PROCEEDINGS OF
THE TENTH
INTERNATIONAL SYMPOSIUM ON
ARTIFICAL LIFE AND ROBOTICS(AROB 10th ’05)

Feb. 4-Feb. 6, 2005
B-Con Plaza, Beppu, Oita, JAPANSupported by Air Force Office of Scientific Research,
Asian Office of Aerospace Research and Development(AFOSR/AOARD)
Editors : Masanori Sugisaka and Hiroshi Tanaka
ISBN4-9900462-5-0

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05), B-con Plaza, Beppu, Oita,
Japan, February 4-6, 2005

Program of The Tenth International Symposium on

ARTIFICIAL LIFE AND ROBOTICSth(AROB 10 ’05)

February 4-6, 2005
B-Con Plaza, Beppu, Oita, Japan

Editors: Masanori Sugisaka and Hiroshi Tanaka©ISAROB 2005

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

THE TENTH INTERNATIONAL SYMPOSIUM

ONARTIFICIAL LIFE AND ROBOTICS(AROB 10th '05)

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

OPERATED BY
Santa Fe Institute (SFI, USA)The 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
Air Force Office of Scientific Research, Asian Office of Aerospace Research andDevelopment (AFOSR/AOARD)
Mitsubishi Electric Corporation Advanced Technology R&D Center
Oita Gas Co., Ltd.
STK Technology Co., Ltd.
Sanwa Shurui Co., Ltd.
Yatsushika Brewery Co., Ltd.

SUPPORTED BY
Kyushu Bureau of Economy, Trade and Industry, Ministry of Economy, Trade andIndustry
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.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The NIKKAN KOGYO SHIMBUM. LTD.
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)

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

CO-CHAIRMAN
J.L. Casti (Santa Fe Institute, USA)ADVISORY COMMITTEE
F. Harashima (Tokyo Denki University, Japan)
H. Kimura (The University of Tokyo, Japan)
T. Fukuda (Nagoya University, Japan)
S. Ueno (Kyoto University, Japan)
M. Tomizuka(University of California at Berkeley, USA)
T.J. Lyons(Asian Office of Aerospace Research & Development, USA)

INTERNATIONAL ORGANIZING COMMITTEE
K. Aihara (The University of Tokyo, Japan)
W. B.Arthur (Santa Fe Institute, USA)
W. Banzhaf (University of Dortmund, Germany)
C. Barrett (Los Alamos National Laboratory, USA)M. Bedau (Reed College, USA)
Z. Bubnicki (Wroclaw University of Technology, Poland)
J.L. Casti (Santa Fe Institute, USA)
H.S. Cho (KAIST, Korea)
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)
S. Kauffman (Santa Fe Institute, USA)
K.Kosuge(Tohoku University, Japan)
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)©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

J.J. Lee (KAIST, Korea)
M. Nakamura (Saga University, Japan)
G. Obinata (Nagoya University, Japan)
S. Rasmussen (Santa Fe Institute, USA)
T.S. Ray (University of Oklahoma, USA)
P. Sapaty (National Academy of Science of Ukraine, Ukraine)
M. Sugisaka (Oita University, Japan) (Chairman)
H. Tanaka (Tokyo Medical & Dental University, Japan)
C. Taylor (University of California-Los Angeles, USA)
K. Tsuchiya (Kyoto University, Japan)
G. Wang (Tsinghua University, China)
W.R. Wells (University of Nevada-Las Vegas, USA)
Y.G. Zhang (Academia Sinica, China)

INTERNATIONAL PROGRAM COMMITTEE
K. Abe (Tohoku University, Japan)
K. Aihara (The University of Tokyo, Japan) (Co-chairman)
T. Arita (Nagoya University, Japan)
H. Asama (The University of Tokyo, Japan)
M. Bedau (Reed College, USA)R. Belew (University of California-San Diego, USA)
Z. Bubnicki (Wroclaw University of Technology, Poland)
T. Christaller (Fraunhofer Institute for Autonomous intelligent Systems-AiS)
C.S. Han (Hanyang University, Korea)
I. Harvey (University of Sussex, UK)
H. Hashimoto (The University of Tokyo, Japan)(Co-chairman)
K. Hirasawa (Waseda University, Japan)
H. Hirayama (Asahikawa Medical College, Japan)
N. Honma (Tohoku University, Japan)
T. Ikegami (The University of Tokyo, Japan)
H. Inooka (Tohoku University, Japan)
K. Ito (Tokyo 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)
Y. Kakazu (Hokkaido University, Japan)
M. Kaneko (Hiroshima University, Japan)
O. Katai (Kyoto University, Japan)
S. Kawaji (Kumamoto University, Japan)
S. Kawata (Tokyo Metropolitan University, Japan)
S. Kitamura (Kobe University, Japan)
T. Kitazoe (University of Miyazaki, Japan)
S. Kumagai (Osaka University, Japan)
J. M.Lee (Pusan National University, Korea)
C. G.Looney (University of Nevada-Reno, USA)
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)
R. Nakatsu (KWANSEI GAKUIN University, Japan)
S. Omatu (Osaka Prefecture University, Japan)©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

M. Okamoto (Kyushu University, Japan)
M. Osano (Aizu University, Japan)
R. Pfeifer (University of Zurich-Irchel, Switzerland)
T.S. Ray (University of Oklahoma, USA) (Co- chairman)
T. Sawaragi (Kyoto University, Japan)
T. Shibata (AIST, Japan)
K. Shimohara (ATR, Japan)
M. Sugisaka (Oita University, Japan)
H. Tanaka (Tokyo Medical & Dental University, Japan) (Chairman)
M. Tanaka-Yamawaki (Tottori University, Japan)
N. Tosa (ATR, Japan)
K. Ueda (The University of Tokyo, Japan)
K. Uosaki (Osaka University, Japan)
H. Wakamatsu (Tokyo Medical & Dental University, Japan)
K. Watanabe (Saga University, Japan)
M. Yano (Tohoku University, Japan)

LOCAL ARRANGEMENT COMMITTEE
X. Feng (University of Singapore, Singapore)
A. Loukianov (Baikal State University of Economy & Law, Russia)
M. Rizon (Northern Malaysia University College of Engineering, Malaysia)
M. Sugisaka (Oita University, Japan) (Chairman)
Y. Suzuki (Tokyo Medical & Dental University, Japan)
J. Wang (Oita University, Japan)
X. Fan (Oita University, Japan)
X. Wang (Tokushima University, Japan)
F. Dai (Matsue National College of Technology, Japan)
M. Ito (Konan University, Japan)

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 inTokyo, Japan (AROB 6th ’01). The tenth symposium will be held on 4-6 February,
2005, at B-Con Plaza, Beppu, Oita, Japan. This symposium invites you all to
discuss development of new technologies concerning Artificial Life and Robotics
based on simulation and hardware in the twenty first century.

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

TOPICS
Artificial brain research
Artificial intelligence
Artificial life

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Artificial living
Artificial mind research
Bioinformatics chaos
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

COPYRIGHTS
Accepted papers will be published in the proceeding of AROB and some of highquality papers in the proceeding will be requested to re-submit their papers for theconsideration of publication in an international journal ARTIFICIAL LIFE ANDROBOTICS (Springer) and APPLIED MATHEMATICS AND COMPUTATION(North-Holland). All correspondence related to the symposium should be addressedto 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 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, 4-6 February, 2005

MESSAGES

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

Fumio Harashima
Chairman of Advisory Committee(President, Tokyo Denki University)

Masanori Sugisaka
General Chairman of AROB

Masanori Sugisaka
General Chairman(Professor, Oita University)©ISAROB 2005

It is my great honor to invite you all to the Tenth International Symposium
on Artificial Life and Robotics (AROB 10th ’05).
The symposiums from the first (February 18-20, 1996) to the ninth(January 28-30, 2004) 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, AFOSR/AOARD
and scientific societies for their repeated support.
This Tenth symposium is sponsored by AFOSR/AOARD and Japanesecompanies (Mitsubishi Electric Corporation Advanced Technology R&D
Center, Oita Gas Co. Ltd., STK Technology Co. Ltd, Sanwa Shurui Co. Ltd.,
Yatsushika Brewery Co. Ltd. And others. I would like to express special
thanks for AFOSR/AOARD and the companies stated above.
The symposium invites you to discuss the development of newtechnologies in the 21st century concerning Artificial Life and Robotics,
based on simulation and hardware.
We hope that AROB 10th ’05 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, 4-6 February, 2005

Hiroshi Tanaka
Program Chairman of AROB

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

On behalf of the program committee, it is truly my great honor to invite
you all to the Tenth International Symposium on Artificial Life and Robotics(AROB 10th ’05). 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 complex systems or Alife approach now attracts
wide interests as a new paradigm of science and engineering. Take an
example in the field of bioscience. The accomplishment of HGP (Human
Genome Project) and subsequent post-genomic comphrensive “Omics dat”
such as transcriptome, proteome and metabolome,brings about vast amountof bio-information. However, as a plenty of omics data becomes available, it
becomes sincerely recognized that the framework by which these omics data
can be understood to make a whole picture of life is critically necessary,thus, in the post-genomic era , the complex systems or Alife approach is now
actually expected to be an efficient methodology to integrate this vast
amount of bio-data.
This example shows the complex system approach is very promising andbecomes widely accepted as a paradigm of next generation of science andengineering. We hope this symposium becomes a forum for exchange of theideas 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.

John L. Casti
Co-Chairman of AROBJohn Casti
Co-Chairman(Professor, Technical Universityof Vienna)©ISAROB 2005

Since its inception by Masanori Sugisaka in 1996, the annual AROB
meeting has become probably the single most important event on thecalendar of Asian workers in the fields of artificial life and robotics. It has
been a distinct pleasure and privilege for researchers from the Santa Fe
Institute community to actively contribute to this AROB "phenomenon"
from the very beginning. Starting with the "father" of artificial life, Chris
Langton, who delivered a plenary talk at the first AROB meeting, SFI
researchers Tom Ray, Steen Rasmussen, Josh Epstein, Brian Arthur andothers have ensured an ongoing SFI presence at every one of the nineprevious meetings.
Everyone in the SFI community congratulates Masanori Sugisaka onmaking the AROB meeting just a bit better each year. This is strong
testimony to his untiring efforts to promote the field of A-Life through not
only the meeting itself, but by helping to form networks of researchers
around the world by means of the journal, *Artificial Life and Robotics*. It
is a pleasure to acknowledge these efforts and to wish Masanori anotherdecade or more of success in the AROB field.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Plenary talker:

Prof. Kazuyuki Aihara

PT Complexity Modeling and its Applications
Kazuyuki Aihara
Institue of Industrial Science
University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JapanThe brain is a typical example of a complex system with much complexity
and many functions. In this plenary talk, I will review our study on creating
an artificial brain with chaotic dynamics, or a chaotic brain. I will also
discuss importance of several bifaces in modelling complex systems, namely
generality / universality & individuality / specialty, abstracted & detailed,and stability & instability.

Special session talkers for artificial life and robotics
SS-1 THE WORK OF
SUEO UENO, ROBERT KALABA, AND RICHARD BELLMAN
Harriet H. Natsuyama
Los Angeles, CA 90066, hhnatsu@yahoo.com
Abstract:This paper recalls the critical developments of three exceptional
individuals in the fields of nonlinear analysis, systems modeling,
computational solution of initial value problems, system identification andoptimal control

Dr. Harriet H. Natsuyama
SS-2

Prof. Yang-gang Zhang

Prof. Henrik Hautop Lund

©ISAROB 2005

Machinery Cognition and Artificial Intelligent
Y.G. Zhang1 and M. Sugisaka2
Institute of Systems Science, Academia Sinica, Beijing China, 100080
yzhang@iss.ac.cn
The Dept. of Electrical and Electronic Engineering, Oita University, Oita,
Japan, 870-1192 msugi@ee.u-oita.edu.jp
Abstract: In this paper authors show some heuristic thinking first, andthen proposed an engineering definition of artificial intelligence inartificial brain that is the abilities to obtain knowledge, to use knowledge
and to operate knowledge. There are still two open important problems:
one is the expression of knowledge for this goal and the other is how toconstruct the integration up operation and detailed down operation.
Keywords: Artificial Intelligence, Creating intelligence, Knowledge,
Cognition, Evolution of intelligence.

SS-3 ATRON Hardware Modules for Self-reconfigurable Robotics
Henrik Hautop Lund, Richard Beck, Lars Dalgaard
Maersk Mc-Kinney Moller Institute for Production Technology
University of Southern Denmark, Campusvej 55, 5230 Odense M., Denmark
hhl@mip.sdu.dk beck@mip.sdu.dk dalgaard@mip.sdu.dk
www.adaptronics.dk
Abstract: We exploit a holistic behavioural and morphological adaptation in
the design of new artefacts, and exemplify the potential of the new designprinciple through the construction of robotic systems that can changemorphology. Here we present the ATRON design in which the modules areindividually simple, attach through physical connections, and perform 3D
motions by collective actions. We produced 100 ATRON modules, andperformed both simulation and real world experiments. In this paper, we
report on the ATRON hardware design and investigations related to theverification of the suitability of the ATRON module design for
self-reconfigurable robotics.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The Tenth International Symposium on
ARTIFICIAL LIFE AND ROBOTICS(AROB 10th '05)

Opening Ceremony
Chair: J.Johnson (Open University, UK)

February 4 (Friday)
Room D: 10:20-10:40
Welcome Addresses
1. General Chairman of AROB

M. Sugisaka (Oita University, RIKEN, Japan)

2. Advisory Committee Chairman

F. Harashima (Tokyo Denki University, Japan)

3. President of SOFT,
President of IEEE
Nanotechnology Council

T. Fukuda (Nagoya University, Japan)

4. Program Chairman of AROB

H. Tanaka(Tokyo Medical and Dental University, Japan)©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

TIME TABLE
RoomA

RoomB

RoomC

RoomD

2/3(Thu.) 8:00
13:00
Registration (Registration Desk)
17:30
Welcome Party (at Beppu Kamenoi Hotel 10th Floor)
2/4(Fri.)

8:00

Registration (Registration Desk)

8:40
GS6(4) Chair H.Tanaka

GS7(4) Chair Y.G. Zhang

IS4(4)
Chair H.Yanagimoto

10:00
10:20

Coffee BreakOpening
Ceremony
Plenary Talk
PT K. Aihara
Chair J.J. Lee

10:40
11:40
Lunch
12:40
GS11(6) Chair H. Umeo

GS15(6) Chair X.Wang

GS14(4)Chair M.Okamoto
will end at 14:00

14:40
14:50

Coffee BreakIS8(5) Chair J.J.Lee

GS17(4) Chair K.Ohnishi

will end at 16:50

will end at 16:30

will end at 16:10

GS5(3) Chair M.Oswald

IS5(4) Chair H.Suzuki

GS12(4) Chair K.B. Sim

will end at 17:50

will end at 17:50

will end at 17:30

GS21(6)Chair S.M. Chen

16:50

18:10
GS: General Session

IS: Invited Session

GS1 Artificial Intelligence
GS2 Artificial Life-I
GS3 Artificial Life-Ⅱ
GS4 Artificial Living
GS5 Bioinformatics
GS6 Complexity
GS7 Cognitive Science
GS8 Computer Graphics
GS9 Evolutionary Computations-I
GS10 Evolutionary Computations -Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms-I
GS13 Genetic Algorithms-Ⅱ
GS14 Intelligent Control and Modeling-I
GS15 Intelligent Control and Modeling-Ⅱ
GS16 Micro-Robot World Cup-Soccer Tournament
GS17 Molecular Biology
GS18 Mobile Vehicles-I
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-I©ISAROB 2005

GS21 Neural Networks-Ⅱ
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Image Processing
IS1 Soft Robotics
IS2 Computer Vision and Mobile Robot
IS3 Intelligent Pattern Classification
IS4 Intelligent Information Retrieval
IS5 Analysis and Implementation of Nonlinear Models
IS6 Hyper Human Technology
IS7 Biomimetic Machines and Robots
IS8 Robot Sensing and Control
IS9 Robot Control and Application
IS10 Human and Agents: Social Interaction andOrganization (1)IS11 Human and Agents: Social Interaction andOrganization (2)IS12 BIO MEDICAL field

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

RoomA

RoomB

2/5(Sat.) 8:00

RoomC

RoomD

Registration (Registration Desk)

8:40
GS18(5) Chair J. Wang

GS4(5) Chair T.Arita

10:20
10:30

GS20(5) Chair A.Buller

Coffee BreakSpecial Session
Y.G. Zhang,
H.LundH.H.Natsuyama
Chair Y.Yin

11:30
Lunch
12:30
IS1(4) Chair T.Yamamoto

GS9(4) Chair K. Uosaki

IS10(4) Chair K.Shimohara

13:50
Coffee Break

14:00
GS19(4) Chair K.Sugawara

GS10(5)

IS11(4) Chair K.Shimohara

will end at 15:20

Chair H. Kawamura

will end at 15:20

15:20

will end at 15:40

15:40
GS1(7) Chair P. Sapaty
16:40

GS25(7)

GS13(4) Chair J. Johnson

Chair S. Ishikawa

will end at 16:40.
IS12(3) Chair T.Ishimatsu

will end at 17:40.

will end at 18:00.

will end at 17:40.

18:10
AROB Award Ceremony (Chair: K. Watanabe)
Banquet (Hotel Shiragiku)
20:30
GS: General Session

IS: Invited Session

GS1 Artificial Intelligence
GS2 Artificial Life-I
GS3 Artificial Life-Ⅱ
GS4 Artificial Living
GS5 Bioinformatics
GS6 Complexity
GS7 Cognitive Science
GS8 Computer Graphics
GS9 Evolutionary Computations-I
GS10 Evolutionary Computations -Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms-I
GS13 Genetic Algorithms-Ⅱ
GS14 Intelligent Control and Modeling-I
GS15 Intelligent Control and Modeling-Ⅱ
GS16 Micro-Robot World Cup-Soccer Tournament
GS17 Molecular Biology
GS18 Mobile Vehicles-I
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-I©ISAROB 2005

GS21 Neural Networks-Ⅱ
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Image Processing
IS1 Soft Robotics
IS2 Computer Vision and Mobile Robot
IS3 Intelligent Pattern Classification
IS4 Intelligent Information Retrieval
IS5 Analysis and Implementation of Nonlinear Models
IS6 Hyper Human Technology
IS7 Biomimetic Machines and Robots
IS8 Robot Sensing and Control
IS9 Robot Control and Application
IS10 Human and Agents: Social Interaction andOrganization (1)IS11 Human and Agents: Social Interaction andOrganization (2)IS12 BIO MEDICAL field

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

RoomA
2/6(Sun.) 8:00

RoomB

RoomC

RoomD

Registration (Registration Desk)

8:40
IS9(5) Chair S.H. Han

IS2(5) Chair M.Kono

GS22(5)Chair K. Nakano

10:20
10:40

Coffee BreakIS7(5) Chair K. Watanabe

GS2(5) Chair M. Osano

GS23(4) Chair J.M. Lee
will end at 12:00

12:20
Lunch
13:20

IS6 (5) Chair M. Kaneko

GS3(5) Chair N. Mirenkov

GS24(6)will end at 15:00.

will end at 15:00.

Chair S.Sagara
will end at 15:20.

15:00
15:20

GS8(4) Chair M. Yokota

IS3(4) Chair S. Omatu

will end at 16:20.

will end at 16:20.

GS16(3) Chair J. Nishii
will end at 16:20.

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

IS: Invited Session

GS1 Artificial Intelligence
GS2 Artificial Life-I
GS3 Artificial Life-Ⅱ
GS4 Artificial Living
GS5 Bioinformatics
GS6 Complexity
GS7 Cognitive Science
GS8 Computer Graphics
GS9 Evolutionary Computations-I
GS10 Evolutionary Computations -Ⅱ
GS11 Fuzzy Control
GS12 Genetic Algorithms-I
GS13 Genetic Algorithms-Ⅱ
GS14 Intelligent Control and Modeling-I
GS15 Intelligent Control and Modeling-Ⅱ
GS16 Micro-Robot World Cup-Soccer Tournament
GS17 Molecular Biology
GS18 Mobile Vehicles-I
GS19 Mobile Vehicles-Ⅱ
GS20 Neural Networks-I©ISAROB 2005

GS21 Neural Networks-Ⅱ
GS22 Robotics-I
GS23 Robotics-Ⅱ
GS24 Robotics-Ⅲ
GS25 Image Processing
IS1 Soft Robotics
IS2 Computer Vision and Mobile Robot
IS3 Intelligent Pattern Classification
IS4 Intelligent Information Retrieval
IS5 Analysis and Implementation of Nonlinear Models
IS6 Hyper Human Technology
IS7 Biomimetic Machines and Robots
IS8 Robot Sensing and Control
IS9 Robot Control and Application
IS10 Human and Agents: Social Interaction andOrganization (1)IS11 Human and Agents: Social Interaction andOrganization (2)IS12 BIO MEDICAL field

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

TECHNICAL PAPER INDEX
February 4 (Friday)
Room D
10:40~11:40 Plenary Talk
Chair J.J. Lee (KAIST, Korea)
PT Complexity modeling and its applications
K. Aihara (University of Tokyo, Japan)

February 5 (Saturday)
10:30~11:30 Special Session on Artificial Life and Robotics
Chair Y. Yin (The Beijing University of Science and Technology, China)
SS-1

The work of Sueo Ueno, Robert Kalaba, and Richard Bellman
H.H. Natsuyama

SS-2

Machinery cognition and artificial intelligent
Y.G. Zhang (Academia Sinica, China)
M. Sugisaka (Oita University, Japan)

SS-3

ATRON hardware modules for self-reconfigurable robotics
H.H. Lund, R. Beck, L. Dalgaard (University of Southern Denmark, Denmark)©ISAROB 2005

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February 4 (Friday)
8:00~ Registration

Room A
8:40~10:00 GS6 Complexity
Chair: H. Tanaka (Tokyo medical & Dental University)
GS6-1 An investigation into state-change complexities in synchronization algorithms forcellular automata
K. Matsumoto, H. Umeo (Osaka Electro-Communication University, Japan)
GS6-2 State-efficient implementations of synchronization algorithms for two-dimensional
cellular automata
H. Umeo, M. Teraoka, M. Hisaoka,M. Maeda (Osaka Electro-Communication
University, Japan)
GS6-3 A Study of the basic concept of information in a complex system
Y. Kinouchi, T. Komiyama (Tokyo University of Information Sciences, Japan)
GS6-4 Qualitative analysis of self-organizing multi agent interaction with entropy and mutual
information
K. Nishikawa, H. Kawamura, M. Yamamoto, A. Ohuchi (Hokkaido University, Japan)

12:40~14:40 GS11 Fuzzy Control
Chair: H. Umeo (Osaka Electro-Communication University, Japan)
GS11-1 Development of the fuzzy control for a GPS-located airship
M. Sugisaka (Oita University, Japan)
F. Dai, Y. Fujihara, T. Kamoika (Matsue National College of Technology, Japan)
GS11-2 Fuzzy information retrieval based on weighted power-mean averaging operators
W-S Hong, S-M Chen (National Taiwan University of Science and Technology, Taiwan)
S-J Chen (Ching-Yun University, Taiwan)
GS11-3 Temperature prediction based on genetic simulated annealing techniques andhigh-order fuzzy time series
L-W Lee (National Taiwan University of Science and Technology, Taiwan)
L-H Wang (Chihlee Institute of Technology, Taiwan)
S-M Chen (National Taiwan University of Science and Technology, Taiwan)
GS11-4 Design of a fuzzy expert system for electric vehicle speed control
M. Sugisaka, Z. Mbaitiga (Oita University, Japan)
GS11-5 A harness line color recognition method based on fuzzy similarity measure
Y-T. Kim, H. Bae, Y-I. Kim, S. Kim (Pusan National University, Korea)
GS11-6 An EPS system control for fuzzy logic in HILS system©ISAROB 2005

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M.K. Lee, J.Y. Choi, S.K. Ha, M.H. Lee (Pusan National University, Korea)

14:50~16:50 GS21 Neural Networks-Ⅱ
Chair: S-M. Chen (National Taiwan University of Science and Technology, Taiwan)
GS21-1 Applying FIFO-Queue ACO algorithm to broadcast problem of wireless Sensor
Networks
J. Katsuki, T. Isokawa, N. Kamiura, N. Matsui (University of Hyogo, Japan)
GS21-2 Predicting selection of artificial network by cluster coefficient
S. Yoshimura, S. Yoshii (Hokkaido University, Japan)
GS21-3 Evolution of development and heterochrony in artificial neural networks
A. Matos, R. Suzuki, T. Arita (Nagoya University, Japan)
GS21-4 Multi-procedure ozone concentration prediction using fuzzy clustering and DPNN
S-P Cheon, S-T Lee, S. Kim (Pusan National University, Korea)
GS21-5 A model of emergence of reward expectancy neurons using reinforcement learningand neural network
S. Ishii (Oita University, Japan)
M. Shidara (National Institute of Advanced Industrial Science and Technology)
K. Shibata (Oita University, Japan)
GS21-6 Shape-recognition using randomly selected pixel-pair neurons
V. Rose, J. Johnson (Open University, Uk)

16:50~17:50 GS5 Bioinformatics
Chair: M. Oswald (Vienna University of Technology, Austria)
GS5-1 Information-theoretic approach to embodied category learning
G. Gomez (University of Zurich, Austria)
M. Lungarella (Tokyo University, Japan)
D. Tarapore (Indian Institute of Technology)
GS5-2 On the diversity of HIV using cellular automata approach
H. Ueda, Y. Iwaya, T. Abe, T. Kinoshita (Tohoku University, Japan)
GS5-3 Construction and strategy of a soccer team by the agent using immune concept
N. Kogawa, M. Obayashi, A. Maeda, K. Kobayashi, T. Kuremoto (Yamaguchi
University, Japan)

Room B
8:40~10:00 GS7 Cognitive Science
Chair: Y.G. Zhang (Academia Sinica, China)
GS7-1 Transforming information to Knowledge and intelligence
Y.X. Zhong (University of Post and Telecommunication, China)
GS7-2 Establishment of sound-correspondence laws of word-initial consonants between©ISAROB 2005

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Finnish/Uralic and Malayo-Polynesian/ Proto-Austronesian languages:
Towards making comparative analysis of word-initial consonant frequencies
K. Ohnishi, S. Akiyama,Y. Murayama, M. Goda (Niigata University, Japan)
GS7-3 Effect of action selection on the emergence of one-way communication using Q-learning
M. Nakanishi, K. Shibata (Oita University, Japan)
GS7-4 Design of a robust adaptive controller for a class of uncertain nonlinear systems with
time-delay input
T-M-H. Nguyen, T-N. Dinh, T-N. Nguyen, V-T.Tran (Center for Automation
Technology-CAT, Vietnam)

12:40~14:00 GS14 Intelligent Control and Modeling-Ⅰ
Chair: M. Okamoto (Kyushu University, Japan)
GS14-1 Solving Constrained Motion Problems Using the GI Method
Y. Y. Fan (University of California, U.S.A.)
GS14-2 Moving robot path search including obstacles by GA using quadrant idea
H. Yamamoto, E. Marui (Gifu University, Japan)
GS14-3 Design of novel adaptive routing by mimicking enzymic feedback control mechanism inthe cellT. Kawauchi (Kyushu University, Japan)
M. Hirakawa (Fukuoka International University, Japan)
M. Okamoto (Kyushu University, Japan)
GS14-4 Agent based plant allocation and transfer routing of products in case of emergency
S. AlSehaim, M. Konishi (Okayama University, Japan)
K. Nose (Osaka Sangyo University, Japan)

14:50~16:30 IS8 Robot Sensing and Control
Chair: J.J. Lee (Korea Advanced Institute of Science and Technology, Korea)
J.M. Lee (Pusan National University, Korea)
IS8-1 The hybrid SOF-PID controller for the control of a MIMO biped robot
T-Y Choi, S-Y Park, Ju-Jang Lee (Korea Advanced Institute of Science and Technology,
Korea)

IS8-2 Strategy of cooperative behaviors for distributed autonomous robotic systems
H-U Yoon, S-H Whang, D-W Kim, K-B Sim (Chung-Ang University, Korea)
IS8-3 Development of the tactile sensor system using fiber Bragg grating sensors
J.S. Heo, Jung Ju Lee (Korea Advanced Institute of Science and Technology, Korea)
IS8-4 A distributed precedence queue mechanism to assign efficient bandwidth in CAN
networks
H.S. Choi, J.M. Lee (Pusan National University, Korea)
IS8-5 Adaptive occupancy grid mapping with clusters©ISAROB 2005

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B.G. Jang, T.Y. Choi, Ju-Jang Lee (Korea Advanced Institute of Science & Technology,
Korea)

16:30~17:50 IS5 Analysis and Implementation of Nonlinear Models
Chair: H.Suzuki (University of Tokyo,Japan)
Co-Chair: S. Horai (University of Tokyo,Japan)
IS5-1 MOSFET implementation of class I*neurons coupled by gap junctions
T. Takemoto ( Tokyo University, Japan)
T. Kohno (Aihara Complexity Modeling Project, ERATO, JST, Japan)
K. Aihara, H. Suzuki (Tokyo University, Japan)
IS5-2 Parameter tuning of a MOSFET-based nerve membrane
T. Kohno (Aihara Complexity Modeling Project, ERATO, JST, Japan)
K. Aihara, H. Suzuki (Tokyo University, Japan)
IS5-3 Wayland test, noise, and surrogate
Y. Hirata ( Tokyo University, Japan)
S. Horai (Aihara Complexity Modeling Project, ERATO, JST, Japan)
K. Aihara, H. Suzuki (Tokyo University, Japan)
IS5-4 Analysis of bifurcation and optimal response on the evolution of cooperation
Y-H Otake, K. Aihara, H. Suzuki (Tokyo University, Japan)

Room C
8:40~10:00 IS4 Intelligent Information Retrieval
Chair: H.Yanagimoto (Osaka Prefecture University, Japan )IS4-1 Information filtering using probabilistic model
H. Yanagimoto, S. Omatu (Osaka Prefecture University, Japan)
IS4-2 Modification of user profile using the genetic algorithm
H. Yanagimoto, S. Omatu (Osaka Prefecture University, Japan)
IS4-3 Information filtering using SVD and ICA
T. Yokoi, H. Yanagimoto, S. Omatu (Osaka Prefecture University, Japan)
IS4-4 Improvement of information filtering using topic selection
T. Yokoi, H. Yanagimoto, S. Omatu (Osaka Prefecture University, Japan)

12:40~14:40 GS15 Intelligent Control and Modeling-Ⅱ
Chair: X. Wang(Tokushima University, Japan)
GS15-1 Decision method of reference input time interval and sampling time interval thatconsidered contour control performance in software servo system
S. Satou, M. Nakamura, S. Goto (Saga University, Japan)
N. Egashira (Kurume Institute of Technology, Japan)
N. Kyura (Kinki University, Japan)©ISAROB 2005

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GS15-2 An automatic decoupling control system for ship harbor maneuvers and its
robustness evaluation
M-D Le (VINASHIN, Vietnam)
A-T Dang (SHIPSOFT JSC., Vietnam)
GS15-3 CPU resource double auction system with an anonymous protocol
T. Matsumoto, H. Kawamura, A. Ohuchi (Hokkaido University, Japan)
GS15-4 Issues and applications of robot control using internet
M. Sugisaka, H. Desa (Oita University, Japan)
GS15-5 Hard / soft switching particle filters for efficient real-time visual tracking
T. Bando, T. Shibata, K. Doya, S. Ishii (Nara Institute of Science and Technology, Japan)
GS15-6 Robotics and the Q-analysis of behaviour
I. Pejman, J. Johnson, L.Rapanotti (The Open University, UK)

14:50~16:10 GS17 Molecular Biology
Chair: K. Ohnishi (Niigata University, Japan)
GS17-1 Nanoparticles as biosensors components―a brief review
T. Kubik (Wroclaw University of Technology, Poland)
M. Sugisaka (Oita University, Japan)
GS17-2 P systems with dynamic channels transporting membrane vesicles
R. Freund, M. Oswald (Vienna University of Technology, Austria)
GS17-3 Design of soccer-ball-shape DNA molecules and preliminary experiments in vitro
Y. Kita ( Hokkaido University, Japan)
A. Kameda, (CREST, Japan Science and Technology Agency, Japan)
M. Yamamoto, A. Ohuchi (CREST, Hokkaido University, Japan)
GS17-4 Amoebic ability to arrive at signal sources in obstacle-rich space
S. I. Nishimura, M. Sasai (Nagoya University, Japan)

16:10~17:30 GS12 Genetic Algorithms-Ⅰ
Chair: K-B. Sim (Chung-Ang University)
GS12-1 The analysis for the movement characteristics of the flying object with genetic
algorithms
R. Goto, Y. Sato (Hosei University, Japan)
GS12-2 Proposal of serially and dynamically separating genetic algorithm and its application
to optimization of robot control systems
K. Nakayama (Kyoto University, Japan)
H. Matsui (Mie University, Japan)
K. Shimohara (ATR Network Informatics Laboratories, Japan)
O. Katai (Kyoto University, Japan)©ISAROB 2005

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GS12-3 Proposal of genetic operations reducing the evaluator workload to the voice quality
conversion using interactive GA
T. Nishizono, S. Noami, Y. Sato (Hosei University, Japan)
GS12-4 Fitness modification in genetic algorithms for function optimization problems
T. Yoshida, T. Nakashima, H. Ishibuchi (Osaka Prefecture University, Japan)

February 5 (Saturday)
8:00~ Registration

Room A
8:40~10:20 GS18 Mobile Vehicles-I
Chair: J. Wang (Oita University, Japan)
GS18-1 A Controller design method for an articulated vehicle employing self-organizing
relationship(SOR) network
T. Koga, K. Horio, T. Yamakawa (Kyushu Institute of Technology, Japan)
GS18-2 Automatic control of an electric vehicle using visual information
K. Tokuda, M. Sugisaka, Z. Mbaitiga (Oita University, Japan)
GS18-3 Mobile sensor device in intelligent space
H. Isu, T. Sasaki, H. Hashimoto (Tokyo University, Japan)
GS18-4 Real-time path planning for senor-based mobile robot based on probabilistic
roadmap method
Z. Li, X. Chen (Fudan University, China)
GS18-5 Gesture clustering and imitative behavior generation for partner robots
Y. Nojima (Osaka Prefecture University, Japan)
N. Kubota (Tokyo Metropolitan University, Japan)
F. Kojima (Kobe University, Japan)

12:30~13:50 IS1 Soft Robotics
Chair: T. Yamamoto (University of Ryukyus, Japan)
Co-Chair: H. Kinjo (University of Ryukyus, Japan)
IS1-1 Improvement of the real-coded genetic algorithms for optimization problems
H. Kinjo, H. Nakanishi, T. Yamamoto (University of the Ryukyus, Japan)
D.S. Chau (Hanoi Agricultural University, Vietnam)
IS1-2 Enhanced performance for multi- variable optimization problems by use of GAs withrecessive gene structure
E. Muhando, H. Kinjo, T. Yamamoto (University of the Ryukyus, Japan)©ISAROB 2005

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IS1-3 Identification of time series signals using dynamical neural network with GA-based
training
K. Nakazono (University of the Ryukyus, Japan)
K. Ohnishi (Keio University, Japan)
H. Kinjo, (University of the Ryukyus, Japan)
IS1-4 Spontaneous speciation by GA for division of labor in two-agent systems
N. Oshiro, K. Kurata, T. Yamamoto (University of the Ryukyus, Japan)

14:00~15:20 GS19 Mobile Vehicles-Ⅱ
Chair: K. Sugawara (Tohoku Gakuin University, Japan)
GS19-1 How to make a mobile robot move more reliably?
J. Wang, M. Sugisaka (Oita University, Japan)
GS19-2 PID orbit motion controller for indoor blimp robot
H. Kadota, H. Kawamura, M. Yamamoto (Hokkaido University, Japan)
T. Takaya (RICOH SYSTEM KAIHATU COMPANY, LTD.)
A. Ohuchi (Hokkaido University, Japan)
GS19-3 Automated moving objects detection with an on-board camera for avoidance of
car accident
S. Sunahara, Y. Tsuboi, O. Ono (Meiji University, Japan)
GS19-4 Improving odometry accuracy for a car using tire radii measurements
H.C. Lee, C.S. Kim, K-S Hong, M.H. Lee (Pusan National University, Japan)

15:20~17:40 GS1 Artificial Intelligence
Chair: P. Sapaty (Aizu University, Japan)
GS1-1 Learning algorithms and uncertain variables in knowledge-based pattern recognition
Z. Bunicki (Wroclaw University, Poland)
GS1-2 Association rule mining using genetic network programming
K. Shimada, K. Hirasawa, T. Furutsuki (Waseda University, Japan)
GS1-3 Evolution of metaparameters for efficient real time learning
G.. Capi (Fukuoka Institute of Technology, Japan)
M. Yokota (Fukuoka Institute of Technology, Japan)
K. Doya (CREST, Japan Science and Technology Agency, Japan)
GS1-4 A reinforcement learning scheme of adaptive flocking behavior
M. Tomimasu, H. Nishimura, K. Morihiro, T. Isokawa, N. Matsui (University of Hyogo,
Japan)
GS1-5 Learning control of manipulator with a free joint
T. Goto, H. Lee (Tohoku University, Japan)
K. Abe (Tohoku University, Japan)
H. Kamaya (Hachinohe National College of Technology, Japan)©ISAROB 2005

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GS1-6 Evolutionary simulations based on a robotic approach to emotion
T. Kato, T. Arita (Nagoya University, Japan)
GS1-7 Models of individuals for constructive approach of dynamic view of language and society
T. Hashimoto, T. Sato, A. Masumi (Japan Advanced Institute of Science and Technology,
Japan)

Room B
8:40~10:20 GS4 Artificial Living
Chair: T. Arita (Nagoya University, Japan)
GS4-1 Interactive musical editing system for supporting human error and offering personal
preferences for an automatic piano-preference database for crescendo and decrescendoE. Hayashi, Y. Takamatsu (Kyushu Institute of Technology, Japan)
GS4-2 The role of population structure in language evolution
Y. Lee, T.C. Collier, E.P. Stabler, C.E. Taylor (University of California, Los Angeles,
U.S.A.)
GS4-3 Truth table language for generating self-replicating systems
H. Harada, Y. Toquenaga (Tsukuba University, Japan)
GS4-4 Effectiveness of emerged pheromone communication in an ant foraging model
Y. Nakamichi, T. Arita (Nagoya University, Japan)
GS4-5 Foraging behavior of ant-like Robots with virtual pheromone
K. Sugawara, T. Kazama (Tohoku Gakuin University, Japan)

12:30~13:50 GS9 Evolutionary Computations-I
Chair: K. Uosaki (Osaka University, Japan)
GS9-1 DNA computing approach to evolutional reasoning algorithm by using restriction
EnzymeY. Tsuboi, I. Zuwairie, O. Ono (Meiji University, Japan)
GS9-2 A model for coevolution
K. Makino (Tokyo Institute of Technology, Japan)
K. Nakano (Tokyo University of Technology, Japan)
GS9-3 Simultaneous state and parameter estimation of nonlinear models by evolution
strategies based particle filters
K. Uosaki, T. Hatanaka (Osaka University, Japan)
GS9-4 Evaluating a solution of tour planning problem based on the partially exhaustive
exploration Monte Carlo method
M. Onodera, H. Kawamura, A. Ohuchi (Hokkaido University, Japan)©ISAROB 2005

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14:00~15:40 GS10 Evolutionary Computations-Ⅱ
Chair: H. Kawamura (Hokkaido University, Japan)
GS10-1 A comprehensive evaluation of the methods for evolving a cooperative teamY. Suzuki, T. Arita (Graduate School of Information Science, Nagoya University)
GS10-2 A fast algorithm in finding communities of book network
S. Wang, C. Zhang (Tsinghua University, China)
GS10-3 Autonomous evolutionary machine vision systems
J.H. Johnson, V. Rose (Open University, United Kingdom)
GS10-4 Real-time adaptive maintenance for performance improvement on daily-use computer
S. Hirose, S.Yoshii (Hokkaido University, Japan)
GS10-5 Swarm search for fast face detection with neural networks
X. Fan, M. Sugisaka (Oita University, Japan)

15:40 ~18:00 GS25 Image Processing
Chair: S. Ishikawa (Kyushu Institute of Technology, Japan)
GS25-1 An optimal capturing trajectory planning for a moving object
B-S. Choi, J-M. Lee (Pusan National University, Korea)
GS25-2 Proposing a passive biometric system for robotic vision
M.M. Rahman, S. Ishikawa (Kyushu Institute of Technology, Japan)
GS25-3 Human motion recovery by mobile stereoscopic cameras
J.K. Tan, I. Yamaguchi, S. Ishikawa (Kyushu Institute of Technology, Japan)
T. Naito, M. Yokota (Kyushu Dental College, Japan)
GS25-4 A high-speed human motion recovery based on back projection
M. Uchinoumi, J.K. Tan, S. Ishikawa (Kyushu Institute of Technology, Japan)
T. Naito, M. Yokota (Kyushu Dental College, Japan)
GS25-5 Auto-correlation probabilistic relaxation matching method
X. Wang (The University of Tokushima, Japan)
M. Sugisaka (Oita University, Japan)
J. Wang (Hebei University of Science & Technology, China)
GS25-6 Segmentation and object recognition for robot bin picking systems
R. Nagarajan, (Universiti Malaysia Sabah, Malaysia)
Y. Sazali (Northern Malaysia University College of Engineering, Malaysia)
P. Pandiyan (Universiti Malaysia Sabah, Malaysia)
C. R. Hema (Universiti Malaysia Sabah, Malaysia)
A. Shamsudin (Universiti Teknologi Malaysia, Malaysia)
K. Marzuki (Universiti Teknologi Malaysia, Malaysia)
M. Rizon (Northern Malaysia University College of Engineering, Malaysia)
GS25-7 Stereo camera based artificial vision for blind through hearing
G. Balakrishnan, G. Sainarayanan, R. Nagarajan (Universiti Malaysia Sabah, Malaysia)©ISAROB 2005

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Y. Sazali, M. Rizon (Northern Malaysia University College of Engineering, Malaysia)

Room C
8:40~10:20 GS20 Neural Networks-Ⅰ
Chair: A.Buller (ATR Network Infomatics Laboratories, Japan)
GS20-1 Improving the tuning capability of the adjusting neural network
Y. Sugita (Hitachi,Ltd. Hitachi Research Laboratory, Japan)
K. Hirasawa (Waseda University, Japan)
GS20-2 Fault diagnosis for electro-mechanical control system by neural networks
T. Torigoe, M. Konishi, J.Imai, T. Nishi (Okayama University, Japan)
GS20-3 Image processing for GIS applications supported by the use of artificial neuralnetworks
T. Kubik, W. Paluszynski (Wroclaw University of Technology, Poland)
A. Iwaniak, P. Tymkow (Agricultural University of Wroclaw, Poland)GS20-4 A supervised learning rule adjusting input-output pulse timing for pulsed neuralnetwork
M. Motoki, S. Koakutsu, H. Hirata (Chiba University, Japan)
GS20-5 Remarks on tracking method of neural network weight change for learning type neuralnetwork direct controller
T. Yamada (Ibaraki University)

12:30~13:50 IS10 Artificial Human and Agents: Social Interaction andOrganization (1)Chair: K. Shimohara (Kyoto University, ATR Network Laboratories, Japan)
Co-Chair: H. Fujii
IS10-1 Sociable and affective artificial cohabitant
N. Matsumoto and A. Tokosumi (Tokyo Institute of Technology, Japan)
IS10-2 Child-robot interaction mediated by building blocks: From field observation ina public space
M. Goan, H. Fujii, M. Okada (ATR Network Informatics Laboratories, Japan)
IS10-3 Social influence of overheard communication by life-like agents to a user
S.V. Suzuki (DCISS, IGSSE, Tokyo Institute of Technology, Japan)
S. Yamada (National Institute of Informatics, Japan)
IS10-4 Do complementarities exist in agent interactions?
M. Lee, M. Goan, M. Okada (ATR Network Informatics Laboratories, Japan)

14:00~15:20 IS11 Artificial Human and Agents: Social Interaction andOrganization (2)Chair: K. Shimohara (Kyoto University, ATR Network Laboratories, Japan)
Co-Chair: M. Goan (ATR Network Informatics Laboratories, Japan)©ISAROB 2005

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IS11-1 Multi user learning agent based on social interaction
D. Katagami, H. Ohmura, Y. Yasumura, K. Nitta (CISS, IGSSE, Tokyo Institute ofTechnology, Japan)
IS11-2 Can robots get "membership" through social interaction?
H. Fujii, M. Okada (ATR Network Informatics Laboratories, Japan)
IS11-3 Minimal design for human-agent communication
N. Matsumoto, H. Fujii, M. Okada (ATR Network Informatics Laboratories, Japan)
IS11-4 Development of a speech-driven embodied laser pointer with a visualized response
equivalent to nodding
H. Nagai, T. Watanabe, M. Yamamoto (Okayama Prefectural University, Japan)

15:20 ~16:40 GS13 Genetic Algorithms-Ⅱ
Chair: J. Johnson (Open University, UK)
GS13-1 Data mining using genetic network programming
T. Fukuda, K. Shimada, K. Hirasawa, T.Furuzuki (Waseda University, Japan)
GS13-2 Performance comparison between fuzzy rules and interval rules in rule-baseclassification systems
S. Namba, Y. Nojima, H. Ishibuchi (Osaka Prefecture University, Japan)
GS13-3 Design of an augmented automatic choosing control with the weighted automaticchoosing functions using Hamiltonian and genetic algorithm
T. Nawata (Kumamoto National College of Technology, Japan)
H. Takata (Kagoshima University, Japan)
GS13-4 Evolution and niche construction in NKES fitness landscape
R. Suzuki, T. Arita (Nagoya University, Japan)

16:40 ~17:40 IS12 Bio Medical Field
Chair: T. Ishimatsu (Nagasaki University, Japan)
IS12-1 Development of pointing device to use vision for people with disability
M. Kubo, M. Tanaka, S. Moromugi, Y. Shimomoto, Y. Ohgiya, T. Ishimatsu (Nagasaki
University, Japan)
IS12-2 Monitoring system of body movements for bedridden elderly
R-S. Dong, M. Tanaka, M. Ushijima, T. Ishimatsu (Nagasaki University, Japan)
IS12-3 Development of a training machine for elderly people with muscle activity sensor
S-J. Yoon, S-H. Kim, M. Tanaka, S. Moromugi, Y. Ohgiya, N. Matsuzaka, T. Ishimatsu(Nagasaki University, Japan)

February 6 (Sunday)©ISAROB 2005

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8:00~ Registration

Room A
8:40~10:20 IS9 Robot Control and Application
Chair: S.H. Han (Kyungnam University, Korea)
IS9-1 Real time implementation of visual servoing control for SCARA robot with eight joints
D-Y. Jung, H-R. Kim, S-H. Han (Kyungnam University, Korea)
IS9-2 Tolerance of permanent magnet Biased bidirectional magnetic bearings and its robotic
application
Y- T. Kim, U-J. Na (Kyungnam University, Korea)
IS9-3 Detecting method of friction force on linear actuators of a parallel manipulator based on
the gravitational force
H-B. Shin, S-H. Lee (Kyungnam University, Korea)
IS9-4 Real time control of feature based visual tracking for dual-arm robot
J-S. Kim, H-R. Kim, S-H. Han (Kyungnam University, Korea)
IS9-5 Calibration and control experiments on redundant legs of a Stewart platform based
machine tool
W-S. Lee, H-S. Kim (Kyungnam University, Korea)

10:40~12:20 IS7 Biomimetic Machines and Robots
Chair: K. Watanabe (Saga University, Japan)
Co-Chair: K. Izumi (Saga University, Japan)
IS7-1 A view-based navigation system for autonomous robots
C. Pathirana, K. Watanabe, K. Izumi (Saga University, Japan)
IS7-2 Intelligent vision system for dynamic environments
C. Pathirana, K. Watanabe, K. Izumi (Saga University, Japan)
A. Hewawasam, L. Udawatta (University of Moratuwa, Sri Lanka)
IS7-3 Human interaction with binocular vision robots in ubiquitous environment
J.C. Balasuriya, K. Watanabe, K. Izumi (Saga University, Japan)
IS7-4 Fuzzy coach player method with shared environmental data
K. Izumi, K. Watanabe, Y. Tamano, A. Oshima (Saga University, Japan)
IS7-5 Cost function analysis of optimizing fuzzy energy regions in control of underactuatedmanipulators
K. Izumi, K. Ichida, K. Watanabe, (Saga University, Japan)

13:20~15:00 IS6 Hyper Human Technology
Chair: M. Kaneko (Hiroshima University, Japan)
IS6-1 Dynamic preshaping based design of capturing robot driven by wire

©ISAROB 2005

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S. Nishio, M. Higashimori, M. Kaneko (Hiroshima University, Japan)
IS6-2 Design of tracing type jumping robots
M. Harada, M. Higashimori, I. Ishii, M. Kaneko (Hiroshima University, Japan)
IS6-3 Development of a laparoscopic surgery training system and preliminary experiments
H. Masugami, T. Kawahara, H. Egi, M. Yoshimitsu, M. Okajima, M. Kaneko (Hiroshima
University, Japan)
IS6-4 Toward a real time force measurement by vision
T. Mizoi, I. Ishii, M. Kaneko (Hiroshima University, Japan)
IS6-5 Non-contact impedance sensing
T. Kawahara, K. Tokuda, M. Kaneko (Hiroshima University, Japan)

15:00~16:20 GS8 Computer Graphics
Chair: M.Yokota (Fukuoka Institute of Technology, Japan)
GS8-1 Optimization of camera positions for taking all indoor sceneries by GA
Y. Tominari, T. Nakagawa, K. Yamamori, I. Yoshihara (University of Miyazaki, Japan)
H. Takeda (Office of Strategic Planning Systems Development Laboratory Hitachi,Ltd.)
GS8-2 Real time structure preserving image noise reduction for computer
vision on embedded platforms
W. Nistico, U. Schwiegelshohn, M. Hebbel, I. Dahm (University of Dortmund,
Germany)
GS8-3 Model-less visual servoing using modified simplex optimization
H. Inooka, K. Hashimoto (Tohoku University, Japan)
J. Gangloff, M. de Mathelin (Louis Pasteur Strasbourg University, France)
K. Miura (Tohoku University, Japan)
GS8-4 Impulsive noise reduction using M-transform and wavelet with applications to AFM
signals
H. Harada, S.K. Jeon, H. Kashiwagi (Kumamoto University, Japan)

Room B
8:40~10:20 IS2 Computer Vision and Mobile Robot
Chair: M. Kono (University of Miyazaki, Japan)
Co-Chair: M. Yokomichi (University of Miyazaki, Japan)
IS2-1 Indication of object spatial position by finger pointing
M. Hirasawa, M. Oshima (Tokyo University of Marine Science and Technology, Japan)
IS2-2 Accepting powers of four-dimensional alternating turing machines with only
universal states
Y. Nakama, M. Sakamoto, M. Saito, S. Taniguchi, (University of Miyazaki, Japan)
T. Ito (Ube Natinal College of Technology, Japan)
K. Inoue (Yamaguchi University, Japan)
H. Furutani S. Katayama (University of Miyazaki, Japan)©ISAROB 2005

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IS2-3 Some properties of four-dimensional multicounter automata
M. Saito, M. Sakamoto, Y. Nakama (University of Miyazaki, Japan)
T. Ito (Ube Natinal College of Technology, Japan)
K. Inoue (Yamaguchi University, Japan)
H. Furutani1, S. Katayama (University of Miyazaki, Japan)
IS2-4 Simulation study for intelligent wheelchair vehicle with ultrasonic and infrared sensors
K. Kamimura, T. Kai, M. Yokomichi (University of Miyazaki, Japan)
T. Kitazoe (The Inter National University of Kagoshima, Japan)
IS2-5 Object recognition using a self-organizing map for an autonomous mobile robot
M. Tabuse, H. Kaneko (Kyoto Prefectural University, Japan)

10:40~12:20 GS2 Artificial Life-Ⅰ
Chair: M. Osano (Aizu University, Japan)
GS2-1 Grasping the distributed entirety
P. Sapaty (National Academy of Science, Ukraine)
M. Sugisaka (Oita University, Japan)
N. Mirenkov, M. Osano (University of Aizu)
R. Finkelstein (Robotic Technology Inc., U.S.A)
GS2-2 Motion control of biped robot bending the knees
K. Umezaki, M. Sugisaka (Oita University, Japan)
GS2-3 Acquisition of common symbols with development of cooperative behaviors
Y. Hashizume, J. Nishii ( Yamaguchi University, Japan)
GS2-4 A hierarchical learning model for basic locomotor patterns
T. Hioki, J. Nishii ( Yamaguchi University, Japan)
GS2-5 Artificial life-based search technique on the solution of singular configurations
concerning screw parameters in helicoidal robots
I. Juarez-Campos ( Universidad Michoacana, Mexico)

13:20~15:00 GS3 Artificial Life-Ⅱ
Chair: N. Mirenkov (Aizu University, Japan)
GS3-1 Human-following robot using the particle filter in ISpace with distributed visionsenosrs
T-S. Jin, K. Morioka, H. Hashimoto (University of Tokyo, Japan)
GS3-2 On vector autoregressive model for action of human arm’s
K. Tomizawa (Tokyo Denki University, Japan), K. Oura (Kokushikan University, Japan)
I. Hanazaki (Tokyo Denki University, Japan)
GS3-3 Outwit game -- a dynamical systems game for market dynamics
M. Konno, T. Hashimoto (Advanced Institute of Science and Technology (JAIST),
Japan)©ISAROB 2005

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GS3-4 Artificial ecosystem on the resource-conservative tierra structure
S. Matsuzaki (Aizu University, Japan)
H. Suzuki (ATR Human Information Science Labs., Japan)
M. Osano (Aizu University, Japan)
GS3-5 Rule ecology dynamics for studying dynamical and interactional nature of social
institutions
T. Hashimoto (Japan Advanced Institute of Science and Technology, Japan)
M. Nishibe (Hokkaido University, Japan)

15:00~16:20 IS3 Intelligent Pattern Classification
Chair: S. Omatu (Osaka Prefecture University, Japan)
IS3-1 Image compression for bill money by neural networks
S. Omatu, K. Kibi (Osaka Prefecture University, Japan)
M. Teranishi (Nara University of Education, Japan)
T. Kosaka (Glory Ltd, Japan)
IS3-2 Intelligent classification of bill money
S. Omatu (Osaka Prefecture University, Japan)
T. Kosaka (Glory Ltd., Japan)
IS3-3 An image segmentation method using the histograms and the human characteristics of
HSI color space for a scene image
S. Ito, M. Yoshioka, S. Omatu (Osaka Prefecture University, Japan)
K. Kita, K. Kugo ( Noritsu Koki Co. Ltd., Japan)
IS3-4 An image recognition method by rough classification for a scene image
S. Ito, M. Yoshioka, S. Omatu (Osaka Prefecture University, Japan)
K. Kita, K. Kugo (Noritsu Koki Co. Ltd., Japan)

Room C
8:40~10:20 GS22 Robotics-Ⅰ
Chair: K. Nakano (The University of Electro-communications, Japan)
GS22-1 Development of the autonomous driving personal robot “The visual processing system
for autonomous driving”
T. Umeno, E. Hayashi (Kyushu Institute of Technology, Japan)
GS22-2 Development of a system for self-driving by an autonomous robot
E. Hayashi, K. Ikeda (Kyushu Institute of Techonology, Japan)
GS22-3 Research about ZMP of biped walking robot
K. Imamura, M. Sugisaka (Oita University, JAPAN)
GS22-4 Verification of trajectory generation of bipedal walking robot
N. Masuda, M. Sugisaka (Oita University, Japan)

GS22-5 Run control of the mobile robot using visual information
T. Hashizume, M. Sugisaka (Oita University, Japan)©ISAROB 2005

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10:40~12:00 GS23 Robotics-Ⅱ
Chair: J.M. Lee (Pusan National University, Korea)
GS23-1 Digital RAC for underwater vehicle-manipulator systems considering singularconfiguration
S. Sagara, M. Tamura, T. Yatoh, K. Shibuya (Kyushu Institute of Technology, Japan)
GS23-2 Modeling of pneunatic artificial muscle actuator
H. Zhao, M. Sugisaka (Oita University, Japan)
D. Yu (Zhengzhou University, China)
GS23-3 Secure cooperation in a distributed robot system using active RFIDs
M. Obayashi (Tokyo Metropolitan Industrial Technology Research Institute, Japan)
H. Nishiyama, F. Mizoguchi (Tokyo University of Science, Japan)
GS23-4 A soccer robot control design based on the immune system
J. Ito, K. Sakurama, K. Nakano (The University of Electro-Communications, Japan)

13:20~15:20 GS24 Robotics-Ⅲ
Chair: S. Sagara (Kyushu Institute of Techonology, Japan)
GS24-1 Robot’s behavior driven by internal tensions regulated by pulsed para-neural networks
J. Li, (University of Science and Technology, China)
A. Buller, J. Liu (ATR Network Informatics Labs., Japan)
GS24-2 Robotic-control blocks (RCB) for research and education
A. Stefanski, A. Buller (ATR Network Informatics Labs., Japan)
GS24-3 Human-robot communication through a mind model based on the mental image
Directed Semantic Theory
M. Yokota (Fukuoka Institute of Technology, Japan)
M. Shiraishi (Fukuoka University of Education, Japan)
G. Capi (Fukuoka Institute of Technology, Japan)
GS24-4 The case for radical epigenetic robotics
A.I. Kovacs, H. Ueno (The Graduate University for Advanced Studies, National Instituteof Informatics, Japan)
GS24-5 Development of a robotic surgical manipulator for minimally invasive surgery system
H-S. Song, J-H. Jung, Jung Ju. Lee (Korea Advanced Institute of Science andTechnology, Korea)
GS24-6 Identification of nonlinear mechatronic servo motor system having backlash
Y. Toyozawa (FANUC Corp., Japan)
H. Harada, H. Kashiwagi (Kumamoto University, Japan)

15:20~16:20 GS16 Micro-Robot World Cup-Soccer Tournament
Chair: J. Nishii (Yamaguchi University, Japan)
GS16-1 Cooperative behavior acquisition for multiple autonomous mobile robots©ISAROB 2005

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K.Nakano, M. Obayashi, K. Kobayashi, T. Kuremoto (Yamaguchi University, Japan)
GS16-2 Multi-agent learning mechanism based on diversity of rules: from the view point of
LCS
H.Inoue (Kyoto University, ATR Network Informatics Laboratories, Japan)
Y.L. Suematsu (Kyoto University, ATR Network Informatics Laboratories, Japan)
K. Takadama (Science, Tokyo Institute of Technology ATR Network Informatics
Laboratories, Japan)
K. Shimohara (Kyoto University, ATR Network Informatics Laboratories Japan)
O. Katai (Kyoto University, Japan)
GS16-3 Gradual emergence of communication in multi-agent environment
S. Tensho(Nara Institute of Science and Technology, Japan)
S. Maekawa (National Institute of Information and Communications Technology, Japan)
J. Yoshimoto (Okinawa Institute of Science and Technology, Japan)
T. Shibata, S. Ishii (Nara Institute of Science and Technology, Japan)©ISAROB 2005

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Complexity Modelling and its Applications
Kazuyuki Aihara
Institute of Industrial Science, University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo153-8505, JapanandAihara Complexity Modelling Project, ERATO, JST
45-18 Oyama-cho, Shibuya-ku, Tokyo 151-0065, Japan
aihara@sat.t.u-tokyo.ac.jp
Abstract

Finally, I discuss importance of several bifaces inmodelling complex systems, namely generality / universality & individuality / specialty, abstracted & detailed, and stability & instability.

In this plenary talk, I review our study on creating an artificial brain with chaotic dynamics and its
possible applications.

keywords: Complexity, Chaos, Mathematical Modelling, Neural Networks, Combinatorial Optimization

References[1] T. Miyano, S. Kimoto, H. Shibuta, K. Nakashima,
Y. Ikenaga, and K. Aihara: “Time Series Analysis
and Prediction on Complex Dynamical Behavior
Observed in a Blast Furnace,” Physica D, Vol.135,
pp.305–330 (2000).

Recent progress in nonlinear systems analysis has
made possible mathematical modelling of complex
phenomena not only in natural systems but also inengineering systems [1, 2]. Among various complex
systems in this real world, the brain is a typical example of a complex system with much complexity andmany superior functions. In this plenary talk, I review
our study on creating an artificial brain with chaotic
dynamics, or a chaotic brain [3].
Biological neurons are highly nonlinear and dynamical devices. For example, we can observe chaotic
responses and different bifurcations in nonlinear dynamics of nerve membranes both experimentally withsquid giant axons and numerically with nerve equations [4]. The chaotic properties of nerve membranes
have provided a clue of making a chaotic brain composed of chaotic neural networks with spatio-temporalchaos, which are derived based on the experimentallyobserved neuronal chaos and described by coupled bimodal maps [5]. Moreover, the chaotic brain has
possible applications to biologically inspired computation like combinatorial optimization such as traveling salesman problems and quadratic assignment problems [6, 7] and hardware implementation with analog
electronic circuits [8, 9].

©ISAROB 2005

[2] K. Mitsubori and K. Aihara: “Delayed-feedback
Control of Chaotic Roll Motion of a Flooded Shipin Waves,” Proceedings of the Royal Society of London, Series A, Vol.458, pp.2801–2813 (2002).
[3] K. Aihara: “Chaos Engineering and its Application to Parallel Distributed Processing with
Chaotic Neural Networks,” Proceedings of theIEEE, Vol.90, No.5, pp.919–930 (2002).
[4] K. Aihara, G. Matsumoto, and Y. Ikegaya: “Periodic and Non–Periodic Responses of a Periodically Forced Hodgkin–Huxley Oscillator,” Journalof Theoretical Biology, Vol.109, pp.249–269 (1984).
[5] K. Aihara, T. Takabe, and M. Toyoda: “Chaotic
Neural Networks”, Physics Letters A, Vol.144,
No.6/7, pp.333–340 (1990).
[6] L. Chen and K. Aihara: “Strange Attractors in
Chaotic Neural Networks,” IEEE Trans. CAS–I,
Vol.47, No.10, pp.1455–1468 (2000).

1The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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[7] M. Hasegawa, T. Ikeguchi and K. Aihara: “Combination of Chaotic Neurodynamics with the 2-opt
Algorithm to Solve Traveling Salesman Problems”,
Physical Review Letters, Vol.79, No.12, pp.2344–
2347 (1997).
[8] Y. Horio, K. Aihara and O. Yamamoto: “Neuron–
Synapse IC Chip-Set for Large-Scale Chaotic Neural Networks,” IEEE Trans. Neural Networks,
Vol.14, No.5, pp.1393–1404 (2003).
[9] T. Kohno and K. Aihara: “A MOSFET-based
Model of a Class 2 Nerve Membrane,” IEEE Trans.
Neural Networks (in press).

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

THE WORK OF
SUEO UENO, ROBERT KALABA, AND RICHARD BELLMAN
Harriet H. Natsuyama
Los Angeles, CA 90066
hhnatsu@yahoo.com

Dick Bellman had already introduced the principle of
optimality and the concept of dynamic programming for
nonlinear optimization problems. Some numericalexamples had been worked out and more computations
were needed. Dynamic programming, I learned during
my later years, applies to life as well as manmade
systems

Abstract
This paper recalls the critical developments of three
exceptional individuals in the fields of nonlinear analysis,
systems modeling, computational solution of initial value
problems, system identification and optimal control.

Bob Kalaba had created the technique called
quasilinearization to solve nonlinear two-point boundary
value problems, such as those that arise when solving
Euler equations from the calculus of variations. One of
the first problems that we attacked together was that oforbit determination. Remember this was going on during
the early days of the space program. We formulated the
problem of estimating the orbit of a moving body whose
angular position has been observed at various instants oftime. In other words, a complete set of initial conditions
would be sought such that the theoretical orbit explained
the observations in a least squares sense. This is amultipoint boundary value problem.

1 Introduction
This paper was suggested by Masanori Sugisaka who
initiated the AROB symposia ten years ago and who
attributes their success to the inspiration andcontributions of three giants: Sueo Ueno, Robert Kalaba,
and Richard Bellman. They are special for their great
talents, productivity and personalities. I am privileged tohave worked with them since the early 1960s.
2 Decades of Research
My relationship with these three giants of modern
analysis and computing is very special to me. It goesback to the early days of the Rand Corporation, thequasi-governmental think tank in Santa Monica. In thedecade of the 1950s, after their World War II efforts hadceased, many of the top scientists from Los Alamos
National Laboratories joined the exciting new work at
Rand.

We extended Bob’s quasilinearization to handle themultiple conditions. We were delighted to see thatsolutions converged quadratically and we could
determine the missing initial conditions even when theinitial estimates were rather crude. We also introduced
noise into the measurements and it still worked. Then we
tried estimating the unknown mass of the moving object,letting its dynamical equation be that the time derivativeof mass is zero and requiring that its initial condition be
determined along with the other initial conditions for theequations of motion. This experiment was successful
also. The results were published in the Proceedings of
the National Academy of Sciences, and there were many,
many requests from aerospace companies for reprints ofthis paper.

John von Neumann developed the general-purpose
digital computer (as opposed to special purpose militarycomputers) in the basement and it was fondly called theJohnniac. Programmers used machine language to code
paper tapes. The field of computer science had yet to be
named, and the mathematicians using these computers
called themselves numerical analysts.
When I joined Rand in 1961, IBM had introduced theirmainframe computers and I learned Fortran by reading
the manual written by McCracken. Fresh out of graduate
school with a masters degree in physics, I had been hired
to assist Bob Kalaba and Dick Bellman in their
computational experiments.

©ISAROB 2005

A couple of years later, Sueo Ueno from Kyoto
University arrived at Rand. This distinguishedastrophysicist had been in correspondence with Dick
Bellman for a number of years. Dick wanted to learn
more about the multiple scattering of radiation throughslabs of finite thickness. Dick saw that there was a3

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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new library of Kyoto University where they are available
for researchers and students alike. Prof. Ueno lives
quietly in Yokohama.

resemblance with the problems of neutron transport
which he had dealt with at Los Alamos with Milt Wing
using the new method of invariant imbedding. But thephysical aspects seemed more complex. He wanted Prof.
Ueno to spend some time at Rand so they could apply
invariant imbedding to radiative transfer.

In the late sixties both Dick and Bob left Rand to become
professors at the University of Southern California. Dick,
who established a program in biomathematics, passed
away in 1984.

Prof. Ueno, during these correspondences, was spendinga year with Jacqueline Lenoble and other French
scientists in Paris, with side trips to visit Ida Busbridge in
Cambridge, Ambarzumian in Leningrad, and Sobolev in
Moscow. Then he had to return to Kyoto University for
another year before he could go abroad again. Thus it
was that he arrived in Santa Monica after I had been
there for a while.

Robert Kalaba held a joint appointment at the Universityof Southern California in the departments of economics
and biomedical engineering. There, he prepared a newgeneration of students and was well-loved by students
and staff alike. Bob passed away at the end of September,2004. Dr. Yueyue Fan, the last of Bob’s doctoral
students, and I are in the process of organizing Bob’s
papers so that a library can be established in his memory
at USC.

Prof. Ueno taught us the physical principles of multiple
scattering so that we could “count photons” for invariant
imbedding and derive initial value problems for the
Riccati equations of reflection functions. We, or rather it
was I, who wrote the Fortran programs for large systemsof ordinary differential equations with initial conditions.
When we integrated these equations using fourth-order
Runge-Kutta, the solutions agreed with those previously
published for very thin or very thick slabs. Furthermore,
we obtained solutions for all thicknesses between zeroand effectively infinity. We saw reflection functions thatno one else had seen before!

3 Summary
Richard Bellman, Robert Kalaba, and Sueo Ueno – tothese pioneers we owe great thanks. And I thank you foryour interest in continuing these explorations.
4. Bibliography
Richard E. Bellman: see Memorial Tributes, National
Academy of Engineering, Vol. 3, pp 22-29, 1989, and
The Bellman Continuum, Robert S. Roth (ed.), World
Scientific Pub. Co., 1986.

This research program in which I was involved led toProf. Ueno becoming my advisor for the doctoral degree
in uchu butsuri in the Department of Astrophysics of
Kyoto University. In particular I investigated anddemonstrated various techniques for solving inverse
problems of atmospheric physics as system identification
problems.

Robert E. Kalaba: see the special issue, Applied
Mathematics and Computation, Vol. 45, n. 2,
September 1991.
Sueo Ueno: see the special issue, Applied Mathematics
and Computation, Vol. 116, n. 1-2, November 2000, andTerrestrial Radiative Transfer: Modeling, Computation,
and Data Analysis, H. H. Natsuyama, S. Ueno, A.P.
Wang, Springer-Verlag Telos, 1998.

While the approach of invariant imbedding was
regarded by Bob and Dick as dynamic programming
without the optimization, it led to a nonlinear filter, apowerful extension of the Kalman filter. This filter wasdeveloped in collaboration with R. Sridhar, and further
developed by M. Sugisaka. Indeed, Prof. Sugisaka wasintroduced to this nonlinear filter by Sueo Ueno, and thatis how we came to meet each other and how I spent threedelightful months in his department at Oita University inthe fall of 1995.
In subsequent years, I had the opportunity to spend timeon various occasions with Prof. Ueno and learn about hislife and his work. By this time, he had retired from
Kyoto University and Kanazawa Institute of Technology,
and he was head of information systems at Kyoto
Computer Gakuin. We produced the Springer book onterrestrial radiative transfer with Alan Wang. I compiled
Prof. Ueno’s collected works and deposited them in the©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Machinery Cognition and Artificial Intelligent
M. Sugisaka
The Dept. of Electrical and Electronic
Engineering, Oita University, Oita,
Japan, 870msugi@ee.u-oita.edu.jp

Y.G. Zhang
Institute of Systems Science,
Academia Sinica, Beijing China,
100080yzhang@iss.ac.cn
HTU

UTH

HTU

UTH

ABSTRACT In this paper authors show some heuristic thinking first, and thenproposed an engineering definition of artificial intelligence in artificial brain that is
the abilities to obtain knowledge, to use knowledge and to operate knowledge. There
are still two open important problems: one is the expression of knowledge for thisgoal and the other is how to construct the integration up operation and detailed down
operation.
U

U

Keywords: Artificial Intelligence, Creating intelligence, Knowledge, cognition,
Evolution of intelligence.
U

I.

U

as memory of something, this king memory isdifferent from the process in computer. For
biological individuals, usually the memory inbrain is a gradual process to accept and strengtha cognized result. Once it is be memorized andunderstood, it is formed a scheme in which
possesses certain structure. The knowledge wetalked in this paper can be expressed as arelation of several schemes, and this relation iswith fixed pattern. So far, we did not see thiskind of knowledge warehouse. If we want toimplement a true artificial intelligence based on
knowledge using some equipments and machineor computer, machinery cognition must be
needed. Following this introduction we will
explain and describe more deep thinking.

Introduction

Animal Brain’s basic function is to process
the information received from the sensing
organs build in the body. That means it could
process the visual sensing information, auditory
sensing information and others simultaneously,
which include tactual, gustatory and olfactory
information. However, the observations resultsfrom the wolf boy told us that although the wolf
boy has human’s sensing organs and human’s
brain, but he has only the intelligence level ofwolf. The reason is that his “mother” is a wolf
but not human’s mother. His ability to cognize
the world surrounded him is kept in the level ofwolf. So, the appearance of intelligence is not
only depends on the physical device—Brain, butalso mother’s teaching and communication withthe “society” surrounded him.

II.

Emerge multiple-modal information1
TP

If we want to create a machine which
possesses certain intelligence, the machine has
to have two functions, one is to cognize theouter world by perceptron equipped on its“body”, the other is to accept the teaching andcommunication from “mother” (or “teacher")and the other members in the “society”
surrounded it. In animal world mammal motherteaches their child how to find food and avoid
enemy by her body language and very simple
primitive natural language (different voice).

Now many achievements about computer
vision and computer hearing appeared and new
equipments related them have been developed
very fast. The problem is how to emerge thiskind of information as the result of machinery
cognition.

2.1

SchemeGeneral speaking, the computer vision and
1

Usually, the results of cognition are theunderstanding outer world. It must be expressed©ISAROB 2005

PT

TP

5

This work is supported by Chinese Natural Science
Foundation, Number # 60275016
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hearing are different type of information, vision
is image information and hearing is sonicinformation, processing methods of them are
different. When mother shoes baby an apple inher hand it give baby a impression that consistof a moving pictures and mother’s voice or
language, baby memories the both components
together simultaneously in his/her brain, though
they are input from different path. This
impression is on a concrete subject or thing with
their attributes, sometimes the impression is onan action with dynamic atributtes. We say allthese presentative impressions as a scheme, they
are the basic components of memories.

key from scheme to presentative knowledge.

2.3

From the above we could try to explain
the structure of presentative knowledge. First,
the schemes are divided into several groups
according to the noun, verb, adjective, andadverb and so on. The relations are definedaccording to grammar. The relations aregrouped as several layers, such that “fragment”,“very simple”, “simple”, “usual”, “complex”,“very complex”, etc. any relation in thosegroups will combine some schemes, thiscombination is very similar to the structure of asentence but it is not a sentence. A combination
corresponds a mapping between the semantic of
combination and a real scene or subject. So, theknowledge expression is to define those rules
grouped schemes as different layers.

To express a scheme in computer we need aunit, in which store a presentative impression
which may be a subject image plus a nounsound, or an action moving picture plus a verb
sound. Of course, for real biological individual
this unit (the scheme) not only includes visionaland hearing information, but also includes
tactual, gustatory and olfactory information.
However, to implement machinery cognition welimit our attention into the computer vision andcomputer hearing. Once we build up this kind ofunit in computer we should build up therelations between units and to form knowledge.

2.2

Examples of the relations:“fragment”:(Food--Noun scheme);(Eating--Action scheme);(Color—Adjective scheme).
“very simple”:(Eating）+ (food).
“usual”:(Look) + ( at ) + (here).

From scheme to knowledge

Scheme is essentially some primitive
knowledge due to it has incomplete structure ofknowledge comparison with human knowledge
structure, even if for a human child. Scheme is
only a fragment of knowledge discussed in thispaper and it is not the final result of cognition.
In this paper we try to discuss the knowledge
with certain structure and similar to human’s
knowledge, or the evolutionary configuration ofknowledge. In fact, we could observe theprocess of mother teaching baby language andyou could find that “mother” use natural
language to make a link of schemes stored inbaby’s brain. Natural language is the link
between scheme and human baby knowledge. At
beginning the human baby could know mother’s
face, the taste of mother’s milk, some toys andmother’s sound, and then they try to understandsome very simple repetitive worlds. The baby
gradually learned them and could repeat them.
Thus baby got some schemes and obtained
primitive knowledge about the surroundedworld. Later they could understand some simple
sentence; they start to get complete knowledge.

The people in different countries speak
various languages, and the grammar is different.
The rules defined groups of relation layers isreally different, however, if the mapping
between the semantic and the scene or subject isthe same we could say that it means the sameknowledge.
If using several combination to construct
a bigger combination, it is similar to aparagraph consists of several sentences in a
article. At this time the mapping is a composite
of mapping. Note, the structure of knowledge
must can be extended in the same manner. So,
the knowledge in a human brain is definitely notisolated, it is similar to an article or a tree, it has
fractal property.

2.4

Knowledge warehouse

The result of machinery cognition is tocombine with the schemes as the presentativeknowledge. So, we expect a special storage
form in computer, in which there are “units” tosave schemes simultaneously formed by using

So, we say that the natural language is the©ISAROB 2005

Structure of presentativeknowledge

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

IV.

of machinery visual and sonic perceptron; inwhich there are defined rules to constructknowledge; also, the real knowledge--mapping
to obtained.

To study machinery cognition is try toinvestigate the new ways creating artificial
intelligence. Once artificial brain is mounted
computer vision and hearing the machinery
cognition will be basic component. We cannot
expect artificial brain has the same intelligencelike human being’s, however, our research focus
on the creation of intelligence using artificial
brain. We could hope that to do someexperiments on it. What kind intelligence could
appear on artificial brain? We limit thedefinition here as the following:“Intelligence” is the abilities that to
obtain knowledge, to use knowledge and tooperate knowledge.
Based on our analysis here we want topromote the study of knowledge based artificial
brain. We propose the key techniques on the
research as the following:a. Knowledge expression;b. Knowledge warehouse;c. Knowledge emergence and operation.
After these key techniques were implemented,
we could play as “mother” to teach and train theartificial brain gradually. The human being’s
intelligence is evolutionary result with theevolutionary process of human being’s natural
language. So, artificial intelligence mounted on
artificial brain should be evolutionary either.

This kind of knowledge warehouse mustbe parallel operation. The storage of scheme
may be not very difficult, but the retrieve ofscheme may be more difficult.

III.

Knowledge’s emergence

Machinery perceptrons are not enough if
we want to create an Artificial Brain. Naturally,
it concern the question that what is knowledge
stored in computer? How dose the knowledge isemerged from the information cognized bycomputer vision and computer hearing? The firstquestion has been discussed previously. The
second question concerns with a very difficult
field that the creation of intelligence or
knowledge emergence.
In fact, knowledge emergence is a process
to operate knowledge. It contains two operations;
one is the detail down process. This operation
make the obtained knowledge has more detailed
contents. On the other hand, baby can find thecommon attributes between some similar
schemes himself or by mother’s teaching, forexample, baby learned “apple”, “strawberry”,“pear” and know all these are eatable and a newscheme “fruit” was established. This is also a
process to operate knowledge; we say it as anintegrated up process. This process maybe
cause knowledge emergence. Both processes areto make the relation and mapping between
schemes and subject (or scene) more
complicated. Knowledge emergence will makeall knowledge has the free-scale structure

REFERENCES[1] Zhang,Y.G. and Sugisaka,M., “Might we train an
artificial brain as a baby?”, The Proceedings of theseventh international symposium on artificial life androbotics (AROB’02), pp.92-97, Beppu, Japan,2002[2] Zhang, Y.G. and Sugisaka. M., “KANSEI Technology
and Artificial Brain”, (invited talk) The Proceedingsof International Symposium on KANSEI technology,
Muroran Institute of Technology, Hokkaido, Japan,
October 5-6, 2001
[3] Zhang, Y.G., Sugisaka, M. and Wu, X.Q., “Bottom-up
Way To Development of Multi-Cellular Digital
Organization”, Journal of Artificial Life and Robotics,
Springer(Tokyo), No. 3, Vol.5, 2001.
[4] Zhang, Y.G. and Sugisaka, M., “Trainable Artificial
brain (TAB)”, The Proceedings of International
Symposium of Knowledge and Systems Science(KSS), Dalian, China, September, 2001
[5] Zhang, Y.G., “Zhang, Y.G. and Sugisaka, M, “A mode
to create artificial intelligence—From knowledge tointelligence”, The proceedings of The nineth
international symposium on artificial life and robotics(AROB’04), pp. I-28-31, Beppu, Japan, 2004

A lot of cognitive and psychological study
experiments for children’s intelligence tell us
how they cognize outside environment andobtain knowledge, these are very important.
Here we want to emphasize mother’s role. At
beginning period of babyhood they just could
see some objects surrounding them and could
hear the voice from mother mainly. Mother’svoice and gesture language make baby establish
a mapping gradually. This mapping is theprocess to establish a structure to fit attributes ofscheme Of course, not only vision and hearing.
This mapping will store in baby’s brain. When
this mapping was established firmly, the baby
had obtained knowledge.
©ISAROB 2005

Intelligence on artificial
brain

U

U

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Environment

ObjectRelation 1

Structure ofknowledge

Sensitive
equipment
Schemes

ObjectRelation N

Machinery Cognition©ISAROB 2005

Knowledge forming

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

ATRON Hardware Modules for Self-reconfigurable Robotics
Henrik Hautop Lund, Richard Beck, Lars Dalgaard
Maersk Mc-Kinney Moller Institute for Production Technology
University of Southern Denmark, Campusvej 55, 5230 Odense M., Denmark
hhl@mip.sdu.dk beck@mip.sdu.dk dalgaard@mip.sdu.dk
www.adaptronics.dk

perform an extensive comparison of state-of-theart, and afterwards make several design,realization and evaluation cycles for developing
the final, mechanical solution for the modules.
The biological designs pointed us towards a
minimal design of “cells” that each should bevery simple, but provide extensive possibilities
for self-design and self-repair when combined inhuge numbers. The survey of the state-of-the-art[1] told us that such a system based on these
considerations would be novel. Related work
includes the CONRO [2] and the M-TRAN [3]self-reconfigurable robotic systems. Other
approaches include the Telecubes [5], the
PolyBots [6] and the Crystalline [7]. Based on
the inspiration from cell biology, we wanted todesign a module to be simple and provide simple
ways of avoiding many of the motion constraints
known from other systems.

Abstract
We exploit a holistic behavioural andmorphological adaptation in the design of new
artefacts, and exemplify the potential of the new
design principle through the construction ofrobotic systems that can change morphology.
Here we present the ATRON design in which themodules are individually simple, attach throughphysical connections, and perform 3D motions
by collective actions. We produced 100 ATRON
modules, and performed both simulation and realworld experiments. In this paper, we report on
the ATRON hardware design and investigations
related to the verification of the suitability of the
ATRON module design for self-reconfigurable
robotics.

Introduction
One of the main objectives of our work is toprovide an architecture made up of simple
building blocks, simple connections, and simple
interactions, in order to allow end-users of ourapproach to design new artefacts in an easymanner. Indeed, the ATRON modules are simple
building blocks that can be viewed as simple“cells” or “atoms” in a larger system composedof numerous of these individually simple
building blocks. Ultimately, the design should
allow a suitable way of performing selfreconfiguration by limiting the motion
constraints in the system of building blocks asmuch as possible.
The connector mechanism of selfreconfigurable robots is known to be difficult todesign because of the many constraints on theconnector mechanism. Therefore, this problem
was addressed with highest priority, since 3D
motion in a terrestrial scenario demands
attachment of modules to each other.
The methodology for choosing the rightdesign for the connectors was to extract
inspiration from the biological designs and to©ISAROB 2005

Figure 1. ATRON modules for self-reconfigurable
robotics.

Module Design
The ATRON modules are placed in a surfacecentred cubic lattice structure that corresponds tothe titanium atoms in the CuTi3 crystal lattice.
The basic idea behind the ATRON modules is tohave two half cells joint together by a rotation
mechanism. On each half cell, there are two
female and two actuated male connectors, bywhich a module can connect to the neighbouring

9The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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modules. A major advantage for reaching theobjective of producing many modules is that the
ATRON modules are designed to behomogenous. Also, an ATRON module canswitch or rotate either rotation axis 180°,
maintaining the same global function of themodule. Further, the shape allows one module tomove to an adjacent hole in an otherwise fully
packed structure (without colliding with othermodules). So the module design seems to fulfill
our objectives. Indeed, the design was guided by
the considerations on how to reduce control
complexity of self-reconfiguration by anappropriate module design. However, the designthat meets these objectives demands strong andreliable point-to-point connectors, which also isachieved in the ATRON module with the novel,
mechanical connectors, which are fast, strong
and reliable.
A module may communicate withneighbouring
modulesthroughIRcommunication. When placed in the surfacecentred cubic lattice structure, the modules canmove in this structure to self-reconfigure into
different overall arrangements or movements.

Mechanical Design
The mechanical design was guided by thedemand for strong point-to-point connectors inmodules being able to lift another two modules
and being placed in the surface-centred cubic
lattice structure.

Figure 3. An ATRON module:
hemisphere, b) southern hemisphere.

Northern

Connectors
Based on the knowledge gained from thesurvey of state-of-the-art, three alternative
connector designs were developed andconsidered. The first was based on a screw for
holding two connected modules together. The
second connector design was based on a pushingblock, while the third is based on a triangularconfiguration of hooks, with a large base line forgood rigidity. The screw mechanism was used in
the first prototype, but shown not to be robust.
Therefore, this design was abandoned. The
remaining two approaches originated from thesame idea and conformed to the same overall
constraints. They differed in several mechanicalaspects, the major differences between them
being in the mechanical parts, which physicallyconnect two neighbouring modules and in therotation mechanism coupling the twohemispheres of the ATRON module. The twoalternative connector designs were tested using
two different hardware prototypes, and bothconnector prototypes performed comparably onmost criteria, but the hook-based design was
found to be most robust. The pushing-block
design had a “weak” direction in which it
sometimes would lock due to friction forces. For
this reason the hook-based design was selected
for the final design of the ATRON modules forthe terrestrial scenario.

Figure 2. The first, second, and third (final) hardware
prototype of the ATRON modules.

If a first ATRON module is attached to asecond neighbouring module and detached onother connection points, the second neighbouring
module may move the first ATRON module byturning around equator with the rotation
mechanism. Hence, the first ATRON module
may be moved to another position in the lattice
structure where it may attach itself to anothermodule in the structure and, for instance, detachitself from the second ATRON module thattransported it to the new position.
As illustrated above, the reconfiguration of
the overall system becomes a process oftransitions in the lattice structure. Simulations(e.g. [xx, xx]) showed that, if we can perform theindividual transitions in the hardware
implementation in a reliable manner, numerous
distributed control possibilities exist and willlead to self-reconfigurable and mobile systems.
Hence, the module design and tests described
below were guided by this demand.

©ISAROB 2005a)

Active Connector
The push mechanism in the active maleconnectors is designed in such a way that a leadscrew is used to transmit rotation to linear
movement, and both lead screws and motors are
installed in the same frame as the passive female

10The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

slipring has been gold plated to ensure stableelectrical connections. Furthermore the reflection
abilities for the optical encoders are enhanced
using a black diffuse background for better
contrast (the outer fields are for the opticalencoder). The encoders and carbon shoes areillustrated in figure 5a.

connector parts. Figure 4 right illustrates thefemale frame parts where the motor and leadscrew are installed. The push mechanism has
been designed so that it is self-locking and very
stiff, thereby ensuring that the connection
between modules is always stable. The
remaining parts of the active male connector
mechanism are illustrated in figure 4 left.
The new teflon coated lead screw used in theactuation mechanism of the active maleconnectors has reduced the required torque fromthe connector motor, such that the connection
time could be reduced to two seconds (the better
efficiency compared to earlier prototypes made it
possible to achieve the same force using a threadwith greater pitch).

Figure 5. Centre arrangement with gears and slip ringfor transferring power and data between the two half
spheres of the ATRON module. a) northernhemisphere, b) southern hemisphere.

Rotational Lock
The mechanism to drive the rotation of themodule consists of the 1-stage planet gear, andthe industrial planet gearbox and is thus not a
self-locking transmission. The module has
therefore been equipped with a solenoid that candrive a bar into rotational lock holes placed inboth the northern and southern centre plates. The
holes are positioned, such that the rotation can belocked in 90° intervals.
Figure 4. Left: Male hook that may transfer positive
voltage. Right: An electrical isolated plate behind thefemale connector allows the male hook to touch theplate and power-share.

Wheel
The centre plates are made circular to aid
manufacturing but also to add “wheel
functionality” to the module. One of the centre
plates is equipped with a O-ring (see figure 1).
The O-ring acts as a tire, and a module cantherefore be used as a wheel when placed in an
upright position. This facilitates the creation ofwheeling organisms.

Centre Arrangement
A 1-stage planet gear in the centre of themodule reduces the load requirements of theindustrial gearbox with a factor of 118/10.
Therefore the size of the gearbox is reduced
compared to earlier prototypes, such that thecentre motor and gearbox are fully contained in
the northern hemisphere of the module.
Also, the identical frames used in thenorthern and southern hemispheres only differ intheir centre part.

Shell
The plastic shell illustrated in figure 1 has
been designed to protect the electric andmechanical parts of the module and to ease thevisual distinction and orientation of each modulein a given structure. The shell has been producedin four different colors, which extends the visual
distinction. Since the shell also gives a module asurface the infra-red proximity detection is mademore stable and reliable. The shell also gives the
ATRON module a more compact look.

Slip Ring
To facilitate the ATRON module with intramodule power and signal distribution overequator, a slipring combined with carbon shoes
has been installed in the module. The slipring isshown in figure 4b, where the inner five rings areused for electrical signals. The specialized©ISAROB 2005

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no matter the voltage level of connected modules
or external power supply.
The South-AT8 is also connected to an
ATmega128 (henceforth denoted South-AT128)
through the I2C-bus which is responsible forcontrolling the IR communication to and fromthe Southern Hemisphere and also for opening
and closing the two actuated male connectors.
The two ATmega128 run at a clock frequencyof 16 MHz and the two ATmega8 at 1 MHz.
The Northern and Southern Hemispheres areelectrically connected through the rotational
centre axis through a gold plated slipring (seeabove) with which carbon shoes maintain electrical contact also during rotation. The communication between the two hemispheres is conducted
through a RS485 network of which the NorthAT128 and the South-AT128 are the only nodes.
The encoder disc for measuring the rotationalangel is also placed on the slipring but is read
optically. Figure 6 shows a block diagram of theelectronic components in an ATRON module towhich unit numbers in the following refer.

Electronics Design
Every ATRON module has, besides its fivemain actuators, a solenoid actuator, a tilt sensor
for measuring its two tilt angles with respect tothe horizontal plane, eight Light Emitting Diodes(LEDs) useful for easy readout, a solenoid
controlled plug for keeping the rotational angle
between the two hemispheres at strict 90°
intervals and an encoder disc placed
perpendicular to the centre axis for measuring
both the absolute rotational angle and relativerotational angle (for velocity calculations).
For neighbouring modules to intercommunicate, each connector is equipped withan infra red (IR) light transmitter and receiver
allowing a wireless communication channel to beestablished between two modules. The IR diodes
are also used for simple distance measurements.
Each ATRON module also contains two
rechargeable Lithium-Ion Polymer batteries
wired to enable power-sharing among connected
ATRON modules. Power-sharing is necessary to
power compensate the more motion active
modules with power from the more motion
passive modules. This is implemented throughthe connectors such that a mechanical connection
between two ATRON modules also results in anelectrical connection between the modules. A
consequence of this is that re-charging thebatteries of an ATRON module can be done even
if it is placed within a structure formed byseveral ATRON modules.
To be able to electronically control all these
hardware components several printed circuitboards (PCB) were constructed. On the Northern
Hemisphere an ATmega8 (henceforth denoted
North-AT8) microcontroller from ATMEL is
responsible for reading the tilt sensor, controllingthe centre motor in conjunction with the encoder
disc, toggling the solenoid plug and opening andclosing the two actuated male connectors.
The North-AT8 is also connected to an
ATmega128 (henceforth denoted North-AT128)
through the I2C-bus. The North-AT128 is
responsible for controlling the IR communication
to and from the Northern Hemisphere but its
main task is to function as the main coordinatorof the components of the entire module (i.e. themain part of the ATRONcontroller).
On the Southern Hemisphere an ATmega8(henceforth denoted South-AT8) is used for
implementing the power control of the ATRON
module. That is, charging the battery and making
sure that a correct internal voltage levels is kept

©ISAROB 2005

Figure 6. Overview of the electronic components in an
ATRON module.

Tilt Sensor Interface
The Tilt Sensor Interface produces two DC
voltage levels to the North-128 proportional to
the level of tilt (±90 degrees) in the planar x andy axis.
Connector Motor Interface
The connector actuator (figure 6, Unit 6 andUnit 13) is used for actuating the two male,active parts of a connection mechanism in eachhemisphere. The actuator is a single DC motor
rotating the arms until a firm connection isachieved.

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IR Interface
Unit 5 and Unit 10 are responsible for the IR
communication and proximity measurement.
Each hemisphere has four IR send-receive pairs(channels) able to communicate via the IrDA
protocol at 9600 Baud or they can be used forproximity measurements. However, only one
channel can be used at a time for either
communication or for proximity measurement oneach hemisphere.
The communication part is implemented
using an IrDA physical-layer controller(MCP2120) that interfaces directly to the RS232
serial port of the North-AT128 and South-AT128
and the channel to use is selected by IO-pins.
The proximity measurement is implemented
mostly in, but it relies on the ability of changing
the strength of the light emitted thus a power
control circuit was implemented by low-passfiltering a microcontroller generated PWM
signal.

Power electronics
The power electronics (Unit 6a and Unit 13a)used for controlling the DC motor is a fullbridge
H-bridge build from an integrated circuit A3966.
Feedback
The only feedback from the actuators (Unit
6b and Unit 13b) that gives important
information on the actuation is the power
consumed by the motors. This is monitored
through shunt resistors and when the current
consumption reaches its highest level the arms
are either fully extended or retracted and the
motors are therefore stalled. This information isused to disable the motors to avoid overloading.
Centre Motor Interface
The Centre Motor Interface (Unit 14 called
Main Actuator) consists of a brushless AC motor
controlled by a dedicated driver circuit generating the necessary control signals (ASIC5660)
and delivering the necessary power (L6234).
The torque delivered to the motor iscontrolled from the North-AT8 which generates a
PWM signal to the ASIC5660 with a duty-cycle
proportional to the desired torque. Two output
pins from the North-AT8 controls the rotational
direction and brake status (on/off), respectively.

RS485 Interface
The RS485 Interface is implemented using
two RS485 line-drivers connected through theslipring. One line-driver is connected to theRS232 serial interface of the North-AT128 andthe other similarly to the South-AT128.
LED Interface
The LED Interface consists of 8 LEDs which
can be toggled on/off by the North-AT128.

Encoder Interface
The Encoder Interface implements the opticalreader of the three outer rings of the slipring (seeFigure 5b). The outermost ring has 108 “slots”
which are read optically by two infrared readers(see Figure 5a) phase shifted 90° with respect toeach other. This means that in one rotational
direction one signal lags the other and if therotational direction is changed it will instead leadthe other. By feeding these signals to a D typeFlip-Flop using one signal as clock and the otheras data, the output will be either high or
low depending on the rotational direction. This
information along with the “clock” is read by theNorth-AT8.
The other two rings on the slipring implementa 2-bit gray-code (meaning only one bit changes
at a time) allowing detection of 90° intervals
when a transition occurs. This is used for
accurate positioning of the rotational angle and isalso read by the North-AT8.

Power Interface
The power management unit (Unit 1) ismainly responsible for supplying the ATRON
module with power based on the current state of
the batteries, the unregulated power and thecurrent energy consumption. In addition, if thepower manager detects that the voltage on theunregulated power supply meets a certain
criterion it knows that the ATRON module isconnected to a recharger and informs the battery
charger that it may start charging the batteries.
The power management unit is supported by four
subparts each of which is described below:
Sharemanager
The Sharemanager (Unit 1a) pays attention to
the modules battery supply and compares it to
the voltage on the unregulated power supply anddecides if it is safe to share power with other
ATRON modules. The Sharemanager can choose
how much current to deliver to the bus and, if
necessary, entirely turn off the sharing.

Solenoid Interface
The Solenoid Interface (Unit 11) delivers
power to the solenoid that drives the metal bar
for the rotational lock mechanism. The solenoid
is controlled by the North-AT8.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Charger
The charging unit (Unit 1b) maintains all
aspects of the actual charging of the battery pack
and is ordered to start or stop charging by thepower manager. Since the batteries are coupled
and thus charged in series it is essential that thevoltage difference between the two batteries is
kept very low to avoid that the battery with thelowest potential drains the other.
This prevents proper charging and could
potentially lead to battery malfunction. To
account for this a voltage divider has been placed
across one of the batteries and the voltage ismeasured by an AD-converter and compared tothe series voltage also measured. This allows thepower circuit to detect a skew voltage level andto react by preventing battery charging (however,
we have also implemented a manual disconnectof the voltage divider when the module isinactive).

Module Tests
In order to test the mechanical andelectronical design of the ATRON module, we
performed a number of simple tests before goingto the larger experiments related to selfreconfiguration and self-repair. These simple
tests are reported below.
Battery Discharge Test
In order to test the operational time of an
ATRON module with fully charged batteries, aburnout test of a module was performed. The
code executed was a repetitious open-closesequence of the male connectors on the Northern
Hemisphere. Current measurements during thetest showed that the current used was somewhere
between 200-400mA which is about half the
maximum level. It was estimated that this level isa good representation of an average working
condition.

Battery packThe batteries (Unit 1c) powering a single
module consists of two series coupled IonLithium Polymer batteries which can be charged
to a maximum level of 4.2V each, giving a totalof 8.4V maximum. Experiments on theiroperational time (before recharging is necessary)
were conducted (see below) and the conclusion
was that the batteries could sustain about 150
minutes at medium current load (300 mA).

Figure 7. The battery discharge curve during burn-out
test.

Power Selector
The power selector (Unit 1d) monitors thevoltage drop across the batteries and the voltage
on the unregulated power supply. The highest is
selected and used for supply. This ensures that a
module will consume power from the sourcewith the highest energy supply at any time.

Figure 7 shows the voltage over the twoseries coupled batteries as the experiment
progressed. It is noted that the starting level was
8.3V and after about 150 min. the voltage level
had dropped to about 7.2V. From here it drops
rapidly to about 6.0V in only 15 min. This
discharge curve is consistent with the supplied
data sheet for the batteries which also states thatthe battery voltage of any one battery must not
fall below 3V where it may suffer damage.
From these information it can be concluded
that 7.2V across the batteries is a good and safe
choice for indicating that the batteries urgently
need recharging. Furthermore the test showedthat about 150 min. of operational time can be
expected on fully charged batteries, which isacceptable.

Power converter
The unregulated power supplied from thePower Manager (Unit 1) in the Southern
Hemisphere is passed through to the Northern
Hemisphere. The Power Electronics (Unit 6a,
Unit 11a, Unit 13a and Unit 14a) is directly
connected to the unregulated power.
In each hemisphere the unregulated power isdown converted to 5V by the power converter
units (Unit 3 and Unit 8). The 5V from the power
converters is needed to drive the basic electronic
components and the micro controllers.

©ISAROB 2005

Mechanical Deformation Test
A mechanical test has been performed, tomeasure the vertical deformation of a module
structure due to slackness, elasticity and

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In figure 9 a) Module B has started aconnection to module C. In b) an electricalconnection of the power sharing circuit has been
established and the LEDs are turned on (marked
with a circle). In c) the mechanical connection
has completed and the two connectors in theupper hemisphere of module C is being actuated.
In d) the connectors are fully extended.

inaccuracies in the manufactured components
and the angular tilt of the ball bearings. Figure 8
shows the setup used for the test where the redline illustrates a horizontal laser beam. The
points A and B indicate where the deformation
was measured.
A laser was placed on a table on the left sideon the picture (not shown). The laser beam waslevelled out horizontally by a mirror fixed to thesteel bar seen on the right side of the picture andwas adjusted such that the reflected beam wasreturned to the same horizontal level as its origin.
After this the mirror was replaced by the module
structure as shown in the picture such that thelaser beam met the topmost point of the module
attached to the steel bar (denoted A in the
picture). A piece of card board was fixed at the
topmost point on the leftmost module and thelaser dot was marked at point B and the verticaldisplacement (the deformation) was measured tobe 3.1mm. This value is surprisingly low but
underlines the fact that the mechanicalcontruction is very stiff.
According to the manufacturer of the bearing
the maximum angular tilt of the bearing is
0.0009 radians which result in a maximum
vertical deformation due to the bearing of
0.42mm. Since our measurement shows a total
deformation of 3.1mm, the remaining 2.68mm
are therefore due to slackness, inaccuracies andelasticity in the components.

Figure 9. The setup for the power sharing test.

Rotation Test
In Figure 10 a setup for testing the rotationalload abilities of an ATRON module is shown.
The four pictures are screenshots from a video
where the battery powered module B
continuously rotates the two passive modules C
and D. To obtain a stable platform, module B isconnected to module A which is a half passive
module fixed to metal plate.

Figure 8. The setup for the mechanical deformation
test.

Power Sharing Test
This test verifies that the modules are able toshare power through their power sharing
facilities. Figure 9 shows 4 pictures dumped
form a video which illustrate a successful power
sharing test.
Module A in figure 9 a) is a half passive
module fixed to metal plate, and its power
sharing circuit is externally connected to a power
supply. Module B is switched off and is therefore
supplied through its connection with module A.
Module C is switched off.

©ISAROB 2005

Figure 10. The setup for the rotation test.

In Figure 10 a) the initial position is shown.
The rotation is initiated with an angular velocity
of approximately 0.8 rad/s. In b) the modules
have been rotated about 45° in counterclockwise
direction and are approaching the 90° position c)where the inertial load is greatest.

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Here the rotation is stopped and the solenoid
lock is activated holding the modules in place for
3 sec. Hereafter the lock is released and therotation continues as seen in d) pausing at the
180°, 270°, 360°, and 90° positions.
One of our initial design criteria for an
ATRON module’s rotational load abilities wasthat it should be able to smoothly rotate twoother modules in a configuration as seen on
Figure 10. This criterion was required to allow
the overall system to have the necessary degreesof-freedom to build complex structures and tolimit motion constraints, and has now beenverified to hold for the physical platform.

for future miniaturization. Of great importance isthe connector mechanism that is strong, fast andreliable despite being a point-to-point connector.
Tests showed that the modules connect evenwhen misaligned in the three dimensions, that a
module can lift another two modules, that onboard batteries provide energy for approximately
150 minutes, and that power sharing between
modules may be possible with the design
outlined in this paper. We therefore believe thedesign to be suitable for self-reconfiguration andself-repair experiments (e.g. [8]), and will showsuch in future work with the 100 ATRON
modules that we have produced now.

Misalignment Test
The purpose of this test is to show the
ATRON module’s abilities to connect tomisaligned modules. Figure 11 shows 4 picturesof a successful connection between an active
module and a passive misaligned module. In a)
the active module has started the connection
process. In b) the passive module is pulled
towards the active module. In c) the passive
module is twisted into its right lattice position.

Acknowledgements
This work is partly funded by the EU FET
project HYDRA, and the Self-assembling
Robotic Artefacts project sponsored by theDanish Technical Science Council. C. Ryberg,
D. Kyrping, D. Brandt, D. Christensen, E.
Østergaard, J. Hallam, K. Støy, K. Kassow, L.
Paramonov have contributed to the ATRON.

References[1] E. Østergaard. Ph.D. dissertation, Universityof Southern Denmark, December 2004.
[2] W.-M. Shen and P. Will. Docking in selfreconfigurable robots. In Proceedings IEEE/RSJ
Intl. Conf. on Intelligent Robots and Systems(IROS), pp 1049–1054, 2001.
[3] S. Murata, E. Yoshida, K. Tomita, H.
Kurokawa, A. Kamimura, and S. Kokaji.
Hardware design of modular robotic system. In
Proceeding of Intl. Conf. on Intelligent Robots
and Systems (IROS), pp 2210–2217, 2000.
[5] J.W. Suh, S.B. Homans, and M. Yim.
Telecubes: mechanical design of a module forself-reconfigurable robotics. In Proceedings on
the IEEE Intl. Conf. on Robotics and Automation(ICRA), pp 4095–4101, 2002.
[6] Craig Eldershaw, Mark Yim, David Duff,
Kimon Roufas, and Ying Zhang. Modular selfreconfigurable robots. In Robotics for future land
warfare seminar and workshop. Defence Science
Technology Organisation, Adelaine, 2002.
[7] D. Rus and M. Vona. A physicalimplementation of the crystalline robot. In
Proceedings of IEEE Intl. Conf. on Robotics &Automation (ICRA), pp 1726–1733, 2000.
[8] E. Østergaard and H. H. Lund. Distributed
cluster walk for the ATRON self-reconfigurable
robot. In Proceedings of the The 8th Conferenceon Intelligent Autonomous Systems (IAS-8), pp.
291– 298, 2004.

Figure 11. The setup for the misalignment test.

The misalignment corrections guides areillustrated in figure 12. The construction allows
misalignment corrections of ± 5.7mm in the x
direction, and at least ± 3.0mm in the y direction.

Figure 12. CAD drawings illustrating the possible
misalignment corrections. (Left) The female framepart with guides, which allow misalignment
corrections of ± 5.7mm in the x direction. (Right) The
lower active male arm, which allows misalignment
corrections of at least ± 3.0mm in the y direction.

Conclusion
We developed the ATRON design as simple
building blocks with simple connections for
simple interactions, in order to provide a design
suitable for performing self-reconfiguration and©ISAROB 2005

16

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Improvement of Real-coded Genetic Algorithms
for Optimization Problems
Hiroshi Kinjo, Hiroki Nakanishi and Tetsuhiko Yamamoto
Duong Sam Chaukinjo,hiroki,yamamoto@mibai.tec.u-ryukyu.ac.jp
Faculty of Electric Mechanics
Faculty of Engineering, University of the Ryukyus
Hanoi Agricultural University
Nishihara, Okinawa 903-0213, Japan
Gialam, Hanoi, Vietnam
Abstract

genes [4]. The crossover operators of the real-coded
GA called the blend crossover (BLX-α). The BLX
has a range parameter that determines the production
ranges of the offspring. The evolution performance of
GAs is dependent on the value of the range parameter.
Selection the range parameter is determined by trial
and error. However, the determination of the range
parameter is sometimes difficult.
The BLX is an offspring production method using arandom number of uniform distributions based on theintervals of two parents and a range parameter. Thatis, the method is not based on the parents them selves
but on the intervals between parents. We consider that
offspring production should be based on the parents
them selves. Based on the above-mentioned idea, inthis paper, we present a new method, i.e., a close-toparent crossover. Furthermore we applied a mutation
operator to the crossover system to improve the search
performance for optimization problems.
In section 2, we describe the close-to-parent offspring production method. In section 3, we describe
the search performance of the crossover for three twovariable functions. In section 4, we describe the evolution performance for a neural network training problem. In section 5, we conclude this paper.

Genetic algorithms (GAs) are widely used in solving optimization problems. In this paper, we present
a crossover method used in real-coded GAs for
multivariable optimization problems. A well-known
crossover operator of real-coded GAs is the blend
crossover (BLX). The BLX has a range parameter that
determines the offspring production range. The search
performance of the GA depends on the value of therange parameter. However, determination of the range
parameter is sometimes difficult. In this paper, we
propose a crossover operator for real-coded GAs thatis a close-to-parent offspring. The crossover is based on
the idea that offspring should be close to their parents.
In order to improve the evolution performance, we applied a mutation operator to the real-coded GA. Simulation shows that the use of a close-to-parent crossoverwith the mutation operator effectively improves search
performance.
Keywords: Real-coded GA, Crossover method,
Close-to-parent offspring, Optimization problem.

1

Introduction

Recently, genetic algorithms (GAs) have been applied widely and effectively in various fields [1]-[3]. The
most useful property of GAs is their ability to solve
search and optimization problems with very little required information about the problems. The performance of GAs depends, to a great extent, on the performance of the crossover operator used. The crossover
operation is performed upon the selected chromosome.
The crossover operates on two chromosomes at a timeand generates offspring by combining the features ofboth chromosomes. A chromosome is usually a binary bit string, but not necessarily. There are several
different variants of basic GAs. It is possible to use
real-coded (or floating-point) genes and actually, several methods have been presented that use real-coded©ISAROB 2005

2

Close-to-parent offspring

Figure 1 (a) shows, a conventional crossover, theBLX. In the figure, offspring are produced using random values on a uniform distribution in the range of[p1 − αI, p2 + αI], where p1 and p2 are the real values of the parents and α is the range parameter. α
determines the search area of the offspring.
Figure 1(b) shows, the proposed crossover, that is,the close-to-parent offspring (CPO). CPO also has arange parameter α that determines the offspring production range based on each parent. For producing
offspring using CPO, two individuals are produced using random values that have uniform distributions in

17

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

P2

Successful evolution [%]

P1

I
αI

αI
(a) BLX

P1

P2

Successful evolution [%]

P1’

P2

I

I

αI

αI

0
0.5

1.0
α

1.5

2.0

the ranges of [p1 − αI, p1 + αI] and [p2 − αI, p2 + αI].
Figure 1 (c) shows a mutation of the CPO. In the
figure, p01 denotes the center of the range of the mutation of parent p1 . The range of the offspring applied
to the mutation is [p01 − αI, p01 + αI].

In this section we investigate the search performance of the proposed method for optimization problems for three two-variable functions: Sphere function,
Rosenbrock function, and Rastrigin function [5].
The search performance is measured using successful evolution rates obtained from the minimum valuesof the functions in the GA.
In this test, we use the following GA parameters:
population size is Np = 100 and generation number islimited to 300. The produced offspring is 60% of the
population Np . The parents selection is the roulette
wheel selection.
The sphere function, which is the simplest case inthis test, is described by the following equation.
x, y ∈ [−1.5, 1.5]

80
60
40

CPO1
CPO2

20

BLX

0
0.5

1.0
α

1.5

2.0

the result of the cross-to-parent offspring and the
CPO2 line shows the results of CPO with mutation,
where the mutation rate is 20 %. We define a successful evolution as that having a minimum value of thefunction f (x, y) < 0.001. The lines of successful ratein the figure show the mean value of 1000 trials. We
can see that the range of α in CPO2 which has a good
performance is wider than that in BLX.
Figure 3 shows the evolution performance for the
Rosenbrock function. The Rosenbrock function is described by the following equation.

Two-variable optimization problem

f (x, y) = x2 + y 2 ,100

Fig. 3 Evolution performance for RosenbrockfunctionFig. 1 Offspring production methodsf (x, y) = 100(x − y 2 )2 + (y − 1)2 ,x, y ∈ [−0.25, 1.25](2)
The minimum value of this function is 0.0 and occurs
when (x, y) = (0, 0). We can see that the rate of successful evolution of CPO1 is approximately two times
better than that of BLX at α = 0.3. From the figure,the result of CPO2, i.e., with the mutation operator,
is poor compared with the result obtained when mutation operator is not used. In this case the mutation
does not have a good effect on search performance.
Figure 4 shows the evolution performance for the
Rastrigin function. The Rastrigin function, which is
the most complicated function in this research, is described by the following equation.

(1)

The minimum value of this function is 0.0 and occurs
when (x, y) = (0, 0).
Figure 2 shows the evolution performance of theSphere function. In the figure, the CPO1 line shows©ISAROB 2005200(c) Mutation of P1

3

CPO1
40

αI

(b) CPO

P1

60

Fig. 2 Evolution performance of Sphere function
αI

αI

BLX

80

0

I

αI

CPO2

100

f (x, y) = 20 + {x2 − 10 cos(2πx)}+{y 2 − 10 cos(2πy)}, x, y ∈ [−0.25, 1.25](3)18Successful evolution [%]Successful evolution [%]The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

100
80
CPO2

60
BLX

40

CPO1
20
0
0

0.5

1.0
α

1.5

100
80
60

20
0

2.0

0

0.5

Successful evolution [%]Successful evolution [%]

80
60
40
20

Nh=7

Nh=5

40
Nh=3
20
0
0.5

1.0
α

1.5

2.0

(b) CPO1
Successful evolution [%]The minimum value of this function is 0.0 and occurs
when (x, y) = (0, 0). We can observe that the COP2
has a better search performance than BLX and CPO1.
In the case of the Rastrigin function, it is difficult tosearch the solution because the surface of the function
is in the shape of valleys and peaks [5]. We considered that a mutation more effective and severer than
the range mutation described in Fig. 1 (c) is required.
Figure 5 shows a result of CPO with a sign mutation. The sign mutation means a change in the signof the real value of the offspring. This figure shows
the result when the mutation rate is 20 %. We cansee that CPO with a sign mutation has a better evolution performance than the previous results of the threemethods shown in Fig. 4.

100
80

Nh=7

Nh=5

60
40

Nh=3

20
0
0

0.5

1.0
α

1.5

2.0

(c) CPO2
Fig. 6 Evolution performance for neural network(Np = 25)

works for various population sizes of GAs: Np =25, Np = 50 and Np = 100. In each case of Np , wealso change the number of neurons in the hidden layerof the network: Nh = 3, Nh = 5 and Nh = 7. The
performance results of the XOR network training areshown in Figs. 6-8; in this training we use GAs withcrossover operators: BLX, CPO1 and CPO2. We cansee that, in all of the cases, CPO achieved better performances than BLX could. Moreover, in CPO, therange of α, which has a good performance, is significantly wider than that in BLX. That is, CPO could
obtain good results several times better than those of
BLX. We can also observe that the addition of mutation effects the good results of the neural network
training problem.

Neural network training problem

This section presents simulation results of neuralnetwork training problems. The training of neuralnetwork is known as a nonlinear multivariable optimization problem.
In order to obtain results comparable to those of
the BLX, We should select the well-known exclusiveor (XOR) problem.
In this test, we use GAs for training a XOR network. For clearer results, we examine the XOR net-

©ISAROB 2005

2.0

60

2.0

Fig. 5 Evolution performance for Rastrigin function
using CPO with sign mutation

4

80

0
1.5

1.5

100

0
1.0
α

1.0
α

(a) BLX

100

0.5

Nh=3

40

Fig. 4 Evolution performance for Rastrigin function

0

Nh=5

Nh=7

19

100

Successful evolution [%]Successful evolution [%]The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Nh=5

80

Nh=3
Nh=7

60
40
20
0
0

0.5

1.0
α

1.5

100

Nh=7

Nh=3

60
40
20
0

2.0

0

0.5

Nh=5

Nh=7

80

Nh=3

60
40
20
0
0

0.5

1.0
α

1.5

Nh=5

40
20
0
0.5

Nh=3

40
20
0
0.5

1.0
α

1.5

2.0

1.5

2.0

Nh=5

Nh=3

80
60
40
20
0
0.5

1.0
α

1.5

2.0

(c) CPO2
Fig. 8 Evolution performance for neural network(Np = 100)

[2] L. Davis, Hand Book of Genetic Algorithms, Van
Nostrand Reinhold, 1991.

Summary[3] M. Gen and R. Cheng, Genetic Algorithm and Engineering Design, Wiley-Interscience, 1997.

Improving crossover operator in GAs is an area ofactive research for developing GAs. In this paper, we
presented a new method of improving real-coded GAs
by examining new search spaces of the crossover operator. The simulations show that GAs can achieve agood performance by changing search spaces to generate two close-to-parent offspring.

[4] L. J. Eshelman and J. D. Schaffer, “Real-Coded
Genetic Algorithms and Interval-Schemata” Foundations of Genetic Algorithms 2, Edited by L. Darrell Whitley, Morgan Kaufmann Publishers, 1993.
[5] H. Okamura, K. Kitasuka and T. Dohi, “Performance Evaluation of Genetic Algorithms Basedon Markovian Analysis - Evaluation by Relaxation
Time and Application to Real-Coded Genetic Algorithms,” Transactions of the ISCIE, Vol. 16, No.
7, pp. 303-312, 2003. (in Japanese)

References[1] D. E. Goldberg, Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley,
1989.

©ISAROB 2005

Nh=7

100

0

(c) CPO2
Fig. 7 Evolution performance for neural network(Np = 50)

5

1.0
α

(b) CPO1

60

0

Nh=3

60

0

Successful evolution [%]Successful evolution [%]

80

2.0

Nh=7

80

2.0

Nh=5

Nh=7

1.5

100

(b) CPO1
100

1.0
α

(a) BLX
Successful evolution [%]Successful evolution [%](a) BLX
100

Nh=5

80

20

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Enhanced Performance for Multi-variable Optimization Problems by Use of GAs
with Recessive Gene Structure
MUHANDO Endusa
KINJO Hiroshi
muhando@mibai.tec.u-ryukyu.ac.jp kinjo@tec.u-ryukyu.ac.jp

YAMAMOTO Tetsuhikoyamamoto@tec.u-ryukyu.ac.jp

Mechanical Systems Engineering
University of the Ryukyus
Senbaru 1, Nishihara, Okinawa. 903-0213 JAPAN
Abstract

next generation, thus maintaining the diversity of thecharacteristics of the living organism. RGM utilizes
both dominant and recessive genes in the cross-overand mutation operations in the mating phase of the
GA. To confirm the efficiency of the scheme we applied
RGM to two multi-variable optimization problems.
The structure of this paper is as follows: in Section
2 we present the RGM and in Section 3 we describe
the test functions. Simulation results are detailed in
Section 4 and a discussion makes up section 5. Finally,
we draw some general conclusions in Section 6.

In this paper we propose a dual gene system using the recessive gene model, RGM, to solve difficult
multi-variable optimization problems. Genetic algorithms (GAs) are widely applied to many optimization problems and usually suffer loss of diversity, leading to evolutionary stagnation. The dual gene system
is exploited to maintain diversity, significantly boost
evolutionary computation precision and avoid stagnation. We show by computer simulations that RGM
has a higher search efficiency in multi-variable optimization functions. Further, RGM performs better on
small populations than the single, dominant gene approach for the same computational cost.

2

Recessive Gene Model, RGM

Keywords: Recessive gene, Multi-modal function,
Multi-variable optimization, Computational cost.

Dominant Chromosome

F1

F2

A

B

D

C

1

Introduction

Recessive Chromosome

First Generation

Evolutionary computation in optimization relies onprocesses loosely based on natural selection, cross-overand mutation, that are repeatedly applied to a population of binary strings which represent potential solutions. Most GAs experience problems of convergence
due to loss in diversity [1]. There is need to devise
ways of avoiding the mechanism of evolutionary stagnation.
We used the basic information on Mendelian genetics to illustrate that a recessive characteristic mightsignificantly affect a closed population [2]. In observing living organisms, characteristics of the offspring do
not always resemble those of parents. A dual gene system exists whereby some alleles are dominant hence always expressed, while some are recessive, that is, only
expressed under certain conditions. However, the individual preserves the recessive gene, which is sent to the©ISAROB 2005

S1
A

B

C

D

A

D

B

C

S4

S3

S2
B

C

C

DA

A

B

D

E

F

G

H

Second Generation

T1
A
D
E
F

Third Generation

....

B
C
G
H

Figure 1: Schematic of the Recessive Gene Model
Figure 1 shows the structure of the double gene system. In usual GA systems only the dominant genes
appear, hence in the first generation of two individuals F1 and F2 , only the dominant characteristics A

21

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and B appear, respectively. In the second generation,
each of the individuals S1 , S2 , S3 and S4 will display
two chromosomes: 1 dominant and 1 recessive. As an
example, F1 and F2 will produce two offspring, S3 andits complement, S30 , in the second filial generation simultaneously. Offspring S3 will have the chromosomes
BC (dominant) and AD (recessive), while S30 , has CB
and DA as dominant and recessive, respectively.
P is the probability that a recessive chromosome
is selected to be a dominant chromosome in the next
generation. In the special cases of P = 0% and P =100% then the offspring will be S1 and S4 respectively.
The essence of the dual gene system is to provide alarger variety of offspring for the search.

’F6.dat’f(x,y)

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
15
10
-15

5
-10

0
-5

0

x VALUESy VALUES

-5
5

10

-10
15-15

Figure 3: Schaffer’s F6 function in 3-D
Figure 3 shows Schaffer’s F6 function; the centermost ring represents the circular global optimum.
Table 1: Analytical results for Schaffer’s F6

3

OptimaGlobal
1st local
2nd local
3rd local

Problem Formulation

Our two test functions were Easom’s and Schaffer’s
F6, from the classical benchmark similar to those defined by Kenneth De Jong [3], [4], [5].

3.1

f (x, y)0.996989
0.838081
0.606185
0.532483

r
π
2
3π
2
5π
2
7π
2

Table 1 gives the analytical values for the various
maxima. For eachpset of values at the various optima,
the relation r = x2 + y 2 exists.

The Easom Unimodal Function

For this function the global minimum has a small
area relative to the search space; the function was inverted for minimization, and takes the form:

4

Simulation Resultsf (x, y) = − cos(x) cos(y)e−((x−π)+(y−π))

4.1

GA Parameters’eft.dat’A random generation of (x, y) values in Euclidean
space was used in the GA search for both functions.

f(x,y)

0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
-1

Table 2: Constant parameters
Binary bit length, B
Selection pressure, parents Pp
Selection pressure, children Pc
Selection method
Crossover

10
5
-10

0

-5

0

x VALUES

5-5

y VALUES

10-10

Figure 2: Easom function in 3-D
Figure 2 shows a 3-D depiction of the Easom function. The analytical global minimum of the Easom
function is -1 when (x, y) = (π, π).

3.2

16
0.5
0.6
Roulette wheel
2-point

Table 2 shows the constant parameters in the GA
search. The sample size, N , was kept at 50 for mostof the simulations.

Schaffer’s F6 Multi-modal Function
Table 3: Variables
Percentage of recessive gene, P %Rate of mutation, M %No. of generations, G
Sampling population, NThis parametric optimization problem is multimodal,
represented by the equation:√sin2 ( x2 +y 2 )−0.5
f (x, y) = 0.5 + 1+0.001(x2 +y2 )2 .

Table 3 shows the variables utilized in the GA
search.

The function is a two-parameter ”ripple”, like thewaves in a pond caused by a pebble.

©ISAROB 2005

0 < P < 100
0 < M < 100
0 < G < 100
20 < N < 100

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4.2

0

Searching performance results for the
Easom function

P = 0%
P = 10%P = 20%P = 30%

-0.1
-0.2

100

-0.3
-0.4

Em

80

-0.5
-0.6

60

S [%]

-0.7
-0.8

40

-0.9
-1

20

0

M = 0%
M = 10%M = 20%M = 30%

10

20

30

40

50
G

60

70

80

90

100

Figure 6: Effect of generations and recessive gene on
efficiency for the Easom function. (M = 20%).

100

80

60

40

20

0

0

P [%]

Figure 4: Recessive gene performance for the Easom
function.

Figure 6 shows the mean of the best values of thefunction on 100 trials per generation. It gives a strong
indication of the advantage of the recessive gene inthis unimodal search, where the attainment of the best
mean value, Em , of the function is faster when P 6= 0%
and there is stagnation when P = 0%. For the Easom function, rate of convergence to solution is fastest
when P = 20%.

Figure 4 shows the searching performance for the
Easom function. In the case of P = 0%, only using dominant chromosomes, success rate is lower thanfor for the case of the recessive gene, when P 6= 0%;
however, when P is too large the search is not very efficient. Mutation plays a significant role in the search.
It is seen that in the absence of mutation, for the caseM = 0%, then the search improves with P . Search
space was in the whole region, (x, y) = [-10,10].

4.3

Searching performance results for
Schaffer’s F6 function

100

100

80

80

S [%]

60

60

S [%]

40

40

20

20

0

0

P = 0%
P = 10%P = 20%P = 30%
20

30

40

50

60
N

70

80

90

0

20

60

40

80

100

P [%]

Figure 7: Recessive gene performance, Schaffer’s F6.

100

Figure 7 shows that success in search for the global
optimum is best when P 6= 0%. Mutation is essential
in this multimodal search, as there is practically nosearch when M = 0%. Mutation enhances the searchby offering diversity among the population. Search
for the global optimum was investigated from an initialsampling in the range [11,13] of the global search space[-15,15] for (x, y) values.

Figure 5: Effect of varying sampling population on
performance for the Easom function. (M = 20%).
From Figure 5 we can observe that though thesearch performance improves with increase in the population size, the recessive gene performs better at low
populations than for the case where P = 0%.

©ISAROB 2005

M = 0%
M = 10%M = 20%M = 30%23

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the unimodal search the recessive gene performs thefunction of mutation. For the Schaffer’s F6, search inproves with P ; mutation is integral to the evolution as
there is practically no success for M = 0%.
Figures 5 and 8 show that for the respective functions, though N greatly influences the search, there
is inferior evolutionary success for P = 0%. GA efficiency is enhanced by a large N , though this requires
more memory and takes longer to converge. Sincecomputational cost, Cc , in terms of time and memory, is in direct proportion to N , then Cc for N = 100
should be twice the Cc for N = 50. It can be seenfrom Figures 5 and 8 that performance is superior forthe case P 6= 0%, N = 50 than for P = 0%, N = 100.
Figures 6 and 9, for Easom and Schaffer’s F6 respectively, show that the rate of convergence is highfor P 6= 0% and that there is stagnation when P = 0%.
Maintaining diversity in the GA search ensures highefficiency yet avoids quick convergence and stagnation.
RGM ensures accurate convergence at low N and thisis desirable for memory storage during computations.
However, RGM may be computationally expensive.

100

80

S [%]

60

40

20

0

P = 0%
P = 10%P = 20%P = 30%
20

60
N

50

40

30

100

90

80

70

Figure 8: Effect of varying sampling population on
performance for Schaffer’s F6 function. (M = 20%).
Figure 8 shows that though increasing N greatly
improves the search, RGM performs relatively wellwith small population when P 6= 0% and it is inadequate when P = 0%.
1
0.95

6

0.9
0.85

In this paper, we have shown that RGM avoids stagnation due to diversity, works very well at low sampling populations and that the recessive gene performs
the function of mutation. Further, performance withrecessive chromosomes is superior to the purely dominant chromosomes case. We believe the managementof the computational cost could be further improved.

Em

0.8
0.75
0.7
0.65
0.6

P = 0%
P = 10%P = 20%P = 30%

0.55
0.5

0

10

20

30

40

50
G

60

70

80

90

100

References

Figure 9: Effect of generations and recessive gene on
efficiency for Schaffer’s F6 function. (M = 20%).

[1] D. E. Goldberg: GAs in Search, Optimization & Machine Learning, Addison-Wesley (1989)

Figure 9 indicates the advantage of using the recessive gene as convergence is influenced by P ; for
P = 0% there is stagnation at some local optima.

5[2] Holland H. John: Adaptation in Natural and Artificial
Systems: An Introductory Analysis with Applications
to Biology, Control and AI, MIT Press (1995)[3] De Jong, K. A., 1975: An Analysis of the Behaviour ofa Class of Genetic Adaptive Systems. Dept. of Computer and Comms., Univ. of Michigan, Ann Arbor.

Discussion

Using RGM, we compared the performance of the
GA driven by a gradually increasing recessive gene tothe dominant case. Figures 4 and 7 are the basis of
our research; for the Easom function the success rate
is lower when using only dominant chromosomes thanfor P 6= 0%. However the search performance is degraded when P is too large. It can be inferred that for©ISAROB 2005

Conclusions[4] Shaffer J., Caruana R., Eshelman L.: A study of control parameters affecting online performance of GAs forfunction optimization. Proceedings of the 3rd Int’l Conference on GAs. Morgan Kaufman Publishers. 1989
[5] Xavier Hue: GAs for Optimization: Background andApplications. EPCC, Univ. of Edinburgh, (Feb 1997).

24

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Identification of Time Series Signals Using Dynamical Neural Network with
GA-based Training
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
Abstract

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 method [5]. However, that algorithm
modified only the connecting weights and the property
parameters for the DNN had to be determined by trial
and error. We design a GA-based training [6] both theconnecting weights and the parameters of the DNN.
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]. In this paper, it is verified by identifying
the time series signals of linear system and nonlinear
system. Simulation results show that the proposed
DNN provides higher performance than the conventional neural network.

In this paper, we propose a dynamical neural network (DNN) having the properties of inertia, viscosity, and stiffness and its training algorithm based ona genetic algorithm (GA). In a previous study, we
proposed a modified training algorithm for the DNN
based on error backpropagation method. However, inthe former method it was necessary to determine thevalues of the DNN parameters by trial and error. In
the proposed DNN, the GA is designed to train not
only the connecting weights but also the parametersof the DNN. Simulation results show that the DNN
trained by GA obtains good training performance fortime series patterns generated from unknown system.

1

Introduction

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 compared 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©ISAROB 2005

2

Structure of DNN

In this paper, a DNN is configured using a neuron
having the properties of inertia, viscosity, and stiffness.
In this neuron model, we assume the image output
from neuron possesses the properties of inertia, viscosity, and stiffness, and that the output is propagated in
the next neuron. The proposed DNN is composed ofthree hierarchy layers and the proposed neuron adopts
a hidden layer and an output layer. The structure of
the DNN is shown in Figure 1.
The equations for the DNN are expressed as follows.
yi

25

=

ui ,

(i = 1, 2, · · · , NI )(1)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005



 

 

wij

 

wjk

Initial population
DNN

DNN

・・・

・・・

・・・

Evaluation
Selection

Figure 1: Structure of DNN

yj

=

Death
pressure: P

Survival

DNN

Kj fj (netj ) + Dj f˙j (netj ) + Mj f¨j (netj )Parents

DNN

(2)
netj

=

NI
X

wij yi ,(j = 1, 2, · · · , NJ )(3)

Children
Crossover

i=1

yk

=

Kk fk (netk ) + Dk f˙k (netk ) + Mk f¨k (netk )

DNN

(4)
netk

=

NJ
X

wjk yj ,(k = 1, 2, · · · , NK )DNN

Mutation
rate: α(5)j=1Here, ui shows input value to the DNN, and yi , yj ,and yk show output values in input, hidden, and output layers, respectively. The Connecting weight fromunit i in input layer to unit j in hidden layer is denotedby wij . Similarly, wjk is a connecting weight from unit
j in hidden layer to unit k in output layer. The totalsum of products of the connecting weight and the output value is denoted by net. Mj , Dj , and Kj are theproperty parameters of inertia, viscosity, and stiffness,
respectively. NI , NJ , and NK are the number of neurons in input, hidden, and output layers, respectively.
The threshold function uses a sigmoid function in therange of [−1, 1].

3

Figure 2: Flowchart of GA-based training

are transformed to the chromosome. The genetic
code is transformed to the binary code (16 bit).
STEP2: Sum all of the fitnesses for the DNNs.
STEP3: Select the parent DNNs by means of roulette
wheel parent selection.
STEP4: Perform a crossover operation for the chromosome to produce new DNNs.

Training algorithm based on GA

STEP5: Perform a mutation operation for some additional new DNNs.

The DNN is trained using a GA in an off-line process. Figure 2 shows the flowchart of the evolution
process in the DNN. The evolution algorithm for the
DNN is as follows.

STEP6: Sum all of the fitnesses for the DNNs including the new DNNs. Go back to STEP2 until theevolution process arrives at generation 10,000.

STEP1: Produce the initial DNNs at random. The
connecting weights wij and wjk in the range of[−1, 1] and the property parameters Mj , Dj , Kj ,Mk , Dk , and Kk of DNNs in the range of [0, 10]

Further information regarding the parameters of the
GA is shown in Table 1.
During the GA-based training process, an error
function E is used to evaluate the performance of each

©ISAROB 2005

26

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

In numerical simulation, the validity of the proposed DNN is verified by identifying the unknown system such as linear system expressed in Equation (7)and nonlinear system expressed in Equation (8).
• Simulation 1 (linear system)

Table 1: Simulation parameters of GA
400 individuals
Initial DNNs
Roulette wheel parent selection
Selection
P = 0.6
One-point crossover
CrossoverBit mutation
Mutation
α = 0.10
10,000
Final generationd(t) = 0.1d(t−1)+0.2d(t−2)+0.3u(t)+0.4u(t−1) (7)• Simulation 2 (nonlinear system)³ πu(t) ´d(t) = 0.1d(t − 1) + 0.2d(t − 2) + sin
4

DNN. The error function E is described by the following equation as
1X
1X(d(t) − y(t))2e(t)2 =
E=
2
2

4.1

(6)

Evolution process

In GA simulation, we set the GA parameters shownin Table 1 and the number of neurons in hidden layer
is 12 units. The input signal u(t) uses 1,000 samplingdata. In simulation 1 and simulation 2, the evolution
processes of the best DNN with the GA-based training
are shown in Figure 4.

k

k

(8)

where d(t) is the desired signal. The fitness of DNN isexpressed in terms of the inverse of the error function
E. The connecting weights and property parametersof the DNN are modified in order to maximize thefitness function determined by the error function in
Equation (6).

6simulation 1
simulation 2

5

Numerical simulation

Best fitness function

4

The effectiveness of the proposed DNN is verified
by numerical simulation in order to identify time series signal. The method by which a time series signal
from an unknown system can be identified is shown in
Figure 3. The DNN is structured to have a single input

4
3
2
1
0

Unknown
System

0

d(t)

+

u(t)

2500

5000
Generations

7500

10000

Figure 4: Evolution process
e(t)

GA

It is observed that either of the evolution processes
provide performance to some extent. The fitness values increase gradually and the evolutions almost stagnate at generation 7,000 in simulation 1 and at generation 9,000 in simulation 2, respectively.

−
y(t)

DNN

Figure 3: System identification of time series signal

4.2

and single output (SISO). The input signal u(t) of the
DNN and the unknown system is a random number ofnormal distribution (mean 0.5 and variance 0.5). The
desired signal, namely the training data d(t), is theoutput signal of the unknown system.

The result of the regenerating signal using thetrained DNN is shown in Figure 5. The figure shows
the regenerating signal in range of [100, 200].
The output of the DNN deviated negligibly fromthe desired signal, but the DNN could not cope witha quick transition.

©ISAROB 2005

27

Simulation 1 (linear system)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

1.5

method. Simulation results showed that the DNN
trained by the GA realized good training performancefor time series signals generated from either of unknown systems with linearity and nonlinearity.
In future work, we will try to identify a unknown
system with strong nonlinearity.

DNN
d(t)

Output signals

1

0.5

0

-0.5
100

References
120

140

160

180

[1] D. E. Rumelhart, J. L. McClelland, and the PDP
Research Group (1989), Parallel Distributed Processing, The MIT Press.

200t

Figure 5: Regenerated result (simulation 1)

4.3[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.

Simulation 2 (nonlinear system)[3] H. Kinjo, K. Nakazono, and T. Yamamoto (1997),
Pattern Recognition for Time Series Signals Using Recurrent Neural Networks by Genetic Algorithms (in Japanese), Trans. of ISCIE, Vol. 10, No.
6, pp.304–314.

The result of the regenerating signal using thetrained DNN is shown in Figure 6. The figure shows
the regenerating signals in range of [100, 200].
1.5

DNN
d(t)

[4] W. Mass and C. M. Bishop (1999), Pulsed Neural
Networks, pp. 16-53, MIT Press.

Output signals

1[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.

0.5

0

-0.5
100

[6] Edited by L. Davis (1991), Handbook of Genetic
Algorithms, Van Nostrand Reinhold.
120

140

160

180

[7] K. Nakazono, K. Ohnishi, and H. Kinjo (2004),
System Identification Using Dynamical Neural
Network with GA-based Training, AROB 9th ’04,
Vol.1, pp. 75–78.

200

tFigure 6: Regenerated result (simulation 2)
The output y(t) of the best DNN deviated negligibly from the desired signal d(t). The DNN trained by
GA obtained good training performance for time series signal generated from the output of the nonlinear
system.

5

Conclusion

In this paper, the proposed DNN, exhibiting theeffectiveness of dynamical neuron with properties
of inertia, viscosity, and stiffness, was configured.
The training algorithm adopt the GA-based training©ISAROB 2005

28

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Spontaneous speciation by GA for division of laborin two-agent systems
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

z1
A1

In this paper, we propose a framework of genetic
algorithm in which division of labor among the agents
emerges. The agents in the system spontaneously develop different charactaristics to cooperate effectively
to achive a task and speciate to different species. We
assume the agents recieve input to specify the task andcan observe each other. We apply the framework to amechanical system. The task is designed simple andessential to elucidate the effectiveness of the algorithm.

x

-

∆θ

x

+

y2

-

A2

A2

z2

(a) Generalθ2α2(b) Customized

Figure 1: Information flow between two agents. (a) General framework. Each agent receives external input x andpart of the other agent’s output yi . To achive given tasks,
the agents use these information appropriately. (b) Customized framework for our task. Each agent can obtain the
difference ∆θ between its angle and its coleage’s. Agentschange their location θ1 , θ2 only once using this information.

Introduction

There are many researchs about multi-agent system
to solve complex problem like n-Traveling Salesman
Problem (nTSP)[1], and Genetic Algorithm (GA)[3]with speciation is an important and effective idea fororganiging cooperative sharing of a task by agents.
Speciation is also an important aspect in evolutional
biology. There are also researchs to build a virtual
ecosystem with GA-like framework[2].
In this paper, we apply this framework to a mechanical system. We assume two agents cooperating
for a task (Figure 1). Each agent Ai receives external
input x and generates its outputs yi and zi , generally.
zi is the action of the agent, and yi is the signal tobe observed by the other agent. yi can be identicalto zi or a part of zi . We assume rather a poor ability for the agents, so that the task is too difficult andtwo agents must cooperate. For better performanceeach of the cooperative pair must play a different role.
This formulation will give rise to the division of labor.
We want to make this division emerge spontaneously.
For this purpose, we assume every agents have a same

©ISAROB 2005

A1

y1

Key Words: spontaneous speciation, coevolution,
multi-agent system, genetic algorithm

1

+

α1
θ1

structure, learning mechanism and live in a same environment.
The task adopted in this paper is “disk moving
problem”, which is simple and essential to test theframework.

2

Disk moving problem

We assume two agents set on a disk in two dimensional space (Figure 2). Initially, the agents are located on the edge of the disk randomly and recieve
input x to specify one of two target motions to becaused, “rotation” or “translation”. Then they observe the relative angle between them. Based on theobservation each of them moves along the circumference to the final position, where the two agents apply
force simultaneously in the directions determined byx to cause rotation or translation.
Each agent has two state variables the angle rep-29

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

resenting the position of the agent on the edge ofthe disk θi and force angle αi measured counterclockwise from the direction of center of the disk. f¯1 , f¯2
are the impulsive force applied by the agents. The
forces are assumed to have a fixed absolute value f(|f¯1 | = |f¯2 | = f ). The forces give two kind of
velocities to the disk. One is the angular velocity
ω = M2fR (sin θ1 + sin θ2 ), where R, M are radius andmass of the disk. The other is the translation velocity
1 ¯(f 1 + f¯2 ).
v=M
Each agent has four parameters {ai , bi , ci , di } todetermine its final position and force angle. These
parameters will be coded binary and used as chromosomes for GA. Movement from the initial position θiinitto the final position θifin is determined by parameters
ai and bi : fin
θ1 := θ1init + a1 (θ2init − θ1init ) + b1 ,(1)
θ2fin := θ2init + a2 (θ1init − θ2init ) + b2 .

θ1
α1

α2

Rotation

two state variables, location angle θi , force angle αi . To
generate rotational or translational velocities effectively.

For this evaluation function |ω| must be maximized(minimized) and |v| minimized (maximized) when x =1 (x = −1). This evaluation process is repeated several
times with x = 1 and with x = −1 same number oftimes to obtain the averaged evaluation F̄i,j , which isused for evaluation of the pair of agents.
After testing all combination of agents, we define
the fitness function of agent i as follows:(2)

Fi = max{F̄i,j },which is the average quality of the motion given byagent i with its best partner.
After all combination are tested, we apply the standard genetic algorithm to the population of agents.
The agents, namely, is sorted according to the fitness values Fi and inferior half of the population aredeleted. From the superior half of the population apair of agents are randomly chosen for mating and two
new agents are generated from the pair with crossover
operation and mutation (see Figure 3). New agents are
generated until the deleted population is recovered.

Let us consider a population of agents with a chromosome {ai , bi , ci , di } which are coded binary. These
parameters range over the following intervals: ai ∈[−1, 1], bi , ci , di ∈ [−π, π). We initialize all agents byassigning random values to all the parameters. We
evaluate every two combination of different agents Ai ,Aj , (i 6= j). These two agents are set on the edge ofthe disk randomly and input x = ±1 is given. Then
rotational velocity ω and translational velocity v is
calculated as described before. The quality Fi,j of theresulting motion is evaluated as:©ISAROB 2005

(4)

jLearning method

Fi,j = x(ω 2 − |v|2 ).

Translation

Figure 2: Action of two agents on a disk. Agents havewhere x is motion specification (x = 1 is for “rotation”
and x = −1 for “translation” of the disk). Then therotational and translational velocities of the disk are
obtained as described before and are used for evaluating the fitness function of the agents.
It will be easily understood that the agents cannot
achive the task with a same behavior, so they must
emerge their different characteristics to play different
roles. It means division of labor between the agents.

3

θ2

f̄ 2

Here we assumed the agents can observe only the relative angle between them, and the positional change
is determined by the observation (Figure 1 (b)). After
this, each agent apply impulsive force in direction αi ,which is determined as
αi = xci + di ,

f̄ 1

4

Simulation results

We set the absolute value of impulsive force f = 1
and moment of inertia I = 1 (M = 1, R = 2). Thus,
the maximum value of two kinds of velocities are |ω| =2 and |v| = 2, and the maximum evaluation value is2
Fi = 4 (= ωmax
= |v|2max ).
In our GA, we set the parameters as follows: number of the units is 40, bit length of binary expression of

(3)

30

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Two agents are selected
randomly from the superior
half of the population

New genes generated
by crossover of parents genes

parents genecrossoverNew genes replace
the inferior halfof the population

children’s genecrossoverpoint(a) gen=0(b) gen=10(c) gen=30(d) gen=100

mutation

Figure 3: Genetic algorithm of parameters of the agentseach parameter is 28, mutation rate is 5%, and maximum generation is 100. Simulation results are depicted in Figures 4, 5 showing agents’ behavior on the
disk. An arrow in the figure corresponds to an agent.
Root of the arrow represents the value (angle) of parameter bi and the direction of the arrow represents
the force angle αi . Figure 6 shows the distributionchange of (a) bi and (b) αi . Values of bi clearly splits
to two values with difference π corresponding to two
different types necessary for effective operation.

5

Figure 4: Position shifts bi and force directions αi of
the agents generated in the simulation (x = 1, generation
0 ∼100).

Discussion

In optimal condition for disk rotation (x = 1), two
agents are located at opposite positions on the diskedge (θ1 − θ2 = (2n + 1)π). If the final positions of
the agents satisfy this condition, we can obtain thefollowing equation from Equation (1)θ1fin − θ2fin = (θ1init − θ2init )(1 − a1 − a2 ) + b1 − b2
= (2n − 1)π.

To make this hold regardless of the random initial positions θ1init and θ2init the following condition is necessary
and sufficient: a1 + a2 = 1 and b1 − b2 = (2n + 1)π.
There are infinitely many solutions for these conditions
also. Different values are obtained in each GA simulations. The optimal force angles for rotation (x = 1)
are α1 = α2 = ±π/2 (Figure 4 (d)), and the optimal force angles for translation (x = −1) must satisfy
α1 −α2 = (2n+1)π (Figure 5 (d)). There are infinitely
many solutions for these conditions also. Different values are obtained in each GA simulations.
For disk translation (x = −1) only there is anothersolution in which two agents are located at a same

©ISAROB 2005(a) gen=0(b) gen=10(c) gen=30(d) gen=100(5)Figure 5: Position shifts bi and force directions αi of theagents generated in the simulation (x = −1, generation
0 ∼100).

31

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the disk. Simulation results show emergence of optimal division of labor by the two agents.

Position shift [deg]

180
90
0

References

-90
-180

25

0

50
Generation

75[1] Toma, N., Endo, S., Yamada, K., (1998). Immune
algorithms for nTSP, Bulltin of the faculty of engineering university of the ryukyus. (in Japanese)

100(a) Position shifts bi[2] Clement, R., (2003). Multi-Agent Simulations of
Evolution and Speciation in Cichlid Fish, 15th
European Simulation Symposium and Exhibition
ESS2003. Delft, Holland.

Force angle [deg]

180
90
0

[3] Edited by Davis, L., (1991). Handbook of Genetic
Algorithms, Van Nostrand Reinhold

-90
-180

25

0

50
Generation

75

100

(b) Force angles αi (= ci + di )Figure 6: Evolution of parameters.
position and a force direction angle for both is π, but
this placement is not effective for rotation. Therefore,
the placement did not appear in our simulation.
Figure 4 (c) shows that in the early stage of evolution the force angles almost converged to −π/2, andthen the other optimal force angle π/2 appeared andthe two optimal force angles coexisted for a while,
but π/2 disappeared before the 50th generation. Thisshows the excellent ability of this scheme to search forother optimal solutions. The ability partially comesfrom the definition of fitness function as the maximumof pairwise evaluations (Equation (4)). An alternative
for it is the average of all the pairwise evaluations.
This definition, however, might not give the schemesuch ability because of the following reason. Suppose
a new optimal pair of agents appear, they will not
obtain higher fitness value because they cannot cooperate properly with the majority of the agents andaverage performance will be poor. Thus, they will beeliminated soon.

6

Conclusion

In this paper, we proposed the framework of genetic
algorithm to give rise to spontaneous cooperation bydivision of labor. We apply this method to a simple
mechanical problem of disk motion by two agents on©ISAROB 2005

32

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Indication of Object Spatial Position by Finger Pointing
Masato HIRASAWA

Masaki OSHIMA

Graduate of Mercantile Marine

Faculty of Marine Technology

Tokyo University ofTokyo University ofMarine Science and Technology

Marine Science and Technology

Tokyo-to Koto-ku Etchujima 2-1-6 〒135-8533
Key Words: Spatial Coordinate, Finger Pointing, Conversation, Indication
while it points out [7]. The system which holds a
conference as if people who are present in a mutuallydifferent place meet with a same place is proposed. Oneof the factor reproducing presence, it is made importantthat enables to point out by finger through a screen. In
order to communicate in natural, this also shows that
finger pointing is important. It relates to this, the researchwhich teaches objective fields and the position of thepeak using the laser pointer [8], and the research which
detects the gazing point from a look [9] haveaccomplished.
Even if it dose not use special device, if it is human, it
is possible by directing the target object with finger toaim at communication. The target of this research is toprovide a technology that enables to communicate withrobot by finger pointing in natural way. In order to know
spatial position between finger and object, hand andobject are horizontally taken using stereo camera. Spatial
coordinate of finger and object are acquired based onstereo photos and indication direction is appointed. And,in order to confirm validity, the experiment that directed
object is made to answer computer is conducted.

Abstract
Human often communicate with each other by gesturefor natural communication. The target of this study is toprovide a technology that enables to communicate withrobot by finger indication in natural way.
In order to set the direction of finger to one, at least
two spatial coordinates are required on the finger. In thisresearch, spatial coordinate is acquired using stereo
method. This spatial straight line is called “Finger
indication vector”.
The finger area is extracted by skin color and shapefeature of hand. From the area of the extracted finger, theportion of the tip of a finger and the root of a finger are
made into the corresponding points.
Plane information is calculated by normal vector. And
the object is pointed out by connecting intersection of
finger indicated vector and object plane, and finger.
The result of experiment is shown finger indication
vector run out in the portion without feature in object
plane. It is shown in 3-dimensional space that the finger
indication vector and object plane crossed. Even if thistechnique does not put on special equipment, it can
presume the direction of finger, and subject is able toexperiment it in free state.

2. Decision of Finger Indication Vector

1. Introduction

2.1

Indication direction is conducted by attached spatial
straight line on finger. In order to set the direction of
finger to one, at least two spatial coordinates are requiredon the finger. In this research, spatial coordinate isacquired by stereo method. The direction of finger ispresumed by applying straight line on these spatial
coordinates. Straight line vector is calculated by formula(1). This is called “Finger Indication Vector” in thisresearch.

In order to support life and work of human, the robotthat coexists with people have developed [1],[2]. Humanmust understand with each other for coexistence ineveryday life. Human often communicate by“Hearing”, ”Looking”, ”Speaking”. Study for human androbot talking communication have been done by language
such as character and sound [3][4]. As oppose to this, thetechnology to communicate with robots have beendeveloped by non-language such as gesture [5][6]. In
order to communicate in natural, human often
communicate with each other by gesture. For example,
when speaker wants to take the object in the position
which speaker left, he says “Please take that object to me”©ISAROB 2005

Finger Indication Vectorr rrr = b + t⋅a

(1)

r
r
However, a and b are expressed “Finger Indication
Vector”, and t is expressed real number.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.2 Acquisition of hand area
After moving object is extracted by inter-frame
difference, each extracted areas are done labeling, andsmall areas are removed as noise. Skin color area is
extracted from left-behind area and it is considered thatrecognized as hand area. Inter-frame difference tends to
be influenced of noise, even if indoor environment. It iscaused incorrect detection by sunlight and fluorescentlight. If it thinks that density pattern of picture does notchange, incorrect detection can be reduced by making
luminosity value normalization like formula (2).

f i ' ( x, y ) =Comparison of frame length with widthYesf i ( x, y )∑{f ( x, y ) − f ( x, y )}2

iFig.1 The shape of hand*kNo

Length > Width

(2)

i

x, y

However f ( x, y ) is expressed luminosity, f ( x, y )is expressed average, and k is expressed real number.
Color information is changed Hue by formula (3), andskin color area is extracted. However R, G , B andC max ,C min are expressed color information and value ofmaximum or minimum into R, G , B , respectively, andH is expressed value of Hue.

 if G = C max
H =  ( B − R ) / ( C max − C min ) * 60 + 120
 if B = C max
H =  ( R − G ) / ( C max − C min ) * 60 + 120

Horizontal Scanning

Vertical Scanning

Yes

Left length > Rightlength

Finger directionis left

No

Finger direction
is right

Yes

Upper length > Underlength

Finger direction
is upward

No

Finger direction
is downward

Fig.2 The flowchart of algorism(3)

 if R = C max B > G
H =  ( G − B ) / ( C max − C min ) * 60 + 360
 if R = C max G > B
H =  ( G − B ) / ( C max − C min ) * 60

2.3 Determination of finger direction

The tip and root of finger are
made into a corresponding
point, respectively.

The flow chart of algorithm, which determines finger
direction, is shown in Fig.2. The direction of hand(horizontal or vertical) is determined by form of therectangle of the extracted hand area. If it is in thetendency for the hand to be horizontally suitable, it scansvertically, and the number of pixels of the pixel value
255 is recorded an account for the every sequence. By
the middle of scan, the boundary line of finger and backof hand is made the portion which count value changed alot. If the hand is vertically suitable, the scanning
direction is changed and it processes similarly. If
difference of vertically direction and horizontally
direction are not clear, it corresponds scanning aslant.
2.4

Fig.3 Extraction of correspondence points
In order to determinate direction of finger indication
vector, two spatial coordinates are search on the finger.
Since it calculates by stereo method, corresponding points
have to be searched in the picture on either side. From thearea of the extracted finger, the portion of the tip of afinger and the root of a finger are made into thecorresponding points, respectively, as depicted by Fig.3.
Formula (1) is calculated by searched two spatial
coordinates.
Cameras are used the lens of the same characteristic
and arranged it in the same height. Starting point of

Determination of spatial coordinate on
finger©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

r r
r1 − r0

P0

In order to discover object, the experiment about finger
pointing is conclude. The picture photo by the stereo
camera (3DC-2000Z SONY) is send to computer througha picture edit machine (Accom-WSD-2Xtreem NGC) ,and result is outputted. An experiment is conducted sothat the hand and object are taken under complicatedbackground. Objective spatial position is acquired
beforehand and memorized. The target object is premisedon not changing position frequency as if home electronics.
But, if a position changes, moving area is extracted andmemory is updated.

P1

r r
r2 − r0

P2

Fig.4 The Normal
spatial coordinate is taken as the position of lens on theleft-hand side of a stereo camera.

4.2

The experiment for confirming the accuracy of a finger
indication vector is conducted by comparing actual spatial
angle with the direction of vector calculated from
reproduced spatial coordinates. In the experiment, a laser
pointer is fixed to a tripod and a gazing board is installedin the position 1m away from the tripod. The actual angle
is determined by attaching gazing points to the interval of
5 degrees vertically, attaching to the interval of 10 agrees
horizontally, and irradiating each gazing points by laser
point. This state is photoed and direction vector is
calculated by formula (7) and (8). Here, ( X 1 , Y1 , Z 1 ) ,( X 2 , Y2 , Z 2 ) expresses there produced spatial coordinates.
The graph which compared actual measurements withcalculation values is shown in Fig.5.6. An actual
measurement is shown in vertical axis and a calculation
value is shown in horizontal axis. The graph expresses
comparison with the straight line of ideal which theerror of angle is 0°, and the straight line obtained byexperiment. Moreover, the error of finger pointing of the
1[m] beyond, 5[m] beyond, and each 10[m] beyond
which the error of angle of an angle brings about, isshown in Table1 and 2.

3. Detection of object position
By the experiment, which searches intersection with anobject plane, it shows validity of finger indication vector
searched section 2. Although the plane information is able
to search for also by method by reference [10], this paper
presumes plane information by searching for the threespatial coordinates, which exists on the same plane(Fig.4). First, plane equation is expressed formula (4).
ρ0 is expressed normal and can calculate it like formula(5). Normal ρ is able to calculate by substituting one ofr rrvalue r1 , r2 or r3 . Here it is necessary to search forthree spatial coordinates, which exist on the same plane.
Then three feature points, such as objective edges, areextracted on the same plane. Next, three spatial points are
acquired by taking correspondence between the pictures
obtained from the camera on either side. Formula (6) isdrawn from formula (r1) and (4), coefficient can becalculated. Intersection r can be obtained by substituting
for formula (1) the value calculated here. The object ispointed out by
r connection finger indicated vector andrintersection.

rr ⋅ ρ0 = ρ(5)rρ − ρ0 ⋅ bt=r
ρ0 ⋅ aX 2 − X1)
Z 2 − Z1

(7)

（Vertically）

ϑ = tan −1 (Y2 − Y1)Z 2 − Z1

(8)

（Horizontal）

4.3

The result of finger pointing(6)
The result of experiment is shown in the Fig.7 and 8.
Each experiment shows the result by projecting the
spatial vector obtained by the formula (1) on plane
coordinate. After extracting also for an intersection
spatially, it is produced on plane coordinates. In thisfigure, finger indication vector run out in the portion
without feature in object plane. It is shown in
3-dimensional space that the finger indication vector andobject plane crossed. The desirable method for state that

4. The experiment
The environment of experiment©ISAROB 2005

ϑ = tan −1 (

(4)

r
r
r
r
( r2 − r0 ) × ( r1 − r0 )
ρ0 = rr
r
r( r2 − r0 ) × ( r1 − r0 )4.1

Evaluation of finger pointing

35

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The Value of calculation (degree)

35
30
25
20

Ideal

15
10
Calculation

5
0

0

5

10

15

20

25

30

The value of experiment (degree)

Fig.7 Example of pointing (TV)Fig.5 Calculation value and actual measurementof vertical
Table.1 Digital data of Fig.6
ActualCalculation Angle The point error The point error
error of 1m beyond of 5m beyond
measurement
value
(mm)
(degree)(mm)(degree) (degree)

0
5
10
15
20
25
30

1.23 1.23
4.97 -0.03
9.98 -0.02
15.61 0.61
20.07 0.07
26.89 1.89
30.39 0.39

21.54
-0.52
-0.38
10.68
1.19
33.07
6.86

107.70
-2.62
-1.92
53.41
5.93
165.3434.30

Fig.8 Example of pointing (Display)

Calculation value (degree)object is directed being shown is giving definition as apolyhedron using a solid modeler, and judging theinteraction with the finger indication vector and object
plane. Here, since it is simple, only a certain field on
object is taught, only the spread range is defined, and it isconsidered that the object is directed only within theintersection within the limit of it.

60
40
20

Ideal

0
-20

-40

-20

0

20

5. Conclusion

40

Calcula
tion

-40

-60

In this report, finger indication is recognized from thepicture obtained by the stereo camera, and object isdirected. Even if this technique does not put on special
equipment, it can presume the direction of finger, andsubject is able to experiment it in free state. Position error
and angle error is a maximum of 211 [mm] and 2.42
degree, respectively in the distance of 5[m] around. As aresult, if it is a size like home electronics, the objective
position is able to be taught. It is necessary to raise
pointing accuracy so that it can presume, even if it is thesmall object that distance left from now on. Whether thefinger is correctly suitable in the direction of objective
influences evaluation greatly. As a subject, the roughinstruction of “being around here generally” is also
construction of a system by which information istransmitted.

Actual measurement (degree)

Fig.6 Calculation value and actual measurementof horizontal
Table.2 Digital data of Fig.7
ActualCalculation Angle The point error
5m 先のポイ
Of beyondmeasurement
value
error
ント誤差（㎜）(mm)(degree)(degree) (degree)-40 -42.34 -2.34-30 -32.42 -2.42-20 -20.21 -0.21-10 -9.26 0.74
0 -0.06 -0.06
109.70 -0.31
20 21.89 1.89
30 31.28 1.28
40 38.53 -1.47
50 52.03 2.03

-40.86-42.25
-3.68
12.90
-1.01
-5.32
32.91
22.29
-25.6835.46-204.32-211.22-18.4164.49-5.062-26.62164.56111.46-128.40
177.31

Reference[1] T.Takahashi，S.Nakanishi，Y.Kuno and Y.Shirai： Helping©ISAROB 2005

36

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Computer Vision by Verbal and Nonverbal
Communication ，International Conference on Pattern
Recognition，Vol.2，pp.1216-1218，1998[2] C.Breazeal and B.Scassellati： How to build robots thatmake friends and influence people ，Proc. IEEE/RSJ
International. Conference. Intelligent Robots and Systems，
1999[3] K.Shobaki，J.P.Hosom and R.A.Cole： The OGI Kid’s
speech corpus and recognize Proc. ICSLP2000，Vol.4，
pp.258-261，2000[4] K.Ohtsuki，T.Matsuoka，S.Matsunaga and S.Furui： Topic
extraction based on continuous speech recognition inbroadcast news speech ，IEICE Trans. Inf. & Syst.，
Vol.E85-D，no.7，pp1145-1152，July，2002[5] V.I.Pavlovic，R.Sharma and T.S.Huang： VisualInterpretation of Hand Gestures for Human-Computer
Interaction：A Review ，IEEE Trans.Pattern Analysis andMachine Intelligence，Vol.19，No.7，pp.677-695，1997，
7[6] A.Pentland： Looking at People：Sensing for Ubiquitousand Wearable Computing ，IEEE Trans.Pattern Analysis
and Machine Intelligence，Vol.22，No.1，pp.107-119，
2000，1[7] O. Morikawa and T. Maesako： HyperMirror :Toward
Pleasant-to-use Video Mediated Communication System ，
CSCW’98，pp.149-158，1998[8] T.Hasegawa and S.Kameyama： Geometric Modeling of
Manipulation Environment with Interactive Teaching andAutomated Accuracy Improvement ，20th Int. Symp. on
Industrial Robots，pp.419-426，1989[9] Jacob. R： What you look at is what you get：Eyemovement-based interaction techniques ，Proc. ACM
CHI’90 Human Factors in Computing System Conference，
pp.11-18，1990[10] Y. Kawai，Y. Ueshiba，T. Yoshimi and M. Oshima：
Reconstruction of 3D Objects by Integration of Multiple
Range Data ，ICPR(International Conference on Pattern
Recognition)，Hague，The Netherlands，pp.154-157，1982，
9©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Accepting Powers of Four-Dimensional Alternating Turing Machineswith Only Universal States
Youichirou Nakama1 , Makoto Sakamoto1 , Makoto Saito1 , Shinya Taniguchi1 ,Takao Ito2 , Katsushi Inoue3 , Hiroshi Furutani1 and Susumu Katayama1
1

Dept. of Computer Science and Systems Engineering, Miyazaki University, Miyazaki 889-2192, JAPAN

2

Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN

3

Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN

Abstracttion, motion image processing, and so forth, it has
become increasingly apparent that the study of fourdimensional pattern processing, i.e., three-dimensional
automata with the time axis should be of crucial importance. Thus, we think that it is very useful for analyzing computation of four-dimensional pattern processing to explicate the properties of four-dimensional
automata. From this viewpoint, we introduced somefour-dimensional automata[6, 8].
In this paper, we continue the investigations aboutfour-dimensional alternating Turing machines [6], andmainly investigate fundamental properties of fourdimensional alternating Turing machines with only
universal states which each sidelength of each input
tape is equivalent.
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:

During the past about forty years, many types oftwo- or three-dimensional automata have been proposed and investigated the properties of them as thecomputational model of pattern processing. On theother hand, recently, due to the advances in many
application areas such as computer animation, motion image processing, and so on, the study of threedimensional pattern processing with the time axis has
been of crucial importance. Thus, we think that
it is very useful for analyzing computation of threedimensional pattern processing with the time axis to
explicate the properties of four-dimensional automata.
In this paper, we deal with four-dimensional alternating Turing machines, and investigate several accepting powers of four-dimensional alternating Turingmachines which each sidelength of each input tape isequivalent.
KeyWords : alternation, configuration, four-dimensional input tape, space bound, Turing machine.

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 [5]. Moreover, Sakamoto et al. presented
three-dimensional alternating Turing machines in [7].
On the other hand, recently, due to the advances
in many application areas such as computer anima-©ISAROB 2005(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 time38

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

axis. 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.
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 ) [8].
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

west

the 3rd axis䊶䊶䊶䊶䊶䊶 input headeast

the 1st axis
upfour-dimensional
input tape

m
m

north

south

m

䊶 䊶䊶䊶䊶䊶 䊶䊶䊶䊶䊶䊶down

M = (Q, q0 , U ,F , Σ, Γ, δ), wherepast
the 4th axis

(1) Q is a finite set of states;(2) q0 ∈ Q is the initial state;

futuremFig. 1: Four-dimensional alternating Turing machine.

(3) U ⊆ Q is the set of universal states;(4) F ⊆ Q is the set of accepting states;
natural numbers. That is, for each configuration c =(x, (i1 , i2 , i3 , i4 ), (q, α, j)), let SP ACE(c) = |α|. M
is said to be L(m) space-bounded if for each m ≥ 1
and for each x 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 . We
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 4-(5) Σ is a finite input alphabet (# ∈/ Σ is the boundary
symbol);(6) Γ is a finite storage-tape alphabet (B ∈ Γ is theblank symbol), and(7) δ ⊆ (Q × (Σ ∪ {#}) × Γ) × (Q × (Γ − {B}) ×{east, west, south, north, up, down, future, past,no move} × {right, left, no move}) is the nextmove relation.
A state q in Q − U is said to be existential. As
shown in Fig. 1, the machine M has a read-only fourdimensional input tape with boundary symbols #’sand one semi-infinite storage tape, initially blank. Of
course, M has a finite control, an input head, and astorage-tape head. A position is assigned to each cell of
the read-only input tape and to each cell of the storagetape, as shown in Fig. 1. The step of M is similar tothat of a two- or three-dimensional Turing machine[3–5, 7], except that the input head of M can move ineight directions. We say that M accepts the tape x if
it eventually enters an accepting state. Note that themachine cannot write the blank symbol. If the input
head falls off the input tape, or if the storage head falls
off the storage tape (by moving left), then the 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 to©ISAROB 2005

1 dre means the smallest integer greater than or equal to r.

39

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Cubex (2), there exists a sequence of steps of M inwhich M never enters an accepting state}.
(Note that, for each x in V (m), C(x) is not empty
since x is not in T 1 , and so not accepted by M .) Thenthe following proposition must hold.

XT 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

Accepting Powers of SV 4-U T M ’s

Proposition 2.1. For any two different tapes x, y in
V (m), C(x) ∩ C(y) = φ.
[ Proof: This proposition can be proved by the wellknown technique [7].
¤]Proof of Lemma 2.1(continued) : Clearly, |V (m)| =3
2m and p(m) ≤ s(m + 2)3 L(m)rL(m) , where p(m) denotes the number of possible semi-configurations of M
just after the input head reached the second plane oftapes in V (m). Since L(m) = o(m3 ), we have |V (m)|> p(m) for large m. Therefore, it follows that for large
m there must be two different tapes x, y in V (m) suchthat C(x) ∩ C(y) 6= φ. This contradicts Proposition
2.1 and completes the proof of (2).
¤

We denote by SV 4-U T M (SV 4-U F A) an SV 4AT M (SV 4-AF A) which has only universal states.
For any function L: N → R, we denote by SV 4-U T M(L(m)) an L(m) space-bounded SV 4-U T M , and let£[SV 4-U T M (L(m))] = {T | T = T (M ) for some SV 4U T M (L(m)) M }. £[SV 4-U F A] is defined in a similar way.
In this section, we investigate the relationship between the accepting powers of SV 4-U T M ’s and SV 4AT M ’s (SV 4-N T M ’s or SV 4-DT M ’s).
The following lemma says that there exists a set
accepted by an SV 4-N F A, but not accepted by any
SV 4-U T M (L(m)) for any L such that L(m) = o(m3 ).

We need the following three lemmas. The proof of
the following lemmas is omitted here since it is similar
to that of Lemma 2.1.

(4)

Lemma 2.1. Let T1 = {x ∈ {0, 1}| ∃ m ≥ 2
[l1 (x) = l2 (x) = l3 (x) = l4 (x) = m] & Cubex (1) =Cubex (2)}. Then

Lemma 2.2. Let T2 = {x ∈ {0, 1}(4) | ∃ m ≥ 1 [l1 (x)= l2 (x) = l3 (x) = l4 (x) = 2m & x [(1, 1, 1, 1), (2m,2m, 2m, m)] = x [(1, 1, 1, m + 1), (2m, 2m, 2m,2m)]]}. Then

(1) T 1 ∈ £[SV 4-N F A],2 and/ £[SV 4-U T M (L(m))] for any L: N → R(2) T 1 ∈such that L(m) = o(m3 ).
Proof: The set T 1 is accepted by an SV 4-N F A
which, given an input x ∈ {0, 1}(4) , simply checks
by using nondeterministical states that Cubex (1) 6=
Cubex (2). It is obvious that part (1) of the lemma
holds. Here, we only prove (2). Suppose that there
exists an SV 4-U T M (L(m)) M accepting T 1 , whereL(m) = o(m3 ). Let s and r be the numbers of states(of the finite control) and storage tape symbols of M ,respectively. For each m ≥ 3, let(1) T 2 ∈ £[SV 4-N T M (log m)], and/ £[SV 4-U T M (L(m))] for any L: N → R(2) T 2 ∈such that L(m) = o(m4 ).
Lemma 2.3. Let T2 be the set described in Lemma
2.1. Then(1) T1 ∈ £[SV 4-U F A], and(2) T1 ∈/ £[SV 4-N T M (L(m))] for any L: N → R
such that L(m) = o(m3 ).

V (m) = {x ∈ {0, 1}(4) | l1 (x) = l2 (x) = l3 (x) = l4 (x)= m & Cubex (1)= Cubex (2)Lemma 2.4. Let T2 be the set described in Lemma
2.2. Then& x [(1, 1, 1, 3), (m, m, m, m)] ∈ {0}(4) }.

(1) T2 ∈ £[SV 4-U T M (log m)], andFor each x in V (m), let S(x) and C(x) be sets ofsemi-configurations of M defined as follows:
S(x) = {((i1 , i2 , i3 , 2), (q, α, j)) | there exists a
computation path of M on x, IM (x) `∗M (x, ((i1 , i2 ,
i3 , 1), (q 0 , α0 , j 0 ))) `M (x, ((i1 , i2 , i3 , 2), (q, α, j)))(that is, (x, ((i1 , i2 , i3 , 2), (q, α, j))) is a configurationof M just after the input head reached Cubex (2))},
C(x) = {σ ∈ S(x) | when, starting with the configuration (x, σ), M proceeds to read the segment(2) T2 ∈/ £[SV 4-N T M (L(m))] for any L: N → R
such that L(m) = o(m4 ).
From Lemmas 2.1–2.4, we can get
Theorem 2.1. Let L: N → R be a function such that(i) L(m) = o(m2 ), or (ii) L(m) ≥ log m (m ≥ 1) andL(m) = o(m4 ). Then(1) £[SV 4-U T M (L(m))] ( £[SV 4-AT M (L(m))],2 If T ⊆ Σ(4) , then define T = Σ(4) − T .

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005(2) £[SV 4-U T M (L(m))] is incomparable with£[SV 4-N T M (L(m))], andand L(m) = o(m4 ). Then, £[SV 4-U T M (L(m))] (£[4-U T M (L(m))].

(3) £[SV 4-DT M (L(m))] ( £[SV 4-U T M (L(m))].

Corollary 3.2. £[SV 4-U F A] ( £[4-U F A].

Corollary 2.1. (1) £[SV 4-U F A] ( £[SV 4-AF A].
(2) £[SV 4-U F A] is incomparable with £[SV 4-N F A].
(3) £[SV 4-DF A] ( £[SV 4-U F A].

Theorem 3.3. (1) £[4-U F A] ( £[SV 4-DT M (m4 )],and (2) m4 space is necessary and sufficient for SV 4DT M ’s to simulate 4-U F A’s.

It is natural to ask how much space is necessary
and sufficient for SV 4-DT M ’s and SV 4-N T M ’s tosimulate SV 4-U F A’s. The following theorem answers
this question.

4

In this paper, we investigated the accepting powers
of four-dimensional alternating Turing machines with
only universal states which each sidelength of each input tape is equivalent.
Let Tc be the set of all the four-dimensional connected tapes. If Tc is accepted by four-dimensional alternating Turing machines with only universal states,
it will be interesting to investigate how much spaceis necessary and sufficient for four-dimensional alternating Turing machines with only universal states to
accept Tc .

THEOREM 2.2. (1) £[SV 4-U F A] ( £[SV 4DT M (m3 )]. (2) m3 space is necessary and sufficientfor SV 4-DT M ’s and SV 4-N T M ’s to simulate SV 4U F A’s.
Moreover, by using a technique similar to that in
the proof of Theorem 3.2 in [2], we can get the following theorem.
THEOREM 2.3. m4 space is necessary and sufficient for SV 4-DT M ’s to simulate SV 4-AF A’s and4-AF A’s.

3

Conclusion

References[1] M. Blum et al., IEEE Symposium on Switchingand Automata Theory :155-160(1967).

Accepting Powers of 4-U T M ’s

[2] A. K. Chandra et al., J. ACM 28(1):114-133(1981).

We denote by 4-U T M (4-U F A) a 4-AT M (4-AF A)
which has only universal states. For any function L:
N → R, we denote by 4-U T M (L(m)) an L(m) spacebounded 4-U T M , and let £[4-U T M (L(m))] = {T |T = T (M ) for some 4-U T M (L(m)) M }. £[4-U F A]
is defined in a similar way. This section first investigates a relationship between the accepting powers of
4-U T M ’s and 4-AT M ’s (4-N T M ’s or 4-DT M ’s).
From Lemma 5.2 in [7], we can get the following
results.

[3] J. E. Hopcroft et al., J. Assoc. Comput. Mach.
16:168-177 (1969).
[4] J. E. Hopcroft et al., Introduction to Automata
Theory, Languages, and Computation, AddisonWesley, Reading, MA, (1979).
[5] K. Inoue et al., Theoret. Comput. Sci. 27:61-83(1983).

Theorem 3.1. Let L: N → R be a function suchthat L(m) = o(log m). Then, £[4-DT M (L(m))] (£[4-U T M (L(m))] ( £[4-AT M (L(m))].

[6] H. Okabe et al., Proc. 7th Words Multiconf. on
SCI :241-246 (2003).

Corollary 3.1. £[4-DF A] ( £[4-U F A] ( £[4-AF A].

[7] M. Sakamoto et al., Inform. Sci. 72:225-249(1993).

We then investigate relationships between the accepting powers of eight-way and seven-way fourdimensional machines. By using the same way as in
the proof of Theorems 2.1–2.3, we can get the following results.

[8] M. Sakamoto et al., WSEAS Trans. on Computers, Issue 5, Vol. 3 :1651-1656 (2004).
[9] H. Taniguchi et al., Inform. Sci. 26:65-85 (1982).

Theorem 3.2. Let L: N → R be a function suchthat (i) L(m)3 = o(m3 ), or (ii) L(m) ≥ log m (m ≥ 1)

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Some Properties of Four-Dimensional Multicounter Automata
Makoto Saito1 , Makoto Sakamoto1 , Youichirou Nakama1 , Takao Ito2 ,Katsushi Inoue3 , Hiroshi Furutani1 and Susumu Katayama1
1

Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN
Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN
3
Dept. of Computer Science and Systems Engineering, Yamaguchi University, Ube 755-8611, JAPAN
2

Abstract

In this paper, we introduce and investigate about
eight-way four-dimensional multicounter automata as
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.

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 four-dimensional pattern processing has been of crucial importance. Thus,
we think that the research of four-dimansional automata as a computational model of four-dimensional
pattern processing has also been meaningful. This
paper introduces four-dimensional multicounter automata, and investigates some their properties. We
show the differences between the accepting powersof seven-way and eight-way four-dimensional multicounter automata, and between the accepting powersof deterministic and nondeterministic seven-way fourdimensional multicounter automata.
Key Words : computational complexity, four-dimensional automaton, multicounter, nondeterminism.

1

Introduction and Preliminaries

Inoue et al. [5] 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 [6,8]. 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].

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We now introduce a seven- or eight-way fourdimensional multicounter automaton. An eight-way
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[5, 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 symbol ]’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 directions — east, west, south, north, up, down, or future(see Fig.1).

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Fig. 1: Four-dimensional k-counter automaton.

trol, an input head, and a storage-tape head.) The
action of M is similar to that of the two- or threedimensional Turing machine [6,8] which has a readonly input tape with boundary symbols ]0 s and one
semiinfinite storage tape, except that the input headof M can move in seven directions — east, west, south,
north, up, down, or future. M starts in its initialstate, with the input head on position (1, 1, 1, 1) of
an input tape x, and with all the cells of the storage tape blank. We say that M accepts the tapex if M eventually halts in an accepting state. Let
L(m) : N 7→ N be a function with one variable
m. By N SV 4-T M (L(m)) (DSV 4-T M (L(m))) wedenote a nondeterministic (deterministic) seven-way
four-dimensional Turing machine whose each sidelength of each input tape is equivalent and which does
not scan more than L(m) cells on the storage tapefor any input tape x (accepted by M) with l1 (x) =l2 (x) = l3 (x) = l4 (x) = m. Let L[N SV 4-T M (L(m))]( L[DSV 4-T M (L(m))] ) denote the class of sets accepted by NSV4-TM(L(m))’s (DSV 4-T M (L(M ))’s).
We denote a nondeterministic (deterministic) fourdimensional finite automaton by N 4-F A (D4-F A). A
seven-way N 4-F A (seven-way D4-F A) is an N 4-F A(D4-F A) whose input tape head can move in sevendirections — east, west, south, north, up, down, or future. By N 4-F A (D4-F A, N SV 4-F A, DSV 4-F A) wedenote an N 4-F A (D4-F A, seven-way N 4-F A, sevenway D4-F A) whose each sidelength of each input tapeis equivalent [9]. For example, let L[D4-F A] denotethe class of sets accepted by D4-F A’s. As is easily

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.
We briefly recall seven-way four-dimensional Turing machines [9]. A seven-way four-dimensional Turing machine M has a read-only four-dimensional input tape with boundary symbols ]0 s and one semiinfinite storage tape. (Of course, M has a finite con-

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seen, it follows that for any constant k, L[D4-F A] =L[D4-1CA(k)], L[DSV 4-F A] = L[DSV 4-1CA(k)],and so on.
We conclude this section by giving a relationshipbetween seven-way four-dimensional multicounter automata and seven-way four-dimensional Turing machines, which will be used in the latter sections.

Clearly, each tape in V (m) is accepted by M . For
any (seven-way) four-dimensional Turing machine M ,we define the configuration of M to be a combinationof the (1) state of the finite control, (2) position of
the input head within the input tape, (3) position of
the storage-tape head within the nonblank portion of
the storage tape, and (4) contents of the storage tape.
For each x ∈ V (m), let conf (x) be the set of configurations of M just after the point, in the accepting
computations on x, where the input head left the firstcube of x. Then the following proposition must hold.

[Theorem 1.1]
S(1) 1≤k<∞ L[XSV 4-kCA(L(m))] ⊆L[XSV 4-T M (log L(m))]
for any L(m) : N 7→ N and any X ∈ {D, N },S(2) 1≤k<∞ L[XSV 4-kCA(m)] =L[XSV 4-T M (log m)]
for any X ∈ {D, N }.

[Proposition 2.1] For any two different tapes x, y ∈V (m),
conf (x) ∩ conf (y) = φ (empty set).
(Proof) Suppose that conf (x) ∩ conf (y) 6= φ andσ ∈ conf (x) ∩ conf (y). It is obvious that if, startingwith this configuration σ, the input head proceeds toread the [(1, 1, 1, 2), (m, m, m, m)]-segment of x, thenM could enter an accepting state. Therefore, by assumption, it follows that the tape z[ l1 (z) = l2 (z) =l3 (z) = l4 (z) = m] satisfying the following two conditions must be also accepted by M : (i) z[(1, 1, 1, 1), (m, m, m, 1)] = y[(1, 1, 1, 1), (m, m, m, 1)]; (ii) z[(1, 1, 1,
2), (m, m, m, m)] = x[(1, 1, 1, 2), (m, m, m, m)]. This
contradicts the fact that z is not in T1 .
¤(Proof) (1) : Let M be an XSV 4-kCA(L(m)). The
set T (M ) is also accepted by the XSV 4-T M (log L(m)) which divides the storage tape into k tracks andmakes each track play a role of the corresponding
counter of M .
S(2) : From (1), 1≤k<∞ L [XSV 4-kCA(m)] ⊆L[XSV 4-T M (log m)]. It is well known that any log m
tape-bounded one-dimensional off-line Turing machine
can be simulated by a one-dimensional two-way multihead finite automaton [4]. By using the same argument as in the proof of this fact, we can easily showthat any XSV 4-T M (log m) can be simulated by an
XSV 4-kCA(m)for some k ≥ 1. Thus L[XSV 4-T M (S
log m)] ⊆ 1≤k<∞ L[XSV 4-kCA(m)].
¤(Proof of Lemma 2.1 (continued)) Clearly, |V (m)|3
= 2m , where for any set S, |S| denotes the numberof elements of S. Let c(m) be the number of possible
configurations of M just after the input head left thefirst cube of tapes in V (m). Then02c(m) ≤ s(m + 2)3 L01 (m)tL1 (m) .

Seven-Way versus Eight-Way(The factor s is the number of possible states of finite
control, (m + 2)3 is the number of possible positionsof the input head, L01 (m) is the number of possible
positions of the storage-tape head, t is the number0of storage-tape symbols, and tL1 (m) is the number ofpossible contents of the nonblank portion of storagetape.) Since lim [L01 (m)/m3 ] = 0, we have

In this section, we investigate the difference between the accepting powers of counter-bounded eightway and seven-way four-dimensional multicounter automata.
We need the following two lemmas.

m→∞[Lemma 2.1] Let T1 = {x ∈ {0, 1}(4) | ∃m ≥ 2 [ l1 (x)= l2 (x) = l3 (x) = l4 (x) = m] & Cubex (1) = Cubex (2)},and let L1 (m) : N 7→ N be a function such thatlim [(log L1 (m))/m3 ] = 0. Then,|V (m)| > c(m) for large m.
Therefore, it follows that for large m there must be
different tapes x, y ∈ V (m) such that conf (x) ∩conf (y) 6= φ. This contradicts Proposition 2.1,
and thus T1 is not in L[N SV 4-T M (L01 (m))], where
L01 (m) : N 7→ N is a function such that lim [L01 (m)/m→∞(1) T1 ∈ L[D4-F A], andS(2) T1 ∈/ 1≤k<∞ L[N SV 4-kCA(L1 (m))].

m→∞

m3 ] = 0. From this result and from the condition thatlim [(log L1 (m))/m3 ] = 0, it follows that T1 is not(proof) The proof of (1) is omitted here, since it isobvious. We now prove (2). Suppose that there is an
N SV 4-T M (L01 (m)) M accepting T1 , where L01 (m) :N 7→ N is a function such that lim [L01 (m)/m3 ] = 0.
m→∞
For each m ≥ 3, letm→∞in L[N SV 4-T M (log L1 (m))]. Part (2) of the lemma
follows from this fact and Theorem 1.1 (1).
¤
[Lemma 2.2] Let T2 = {x ∈ {0, 1}(4) | ∃m ≥ 1 [ l1 (x)= l2 (x) = l3 (x) = l4 (x) = 2m & x[(1, 1, 1, 1), (2m, 2m,
2m, m)] = x[(1, 1, 1, m + 1), (2m, 2m, 2m, 2m)] (thatis, the top and bottom halves of x are identical)]},V (m) = {x ∈ T1 | l1 (x) = l2 (x) = l3 (x) = l4 (x) = m& x[(1, 1, 1, 3), (m, m, m, m)] ∈ {0}(4) }.

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4

and let L2 (m) : N 7→ N be a function such thatlim [(log L2 (m))/m4 ] = 0. Then,m→∞

In this paper, we introduced four-dimensional multicounter automata, and we investigated the accepting
powers of counter-bounded seven-way and eight-way
four-dimensional multicounter automata. Then, weinvestigated a relationship between determinism andnondeterminism. 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’).

(1) T2 ∈ L[D4-1CA(m)], andS(2) T2 ∈/ 1≤k<∞ L[N SV 4-kCA(L2 (m))].
(Proof) The proof of Part (1) is omitted here, since it
is obvious. Part (2) is given by using the same technique as in the proof of Lemma 2.1 (2).
¤From Lemmas 2.1 and 2.2, we can get the following
theorem.
[Theorem 2.1] (1) Let L(m) : N 7→ N be a function
such that lim [(log L(m))/m3 ] = 0. Then, L[D4-F A]
m→∞S− 1≤k<∞ L[N SV 4-kCA(L(m))] 6= φ. (2) Let L0 (m) :N 7→ N be a function such that lim [(log L0 (m))/m4 ]S m→∞= 0. then, L[D4-1CA(m)]− 1≤k<∞ L[N SV 4-kCA(
L0 (m))] 6= φ.

3Conclusion

References[1] M.Blum et al., Automata on a two-dimensionaltape, in IEEE Symposium of Switching and Automata Theory, pp. 155–160 (1967).

Nondeterminism versus Determinism[2] A.K.Chandra et al., Alternation, J.ACM, Vol. 28,
No. 1 pp. 114–133 (1981).

In this section, we investigate the difference
between the accepting powers of counter-bounded
deterministic and nondeterministic seven-way fourdimensional multicounter automata.
We need the following two lemmas. The proof of
the following lemmas is omitted here since it is similar
to that of Lemma 2.1.

[3] S.A.Greibach, Remarks on the complexity ofnondeterministic counter languages, TheoreticalComputer Science, Vol. 1, pp. 269–288 (1976).
[4] J.Hartmanis, On non-determinancy in simple
computing devices, Acta Informatica, Vol. 1,
pp. 336–344 (1972).

[Lemma 3.1] Let T3 = {x ∈ {0, 1}(4) | ∃m ≥ 2 [ l1 (x)= l2 (x) = l3 (x) = l4 (x) = m] & Cubex (1) 6= Cubex (2)},and L1 (m) : N 7→ N be a function such thatlim [(log L1 (m))/m3 ] = 0. Then,[5] K.Inoue et al., Three-way two-dimensional multicounter automata, Information Sciences, Vol. 19,
pp. 1–20 (1979).

m→∞(1) T3 ∈ L[N SV 4-F A], andS(2) T3 ∈/ 1≤k<∞ L[DSV 4-kCA(L1 (m))].

[6] K.Inoue et al., A survey of two-dimensional automata Theory, Information Sciences, Vol. 55,
pp. 99–121 (1991).

[Lemma 3.2] Let T4 = {x ∈ {0, 1}(4) | ∃m ≥ 2 [ l1 (x)= l2 (x) = l3 (x) = l4 (x) = 2m & x[(1, 1, 1, 1), (2m, 2m,
2m, m)] 6= x[(1, 1, 1, m + 1), (2m, 2m, 2m, 2m)]]}, andlet L2 (m) : N 7→ N be a function such that lim [(log[7] M.Sakamoto et al., Three-dimensional Multicounter Automata, in Proceedings of the 6th International Workshops on Parallel Image Processing and Analysis, pp. 267–280 (1999).

L2 (m))/m4 ] = 0. Then,m→∞[8] M.Sakamoto, Three-Dimensional Alternating
Turing Machines, Ph.D. Thesis, Yamaguchi
Univ. (1999).

(1) T4 ∈ L[N SV 4-1CA(m)], andS(2) T4 ∈/ 1≤k<∞ L[DSV 4-kCA(L2 (m))].

[9] M.Sakamoto et al., A note on Four-dimensional
Finite automata, WSEAS Trans. on Computers,
Issue 5, Vol. 3, pp. 1651–1656 (2004).

From Lemmas 3.1 and 3.2, we can get the following
theorem.
[Theorem 3.1] (1) Let L(m) : N 7→ N be a function such that lim [(log L(m))/m3 ] = 0. Then,m→∞
S
L[N SV 4-F A] − 1≤k<∞ L[DSV 4-kCA(L(m))] 6= φ.
(2) Let L0 (m) : N 7→ N be a function such thatlim [(log L0 (m))/m4 ] = 0. Then, L[N SV 4-1CA(m)]m→∞S− 1≤k<∞ L[DSV 4-kCA(L0 (m))] 6= φ.

©ISAROB 2005[10] A.Szepietowski, On three-way two-dimensional
multicounter automata, Information Sciences,Vol. 55, pp. 35–47 (1991).

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Simulation Study for Intelligent Wheelchair Vehicle with Ultrasonic and Infrared
Sensors
Kouichi Kamimura∗ , Takahiro Kai∗ , Masahiro Yokomichi∗ and Tetsuro Kitazoe∗∗∗ Faculty of Engineering, University of Miyazaki ∗∗International University of Kagoshima
Gakuen Kibanadai Nishi 1-1, Miyazaki City, Japan, 889-2192
kai@cs.miyazaki-u.ac.jp
Abstract

We focus the task in the present paper to the navigation going along a right side wall by using front andright side sensors. If we are successful to get a task ofgoing along a right side wall, then we can easily extend it to the task of going along a left side wall. If arobot can move along any side of a wall or can proceedstraight, it will be possible to build a robot to go fromone place to a destination, following an ordinary corridor in a building. We connected input sensors andoutput wheels by neural networks and developed G.A
for the wheelchair simulator in a rather complicatedtest course to obtain the best robot. The simulation
result was applied to the real wheelchair navigation ina corridor inside our building and eventually we had asmoothly moving nice wheelchair robot.

In this paper, we propose wheelchair navigation
system with infrared sensors and ultrasonic sensors
to aid senior people and the handicapped to drive awheelchair. We consider neural network to connect input sensor signals and output wheel speed. To get nicewheelchair controller, we constructed wheelchair simulator and applied G.A. for synaptic couplings in theneural networks. We investigated that a wheelchair
controlled by this system ran safely and comfortably.

1

Introduction

Recently, many people have been researching autonomous mobile robots extensively. The purpose of
these researches is to build the robots that are able torun safely in unknown or dynamically changing environments. In order to perceive the surrounding environments, robots mount many kinds of sensors, e.g.,
infrared sensors, ultrasonic range sensors and CCD
cameras.
There is a growing demand for more safe and comfortable wheelchairs as mobile aids, as the populationof senior people has been increasing. There are two
research issues with these wheelchairs. One is autonomous (or semi-autonomous) and safe navigation,such as avoiding obstacles, wall following, going to agoal using various sensors [1,2]. The other is to develop
human interfaces for easy operation [3,4].
In this paper, we aim to build an autonomous
wheelchair robot so as to reduce navigation eﬀorts
of a handicapped person as far as possible. We use
wide ranged ultrasonic sensors in front of wheelchair
to detect obstacles and sharp ranged infrared sensors
mounted on both sides of wheelchair to measure a distance to a wall.
The most major problem to construct the bestrobot is to make a realistic simulation of a robot. We
use Webots by Cyberbotics[3] which is an excellent software to take account of noises in real world and toenable us to build any shape of mobile robot easily.

©ISAROB 2005

2

Outline of Wheelchair system

Fig. 1: Wheelchair system
The wheelchair system in our research is composed of a commercial electric wheelchair (Matsunaga
MD-100), a desktop PC (CPU:PentiumIV 2.66GHz,
OS:Linux), infrared sensors (GP2D12) and ultrasonicsensors (BTE054) as shown in Fig.1 The wheelchair
has the maximum speed of 4.5 [km/h]. The infrared
sensors are mounted on the side of the wheelchair andthe ultrasonic sensors are mounted on the front. The
PC processes infrared and ultrasonic sensor input values and controls wheel motors of the robot.

46

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
2.5

2voltage (v)wall in the front direction are normalized in the range0∼1.0 by deﬁning"Aluminum""Concrete""Corrugated_paper""Clothes""Wood"

1.5

1

0.5

0
10

20

30

40
50
distance (cm)

60

70

80

Fig. 2: Distance-Voltage graph of an infrared sensor
Fig. 4: Sensor configurations for wheelchair robot
5"Aluminum""Concrete""Corrugated_paper""Clothes""Wood"

voltage (v)43X03 = αultra s /V 03
αultra s = 75.0

(2)

X0 =
X1 =

1

0

50

100

150
200
distance (cm)

250

300

To normalize Xi to take 0∼1.0, we choose
Vinf ra s = 2.78

The infrared and ultrasonic sensors output a distance to an object in voltage. Fig.2 shows distance- voltage graph for several kinds of objects. We ﬁndthat the maximum detection ranges of the infraredand ultrasonic sensors are about 80 cm and 300cm,
respectively. They have almost simulator response to
the diﬀerent objects as shown in Fig.2 and Fig.3.

(3)

(4)

(5)

The control system of the wheelchair consists ofneural networks shown in Fig.5. To train the synaptic couplings by means of genetic algorithms, we investigated robot movement in a computer simulation
model. A diﬃcult problem in computer simulations is
how to simulate the real world with respect to noises.
Webots ver.4.0.24 by Cyberbotics[5], is an excellent
software tool that takes account of uncertainties assumed to exist in the real world. Noises of ±10% and±10% are randomly added to the amplitude of thesensor value, and the amplitude of the motor speed,
respectively.
Another diﬃculty in computer simulation is how tobuild a simulator to imitate the real object as far as
possible. The webots software package is composed of
VRML coding and any parts of an object are easily
replaced or modiﬁed. As a typical example, we take arectangular area with concave spaces surrounded by awall as shown in Fig.6, in which wheelchair webots isrequired to follow the wall counterclockwise. It must
keep a safe and short distance from the wall on theright, and enters wide spaces and narrow spaces.

Control methods

We consider control methods of a wheelchair sothat a wheelchair runs along a right-hand side wall.
We mount infrared and ultrasonic sensors on thewheelchair as shown in Fig.4. Since the ultrasonicsensors cover wide range of angle, three ultrasonicsensors(X 01, X02, X03) are installed on the front todetect front obstacles. Since the infrared sensors
have narrow range sensing angle, four infrared sensors
X1, X2, X3, X4 are installed on left and right sides tomeasure the distance to a wall. Since we do not use left
side sensors X3, X4 for our present task of going along
a right side wall, we can easily extend our task to go
along a left side wall by using X3, X4. The ultrasonicsensors (X01, X02, X03) which detect an obstacle or a

©ISAROB 2005

(X01 + X02)/2.0
X03

Side infrared sensor values are denoted by
X2 = (V1 + V2 )/2Vinf ra s
X3 = (V3 + V4 )/2Vinf ra s

Fig. 3: Distance-Voltage graph of an ultrasonic sensor

3(1)

where V 01 is the output voltage of the sensor and
αultra s is a normalization constant. To reduce variables, we take the mean value of X01, X02 as

20

X01 = αultra s /V 01, X02 = αultra s /V 02

47

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

ω0 ∼ ω7 are synaptic couplings, as shown in Fig.5,
connecting data from sensors with the control of theright or left motors. We determine ωi by using thegenetic algorithms. The algorithms for obtaining thebest genes are as follows:
Fig. 5: Neural Network to control left and right wheel

1)Make N1 robots with randomly generated synaptic couplings and left them run in the area shownin Fig.6 for certain period.

2)

Make new N2 robots from the old N1 robots by using genetic algorithms in which the synaptic couplings of new robots are generated by real-coded
genetic algorithms, in which α of BLX − α[5] is
set to 0.5. Then let them run for the same period
as in step 1.

3)

Make new N3 robots from the old N1 robots byusing mutation.

4)

Measure all the robots N1 + N2 + N3 by using agiven evaluation function, and choose N1 robots
with the highest scores. If the total score of theN1 robots exceeds a given threshold, stop theloop; otherwise go to step 2.

5

Experimental results and conclusion

Fig. 6: Test course for simulator wheelchair robot

4

Evolutionary Adaptation

To train robot control systems, we perform adaptation under a computer simulation model of a robot
and its environment. As shown in Fig.5, the data
X0, X1, X2 output from the sensors are fed to theneural networks with self-training ability. The synaptic couplings are then revised by genetic algorithms.
The control signals to the right wheel and left wheel,
L and R are given byL

We performed simulation in the fairly complicatedtest course shown in Fig.6. The evaluation function in
genetic algorithms is given as  ST EP
 ST EP
speed  
X2 

 

g=
 ST EP  ·  ST EP  ·= F (ω0 X0 + ω1 X1 + ω2 X2 + ω3 ) (6)R= F (ω4 X0 + ω5 X1 + ω6 X2 + ω7 ) (7)F (x) = tanh(x)(8)

where L and R are normalized as −1.0 ≤ L, R ≤1.0. We deﬁne mean voltage Vm and diﬀernce voltage
Vd supplied to the wheel motors so as to control wheel
speeds asVm= Vmax · (L + R)/2.0

(9)

Vd


= Vmax· (L − R)/2.0

(10)

1+

©ISAROB 2005

= fR (Vm , Vd )= fL (Vm , Vd )

−1

X3

·



1+

ST
EP


−1

collision(13)

Each term in Eq.(13) evaluates the robot performance from diﬀerent points of view, 
1 measuring
wheelchair robot’s speed, 
2 going along a right side
wall, 3 movements without obstacles on the left side,and 
4 avoiding collision against a wall. The evaluation function g has a high value if a robot fallows a wall without colliding with anything and if
it moves forward as far as possible. We take N1 =10, N2 = 4, N3 = 11, speed = (vR + vL )/2vmax , andvmax = 0.88[m/s], ST EP = 400.
As a result of evolutionary adaptation after 400 generations, the coupling values of the best 10 robots became to have almost the same value which are shown in
Table.1 together with the ﬁtness value. Table.2 shows
where Vmax = 1.12volt, Vmax
= 1.0volt. A given
voltage Vm , Vd determine left and right wheel speeds
vL , vR . The functional relations between these variables are decided by moving our actual wheelchair.
We obtained the best ﬁt functions experimentally anddenote them as

vR
vL

ST
EP


(11)
(12)

48

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the behaviors of the best robot at the typical places
shown in Fig.7. The trace of the robot in the test
course is also shown in Fig.7.

25.0m
2.2m

2.5m

Table 1: The coupling values of the best robot after 400
generations.

1.6m
2.0m

ω0
ω1
ω2
ω3
ω4
ω5
ω6
ω7 fitness-5.24 -4.09 -0.23 1.59 1.77 1.05 0.73 0.05 9011.29

2.0m

Fig. 9: Course of the corridor inside our building andtrace of the best robot

Table 2: Typical behaviors of the best robot in Table.1
X0 X1 X2
LRvLvR
A 0.04 0.04 0.25 0.82 0.34 2.80 1.83 Turn right
B 0.24 0.21 0.25 -0.52 0.71 -0.13 1.18 Turn left
C 0.04 0.19 0.29 0.49 0.49 1.90 1.93 Go straight

References[1] T.kitazoe, M.Tabuse, T.Uemura, S.kitazato,
T.shinchi, Wheelchair Navigation System with Infrared Sensors, Proc. of The Eighth International
Symposium On Artiﬁcial Life And Robotics, p.p.
102-105

A[2] Webots User Guid release 4.0.25 (2004).
B[3] webots
Packageversion
4.0.24.
Mobilerobot simulator. The World Wide Web at
http://www.cyberbotics.com

C[4] M.Tabuse, T.shinchi, A.Todaka, and T.kitazoe,
Evolutionaly Robot with Competitive-Cooperative
Neural Network, IPSJ Journal, Vol.44 No.10,
pp.2530-2513, 2003.

Fig. 7: Result of test course simulation

10000"fit_best1""fit_average"

9000[5] Eshelman, L.J. and Schaﬀer, J.D, Real-Coded Genetic Algorithms and Interval-Schemata, Foundations of Genetic Algorithms 2, pp.187-202 (1993).

8000
7000
6000

[6] D.P. Miller, M.G. Slack, ”Design and Testing ofa Low-Cost Robotic Wheelchair Prototype”, Autonomous Robotics, 2, pp.77-88, 1995.

5000
4000
3000
2000

[7] T. Gomi, A.Griﬃth, ”Developing Intelligent
Wheelchairs for the Handicapped”, Assistive Technology and Artiﬁcial Intelligence, Lecture Notes inAI, 1458, pp.150-178, Springer, 1998.

1000
0
0

50

100

150

200
250
generation

300

350

400

Fig. 8: Evaluation function in GA[8] Y. Matsumoto, T. Ino, T. Ogasawara, ”Development of Intelligent Wheelchair System with Faceand Gaze Based Interface”, Int. Workshop on
Robot and Human Interactive Communication,
pp.262-267, 2001.

The evaluation function for the best robot and theiraverage value among robots are given in Fig.8 against
generation development.
We made simulation experiments of wheelchair navigation on the second ﬂoor in the north building of
the faculty of engineering, University of Miyazaki asshown in Fig.9. The robot moved smoothly as thetrace is shown by a solid line in Fig.9. We conclude
that our two step strategies, test course simulation andits application to real mapped course led to a successful result.

©ISAROB 2005[9] S. Nakanishi, Y. Kuno, N. Shimada, Y. Shirai,”Robotic Wheelchair Based on Observations ofBoth User and Environment”, Int. Conf. on Intelligent Robotics and Systems, pp912-917, 1999.

49

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Object Recognition Using a Self-Organizing Map for an Autonomous
Mobile Robot
Masayoshi Tabuse
Haruna Kaneko
Department of Environmental Information, Kyoto Prefectural University, Kyoto, 606-8522, Japan
tabuse@kpu.ac.jp

Abstract
In this research, we propose a control system by using
the self-organizing map, a neural network and the genetic
algorithm. The control system autonomously extracts visual
information and decides behaviors. We investigate that amobile robot controlled by this system performs a given taskvery well.
Key words: object recognition, Self-organizing map, neuralnetwork, mobile robotthat Kohonen's self-organizing map classifies a floor, a wall,coloring blocks and coloring areas and that the robot
accomplishes the task very well.

2 Outline of the System
2.1 A mobile Robot
We use a miniature mobile robot Khepera II in our
experiments as shown in Figure 1. This robot is 70mm indiameter and has two independent wheels and eight infraredproximity sensors (six in front and two in rear). We attach agripper to this robot so that it grips a small block.
Furthermore, we place a small CCD camera (KEYENCE
CK-200) on the gripper to capture images.

1 Introduction
Recently, many people have been researching autonomous
robots extensively. One purpose of this research is to build
the robots that are able to behave in unknown or dynamicallychanging environments. In the case of a robot with visualsensors, most of methods are that useful information is
extracted using image processing and robot behaviors are
decided based on this information. However, appropriate
image processing methods are generally constructed by adesigner who has a good knowledge of tasks, so that
autonomy and flexibility of a robot may be reduced [1].
Therefore we propose a control system in which a robot
autonomously extracts visual information and decides
behaviors.
In this system, we use a CCD camera as a visual sensor.
Images obtained from the camera inputs Kohonen's
self-organizing map [2], which classifies images, and a robot
recognizes objects in the environment. A behavior decisionpart consists of a neural network and parameters of theneural network are chosen so that the robot accomplishes agiven task by using a genetic algorithm [3]. We examine thissystem using a miniature robot Khepera with a CCD camera.
A task is that the robot moves in a field, finds a coloring
block and carries it to a same coloring area. We investigate©ISAROB 2005

Figure 1: A mobile robot Khepera II

2.2 Control System
The control system consists of Kohonen's self-organizing
map (SOM) and a two-layer neural network (NN).
Kohonen's self-organizing map classifies images and theneural network control each wheel and a gripper. A CCD
camera mounted on Khepera captures front images. These
images are inputted into SOM. The output data from SOM
are fed to a two-layer neural network. Weights of the neuralnetwork are determined using the genetic algorithms so that

50

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The winner node c is determined by

Khepera performs appropriate behaviors. The outline ofcontrol system is shown in Figure 2.

r r
c = arg min i || x − mi || .
(3) The reference vectors of the winner and its neighboring
nodes on the grid are changed towards the input vector
according to the following equation:rrrr
mi (t + 1) = mi (t ) + hci (t )[ x (t ) − mi (t )] ,Figure 2.: The outline of control systemhci (t ) = α (t ) ⋅ exp( −2.3 SOM
The self-organizing map (SOM) is a neural network of
unsupervised learning proposed by T. Kohonen in the early
1980s. The goal is to discover some underlying structure of
the data. SOM consists of two layer structures. One is an
input layer and the other is an output layer, which is usually
represented by 2 dimensional grid. The network is fully
connected, i.e., all nodes in input layer are connected to allnodes in output layer, as shown in Figure 3.

r r|| rc − ri ||2),
2σ 2 (t )where t=0,1,2,… is a discrete learning time, hci (t ) is aneighborhood function, α (t )is a learning rate,

σ (t ) is a decreasing function which defines a radius ofrrneighborhood and rc and ri are coordinate vectors of
the winner node c and the neighborhood node i on the grid,
respectively.
(4) Repeat processes (2) and (3).
According to this algorithm, the network organizes
itself and a self-organizing map for input vectors is built.
This map classifies input vectors. In Figure 5 we show aself-organizing map obtained in our experiments. Light andshade of the map represent similarity of reference vectors.
Light color means that reference vectors of both sizes are
similar. Thus we guess that a robot with our control system
recognizes objects without appropriate image processing
methods constructed by a designer.

Figure 3: SOM
The learning algorithm for n dimensional input vectors
is as follows:

2.4 Neural network and genetic algorithm
A two-layer neural network controls left and right wheels
and a gripper according to output data of SOM as input data.
We think that a neural network is good controller because it
outputs continuous signals for various input data. Usually,learning methods of the neural network are supervised
learning. In supervised learning, we must have knowledge ofcorrect outputs of the neural network for any input data.
However, in dynamical changing environments, we supposethat it is impossible to teach correct outputs in any cases.
Therefore, We use the genetic algorithm to find appropriate
weights of the network to perform a given task.

(1) Properly choose a reference vector of i-th output noder
mi = (mi1 , mi 2 ,..., min ) , i = 1,2,..., M ,connected with n input nodes, where M is the number of
output nodes.

r(2) An input vector x = ( x1 , x2 ,..., xn ) is comparedrwith all the reference vectors mi . The best-matching
output node where the reference vector is most similar to
the input vector in some metric (e.g. Euclidean) is
identified. This best matching node is called the winner.
©ISAROB 2005

51

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3 Experiments
3.1 Experimental Environment
Figure 3 shows the environment in our experiments. The
color of a floor and a wall is white and we set red, blue andgreen blocks and the same coloring areas on the floor. A task
is that Khepera carries a block to the same coloring area.

129

69

9

床

0.9 m

0.6 m

Figure 5: SOM of our experiments
Next we determine weights of the neural network to
perform a given task using the genetic algorithm in computer
simulation. The neural network consists of two layers, as
shown in Figure 6. Input data are 48 dimensional data from
SOM and 3 internal states of Khepera. Internal states denotethat Khepera is gripping a block, i.e.,

Figure 3: Experimental environment

3.2 Learning methods
First we capture total 190 sample images of a block, acoloring area, a wall and a floor using a CCD camera
mounted on Khepera. Each image is 320×240 pixels, but toreduce the dimension of input data and to obtain a image ofthe nearest object, we use only the lower half of the image
after dividing into tiles (tile size : 20×20) and averaging ineach tile. A capture image and its lower half average image
are shown in Figure 4. Then 16×6 = 96 dimensional images
are the input data of SOM. SOM consists of 96 nodes in
input layer and 6×8 nodes in output layer, and SOM
classifies input data using the learning algorithm. Figure 5
shows a self-organizing map for 190 sample images. Imagesof a coloring block, a floor or a wall and a red area areclassified in the left upper side, in the center and in the lower
side of this map, respectively. The number on the map
represents a sample image number.

(0,0,0) for gripping no block(1,0,0) for gripping a red blockinternal state = (0,1,0) for gripping a green block(0,0,1) for gripping a blue block

Figure 6: The neural network
In this research, we investigate object recognition byusing SOM. Therefore, we consider that learning behaviors
of Khepera are finding a block, approaching a block,carrying a block to a same coloring area and outputting
signals of gripping and placing a block to the gripper. The
behaviors of gripping and placing a block are constructed byhand so that if the signal to the gripper indicates gripping a
block and infrared sensors detect a object in front, Khepera
grips a block and that if the signal indicates placing a block,
Khepera places a block.

Figure 4: A example of a capture image and its lower half
average image©ISAROB 2005

52

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

In the genetic algorithm we set that the population has
50 individuals, it runs for 2000 generations and individuals
run 200 steps for each generation. Each individual has achromosome, which represents weight of the neural network
in a real number. At each generation, individuals with highfitness are selected and produce descendants using
crossovers and mutations of their chromosomes. The fitness
function is given by1000 for gripping a block1000 for placing a block
on the same coloring areafitness = - 500 for going back or rotatingin a free space10 for capturing a block- 100 for missing a block

Figure 8: Placement of a block on the same color area

4 Conclusion
We have presented the control system for an autonomous
mobile robot using a combination of Kohonen's
self-organizing map and neural network. In this system, therobot autonomously extracts visual information andrecognizes objects in the environment. Thus the robot finds acolored block, grips and carries it and places it on a samecolored area.
In our experiments, we use a map of 6×8 in SOM. If
the size of a map is larger, SOM may classify visual images
more precisely. However, for a larger size of a map it
requires more time to find proper weights of neural network,so that we will find a proper size of a map.
As the future works, we will consider a control system
in which a robot recognizes a shape of objects and constructa robust system in dynamically changing environments.

After 2000 generations, we select an individual withhighest fitness, decide weights of the neural network andinvestigate Khepera’s behaviors with the control system.

3.3 Result
The mobile robot Khepera moves around in the field andfinds a coloring block. Then the robot approaches the block,and grips and carries it and places it on a same coloring area.
Thus we find that Kohonen's self-organizing map classifies afloor, a wall, coloring blocks and coloring areas and that the
robot accomplishes the task very well. Figure 7 and 8 show
scenes of gripping and placing a block, respectively.

References[1] K. Shibata, M. Iida, "Acquisition of box pushing bydirect-vision-based reinforcement learning", SICE 2003
Annual Conference, pp.2322- 2327, 2003.
[2] T. Kohonen, “Self-Organizing Maps”, Springer-Verlag,
1995.
[3] S. Nolfi, D. Floreano, “Evolutionary Robotics; The
Biology, Intelligence, and Technology of Self-Organizing
Machines”, MIT Press, 2000.
Figure 7: Griping a block©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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   %      ,    "            

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 .       /      "(           %        (                        %
 (           #   "            #%   (                        !     " #   "    "      # $ #      0  ©ISAROB 2005$       &()*+,&()*+ ,&)())) 4    5     !      "  
  "      #  $        $&0          8   . )&       .   " #   )& $   &)())) 4 
               "     0& 9(                              "            "   "    $ (              "           0* %     (            ( .((:
     

    

$ &; $    
% &()*+,&()*+ ,%
*'6:  # ' . :
- " *7 )& *7

54

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

 /      )&  *7%   (                                          

0 &; . ,    "  %  / 
  ,   (       ,  /          4  ,    !       
$(               "          $       
0'     0 =; 00 *; :,     

 !   %   (   ,         $     !               $(    (  (       $ (     "         . ,              0+<    (              /  "  $        (    ,         
,    ,  0       / ©ISAROB 2005 

0 +; >        $                     ,   
 06 ?  #     
    &()*+,&()*+  *()+@     $ & $(    .    *7  *7 "        0A  @ $ (     "                "     


55

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

0 ';   " 0 A; . ,   .

    )& &'0 @; . ,   .

    &6 *70 6; B             ,            ?          
     ,          4   /    ,  ,    (         &+'  **      /      07 $    . )&(&+  C &( 4 )&()*(*6(< )&()'(*@  C *( B )&()+(&7(*@  C

$ *; C
C
>  $   D %    

©ISAROB 2005

=(  : )&()'()@(*6  C + C &      ( C *  =          (  C
+        &)())) $            5
','
&()))
)@
=

0 &)    "     "         $       *()+@        ',' $       =(     )@(        &()))
    $ *

56

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

0 7; -    %   (                     $   
    /     "          "  "  $    /       0 &); 

<"    !  
2&3 $ 1  ( E   F(  
5( &77A
2*3 - C ( C -(  (%     %
 %
 % -  (   "(*)))0 &&; . ,   "  

0 &*; 0 ,     
©ISAROB 2005

57

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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

ABATRACT
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.

We will explain the competitive neural networks thatare used to classify the bill money. The structure of 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 office automation[1]. Since sizes of bills are different according to kindsof bills, the measurement data of bills include various
variations. Human being can classify the bills correctly
even if they are suffered from those variations such as
rotation and shift. But usual pattern recognition using a
conventional transaction machine cannot give us thecorrect classification result under such cases since thebasic method is a pattern matching principle.
Furthermore, the conventional pattern matching
method requires many template patterns for manykinds of bills, which takes 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 to©ISAROB 2005

COMPETITIVE NEURAL NETWORKS

weight 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 the58

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

If c belongs to
the different

Output Layer
Category１

Category２

Wcategoryc(t+1)

Category

with Wc(t)If c belongs to
the same category
Wc(t+1)as x(t)

X(t)

x(t)

Input
Fig. 1.

Layer
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.

Structure of the LVQ networks.

connection weight vector is used.

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
LVQ, we havew c (t + 1) = w c (t ) + s (t )α c (t )(x(t ) − w c (t ))= (1 − s (t ))α c (t )w c (t ) + s (t )α c (t )x(t )andw c (t ) = wc ( t − 1) + s(t − 1)α c (t − 1)

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

wheredenotes the Euclidean norm and t
denotes the iteration time.

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 ))w i (t + 1) = w i (t ) , i ≠ cand if the input x (t) belongs to the other Category j(j ≠ c), thenw c (t + 1) = w c (t ) − α (t )(x(t ) − w c (t ))(x(t − 1) − wc (t − 1))= (1− s(t − 1))α c (t − 1)wc (t − 1)

w i (t + 1) = w i (t ) , i ≠ c+ 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)

where α (t ) is a positive function and denotes learningrate.

+ s(t)αc(t)x(t) + s(t − 1 )αc(t − 1 )( 1 − s(t)αc(t))x(t − 1 ).

In the usual LVQ α (t ) is given bytα (t ) = α 0 (1 − )TWe 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

where (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 .

©ISAROB 2005

59

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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.

100,000 Liras, and old 100,000 liras. Those Lira bills
are used at the input of the transaction machine where

A direction
3.

Ｄdirection

PREPROCESSING ALGORITHM

The images obtained by transaction machine, there arevariations such as rotation or shift. Therefore, we must
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.

Ｂ direction
Fig. 4.

Ｃdirection

Transfer direction

Four directions of bill money.

(a) A direction of 1,000Lira

original image(64x128 pixels)

rotation and shiftcorrection(b) B direction of 1,000 Lira

extraction oftheprocessing

input to NN(c) C direction of 1,000 Lira

(15x64 pixels)

ＮＮ
Fig. 3. Preprocessing algorithm.

4.

ITALIAN LIRA CLASSIFICATIONThe 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©ISAROB 2005(d) D direction of 1,000 Lira

Fig.5. Image of four directions of 1,000 Lira.
60

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

four 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.
The typical images of 1,000 Lira for four directions
are shown in Fig.5. Thus, thirty-two bill images are
one set of the classification pattern of the experiment.
Total number of data sets is 30 and 10 data sets areused 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 timeT=150
α i (0) = 0.5 , i = 1, L M
Initial values of the weight vectors=mean vectors
for training patterns

Table 2.

Not fired rate (%) at t=0.

Directions
Italian
Liras

Ａ

Ｂ

Ｃ

Ｄ

1,000

20

15

15

10

2,000

5

10

25

25

5,000

15

20

5

0

10,000

10

10

10

5

50,000(new)

5

0

20

5

50,000(old)

0

0

0

0

100,000(new)

0

0

0

0

100,000(old)

0

5

0

0

Table 3.

Recognition rate (%) at t=160.

Directionsdθ = min (mc + 4.5σ c )cItalian
Liras

After training the neural network, 20 data sets aretested how well the LVQ network could work. Table 1
denotes the recognition rate in the beginning (t=0),
which means the result by the conventional patternmatching method. Table 2 shows the number of not
fired numbers at t=0. Tables 3 and 4 show those values
at t=160. We can see the improvement by learning.
Table 5 shows the number of the neuron units at t=160
which are determined by increasing them.

Ａ

Ｂ

Ｃ

Ｄ

1,000

100

100

100

100

2,000

100

100

100

100

5,000

100

100

100

100

10,000

100

100

100

100

50,000(new)

100

100

100

100

50,000(old)

100

100

95

95

100,000(new)

100

100

90

100

100,000(old)

100

100

95

90

Table 1. Recognition rate (%) at t=0.
Table 4.

Directions
Italian
Liras

1,000
2,000
5,000
10,00050,000(new)50,000(old)100,000(new)100,000(old)©ISAROB 2005

Ａ

Ｂ

Ｃ

Ｄ

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

100

85

100

80

95

100

100

90

100

100

100

95

90

Not fired rate (%) at t=160.

Directions
Italian
Liras

61

Ａ

Ｂ

Ｃ

Ｄ

1,000

5

0

5

0

2,000

0

10

25

25

5,000

15

20

5

0

10,000

10

0

0

5

50,000(new)

5

0

0

0

50,000(old)

0

5

0

0

100,000(new)

0

0

0

0

100,000(old)

0

5

0

0

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 5.

Number of units after learning.

Directions(a) 50,000 Lira(new)A direction

ItalianLiras(b) 50,000 Lira(old)A direction

1,000
2,000
5,000
10,00050,000(new)(c) 50,000 Lira(new)D direction

Fig. 6.

50,000(old)(d) 50,000 Lira(old)D direction

100,000(new)100,000(old)New and old 50,000 Liras.

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

6. CONCLUSIONS(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

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.

REFERENCES

Fig.7. New and old 100,000 Liras.

[1] S. Fukuda, T. Kosaka, and S. Omatu: Bill Money

From the original image data we can see thatthe difference between 50,000 Lira old and newis slight and the difference between old andnew100, 000 Liras as shown in Figs. 6 and 7.
Therefore, it is rather difficult to recognize them so
perfectly. But in this case the misclassification like oldand new bills within the same values is not serious.
Thus, we have regarded this misclassification as thecorrect one. Furthermore, we have introduced thethreshold value to prevent from making themisclassification. Thus, even if the minimum distancecriterion results in the correct classification, we havedecided these bells are unknown. Without thresholdconstraints, we could obtain 100% classification rate.

©ISAROB 2005

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,
1995.

62

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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63

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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4    > 5  (  17  1(  75  %    5  ( =     
7   7    1    5     7    5%      ( 1 =     15 7(     7   
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        
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67

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Information Filtering Using SVD and ICA
Takeru Yokoi
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
takeru@sig.cs.osakafu-u.ac.jp

Hidekazu Yanagimoto
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
hidekazu@cs.osakafu-u.ac.jp

Abstract

the user’s interests and filtering systems which select the information depending on the user’s interests. It is reported the method such as Latent Semantic Analysis(LSA)[1]. LSA is the method whichchooses axes to focuse on the variance and cuts off
noise. In LSA, Singular Value Decompositon(SVD) is
often used to search the axes.
We search the axes from the viewpoint of independence instead of variance. Independent Component
Analysis(ICA) is the method to find the axes depending on the independene. ICA is recently used for signal
processing, image processing and so on. It has been
already reported that we can obtain topics includedin the documents on applying ICA to documents[2][3].
We use the topics for transformation of document vectors and improve recommendation accuracy of documents.
However, it is difficult to clearly understand meaning of some topics obtained by ICA. This is why input
documents includes some noise. Hence, we use SVD
to cut off the noise. It is reported to use SVD before
applying ICA, for the accuracy of topic extraction to
become better[4].
In this paper, we combine denoising by SVD and
topic extraction by ICA to improve the user profile.
Then we map the document vectors into the space
which consists of the topics and construct the user
profile with the transformed document vectors by GA.
Finally, to confirm the proposed method, we carry out
an experiment on test collection (NTCIR2[5]). In addition, we discuss about the topics obtaind by thisalgorithm.

We propose an information filtering system using
Singular Value Decomposition(SVD) and Independent
Component Analysis (ICA). The number of the independent components to estimate increases as the number of documents increases. Therefore, ICA requires
much calculation amount according to it. When ICA
is applied to documents, it is considerd that topics included in the documents are obtaied. However, it isdifficult to clearly recognize the meaning of topics obtained by ICA. To solve these problem, before applying ICA, we transform the documents with SVD. Using SVD, we expects accuracy of the topic extraction
becomes better and effects a user profile well. Then,in our proposed method, we map the document vectors into topic space. We create the user profile fromtransformed documents with Genetic Algorithm. Finally, we recommend documents by the user profileand evaluate accuracy by 11-point average precision.
We carry out an experiment to confirm advantage ofthe poposed method.
Keywords: Independent Component Analysis, Singular Value Decomposition, Information
Filtering System, User Profile

1

Introduction

As information technologies have been advanced, aplenty of information are served in the Internet. It
has been difficult to find what we demand from large
amount of information by existing retrieval systems
since we cannot express our query exactly. A lot of researchers pay attention to developing information retrieval systems which automatically select the information depending on our interests.
The information retrieval systems with user’s interests have been studied. For example, there are
ranking methods that sort information depending on©ISAROB 2005

Sigeru Omatu
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
omatu@cs.osakafu-u.ac.jp

2

Our proposed method

In this chapter, we explain a user profile, ICA and
SVD.

79

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.1

User Profile

data recorded by microphones and independent variables are source signals. On the other hand, when ICA
is applied to documents, the inputs of microphones
correspond to document vectors.
Now m independent components are denoted as
s1 , s2 , · · · , sm and independent component matrix S
are denoted by equation(6).

A document vector is a row vector whose elements
are weights of words in a document. When the numberof words is n and the weight for the ith word is wi ,the document vector x is denoted as
x =

[w1

···

w2

wn ]T

(1)

S

where [·]T means transportation.
A user profile is also denoted by a row vector whose
elements are weights of words like a document vector. In the user profile, interesting words need tohave large weights and uninteresting ones need to havesmall weights. We construct the user profile using Genetic Algorithm(GA). In order to create a user profile
which fulfills the conditions mentioned above, the fitness function of GA is defined asXf =αi GT Di(2)

SVD is a method to look for an axis of large variance. It is reported that when SVD is applied to documents, we can analysis the word cooccurrence[1].
Now, we assume that m document vectors denoted
as x1 , x2 , · · · , xm and a document vector matrix X isdenoted by equation(3).
[x1

x2

···

xm ]T

Y

(3)

= AS.

= W Xk .

w+= E[Y g(wT Y )] − E[g 0 (wT Y )]w
g(u) = tanh(u)The row vectors of U are called singular vectors andorthogonal bases of X. In this paper, we select k singular vectors with a large singular value to remove
noise. In addition, k is determined by the rate of contribution. Uk represents these k singular vectors. The
input matrix X is changed into Xk as follows using
Uk .
Xk = UkT X
(5)

(6)

(7)

(8)

(9)

In this paper, A which is inverse matrix of W is used
as a feature axis showing a topic included in the documents. In fact, the topic is denoted by equation(10)since space conversion by Uk is performed before applying ICA.
T opic = Uk A
(10)
A Document vectors xi is mapped to the space constructed by topics and represented with the topics.
Here, we construct a user profile with X̂ represented
by topics in equation(11).

Extraction of Topics Using ICA

In signal processing, ICA extracts independent signals from some mixed signals. When ICA is applied
to speech processing, observed variables are time series©ISAROB 2005

sn ]TIn addition, by the property of evaluation criteria, amagnitude and an order of the restored signals havenot been determined uniquely.
Fast ICA[6] is one of ICA solution algorithms. This
paper uses Fast ICA to find the independent components. The update criteria by hyperbolic tangent tofind the independent components is equation(9).

Then SVD decomposes X into the equation(4), using two orthogonal matrices U , V and one diagonal
matrix Σ.
X = U ΣV T
(4)

2.3

···

Here, A is an m k full rank mixing matrix. In addition,
we assume m ≥ k. If A is known, we can obtain thegeneralized inverse matrix A† of A easily. However, A†
cannot be generally found because the mixing matrix
A is unknown.
The purpose of ICA is to estimate a topic matrix S
with only observed variables Xk under the condition
where under the condition where mixing matrix A is
unknown. In the other word, ICA finds a restored signal matrix Y which is statistically independent using
the restored matrix W in the following equation.

Abstract of SVD

=

s2

Xk

where G means a gene, Di means a document and αi
is a coefficient for each document.

X[s1

At that time, Xk , which is mensioned previous section, is assumed to follow a formula(7).

i

2.2

=

X̂

80

= T opic ∗ X T .

(11)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3
3.1

Experiment and result

Table 2: The Parameter of GA

Experiment environment and procedure@@@@
Only GAICA+GA
SVD+ICA+GA

The data for an experiment are the 625 documents
concerning with information retrieval from test collection NTCIR2. These documents have already been
evaluated whether each document is relevant or not.
In the documents, there are 34 relevant documents.
Each document is represented as a vector with vector space model[7]. As a methodology to represent a
document with a vector, at first, we apply morphological analysis tool ChaSen[8] to documents and extract
nouns. After that, we remove stop words and highfrequency words thorough all documents. We set thethreshold of frequency with 20 documents. With theabove process, we get 5,948 words and the dimensionof document vector is 5,948. Using tf-idf, these words
are weighted.
We apply SVD to 625 document vectors and pickup axes until these contribution ratio is 0.8. Then we
obtain 409 unique vectors. The input document vectors are changed with these vectors and we apply ICA
to them. After that, we converted input documentswith topics obtained by ICA.
We use GA for construction of a user profile in crossvalidation. We provide 625 documents into 5 subsets
which include 125 documents. The number of relevant
documents and non-relevance ones included in eachsubset is showed in Table 1. We put 3 subsets together
as training data for the construction of the user profileand set the others with evaluate data. Hence, we carryout experiments on 10 patterns of training data.
In GA, each element of individual is expressed with
5 bit. To make the ratio of relevant documents andnon-relevance ones set to 1:1, the coefficient αi in theequation(2) is defined as
+1
Di : Relevant
αi =−NI /NJ
Di : N on − relevance©ISAROB 2005

set4
3
122

Cross Over1
11

Mutation
0.005
0.05
0.05

Finally, we recommend evaluation documents depending on the user profile and evaluate accuracy ofrecommendation with 11-point average precision ratio.
The recommended documents are determined depending on the similarity Si between the user profile andthe ith document vector Di . Similarity Si is definedasSi= U T Di

(12)

where U means user profile.
We summarize the experiment in the following
steps.
Step1 Make document vectors with vector spacemodel.
Step2 Apply SVD to document vectors and convert
the space of input documents with unique vectors.
Step3 Apply ICA to converted documents.
Step4 Return the space of A with unique vectors andit is defined topics.
Step5 Transform the input documents with topics.
Step6 Construct the user profile with Genetic Algorithm.
Step7 Recommend documents and evaluate the user
profile with 11-point average precision ratio.
Moreover, we construct the user profile with other2 methods to confirm the advantage of the proposed
method, which are construction of the user profile using only GA with original documents and ICA andGA without SVD. The parameters of GA used in thesecomparable experiments are shown in Table 2.

where NI means the number of relevant documents
and NJ means the number of non-relevance documents. Other parameters of GA are shown in Table 2
and we use two point crossover. In addition we construct the user profile at 5 times and the average of 5
times results since the GA is a probabilistic approach.

Table 1: The Input Data.
@@@@
all set1 set2 set3
relevant
34
7
13
5
non-relevance 591 118 112 120

Generation
10000
10000
10000

3.2

Results

In this section, we show the result of the evaluation
experiment. Figure 1 shows recall precision curves andTable 3 shows 11-point average precision ratios. In addition, Table 4 shows the feature axes. Table 4 shows
the some words sorted by absolute of word weights.

set5
6
119

81

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 4: Example of Feature Axes
Axis1
Axis2
Language crossing
Array
Information access
Processor array
Use field
Processor
List stage
MappingDocument selection
Physical array
Translation display
Routing
Real use
Size
Technical know-how
Failure processor
International exchange Logic array
Gate
Mapping algorithm
topic. Then, we confirmed the advantage of information filtering accuracy. Consequently, it checked thata user profile became good by improvement of topic
detection.

Figure 1: Imputation Precision with GA.
Table 3: 11-points Average Precision Ratio
Original ICA ICA+SVD
0
0.160
0.322
0.331
0.1
0.133
0.218
0.294
0.2
0.126
0.139
0.219
0.3
0.121
0.115
0.171
0.4
0.118
0.099
0.145
0.5
0.116
0.096
0.133
0.6
0.112
0.089
0.124
0.7
0.108
0.082
0.113
0.8
0.106
0.075
0.103
0.9
0.101
0.070
0.087
1
0.098
0.067
0.076
Average
0.118
0.125
0.163

4

References[1] Deerwester.S, Dumais.T, Landauer.T, Furnas.W,
Harshman.A, “Indexing by Latent Semantic Analysis”, Journal of the Society for Information Science Vol.41, No.6, pp.391–497.
[2] Ata Kabán and Mark Girolami, “Topic
Separation and Keyword Identification inDocument Collections :A Projection Approach”,
Technical
Reportavailablein
http://cis.paisley.au.ck/reseach/reports/index.html.

Discussion[3] T.Kolenda and L.K.Hansen, “Independent Components in Text”, Advances in Independent Component Analysis, Springer-Verlag, 2000.

From the Figure 1, the precision ratio of ICA version becomes better than original version. This is thereason why the concretization of topics contributes the
improvement of precision ratio. The proposed method
which uses SVD also left much better result than only
ICA at low recall ratio especially. In addition, comparing the result from the viewpoints of 11-points average ratio in Table 3, the proposed method left goodaverage precison. According to this, it can be said
the proposed method has realized the totally improvement. This is because the accuracy of the topic used
for conversion went up, which is shown in Table 4.
Seeing Table 4, we can estimate the topics clealy. It
is considered that Axis1 means “Cross Language Retrieval” and Axis2 means “Processor Array”.

5[4] Masafumi Hamamoto, Hiroyuki Kitagawa, Jia-Yu
PAN and Christos Faloutsos, “Topic Detection
from Text Data Using Independent Component
Analysis”, DEWS2004 3-B-04[5] “NTCIR2”, NII-NACSIS Test Collection for
IR System, http://research.nii.ac.jp/ntcir/indexen.html.
[6] Aapo Hyvārinen, Erkki Oja, “Independent component analysis: A tutorial”, Neural Network,
Vol. 13, pp. 411–430, 2000.
[7] G.Salton, M.J.McGill, “Introduction to Modern
Information Retrieval”, McGraw-Hill Book Company, 1983.

Conclusion[8] Y.Matsumoto, “Japanese Morphological Analysis
System:CHASEN”, Information Science Technical
Report NAIST-IS-TR97007, Nara Institute of Science Technology,1997.

In this paper, we proposed the method of improving
the accuracy of the user profile by detection of correct©ISAROB 2005

82

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Improvement of Information Filtering Using Topic Selection
Takeru Yokoi
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
takeru@sig.cs.osakafu-u.ac.jp

Hidekazu Yanagimoto
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
hidekazu@cs.osakafu-u.ac.jp

Abstractof information filtering, it is a promising method to
cut off the noise from documents. It is reported thatthe method such as LSA[1], which transforms document space, is effective in denoising. The LSA focuseson variance of documents and cuts off the components
of low variance for denoising.
We take account of independence of topics included
in documents and introduce Independent Component
Analysis (ICA) to obtain the topics. ICA is recently
used for signal processing, image processing and soon. It has been already reported that the independent
components mean topics included in the documents inapplying ICA to documents[2][3]. We use the independent components for transformation of document
vectors and improve recommendation accuracy of documents.
However, some components obtained by ICA are
unnecessary components(noise) for the object of information filtering. Though it is important to remove
the noise, there is no criterion to select the independent components. Hence, to focus on the similarity of topics, we use Maximum Distance Algorithm(MDA)[4] which is often used in the field of patternrecognition. This algorithm is applied to classifying
the independent components and extracting the usefulcomponents for document recommendation. Then we
map the document vectors into the space which consists of the selected topics and construct a user profile
with the transformed document vectors by relevance
feedback(RFB)[5]. Finally, to confirm the proposed
method, we carry out an experiment on test collection(NTCIR2[6]).

We propose an information filtering system using Independent Component Analysis (ICA). A
document-word matrix is generally sparse and has ambiguity of synonyms. To solve this problem, we propose a method to use document vectors represented
by independent components generated by ICA. The
independent component is considered as a topic. Concretely speaking, we map the document vectors into
topic space. Since some independent components areuseless for recommendation, we select necessary components from all independent components by Maximum Distance Algorithm. We create a user profile
from transformed documents with Relevance Feedback. Finally, we recommend documents by the user
profile and evaluate accuracy of the user profile by 11point average precision. We carry out an experiment
to confirm the advantage of the poposed method.
Keywords: Independent Component Analysis, Information Filtering System, User Profile,
Maximum Distance Algorithm

1

Introduction

As information technologies have been advanced, aplenty of information are served in the Internet. It
has been difficult to find what we demand from large
amount of information by existing retrieval systems
since we cannot express our guery correctly. A lotof researchers pay attention to developing information
retrieval systems which automatically select the information depending on our interests.
The information retrieval systems with user’s interests have been studied. For example, there are ranking methods that sort information depending on theuser’s interests and filtering systems which select the
information depending on the user’s interests. Since
documents usually have noise which worsens accuracy©ISAROB 2005

Sigeru Omatu
Computer and Systems Science
Osaka Prefecture University
1-1 Gakuencho Sakai-City
Osaka 599-8531
omatu@cs.osakafu-u.ac.jp

2

Our proposed method

In this chapter, we explain a user profile, ICA and
MDA.

83

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.1

User Profile

topic, the solutions are unspecified. Thus, we assumem ≥ n. If A is known, we can obtain the generalized
inverse matrix A† of A easily. However, A† cannot be
generally found because the mixing matrix A is unknown.
The purpose of ICA is to estimate a topic matrix
S with only observed variables X under the condition
where each topic is independent. In other words, ICA
finds a restored signal matrix Y which is statisticallyindependent using the restored matrix W in the following equation.

A document vector is a row vector whose elements
are weights of words in a document. When the numberof words is n and the weight for the ith word is wi ,the document vector d is denoted as
d

=

[w1

w2

wn ]T

···

(1)

where [·]T means transportation.
A user profile is also denoted by a row vector whose
elements are weights of words like a document vector.
Interesting words have large weights and uninterestingones have small weights. We construct the user profile
using RFB. The update fomula of RFB is denoted as
X
X
U = a
Di − b
Dj
(2)
i

Y

j

w+
= E[Y g(wT Y )] − E[g 0 (wT Y )]w
g(u) = tanh(u)In signal processing, ICA extracts independent signals from some mixed signals. When ICA is applied
to speech processing, observed variables are time series
data recorded by microphones and independent variables are source signals. On the other hand, when ICA
is applied to documents, the inputs of microphones
correspond to document vectors and the independent
components are equivalent to independent topics included in the documents.
Now, we assume that m document vectors denoted
as x1 , x2 , · · · , xm are described with the combinationof n unknown topics denoted as s1 , s2 , · · · , sn . Each
topic vector is statistically independent and its mean
is 0.
A document vector matrix X and a topic vector
matrix S are denoted by equation(3).
= [x1
= [s1

x2
s2

···
···

xm ]T
sn ]T

X̂

2.3

= AS

(7)

Abstract of MDA

Step1 Set the threshold ratio r which denotes the distance between the farthest clusters.

(3)

Step2 Set topic y1 as cluster center Z1 .
Step3 Calculate Di = min(yi , Zj ) for y2 , y3 , · · · , yn .
j

(4)

D(yi , Zj ) is defined as equation(8).
qD(yi , Zj) =(yi − Z¯j )T (yi − Z¯j ) (8)Here, A is an m n full rank mixing matrix. In addition, if the number of document is larger than that of©ISAROB 2005

= Y XT

Some topics obtained by ICA are unnecessary topics for information filtering which worsen accuracy of
information filtering. For instance, a document about
GIS system includes topics such as city plan and information retrieval. Considering with land-use plan,
the topic of information retrieval is regarded as noise.
In this paper, to remove those topics, we apply MDA,
which does not have to decide the number of classes,
to categorize similar topics and select topics.
MDA is stated in the follow steps.

At that time, we assume X is linear combination of
topic vectors.
X(6)

The independent components Y obtained by ICA
mean topics included in the documents. In this paper, document vectors xi is mapped to the space constructed by the topics and represented with the topics.
Here, we construct a user profile with X̂ represented
by topics in equation(7).

Abstract of ICA and Space Transformation

X
S

(5)

In addition, by the property of evaluation criteria, amagnitude and an order of the restored signals havenot been determined uniquely.
Fast ICA[7] is one of ICA solution algorithms. This
paper uses Fast ICA to find the independent components. The update criteria by hyperbolic tangent tofind the independent components is equation(6).

where U means a user profile, Di means an interesting document, Dj means an uninteresting document.
Both a and b are arbitary positive numbers. When weconstruct the user profile with documents, the weightof the word included in the interesting document increases.

2.2

= WX

84

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Step4 Calculate l = max Di and set the element of l

profile and set the others with evaluate data. Hence,
we carry out experiments on 10 patterns of trainingdata.
In RFB, to make the ratio of relevant documents
and non-relevant ones set to 1:1, the coefficients, aand b, in the equation(2) are defined asiwith yk .
Step5 If l/M AX > r, where M AX = max(Zi , Zj )means the distance between the farthest classes,
make new class whose center Zj+1 is yk andreturn Step3. Otherwise, go to Step6.

a = +1b = −Ni /Nj

Step6 Output all classes.
After classification, we extract all components of aclass which has the most components. The components in the class are considered as specific topics ona theme. In other words, this selection method means
to extract the detail components for a main topic.

3
3.1

where Ni means the number of relevant documents
and Nj means the number of non-relevant documents.
Finally, we recommend documents depending on
the user profile and evaluate accuracy of recommendation with 11-point average precision ratio. The recommended documents are determined depending on
the similarity Si between the user profile and the ith
document vector Di . Similarity Si is defined as

Experiment and result
Experiment environment and procedureSi= U T Di .

(10)

We summarize the experiment in the following
steps.

The data for an experiment are the 625 documents
concerning with information retrieval from test collection NTCIR2. These documents have already been
evaluated whether each document is relevant or not.
In the documents, there are 34 relevant documents.
Each document is represented as a vector with vector space model[8]. As a methodology to represent a
document with a vector, at first, we apply morphological analysis tool ChaSen[9] to documents and extract
nouns. After that, we remove stop words and highfrequency words thorough all documents. We set thethreshold of frequency with 20 documents. With theabove process, we get 5,948 words and the dimensionof document vector is 5,948. Using tf-idf, these words
are weighted.
We apply ICA to 625 document vectors and obtain
623 independent components. The number of topics isless than the numbers of documents since some documents are dependent. Next we normalize the 623
independent components and remove the unnecessarycomponents with MDA. It has been already mentioned
in Section2.3 how to select the useful components. In
consequence, we select 324 topics. After that, we converted input documents with the components selectedby MDA.
We use RFB for construction of a user profile incross validation. We provide 625 documents into 5
subsets which include 125 documents. The number of
relevant documents and non-relevant ones included ineach subset is showed in Table 1. We put 3 subsets together as training data for the construction of the user

©ISAROB 2005

(9)

Step1 Make document vectors with vector spacemodel.
Step2 Apply ICA to document vectors
Step3 Classify the independent components with
MDA and remove unnecessary components.
Step4 Transform the input documents.
Step5 Construct the user profile with RFB.
Step6 Recommend documents and evaluate the user
profile with 11-point average precision ratio.
Moreover, we construct the user profile with othertwo methods to confirm the advantage of the proposed
method, which are construction of the user profile using only RFB with original documents and ICA and
RFB without topic selection.

3.2

Results

Tablewe
1: show
The Input
Data.of the experiIn this section,
the result@@@@
all recall
set1 precision
set2 set3
set5
ment.
Figure 1 shows
curvesset4
and Table
relevant
13 ratios.
5
3
6
2 shows
11-point 34
average7 precision
non-relevant 591 118 112 120 122 11985

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

5

Conclusion

In this paper, we proposed a new method to select necessary topics for information filtering using the
MDA and confirmed the advantage of the proposed
method. This gives that the accuracy of information
filtering can be improved with removing unncecessary
topics.
Since we deal with the one topic data in this experiment, we will have to extend the data including
multiple topic in future.

References[1] Deerwester.S, Dumais.T, Landauer.T, Furnas.W,
Harshman.A, “Indexing by Latent Semantic Analysis”, Journal of the Society for Information Science Vol.41, No.6, pp.391–497.

Figure 1: Imputation Precision with RFB.

4

Table 2: 11-points Average Precision Ratio.
Original ICA Euclid
0
0.458
0.463 0.638
0.1
0.420
0.432 0.535
0.2
0.325
0.368 0.525
0.3
0.301
0.346 0.459
0.4
0.270
0.320 0.381
0.5
0.257
0.288 0.325
0.6
0.238
0.237 0.250
0.7
0.152
0.198 0.165
0.8
0.095
0.160 0.122
0.9
0.080
0.103 0.106
1
0.248
0.085 0.092
Average
0.248
0.273 0.327[2] Ata Kabán and Mark Girolami, “Topic
Separation and Keyword Identification inDocument Collections :A Projection Approach”,
Technical
Reportavailablein
http://cis.paisley.au.ck/reseach/reports/index.html.

Discussion[6] “NTCIR2”, NII-NACSIS Test Collection for
IR System, http://research.nii.ac.jp/ntcir/indexen.html.

[3] T.Kolenda and L.K.Hansen, “Independent Components in Text”, Advances in Independent Component Analysis, Springer-Verlag, 2000.
[4] Tou,J.T. and Gonzalez,R.C., “Pattern Recognition
Principles”, Addison-Wesley, Reading, MA. 1974.
[5] J.Rocchio, “Relevance feedback in information retrieval” , The SMART Retrieval System Experiments in Automatic Document Processing, pp.
313–323,1971

From the Figure 1, the precision ratio of ICA version becomes better than one of original version. Thisis the reason why the concretization of topics contributes the improvement of precision ratio. The proposed method which selects topics with MDA also left
much better result than only ICA. Especially, the improvement is clear at low recall ratio points. We cannot clearly recognize some precision ratios at high recall ratio points to become better in Figure 1. Therefore, comparing the result from the viewpoints of 11points average ratio in Table 2, it is clearly found thatthe proposed method can improve the precision ratio. This is because the unnecessary components for
information filtering are removed with selecting components.

©ISAROB 2005

[7] Aapo Hyvārinen, Erkki Oja, “Independent component analysis: A tutorial”, Neural Network,
Vol. 13, pp. 411–430, 2000.
[8] G.Salton, M.J.McGill, “Introduction to Modern
Information Retrieval”, McGraw-Hill Book Company, 1983.
[9] Y.Matsumoto, “Japanese Morphological Analysis
System:CHASEN”, Information Science Technical
Report NAIST-IS-TR97007, Nara Institute of Science Technology,1997.

86

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

MOSFET Implementation of Class I* Neurons Coupled by Gap Junctions
Takashi Takemoto∗ , Takashi Kohno† and Kazuyuki Aihara† ‡∗ Complexity Science and Engineering Frontier Science, The University of Tokyo.
† Aihara Complexity Modelling Project, ERATO, JST.

‡ Institute of Industrial Science, The University of Tokyo.

Abstract

well known that saddle-node bifurcation produces class I
excitability, and the subcritical Hopf bifurcation class II.
These bifurcations can be generated in two-dimensional
systems. Thus, we adopt two-dimensional reduction models of neurons so that the temporal evolution of the variables can be visualized in the phase plane.
Recent physiological data indicate the massive presenceof gap junctions (GJs) among the interneurons in the neocortex [6] [7]. They raise a serious question about the roleof interneurons for neural coding and the dynamics of system levels for inhibitory neurons electronically coupled by
GJs. Fujii et al. claimed that some neurons that belong to a
subclass of class I, when coupled by GJs, can exhibit extensive spatio-temporal chaos in some parameter regions [8].
This subclass is characterized by a phase plane structure
called a narrow channel. They named these neurons class
I*. They show perfectly regular firings when isolated, so
this chaotic behavior is an emergent property of coupledsystems.
In this paper, we propose a class I* silicon neuron circuit, that has a simple phase plane structure. In the following section we briefly summarize the class I* neuronmodels. In Section 3 we describe the characteristics of thesilicon neuron circuitry and PSPICE simulation results fora circuitry implemented with discrete elements. Finally in
Section 4 we make concluding remarks.

This paper proposes a class I* neuron circuit coupled by
gap junctions (GJs) consisting of enhancement-type MOSFETs. Some neurons that belong to a subclass of class
I, when coupled by GJs, exhibit extensive spatio-temporalchaos in some parameter regions. This subclass is calledclass I* and characterized by a phase plane structure calleda narrow channel. In the proposed circuit, one can metamorphose the model continuously - class I*, class I without a narrow channel, and even to class II - by changing the value of the resistance. The circuitry is compatible with standard CMOS semiconductor processes. Henceit can be implemented in an analog very-large-scale integrated circuit (aVLSI) and the construction of a relatively
large network is possible. We show PSPICE simulation results for a circuit implementeted with discrete elements. A
GJ-coupled network consisting of twenty such neurons isshown to reveal itinerant dynamics similar to class I* GJcouple systems.

1 Introduction
Neuromorphic hardware has been studied extensively,
and various circuitries have been proposed to emulate biological neurons [1]. Some studies implement conductancebased models [3] such as the Hodgkin-Huxley (HH) modeland others implement phenomenological models [4] suchas the integrate-and-fire (I&F) model. The I&F neuron
is very simple and more popular for studies of large network dynamics, but biological neurons have more complex
electrical dynamics. On the other hand, the HH model
describes in detail the electrical dynamics of biologicalneurons. It is difficult to analyze mathematically because
this model includes four-dimensional nonlinear differential
equations.
As Hodgkin pointed out, there are two classes of neurons: class I and class II [5]. When a strong enough sustained current is applied, neurons fire repetitively. The firing frequency of class I neurons at the onset is asymptotically zero, whereas that of class II neurons is nonzero. It is©ISAROB 2005

2 Class I* Neuron Models
The two-dimensional reduction models of a single cellare written as [10] τV dV = F(V, R) + I
dt
,
(1)
dR

 τR
= G(V, R)
dt
where V represents the membrane potential, R is the
conductance parameter of the ion channels, and I is thestimulus current. In two-dimensional reduction models,
dynamic behavior can be characterized in the two nullclines corresponding to the two variables. In many case,87

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the class I neurons have U-shaped R-nullclines, while theclass II neurons have inclined I-shaped R-nullclines. Most
neurons have inverted-N-shaped V-nullclines.
The essential nonlinearity of class I* is characterizedby (1) the presense of a narrow channel, (2) the presenceof an unstable spiral, and (3) the presence of orbits (withpositive measure) reentering into the channel. It is not difficult to construct class I* models. The neuron models withinverted-N-shaped V-nullclines and U-shaped R-nullclines
can be turned into class I* by adjusting the parameters.

suppressing the nonlinearity of the input/output (IO) characteristics. Hence the output current of this circuit may be
0 + ∆I, where k is the ratio ofexpressed as IconV I ' −kVIN
the V-I converter. This ratio can be altered by RconV I .
VDD
VconVI

M36

M31
’
VIN

VIN

M37

M33

M32

M35

M34

3 The Silicon Neuron

IconVI

IoffsetVI

RconVIVSSIn this section, we describe the characteristics of the
proposed circuits and present PSPICE simulation resultswith discrete elements (NEC µ PA602 and µ PA603). These
two devices are a complementary pair. The supply voltages
are VDD = 5.0 V and VSS = −5.0 V.

3.1

Figure 2: Schematic of the V-I converter.

Fig. 3 shows the IO characteristics of the V-I converter.
This circuit is very simple and has good linearity.

Circuit Operation and Characteristics

Tne neuron circuit mainly consists of three blocks (seeFig. 1): inverted-N-shaped nonlinear resistance, U-shaped
nonlinear resistance, and V-I converter.
Rij

Rhi
iN=f(V)

iconVI=-kµiU=g(V)

V
Rµ

µ

CV

Figure 3: IO Characteristics of V-I converter. VconVI =2.8
V, RconV I =10 kΩ, Io f f setV I =0.24 mA.

CµNeuron-i

Figure 1: Block diagram of class I* neuron circuits.
The circuit equations of this silicon neuron are

dV

 CV
= f (V ) − k µ
dt
dµ
µ ,

 Cµ
= g(V ) −dt
RµThe inverted-N-shaped nonlinear resistance is schematically shown in Fig. 4. This circuit consists of a differential
pair M1-M2 and a V-I converter M5-M9. The output current of this circuit may be expressed as IN = Idi f f N + IconN ,where Idi f f N is the output current of the differential pairand IconN is the output current of the V-I converter respectively.

(2)

where CV is the membrane capacitance, V represents themembrane potential, k is the ratio of V-I converter, µ is
the channel conductance variables, Cµ represents the time
constant of µ , R µ is the constant resistance, f (V ) is theinverted-N-shaped-nonlinear resistance, and g(V ) is the Ushaped nonlinear resistance.
Fig. 2 shows a schematic of the V-I converter. The
0 ' V +V
source-follower M31-M32 produces VININ
DD −VconVI , where VIN is the input voltage and VconVI is a constant bias voltage. The feedback M33-M34 has the effect of©ISAROB 2005

VDD
M3

M4

VconN

M10

M5

IdiffN

VIN

M7

IconN

M6
M1

M11

M9

IN

M2

M8

IbN

IoffsetN

RconN

VSS

Figure 4: Schematic of inverted-N-shaped circuit.

88

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fig. 5 shows the I-V characteristics of this circuit.
These characteristics represent the V-nullcline of the silicon neuron.

morpose the model continuously: class I*, class I withouta narrow channel, and class II.

Figure 7: Nullcline of the proposed silicon neuron. The
dashed line is µ -nullcline and the solid line is V -nullcline.
IbN =0.25 mA, VconN =2.8 V, RconN =1 kΩ, Io f f setN =2.54
mA, IbU1 =0.9 mA, Vbump =5 mV, IbU2 =4 mA, VconU =2.8
V, RconU =4 kΩ and Io f f setU =1.35 mA.

Figure 5: I-V characteristics of inverted-N-shaped nonlinear resistance. IbN =0.25 mA, VconN =2.8 V, RconN =1 kΩ,
Io f f setN =2.54 mA.

A schematic of the U-shaped nonlinear resistance isshown in Fig. 6. This circuit consists of an improved
anti-bump circuit M12-M19 and the V-I converter M22M26. The differential pair M12-M15 is used to shift the
input voltage VIN by a constant voltage Vbump . The original anti-bump circuit [2] takes two input voltages, V1 andV2 , and generates three output currents. The two outsidecurrents, I1 and I2 , are a measure of the dissimilarity betweeen the two inputs; the center current Imid is the measure of the similarity. Hence this circuit has Gaussian-likeI-V characteristics. In the proposed circuit, V2 is connected
to GND, and the amplitude of V1 can be altered by theresistance Rbump to adjust the left slope of the Gaussian
curve. The output current of this circuit may be expressed
as IU = Ibump − IconU , where Ibump is the output current of
the improved anti-bump circuit and IconU is the output current of the V-I converter. The I-V characteristics of thiscircuit represent the µ -nullcline of the silicon neuron.

Fig. 8 shows the membrane potential V of the siliconneuron for the phase plane structure in Fig. 7. The single
silicon neuron shows perfectly regular firings.

Figure 8: Time course of the membrane potential of the
proposed silicon neuron. CV =10 nF, Cµ =100 nF, Rµ =10kΩ, k '0.1 mS and Rbump =205 Ω

VDD
M14

I1
V1
VIN

M12

M13

VconV

IbU2

M15

M18
M16

M27

M22

I2

M28
IoffsetU

3.3

M24

Imid
V
M17 M19 2

M23

M26

IU

Vbump
Rbump

Ibump
M20

M21

M25

IbU1

RconU

Itinerant Dynamics in Class I* GJ-Coupled
Systems

M29

The currents induced by GJs are [9]

IconU
M30
VSS

Figure 6: Schematic of U-shaped nonlinear resistance.

Ji =1(Vnbi −Vi )
RGJ ∑
nbi

= gGJ ∑(Vnbi −Vi ), (nbi ∈ coupledneigborcell),

3.2

nbi

Behavior of The Single Neuron

where gGJ is a coupling constant, which is assumed tobe identical for all connections in this simulation. The neurons at the boundaries connect only to the inner neurons.

The phase plane structure of the silicon neuron is shownin Fig. 7. By changing the value of Rbump , we can meta-©ISAROB 2005

(3)

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

References

Fig. 9 shows the time series of the GJ-coupled networkthat consists of twenty of the proposed circuits.

[1] M. Mahowald and R. Douglas, “A Silicon Neuron”,
Nature, Vol. 354, pp. 515-518, 19/26 Dec. 1991.
[2] T. Delbruck, “Bump Circuits for computing similarity and dissimilarity of analog voltages”, IJCNN, pp.
I-472-479, 1991.
[3] Simoni MF., Cymbalyuk GS., Sorensen ME., Calabrese RL. and DeWeerth SP., “A Multiconductance
Silicon Neuron With Biologically Matched Dynamics”, IEEE Trans Biomed Eng., Vol. 51(2) pp. 342-54,
Feb. 2004.

Figure 9: Time series of the GJ-coupled network consistingof twenty of the silicon neurons. RGJ =10 kΩ.

[4] G. Indiveri, “A low-power adaptive integrate-andfire
neuron circuit”,In ISCAS 2003. The 2003 IEEE International Symposium on Circuits and Systems, 2003.
IEEE , 2003.

At time t=0, there is a slight variation in the charges of
the capacitors (CV , Cµ ) of the neurons. This system has
been shown to exhibit chaotic dynamics as time elapses.
Note that each neuron shows perfectly regular firings whenisolated. Hence this chaotic behavior is an emergent property of coupled systems.

[5] Hodgkin A.L., “The local electric changes associatedwith repetitive action in a non-medullated axon”, J.
Physiology, Vol. 107, pp. 165-181, 1948.
[6] Galarreta M. and Hestrin S., “A network of fastspiking cells in the neocortex connected by electricalsynapses”, Nature, Vol. 402, pp. 72-75, 1999.

4 Concluding Remarks[7] Gibson JR, Beierlein M and Connors BW, “Two networks of electrically coupled inhibitory neurons in
neocortex”, Nature, Vol. 402, pp. 75-79, 1999.

We have proposed a class I* silicon neuron circuitry
with a simple phase plane structure. We combined
inverted-N-shaped and U-shaped nullclines to reproduce
the narrow channel structure. Differential pair circuitries,
bump circuitries, and linear resistances were employed to
implement these nullclines. Their I-V characteristics canbe easily altered by external voltages and resistances. The
varieties of I-V characteristics allow the proposed circuit
to behave as a class I*, class I, or class II neuron. We
have also shown PSPICE simulation results for a circuit implemented with discrete elements. A GJ-coupled network
consisting of twenty neurons was shown to generate itinerant dynamics similar to class I* GJ-coupled systems in
the work of Fujii et al. Moreover, this circuitry is compatible with standard CMOS semiconductor processes. Henceit can be implemented in an analog very-large-scale integrated circuit (aVLSI) and the construction of relatively
large networks is possible as well.

[8] H. Fujii and I. Tsuda, “Itinerant dynamics of class
I* neurons coupled by gap junctions”, COMPUTATIONAL NEUROSCIENCE: CORTICAL DYNAMICSLECTURE NOTES IN COMPUTER SCIENCE, Vol.
3146, pp. 140-160, 2004.
[9] N. Schweighofer, K. Doya and M. Kawato, “Electrophysiological Properties of Inferior Olieve Neurons:
A Compartment model”, J. Neurophysiol., Vol. 82 pp.
804-817, 1999.
[10] Rinzel J., “Excitation dynamics: insights from simplified membrane models”, Fed. Proc., Vol. 44, pp.
2944-2946, 1985.

Acknowledgements
This study is partially supported by the Superrobust
Computation Project in 21st Century COE Program on Information Science and Technology Strategic Core from the
Ministry of Education, Culture, Sports, Science, and Technology, the Japanese Government.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Parameter tuning of a MOSFET-based nerve membrane
Takashi Kohno∗ and Kazuyuki Aihara∗†
Aihara Complexity Modelling Project, ERATO, JST., Shibuya-ku, Tokyo, 151-0064†
Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, 153-8505∗

Abstract

be inherited in these implementations but circuitries
tend to be complex. It is impossible to reproduce thosemechanisms completely.
In the previous work [1][4], we proposed a design
methodology that allows us to implement simple andbiologically realistic silicon nerve membranes. It isbased upon phase plane analyses that have been utilized to reveal the mathematical structures behind theproperties of biological neurons. These properties canbe given to simple silicon neurons by constructing themathematical structures similar to that of biologicalneurons with silicon-friendly functions.
One of the well-known properties of biological neurons is oscillation against sustained input currents.
Hodgkin [5] found in his biophysical experiments ofstimulating various nerve membranes with sustained
currents that some membranes start oscillating withan arbitrary low frequency when the currents exceed
thresholds and others with non-zero frequency. He
named the former Class 1 and the latter Class 2. Masuda and Aihara [6] indicated that these differences in
excitation mechanisms may play a key role in neuralcoding. Bifurcation analysis studies on biological neuron models have revealed the mathematical structure
behind the Class 1 and 2 excitabilities [7][8]. Typical bifurcations that lead to Class 1 excitability area saddle-node on an invariant circle bifurcation anda saddle loop homoclinic orbit bifurcation. It is wellknown that Class 2 excitability can be produced by a
Hopf bifurcation.
In this paper, we show how to tune the mathematical structure of silicon nerve membranes to make them
reveal Class 1 or Class 2 excitabilities, utilizing a simple MOSFET-based nerve membrane as an example.

Two major principles for silicon neuron implementations are the phenomenological and the
conductance-based ones. The former reproduces someproperties perceived by the designers and does notclaim mechanism consistency. The latter reproduces
the dynamics of the ion channels on the nerve membranes. It makes the silicon neurons more similar tobiological ones, but the implementations tend to becomplicated because it attempts to replicate the detailed dynamics of the biological components. In theprevious work [1], we proposed a new principle based
upon phase plane analyses. It reproduces the mathematical structures of biological neurons, which makes
the silicon neurons simple and faithful. In this paper,
we show how Class 1 and Class 2 nerve membranes are
realized by parameter tuning based on simple phaseplane analyses with an illustrative MOSFET-based
nerve membrane.

1

Introduction

Neural systems process massive and various incoming information flexibly, appropriately, and in realtime. One of the purposes of studies on silicon neurons is to implement artificial systems that inherit
these exquisite properties, and another is to produce
some devices to interface between electrical circuits
and living nerve systems [2]. There are two major types of silicon neuron design principles. One
is the phenomenological implementation, which aims
to reproduces some phenomena of biological neurons.
Integrate-and-fire silicon neurons are good examples[3], which reproduce the integration property of spatiotemporal inputs and the threshold property of generating action potentials. Circuitries can be simple
in these implementations but some properties thatare not regarded may be lost. The other is the
conductance-based implementation, which aims to reproduce some or all mechanisms in biological neurons
faithfully. Most phenomena of biological neurons can©ISAROB 2005

2

MOSFET-based nerve membrane

The design principle we proposed in the previous
work [1] replicates the mathematical structures lying
behind the properties of biological neurons with some
’MOSFET-friendly’ functions. One of the simplest sil-91

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

icon nerve membrane models based upon the generalized Hodgkin-Huxley equations can be described asfollows:dyyβm 2 βn 2=−+m −n + a + Istim ,
dt
Ry
2
2
dm fm (y) − m
=
,
dt
Tm
dn fn (y) − n
=
,
dt
Tn

Cy

(1)
(2)
(3)

where y represents membrane potential, m is a fasterconductance variable, n is a slower conductance variable, Cy is membrane capacity, Ry is a constant resistance, a is a constant, Istim is a stimulus current input, βm and βn are the transconductance coefficientsof the MOSFETs for the ion channel current corresponding to m and n, respectively, and Tm and Tn
are the time constants for m and n, respectively. Ordinarily we make Tm an order of magnitude smaller
than Tn because the faster conductance corresponds tothe sodium channel activation variable in the HodgkinHuxley equations and the slower one to the potassiumchannel activation variable. If we use the V-I characteristic curves of differential pairs (see appendix B for
x = m and n) as the sigmoidal functions fm (y) andfn (y), this system can be implemented with a very
simple MOSFET circuitry [4].
Because the time scale for m is sufficiently smaller
than that for n, we can reduce the faster conductancem by assuming it relaxes to fm (y) instantaneously.
This reduction gives the slower subsystem that allows
us to trace the system’s behavior on the time scale ofa whole generation of an action potential on a phaseplane. The system equations are as follows:
Cy

Figure 1: Typical phase planes of the slower subsystem. (S) is a stable equilibrium (the rest state). (T)is a saddle and (U) is an unstable equilibrium. a) The
system is near a saddle-node on an invariant circle
bifurcation. The parameters are shown in appendix
A. b) The system is near a saddle loop homoclinic
orbit bifurcation. The right segment of the unstable manifold of (T) wraps around a stable limit cycle around (U). The parameters are as in a) except
Cy =0.0140(mF).

3

dy
y
βm 2
βn 2
=−
f (y) −
n + a + Istim , (4)
+
dt
Ry
2 m
2
dn fn (y) − n
=
.
(5)
dt
Tn

3.1

n=

2 βm 2y(fm (y) −+ a + Istim ),βn 2
Ryn =fn (y),

(6)
(7)

respectively. A typical relation between the two nullclines is shown in Fig. 1. The conditions that they
have to satisfy for the system to inherit the fundamental properties of the biological neurons are discussed
in [1].

©ISAROB 2005

Bifurcations of the rest state

The increase in the stimulus current transforms thephase space structure and destabilizes the rest statein our illustrative silicon nerve membrane like in most
biological ones, which induces the repetitive firing. As
described in the introduction, it is well known that
Class 1 excitability is observed if the rest state loses
stability via a saddle-node bifurcation or a saddle loop
homoclinic orbit bifurcation, and the Class 2 excitability via a Hopf bifurcation [7][8]. These are the mostmajor scenarios of repetitive firings. The phase planestructure near a saddle-node on an invariant circle bifurcation is shown in Fig. 1a). In this case, the stableThus the y-nullcline and the n-nullcline are:s

Parameter tuning for Class 1 andClass 2 excitabilities

92

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

equilibrium point (S) and the saddle point (T) approach each other as the y-nullcline (eq. (6)) moves
up in response to the increase in stimulus current
Istim . The smaller segment of the unstable manifoldof (T) vanishes when (S) merges with (T), and theother segment of the unstable manifold turns to a stable limit cycle when they disappear. This limit cycle
passes near the narrow channel between the y- and nnullclines around the vanished saddle point, where thevelocity is very slow. By narrowing this channel, we
can delay the period of the limit cycle arbitrarily up to
infinity when the channel width becomes zero. Figure1b) shows the phase plane structure near a saddle loop
homoclinic orbit bifurcation. In this case the right unstable manifold of (T) gets closer to the upper stablemanifold as the y-nullcline moves up, until it merges
with the upper segment of the stable manifold and becomes a closed homoclinic orbit. Then a stable limit
cycle is born around (U), which passes near the saddle
point where the velocity is very slow. In contrast to
above two scenarios, the saddle point (T) has no rolein the Hopf bifurcation. The rest state loses stability
singularly, and the system moves to a stable limit cycle around it if one exists. Because the system jumps
to a limit cycle, the repetitive firing begins abruptly
with a certain non-zero frequency.

3.2

Figure 2: The bifurcation diagrams for a) a saddlenode on invariant circle bifurcation and b) a saddle
loop homoclinic orbit bifurcation. (N) represents thepoint where the stable and saddle points merge and(H) the point where the stable limit cycle emerges. a)
The limit cycle vanishes at Ia =-0.00829(A) where (N)exists. The parameters are as in Fig. 1 a) except Ia is
swept. b) The limit cycle vanishes at Ia =-0.00839(A).
The parameters are as in Fig. 1 b) except Ia is swept.

Tuning the bifurcations

As described in the above subsection, the stabilityof the rest state plays a key role in neural excitabilities. The local linearization method tells how it depends on the parameters. The necessary and sufficientconditions for an equilibrium point to be stable are:Cy,n 0 βn T n
λ1 · λ2 > 0 ⇔ η 0 (y0 ) < fn0 (y0 ),λ1 + λ2 < 0 ⇔ η 0 (y0 ) <and (T) get closer as the y-nullcline moves up and (S)loses stability when they merge because condition (9)is no longer satisfied. This is the common scenario forClass 1 excitability. If the rightmost side of condition(8) is smaller than fn0 (y) at the point where (S) and(T) merge, (S) loses stability singularly via the Hopf
bifurcation before merging with (T). This is the basic
scenario for Class 2 excitability. To obtain a Class 2
nerve membrane that generates action potentials of areasonable size and keeps firing repetitively for a reasonably wide range of stimulus current, the y-nullcline
should be shifted right to make (T) and (U) disappear or, at least, to make (S) and (T) further aprat
than in Fig. 1. Class 2 excitability in our silicon nerve
membrane is discussed in [1].
The local stability analysis does not tell which of
the two scenarios appears. The bifurcation diagramsfor these scenarios are shown in Fig. 2. Let us trace

(8)
(9)

where λ1 and λ2 are the eigenvalues of the Jacobianmatrix in equations (4)-(5), (n0 , y0 ) denotes an equid
librium point, 0 denotes dy, and η(y) is the right sideof eq. (6). These conditions indicate that at a stable equilibrium point the y-nullcline should cross then-nullcline from over to under and the gradient of they-nullcline should keep smaller than a certain value.
Thus the stabilities of the equilibrium points can be
configured as in Fig. 1 if we place the leftmost crosspoint of the y-nullcline and the n-nullcline where thegradient of the y-nullcline is sufficiently small and therightmost one where it is sufficiently large. Of courseC
we must tune Cy and Tn properly so that n0 βnyTn is
between these two gradients. In this situation, (S)©ISAROB 2005

93

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

limit cycle, Tn can also be tuned. This paper indicatesthat our illustrative MOSFET-based nerve membrane
can function as a Class 1 or Class 2 nerve membrane
according to the proposed parameter tuning.

References[1] T. Kohno and K. Aihara, “Design of Neuromorphic
Hardware Based upon Mathematical Methods,” Proc.
SBRN 2004, 3470, 2004.
[2] M. Simoni, G. Cymbalyuk, M. Sorensen, et al., “A
Multiconductance Silicon Neuron With Biologically
Matched Dynamics,” IEEE Trans. Biomed. Eng., Vol.
51, No. 2, pp. 342-354, Feb., 2004.
[3] D. Rubin, E. Chicca, and G. Indiveri, “Characterizing
the firing properties of an adaptive analog VLSI neuron,” Proc. Bio-ADIT 2004, Lausanne, pp. 314-327,
2004.
[4] Takashi Kohno and Kazuyuki Aihara, “A MOSFETbased model of a Class 2 Nerve membrane,” Submitted
to IEEE Trans. Neural Networks.
[5] A. L. Hodgkin, “The local electric changes associatedwith repetitive action in a non-medullated axon,” J.
Physiol., Vol. 107, pp. 165-181, 1948.
[6] Naoki Masuda and Kazuyuki Aihara, “Band-pass filtering properties of interspike interval encoding with
Morris-Lecar neurons,” Proc. SBRN 2004, 3611, 2004.
[7] J. Rinzel and B. Ermentrout, “Analysis of Neural Excitability and Oscillations,” in “Methods in Neural
Modeling”, ed. C. Koch and I. Segev, pp. 251-291, MIT
Press, 1998.
[8] E. M. Izhikevich, “Neural Excitability, Spiking, andBursting,” International Journal of Bifurcation and
Chaos, Vol. 10, pp. 1171-1266, 2000.

Figure 3: The bifurcation diagram of the limit cycle
along Cy . Parameters are as in appendix A except
Ia = −0.00829(A) and Cy is swept. The vertical axis
represents the upper and lower limits of the limit cycle.
The white circles at Cy =0.01283(mF) represent thelimit cycle is unstable around it.

them from right to left. In both cases, the limit cycle that emerges via the Hopf bifurcation continues
with its amplitude increasing. If the amplitude is sufficiently large at the point where the pair of stable
and saddle points emerges ((N) in Fig. 2, where Ia =-0.00829(A)), the orbit of the limit cycle gets ‘trapped’
and vanishes; otherwise it persists until it merges withthe unstable manifold of the saddle point.
The amplitude of the limit cycle can be controlled
by Cy , which works as a time scale factor. It getssmaller as Cy becomes larger because the velocity ofthe system in the y direction decreases. In Fig. 3, weshow the bifurcation diagram of the limit cycle for an
Ia value just near the point (N) in Fig. 2. The limit
cycle is stable except when the limit cycle passes atthe point (N) (Cy ' 0.01283(mF) in Fig. 3). Thus, we
obtain the saddle-node on invariant circle bifurcation
if Cy is below 0.01283 (mF) and the other one if it isabove.

4

AppendixA

Parameter
βm
δm
m
m̄
Tm
Cy
a

Conclusions

We have shown how Class 1 and Class 2 excitabilities are obtained with a MOSFET-based nerve membrane. Once the phase plane structure is configured
properly by tuning the parameters that affect theforms of the nullclines, we can easily realize the desired neural excitability by tuning Cy and Tn . We
have shown that the two types of Class 1 excitabilities
can be selected only by tuning Cy . Because the ratio of
the time scales of y and n affects the amplitude of the©ISAROB 2005

Parameters:

B

0.0406 (A/V2 )-0.5200 (V)2.000 (V)
1.300 (V)
0.1300 (ms)0.0100 (mF)
0 (A)

Value
0.0799 (A/V2 )0.8000 (V)2.600 (V)
1.400 (V)
1.500 (ms)200 (Ω)-0.00834 (A)Characteristics of differential pairs:fx (y) ≡8x̄
wheny > δ x + x ,>>< x̄ {1 + 1 (y − δ )p22 − (y − δ )2 }
2

>
>
:

94

Parameter
βn
δn
n
n̄
Tn
Ry
IA

Value

0

2
x

when
when

x

x

x

δx − x ≤ y ≤ δ x + x ,y < δ x − x ,

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005     34 "  7   34
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97

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
3

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3G> 4  L©ISAROB 2005

2

98

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Analysis of bifurcation and optimal response on the evolution of cooperation
Yo-Hey Otake

Kazuyuki Aihara

Graduate School of Frontier Sciences

Institute of Industrial Science

The University of Tokyo

The University of Tokyo

4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japanand Aihara Complexity Modelling Project, ERATO, JST
45-18 Oyama-cho, Shibuya-ku, Tokyo 151-0065, Japan

yoheyo@sat.t.u-tokyo.ac.jp

aihara@sat.t.u-tokyo.ac.jp

Abstract

Evolutionary game theory is now widely applied tomany fields such as behavioral biology, ecology andeconomics.

A variety of strategies are needed to maintain cooperative behavior. Such strategy diversity in replicationarises from various circumstances; for example, mutation in replication, noise, mistakes and moods. In thispaper, we deal with the iterated prisoner’s dilemma
game, which has been widely used to study the evolution of cooperation. We approach the question of how
cooperation evolves from the standpoint of dynamical systems and also analyze the evolution in termsof optimal response. Through these analyses, we haveconfirmed that strategy variation is important for theevolution of cooperation. In addition, we show that
this approach is more useful than previous approaches
because use of dynamical systems theory allows us toexpress a transient process dynamically.
Keywords: evolutionary game dynamics, replicator
equation, cooperative behavior, iterated prisoner’s
dilemma, bifurcation analysis, optimal response.

1
1.1

1.2

Most evolutionary researches on cooperation haveemphasized an invasion condition of cooperative behavior in a population characterized by selfish behavior. To make up for the insufficiency of this approach,
we need mathematical tools for analyzing dynamic
processes as well as static states. Therefore, we haveused nonlinear dynamical systems analysis to analyze
the global behavior of evolutionary dynamics in thephase space of a strategy set.

1.3

Analysis of mutation in the evolution

Past researches have not accounted for all properties of evolution. Our goal is thus to consider situations containing various evolutionary factors, especially effects of mutation.

Introduction

Through consideration of mutation, we examine effects of diversity on the evolution of cooperation. For
this analysis, we emphasize the importance of dynamical systems, especially through bifurcation analysis.

Evolutionary game theory

An aim of sociology and economics is to understandhow cooperative behavior is maintained. It is important for research on behavior to look at the sustainability of such phenomena as well as their initial causes.

2

For this purpose, Maynard Smith applied game theory to ecological situations[1], which is known as evolutionary game theory. The main concept of this approach is that an evolutionarily stable strategy can beachieved. This concept of stability enables us to discuss sustainability.

©ISAROB 2005

Need for dynamic analysis

Theory and methods

First, we introduce the game used to model the situations we consider, namely the prisoner’s dilemma
game, and prepare a strategy set for playing the game.
After that, we introduce tools for the analysis. We
describe the replicator equation and the form of mutation.

99

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.1

The Iterated
gamePrisoner’s

2.3

Dilemma

The iterated prisoner’s dilemma (IPD)1 is the mostwidely used model for the evolution of cooperation.
Axelrod held a competition to study IPD strategy
through a computer simulation [2]. This competition
showed that a “tit-for-tat” (TFT) strategy was themost advantageous. A player using this strategy cooperates in the first interaction and then in subsequentinteractions repeats (imitates) what the opponent didin the immediately preceding interaction.

We consider a replicator map of the following form:xi (t)wi (~x(t)),
xi (t + 1) = Fi (~x(t)) = PNx(t))
j=1 xj (t)wj (~We must consider a class of various types of strategy
ranging from selfish behavior to cooperative behavior.
Thus, we prepared a strategy set (Table 1) like thoseused elsewhere [3, 4, 5].

If the number of iterations of an IPD is finite andfixed2 , this map is deterministic and has no stochasticity.

explanation
Tit for tat
Tit for tat and defect in the last interaction
Tit for tat and defect in last two interactions..
.
AllD (The number of interactions is k)

Ek

Table 1: Strategy set
TFT represents cooperative behavior (not being thefirst to defect) and AllD represents selfish behavior.
This strategy set is filled from cooperation to defection.

The elements of the evolution are as follows:• Heredity• Selection• Mutation.
The replicator system mentioned above does not allow
mutation, then we introduce the effect of mutation.

x(t))qji
j=1 xj (t)wj (~x(t))
j=1 xj (t)wj (~,(2)

where each setting is the same as for the replicatormap (1). The probability that replication of strategy jgives rise to strategy i is given by qji . These quantities
define the mutation matrix (a Markov matrix). Thismap describes both frequency-dependent selection andP

S < P < R < T and T + S < 2R. This situation leads to theself’s conflict and dilemma: if each of two players chooses the
behavior maximizing self payoff, it brings about the situation
minimizing the sum of the payoffs. Through iteration of thePD, the tendency towards cooperation increases.

©ISAROB 2005

Mutationxi (t + 1) = Fi (~x(t)) = PN

Opponent
Cooperation Defection
R
S
T

2.4

PN

Self
Defection

We carried out numerical simulations based on thismap. Similar analyses of population dynamics basedon game theory have been done in [7].

In this work, we discuss the replicator-mutator mapof the following form [8, 9, 10]:1 A payoff matrix for the prisoner’s dilemma game (PD) isshown below.

Cooperation(1)

where the variable xi denotes the frequency of strategy
i, which fitness, wi (~x), is a function of the distributionof the population given by the vector ~x = (x1 , · · · , xn ).
PN
The denominator,x(t)), ensures thatj=1 xj (t)wj (~
PN
x
(t)
=
1.
This
map
describes
frequencyj=1 jdependent selection. The evolutionary game theory
assumes that Darwinian fitness is determined by the
payoff matrix of a game (e.g., the IPD).

Strategy set

strategy
TFT
E1
E2
..
.

the

The fundamental law of evolutionary game theory
is described by the replicator equation [6], and theevolutionary path can be understood as the dynamics
on the phase space spanned by the frequency of eachstrategy.

On the other hand, the evolutionarily stable strategy (ESS) [1] in a finite IPD is the “always defect”(AllD) strategy. A violation of AllD, which is equivalent to destabilization of the ESS, is necessary for theevolution of cooperation. The evolution of cooperation in such cases can arise from various conditions(for example, groupings or spatial structures).

2.2

Evolutionary game dynamics:
replicator equation

2 As in previous studies [3, 4, 5].

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3.2

mutation.

Next, we investigated effects of mutation.

Here, we consider a mutation matrix (Markov matrix) with uniform mutation3 of the following form:½(qji ) =1 − (N − 1)²²(j = i)(j 6= i).

While the orbit property is the same as in the case ofno mutation, the population dynamics are drasticallychanged even by a slight mutation.

(3)

A numerical simulation result of evolutionary dynamics with a slight mutation is shown in Figure 2.

We consider the population dynamics of this modeland analyze the stability and bifurcations of this system.

3

If there is a slight mutation, the sojourn time ineach strategy except the final one (AllD) is constant.
Therefore, the evolution time of non-cooperative behavior is linear and selfish behavior evolves in actual
time. Since the models used in previous studies cannot
tolerate even a small amount of mutation, we consider
them insufficient for interpreting the evolution of cooperation.

Results

In this section, we analyze the effects of mutation.
First, we show time series obtained by numerical simulation with several mutation rates. After that, weshow a bifurcation diagram and discuss its bifurcation
structure. The number of strategies is N and the number of iterations of the IPD is k (N=k+1).

3.1

3.3

No mutation: the replicator equation

One factor that helps explain the evolution of cooperation is effective mutation.
We make the variation of strategy wider. In addition, the population dynamics change drastically depending on the mutation rate. In this instance, moreover, the orbit property differs from those in the casesof no mutation and a slight mutation.

A numerical simulation result of the equation isshown in Figure 1.
The orbit in the simulation resembles a heteroclinic
cycle, and approaches each of the corners on the kdimensional simplex.

A numerical simulation result of the evolutionary
dynamics with a fairly weak mutation is shown in Figure 3.

The sojourn time in each strategy exponentially increases. Various researchers have discussed the possibility that because the evolution of non-cooperative
strategies cause the waste of much time, cooperation
can evolve.

For any initial condition, the dynamics fall into thisquasi-periodic orbit. Thus, non-cooperative behavior cannot evolve. The result of evolution is periodic
change of behavior.

3 We can also consider other types of formalization of themutation matrix. A simple model isn

1 − 2²²(j = i)(j = i − 1, i + 1)

3.4

(1 − ² if j = 1 or N ).

Bifurcation analysis

We measure the level of cooperation in the population as a function of the mutation parameter ². This is
the level of cooperation after sufficient time has passed
and the influence of the initial conditions is negligible.
The bifurcation diagram is shown in Figure 4.

A strategy easily mutates into a similar strategy on the phenotype, but cannot mutate into a radically different strategy. The
distance on the phenotype is strictly determined by the distanceon the genotype. In this model, mutation will similarly affectthe evolution of cooperation, but various dynamics would not
be able to arise.

©ISAROB 2005

Greater mutation

Since there are reasons other than those consideredin previous studies that can account for the evolutionof cooperation, we look at the conditions affecting theevolution.

Previous studies [3, 4, 5] considered the case of no
mutation. No mutation is equivalent to the mutation matrix being an identity matrix, namely ² = 0
in Equation (3).

(qji ) =Slight mutation

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A mutation threshold exists and the dynamics ofthis system drastically change with changing the valueof ². The period of the limit cycle also continuously
changes.

This is what makes the dynamical system approach so
effective.

4.2

This system contains a saddle-node bifurcation on
an invariant circle and a Neimark-Sacker bifurcation
[11].

4

In this paper, we have dealt with a finitely iterated
prisoner s dilemma game; that is, we have discussed
the situation where the period of interaction is fixedand known. This is a difficult problem in the sensethat it is difficult for cooperative behavior to evolve.

Discussion

In biological and economic situations, the period ofinteraction may be known to everyone. In this case,cooperative behavior is maintained. Nevertheless, it isdifficult for cooperation to evolve theoretically becauseof backward induction reasoning, and few theoretical
approaches have allowed the evolution of cooperation.

We assert that mutation may play an importantrole in the evolution of cooperation, because population dynamics are drastically changed by mutation in
replication. Here, we discuss the bifurcation at thischange.

4.1

Comparison with recent research

When the period of interaction is fixed and known,
previous studies have shown that it is difficult for cooperation to evolve because of backward induction
reasoning [3, 4, 5]. Backward induction is powerfulpremise, leading some to consider theoretical researchon the evolution of cooperation to be impossible. The
few studies that supported the evolution of cooperation [3, 4, 5, 14] used models that did not contain allproperties of evolution. Therefore, we wanted to consider situations involving various evolutionary factors,
especially the effect of mutation.

Recent research based on dynamic programming
suggested similar outcomes [12]. Therefore, we compare a dynamical systems technique to an analysis of
optimal response and discuss the similarities and differences between the former research and our own.
The earlier research on the evolution of cooperation
through analysis of optimal response has two limitations. First, a limited situation was assumed wheremost of the population adopted one strategy and otherstrategies were distributed among the population according to a distribution of a given variance. If a stateis a mixture of strategies, it is difficult to analyze. Second, the response could only be examined for certain
static situations, and dynamic processes could not be
accommodated. McNamara et al. [12] demonstrated
the importance of mutation (variation) from this viewpoint.

Our goal is to bridge the gap between the theoretical and empirical researches. In this paper, wesought to do this by incorporating the role of mutation. By considering mutation, we found a remarkablephenomenon: mutation can promote the evolution ofcooperation. This phenomenon arises from a bifurcation of the equation solution.

To determine whether cooperative behavior is sustainable, we need to describe the time evolution of
behavior. An approach based on a dynamical systemallows the expression of various states like mixturesof strategies and transient processes. The resulting
abundance of information makes this a more fruitfulapproach. This is important for studying the evolution of cooperation [13].

5Conclusion

We have examined the factors affecting the evolution of cooperation in a finitely IPD. Players cooperate in a situation where the interaction term is finite
and known. This evolution is supported by mutation.
Our research indicates that mutation plays an important role in the evolution of cooperation. Moreover,through a comparison with another approach, we haveconfirmed the effectiveness of applying dynamical systems theory to the evolution of behavior.

The advantage of the approach with a dynamicalsystem is that it allows us to examine not only the
direction of evolution but also the time evolution of thefrequencies of the strategies and transient processes.

©ISAROB 2005

Consideration as game theory

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Acknowledgements[12] J. H. McNamara, Z. Barta & A. I. Houston,“Variation in behaviour promotes cooperation in
the Prisoner’s Dilemma game,” Nature, vol.428,
pp.745–748, 2004.

We would like to thank Koh Hashimoto, Joe
Yuichiro Wakano (The University of Tokyo) andHisashi Ohtsuki (Kyushu University) for their fruitful suggestions and comments.

[13] M. A. Nowak & K. Sigmund, “Chaos and theevolution of cooperation,” Proc. Natl. Acad. Sci.
USA, vol.90, pp.5091–5094, 1993.

References[14] M. A. Nowak & K. Sigmund, “Invasion dynamics of the finitely repeated prisoner’s dilemma,”
Games Econ. Behav., vol.11, pp.364–390, 1995.

[1] J. Maynard Smith, Evolution and the Theory ofGames, Cambridge University Press: Cambridge,
1982.
[2] R. Axelrod, The Evolution of Cooperation, Basic
books: New York, 1984.
[3] J. H. Nachbar, “Evolution in the finitely repeated prisoner’s dilemma,” J. Econ. Behav. Organ., vol.19, pp.307–326, 1992.
[4] R. Schuessler, “The gradual decline of cooperation: endgame effects in evolutionary game theory,” Theory Decis., vol.26, pp.133–155, 1989.
[5] T.
Shimizu, “Evolution of cooperation in
the finitely repeated Prisoner’s Dilemma (inJapanese),” Koudoukeiryougaku, vol.24, pp.101–
111, 1997.
[6] P. D. Taylor & L. B. Jonker, “Evolutionary stable strategies and game dynamics,” Math. Biosci.,
vol.40, pp.145–156, 1978.
[7] E. C. Zeeman, “Population dynamics from gametheory,” In: Global theory of dynamical systems.
Springer Lecture Notes in Mathematics, vol.819,
pp.471–497, 1978.
[8] J. Hofbauer & K. Sigmund, Evolutionary Gamesand Population Dynamics, Cambridge University
Press: Cambridge, 1998.
[9] K. M. Page & M. A. Nowak, “Unifying Evolutionary Dynamics,” J. Theor. Biol., vol.219,
pp.93–98, 2002.
[10] N. L. Komarova, “Replicator-mutator equation,
universality property and population dynamics oflearning,” J. Theor. Biol., vol.230, pp.227–239,
2004.
[11] Y. A. Kuznetsov, Elements of Applied Bifurcation Theory (2nd ed.), Springer Verlag: New
York, 1998.

©ISAROB 2005

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1

1

0.9

0.8

0.8

0.7

0.6

0.6

0.5

0.4

0.4

0.3

0.2

0.2

0.1

0

0
0

500

1000

1500

2000

2500

0

3000

500

1000

1500

2000

2500

3000

Figure 3: Population dynamics with a weak mutation(k = 10, ² = 3.5 × 10−4 ). The horizontal axis is time
(generation) and the vertical axis is the population
frequency of each strategy.

Figure 1: Population dynamics without mutation (k= 10, ² = 0). The horizontal axis is time (generation)and the vertical axis is the population frequency ofeach strategy.

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0
0

500

1000

1500

2000

2500

3000

Figure 2: Population dynamics with a slight mutation(k = 10, ² = 1.0 × 10−6 ). The horizontal axis is time
(generation) and the vertical axis is the population
frequency of each strategy.

©ISAROB 2005

Figure 4: Bifurcation diagram: The horizontal axis is
the mutation rate (the bifurcation parameter) and thevertical axis is the frequency of cooperative behavior
after sufficient time has passed.

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Dynamic Preshaping Based Design of Capturing Robot Driven by Wire
Shoichi NISHIO∗ , Mitsuru HIGASHIMORI∗ , and Makoto KANEKO∗∗

Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, JAPAN
Abstract

This paper discusses the design of wire drive capturing robots based on dynamic preshaping. As the
speed of a robot hand increases, dynamic eﬀects become dominant. In order to successfully catch an object by a high speed capturing robot, it is important
to consider mass distribution, wiring, and joint springwhen designing the robot. After explaining the design
procedure by considering those mechanical parameters,
experiments as well as simulations are shown to verify
the basic idea.

1(a) 38[ms](b) 44[ms](c) 46[ms](d) 48[ms]Introduction

While a number of robots have been developed sofar, we are particularly interested in high speed robots
that can capture a moving object quickly. As the ﬁrst
trial of such a high speed robot, we have designed anddeveloped the 100G capturing robot[1] that can catcha ﬂying object in the air within the motion time of
50[msec]. Fig.1 shows an experimental result where
the 100G capturing robot is capturing a ball by thegripper. Due to such a high speed, we cannot follow
by our eyes what is really happening during the capturing motion. Therefore, we can not see directly why
the robot fails in catching the object while it looks likethat the gripper successfully reaches the target object
position. After precise analysis utilizing a high speed
camera with the frame rate of 1[msec], we found thatthe preshape of the gripper is not appropriate, especially for the ﬁnal stage of catching motion. Considering the ﬁnger link posture during the high speed capturing motion, it is impossible to neglect inertia force
applied to the ﬁnger links. Therefore, the preshaping
issue under such a high speed condition results in an
optimum design problem to determine the joint torquedistribution with considering the eﬀects of dynamics.
The goal of this work is to provide a design procedureon preshaping issue for a high speed capturing robot.
Reducing mass of the robot is really important forachieving a high acceleration. In such sense, wire drive
robots are good candidates for reducing mass of botharm and gripper, since we can install all actuators at©ISAROB 2005

Fig. 1: A two-ﬁngered robot hand for capturing an
object.

the base. We suppose a robot where all joints are controlled by a single wire. A feature of such robots isthat the torque distribution is determined by both thesize of pulley in each joint and the way of wiring. By
focusing on such a wire drive robot, we deﬁne thatthe dynamic preshaping problem is to produce a joint
torque set so that all links make contact with an object. We explore the geometrical issue where the design for wiring is precisely discussed. After explaining
the design procedure for achieving the reference posture under high speed condition by considering mechanical parameters of the robot, experiments as wellas simulations are shown to verify the basic idea.

2

Related Works

There have been a couple of works discussing preshaping issues so far[3]–[8]. Bard and Troccaz[3] havediscussed an automatic preshaping for a dexterous
hand from a single description of objects, where theobject model is automatically extracted from a lowlevel visual data and a system for preshaping a planar
two-ﬁngered hand with four joints grasping planar ob-

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torque driving each joint is transmitted through wire,
has been utilized[1][2]. Consider a multi-linked robot
hand where all joints are controlled by a single wire,as shown in Fig.2. Now we focus on only open-closemotion of ﬁngers. The motions of the arm are ignored.
Since all joints of the robot are driven by the tensionof one control wire[1], the distribution of drive torquefor each joint is determined by the wiring, i.e., pulley
position and radius. In addition, a spring is attachedto each joint to keep the initial link posture when wiretension is zero. This spring produces a resistant force
for the joint torque coming from wire tension.

Ob ject

Wire

PulleySpring

(a)

(c)

(b)

Fig. 2: Basic mechanism of a high speed capturing
robot driven by a single wire.

3.2

Reference Posture

Suppose a capturing robot trying to capture an object, as shown in Fig.3, where (a) and (c) are examplesof failure in catching an object, and (b) is an example
of successful catching, respectively. From the geometrical point of view, it is more likely that the simultaneous contact between links and the object leads to a
successful catching. Based on this consideration, wedeﬁne the reference posture of ﬁnger links as shown in
Fig.3(b), and this is given by following.
(a)

(b)

(c)

Fig. 3: Various behaviors of object when ﬁnger links
make contact with it.

Reference Posture: The reference posture isgiven by the grasping form where all links makecontact with an object, and is expressed by theUjoint angular vector θ r ∈ RΣs=1 Ns ×1 , where U
and Ns denote the number of ﬁngers and the number of joints (= the number of links) of the s-th
ﬁnger, respectively.

jects is described. Kang and Ikeuchi[4] have proposed
an automatic robot instruction for recognizing a grasp
from observation. Hong and Slotine[5] have proposeda catching algorithm by which they succeeded in realtime catching free-ﬂying spherical balls being tossed
from random locations. The postures of the robot
hand and arm at the moment of capturing are plannedby the information obtained by vision. Nakamura
et. al. [6] have challenged the reactive grasp of a threeﬁngered robot hand by using a learning method, where
the preshaping is planned by integrating 48 kinds ofsensor signals and 29 primitive behaviors. As far as weknow, our paper is the ﬁrst work discussing dynamic
preshaping.

3
3.1

3.3

In this work, we suppose that capturing motions by
robot hands can be regarded as realizing the given reference posture θ r . We deﬁne the dynamic preshaping
problem as follwing.
Dynamic Preshaping Problem: By giving
the reference joint angle θ r , determine the mechanical parameters (pulley radius, pulley position, spring constant, and mass of ﬁnger link), sothat the following condition is satisﬁed.
¯¯∆tθr = max ¯ tθsir − tθsjr ¯ < ε(1)(s = 1, . . . , U, i = 1, . . . , Ns , j = 1, . . . , Ns )Problem Formulation
Robot Hand Driven by a Single Wire

Suppose a typical robot hand where each actuator is placed at each joint. In such a case, the whole
weight of the robot increases and the motion of therobot becomes slow as a result. In order to decrease
the weight, the wire drive method where all actuators are arranged at the base of the robot and the©ISAROB 2005

Dynamic Preshaping Problem

where tθsir denotes the time when the i-th jointof the s-th ﬁnger reaches the reference angle andε is a small positive value.

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following equation:N-th linkpNa

pN

Fixed point(pai+1 − pai ) ⊗ tbi+1

i+1-th link
a
pi+1

f

ti

f

Ri ri

pia

tib

pi 1 = pia 1

p1a

τiwirei-th link

ki

Rivp

Wire

Fig. 4: A 2D ﬁnger model driven by a single wire.

4
4.1

WiringSuppose a 2D ﬁnger is driven by a single wire asshown in Fig.4, where pi , pai , and pfNix ∈ R2×1 are theposition vectors expressing the i-th joint position, thei-th pulley position at inner link, and the wire ﬁxing
point at the N -th link, respectively, and all pulleys canbe freely rotated around their axes. pa0 is the position
vector expressing the pulley position at the palm, andwhole link system is ﬁxed with the base at p1 . Let
tfi ∈ R2×1 and tbi ∈ R2×1 be the tension vectors before
and after the i-th pulley, respetively, as shown Fig.4.
Supposing that there is no friction around each pulleyaxis, we have ∥tbi ∥ = ∥tfi ∥ = T where T is the wiretension. Also, let r fi ∈ R2×1 and r bi ∈ R2×1 be thevectors denoting the positions where the wire tensions
are given. Under the above preparation, we have thefollowing relationship.
tfi
r bi ⊗ tbi= (pai − pi + r bi ) ⊗ ebi−(pai+1 − pi+1 + r bi+1 ) ⊗ ebi+1(8)= −tbi+1=−r fi ⊗ tfi

τ wire=pai + r fi + di+1
− r bi+1i= T [A + B] eb

(9)

where
[
]
wire Tτ wire = τ1wire , · · · , τN
∈ RN ×1(10)
[q 1 ⊗] − [q 2 ⊗]
0


..


. [
A = 

]

q N −1 ⊗ − [q N ⊗] 
0
[q N ⊗]∈ RN ×2N
qi =(2)

pa − p ∈ R2×1[]
i [ b i]
r 1 ⊗ − r b2 ⊗



B = 


(3)

0

where ⊗ denotes the operator providing x1 y2 − x2 y1
Tfor two vectors x = [x1 , x2 ]T and y = [y1 , y2 ] . By
i+1
2×1
express the vector from the wire
letting di ∈ Rrelease point of the i-th pulley to that of the i + 1-th
pulley, we get

(11)
0

..

. [
]
r bN −1 ⊗

]
[ T
T T
∈ R2N ×1
eb = eb1 , · · · , ebN

(12)




[ b ]
−[ r N ⊗] 
r bN ⊗∈ RN ×2N

(13)
(14)

where [x⊗] means [−x2 , x1 ] ∈ R1×2 for a vector
x = [x1 , x2 ]T . Aeb and Beb express the torque components controlled by the position of pulley and byboth the radius of pulley and the way of wiring, respectively.

(4)

By multiplying ⊗tbi+1 by right side to each term on⊗ tbi+1 = 0, we obtain theeq.(4) and considering di+1i

©ISAROB 2005

(7)

Let ebi (= tbi /T ) ∈ R2×1 be the unit vector expressing
the direction of tension. Eq.(7) means that the torqueproduced by a wire can be expressed by the multiplying the wire tension with the radius of virtual pulley.
We would note that the radius of such a virtual pulleyvaries depending upon the link posture.
The above discussions provide us with the relationship between the torque and the wire tension in thefollowing form:

Mechanical Analysis

pai+1= T Rivp

where

Palm

T

(pai − pi ) ⊗ tbi + (pai − pi+1 ) ⊗ tfi (6)τiwirei 1-th link

p1

p0a

=

From eqs.(2), (3), (5), and (6), τiwire can be rewrittenwith r bi by the following form.

Üopi­iwire(5)

The torque around the i-th joint is given by the following form:pi 1i-th pulleyrib= (r fi − r bi+1 ) ⊗ tbi+1

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rball

Table 1: Mechanical parameters used for simulation.

θ2

l1
l2
m1
m2
I1
I2
w1
w2

θ1

I1
m1

1p a
1

Σ1

R0

I2
m2

2p a
2

Σ2

k1
lpalm

w2 w1
k2

l1

larm

l2

lpalm
larm
ini
lmain
kmain
dmain
lkmain
rball
θ1ini
θ2ini
k1
k2
R0
R1

kmain

ini

lmain

dmain

Fig. 5: Finger model for determining parameters.

4.2

Joint Compliance

where

[

springﬁ spring = τ1spring , · · · , τN





Q = 



∈ RN ×1

θ1 θ1 − θ2

5








θi − θi−1 θi − θi+1..

.
θN − θN −1

0∈ RN ×N
T

N ×1

k = [k1 , · · · , kN ] ∈ RParameter Determination

Since the equation of motion given by eq.(20) is
highly nonlinear, it is really diﬃcult to formulate analgorithm for solving the dynamic preshaping problem. Through the analysis of wiring, we learnt thateach joint torque is very sensitive to the pulley position and even negative torque can be generated according to the location, while it is hard to achieve sucha characteristic by changing either spring constant or
mass distribution. Based on these considerations, let
us now replace the problem obtaining the mechanicalparameters for realizing θ r by the following optimization problem.

.

Dynamic Preshaping

5.1

(16)
0

..

M (θ)θ̈ + h(θ̇, θ) = T [A(θ) + B(θ)] eb (θ) − Q(θ)k
(20)

(15)
]T

60.0[mm]45.0[mm]55.0[g]30.0[g]27.6[kgmm2 ]7.1[kgmm2 ]20.0[mm]17.0[mm]10.0[mm]260.0[mm]70.0[mm]
0.60[N/mm]
0.01[Ns/mm]
180.0[mm]32.5[mm]0.0[rad]0.0[rad]90.0[Nmm/rad]120.0[Nmm/rad]15.0[mm]0.01[mm]rewritten in the following form:

Let us now consider the eﬀect of spring distribution
among joints, where ki is the i-th joint spring constant
as shown in Fig.4. Each joint spring plays an important role for controlling the ﬁnger link posture when
the ﬁngers approach an object. We have the following relationship between the joint torque and spring
constant.
τ spring = −Qklength of the 1st link
length of the 2nd linkmass of the 1st linkmass of the 2nd link
moment of inertia of the 1st link
moment of inertia of the 2nd link
width of the 1st link
width of the 2nd link
position of the 1st joint
length of arm
initial length of main spring
spring constant of main spring
damping coeﬃcient of main spring
natural length of main springradius of object
initial angle of the 1st joint
initial angle of the 2nd joint
spring constant of the 1st joint
spring constant of the 2nd joint
radius of base pulleyradius of pulley on the 1st link

(17)

Minimize(18)

Z = ∆tθr

4.3

Equation of Motion

Subject to

The equation of motion of the ﬁnger link system
can be modeled by the following,
M (θ)θ̈ + h(θ̇, θ) = τ wire + τ spring
N ×N

i amin≤ i pai ≤ i pamaxpii(i = 1, . . . , N )M (θ)θ̈ + h(θ̇, θ) = T [A(θ) + B(θ)] eb (θ) − Q(θ)k(19)

where i paminand i pamax∈ R2×1 are the minimumiiand the maximum limitations of the pulley position,
respectively. Now, the evaluation function Z is corresponding to eq.(1).

where M (θ) ∈ R
and h(θ̇, θ) ∈ R
are theinertia matrix and velocity related torque vector, respectively. From eqs.(9) and (15), eq.(19) can be©ISAROB 2005

N s×1

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Start
a

a

Pulley positions 1p1 and 2p2

ADAMS

t=0
Matrices A, B, and K
Wire Tension T
Changepulleypositionsúwire = T [ A + B ] ebúspring = Kíúwire + úspring

(c)

(d)

No
T

(a) 1p1a = [32.0 2.0] [mm]

Yes

No

(b)

T = T + ÅTt = t + Åt

Dynamic computationt > End time ?

(a)

2

T

p2a = [25.0 2.5] [mm]Åtθ r < " ?
Yes

End

Fig. 6: Flow chart for computing the pulley positions.

5.2

(a)

(b)

(c)

(d)

Simulations

In order to obtain the pulley position satisfying thecondition expressed by eq.(1), we repeat computation
until it converges. Consider a two-ﬁngered hand anda sphere object as shown in Fig.5. All parameters
except for 1 pa1 and 2 pa2 are given by Table.1. We also
suppose that one tip of the wire is ﬁxed at the one sideof the spring implemented in the arm and the other tip
of wire is ﬁxed at the second ﬁnger link. We assume
that the wire tension T is generated by the following
equation.
T= kwire ∆lwire − dwire ∆l˙wireT(b) 1p1a = [32.0 2.0] [mm]T

6.5] [mm]Fig. 7: Simulation results for two diﬀerent sets of pulley position.

(21)
As shown in Fig.7(a), the ﬁrst link makes contact
with the object earlier than the second one and theobject is ﬁnally pushed away. This result suggeststhat ε = 13[ms] is not small enough. In case of
ε = 0.4[ms], we obtain 1 pa1 = [32.0, 2.0]T [mm] and2 ap2 = [25.0, 6.5]T [mm] after convergence. In this case,all links make contact with the object with in 1[ms].
As shown in Fig.7(b), the robot can capture the object successfully while it includes a small oscillationalmotion.

where kwire , dwire , and ∆lwire are virtual stiﬀness,damping coeﬃcient, and the amount of stretch, respectively. Now, we give kwire = 1.0 × 106 [N/mm] anddwire = 1.0 × 10−2 [Ns/mm]. We also suppose thatthe friction between each ﬁnger link and the object isgiven by µ = 0.0. The dynamic simulator ADAMS(Mechanical Dynamics, Inc.) is utilized for computing ﬁnger motion for a given set of parameters. Fig.6
shows the ﬂow chart for computing the pulley positions 1 pa1 and 2 pa2 in Fig.5 for achieving the reference ﬁnger posture at the instance of contact. Fig.7
shows two simulation results where (a) and (b) are
ε = 13[ms] and ε = 0.4[ms], respectively. In case of
ε = 13[ms], we obtain the optimum pulley positions
1 a
p1 = [32.0, 2.0]T [mm] and 2 pa2 = [25.0, 2.5]T [mm].

©ISAROB 2005

2 p a = [25.026Experiments

We designed and developed an experiment systemfor conﬁrming the simulation results, where we canchange the wiring route by changing the pulley posi-

109

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

7

We discussed the design of capturing robot driven
by a wire from the viewpoint of dynamic preshaping.
The main results are summarized as follows;(1) Dynamic preshaping is important, especially justbefore ﬁnger links make contact with the object.
We choose the preshape so that all links makecontact with the object simultaneously.
(2) The mechanical parameters which inﬂuences joint
drive torque were considered and the relationshipbetween the wire tension and the joint torque distribution was introduced precisely.
(3) The design procedure for achieving the reference
posture under dynamic condition was explained.
A couple of simulations were executed to obtain
the optimum parameter and the validity is conﬁrmed experimentally.
This work was supported by CREST of JST (Japan
Science and Technology). We are very thankful for the
support.

9 [ms]

0 [ms]

30 [ms]19 [ms]T(a) 1p1a = [32.0 2.0] [mm]2T

p2a = [25.0 2.5] [mm]9 [ms]

0 [ms]

Concluding Remarks

References[1] M. Kaneko, R. Takenaka, M. Higashimori A. Namiki
and M. Ishikawa: “The 100G Capturing Robot –Too
Fast to See–,” IEEE/ASME Trans. on Mechatronics,
vol.8, no.1, pp.37-44, 2003.

31 [ms]19 [ms]T(b) 1p1a = [32.0 2.0] [mm]2 p a = [25.02T

6.5] [mm][2] S. Hirose and Y. Umetani: “Soft Gripper,” Proc. of
ISIR, pp.112–127, 1983.

Fig. 8: Experimental results for two diﬀerent sets ofpulley position.

[3] C. Bard and J. Troccaz: “Automatic Preshaping
for a Dexterous Hand from a Single Description of
Objects,” IEEE Proc. of Int. Workshop Intelligent
Robots and Systems, pp.865–872, 1990.
[4] S. B. Kang and K. Ikeuchi: “Toward Automatic
Robot Instruction from Perception–Recognizing a
Grasp from Observation,” IEEE Trans. on Robotics
and Automation, vol.9, pp.432–443, Aug, 1993.

tion and the pulley size, the joint spring constant, andthe mass distribution of the links, respectively. The
tension of the wire is generated by the elastic energyof the spring mounted in the arm. Mechanical parameters except for the pulley positions are given by
Table.1 where parameter explanation is given in Fig.5.
We observe the change of the link posture by shifting
the pulley positions along the simulation results. Fig.8
shows a series of photo taken by a high-speed camera
with 1 [ms/frame], where the hand is taking action
for capturing a sphere object. The hand as shown in
Fig.8(a) and (b) are designed under the pulley position
as shown in Fig.7(a) and (b), respectively. While theﬁrst link makes contact with the object earlier than
the second one in Fig.8(a), all links almost make contact with the object simultaneously in Fig.8(b). From
Fig.8, we can see that the behaviors of ﬁnger links inthe simulation and experiment nicely coincide. Therefore, the validity of the optimum parameters obtainedby the simulation are supported by experiments.

©ISAROB 2005

[5] W. Hong and J.E. Slotine: “Experiments in HandEye Coordination Using Active Vision,” Proc. of the
4th Int. Symp. of Experimental Robotics, pp.130–139,
1995.
[6] Y. Nakamura, T. Yamazaki and N. Mizushima: “Motion Synthesis, Learning and Abstraction through Parameterized Smooth Map from Sensors to Behaviors,”
Proc. of the Int. Symp. of Robotics Research, pp.69–
80, 1998.
[7] L. Deman, A. Konno and M. Uchiyama: “Flexible
Manipulator Trajectory Learning Control with Input
Preshaping Method,” Proc. of the 38th SICE Annual
Conf. Int., pp.967–972, 1999.
[8] A. Namiki and M. Ishikawa: “Optimal Grasping Using Visual and Tactile Feedback,” IEEE Proc. Int.
Conf. on Multisensor Fusion and Integration for Intelligent Systems, pp.584–596, 1996.

110

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Design of Tracing Type Jumping Robots
Manabu HARADA∗ , Mitsuru HIGASHIMORI∗ , Idaku ISHII∗ , and Makoto KANEKO∗∗
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, JAPAN
Abstract
This paper discusses a leg design for a tracing type jumping robot driven by an actuator with an extremely highoutput/weight. We found that there is a close relationship between the jump ratio(= h/l) and the leg length.
Through analysis and simulation, we found that there
exists the optimum design speciﬁcation where the jump
motion becomes maximum. After explaining an designorientation by considering joint torque and mechanicalparameters, experiments are shown to verify the basic
idea.

h
g

!max ­0mb

Keywords—Jumping Robot, Motor Based Actuation,
Jump Ratio1

To answer these questions, we consider a simple model
where the robot is composed of one actuator and twolegs, as shown in Fig.1. The open-close motion of bothlegs is controlled by one actuator. As a result, this robot
realizes the jumping motion by tracing the surface of theground by both tips of leg. For such a simple model, we
obtain the analytical result of the jumping height ratio
deﬁned by the height normalized by the leg length. Although the result is available only under a couple of limited assumptions, it includes really useful information
for determining the speciﬁcation of a jumping robot.

©ISAROB 2005

111

θ

l
Fig. 1: Model for tracing type jumping robot.

Introduction

There have been many works discussing legged robots.
Legged robots can be classiﬁed into two groups; static
based locomotion where the center of gravity is always
ensured within the support polygon constructed by the
supporting legs, and dynamic based locomotion where
the center of gravity is allowed to be away from the support polygon. Jumping robot can be categorized into
the latter one. While various jumping robots have been
proposed so far, accumulated energy using either springor pneumatic actuator is utilized for most of them. Thisis because there have been no powerful actuator ensuring an enough jump height. With the increase of actuation technique, it has become possible to see the chancethat a motor based robot can jump since the eﬀect togravitational force is reduced. For a given actuator, howmuch size is appropriate for achieving a jump? How todesign a robot for achieving the maximum jump height?

θbThrough above analysis, we show an interesting observation is that there exists the optimum design where thejump ratio becomes maximum. For a given speciﬁcationof actuator, we explain how to determine the robot speciﬁcation leading to the optimum design. Along the proposed approach, we design a jumping robot which canachieve the maximum jump ratio. Finally, We develop
the jumping robot with optimum design and show thatexperiments strongly support our design. Nice qualitative coincidences are observed between analysis andexperiments.

2

Related Works

While there are many works discussing walking
machine[1][2], most of them supposed that at least one
leg makes contact on the ground. On the other hand,
there exists a phase where all legs are away from theground in jumping robots[3]. Raibert et al.[4] have developed one-legged hopping machine driven by a pneumatic actuator. Okubo et al.[5][6] have proposed ajumping machine with small output actuator where theyutilized self-energizing spring system. The jump ratio
which we utilize as the index of evaluation can be regarded as “Jumping height index” proposed in [5] by
replacing the maximum distance that body can movewith the leg length. Tsukagoshi et al.[7] have developed
the jumping & rolling inspector as a rescue robot anddiscussed the control of the jumping height and energy

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3
3.1

Jc

saving by using a pneumatic actuator. Arikawa andMita[8] have developed the design of multi-DOF jumping robot. They have discussed planning parameters ofrobot motions for achieving jumping and somersault.

Design of Jumping Robots
Model for Analysis

Let us consider the two-legged model as shown in Fig.1,
where h, l, mb , τ0 , ωmax , and g are the maximum heightof jump, the length of leg, mass of body, torque of actuator, the maximum angular velocity of actuator, andthe gravitational acceleration, respectively. We supposethat the actuator operates with constant torque τ0 withrespect to angular velocity. While a regular DC servo
motor has a decreasing characteristics with respect toangular velocity, there are a couple of AC servo motors
approximately supporting this characteristics by supplying current depending upon the angular velocity. We
also suppose that the actuator has the limitation of angular velocity with its maximum value of ωmax and eachleg is controlled by a single motor. We ignore the eﬀectof the friction between the tip of leg and the ground,since we regard it as a secondary factor for reducing thejumping height.
In this work, we utilize jump ratio Jc to evaluate theheight of jump as follows;
Jc

=

(1)

Optimum Design on Leg Length

We now consider an extremely simple model as shownin Fig.1, where we assume that two rigid links without
mass are connected to an actuator with the mass of mb
and the robot motion is symmetry with respect to thez-axis. Suppose that the robot is jumping as shownin Fig.1 where the link angle is θ with respect to thehorizontal line. Let the joint torque τ0 is given until
θ > θb .
The work where the torque executes during 0 ≤ θ ≤ θb
is given as follows;
Z 2θb
W

=
0

=

2τ0 θb

τ0 dθ

(2)
(3)

Since W is fully transmitted to the potential energy
when the robot reaches the highest position by jumping©ISAROB 2005

Leg length l [mm]Fig. 2: Relationship among l, llim , and Jc .
motion, we can obtain the jump ratio with the functionof τ0 as follows;Jc=

2θb τ0
mb gl

112

(4)

Eq.(4) is the closed form solution exhibiting the relationship between Jc and mechanical parameters, where
both legs maintain contact with the ground during
0 ≤ θ ≤ θb . Now, we would note that the leg cannot rotate with more than ωmax . The above constraint
for angular velocity leads to the following inequality.
θ̇max ≤ ωmaxhlPhysical meaning of Jc is that it expresses the maximum height which the robot can jump in units of its leg
length.

3.2

lmin=218 [mm](5)The maximum angular velocity of joint θ̇max is achievedat the moment of θ = θb and the energy balance at thismoment is given as follows;
´2
1³
(6)
θ̇max l cos θb + mb gl sin θb = 2τ0 θb
2
From eq.(6), we can obtain θ̇max as follows;¯¯θ̇max = θ̇¯
θ=θb
s µ
¶
1
2τ0 θb=2− gl sin θbl cos θb
mb

(7)
(8)

Now, we would focus on the leg length from the viewpoint of the design for robots. Substituting eq.(8) into
ineq.(5) yields,l ≥ llimwherep

q 2 + 4mb τ0 θb p2
mb p2
p = ωmax cos θb
q = mb g sin θb

llim

=

−q +

(9)

(10)
(11)
(12)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 1: Speciﬁcation of the lightweight high-speed
motor
motor typemax torque [Nm]max speed [r/min]time response to a step input [msec]
weight [g](a) Side View

AC
1.71
300
30
59.6

100

00[mm](b) Top View

Fig. 4: An overview of the developed robot.

Jceq.(4)(a)(b)

Fig. 3: The lightweight high-speed motor and the developed jumping robot.

In the case where l < llim , eq.(4) is not guaranteed sinceeach tip of the leg cannot trace the ground and is awayfrom it. Fig.2 shows the relationship among l, llim , andJc . While we can suppose that jump ratio decrease lessthan the that of eq.(4), we would note that llim maybe the optimum leg length when the speciﬁcation of
actuator is given.

4

eq.(10)llim=218 [mm]Experimental results

Leg length l [mm]Fig. 5: Experimental results.

5Concluding Remarks

We discussed the design of tracing type jumping robots
by focusing on the leg length. The main results are as
follows:

Experiments

Table.1 shows the speciﬁcation of the lightweight powerful motor (Harmonic Drive Systems, Inc.)[9][10]. This
motor has the maximum torque of 1.71[Nm], the maximum rotational speed of 300[r/min] and the mass of
59.6[g]. Fig.3(a) and (b) show an overview of the motor
and a photo of the developed robot, respectively. Fig.4
shows an overview of the developed robot where one leg
is connected to the axis of the motor and the other one
is ﬁxed to the outer case of the motor.
Fig.5 shows the map showing experimental results withrespect to l. The circles shown in Fig.5 are points obtained by experiments. For reference, the results obtained by eq.(4) and eq.(10) are also provided in Fig.5,
where θb = 0.5. A really interesting observation isthat we can ﬁnd the maximum jump ratio between
l = 200[mm] and 300[mm] for analysis, which supports
the idea of this work. Fig.6 and Fig.7 show series ofphotos during a jump motion of the robot with the leg
length of 100[mm] and 200[mm], respectively. The robot
with the length of 200[mm] achieved a big jump with 550[mm] with Jc = 2.75.

©ISAROB 2005

113(1) Jump ratio was chosen as the evaluation index fordesigning a jumping robot.
(2) We obtained the relationship between the jump ratio and the leg length by using a simple model.
(3) It was shown experimentally that there exists the
optimum design point leading to the maximum
jump ratio.
One of features for using motors for a jumping robot
is that we can expect the capability of grasping as well
as jumping. Fig.8 shows an example where the robot is
capturing an object suspended in air after jumping up.
We believe that a kind of dexterity can be anticipatedthrough an AC motor based jumping robot.
This work is supported by CREST of Japan Scienceand Technology (JST). We are very thankful for the
support. Also, we would like to express sincere thanks
for Mr. Masahiro Yuya for his experiments.

References[1] T. Frank: “Dynamic study of a four-bar linkage walking
machine leg”, M.S. thesis, Ohio State University, pp.133,

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

(a)

(b)

(a)

(b)

(c)

(d)

(c)

(d)

(e)

(f)

Fig. 6: A series of photos during a jumping motion(l =100[mm]).
1973.
[2] S. Hirose and Y. Umetani: “The basic considerations onenergetic eﬃciencies of walking vehicles”, Trans. Soc. Instrument Countr. Engineers(In Japanese), vol.15, no.7,
pp.928–933, 1979.
[3] E. Nakano and H. Okubo: “Jumping Machines”, Journalof RSJ (in Japanese), vol.11, no.3, pp.342–347, 1993.

Fig. 7: A series of photos during a jumping motion(l =
200[mm]).

[4] M. H. Raibert, H. B. Brown, Jr, and M. Chepponis:“Ezperiments in Balance with a 3D One-Legged Hopping Machine”, The Int. Journal of Robotics Research,
vol.3, no.2, pp.75–92, 1984.
[5] H. Okubo, E. Nakano, and H. Kimura: “Design of Jumping Machine using Spring and Actuators”, Journal of
RSJ (in Japanese), vol.10, no.7, pp.948–954, 1992.
[6] H. Okubo, M. Handa, and E. Nakano: “Design ofa Jumping Machine using Self-energizing Spring —Jumping by Small Actuators—”, Journal of RSJ (inJapanese), vol.16, no.5, pp.633–639, 1998.
[7] H. Tsukagoshi, Y. Mori, M. Sasaki, T. Tanaka, and A.
Kitagawa: “Development of Jumping & Rolling Inspector to Improve the Debris Traverse Ability”, Journalof Robotics and Mechatronics, vol.15, no.5, pp.482–490,
2003.
[8] K. Arikawa and T. Mita: “Design of Multi-DOF Jumping Robot”, IEEE Proc. of Int. Conf. on Robotics andAutomation, pp.3992–3997, 2002.

(a)

(b)

(c)

(d)

(e)

(f)

[9] A. Namiki, Y. Imai, M. Ishikawa, and M. Kaneko: “Development of a High-speed Multiﬁngerd Hand System
and Its Application to Catching”, In Proc. of IEEE/RSJ
Int. Conf. on Intelligent Robots and Systems, pp.2666–
2669, 2003.
[10] http://www.hds.co.jp/HDS hp english/english/index.
html©ISAROB 2005

114

Fig. 8: A series of photos during a jumping and grasping motion.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Development of a Laparoscopic Surgery Training System
and Preliminary Experiments
Hirokazu MASUGAMI∗ , Tomohiro KAWAHARA∗ , Hiroyuki EGI∗∗
Masanori YOSHIMITSU∗∗ , Masazumi OKAJIMA∗∗ and Makoto KANEKO∗∗Graduate School of Engineering, Hiroshima University∗∗Graduate School of Biomedical Science, Hiroshima University
masugami@hfl.hiroshima-u.ac.jp
Abstract
This paper discusses the direction dependent dexterity of surgeons in laparoscopic surgery. In laparoscopic surgery, a surgeon can observe the concerned
tissue only through a 2D display and is obliged to convert the image from 2D to 3D in his (or her) brain. In
this report, we examine the eﬀect of 2D or 3D visual
information on the manipulation dexterity by utilizing
the newly developed evaluation system.

1

Introduction

Laparoscopic surgery is a well established method
in modern medicine. In laparoscopic surgery, a surgeon inserts forceps and a scope into the abdomen
through small holes, so no large cuts are necessary
as in open surgery. It is a method in minimal invasive surgery. It was well examined scientiﬁcally that it
does not aﬀect a life prognosis, and has a similar rateof cures for malignant diseases[1][2][3].
On the other hand, many reports in operation mistakes by laparoscopic surgery are seen in the news inrecent years. In order to prevent such operation mistakes, the education to surgery residents is necessary.
In laparoscopic surgery, as shown in Fig.1, surgeons
have to operate forceps, looking at a monitor, without
seeing the aﬀected part directly, and have to convert
the image from 2D to 3D in their brain.
The purpose of this study is quantifying the direction dependent dexterity of surgeons in laparoscopic
surgery. In this paper, we analyze how a surgeon obtains a distance feeling for the depth direction whileconverting the 2D display image to a 3D space. Then,
we show that the image conversion capability to 3D
can be considered as an element to judge a surgeon’s
skill.

2

Fig.1: Overview of Laparoscopic Surgery

Fig.2: Overview of the Developed System

Related research

There are many reports that evaluate laparoscopic
surgical skills. Although Kopta[4] presented methods to evaluate surgical skills, they are not widely accepted. Martin[5] and others made an evaluation system called Objective Structured Assessment of Technical Skill (OSATS), which gained consensus for theﬁrst time based on its excellent objectivity. Moreover, Rosser, et al.[6][7][8][9] claimed what makes laparoscopic surgery diﬃcult is the limitation of the©ISAROB 2005

115

spatial information due to the 2D display, and supported their claim by monitoring the learning eﬀect
by change of performance time. Most of these reports
evaluated the improvements in reaching the training
goal, which had not been necessarily quantitive. However, recently, the tip position of forceps is measured,and thus the number of test methods using quantitive
analysis about the operation increases. Cuschieri, etal. developed the Advanced Dundee Endoscopic Psychomotor Tester (ADEPT) which acquires the forceps
tip position information by using an encoder[10][11].
Darzi, et al. developed the Imperial College Surgical Assessment Device (ICSAD) which acquires theforceps tip position information by using an electromagnetic ﬁeld, and utilizes it eﬀectively to improvelaparoscopic skills[12][13][14]. Sokollik, et al. considered the tip speed proﬁle from the forceps tip position
data by the ultrasonic sensor[15]. Also, there are simulators that perform the analysis and evaluation of theThe Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Monitor2D image
Image
ForcepsB

Tip positionA

Endoscope
Table
PC

Fig.3: System Architecture
Fig.4: Coordiate System

position information by vision sensors[16] and others
that use a virtual reality to simulate an actually undergoing operation[17].
Here, the image conversion capability from 2D to
3D is considered as one of the necessary skills in laparoscopic surgery. We focus on the distinction between the vertical movement and the front-and-backdirection movement, as it is regarded as being very
diﬃcult.

3
3.1

Then, we considered the error due to alignment,
and corrected it by the method shown in the following paragraph. As a result, the maximum error was
3.23[mm], the average error was 0.92[mm], and standard deviation 0.62[mm] after the correction as shownin Fig.5(b). Thus the position accuracy was greatly
improved. The circles of Fig.5 shows the average error
before and after compensation, respectively.

3.4

Materials
The feature of the developed device

Fig.2 and Fig.3 show the experiment device and thesystem architecture. As can be seen from Fig.2, theendoscope is installed in front of a table and the 2D image is shown on the monitor. A gimbals mechanism isused in this system to grasp the forceps. Two encoders
are attached to the two rotational axes. In addition,
we ﬁxed a scale seal (LBP = 0.1[mm]) on the stem partof forceps, in order to be able to acquire the displacement of the length. Then, we used the heat shrink
tube to secure the seal on the stem. Furthermore, theopinion of surgeons was taken into account. In orderto reproduce the load felt in an actual operation, weused a spring mechanism that enables to adjust rotation friction of the gimbals. Furthermore, in order toprevent the candidate to look at the subject directly,
instead of the monitor during an experiment, we manufactured a cover made of a black plastic plate. Fig.3
shows the information ﬂow of this system by arrows.

3.2

v1

(1)

vt2
vt3

=
=

v2 + ∆v2
v3 + ∆v3 .

(2)
(3)

=

Vk

(4)

where V = [v1 v2 v3 ] and k = [k1 k2 k3 ]T .
That is, if v and V = [v1 v2 v3 ] are known, equation(4) can be changed intok=

V −1 v(5)in order to obtain k = [k1 k2 k3 ]T .
Therefore, k = [k1 k2 k3 ]T can be found, if vt1 , vt2 ,
vt3 , v1 , v2 and v3 are known beforehand. Also, thetrue value v̂t can presume as follows,

Position detection accuracy©ISAROB 2005=v

The forceps tip position detection
methodWe acquired the position data for both tips by
putting the tip of the forceps on pre-deﬁned 14 points
on the table that were separated by 50[mm]. Then, weinvestigated the deviation from the true value when
acquiring position information for the 14 points of thetwo tips 10 times. As shown in Fig.5(a), the maximum
error was 6.20[mm], the average error was 2.54[mm],and standard deviation 1.28[mm].

vt1

As is commonly known, the arbitrary positions in
the 3D space can be expressed as follows by using thethree position vectors:

We can measure the two rotation angle parameters
and one distance parameter by using three encoders.
As shown in Fig.4, by measuring the three parametersl, θ and φ, the relative position coordinates x = l·sinφ,y = l · cosφ · sinθ and z = −l · sinφ · cosθ of the gimbal
central point can be calculated.

3.3

Compensation method

In a ﬁrst step of the compensation method of thesystem, the three points vt1 , vt2 and vt3 were chosen as calibration points and their true x-y-z values
are known. Next, using vt1 as a reference point, theposition data on the remaining points vt2 and vt3 isacquired by the parameter from each encoder of thesystem and the equations x = l · sinφ, y = l · cosφ · sinθ,z = −l · sinφ · cosθ. Then, the deviations ∆v2 , ∆v3 ofthe acquired values v2 , v3 from the true values vt2 , vt3
were derived from the equations:v̂t= vt1 k1 + vt2 k2 + vt3 k3(6)using equations (1), (2) and (3), equation (6) can bechanged intov̂t=

v1 k1 + (v2 + ∆v2 )k2 + (v3 + ∆v3 )k3 . (7)With equation (4), we ﬁnally obtain
v̂t
116

=

v + ∆v2 k2 + ∆v3 k3 .

(8)The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

5
Errorz[mm]Errorz[mm]

5
0
−5
−5
0
Error [m
x

m]

5 −5

5
0
]
[mm
r
o y
r
r
E

(a)Before Compensation

0
−5
−5
0
Error [m
x

m]

5
0
]
5 −5 r [mm
o
Err y

y[mm]

(b)After Compensation

Fig.5: Position Errors before/after Compensation

4
4.1

Preliminary Experiment
PurposeThis chapter shows that the human characteristic
when only on carrying out 3D movements of the forceps relying the information from a 2D display. It isshown that the diﬀerence in skill of an amateur and asurgeon is appearant in this setup.

4.2

Fig.6: Tip Trajectory of an Amateur

Methods

In the experiment point A and point B on the tablein Fig.3 are 150[mm] apart. The subject was asked
to move the forceps tip from point A to point B andback again to point A. The tip position information
was acquired during the whole movement.

4.3

y[mm]

Definition of the target point attainment

The attainment times ti (i = 1, 2) to the intermediate point B and the ending point A are taken when
the forceps tip stays with in a distance of dg = 3[mm]from target point for tg = 0.3[s]. They are deﬁned asfollows:
ti

=

 

 pi − p(t) < dg ,inf tn .
tn − tg ≤ t ≤ tn

Fig.7: Tip Trajectory of an Expert
to the actual side motion. Therefore, a subject considers the correction of the deviation in the vertical direction diﬃcult, while the correction of the deviationin the side direction is considered comparably easy.

(9)Here, p(t) is the forceps tip position vector after t[s]from start, and pi (i = 1, 2) is a target position vector,
respectively. Also, t1 < t2 .

4.4

5
5.1

Results

Discussion

That the diﬀerent deviations discussed the lastparagraph are due to the shortage of the information
by a 2D display. A motion in the actual vertical direction and a motion of the front-and-back direction
cause a both vertical movement on a monitor, while amotion in the side direction on a monitor corresponds©ISAROB 2005

Integration according to direction ingredientIn case of the experiment shown in the preceding
chapter, it is characteristic deviation of the vertical direction was large, compared to the deviation of the side
direction was very small. Here, the deviation in theside direction and the deviation in the vertical direction are integrated over the move distance according
to the ingredient. Then, the result of the amateur’s tip
movement seen from Fig.6, the deviation integration
value Ax of the side direction is Ax = 616.27[mm2],
and the deviation integration value Az of the vertical direction is Az = 2934.37[mm 2]. The result of
the expert’s tip movement seen from Fig.7 is Ax =954.99[mm2], Az = 1347.27[mm 2].

The experimental results of an amateur and an expert are shown in Fig.6 and Fig.7. They show the tip
trajectory in the xy plain (top view) and in the yz
plain (side view) in the experiment done by an amateur (Fig.6) and by an expert (Fig.7). Here, looking at
both top views, deviation from the ideal trajectory inthe side direction is very small. Looking at both sideviews respectively, a large diﬀerence in the deviationin the hight direction of the expert’s and amateur’sforceps tips trajectory can be seen.

4.5

Direction dependent dexterity

5.2

117

Direction dependence line

We express the relation m between the deviation
integration value Ax of this side direction and the deviation integration value Az of the vertical direction
as follows.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

References

6000[1] H. Hasegawa, Y. Kabeshima, et al: “Randomized controlled
trial of laparoscopic versus open colectomy for advanced colorectal cancer,” Surg Endosc, vol.17, no.4, pp636-640,2003.
[2] Clinical Outcomes of Surgical Therapy Study Group: “AComparison of laparoscopic Assisted and Open Colectomy
for Colon Cancer,” N Engl J Med, vol.350, no.20, pp20502059,2004.

Az[mm2]

4000[3] R. Gonzalez, C. D. Smith, et al: “Laparoscopic vs open resection for the treatment of diverticular disease,” Surg Endosc, vol.18, no.2, pp276-280,2004.
[4] J. A. Kopta: “An approach to the evaluation of operativeskills,” Surgery, vol.70, no.2, pp297- 303,1971.

2000
amateur1
amateur2
doctor1
doctor2
0
0

2000

4000
Ax[mm2][5] J. A. Martin, G. Regehr, R. Reznick, et al: “Objective structured assessment of technical skill (OSATS) for surgical residents,” Br. J. Surg, vol.84, no.2, pp273- 278,1997.
[6] J. Rosser, L. Rosser, R. Savalgi: “Skill acquisition and assessment for laparoscopic surgery,” Arch Surg, vol.132, no.2,
pp200- 204,1997.

6000[7] J. Rosser, L. Rosser, R. Savalgi: “Objective evaluation of alaparoscopic surgical skill program for residents and senior
surgeons,” Arch Surg, vol.133, no.6, pp657- 661,1998.

Fig.8: Direction Dependence Line
m=

Az
.
Ax

[8] D. Risucci, J. Rosser, et al: “Experience and visual perception in resident acquisition of laparoscopic skills,” Curr
Surg, vol.57, no.4, pp368- 372,2000.

(10)

In order to verify the tendency of a direction dependence in two or more experiment results, we consider
the graph which sets the horizontal axis to Ax , the vertical axis to Az and the inclination to m. Each point in
Fig.8 is the result of one of 5 experiments carried out
by two amateurs and two experts, respectively. The
straight line in the graph marks the direction dependence m obtained by using the least-squares methodin the data for these 20 data points. The inclinationof the straight line is m = 1.95.
Moreover, as can be seen from the graph, it turns
out that each point m > 1. Thus, the correction of thedeviation in the vertical direction is again observed tobe more diﬃcult due to the lack of information fromthe 2D display.

6[9] D. Risucci, J. Rosser, et al: “Surgeon-speciﬁc factors in
the acquisition of laparoscopic surgical skills,” Am J Surg,
vol.181, no.4, pp289- 293,2001.
[10] A. I. Macmillan, A. Cuschieri: “Assessment of Innate Ability and Skills for Endoscopic Manipulations by the Advanced Dundee Endoscopic Psychomotor Tester: Predictive
and Concurrent Validity,” Am J Surg, vol.177, no.3, pp274277,1999.
[11] N. K. Francis, G. B. Hanna, A. Cuschieri: “Reliability of
the Advanced Dundee Endoscopic Psychomotor Tester for
Bimanual Tasks,” Arch Surg, vol.136, no.1, pp40- 43,2001.
[12] K. Moorthy, Y. Munz, A. Darzi, et al: “Motion analysis in
the training and assessment of minimally invasive surgery,”
Taylor & Francis Health Sciences, vol.12, no.3-4, pp137142,2003.
[13] K. Moorthy, Y. Munz, A. Darzi, et al: “Objective assessment of technical skills in surgery,” BMJ, vol.327, no.7422,
pp1032-1037,2003.
[14] K. Moorthy, Y. Munz, A. Darzi, et al: “Validity and reliability of a virtual reality upper gastrointestinal simulator
and cross validation using structured assessment of individual performance with video playback,” Surg Endosc, vol.18,
no.2, pp328-333,2004.

Conclusion

In this report, we introduced an equipment for evaluating a doctor’s skill in laparoscopic surgery, anddescribed the forceps tip position data acquisition
method, and its accuracy. Also, we found that obtaining the distance feeling of the depth direction is very
diﬃcult, due to the conversion of information from a
2D display to 3D that the surgeon has to perform inlaparoscopic surgery, and conducted the basic experiments. As a result, distinction of the vertical direction
and the front-and-back direction from 2D display is
diﬃcult. Thus, the direction dependence might be one
parameter that shows a surgeon’s skill. Furthermore,
we deﬁned the characteristics of human operation inendoscopic condition by the direction dependence, andconsidered its tendency by the results of a preliminary
experiments.
In the next step, we will consider the know-how ofexperts to use one forceps to help to judge the positionof the other forceps in the 3D conversion.
Finally, we would express our sincere thanks to Dr.
Roland Kempf for his valuable comments.
©ISAROB 2005[15] C. Sokollik, J. Gross, G. Buess: “New model for skills
assessment and training progress in minimally invasive
surgery,” Surg Endosc, vol.18, no.2, pp495-500,2004.
[16] Haptica: ProMIS, http://www.haptica.com/.
[17] Reachin: Reachin Laparoscopic Trainer 2.0, http://www.reachin.se/.
[18] G. B. Hanna, A. Cuschieri: “Optimal port locations forendoscopic intracorporeal knotting,” Surg Endosc, vol.11,
no.4, pp397-401,1997.
[19] G. B. Hanna, A. Cuschieri: “Inﬂuence of the optical axisto-target view angle on endoscopic task performance,” Surg
Endosc, vol.13, no.4, pp371-375,1999.
[20] G. B. Hanna, A. Cuschieri, et al: “Inﬂuence of Twodimensional and Three-dimensional Imaging on Endoscopic
Bowel Suturing,” World J. Surg, vol.24, no.4, pp444449,2000.
[21] G. B. Hanna, A. Cuschieri, et al: “Eﬀect of the angle between the optical axis of the endoscope and the instruments’plane on monitor image and surgical performance,” Surg Endosc, vol.18, no.1, pp111-114,2003.

118

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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119

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6,
2005
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120

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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    121

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
Strain gauge

Dynamic method

Strain gauge output

Static method

R=0.0021

R=0.0001

Force stick
Beam
y
y

Time[sec]

Time[sec]

R=0.15R=0.21

Time[sec]

Time[sec]

y sinω t

63              
Searching line(1;3 7      * +pixels)Center-of-gravity

Dynamic method

Strain gauge output

Static method

R=0.15R=0.21

Time[sec]

Time[sec]

F(L,t)xv

x

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122

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"The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Non-Contact Impedance Sensing
T. Kawahara, K. Tokuda and M. Kaneko
Graduate School of Engineering
Hiroshima University
4-1, Kagamiyama 1-Chome, Higashi-Hiroshima 739-8527
Abstract
This paper discusses the non-contact impedance sensing that can measure the mass, viscosity and stiﬀness
of environment. The developed sensor is composed ofa laser displacement sensor and air force supply nozzle.
We applied the developed sensor to fruits to conﬁrm
the possibility for measuring their surface impedance.

Laser sensor

Solenoid valve

Air andlaser axes

1sIntroduction

Distance sensor

There are various needs for measuring the mechanical
impedance (mass, viscosity, stiﬀness) of environment,such as medical examination of a cancer tissue, medical examination of eye pressure, estimation of humanskin age, judgment of the best time for eating fruits ormeats, and evaluation of the degree of completenessof compliant material in industrial products. Whilevarious approaches have been proposed for answering these issues, most of them are based on the direct contact method [1]−[3], where the pushing force
is actively given by a force probe. By the relationshipbetween the applied force and the displacement, we
can compute the impedance parameters. These approaches, however, cause several inherent issues dueto the direct contact between the probe and the environment, for example, imparting damages to environment, receiving damage of the sensing probe itself,and sanitary issue especially for both foods and human beauty care.

ObjectAir unit

Fig. 1: An overview of the developed sensor
of eye surface is measured by an infrared LED sensor
with an inclination angle with respect to the front direction.
We discuss the non-contact impedance sensing capableof estimating the local impedance parameters without
any contact. In this paper, we show the overview ofthe whole system including the non-contact impedance
sensor[6]. As an example of application, we applied
this sensor for measuring the degree of ripeness forpear. We show that impedance parameters for thepear are measured with reasonable estimation.

To cope with these issues, non-contact approaches
have been proposed recently and their eﬀectivenesshas been reported. As for a non-contact method, Shinoda and others [4], have ﬁrst proposed the non-contact
impedance sensor by utilizing air pressure actuated byan acoustic speaker, and showed the capability of measuring impedance parameters through the non-contact
approach. One of most popular examples as a noncontact impedance sensor is perhaps the eye pressure
measuring system [5] where air pressure is given to
the eye from the front direction and the displacement©ISAROB 2005

Nozzle2

Non-Contact Impedance Sensing

2.1

Design of the Developed Sensor

Fig. 1 shows the basic design of the developed sensor
unit and its overview, respectively. The sensor unit
is composed of a laser distance sensor and an air supply adaptor, where s denotes the distance between thenozzle and the surface to be measured. The key idea
is that the hole of air supply adaptor is so designedthat the longitudinal axis perfectly coincides with thesensing axis of the laser sensor. To achieve this tuning
up easily, we attach a sliding mechanism for the air

123

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

supply adaptor, which helps us to change the position
with observing the laser spot. The air supply system
is equipped with a high speed solenoid valve whichis the key element of the sensor toward a high speed
sensing. The valve can operate with the maximum
switching frequency of 500[Hz] under the supply pressure of 0.05[MPa] through 0.25[MPa]. It is composedof only three mechanical components and enables us to
impart a force to the environment with high responsewith respect to time. Such a high response allows us toestimate the impedance parameters with high quality
as well as high speed. Toward a ﬁeld test, we would
note that since the width of air ﬂow becomes large as sincreases. This will bring both the weakness of pushing force to the environment and the low resolution of
local impedance parameters in test surface. We should
design the hole of the air nozzle small enough for ensuring a high resolution in surface and keep a suﬃciently
short s for avoiding a weak force to the environment.
Another remark should be also given to the following
point. Even though the axis of nozzle coincides withthat of the laser sensor, it is not guaranteed that theaxis of actual air ﬂow coincides with the nozzle axis.
This is because the air is supplied to the sensor unit
from the side wall and largely bent with a right angle
within the sensor unit. The appropriateness of design
can be conﬁrmed only through experiments.

τon ,τoff
DriverAD

Non contact
impedance sensor

Fig. 2: Experimental system

Start

Step 1

Sampling

x(t), f(t)Step 2Model selectStep 3

Parameters estimate^ c^, k^
m,
i

i

^
x(t)

Step 4

R

2

2

Rth

no

yes
Sensitivity of parameters
S

How to Operate the Sensor
End

Fig. 2 shows the overview of the whole system including the non-contact impedance sensor. The system
includes a compressor for producing high pressurized
air source, an accumulator for keeping pressurized air,a control driver for sending the switching signal to thesensor, and a PC for collecting measured signals fromthe sensor as well as for sending a command signal tothe driver. The command signal is produced by two
parameters;1, (0 < freq  500[Hz])τon + τof f
τon, (0 < α  1)α=τon + τof f

freq =(a) Sensing flowchart

Step1 Measurement of the force and displacement data set:
Air force is sprayed on object and the displacement data set x(t) = [x1 , x2 , · · · , xn ]T is obtained.
At the same time, the air force data set f (t) =[f1 , f2 , · · · , fn ]T is also obtained, where fj is given
step-wisely.

(1)
(2)

Step2 Select of the impedance model:
Since the behavior diﬀers greatly from objects, theimpedance model has to be carefully chosen. By considering that it generally becomes the complicatedmodel due to the coupled spring eﬀect, it is given by amulti-element impedance model as shown in Fig. 3(b),where m, ci and ki are the mass, the i-th damper, andthe i-th spring, respectively.

How to Sensing Impedance

Step3 Estimation of the model parameter:
The estimated parameters of selected model is determined by the ordinary least square method using f (t)and x(t).

The ﬂowchart for estimating impedance is shown in
Fig. 3(a).

©ISAROB 2005(b) Impedance modelFig. 3: Procedure for estimating impedance parameter

where freq , α, τon , and τof f are the switching frequency for the valve, the duty factor for the valuve,
ON time of time period, and OFF time of time period, respectively. By changing freq and α, we canproduce various force patterns for each frequency.

2.3

ObjectSlider

Step 52.2

Air compressor

PC

124

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Step4 Evaluation of the selected model:
The response of displacement can be reproduced byusing the estimated value m̂, ĉi and k̂i . By comparing
the reproduced displacement x̂(t) = [x̂1 , x̂2 , · · · , x̂n ]Tand the actual x(t) we can evaluate how nicely the
impedance are estimated. By applying the coeﬃcientof determination R2 , we can obtain the evaluation of
the degree of approximation as follows[7];nR2 = 1 −air pressure distribution with more than 80% of the
maximum pressure. From Fig. 4(b), we can also see
w=2.5[mm] for the diameter of nozzle of 2[mm], whichmeans that the width of air ﬂow is not big enough under s=10[mm]. In Fig. 4(c), circles and triangles show
the pushing force to the environment and w, respectively. Fig. 4(c) says that the total force keeps almost
constant with respect to s, while w increases gradually with respect to s. In other words, although thearea aﬀected by the air jet increases with respect tos, the pushing force does not change largely. This isbecause the force obtained by the integration of local
pressure distribution is determined by the change ofthe total momentum of the air ﬂow. The reason why
the force decreases when s becomes small is that thepressurized air is diﬃcult to come out from the nozzle
due to a large ﬂow resistance when the nozzle hole isclose to the environment.

|x̂j − xj |2

j=1
n


(0  R2  1)

(3)

2

|xj − x̄|

j=1

Pressure [kPa]

where x̄ is the average value of xj . We can say thatthe degree of approximation of the reproduced wave2
form is good, when R2 is close to 1. If R2  Rth, we
progress to Step5 by regarding that the model utilized2
is reasonably good where Rth
is the threshold value.
2
Re-selection of a model is required when R2 < Rth.
2
2
We return to Step2 when R < Rth .
Step5 Sensitivity evaluation of estimated parameters:
By Step4, We can check whether the estimated parameters are reasonable or not. However, even if R2
becomes a large value, it is not guaranteed that the estimated parameters nicely coincide with actual ones.
In order to check this, we deﬁne the sensitivity of parameter by the following[8]:
p δg
g δp

0.8

(4)

0.6
0.4

where g is the function of p. The sensitivity for eachparameter obtained by Step3 is examined. We would
note that if S is suﬃciently small, the reliability ofparameter estimation is not guaranteed.

0.2
0

-6
6

0

[m

3

m]

6

Experiments

X

Y

w

-3

Pressure
sensor3

Nozzle1

P/PmaxS=(a) Step response

-6

-3

0
Y [mm]

3

X

Air Flow Characteristics(b) Pressure distribution

Fig. 4 shows experimental results where Fig. 4(a), (b)and (c) are the step responses of the air jet under
α=0.5, f =2.5[Hz] and s=10[mm], the air ﬂow patterns under α=1.0 and s=10[mm], and the pushingforce with respect to s under α=1, respectively. From
Fig. 4(a), we can conﬁrm that the step-up responseis roughly 1[msec] while the step-down response isroughly 2[msec]. We would note that the origin of
the horizontal axis in Fig. 4(b) coincides with the laser
sensor axis. From Fig. 4(b), we can see a symmetry
shape of the ﬂow pattern with respect to the laser
sensor axis. Now, let us deﬁne w by the width of the©ISAROB 2005

12
10
8
6
4
2
0
Distance: s [mm](c) Force with respect to distanceFig. 4: Characteristics of air ﬂow

125

Width: w [mm]3.1

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Application to Fruit

Displacement [mm]3.2

As an example of application, we applied the developed sensor to estimate impedance parameters forpear. Fig. 5 shows the experimental result for pear.
We apply stepwise air jet for 200[msec] for the surfaceof pear and measure the displacement of the surface.
The real line in the ﬁgure shows the displacement. Although the displacement of a pear is only 60[µm], we
can measure such a tiny displacement. Let us supposethat we apply a force for a linear four elements model(i = 2 and m = 0 in Fig. 3(b)). We can describe theequation of motion for the model as follows:b2 ẍ(t) + b1 ẋ(t) + b0 x(t) = a1 f˙(t) + f (t)
(5)


c1 c2
c1 k2 +c2 k1
b2 = k1 +k2 , b1 = k1 +k2
k2
2
b0 = kk11+k, a1 = kc11 +c
+k2
2

0.02
0
0

0.1

0.2

0.3

0.4

0.5

0.6

k̂1 = 3800[N/m], k̂2 = 11100[N/m], ĉ1 = 3.68[Ns/m], ĉ2 = 1550[Ns/m]Fig. 5: Application to pear (R2 = 0.993)
|S|
k1
|S|

Sensitivity

1.5

|S|c1
|S|

1.5

c2

k2

1

1

0.5

0.5

0
0.1

0.15

0.2

0.25

0
0.1

0.3

Time [sec]

0.15

0.2

0.25

0.3

Time [sec]Fig. 6: Sensitivity of the eatimated parameters

Stiffness: k^1 [N/m]

4000

3000

2000

1000

0

0

2

4

6

8

Days

Fig. 7: Time history of stiﬀness of pear

References[1] S. Omata and Y. Terunuma, “New tactile sensor likethe human hand and its applications”, Sensors and Actuators A, Vol.35, pp.9–15, 1992.
[2] Y. Yoshitoshi and M. Nagasaka, “Viscoelasetic Properties of Edematous Skin”, Japanese Heart Journal,
Vol.3, No.3, pp.220–230, 1962.
[3] H. Oka and T. Irie, “Mechanical impedance of layered
tissue”, Medical Progress through Technology, Vol.21,
pp.1–4, 1997.
[4] H. Shinoda and H. Yamasaki, “Noncontact sensing of
surface hardness”, J. of SICE, Vol.27, No.7, pp.749–
754, 1991.
[5] TOPCON MEDICAL SYSTEMS, INC., Non-contact
tonometer, http://www.topcon.com/medical.html.
[6] M. Kaneko and T. Kawahara, “Co-axis type noncontact impedance sensor”, In Proc. of the IEEE Int.
Conf. on Robotics and Automation, pp.709–714, 2004.
[7] B. W. Bolch and C. J. Huang, Development of themultivariate analysis, Morikita Publishing Company,
pp.133–139, 1976.
[8] K. Nonami, H. Nishimura, A foundation for the control
theory, Tokyo Denki University Press, pp.94–95, 1998.

Concluding Remarks

We discussed the developed non-contact impedance
sensor capable of estimating the local impedance parameters without any contact. We precisely explained
the sensing method. As an application example, wetook pear and showed that impedance parameters
can be nicely evaluated by utilizing the four-elements
impedance model.

©ISAROB 2005

0.04

Time [sec]This model is conventionally well known that it approximates the action of viscoelasticity, such as foodand human skin. By using this model, we estimate k̂1 ,k̂2 , ĉ1 , and ĉ2 , as indicated in Fig. 5. The displacement
reproduced by these parameters is also expressed withthe dashed line in Fig. 5. In this case, R2 is higher
2
than the given Rth(= 0.950). Fig. 6 shows the computed sensitivity S between 100[msec] and 300[msec]
in Fig. 5. From these results, |S|k1 is even larger than
|S|k2 , |S|c1 and |S|c2 . Furthermore, we pay our attention to a time history of the sensitivity of |S|c1 . Fig. 6means that the parameter c1 contributes to the response at the initial phase, for example up to 20[msec].
From the viewpoint of good estimation of c1 , we shouldchoose the time interval in the range of 20[msec]. On
the other hand, we need an appropriate time interval
for obtaining suﬃcient number of data for estimation.
There exists a compromise time interval for satisfying
both requirements. We have done this experiment every two days for ﬁve pears. Fig. 7 shows the change ofk̂1 of the pear. Through this experiment, we observe
how stiﬀness changes every two days. In six days after
we start this experiment, stiﬀness is changes as shownin Fig. 7. We found that the taste becomes good aftersix days we start this experiment. We can say this surface stiﬀness provides us with a good hint for judging
the time for eating.

4

measuredestimated

0.06

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A View-Based Navigation System for Autonomous Robots
Chandima Pathirana
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

Abstractbe, e.g., movement decisions connecting the views, ormere adjacencies.
Motivated by the ﬁndings of vertebrate ethology,
researchers have started to investigate topological representations for robot navigation. These systems rely
primarily on local sonar patterns for the identiﬁcationof places, in combination with metric knowledge derived from compasses or wheel encoders. Bachelder
and Waxman [8] have reported results on a visionbased topological system which uses a neural control architecture and object recognition techniques forlandmark detection. In their current implementation,
however, the system has to rely on artiﬁcially illuminated landmarks and a pre-programmed path duringexploration of the environment. For maze-like environments, Schölkopf and Mallot [7] have shown thatlearning a graph of views and movement decisions is
suﬃcient to generate various forms of navigation behavior known from rodents. The scheme has subsequently been implemented in a mobile robot [7].
The purpose of the present study is to extend theview graph approach from the mazes of [7] to open environments. In doing so, we present a navigation system that uses purely topological information based on
visual input. By focusing on just one type of information we want to make the contribution of topologicalknowledge explicit.

We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local
views of the surrounding scene, and ﬁnds traversable
paths between them. The set of recorded views andtheir connections are combined into a graph modelof the environment. To navigate between views connected in the graph, we employ a homing strategy
inspired by ﬁndings of insect ethology. In robot experiments, complex visual exploration and navigation
tasks can thus be performed without using metric information.

1

Introduction

To survive in unpredictable and sometimes hostileenvironments animals have developed powerful strategies to ﬁnd back to their shelter or to a previously visited food source. Successful navigation behaviour canalready be achieved using simple reactive mechanisms
such as association of landmarks with movements [1]or tracking of environmental features [2]. However, forcomplex navigation tasks extending beyond the current sensory horizon, some form of spatial representation is necessary. Higher vertebrates appear to construct representations—sometimes referred to as cognitive maps—which encode spatial relations between
relevant locations in their environment [3, 4].
Under certain conditions, such maps can be acquired visually without any metric information. Humans, for instance, are able to navigate in unknown
environments after presentation of sequences of connected views [5]. This has led to the concept of a viewgraph as a minimum representation required to explain experimentally observed navigation competences[7]. A view graph is deﬁned as a topological representation consisting of local views and their spatial relations. Depending on the task, these relations can©ISAROB 2005

2
2.1

Learning View Graphs
Discrete Representation of Continuous SpaceIn view-based navigation tasks, visual information
is used to guide an agent through space. The reason
why this is feasible at all, is the fact that there is acontinuous mapping between position space (x- andy-coordinates, possibly supplemented by gaze directions) and the space of all possible views: for each spatial position, a certain view is perceived, and this view

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changes continuously as the observer moves around inspace. Unfortunately, this mapping can be singular,
because identical views can occur at diﬀerent spatial
locations, i.e., there is no guarantee for the existenceof a global coordinate system on the manifold of all
possible views. In principle, this problem can be dealt
with using context information: In points with identical views, we can use views from nearby spatial positions to disambiguate between them.
It is suﬃcient to store views which allow the description of relevant paths. This leads to a less detailed representation of the view manifold, namely bya graph consisting of representative views and connections between them.
Since open environments do not impose a structureon the view graph, we have to select a set of views
which are representative for the manifold (in the following referred to as snapshots), and to ﬁnd connections between them. Since the connecting paths between the snapshots are not explicitly coded in theview graph, we have to provide a homing method
which allows us to ﬁnd connected views from a given
start view.
In the following sections, we introduce a systemthat is able to solve these tasks. The vertices of theacquired view graph are panoramic views of the environment, and their edges are connections between
views that can be traversed using a visual homing procedure. This homing procedure allows the system toapproach a location from any direction such that thegraph edges denote mere adjacency relations without
any directional labelling. The resulting view graphdoes not contain any explicit metric information.

2.2

Move in exploration direction
No

Current view
sufficiently
distinct?

No

Current viewsimilar to known
vertices?

Yes

Yes
Home to vertex

Route learning
Vertexfound?

No

Yes
Choose next
exploration direction

Connect it to lastvertex

Exploration

Edge verification

Figure 1: Block diagram of the graph learning algorithm
fulﬁlled, the system takes a new snapshot and adds itto the graph. The classiﬁer is described in Section 2.3.
2.2.2

Route Learning

In this system, a new snapshot is automaticallyconnected to the previously recorded vertex of theview graph. Thus, the system records chains of snapshots, or routes. These routes can be used to ﬁnd a
way back to the start position by homing to each intermediate snapshot in inverted order. We describe thehoming procedure in Section 2.4. The area from whicha speciﬁc snapshot can be reached by the homing procedure is called its catchment area. The view classiﬁer
has to make sure that every snapshot can be reachedfrom its neighbour, i.e., all vertices of the view graphhave to be in the catchment areas of their adjacentvertices.

A Minimal System for Learning aView Graph

View Classifier

As we mentioned above, a crucial component of any
graph learning scheme is the selection of vertices. The
graph vertices have to be distinguishable, because otherwise the representation could not be used for ﬁnding
a speciﬁc location. Since we conﬁne our system to use
only visual information, we must guarantee that therecorded views are suﬃciently distinct. This can be
performed by a classiﬁer which detects whether theincoming views can be distinguished from the alreadystored vertices of the view graph. If this condition is©ISAROB 2005

View classifier

Take snapshots, connect
it to predecessor

The overall architecture of the system is shown in
Fig. 1. Here, we discuss the basic building blocks, thedetails are described in the following sections.
2.2.1

Record non-edge

Start new graph

2.2.3

Choice of Exploration Direction

When the system has taken a new snapshot, a new
exploration direction must be chosen. This choice primarily aﬀects the exploration strategy of this system,
because it determines how thoroughly an area is explored and how fast the explored area grows. In Sec-128

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tion 2.5, we describe several local exploration strategies used in this system.
2.2.4

The robot is not allowed to take snapshots as longas the obstacle avoidance system is active. The resulting graph structure tends to concentrate in the open
space between obstacles. This feature makes the navigation system more eﬀective, because the visual input changes very rapidly near objects. Exploration ofthese areas would require a large number of snapshots
which, in complex natural environments, would ultimately lead to a fractal graph structure near objects.

Edge Verification

The route learning procedure described above has no
way of forming new edges to previously visited views,
i.e., the resulting graphs will be mere chains. By
adding the following behaviour we can obtain nontrivial graphs: during exploration, the system constantly
checks whether the current view becomes similar to thealready recorded snapshots. This again is a view classiﬁcation task which can be solved by the same classiﬁer as used for the selection of the snapshots (see Section 2.3). In a second step, the system checks whether
the detected snapshot is not yet connected to the vertex from which the current exploration step started.
Whenever these conditions hold, the system tries tohome to the snapshot. If successful, an edge connecting the two vertices is included, and the exploration
continues from the detected snapshot. In cases where
the robot gets lost or bumps into obstacles, the system reports a “non-edge” between both vertices thus
preventing the failed action from being repeated. Before starting to home, the veriﬁcation procedure always checks whether a “non-edge” for this action has
already been recorded. After a failed veriﬁcation, we
start a newgraph, which will typically get connectedto the old one in due course by the edge veriﬁcation
procedure.
If an already connected view is encountered during
an exploration step, the system homes to it as well(notshown in Fig. 1). This procedure does not produce
additional knowledge, but has the eﬀect that edges
intersecting previously stored edges are less likely tobe recorded.
2.2.5

2.3

Ideally, the set of snapshots taken to represent theview manifold should satisfy three criteria: ﬁrst, theviews should be distinguishable. In purely graphbased maps, this is the only way to guarantee thatspeciﬁc vertices can be navigated to. This can beachieved by incorporating only distinct views into thegraph. Second, a large proportion of the view manifold
should be covered with a small number of vertices tokeep processing requirements small. Third, the spatial
distance of neighbouring views should be small enoughto allow reliable homing between them.
As we conﬁne this system to use only visual input,
the selection of the snapshots must be based on thecurrent view and the stored snapshots. The above
criteria can be satisﬁed by measuring the degree ofsimilarity between views: dissimilar views are distinguishable by deﬁnition while being distant on the view
manifold, and similar views often are spatially close.
Clearly, a threshold of classiﬁer can also be used
to detect whether the current view becomes similar
to one of the already recorded snapshots. If the image
distance to a snapshot falls below the threshold, the robot starts its edge veriﬁcation procedure (as stated in
Section 2.2) and tries to home to the snapshot. In thissystem, we use the same classiﬁer for both tasks. A
suitable threshold can be determined experimentally.

Arbitration and Obstacle Avoidance

2.4

Since the focus of this work is on navigation, any sophisticated obstacle avoidance systems are not necessarily employed into this system. During exploration,kind of simple sensors like infrared sensors can be used
for the presence of nearby objects. If obstacles weredetected at distances larger than 1 cm, the robot ismade to turn away without slowing down. Smaller
distances can be interpreted as collisions causing therobot to back up and turn away from the obstacle.
Both behaviors and the graph learning system of Fig. 1
are combined into a subsumption architecture where
the collision-induced escape behavior had highest, thegraph learning procedure lowest priority.

©ISAROB 2005

View Classifier

Navigating Between Places:
View-Based Homing

In order to travel between the vertices of the viewgraph, we need a visual homing method. Since thelocation of a vertex is only encoded in the recorded
view, we have to deduce the driving direction from acomparison of the current view to the goal view. Afterthe robot has moved away from the goal, the images ofthe surrounding landmarks in the current view are displaced from their former image positions in the goalview. If the robot moves so as to reduce these displacements, it will ﬁnally ﬁnd back to the goal where
current view and snapshot match. The displacement

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ﬁeld has a focus of contraction in the goal direction.
Driving into the direction of this focus most quickly
reduces the image displacements.
A number of experiments have shown that invertebrates such as bees or ants are able to pinpoint a
location deﬁned by an array of nearby landmarks. Apparently, these insects search for their goal at places
where the retinal image forms the best match to amemorized snapshot. Cartwright and Collett [9] have
put forward the hypothesis that bees might be able toactively extract the goal direction by a homing mechanism as described above.

2.5

Local Exploration
Graph Learning

Strategies

Alternatively, one could use information about neighbouring vertices, such as their connectivity or similarity. For example, exploring areas where neighbouring
views are connected to each other would be more likely
to lead to possibly undesired edge intersections.

3

SummaryThis paper has presented the view graph learning
method that can be used for view based navigation
system of autonomous robots. This method is expected to incorporate into a real robotic system whichis expected to work only on visual inputs.

for

References

The exploration strategies used by this system havebeen motivated by the principle of maximizing knowledge gain [6]. As we have not formalized any notionof knowledge, this principle was used as a qualitative
guideline. In this context, knowledge gain is possible, for instance, through the recording of new edges
and new snapshots. In the following, we describe several exploration strategies, which concern primarily
the choice of the next direction to explore after a snapshot has been taken, or after an existing vertex has
been reached (as discussed in Section 2.2).

[1] R. Wehner, B. Michel, and P. Antonsen, “Visualnavigation in insects: Coupling of egocentric andgeocentric information,” J. Exp. Biol., vol. 199, pp.
129–140, 1996.
[2] T. S. Collett, “Insect navigation en route to thegoal: Multiple strategies for the use of landmarks,”
J. Exp. Biol., vol. 199, pp. 227–235, 1996.
[3] J. O’Keefe and L. Nadel, The Hippocampus as a
Cognitive Map, Clarendon Press: Oxford, 1978.

Exploration Direction During RouteLearning[4] R. Gallistel, The Organization of Learning, MIT
Press: Cambridge, MA, 1990.

The simplest conceivable rule is to choose a random direction and then to go straight until the next snapshot
is taken. The resulting Brownian motion pattern has
the advantage that eventually every accessible point of
the environment will be explored without the danger
that the exploring agent is caught in an inﬁnite loop.
Good results can also be achieved if one uses a ﬁxed
turning angle. Using smaller angles, distant areas arereached faster, whereas angles closer to lead to a morethorough exploration of the local neighbourhood.

[5] M. J . O’Neill, “Evaluation of a conceptual modelof architectural legibility,” Environment and Behavior, vol. 23, pp. 259–284, 1991.

2.5.1

2.5.2

[6] S. Thrun, “Exploration in active learning,” in The
Handbook of Brain Theory and Neural Networks,
M.A.Arbib(Ed.), MIT Press, pp. 381–384, 1995.
[7] B. Schölkopf and H. A. Mallot, “View-based cognitive mapping and path planning,” Adaptive Behavior, vol. 3, pp. 311–348, 1995.
[8] I. A. Bachelder and A. M. Waxman, “A view-basedneurocomputational system for relational mapmaking and navigation in visual environments,”
Robotics and Autonomous Systems, vol. 16, pp.
267–289, 1995.

Exploration of the Largest Open Angle

This navigation scheme is designed such that all vertices of the view graph remain in the catchment areas of their respective neighbours. This property canbe used to choose the next exploration direction, if avertex has already more than one edge: the systemdetermines the directions to all neighbouring vertices
using the homing procedure, and directs the next exploration step to the largest open angle between them.

©ISAROB 2005

[9] B. A. Cartwright and T. S. Collett, “Landmark
learning in bees,” J. Comp. Physiol. A, vol. 151,
pp. 521–543, 1983.

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Intelligent Vision System for Dynamic Environments
Chandima Pathirana, Keigo Watanabe, Kiyotaka Izumi (Saga University, Japan)
H.Y. Aruna Hewawasam, and Lanka Udawatta (University of Moratuwa, Sri Lanka)
E-mail: dpchandima@yahoo.com;{watanabe,izumi}@me.saga-u.ac.jp;lanka@ieee.org
Abstract

ActiveActiveImage
Image
Acquisition
Acquisition

This paper describes an intelligent vision systemthat absorbs useful information from its environmentand draws useful conclusions. This system can give theinstructions to locate vacant seats that are currently
occupying in a cinema theater. Extraction of useful
information without viewing or exposing inside detailsof an environment through an active vision system is
proposed. Reasoning based conclusions are drawn for
optimum searching. The effectiveness of the proposed
method is demonstrated using an experiment.

Feedback

Display
Display

Transmitter
Transmitter

Receiver
Receiver

Image
Image
Processing
Processing
SystemSystemFigure 1: Intelligent vision system architecture

1

Introduction

Computer vision is a new and rapidly growing field,
currently focusing on building intelligent systems. It
basically processes active image data captured by a vision system and draws intelligent conclusions. Much
accurate timely information could be obtained without human involvements. In practice, this would also
save time, avoid the occurrence of any disturbancesand enhance the security. As an example, in a multistorey building, required information of a particularfloor that is used for common seating could be displayed at other floors (see Fig. 1). In a parking, registration numbers and the entering time of the vehicles
could be recorded. Camera system would be used toobtain a view of that particular environment and thedesired data is transmitted to the location where thedata is processed in order to display the useful information. Finally, all these disciplines are needed forbuilding advanced intelligent systems [1].
Image processing plays a great role in this research
field [2] backed by artificial intelligent techniques [3] inorder to build these intelligent vision systems. Combining visual model acquisition and agent control sys-

©ISAROB 2005

tem was presented for visual space robot task specification, planning and control [4]. In [5], an evolutionary
based approach was described to develop an active vision system for dynamic feature selection with simple
neural control system.
In the present example, the system first detects objects or humans on the seats using a wide-angle image
and analyzes them for conclusions. Image data is continuously transmitting from the environment and analyzing for vacant seats using image-processing techniques. Reasoning based conclusions are drawn for
the users entering into the environment for optimum
seat searching. The recognition algorithm with image
processing tools will be used in order to analyze video
images. The experimental results show the feasibility of the system. The rest of the paper is organized
as follows: system features and methodology are described in Section 2 and Section 3, respectively. In
Section 4, analysis used for identification is presented.
Then, some experimental results are given in Section
5. Finally, concluding remarks is given in Section 6.

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Video
CameraTransmitter

Receiver

Theater hallInterface

VACANT
SEAT NUMBERS

Display

05 08 12 45 67 22
67 98 34 08

Processing
SystemFigure 2: System architecture for the experiment

2

System Overview• Connecting a camera to a connector on the frame
grabber board.

In this illustration, we use a cinema theater hall,which should be arranged in such a way that a clearly
distinguishable mark should be stick on each chair andseat locations should be unchanged after setting up
the system. Once the system is set up, seat locations
should be unchanged and if seat locations are going
to be changed the system should be set up again before using the system. If it is going to change, simple recognition mechanism can be employed in order
to apply this technique. The video camera should be
fixed at a correct elevation to get the plan view of
the theater hall. Install and configure the transmitter receiver camera system. Focus the camera, adjust
the position and correct the lighting level if requiredby previewing the video stream. Basically, the systemused for the experiment is similar to the system inFig. 2. Follow the set of instructions that comes withimage acquisition device. Setup typically involves:• Verifying that the camera is working properly byrunning the application software that came withthe camera and viewing a live video stream
The device ID is a number that the adaptor assigns
to uniquely identify each image acquisition device withwhich it can communicate. The video format specifies
the image resolution (width and height) and other aspects of the video stream. However, before starting,you might want to see a preview of the video stream tomake sure that the image is satisfactory. For example,
you might want to change the position of the camera,change the lighting, correct the focus, or make someother change to your image acquisition setup.

3

Methodology• Installing the frame grabber board in your computer.

We develop this algorithm to setup the system explained in Section 2:• Installing any software drivers required by the device.

1. Read the color image frame acquired from image
acquisition device.

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2. Convert color image to an intensity image.

Sub matrix M̂i should be selected from matrix A
such that its center element or elements should alignwith the center element relevant to the mark on seati. Dimension of M̂i should be as large as possible
subjected to the criteria that all the elements should
cover the mark area and some more space beyond themark too.
Then the matrix M̂i should be selected in a waythat Mi ⊂ M̂i and dimensions of M̂i should be (m +p) × (n + p), where p is an integer. Center element of
Mi and M̂i should be aligned with each other.
Then use the following criteria to determine
whether the seat is vacant or not

3. Resize intensity image so that the image matrix
could be viewed.
4. Suppress light structures connected to image border
5. Convert clear border image to a binary image
6. Create sub matrices Mi and M̂i (explained laterin Section 4)
7. Process and determine whether the seats are vacant or not
8. Display the results
Note here that once the system is set up, seat locations
should be unchanged and if seat locations are going to
be changed, the system should be set up again before
using the system.

IF

m X
n
X

mjk = mn

j=1 k=1

AND

m+p
X n+p
X

M̂ij 6= (n + p)(m + p)

j=1 k=1

THEN Seat is Vacant

4(1)

Image Analysis
5

The video camera should be fixed at a correct elevation to get the plan view of the theater hall. Install and configure the transmitter receiver camera system. Focus the camera, adjust the position and correct
the lighting level if required by previewing the video
stream. When nobody is seated in the hall, a color
image frame is acquired and converted to an intensity
image and then processed in order to set up the system as below. Consider intensity image matrix f (x, y)of dimension m × n




f =


f (0, 0)f (1, 0)f (0, 1)f (1, 1)f (m − 1, 0)

f (m − 1, 1)

...
...
..
.
...

f (0, n − 1)f (1, n − 1)

Figure 3 shows the resulting images according to
the algorithm explained in the previous sections. Useof three marks on the seat instead of one mark enhanced the reliability. As shown in Fig. 4(a) when
the seat is occupied by a person all three marks will
be covered and when there is an object placed on thechair such as a bag may not cover all three marks.
Use of remarkable color mark with a fine thick edge
as shown in Fig. 4(b) and suitable lighting conditions
enhanced the system.






f (m − 1, n − 1)6

Here f (x, y) was subjected to reduce overall intensity
level and to suppress structures that are lighter thantheir surroundings and that are connected to the image
border and converted to a binary matrix A.
Next, consider matrix Mi such that
Mi

⊂ A

Mi

⊂ M̂i ,

Conclusions

The system has detected objects or humans on theseats using a wide-angle image and analyzed them forconclusions. Image data was continuously transmitting from the environment and analyzing for vacantseats using image-processing techniques. The reasoning based conclusions were drawn for the users entering
into the environment for optimum seat searching
The system can be further developed and generalized for other applications. In a multi storey building,where i = 1, 2, ..., S. Here, i is the seat number and S
is the total number of seats detected by the camera.

©ISAROB 2005

Results

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original image

Step 1
Original image

original image

Step 2
Intensity image(a) Improved seat marks

Step 3
Clear border image(b) Fine thick edge marks

Figure 4: Improving the accuracy[3] S. Russell and P. Norvig, Artificial Intelligence:
A Modern Approach, New-Delhi, India, Prentice
Hall, 2003.

Step 4Isolating seats[4] M. Jagersand and R. Nelson, “On-line Estimationof Visual-Motor Models using Active Vision,”in Proc. ARPA Image Understanding Workshop,
1996.
[5] D. Marocco and D. Floreano, “Active Vision andFeature Selection in Evolutionary Behavioral Systems,” in Proc. of the Seventh International Conference on Simulation of Adaptive Behavior on
From Animals to Animats , pp. 247–255, 2002.

Figure 3: Image processing steps

required information of a particular floor that is used
for common seating could be displayed at other floors.
In a vehicle park, the registration number and the entering time of the vehicles could be recorded.

[6] S.Y. Huang, C.W. Mao, and K.S. Cheng,“A Codeword-Based Approach to Diagnose theThermal Imaging of Printed CircuitBoards,” inKnowledge-Based Intelligent Information Engineering Systems Allied Technologies, Volume 69,
N. Baba, L.C. Jain and R.J. Howlett, Eds., The
Netherlands: IOS Press, 2001, pp. 663–668.

References[1] J. Nakanishi, T. Fukuda, and D. E. Koditschek,“A Brachiating Robot Controller,” IEEE Trans.
on Robotics and Automation, vol. 16, pp. 109–
123, 2000.
[2] R.C. Gonzalez and R.E. Woods, Digital Image
Processing, Boston, MA, Addison-Wesley Longman Publishing Inc., 1992.

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Human Interaction with Binocular Vision Robots in Ubiquitous Environment
Janaka Chaminda Balasuriya
Keigo Watanabe
Kiyotaka Izumi
Department of Advanced Systems Control Engineering
Graduate School of Science and Engineering
Saga University, 1-Honjomachi, Saga 840-8502, Japan
Abstract

CentralUnit

Human interactive robotics is a fast developing area
today. There are many aspects to be fulﬁlled for arobot to be more similar to human behavior. For example, a robot with a binocular vision head needs torecognize various gestures of the humans in order to
respond to them accordingly. It is also necessary tohave a sense of environmental changes, especially ifinteraction is being made while in motion. This isachieved with the help of an overhead monitoring system. Making the robot be “ubiquitous” will be possible by manipulating Bluetooth Wireless Technology.
In order to apprehend the demand of more human like
intelligent system, this research project is planned tosearch for new solutions for better achievement.

1

Human

Figure 1: Human interaction

Introduction

2

For few years, research is going on to assist people
by ﬁnding new robotic solutions. In the near future
there may be robots in everywhere as our partners.
These ”ubiquitous” or present everywhere robots are
human interactive not only to voice commands but
also to human gestures as well (see Figure 1). In
conversation, humans are capable of getting sense ofother activities going around themselves. These include static / dynamic environment, environmental
conditions, other sounds and noises in the vicinity, etc.
But unfortunately there are human limitations as well.
For an example, they are incapable of presenting themselves in anytime, anywhere or anyway. These may
be due to human limitations or due to some natural
disasters like ﬁre, earthquakes, etc. However, it is useful to have a companion, who can represent a human.
This may include basic things like; watching, listening,understanding, communicating as well as many otherthings. An attempt to build such a human interactive
intelligent system is shown in Figure 1.

©ISAROB 2005

Robot

Binocular Vision

Humans are capable of getting a vision in breadth,
width and depth, i.e. three-dimensional view. Thishas gained humans lot of features of characterizing
the object in concern. In humans, the brain gets images from both (bi) eyes (ocular) at the same time andcombines those two images into one, to make vision.
The images that the brain gets from the eyes are however slightly diﬀerent from each other and this small
diﬀerence is used to work out how far away an object
is. This is called the depth perception. It also helps
to work out how quickly an object is moving towards
or away from a person. This is the movement perception [1]. In order to interact eﬀectively with humans,
Ubiquitous robots require such information. Some examples are, to recognize a particular object or person(as a requested by third party interactor), to clearly
identify face impressions / body gestures (while in conversation), to guidance in motion, etc.

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2.1

Object Tracking

2.3

On the other hand, if we consider the trends in modern communication techniques, video teleconferencing
over the Internet provides an arguably more realistic interface into a remote space; but it is more ofan enhancement to existing telephone communication
technology rather than a new form of communication.
With video conferencing, the participants ﬁnd themselves ﬁxed, staring almost all the time through thegaze of an immovable camera atop of someone’s computer monitor. As actions and people pass across thecamera’s ﬁeld of view, the observers are helpless to
pan and track them or follow them into another room.
In essence, here, it still lacks mobility and autonomy.
The observers cannot control what they see or hear.
Even if there are cameras in every room and the ability to switch between them is provided, the experiencewould still lack the spatial continuity of a walk arounda building.
It will always be better to deliver a more realistic
perception of physical embodiment of the user within
the remote space being explored. Such system must
immerse the user in the remote world by providing
continuity of motion and control. These would provide
the user the visual cues necessary to stitch together
the entire visual experience into a coherent picture ofa building and its occupants. It will be much advantageous and also be thoroughly appreciated if the user
is provided with the necessary means of communication and interaction with the remote world and its real
inhabitants using this new system. Furthermore, asPaulos et al. [2, 3] and Schulz et al. [4] have plannedin their research, such a system can be further developed to give access to any user in the Internet withstandard software running on currently existing computer architecture.

In order to do any of the above tasks, the primaryobjective is to obtain a clear view of the object. Thisrequires continuous gazing at the right place at theright time. In other words, it is required to focus thelenses of the vision system according to the positionchanges of an object, especially if it is moving. As
these trajectories of motion are not always simple asstraight lines, the tracking mechanism becomes complicated and advanced. For a binocular vision system,
both eyes (or cameras) should be focused separately
over the object under consideration continuously.

2.2

Fuzzy Logic System

Matlab simulation results of one such tracking program written using fuzzy reasoning, is given in Figure
2. Once received the position of the object, relative
distance to it from the current focal point is calculated. This is taken as ‘Distance Error (DE)’. In addition to above, considering the motion of the object
and the robot; rate of change of distance error (RateChangDE) is calculated. Obtaining these two values, will serve as the inputs to the Fuzzy Logic system, resulting in controlling the focal length of thelens (FocusLens). Rules such as; If DistanceError ispositive big and RateChangDE is positive big then
FocusLens is positive big, If DistanceError is negativesmall and RateChangDE is negative small then FocusLens is negative small, etc. are used. Here, forsimplicity, a straight-line motion has been considered.
But in realistic situation, the trajectories of motion
will be much more complex. An adaptive neuro-fuzzy
system will be much useful.

3

Localization and Target Tracking

Ubiquitous robots, ﬁnding its own path by means ofvisuals, sounds or any other method, have many problems due to the limitations of the sensory elements.
Robots with binocular vision system have many advantages in this regard; but there are some limitations
such as reduction in visible area [5] once the cameras
are focused to obtain a closer view of an object asshown in Figure 3. This gets worse when there aremoving objects in the surrounding area. There may bepossible collisions due to blank angles and hence necessity of a continuous environmental awareness system
arises.

Figure 2: Fuzzy object tracking©ISAROB 2005

Vision Function

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tributed cameras, ﬁnding correspondence is the key
issue as observed in Lee et al. [8]. Hence a newer,
simpler yet versatile localization and tracking system
is required.

Visible Area

3.1

It should be possible to recruit such ubiquitous
robots covering a large area. Hence to combine information gathered by one robot as well as to seek information / instruction, eﬀective and convenient communication medium is required. In other words, theserobots, while in motion, should be able to interconnect
easily to a computer network as shown in Figure 4.
Here the central unit with overhead camera monitors
the environment continuously. Shapes of the various
objects, positions at time to time, direction of motion,
velocities, etc. are observed. These data is distributed
among the ubiquitous robots in the surrounding area.
In addition to the above, the central unit gathers theinformation send by these robots and sends to the respective recipients.

Robot
Human

Figure 3: Limitation of visibility
There are platforms that estimate relevant quantities in the vicinity formed by combining information from multiple distributed sensors. For an example, robots in a team estimate their relative conﬁguration by combining the angular measurements obtained
from all of the ominidirectional images and performing triangulation operation as described by Spletzer et
al. [6]. There are other variants such as the system
proposed by Gurinaldo et al. [7] for controlling theperceptual process of two cooperative non holonomic
mobile robots by formalism called perceptual anchoring. Their system enhances the awareness of the system by employing an anchor based active gaze control
strategy to control the perceptual eﬀort according towhat is important at a given time. But such a system
is of little use or not adequate for the robots whose
main intention is to interact with humans. The situation gets critical, when such robots require to interact
with a human while in motion. To place other robot
units in the vicinity just to obtain an idea of the surrounding will be redundant and expensive. According
to Splitzer et al. [6], questions of the quality and of
how informative the gathered data are also arise, because they are obtained from individual robot units(sensors). In addition, there may be other issues likehow the robot units (sensors) should be deployed in order to maximize the quality of the estimates returnedby the team, because the robot platforms are mobile.
On the other hand, a diﬀerent set of questions arises
when one considers the problem of integrating information from a number of ﬁxed distributed sensors suchas cameras. Cost associated with transmitting andprocessing data, sensors that should be used to form
an estimation for a given time, coordination among
the sensors, automatically relating events among eachother, sensor geometry, eﬀects due to characteristic
diﬀerences, etc. are some of the problems to be solved.
In multiple video streams generated by multiple dis-

©ISAROB 2005

Networking

MovingObjectObjectMovingObjectCentralUnit
Objects

Human Interaction

Figure 4: Interconnection of robots

3.2

Bluetooth Technology

In order to achieve the above mentioned communication, Bluetooth wireless technology, which facilitates an electronic equipment to make its own connections without wires, cables or any direct humanintervention, will be very useful [9]. This type ofa system will facilitate connection and synchronization; it will control the other electronic devices suchas computers, printers, cell phones, PDA, televisions,
alarm systems and telephone systems. These are theequipment developed by more than 1200 companies

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worldwide who represent the Bluetooth Special Interest Group including the giant leaders in the Electronic/ Computer Industry like Ericson, IBM, Intel, Nokia,
Toshiba, 3COM, Microsoft, Motorola, etc. [10]. Allof these communications can take place in an ad hocmanner, without being aware and totally automatically. This gives the robots the freedom of appearing
anywhere, any time and in any way very eﬀectively.

4

Automation, vol. 2, 22–28 April 1996, pp. 1694 –1699, 1996.
[4] D. Schulz, W. Burgard, A. B. Cremers, D. Fox,
and S. Thrun, “Web interfaces for mobile robots
in public places,” IEEE Robotics and Automation
Magazine, vol. 7, no. 1, pp. 48–56, March 2000.
[5] D. J. Coomb, “Real time gaze holding in binocular robot vision,” Technical Report, University ofRochester, Dept. of Computer Science, 1992.

Summary[6] 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, pp. 676–681, May
2002.

It is diﬃcult to design a human like robot in a ubiquitous environment that caters for interaction of any
kind at the ﬁrst instance. This research project tries
to tackle such diﬃculties by manipulating some of theeﬀective systems in a more realistic way. The approach
is focused on the designing of a more human like robot
while allowing services of many other systems at thesame time. Here it tries to manipulate the services,
capabilities and advantages of other existing systems
as they are without any changes or with minimum alterations if required. But more complex interaction,
will alternatively complicate the interface in return.
This may make obtaining the so called ‘Ubiquitous’
more diﬃcult. In the ﬁrst instance, interaction may
not provide the full facilities and functionalities suchas two humans in conversation or in manipulating atask. But more general approach now will be available
for a greater diversity of interactions to be managed
in a later stage.

[7] S. A. Gurinaldo, K. Watanabe, and K. Izumi,“Enhancing awareness in cooperative robots
through perceptual anchoring,” in Proc. of the 9thInternational Symposium on Artificial Life andRobotics (AROB), vol. 2, pp. 523–526, January
2004.
[8] L. Lee, R. Romano, and G. Stein, “Monitoring
activities from multiple video streams: Establishing a common coordinate frame,” IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol.
22, no. 8, pp. 758–767, Aug. 2000.
[9] M. Miller, Discovering Bluetooth, Sybex Inc. Publications, 2001.
[10] B. R. Miller and C. Bisdikian, Bluetooth Revealed: The Insider‘s Guide to an Open Specification for Global Wireless Communication, 2nd
Edition, Prentice Hall, 2001.

References[11] C. Stauﬀer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE
Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747–757, Aug. 2000.

[1] T. Brown, Stereoscopic phenomena of light andsight: A Guide to the Practice of Stereoscopic
Photography and its Relations to Binocular Vision, Reel Three-D Enterprises Publications,
1994.

[12] T. B. Sheridan, Telerobotics, Automation and Human Supervisory Control, MIT Press, 1992.

[2] E. Paulos and J. Canny, “Space browsers: A
tool for ubiquitous tele-embodiment,” in Proc. ofInternational Conference on Computer Graphics
and Interactive Techniques (SIGGRAPH), Digital Bayou 1996.

[13] H. Monson, “Bluetooth Technology and Implications,” System Optimization Information Article,
December 14, 1999.
(http://www.sysopt.com/articles/bluetooth/)

http://www.siggraph.org/conferences/siggraph96/
core/conference/bayou/26.html[3] E. Paulos and J. Canny, “Delivering real reality to
the world wide web via telerobotics,” in Proc. ofIEEE International Conference on Robotics and©ISAROB 2005[14] http://www.bluetooth.com/news/
dex.asp?A=2&PID=1192[15] http://www.open.ac.uk/oubsfuture/Millennium/ 636.htm

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Fuzzy Coach Player Method with Shared Environmental Data
Kiyotaka Izumi, Keigo Watanabe, Yuya Tamano, and Ayumu Oshima
Dept. of Advanced Systems Control Engineering,
Graduate School of Science and Engineering, Saga University,
1-Honjomachi, Saga 840-8502, Japan
2 Fuzzy Coach Player Method

Abstract
In recent years, voice is used as an interface between
human and robot or computer. An interface between human and robot, based on a fuzzy coach player method,
is discussed in this paper. The concept of a coach player
method comes from the training of athlete with his/hercoach. In general, the coach teaches a skill for the athlete by speech with fuzziness. Therefore, authors propose
a fuzzy coach player method so as to reflect a fuzzy voicecommand. Some simple examples are shown by experiments on a robot manipulator.

In order to smoothly realize a cooperative operation orwork between a human and a robot, some intelligent exchanges need to be found between them. In particular,when a human issues “an operation command” through anatural spoken-language command to a robot, an ambiguous (or fuzzy) expression is always contained in the humanconversation. Through the exchange of the knowledge ofa human and a robot, especially through operator’s voicecommands and the observation of robot behaviors, a seriesof actions in which an operator makes a robot realize a desired behavior or motion is similar to the relation betweena coach and a player who are doing the coaching of skill
acquisition and performance improvement.
In this case, the following three points should be noted:

1 Introduction
In general, speech or voice is used as a major communication method between humans. Now, we are consideringwith teaching using voice for robots, because the communication by voice has several advantages. As for the advantages that one uses voice to an interface, it is easily pointed
out that 1) there are few burdens to users because we need
not necessarily remember the special knowledge and operations on input devices such as keyboards and mouse; 2) itis an available interface to the man who has a handicap inhand and foot, old people, and the man whose both hands
are closed by some manual operation; and 3) since it is asociable interface ordinarily utilized by human beings, we
can have an attached heart to a controlled object.
From the above backgrounds, attention has gatheredalso in the research of a robot control by voice commands[1]∼[4]. However, in the conventional research of a robot
control by voice, it is difficult to apply for an actual task,
because a user has to command by voice repeatedly withobserving carefully the robot motion. In other words, auser provides many voice commands to the robot to perform an actual task.
In this paper, in order to achieve a cooperating between
human and robot, we construct a voice interface with afuzzy coach player method using shared environmental
data. The effectiveness of the present method is illustrated
through some simple experiments.

©ISAROB 2005

1. Intention understanding：A player incorporates the“command” well according to his performance stateand posture, and reflects it on acquiring of skill or
improving of performance, in spite of containing ambiguous expressions in an advice to the player by thecoach voice.
2. Evaluation of the player by the coach：On the other
hand, from the aspect of a coach, the coach judges
subjectively whether performance, as the image which
the coach has, and skill acquisition have been attainedby the player, and decides whether to issue the following improvement command.
3. Improvement of the motion skill by the player itself:
The player itself does not only blindly response to
the spoken-language command from a coach; rather
he usually improves his performance etc. so that theformer effort (or action) result is employed more efficiently. Furthermore, he has an ability of accumulating the knowledge that can realize a more efficient performance by him, using sometimes reading of coachintent from a simple voice command.

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2.1 Constructing a fuzzy coach player model

2.2 Representation of a relationship between thecoach voice indication and the operation input

2.1.1 Interpretation of “fuzzy indication” from acoach

In the framework of a conventional robot control, it
needs to control the joint position and velocity of the robot(or the end-effector position and orientation) using the joint
input torques, or to control the velocity of the end-effector
position and orientation using the joint input velocities.
On the contrary, in the fuzzy coach player system, it
needs to find the followings: For example, the control inputs for a manipulator consist of motion indication commands that are very vague and are composed of a verb
and adverb (or multiple adverbs, or adverb phrase), suchas “move more quickly,” “move to the right more slowly,”“lower an elbow more and move,” “move as it is,” etc.,
which are different from the conventional input torques thatare taken within a real-number region.
Therefore, let the input sequence of a fuzzy human voice
indication from a microphone at time k be v(k) ∈ V, andthe v(k) passed through a speech recognizer is assumed to
be split into the language variable va (k) ∈ V, which is notnecessary for the action indication, the verb vb (k) ∈ V, andthe adverb (or adverb phrase) vab (k) ∈ V,

The voice indication of a coach is based on his experiences
and consists of action (verb) plus action modification (adverb) or repetition of action.

2.1.2 Subjective evaluation of a coach
After giving a voice indication, a confirmation (through a
direct observation or indirect observation) is performed on
the motion of a player and the subjective evaluation basedon the past experience of a coach is performed to the motion of the player. For example, an index of degree-ofsatisfaction of the coach is introduced.

2.1.3 Improvement commands after evaluation
If the player motion is satisfactory based on the coachdata-base, then any further improvement commands are
not given to the motion. Otherwise, a further improvement
command should be provided.

v(k) = va (k) + vb (k) + vab (k)where V denotes the voice space that is a time-series ofsignal or character level. A fuzzy NN is used to generate adesired velocity command u(k) ∈ <m (or torque command
τ(k) ∈ <m ) to the robot, from the effective action indication
language variables vb (k) and vab (k).
For example, we consider the method of changing multiple action FNNs. In this method, an action i is selected
and switched out of N action modules, according to theconsistence with the current pseudo sentence such as

2.1.4 Introduction of a sub-coach
We consider the introduction of a virtual sub-coach who
can support the role of a coach and reduce the load of thecoach, by using a data-base composed of various intelligent
information that could be acquired from the indirect vision
through any display and camera and the voice uttered fromthe coach, i.e., using a set of sequential data composed of
the subjective evaluation and the improvement commandto the player. After the sub-coach obtains the behavior andintelligence of the coach by any way, the sub-coach (i.e.,
computer) and the coach (i.e., operator) control a robot ina cooperative manner.

u(k) =φi (vb (k), vab (k))FNNi (si (k), vab (k))

i=1where FNN i (·) : < × < 7→ <m and it is assumed that(
1 if {vb (k), vab (k)} is consistent with i-th action
φi =0 otherwise
Note here that if there exists no inconsistence between theaction and the action modification languages in the voicecommand uttered from a coach, then the above judgmentof φi (k) can be implemented by only using the informationof vb (k).

2.1.5 Autonomous action modification of player
If there exist multiple commands of action modificationfrom the coach, or repetition of the same commands, thenit may be need to set a function that can suddenly increaseor decrease the amount of the action modification, which isnormally based on the current state of the player. Namely,
by predicting the irritation of the coach, we need to makea function that can cope with a more action modification
than the usual increment (or reduction) for the next step.

©ISAROB 2005

NX

2.3 Observation of robot stateConsidering the general dynamical model of a robot, it
follows thatf ( q̈(t), q(t), q(t)) = τ(t)140The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Display

where q(t) ∈ <n is the generalized coordinate vector andf (·) : <n × <n × <n 7→ <m is a vector-valued function
representing the robot dynamics. Instead of measuring thejoint angles and velocities as the robot states, the coach(i.e., operator) generally observes the velocity of the endeffector or the elbow that is the overall and ambiguous information for the state and orientation of the robot. Therefore, it is assumed that

Image
Dynamic

StaticInitialization
Objectcandidates

Voice command
Color
Human

Image
processing

Voice
processing

Position

Generationof behavior
command

PA-10 Camera

Direction or Sizezh (k) = Md ( q̇(k), q(k))

Derivation of voice commandfor the observation of the robot state through direct humaneyes andFigure 1: Example of fuzzy coach-player model withshared environmental datazid (k) = Mid ( q̇(k), q(k))
for the displayed information of the robot, respectively,
where zh (k), zid (k) ∈ < p and Md (·), Mid (·) : <n × <n 7→< p . Moreover, the observation through human eyes for the
displayed information is assumed to be

3 Example of Fuzzy Coach Player System
The construction of proposed voice interface with vision is shown in Fig. 1. The proposed system is constructed with the derivation part of object candidates andthe generation part of behavior command. At first, a usercommand by voice is given for object’s color at first. The
environmental data is initialized with each geometric center of object candidates and the commanded color. The
initial object is determined as the object which has minimum distance between the initial position of manipulator’s
tip and the geometric center of the object. Next, if a usercommand is given for direction, then the derivation part ofobject candidates outputs the commanded direction to thegeneration part of behavior command. Finally, the generation part of behavior command provides the reference pointof the manipulator’s tip in the world coordinate depending
on direction command and the environmental data.
The derivation part of object candidates is constructedwith the voice processing part and the image processing
part. The voice processing part is constructed with thevoice recognition process, the morphology analysis and thepattern matching. The image processing creates the environmental data for the generation part of behavior command. The image process is carried out at initial stage or atchanging the color of the object. The derivation part of object candidates outputs the command or the environmental
data.
Derivation part of behavioral command provides a reference point of the manipulator using all geometric centersof object candidates and the voice command related to thedirection or size. A user observes the working environment by this image in which the left image is a movie inthe current working environment and the right image is thestatic image when the system was started. As shown in
Fig. 2, when a user commands “red”, right image shows +zhd (k) = Mhd ( q̇(k), q(k))
where zhd (k) ∈ < p and Mhd (·) : <n × <n 7→ < p .
For example, if we focus on the motions of the endeffector and the elbow for a robot manipulator with sevendegrees-of-freedom, then we can set p = 6 and zh (t) =[zThtip zThelb ]T , where the first three-dimensional vector
zhtip = [z1htip z2htip z3htip ]T denotes each subjective andfuzzy (interpretation) variable for the position, velocity
and orientation of the end-effector (e.g., the position is almost good; the velocity is a little late; and the orientationis generally good) and the latter three-dimensional vector
zhelb = [z1helb z2helb z3helb ]T similarly denotes the information of the elbow. In addition, observation vectors zid (t)and zhd (t) are defined similarly.

2.4 Generation of voice commands for the next
step operation through a human subjective
evaluation
After observing the fuzzy position, velocity and orientation of the robot end-effector and elbow through direct eyes
or display, and evaluating such a motion with the subjective
criterion of the coach, the voice command v(t) for the next
step operation is assumed to be generated byv(k) = BF(zh (k)) or v(k) = BF(zhd (k))
where BF(·) : < p 7→ V is a criterion, for realizing a desired robot motion, in which the coach (or human) has been
assumed to have it in advance.

©ISAROB 2005

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Figure 2: Initial movement with voice command “red”

Figure 4: Result of voice command “a little small”

4 Conclusions
The concept of a fuzzy coach player method is discussed
to smoothly realize a cooperative operation or work between a human and a robot, some intelligent exchanges
need to be found between them. The environmental data isshared with image view to understand easy a robot movement. Simple experiment is carried out, so that it was easy
to change the robot movement by voice command at any
time.

References

Figure 3: Result of voice command “move right”[1] K. Komiya, K. Moita, K. Kakegawa, and K. Kurosu,“Guidance of a Wheelchair by Voice,” Proc. of
IECON’2000, pp. 102–107, 2000.

or  on each geometric center of red colored objects, where+ means the target object, whereas  means the red coloredobjects except for the target object. A user can observe thecommand and the position of the target object at the image
coordinate through the bottom area of the image. It is foundfrom Fig. 2 that the manipulator moves to the target object.
The user confirms the desired object from + position in theright image and the displayed data at the bottom area asshown in Fig. 2. Next, when the user commands “moveto left”, the target object is changed, and the robot move tothe left side target. When the user commands “move to up
and small object”, it is found from Fig. 3 that the user canperform the same fact as stated in Fig. 2. Finally, the usercommands “move to smaller object” after completing the
Fig. 3. The user selects a new target object on the manipulator’s way to the former target object. The user can checkthe change of the target object in the right image and the
movement of the manipulator in the left movie, as shownin Fig. 4.

©ISAROB 2005

[2] K. Pulasinghe, K. Watanabe, K. Kiguchi, and K. Izumi,“A Novel Modular Fuzzy Neural Controller Driven by
Natural Language Commands,” Proc. of the 40th SICE
Annual Conference (SICE2001), Int. Session Papers,
312C-4, 2001.
[3] K. Pulasinghe, K. Watanabe, K. Kiguchi and K. Izumi,“Modular Fuzzy Neural Controller Driven by VoiceCommands,” Proc. of ICCAS2001, pp. 194–197, 2001.
[4] C.T. Lin and M.C. Kan, “Adaptive Fuzzy Command
Acquisition with Reinforcement Learning,” IEEE
Trans. on Fuzzy Systems, Vol. 6, No. 1, pp. 102–121,
1998.

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Cost Function Analysis of Optimizing Fuzzy Energy Regions in Control of UnderactuatedManipulators
Kiyotaka Izumi, Keisuke Ichida, and Keigo Watanabe
Dept. of Advanced Systems Control Engineering,
Graduate School of Science and Engineering, Saga University,
1-Honjomachi, Saga 840-8502, Japan
Abstract

Y

Underactuated manipulators have to be controlled byfollowing a restricted way that the number of actuators is
less than the number of generalized coordinates, because
they have some passive joints.
In this paper, we propose a logic based switching mechanism using fuzzy energy region. Boundary curves to separate an energy region into some energy subregions areconstructed by fuzzy reasoning. Therefore, fuzzy relatedparameters are optimized by genetic algorithm (GA). The
present method is applied to an underactuated system withdrift term such as an 2-DOF planar manipulator which
has only one active joint. The effectiveness of the present
method is illustrated with some simulations.

I 2 , m2

µ2

θ2

lg 2

l2

I1, m1
τ1

θ1

µ1

l g1

X

l1

Figure 1: Model of 2-link underactuated manipulator

1 Introductionof GA. The present method is applied to an underactuatedsystem with drift term such as an 2-DOF planar manipulator which has only one active joint. The effectiveness ofthe present method is illustrated with some simulations.

Control of underactuated manipulators is an attractive
research theme in robotics because of complex behaviors
and difficulty of control [1, 2, 3]. Since underactuated manipulators have some passive joints, their energy efficiency
can be better than full actuated manipulators.
As a control method for underactuated systems, authors
have already proposed a switching control, in which somepartly stable controllers were designed by computed torquemethod and the switching low was obtained as the index ofcontroller directly by fuzzy reasoning [4]. The switching
control is proposed to design simply a control low without any complex variable transformation for underactuatedmanipulators. The switching low is a key-point to obtainsufficient results in this method. A logic based switching
method using energy regions [5] is also proposed for a nonholonomic system without drift term.
In this paper, we propose a logic based switching mechanism using fuzzy energy region. Boundary curves to separate an energy region into some energy subregions are constructed by fuzzy reasoning. Fuzzy related parameters andcontrol gains of the partly stable controllers are optimized
by genetic algorithm (GA). We prepare some cost functions©ISAROB 2005

2 Underactuated Manipulator
Figure 1 shows a two-link underactuated manipulator,
in which the second joint is constructed of a passive joint.
Here, τ1 denotes the applying torque of 1st joint, θi denotes
the angle of ith link, mi denotes the mass of ith link, lgi denotes the distance from the joint to the center of mass of
ith link, Ii denotes the moment of inertia of ith link, andµi denotes the coefficient of viscous friction. The dynamical model of the underactuated manipulator is given as follows:
M (θ) θ̈ + h θ, θ̇ = τ
(1)
where
θ
τ

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E2

E2

π2

E2 = EE2 b E1
1aE2 = EE2 a E1
1a

E2b

π1
R1

π1

E2 a

0

π2

E1

0

(a)

E1a

E1

E2

π2

R2

Figure 3: Approximation of regions for a logical switchingπ1−

M12 (θ)
τ1
D

(3)

where
0

2
(θ)
D = M11 (θ) M22 (θ) − M12

E1

Here, it is found that we can design partly stable controllers
for link 1 and link 2 using the computed torque method.
The controller 1 to stabilize the link 1 is given by(b)
Figure 2: Subregions of energy"
M (θ)
M11 (θ)M12 (θ)M22 (θ)h θ, θ̇h1 θ, θ̇h2 θ, θ̇=

M11 (θ)M12 (θ)M12 (θ)M22 (θ)τ1#

2
2+ I2 )+ m2 l12 + I1 ) + (m2 lg2= (m1 lg2
+2m2 l1 lg2 cos θ2
2+ I2 ) + m2 l1 lg2 cos θ2= (m2 lg2

θ̈1∗

2
+ I2
= m2 lg2
h 
 
iT
= h1 θ, θ̇ h2 θ, θ̇

τ1= m2 l1 lg2 θ̇12 sin θ2 + µ2 θ̇2

θ̈2∗

=

©ISAROB 2005


 M12 (θ) 
M22 (θ) 
h2 θ, θ̇h1 θ, θ̇ +D
D
M22 (θ)τ1+
D
 M11 (θ) 
M12 (θ) 
h2 θ, θ̇h1 θ, θ̇ −
D
D

(4)

=


M12 (θ) D
h1 θ, θ̇
θ̈2∗ −
D
M12 (θ)!
M11 (θ) 
h2 θ, θ̇+Dθ̈d2 + Kv2 θ̇d2 − θ̇2 + K p2 (θd2 − θ2 )−(5)

where the desired vector of θ is defined as θd = [θd1 θd2 ]T ,in which the proportional gain of the controller i is K pi andthe derivative gain of the controller i is Kvi .

3.2 Logic based switching method
Energy of each link is defined by

Equation (1) can be described byθ̈2== −(m2 l1 lg2 )(2θ̇1 θ̇2 + θ̇22 ) sin θ2 + µ1 θ̇1

3.1 Partly stable controller==
M22 (θ) D
h1 θ, θ̇
θ̈1∗ +
D
M22 (θ)!
M12 (θ)h2 θ, θ̇−Dθ̈d1 + Kv1 θ̇d1 − θ̇1 + K p1 (θd1 − θ1 )and the controller 2 to stabilize the link 2 is given by

3 Fuzzy Region Based Switching Control

θ̈1

=

4

Ei = e2i + ė2i ,

−

with

(2)
ei = θdi − θiėi = θ̇di − θ̇i

144i = 1, 2(6)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005µ( E2 )

1.0

S

M

Table 1: GA operations and methods
GA operations
MethodSelection for crossover Tournament strategy with
3 individuals
Crossover
Uniform crossover withprobability 0.6
Probability of mutation 1/96
Alternation
Elite strategy with
10 individuals

B

0.5

0

E2 a
E
E1a 1

E2 b
E
E1a 1

E2

Figure 4: Membership functions for E1 ≤ E1a
Table 2: Setting parameters of simulations
Conditions
Setting value
Simulation time
30 [s]
Sampling interval
0.01 [s]Mass of each link
m1 = 0.582 [kg],m2 = 0.079 [kg]Length of each link
l1 = 0.4 [m], l2 = 0.22 [m]Distance between center
lg1 = 0.2 [m]of gravity and each joint lg2 = 0.11 [m]Coefficient of viscousµ1 = 0 [Ns/m2 ]friction of each jointµ2 = 0.02 [Ns/m2 ]Desired state vector[0 0 0 0]T
1st initial state vector[0 π/4 0 0]T
2nd initial state vector[π π/6 0 0]Tµ( E2 )

1.0

S

M

B

0.5

0

E2 a

E2b

E2

Figure 5: Membership functions for E1 > E1a

Energy plane is composed of Ei as shown in Fig. 2.
In Fig. 2, πi is a boundary curve which determines thesubregion of energy to use a partly stable controller, and isplotted by an exponential curve. The region Ri with grayshadow is the subregion to which the controller i is applied.

Note that, a parameter φt−1 which means the index of controller for one-step delay, is introduced, because one-stepdelayed controller must be retained in the overlapped energy region according to ideal energy response. si is theindex of controller that must be used in the fuzzy rule i.
The advantage of the present method is to set design parameters roughly, comparing to the logic based switching
method, because the boundary curves have fuzziness to use
the present fuzzy reasoning.

3.3 Fuzzy energy region method
If a boundary curve comprises an exponential function,
we can suitably design it with the amplitude and the time
constant of a step-response. It is difficult to design such parameters of the function in advance, because we can’t theoretically analyze them depending on the switching control.
Therefore, we propose a fuzzy energy region based switching control method. At first, boundary curves are approximated by several straight-lines as shown in Fig. 3. Afterthese approximations, fuzzy sets for E2 can be defined forE1 ≤ E1a and E1 > E1a cases as shown in Fig. 4 and Fig.
5. E1a , E2a and E2b are design parameters of fuzzy sets.
In order to realize ideal energy responses, fuzzy rules are
given as follows:
Rule 1
Rule 2Rule 3Rule 4::::©ISAROB 2005

If E2 = S
If E2 = M
If E2 = M
If E2 = B

Then s1 = 1
and φt−1 = 1
and φt−1 = 2
Then s4 = 2

4 Optimizing Fuzzy Energy Regions by GA
The present method has the same difficulty to design
parameters in advance as the logic based switching method.
Here, we discuss about the design parameters of fuzzy rules
using GA. These parameters are E1a , E2a , E2b , K p1 , Kv1 ,K p2 and Kv2 . Each parameter is encoded by 32 [bit], thenthe size of an individual is 224 [bit]. The searching domainof E1a , E2a and E2b is set from 0.1 to 15. The searchingdomain of K pi and Kvi , i = 1, 2 is set from 0.01 to 100.
Each parameter is decoded using gray code. The size of a
population is 100. The maximum number of generations is1000. GA operations used here are shown in Table 1.

Then s2 = 1
Then s3 = 2

145

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

20

Link angles [rad]10Fitness

θ1
θ2

Cs=2501
Cs=2001
Cs=1501

−5

10

10

0

−2

−10

−4

0

200
Generations

0400

5 Conclusions

Table 3: Obtained design parameters of fuzzy energy region method
E1a
E2a
E2b
K p1
Kv1
K p2
Kv2

C s = 2001
12.69890.297814.98125.20485.74132.609650.748130

Figure 7: Simulation result with untrained initial value

Figure 6: Optimizing history of cost functions

C s = 2501
11.50970.290612.4336
10.5593
11.56475.240056.333110
20Time t [s]We have proposed a logic based switching method using
fuzzy energy region, in which fuzzy design parameters andgains of partly stable controllers were trained by genetic
algorithm. A cost function was tried to use in optimizing
parameters. Obtained differential gains are a little bit high,so that a simulation result using untrained initial states hasa steady state error. However, each link converges near thedesired value.

C s = 1501
14.0461
1.04178.111910.4885
11.041124.2865
4.0119References
A cost function is given by
fc =[1] Y. Nakamura, T. Suzuki, and M. Koinuma, “Nonlinear
behavior and control of a nonholonomic free-joint manipulator,” IEEE Trans. on Robotics and Automation,
vol. 13, no.6, pp. 853–862, 1997.

2 30002
X
XX

Ek ( j)(7)

i=1 j=C s k=1

where i is the index of simulation trials, j is the index of
discrete times, k is the index of energy of each link andC s is the starting index of discrete time to evaluate the response of an underactuated manipulator. Simulation conditions to train fuzzy parameters are shown in Table 2. Note
that, the fitness function is not evaluated during a transition
segment due to the dynamic characteristics of an underactuated system.
Training history in cost function is shown in Fig. 6. It
is found from Fig. 6 that the case of C s = 2501 is smaller
than other cases. Obtained parameters are shown in Table
3. The obtained parameters in the case of C s = 2501 areapplied to the cases of untrained initial state vectors suchas θ(0) = [π/4 − π/4 0 0]T . Response of link angles isshown by Fig. 7. The small steady state error is found inθ1 from Fig. 7. Differential gain of partly stable controllers
is high, so that a response has such error.

©ISAROB 2005

[2] H. Arai, K. Tanie, and N. Shiroma, “Nonholonomic
control of a three-DOF planar underactuated manipulator,” IEEE Trans. on Robotics and Automation, 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.
[4] L. Udawatta, K. Watanabe, K. Izumi, and K. Kiguchi,“Control of underactuated manipulators using fuzzy
logic based switching controller,” J. of Intelligent andRobotic Systems, vol. 38, pp. 155–173, 2003.
[5] J. P. Hespanha and A. S. Morse, “Stabilization of nonholonomic integrators via logic-based switching,” Automatica, vol. 35, pp. 385–393, 1999.

146

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The Hybrid SOF-PID Controller for a MIMO Biped Robot
Tae-Yong Choi and So-Yeon 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
ermzace@odyssey.kaist.ac.kr, arashi@odyssey.kaist.ac.kr, jjlee@ee.kaist.ac.kr
Abstract

method because only one kind of parameters, proportional gains, can be tuned using his method. So in
here I will suggest the hybrid SOF-PID to adjust all
three kinds of PID gains. Though the hybrid SOF-PID
can tune all of three PID gains, in experiments actually proportional gains and differential gains are tuned
because PD controller is enough to control nonlinear
MIMO robot.
Section 2 describes the structure of SOF-PID controller suggested by Kazemian. Section 3 describes the
structure of Hybrid SOF-PID controller. Section 4 explains the experimental results of MIMO Biped robot’s
trajectory following and compares the results with the
SOF-PID. Finally Section 5 discusses and concludes
the contribution of this work.

The application of the hybrid Self-Organizing
Fuzzy PID controller to a multi input multi output
nonlinear biped robot is studied in this paper. The
SOF-PID controller was initially studied by H. B.
Kazemian on his papers, [1], [2] and etc. Actually
his SOF-PID controller has limits. The supervisoryof the SOF-PID controller can adjust only a kindsof parameters. So, In here the hybrid SOF-PID
controller are introduced to tune some kinds ofparameters and tested on a MIMO biped robot. In
experiment the hybrid SOF-PID controller shows
better performances than the SOF-PID.
Keywords : self-organizing fuzzy, biped, pid controller.

12

Introduction

Over the past few decades many industrial
processes have been controlled using PID. Despite ofextensive use of PID in conventional control problems, its performance in industrial application is limited. For instance in case of step input problem, it
can’t show the best performance. For fast response
you must suffer bad overshoot property. Even if you
succeeded in tuning PID gain to show fine overshootand ripple you must have some steady state error. In
this reason a few replacement algorithm were appeared
but It is not necessary to discard an existing controller
such as conventional PID, which works well and is already in operation with proper performance even if it
is not best. Supervisory controller which can adopt
the conventional PID controller is enough and can begood solution.
Kazemian studied the SOF-PID to adjust PID controller which is applied to complex nonlinear systemand showed fine performances[1],[2],[3]. He tuned only
proportional gains. For other gains of differential
gains and Integral gains he used Ziegler-Nichols tuning©ISAROB 2005

Basic structure of the SOF-PID controller

The block diagram of Fig.1 shows the basic structure of the SOF-PID controller. This diagram shows
the SOF at a supervisory level readjusting the PID
gains at an actuator level. An error from the actuator
level is fed into the supervisory level to enable the SOF
to analyse the process output. There is also an input
from the PID controller block to the history of paststates block via the PID input section to continuously
fuzzify the values of the PID gains. Finally the SOF
adjust the PID gains during operation and feeds theresults from the output section block into the actuator
block. Detail of the SOF block is followed.
• error input section : error and error change aredefined as (1), (2) for each.
error(ei ) = setpoint(si ) − system output(poi ) (1)error change(cei ) = error(ei−1 ) − error(ei )(2)where i is sampling instant. The fuzzification of
the error and error change are also done in thisblock.
147

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

for sampling instant i the following equations.
KP l (reinf orcer) = KP l−n[i] + P(3)

where i is the sampling instant, n represents number of samples before the present sample, and l isnumber of links or robot parameters. KP l−n[i] isthe fuzzified proportional PID gain from the history of past states block.
• rule base : The rule base contains all the appropriate PID gains to be used in the supervisory
controller block. The rules are the exact control
rule strategy which have been obtained from thelearning section of the master controller.

Figure 1: The SOF-PID Controller• Inference mechanism : From this block output isproduced by combining the ei , cei and the rules
from rule base block. Implication function anddefuzzification take place in here.

• PID input section : The PID gains are fuzzified
in this section• Performance index table : The performance index
table is designed as such that if no errors are happened, then no action is taken and the PID gain
correction value P is equal to zero. If errors are
happened. then PID gain correction P is chosen.
Using the equations (1)and (2) a set of thirteen
linguistic rules are produced, Table1, and fromthis a performance index table is obtained.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

• output section : The output section provides anon fuzzy input signal to be fed into the PID controller. In here the fuzzy signal is dequantised anddescaled.
KP (af ter−apps) = KP (bef ore−apps) +UP i ×K1 (4)KD(af ter−apps) = KD(bef ore−apps) +UDi ×K2 (5)KI(af ter−apps) = KI(bef ore−apps) + UIi × K3 (6)If E is NL and CE is NL or NM or NS then P is ZO.
If E is NL and CE is ZO or PS or PM or PL then P is NL.
If E is NM and CE is NL or NM or NS then P is ZO.
If E is NM and CE is ZO or PS or PM or PL then P is NM.
If E is NS and CE is NL or NM or NS then P is ZO.
If E is NS and CE is ZO or PS or PM or PL then P is NS.
If E is ZO and CE is NL or NM or NS or ZO or PS or PM or PL then
P is ZO.
If E is PS and CE is NB or NM or NS or ZO then P is PS.
If E is PS and CE is PS or PM or PL then P is ZO.
If E is PM and CE is NB or NM or NS or ZO then P is PM.
If E is PM and CE is PS or PM or PL then P is ZO.
If E is PL and CE is NB or NM or NS or ZO then P is PL.
If E is PL and CE is PS or PM or PL then P is ZO.

where KP , KI , KD represent the PID gains.
The left is prior to changes by the supervisorylevel and the right is after the change takenplace. K1 , K2 , K3 are the descaled coefficients.
UP i , UDi , UIi are the output from supervisory
controller block.

Table 1: Linguistic rules of The SOF-PIDC

3

NL : -3 or -2.5, NM : -2 or -1.5, NS : -1 or -0.5
ZO : 0, PS : 0.5 or 1, PM : 1.5 or 2, PL : 2.5 or 3

Basic structure of the hybrid SOF-PID controller
is same as the SOF-PID controller except the hybrid structure to provide another PID gains, differential gains. In the previous work only proportional
gains are provided by the SOF and UDi , UIi is directly
valued by Ziegler-Nichols tuning method : UDi =2UP i /T o, UIi = UP i × T o/8, where T o is the oscillation period. In the hybrid SOF-PID controller UDi arealso provided by the hybrid SOF with relation to UP i .
I aimed to control a MIMO biped robot system and
the PD controller is enough to control a biped robot.
In this reason integral parameters aren’t considered.

• History of past states : A storage for the fuzzfied
values of PID gains. These past states could be
retrieved on the basis of first-in and first-out.
• Rule reinforcer : This block update the existing
rules and to create new ones. If the PID gain
correction P is zero from the performance index
table, then no rule modification are taken. If P
is not zero, then the rule modification is required©ISAROB 2005

The structure of the hybrid SOFPID controller

148

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

1. If E is NL and CE is NL or NM or NS and Upi is NL or NM or NS thenD is ZO.
2. If E is NL and CE is NL or NM or NS and Upi is ZO or PS or PM or
PL then D is ZO.
3. If E is NL and CE is ZO or PS or PM or PL and Upi is NL or NM or
NS then D is NL.
4. If E is NL and CE is ZO or PS or PM or PL and Upi is ZO or PS or PM
or PL then D is NL.
5. If E is NM and CE is NL or NM or NS and Upi is NL or NM or NS thenD is ZO.
6. If E is NM and CE is NL or NM or NS and Upi is ZO or PS or PM or
PL then D is ZO.
7. If E is NM and CE is ZO or PS or PM or PL and Upi is NL or NM or
NS then D is NM.
8. If E is NM and CE is ZO or PS or PM or PL and Upi is ZO or PS or
PM or PL then D is NM.
9. If E is NS and CE is NL or NM or NS and Upi is NL or NM or NS thenD is ZO.

Figure 2: The Hybrid SOF-PID Controller
The detailed block diagram is shown in Fig2. The differential gain update part is for differential parametersof PID control. The different with proportional gain
update part is that UP i is used to readjust UDi . Linguistic rule is shown, Table2. The proposed controller
aims to be used on on-line but the hybrid SOF-PID
controller needs more computing power than the SOFPID of previous works. In experiments sampling timeis set to more longer than the SOF-PID to reduce computing time.

4

10.

If E is NS and CE is NL or NM or NS and Upi is ZO or PS or PM or PLthen D is ZO.

11.

If E is NS and CE is ZO or PS or PM or PL and Upi is NL or NM or NS
then D is NS.

12.

If E is NS and CE is ZO or PS or PM or PL and Upi is ZO or PS or PM
or PL then D is NS.

13.

If E is ZO and CE is NL or NM or NS or ZO or PS or PM or PL and
Upi is NL or NM or NS or ZO or PS or PM or PL then D is ZO.

14.

If E is PS and CE is NB or NM or NS or ZO and Upi is NL or NM or
NS then D is PS.

15.

If E is PS and CE is NB or NM or NS or ZO and Upi is ZO or PS or
PM or PL then D is PS.

16.

If E is PS and CE is PS or PM or PL and Upi is NL or NM or NS thenD is ZO.

17.

If E is PS and CE is PS or PM or PL and Upi is ZO or PS or PM or PLthen D is ZO.

18.

If E is PM and CE is NB or NM or NS or ZO and Upi is NL or NM or
NS then D is PM.

19.

If E is PM and CE is NB or NM or NS or ZO and Upi is ZO or PS or
PM or PL then D is PM.

20.

If E is PM and CE is PS or PM or PL and Upi is NL or NM or NS thenD is ZO.

21.

If E is PM and CE is PS or PM or PL and Upi is ZO or PS or PM or
PL then D is ZO.

22.

If E is PL and CE is NB or NM or NS or ZO and Upi is NL or NM or
NS then D is PL.

23.

If E is PL and CE is NB or NM or NS or ZO and Upi is ZO or PS or
PM or PL then D is PL.

24.

If E is PL and CE is PS or PM or PL and Upi is NL or NM or NS thenD is ZO.

25.

If E is PL and CE is PS or PM or PL and Upi is ZO or PS or PM or PLthen D is ZO.

NL, NM, NS, ZO, PS, PM, PL : same as value in Table 1.

Table 2: Linguistic rules of The Hybrid SOF-PIDC

Experiment

A five linked biped robot simulator are used totest The hybrid SOF-PID cotroller and SOF-PID controller. Robot kinematics are used to control robot.
Robot joint values are described like Fig3.
α : torso angle∆β = βR − β( L) : difference of the thigh angles
γ( L) : left leg knee angleγ( R) : right leg knee angle

For four joint values, errors are compared between
the SOF-PID controller and the hybrid SOF-PID controller in Fig.4,5,6 and 7. It shows that roughly thehybrid SOF-PID controller is better than the SOFPID controller.
©ISAROB 2005

Figure 3: A MIMO Biped Robot

149

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

5

error of alpha
0.15
0.1

Both of the hybrid SOF-PID and the SOF-PID controller are tested on a MIMO biped robot simulator.
In experiment the hybrid SOF-PID controller shows
more smaller error than the SOF-PID as operatingtime goes. In effect to adjust conventional PID controller the hybrid SOF-PID can be a good solution
than the SOF-PID.
The hybrid SOF-PID controller have some drawbacks. First, it need more computing power than thethe SOF-PID to apply on on-line control problems. If
all three PID gains are tuned computing power will
be serious problem. Second it is difficult to constructperformance index table without help of experienced
human.

alpha

0.05
0
−0.05
−0.1
−0.15
−0.2

error of SOF−PID
error of HSOF−PID
0

50

100

150

200

250

300

350

400

Conclusion

450

time
error of delta beta
0.2
0.15
0.1

delta beta

0.05
0References

−0.05
−0.1
−0.15
−0.2

[1] H. B. Kazemian, “The SOF-PID Controller for theControl of a MIMO Robot Arm,” IEEE Trans. Fuzzy
Syst.,Vol. 10, pp. 523-532, Aug 2002.

error of SOF−PID
error of HSOF−PID
0

50

100

150

200

250

300

350

400

450

time

[2] H. B. Kazemian, “Comparative study of a learning
fuzzy PID controller and a self-tuning controller,” ISA
Transactions,Vol. 40, pp. 245-253, 2001.

error of gamma L
0.25
0.2
0.15

[3] H. B. Kazemian, “The self organizing fuzzy PID controller,” Proc. IEEE World Congr. Computational Intelligent, Anchorage, AK, May 1998, pp. 319-324.

gamma L

0.1
0.05
0
−0.05

[4] R. K. Mudi and N. R. Pal, “A robust self-tuning
scheme for PI- and PD-type fuzzy controllers,” IEEE
Trans. Fuzzy Syst.,Vol. 7, pp. 2-16, Feb 1999.

−0.1
−0.15
−0.2
−0.25

error of SOF−PID
error of HSOF−PID
0

50

100

150

200

250

300

350

400

[5] Franck Plestan, “Stable Walking of a 7-DOF Biped
Robot,” IEEE Trans. Robotics and Automation.,Vol.
19, pp. 653-668, Aug 2003.

450

time
error of gamma R
0.25[6] Zhe Tang, “Trajectory Planning for smooth Transitionof a Biped Robot,” Proc. IEEE International Conf.
Robotics and Automation, Taiped, Taiwan,Sep 2003,
pp. 2455-2460.

0.2
0.15

gamma R

0.1
0.05
0

[7] Daiki Ito, “An Approach to Generation of Smooth
Walking Pattern for Biped Robot,” AMC, Maribor,Slovenia, 2002, pp. 98-103.

−0.05
−0.1
−0.15
−0.2
−0.25

[8] T. Furuta, “Design and construction of a series ofcompact humanoid robots and development of biped
walk control strategies,” Robotics and Autonomous
Systems,Vol. 37, pp. 81-100, 2001.

error of SOF−PID
error of HSOF−PID
0

50

100

150

200

250

300

350

400

450

time

Figure 4: Error of α[9] G. F. Franklin and J. D. Powell, em Digital Control ofDynamic Systems., Addison-Wesley, 1990.

Figure 5: Error of ∆β
Figure 6: Error of left γ
Figure 7: Error of right γ©ISAROB 2005

150

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Strategy of Cooperative Behaviors for Distributed Autonomous Robotic Systems
Han-Ul Yoon, Se-Hee Whang, Dae-Wook Kim, and Kwee-Bo Sim
School of Electrical and Electronics Engineering
Chung-Ang University
221 Heukseok-Dong, Dongjak-Gu, Seoul, 156-756 Korea
e-mail: huyoon@wm.cau.ac.kr, kbsim@cau.ac.kr(frame), vision system, sensor system, drive (motor) system,
communication system, and main controllers. There aremany ways, which depends on the main task of robot andwhich part of robot will be specially intellectualized, to apply
these systems efficiently [2]. For cooperative works, theKhepera can be the role model of design. It consists of a mainprocessor (Motorola 68331), driven by two d.c. motors, andhas eight infra-red proximity sensors around its body (55mm
diameter, and 30mm height) [3]. This robot still being used
for in many fields like fuzzy control, wall following, obstacle
avoidance. We take similar appearance with Khepera or
RoboSot (one model of the soccer robots) for our robot.
In this paper, we organize DARS to perform the pursuit
competition with one-fugitive robot versus multi-detective
robot. The paper introduce about our robot system and its
functional block component in chapter 2. We present the taskof our DARS, the area-based decision making algorithm, andexperimental results in chapter 3. The paper concludes the
subject and issues future works in chapter4.

Abstract
In this paper, we present the strategy of distributed autonomous robotic systems (DARS) for cooperative behaviors.
The DARS are the systems which consist of multiple
autonomous robotic agents into which required function is
distributed. For building of DARS, the agents can recognize
the environment where they are located, communicate eachother, and generate some rules to act by themselves. In thepaper, we introduce our DARS robot to perform cooperative
behavior which is the task of our research − the pursuit
competition with one-fugitive robot versus multi-detective
robot. The paper also presents the area-based decision
making algorithm to determine the direction of the robot
maneuver.
Keywords: DARS, camera vision, sensor, motor, bluetoothcommunication, main MCU, area-based decision making
algorithm, fugitive and detective robot

2. The Autonomous Robot for DARS
1. Introduction
Nowadays, the robots replace the human working in thefields of rescue a life at highly destroyed building by fire orgas contaminated place, getting some information in the deepsea or the space, and watching the weather condition at theextremely cold area like the Antarctica. Especially, we needto penetrate multiple robots to get more trustful and robust
information data from hardly accessible area, such an ant’s
nest under the ground. In this case multiple robots send thedata by cooperation and communication each other and makea decision to act by themselves.
Distributed autonomous robotic systems (DARS) havebeen focused by many researchers as a new way to control
the multi-agents more flexibly and robustly. The DARS are
the systems being organized by multiple autonomous robotic
agents into which required function is distributed. The mostunique and important feature of DARS is that each system isa distributed system composed of multiple agents (robots) [1].
According to this feature, the subject of DARS can bevarious widely. DARS are now applied to multi-robot behavior, distributed control, coordinated control, and cooperative operation, etc.
Typical mobile robot systems consist of robot body
©ISAROB 2005

Our robot system consists of four sub-parts and a main
micro-controller part. The sub-parts are camera vision,
sensor, motor, and bluetooth communication, respectively.
Each sub-part has its own controller to perform a uniquefunction more efficiently. The main micro-controller part
controls four sub-parts to avoid process collision and performs decision making by the data of its sub-parts.

2.1. Camera vision
The camera, which we use for the robot, is Movicam II
made by Kyosera. The Movicam II is the CCD camera being
used for SKY cellular phone. Its size is 30×47×29 mm (width× height × thick) and weight is 12g approximately. A frame
consists of header, image data, and end maker. Fig. 1 showscamera appearance and the data components of a frame indetail.
When clock is applied to the clock-port (port #2), ti starts
to send the data at rising clock from the header to the end
maker bit by bit. The data-out port (port #1) of camera is
attached to DSP (Digital Signal Processor) TMS320LF2407A which is programmed to perform signal processing.
The size of image data is rather bigger to wait the end of pro-151

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Figure 1. Camera and the data component of a frame
Figure 4. Torque characteristic (left), motor block diagram (right).
Table 1. The relationship of signals and actions.

Signals
0x00
0x01
0x02
0x03
0x04
0x05

Figure 2. The connection between camera and DSP (left), the datatransfer timing (right).

cess with whole image(153,600 byte). Accordingly, we optimize the program to cramp the image data within 25,000
byte. The connection between DSP and camera is showed inFig. 2(left). The data transfer is presented in Fig. 2(right).

frequency-current characteristic curve. By the figure, themaximum self-operating frequency is 600 pps. We assemble
the driver with L297 and SLA7024A combination to control
two motors. Fig. 4(right) and Table 1 present the block diagram and the signals associated actions pair of robot by decision making.

2.2. Sensor
The robot has six of infra-red (IR) sensor pairs (emitter/
detector) to measure the distance around itself. The emitter is
Kodenshi EL-1kl3, high-power GaAs IR mounted in durable,
hermetically sealed TO-18 metal package. The detector is
ST-1kla, high-sensitivity NPN silicon phototransistors
mounted in durable, and the package is same with the emitter’s. The six IR sensors are placed with 60 degree angle ofeach other, so they can cover whole 360 degree.
We chose the emitter, which has narrow beam angle(about 17°), to avoid interference. The detector, however,has almost 50° beam angle, therefore it can detect most of IR
reflection by the object. The appearance is showed in Fig. 3(left). The arrangement, the area where can be covered by thesix of sensor pairs, and the block diagram are depicted in Fig.
3 (right).

2.3. Motor
As driving part, we use NMB PG25L-024 stepping motor.
Its characteristics are drive voltage-12V, drive method 2-2phase, and 0.495° step angle, etc. Fig. 4(left) shows torque-

2.4. Bluetooth communication
Blutooth communication was developed to use for mobile device, which includes battery source, with the motto‘low -cost,’ ‘low-power,’ and ‘compact.’ For these characteristics, bluetooth regard as very suitable for wirelesscommunication system [4].
Bluetooth fundamentally organizes its network with 1
master to 7 slave architecture. The PicoNet is the net which
consists of one master and multiple slaves. The ScatterNet isthe net organized by PicoNets. Fig. 5 depicts the concept of
PicoNet and ScatterNet.
In DARS, there is no pre-defined classification likemaster and slave. All agent robots should be distributed individually. We’d like to apply, however, the concept ofmaster/slave only for communication. The detective robots
need to communicate each other only when they catch the
fugitive robot in their vision. Therefore it can be said that
“We are still in the DARS.”

Figure 3. El-1ka3 and ST-1kla (left), sensor block diagram (right).
©ISAROB 2005

Actions
ForwardRotate 60° clockwise
Rotate 120° clockwise
Turn around (rotate 180°)
Rotate 60° counter clockwise
Rotate 120° counter clockwise

Figure 5. The PicoNet and the ScatterNet diagram.
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2.5. Main Controller

300

250

We design the system as every sub-part has its own controller. Therefore, main controller has little overhead. The
function of the main controller is control the UART Tx/Rx
communication between main controller and sub-controller.
It also generates the rules of next action, change moving
direction or camera vision on/off or send the data to otherrobot, by the sub-parts’ data. Fig. 6 is the block diagram ofwhole robot system connected with main controller and theappearance of our robot.

200

In DARS, the best role model for cooperative behavior isthe ants’ cooperation. When the ants find a food which ismuch bigger than an ant itself, they get around by the foodand start to cooperation. Similarly, the robot cooperationmeans operating multiple robots cooperatively. Therefore,
the communication between robots is essential.
In our system, the fugitive robot must run away from thepursuit of multiple detective robots. To perform their own
task (i.e. escaping and tracking), both the fugitive and thedetective robot recognize the surrounding by their sensors,and generate the rule by recognizing the situation. In thischapter we discuss with two issues − the distance measurement and the area-based decision making to generate rules
and change direction, and show the experimental results.

100

50

0
1

+ offset

5

7

9

11

13

15

17

19

21

23

25

27

29

31

To change the direction of running, we can consider twoalgorithms − the distance-based and the area-based algorithm.
In the distance-based decision algorithm, the fugitive
robot considers the direction which a sensor returns thelongest distance as the safest direction. Thus, the robot takes
the following steps to determine the next direction.
• The robot gets 6 of A/D conv. value from detect sensor.
• The robot decides the direction which returns the longest distance as safe way without any obstacles.
• The robot changes the direction.
In the area-base decision algorithm, however, the robot
follows these steps,• The robot gets 6 A/D conv. value from detect sensor.
• The robot calculates the 6 areas with each A/D conv.
value from detect sensor.

The robots must know its situation by measuring the
distance around itself. In Fig. 7, dashed line presents the
distance-A/D converting value curve. It is expected to haveinversely proportional form rather than linear or hyperbola.
The approximation of this fomula is

A / D conv. value

3

Figure 7. Distance to A/D conv. value curve by equation (1).

3.1. Distance measurement

Distance =Approximation

3.2. Area-based decision algorithm

3. Cooperative Behavior Example of DARSαOriginal Data

150

JG JJG
1 JJG JJG
s × s sin 60 ° , where s1 , s 2 are the2 1 2
distance measured by sensor 1 and 2.

Area =• The robot decides the direction which returns the wideest area as safe way.
• The robot changes the direction.

(1)

We set the coefficient α = 580 and offset = 7 by experiential
result. The solid line presents its approximated value curve in
Fig. 7.

In brief, we can generate the rule of fugitive robot’s behavior
with this simple concept,“Change your direction to where you can guarantee morewide space.”
The rule of the detective robots can be define easily byadding one more condition to above,“If you get the fugitive in your camera, try to occupy
more wide area in the fugitive’s plane.”This is the basic concept of area-based decision making. The
concept is similar with the behavior-based decision making
but is different to change the direction by the area [5]. Fig. 8
shows the difference between the distance-based and thearea-based decision making by example.

Figure 6. The robot and main MCU with 4 sub-parts connection.
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4. Conclusions and Future Works

Figure 8. The different results, with the same sensor values, by the
distance-based (left) and the area-based (right) decision making.

In this paper, we introduced the hardware specification of
the robot and architecture of our DARS. We also proposed
the area-based decision making algorithm and presented thetwo experimental results with the robots in the two different
situations. The result shows us that the area-based decision
making can be a new way for the obstacle avoidance.
The future works would be to complete the implementtation of the detective robots and to find out appropriate andefficient way to pursue the fugitive robot more concretely in
DARS.

3.3. Experiments
We experiment two different situations with our robots.
The results are shown in this chapter.
First, we release the robot on the hallway where no obstacle but walls. Fig. 9 shows the pictures of the robot on
running. We tested the robot’s running by 50 trials. The
robot succeeded 43 times out of 50 trials, but 7 times failed.
The reason of fails was the robot took the turnaround (turn180°) decision continuously, even if both its front and backspace are freely empty. To clear this problem, we modified
area- calculating function by the multiplying of weight, wfront= 1.2, to the calculation of front area.
Second, we try for the robot to explore the hallway where
three obstacles are located. Fig. 10 depicts the robot’s running in the second situation. The result shows the robot
found the next direction more easily when it is surrounded byother obstacles. At the 1st and the 3rd turns, however, therobot has many other choices to avoid the 1st and the 3rd obstacle. It is the main reason that the malfunction (i.e. back to
the 1st obstacle when tries to avoid the 3rd) occurs several
times. By the reason, the robot succeeded its task 31 times
out of 50 trials.

Acknowledgement
This research was supported by the Brain-tech 21 from
Ministry of Science and Technology of Korea.

References[1] H. Asama, T. Fukuda, T. Arai, et al, Distributed
autonomous robotic systems, Springer-Verlag, 1994.
[2] J. H. Kim, H. S. Sim, S. H. Kim, et al, Robot soccer
engineering, Dooyang, 2003.
[3] S. H. Lian, “Fuzzy logic control of an obstacle avoidance robot,” Proc. of the Fifth IEEE International
Conference on Fuzzy Systems, vol. 1, pp.26-30, 1996.
[4] S. H. Whang, K. B. Sim, I. C. Jeong, et al, “Design ofefficient strategies for distributed multi-agent robot
soccer system,” FIRA Robot Congress, pp. 1-5, 2004.
[5] W. Ashley, S. Balch, and T. Balch, “Value-based observation with robot teams (VBORT) using probabilistic techniques,” Proc. of ICAR 2003, vol. 1, pp. 1-8,
2003.

Figure 9. The robot is running in the hallway.

Figure 10. The robot is running through the 3 obstacles.
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Development of the Tactile Sensor System Using Fiber Bragg Grating 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
and sensitivity, they are not flexible and don’t havesufficient durability[1-4]. In this paper, we will show theflexible optical fiber force sensor which can be the basisof the tactile sensor. This force sensor has very simple
structure

Abstract
This paper shows the development of flexible force
sensor using the fiber Bragg grating. This force sensor
consists of a Bragg grating fiber and flexible silicone
rubber. This sensor does not have special structure to
maximize the deflection or elongation, but have goodsensitivity and very flexible characteristics. In addition,
this sensor has the immunity to the electro magnetic fieldand can be multiplexed easily, which is inherited fromthe characteristics of fiber Bragg grating sensor. In the
future, this sensor can be utilized the tactile sensor
system minimizing the sensor size and developing the
fabrication method.

2. The Structure of the Flexible FBG Sensor
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
Many force sensors based on the strain gages, until
now, have been used mainly to monitor the durability ofbridges, buildings from the view point of safety, andcontrol some material test machines and industry robots,and so on. However, recently, as some system is now
small and need high sensitivity and accuracy, a newforce sensor with small size and high sensitivity, notsome conventional load cells, has been required tocontrol small force accurately. On the other hand,
MEMS shows the possibility of development of microforce sensor similar to the pressure sensor. Especially,some researchers have tried to develop tactile sensor
combined small force sensors for intelligent robotics,
teleoperational manipulators and haptic interfaces. Thesetactile sensors can detect normal forces on the taxel for
gripping force control and generating tactile images forobject recognition. However, in addition to acquiring
tactile images and normal forces, detecting tangential
forces is also critical. The fabrication process of sensor isvery simple. This paper shows the development of threecomponent force sensor based on the fiber optic sensors
Until now, a few tactile sensors on behalf of the humanskin have been developed compared with the other kindsof sensors, such as, image sensors and sound sensors.
Because the tactile sensors have some requirements toadapt the practical engineering field, such as humanoid
robot system and telerobotic system and so on. Thesesensors must have the flexibility and must have a good
spatial resolution mimicking the human skin. Some
researchers tried to make the tactile sensors using theMEMS(Micro Electric-Mechanical System) technology.
Although these sensors have a good spatial resolution©ISAROB 2005

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
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and temperature can be expressed as[∆λB = λB (α f + ξ f )∆T + (1 − pe )∆εOnce the external force is applied on the silicone
rubber, the fiber Bragg grating in the silicone rubber isdeformed. The deformation of fiber Bragg gratings is
induced on the change of the Bragg wavelength.
Therefore, this sensor can detect the fore through thechange of Bragg wavelength. Fig.2 shows the flexible
characteristics of the prototype sensor.

] (2)⎛ n2 ⎞
pe = ⎜⎜ ⎟⎟[ p12 − ν ( p11 − p12 )] (3)⎝ 2 ⎠3 Design of the Flexible FBG force sensor

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

3.1 FEM Analysis of Silicone rubber
We simulated the deformation of the silicone rubber toverify the resolution of the sensor. ABAQUS 6.3 wasused and the 2-dimensional element model was applied
to the silicone rubber. As the model is symmetric, thehalf geometric model was used. And the fixed conditionon the all directions was applied on the base of themodel. And the force (1~10N) was applied through therigid ball with the diameter of 3mm to simulate the realexperimental condition.

(4)

Fig. 3 Finite Element Model and FEA (Finite Element
Analysis) of DC 184

The optical fiber was excluded from the FEM model.
Because the optical fiber have the very small diameter(250㎛) compared with the whole size of the sensor.
That is, the sensor model have only the silicone rubber
and we assumed the deformation of the silicone rubber
should be the deformation of the optical fiber. As thedeformation of the silicone rubber is very small, we used
the general elastic solver to verify the deformation[6].
The elastic modulus of the silicone rubber is 9.2MPa andthe Poission’s ratio is 0.49.

Fig.1 FBG sensor encoding operation

2.2 The Structure of Flexible FBG Force Sensors

Y (depth)
0.0 mm
0.3 mm
0.6 mm
0.9mm
1.2 mm
1.5 mm
1.8 mm

0.3
0.2

U1 (mm)0.1

0.1 mm
0.4 mm
0.7 mm
1.0 mm
1.3 mm
1.6 mm
1.9 mm

0.2 mm
0.5 mm
0.8 mm
1.1 mm
1.4 mm
1.7 mm
2.0 mm

0.0
-0.1
-0.2
-0.3

Fig.2 Prototype flexible FBG sensor

-0.4
0

2

4

6

8

10

12

14

16

X (mm)The sensor has the simple structure which iscomposed of FBG and silicone rubber (DC184 Dow
corning Co. Ltd). The fabrication process of this sensor
is easier than that of diaphragm type sensor. The FBG
sensor with the length of 10mm is embedded in thesilicone rubber.
©ISAROB 2005

Fig. 4 U1 distribution of DC184 according to thedepth (Load: 10N)
Fig. 4 shows the deformation of the U1 direction (theaxial direction of the optical fiber). If the optical fiber is
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located on the surface of the silicone rubber, positive
deformation is induced by the transverse load. As the
optical fiber is deeper inside, the positive deformation isappeared. And the positive deformation is in the whole
optical fiber above the 1.5mm deep inside the silicone
rubber.

Fig 6 shows the calculated the change of Bragg
wavelength according to the applied force. The fiber
Bragg grating sensor located in the 2mm depth has theabout 1.0 ×10−3 nm / gf sensitivity.

4. Experimental Results

3.2 Determination of the Depth of FBG sensors

4.1 Experimental Equipment

Using the results of the FEA, the efficient depth of the
optical fiber can be determined. The large deformation is
occurred in 1~4mm depth. And the output signal, thechange of the Bragg wavelength can be easily influenced
in 1~4mm depth. If the optical fiber is located on thesurface of the silicone rubber, the output is most efficient,but the fiber can be easily broken because of the brittle
characteristics of the optical fiber. Fig. 5 shows that thepositive and concentrated deformation was occurred in
the 2mm depth of the silicone rubber. Therefore, the
2mm depth was determined as the prototype flexible
force sensor.
1N
2N
3N
4N
5N
6N
7N
8N
9N
10 N

0.10
0.22

0.05

0.20
0.18

0.00

0.16
0.14

1N
2N
3N
4N
5N
6N
7N
8N
9N
10 N

-0.10
-0.15
-0.20
-0.25
-0.30
-0.35

0.12

U1 (mm)

U1 (mm)-0.05

Fig. 7 shows the experimental setup. The broadband
light source having the 1527~1602nm wavelength isincident in the optical fiber and the light is separated by
the 2 by 1 coupler. The separated light is transmitted in
the Bragg grating. And the reflected light is experienced
the applied load is send the optical spectrum analyzer(OSA). Therefore we can know the applied force
through the Bragg wavelength change of the reflected
light. And the applied load is measured by the load celllike Fig.7.

0.10
0.08
0.06
0.04
0.02
0.00
-0.02
-0.04
-0.06

0

2

4

6

8

10

12

14

16

0

2

4

6

8

0 mm depth

12

14

16

1mm depth

0.32
0.30
0.28
0.26
0.24
0.22
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
-0.02

1N
2N
3N
4N
5N
6N
7N
8N
9N
10 N

1N
2N
3N
4N
5N
6N
7N
8N
9N
10 N

0.35
0.30
0.25
0.20

U1 (mm)

U1 (mm)10

X (mm)X (mm)

0.15
0.10
0.05
0.00
-0.05

0

2

4

6

8

10

12

14

16

0

2

4

6

8

10

12

14

X (mm)X (mm)

2 mm depth

4mm depth

16Fig. 7 Experimental setup

Fig. 5 U1 distribution of DC184 according to the load

4.2 Evaluation of flexible FBG force sensors
1.0

1.4

1 mm
2 mm
4 mm
6 mm

1.3
1.2
1.1

0.8

1.0

0.6

0.9
0.8

∆λΒ (nm)∆λΒ(nm)TEST 1TEST 2TEST 3
Linear fit
y=0.001x-0.01091

0.7
0.6

0.4

0.5

0.2

0.4
0.3
0.2

0.0

0.1
0.0
0

2

4

6

8

10

0

Force (N)

200

400

600

800

1000

Force (gf)Fig. 6 Bragg wavelength shift analysis using FEM

Fig. 8 Shifted wavelength according to the applied force of
flexible FBG force sensor (depth 2mm)

Using the equation (4) and the results of FEA, we cancalculate the change of Bragg wavelength. The
photoelastic constant of the used optical fiber is 0.22[7].

Three flexible fiber Bragg grating force sensors were
fabricated and tested in the same environment. Three©ISAROB 2005

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sensors have the same sensitivity, about 1.0 ×10−3 nm / gf
which is the same resolution of the simulated results.
This means that the 1.0nm Bragg wavelength waschanged by the applied force, 1000gf. As OSA can detect
the 0.01nm wavelength, these sensors can detect at least
10gf (0.1N).

[7] Udd, E., “Fiber optic smart structure”, John Willey andSons, 1995

5. Conclusion
In this paper, the flexible fiber Bragg grating force
sensor was introduced and verified. The experimental
results mated the simulated results by the FEA. The
sensors are very flexible and can detect the absolute
strain. And these sensors can attach the arbitrary surface.
This characteristic is suitable to the artificial skin and thetactile sensors. The resolution of theses sensors is about
10gf (0.1N) and will be improved by the ability of the
OSA or interrogation system of the fiber Bragg grating.
Now these sensors cannot have the good spatial
resolution to adapt the tactile sensors. In the future, thelength of the FBG with the 1~5mm, the sensors can be
minimized, that is, the spatial resolution of the sensor
would be improved.

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 Science and Technology of Korea.

Reference
[1]

[2]

[3]

[4]

[5]

[6]

Mei, T., Li, W.J., Ge, Y., Chen, Y., Ni, L. and Chan, M.H.,“An integrated MEMS three dimensional tactile sensor
with large force range”, Sensors and Actuators (A), Vol.
80, pp155-162, 2000
Wang, L. and Beebe, D.J., “A silicon shear force sensor:
development and characterization”, Sensors andActuators (A), Vol. 84, pp33-44, 2000
Kim, J. H., Kang, D. I., Park, Y. K., “Research Trends on
the Development of Micro Force Sensor and Its
Application”, J. of KSPE, Vol. 19, No. 3, pp. 7~12, 2002
Yoon, M. J., Yu, K. H., Jeong, G. Y., Lee, S. C., Kwon, T.
G., “Development of a Distributed Flexible Tactile
Sensor System”, J. of KSPE, Vol. 19, No. 1, pp. 212~218,
2002
Kenaeth O. Hill and Gerald Meltz, “Fiber Bragg grating
technology: fundamentals and overview”, Journal ofLightwave Technology, Vol. 15, No.8, August 1997
Yang, L.M., Shim, V.P.W., Lim, C.T., “A viscohyperelastic approach to modeling the constitutive
behavior of rubber.”, International Journal of Impact
Engineering Vol. 24, pp545~560, 2000

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A Distributed Precedence Queue Mechanism to Assign
Efficient Bandwidth in CAN Networks
Ho-Seek Choi, Jang-Myung Lee
Department of Electronics Engineering, Pusan National University
San 30 Jangjeon-dong Kumjeong-ku, Busan, 609-735, Korea
chs_chs@hanmail.net, jmlee@pusan.ac.kr
period of time. This paper presents a mechanism that cancreate a fair transmission chance and can reduce delay
time [7,8,9] using a distributed precedence queue, andassigning a precedence queue to relatively low priority andobjects having similar transmission purposes when a
network is overloaded, and which can compensate amaximum tolerance delay time and to remove
ineffectiveness for an identifier assigned statically into an
overload condition [10].
This precedence queue is not assigned statically but
assigned dynamically According to transmission quantity,
so that the transmission efficiency can be optimized in
network. And each queue can independently assign
transmission sequences of data of a relative priority.
So, this paper can contribute to the mechanism that cantransmit data within a constant time to adjust its priority
dynamically based on an extended CAN protocol when alow priority object delays transmission because of an
overload in a network.
Identifier is assigned statically in the CAN protocol, thetwo requirements of a fair transmission chance and delaytime, can not be satisfied because this solve collision
problem by a static identifier. In this paper it is shown thatthe problem can be solved collision by the filtering of
input frames according to the identifier of each object andby redefining the identifier in the identifier field. By
redefinition of the distributed precedence queue (DPQ) to
use the identifier field of the extended CAN, each object
can be transmitted according to a fair transmission
sequence and can thus satisfy the maximum tolerance
delay time.

Abstract
This paper presents a distributed precedence queue
mechanism to resolve unexpected transmission delay of alower priority transaction in a CAN based system, whichkeeps a fixed priority in data transactions. The mechanism
is implemented in the upper sub-layer of the data link layer(DLL), which is fully compatible with the original medium
access control layer protocol of CAN. Thus the mechanism
can be implemented dynamically while the datatransactions are going on without any hardware
modification. The CAN protocol was originally developed
to be used in the automotive industry and it was recently
applied for a broader class of automated factories. Even
though CAN is able to satisfy most of real-time
requirements found in automated environments, it is not toenforce either a fair subdivision of the network bandwidthamong the stations or a satisfactory distribution of theaccess delays in message transmissions. The proposedsolution provides a superset of the CAN logical link layer
control, which can coexist with the older CAN
applications. Through the real experiments, effectivenessof the proposed mechanism is verified.
1. Introduction
The controller area network protocol (CAN) was
developed to solve complex cable problems and reliability
reduction in automotive [1,2]. This availability was built
network of high reliability applied various industry
environment [3,4,5].
Unlike the IEEE 802.3 standard-access-technique-based
CSMA/CD protocol[6], CAN’s medium-access control
mechanism ensures that when collision occurs a nondestructive contention-based arbitration is initiated thatstops all of the transmitting stations except the one which
is sending the frame having the highest priority. The
frames that are transmitted are not addressed to a specificdestination, but they are considered as global objects, eachof which is associated with a network-wide unique
identifier. CAN allocates absolutely priority to messages
or objects transmitted in a network using ID. Thismechanism is a good method to manage collisions in
network.
If a network is overloaded, the data transmission quantity
is rapidly decreased to increase data transmission collision.
If this state is continuous, a network may be groggy andthe state of non-transmission may continue over a long

©ISAROB 2005

2. CAN Analysis
2.1 A basic CAN protocol
The CAN is based on a CSMA/CD channel accesstechnique. It uses a priority modification mechanism fortransmitted-received messages to resolve collisions in a
network. The CAN protocol adopts a layered architecturethat is based on the OSI reference model, even though it is
not fully OSI compliant, and the architecture is composedof three layers the factory automation environment.
1.

2.
3.

The Application Layer; Support to access on a Network
The Data Link Layer; Connection physical address to the upper-low layer
The Physical Layer; Transmission bit stream to physical medium
This paper resolves the transmission delay time problem

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

using the data link layer and the only LLC sub-layer
between the MAC (Medium Access Control) and the LLC(Logical Link Control) of the data link layer.

a

b

c

A
B
C
D
E
F
G

d
A
B
D

C
D
E
F
G

E
F
G
C

Fig 2. Generation of a precedence queue in
DPQ mechanism
Fig 1. CAN arbitration phase

This distributed precedence queue protocol provides theopportunity to create precedence queues for all nodes in a
network. And, in the case that several precedence queues
exist, each precedence queue assigned a priority so thatthey can be implemented independently.
The DPQ mode ID, which is stored in the 11 bit standard
ID field shown in the Fig 4, indicates the precedence
queue order of each node. Whenever a node carries out atransmission, it moves to the end of the queue, thus
lowering its precedence to the minimum. All of the nodes
following the transmitting node advance by one position in
the queue, occupying the space that has just been created.
Using this round-robin policy, collisions among messages
are avoided.
The queue is not stored in some specific location. Instead,
it is distributed among all the nodes in the network. Eachnode is responsible for storing and updating. That is, if the
maximum permission delay time is reached, it creates a
precedence queue, and then it has to dynamically change
priorities to transmit preferentially with other nodes. And a
precedence queue has to be dissolved when is completed
an urgent task.
We suppose a network that is composed of Nodes A to G
as shown in Fig 2. If Node C builds up a queue, the ID thatis entered into the data frame queue can transmit anddesignate to 7 by lower 7 byte. At this time, it will be
designated precedence priority to higher byte. Then, eachnode filters to enter itself into the queue, and it assigns its
queue. After Node C transmits a message, it will go to thelast position in the queue. And the other nodes will move
up one position by order. And the remaining nodes that to
be transmitted are designated using the upper 1 byte as
shown in Fig 3; their queues will be dissolved or
maintained using the upper 1 byte, as shown the Fig 3 after
all transmissions are completed.

3. A Distributed Precedence Queue Mechanism (DPQ)The CAN implicitly assigns to each object exchanged in
the network a priority that corresponds to the identifier ofthe object itself. Even though this mechanism enforces adeterministic arbitration that is able to resolve any conflictthat occurs when several nodes start transmitting at thesame time, it is clearly unfair. If many nodes are connectedin the network, nodes that are of low priority rank cancontinuously lose a transmission opportunity. That is, ifhigh priority objects transmit continuously, finally a low
priority object can miss an important message which isrelatively unimportant compared to that of a high priority
object.
Accordingly, a mechanism that uses a relative priority
according to the consideration of low priority nodes isnecessary although the CAN implicitly assigns a priority.
Fair behavior, which for example enforces a round-robinpolicy among different stations, has to be guaranteed to all
the objects exchanged at a given priority level.
In this paper, it is shown that this kind of behavior can beobtained by slightly modifying the frame acceptancefiltering function of the LLC sub-layer. In particular, only
the significance of the identifier field in the transmitted
frame has to be modified in some way. The resulting
arbitration mechanism is able to enforce a round-robinpolicy among the stations that want to transmit a message
on the bus, and provides two levels of priority for theframe transmission services. Little or nothing has to bechanged at the MAC level; and in this way it is possible toreuse the same electronics components developed for the
implementation of the standard CAN protocol.
3.1 DPQ principle

D LC

The basic idea of this CAN fairness control mechanismthat is to insert into a global queue all of the nodes thatwant to transmit over the shared medium. For Node C, ofwhich transmission is continuously delayed as shown in
Fig 1, a queue is created to transmit Node C and the othernodes that transmit with C. So, several queues can bepartially made in this research, two queue were used.

Q ueueentry(1 b yte)

P reced ence p rio rity (7 b yte)

C RC

8 b yte (data field )Fig 3. Structure of a data field for DPQ

3.2 DPQ Realization Method
The DPQ mechanism can be implemented without any©ISAROB 2005

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Each node used TMS320LF2407 with the CAN moduleand PCA82C251 with the CAN transceiver. Each node
was set to a 250 Kbps transmission time.
The transmission period for the total 10 nodes was set totwo states, 10ms and 2ms. When the transmission period
was 10 ms, Collisions did not often occur. But when it was
2 ms, collisions often occurred. The transmission message
priority was arranged as Node 1 (portable ECU) and node
2 (main ECU) for each transmission period and thispriority decreased gradually. When the transmission period
was 2 ms, Node 8,9,10 suffered a long transmission delay
because of message collision on the bus, and the DPQ
mode was applied to resolve this problem at Node 8,9,10.

modifications to the basic format of CAN frames. It uses
an identifier field to designate the priority queue. Because
the length of the conventional identifier field defined in the
CAN standard is too small, the CAN extended format canbe adopted.
S
O
F

S
S
R

11b itsb ase IDI
DE

18 bit ID E X T

R
T
R

r
0

r
1

D LC

Fig 4. Format of the header of extended CAN frames
The DPQ uses the first 11 bits of the identifier field for its
control information, whereas the remaining lower order 18
bits (ID ext.) are used to dynamically store the effective
identifier of the an exchanged object (EID).
S
O
F

t
0

t
1

P

S
8 bit P L S
RIRD 18 bit EID T
E
R

r
0

r
1

Table 1. Identification Definition (ID)D LC

Fig 5. Format of the header of DPQ frames
The first two bits (t0, t1) must be set at the logical valueof zero as shown in Fig 5. Then, the protocol is divided bya standard CAN communication and DPQ mechanism. So,
DPQ always has a higher priority than a CAN mechanism,and they can exist in this same space.
The priority bit P specifies whether the frame has to betransmitted as a high priority frame (P=0) or as a low
priority frame (p=1). When T1 and P are used, the priority
can be assigned a maximum 4 queues.
The next 8 bits represent the precedence level of theframe. Namely, these 8 bits show the transmission queue
order. The DPQ, which was used in this research, uses t0,
t1, and then distinguishes the standard CAN mechanism,and sets each queue using P, and concludes the precedence
in the queue using 8 bits.

node
P O R TA B LE
M A IN E C U
3s ta n d a rd C A N
11 1 0000 0001
11 1 0000 0010
11 1 0000 0011

DPQ
11 1 0000 0001
11 1 0000 0010
11 1 0000 0011

4
5
6
7
8
9
10

11 1 0000 0100
11 1 0000 0101
11 1 0000 0110
11 1 0000 0111
11 1 0000 1000
11 1 0000 1001
11 1 0000 1010

11 1 0000 0100
11 1 0000 0101
11 1 0000 0110
11 1 0000 0111
00 1 1111 1101
00 1 1111 1110
00 1 1111 1111

5. Result and Analysis
Fig 7 shows the transmission delay time of the Node 1.
From 1 to 50, the X axis values show the transmission
delay time when the transmission period was 10 ms. And
from 51 to 100, the values show the transmission delay
time when the transmission period was 2 ms. And from
101 to 150, the values show the transmission delay time
when the DPQ mode was applied.

4. System Architecture and Experiments
To verify the usefulness of the mechanism presented inthis paper, actuator ECU that are used in throttle-bodycontrollers of vehicles and portable inspection equipment
ECU that can set sensor limit values and can diagnosis
vehicle problems, established the basic nodes.

3000
2500

usec

2000
1500
1000
500
0
1

14

27

40

53

66

79

92

105 118 131 144

num berFig 6. Total system organization
Fig 7. Transmission delay time of node 1

The total system consisted of additional virtual ECU of
10 nodes used in many parts of the vehicles as inhalationfuel ECU, lighting ECU, side-mirror ECU, and exhaust
port ECU.
©ISAROB 2005

From the Fig 7 results, we know that Node 1 increased
the delay time more when the transmission period was 2
ms than when the transmission period was 10 ms. And
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005priority node does not exceed the maximum tolerance
delay time using the DPQ mode, despite frequently
occurring collisions in transmission and the rapidtransmission of each node.
But, in the case of the DPQ mode being applied to highpriority object, the effectiveness was lower than that of astandard CAN application. In future research, algorithms
will be developed to efficiently manage the time delay ofeach object, applying the DPQ mechanism dynamically.
And it will be shown how these algorithms can be applied
conveniently for compatibility with other CAN
applications.

additional delay time occurred for Node 8, 9 and 10 in
DPQ mode.
As shown in Fig 8, in the case of Node 8, the state whicha transmission period is 2ms, a longer delay time occurred
for low priority nodes than other nodes. To overcome thisproblem, we can verify that a transmission chance was
guaranteed and the delay time was advanced outstandingly,
when the DPQ mode was applied instead of changing thepriority permanently, as shown in Fig 9.
In case of experiment 2 shown in the Fig 11, the graphshows a transmission delay time. From 1 to 50, the X axis
values show a transmission delay time for the highest
priority Node 1 when the transmission period was 2 ms.
And from 51 to 100, the values are shown for that when
the node number was 10. From 101 to 150, the values areshown for when the DPQ mode was applied.

Acknowledgments
This work was supported (in part) by the Ministry of
Science & Technology, Korea, under the Region ResearchCenter University (RRCU).

3000

References[1] International Standard Organization, “Road - vehicles
Interchange of digital information - Controller area
network for high-speed communication” ISO 11898,
November, 1993.
[2] International Standard Organization, “Road - vehicles
Interchange of digital information - Controller area
network for high-speed communication” Draft
Amendment, ISO 11898:1993/DAM 1, February, 1994.
[3] CAN in AUTOMATION International Users andManufacturers Group e. V. “CAN Application Layer(CAL)”, CiA/DS201-CiA/DS205, CiA/DS207.
[4] Jin W. Park, Dong K. No, Jae H. Park, Hwa R. Hur,
Jang M. Lee, “Implementation of a Mobile Robot withDistributed Control Structure using CAN” Autumn
Combination Conference . pp 251-255.1999.
[5] Sung S. Hong, “A distributed real time control
systems,” CASE Technical Special: Real-Time Control
System (3), ICASE, 1, 1998.
[6] IEEE Standards for Local Area Networks, “Carrier
Sense Multiple Access with Collision Detection(CSMA/CD) Access Method and Physical Layer
Specifications”, ANSI/IEEE Std 802.3- ISO/DIS 8802/3,
1985.
[7] K. Tindell and A. Burns and A. Wellings, “Calculating
Controller Area Network (CAN) Message Response
Times”, in Proc. 1994 IFAC Workshop on Distributed
Computer Control Systems, Toledo, Spain, September,
1994.
[8] K. Tindell and A. Burns, “Guaranteeing Message
Latencies on Control Area Network (CAN)”, in Proc. 1
st International CAN Conference, Mainz, Germany,
September, 1994.
[9] K. Tendell, A. Burns and A. Wellings, “Analysis ofHard Real-Time Communications”, Report YCS 222,
Department of Computer Science, University of York ,to appear in Real-Time Systems, 1994.
[10] “SDS-Smart distributed system specification”
Hineysell Inc., Micro Switch Division, Phoenix, AZ, GS
052-103/104/1-5/106/107/108.

2500

usec

2000

1500

1000

500

0
1

11

21

31

41

51

61

71

81

91 101 111 121 131 141

num berFig 8. Transmission delay time of node 8
1400
1200

usec

1000
800
600
400
200
0
1

2

3

4

5

6

7

8

9

10

개체

Fig 9. Average transmission delay time of DPQ mode

6. Conclusion
This study applied the DPQ mechanism to correct the
ineffectiveness occurring according to a fixed priority
mechanism and to arbitrate collisions in a network using astandard CAN protocol. The proposed mechanism
established the availability through an experiment of two
different states.
The experiment showed that a transmission of a low©ISAROB 2005

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Adaptive Occupancy Grid Mapping With Clusters
Byoung-Gi Jang and Tae-Yong Choi 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
ee185zzang@kaist.ac.kr, ermzace@odyssey.kaist.ac.kr, jjlee@ee.kaist.ac.kr
Abstract

stereo vision, etc. However, all these sensors are subject to errors often referred to as measurement noise.
In addition, sonar sensors cover an entire cone in spaceand form a single sonar measurement it is impossible
to say where in the cone the object is. The sonar sensors are also sensitivity to the angle of an object surface relative to the sensor and the reflective propertiesof the surface. The above properties of sensors makea mapping problem be difficult and lead inconsistentmap.
The occupancy grid mapping resolves such problems by generating probabilistic maps. As the namesuggests, occupancy grid maps are represented bygirds. Namely, they decompose the high-dimensional
mapping problem into many one-dimensional estimation problems which are then tackled independently.
Because of the independency of neighboring cells, theyoften generate maps that are inconsistent with thedata, particularly in cluttered environments.
To overcome it, we define the cluster which is a setof cells. The cluster is the region that has the highprobability to be inconsistent with the sensor datawhen the standard occupancy grid mapping is used.
Existing occupancy grid mapping algorithms do thetask with the emphasis on individual cells. However,
our approach maps with the emphasis on clusters. As
making the cluster and choosing the optimal mapping
algorithm according to the sensor measurements, maps
generated by our approach more accurate than ones
generated by the previous occupancy grid mapping algorithm. Our mapping algorithm is also as fast as thestandard occupancy grid mapping algorithm.

In this paper, we describe an algorithm for acquiring occupancy grid maps with mobile robots.
The standard occupancy grid mapping developed by
Elfes and Moravec in the mid-Eighties decomposes
the high-dimensional mapping problem into many
one-dimensional estimation problems which are thentackled independently. Because of the independencies
between neighboring grid cells, it often generates
maps that are inconsistent with the sensor data. To
overcome it, we propose the cluster which is a set ofcells. The cells in the clusters are tackled dependently
with another occupancy grid mapping with EM
algorithm. The occupancy grid mapping with EM
algorithm yields more consistent maps, especially inthe cluster. As we use mapping algorithm adaptively
with clusters according to the sensor measurements,
our mapping algorithm is faster and more accurate
than the previous mapping algorithms.
Keywords : occupancy grid, mobile robotics,
mapping, Bayes rule, cluster

1

Introduction

Robotic mapping has been a highly active researcharea in robotics and AI for a few decades. Robotic
mapping addresses the problem of acquiring spatial models of physical environments through mobile
robots. There are a number of mapping algorithms.
However, the occupancy grid mapping is more popular than others, since it has the reputation of being extremely robust and easy to implement. Once
mapped through occupancy grid mapping, they enable various key functions necessary for mobile robot
navigation, such as localization, path planning, collision avoidance, and people finding.
Occupancy maps have been built using various sensors, such as sonar sensor, laser range finders, and©ISAROB 2005

2

Standard Occupancy Grid Mapping

The Standard occupancy grid mapping approach(Elfes, 1989; Moravec, 1988)[2][3] constitutes two algorithms mainly. First, it decomposes a multidimensional (typically 2D or 3D) tessellation of space into

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Standard occupancy grid mapping does not takethe occupancy of neighboring cells into account. It
makes the crucial independence assumption that theoccupancy of a cell can be predicted regardless of acell’s neighbors. Herein lies a major problem of thestandard occupancy approach. This leads to incorrectmap.

many independent cells. Second, each cell calculates
a probabilistic estimate of its state. To calculate thisestimate, techniques such as Bayesian reasoning arethen employed on the grid cell level. And each cell istackled independently.
Let m be the occupancy grid map. The grid cellhas the index hx, yi to store a probabilistic occupancy,
which is mx,y . Occupancy grid maps are estimatedfrom sensor measurements. Let z1 , · · · , zT denote themeasurements from time 1 through time T . The measurement is composed of a sonar scan and the robot
pose at which the measurement was taken. The robot
pose which is assumed to be known is xy coordinatesof the robot and heading direction. Each measurement
carries information about the occupancy of many gird
cells. Thus, the problem addressed by occupancy grid
mapping is the problem of determining the probabilityof occupancy of each grid cell mx,y given the measurements z1 , · · · , zT .
p(mx,y | z1 , · · · , zT )3This section presents an algorithm to improve theproblems of the previous occupancy grid mapping. A
key idea is adapting the cluster which is a set of cells.
The cells in the cluster mean that they have the highprobability to be inconsistent with the sensor datawhen the standard occupancy grid mapping is used.
Unlike existing occupancy grid mapping algorithm,
our approach does the mapping with the emphasison the clusters. One cluster doesn’t affect the others, since the cluster is independent each other. The
occupancy of the cells in the cluster is calculated withthe occupancy grid mapping proposed by Thrun in2003[1]. Using Expectation Maximization algorithm,
in short EM, the alternative mapping algorithm solves
the mapping problem as maintaining the dependencies between neighboring cells. Hence, it leads to themore accurate maps than the standard occupancy grid
mapping in the cluster. The clusters are made with
the neural networks[4][5][6] which is a powerful tool inpattern recognition.
To make the cluster, we use the neighboring sensor
measurements which are the input of neural networks.

(1)

For computational reasons, it is common practice tocalculate the log-odds instead of estimating the above
posterior. The log-odds is defined as follows.
T
= log
lx,y

p(mx,y | z1 , · · · , zT )1 − p(mx,y | z1 , · · · , zT )(2)

The assumption in standard occupancy grid mappingis the static world and conditional independence givenknowledge of each individual grid cell mx,y . Two assumptions and Bayes rule allow us to simplify the posterior to following:p(mx,y | z1 , · · · , zt )p(mx,y | zt )p(zt )p(mx,y | z1 , · · · , zt−1 )(3)=p(mx,y )p(zt | z1 , · · · , zt−1 )P = [p1 , · · · , pR ]Let mx,y be freeness of the grid cell. The probabilityof the freeness of grid cell can be calculated as same
way.

(6)

R is the number of the sensor measurements used. The
output of neural networks, y, is 0 10 if the region swept
by the sensors is cluttered or erroneous place. Otherwise y is 0 00 . That is, if y is 0 10 , we assemble the cells
in that region and make a new cluster.
The occupancy of cells out of cluster is calculatedwith the standard occupancy grid mapping algorithm
explained in section 2. The binary occupancy of cells
in the cluster is calculated with the alternative occupancy grid mapping proposed by Thrun.
Let Ki the number of obstacles in the sensor coneof the i-th measurement. Let Dt = {dt,1 , · · · , dt,Kt }denote the distances to these obstacles and ordered
in increasing order. To describe the multiple causesof a sensor measurement zi , the new variables, calledp(mx,y | z1 , · · · , zt )p(mx,y | zt )p(zt )p(mx,y | z1 , · · · , zt−1 )(4)=p(mx,y )p(zt | z1 , · · · , zt−1 )By dividing (3) by (4) and adapting logarithm, thedesired log-odds is expressed as follow:
1 − p(mx,y )p(mx,y | zt )t−1
+ lx,y+ logp(mx,y )1 − p(mx,y | zt )(5)
Finally, the desired posterior occupancy probabilityp(mx,y |z1 , · · · , zT ) can be recovered from the log-odds
representation of the map.
t
lx,y
= log

©ISAROB 2005

Adaptive Occupancy Grid MappingWith Clusters

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4

correspondence variables, are defined as follow:
ct = {ct,∗ , ct,0 , ct,1 , · · · , ct,Kt }(7)

In order to test our approach, we applied our approach to learning grid maps using simulated data.
Our main finding are that the maps generated our approach are more accurate and the approach has lesstime than the previous occupancy grid mapping algorithm, such as the standard occupancy grid mapping
algorithm and the alternative occupancy grid mapping
algorithm with EM.

Each of these variables corresponds to exactly one
cause of the measurement zt . If ct,k is 1 for 1 ≤ k ≤Kt , the measurement is caused by the k-th obstacle.
If ct,0 is 1, none of the obstacles were detected and thesensor returns a max-range reading. The random variable ct,∗ corresponds to the case where a measurement
was purely random. The log-likelihood of all data andcorrespondences is written as follows:
log p(Z, C|m) =X

log p(zt , ct |m)

Simulation(8)tHere Z denotes the set of all measurements and C is
the set of all correspondences ct for all data. Not calculating the probability of the correspondence variables
but Maximization the likelihood of the data is important, since the probability of correspondence variables
is unobservable. This is achieved by maximizing the
expected log-likelihood E[log p(Z, C|m)|Z, m], where
the expectation is taken over the correspondence variables C. The expected log-likelihood can be obtained
as follows:(a)time:0.172s(b)E[log p(Z, C|m)|Z, m]
X
1[E[log p(ct )|zt , m] + log √
=
2πσ 2
t
z2
1
− [E[ct,∗ |zt , m] log max22πσ2(zt − zmax )2+E[ct,0 |zt , m]σ2KtX(zt − dt,k )2]]+
E[ct,k |zt , m]σ2time:5.047s(d)Figure 1: Narrow open door without error

(9)

k=1

(a)

Maximizing the above expected log-likelihood is the
final goal. To do this, expectation maximization algorithm, EM algorithm, is used. The EM algorithm is
one such elaborate technique. The EM algorithm is a
general method of finding the maximum-likelihood estimate of the parameters of an underlying distribution
form a given data set when the data is incomplete or
has missing values.
As the above way, we choose the optimal mapping algorithm according to the sensor measurements,
namely clusters. Hence, maps generated by our approach are faster and more accurate than ones generated by the previous occupancy grid mapping algorithm.

©ISAROB 2005

time:48.656s(c)time:0.156s(b)time:101.078s(c)time:18.828s(d)Figure 2: Corridor with error
The sensor measurements are gathered in a corridor while driving by an open door. The mobile robot
is equipped with a circular array of 24 sonar sensors.

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Figure 1(a) shows a narrow open door as a first example. The width of the door is two times wider than
the width of mobile robot. Hence, the mobile robot
can pass through the door, but it may be difficult tocontrol. Figure 1(b) shows the result of the standard
occupancy grid mapping algorithm. In the standard
occupancy grid mapping, a narrow open door is notdetected, but other places are similar to Figure 1(a).
Figure 1(c) is obtained by the alternative occupancy
grid mapping with EM. In Figure 1(b), the door is detected, but it takes much time to calculate. In Figure
1(d) generated by our approach, the door is detected
and it takes less time than the occupancy grid mapping with EM. Figure 2 shows the result of corridor
with the error measurements. Figure 2(a) is a simulated environment. As Figure 2(b) shows map of thestandard occupancy grid mapping, map is incorrect
because of the sensor error. In Figure 2(c), the alternative occupancy grid mapping detect incorrectly in
one place though it is better than (b). Unlike Figure2(b) and Figure 2(c), Figure 2(d) shows an accurate
map. As Figure 2(d) is generated by our approach,the map is similar to the environment(a). Because ofclusters, our approach is more accurate than the occupancy grid mapping with EM in erroneous place. Our
approach takes also less time than others.
As a result, because our approach maps with theemphasis on the clusters, maps generated with adaptive occupancy grid mapping algorithm, more accurate
and faster than the others.

5[2] A. Elfes, “Using Occupancy Grids for Mobile
Robot Perception and Navigation,” IEEE Computer,Vol. 22, pp. 46-57, 1989.
[3] H.P Moravec, “Sensor Fusion in Certainty Grids
for Mobile Robots,” IEEE Computer,Vol. 22, pp.
46-57, 1989.
[4] L, Fausett, “Fundamentals of Neural Netwoks:
Architectures, Algorithms, and Application,”
Prentice-Hall, 1994.
[5] Riedmiller, M, “A Direct Adaptive Method for
Faster Backpropagation Learning: The RPROP
Algorithm,” IEEE Neural Network,Vol. 1, pp. 586591, 1993.
[6] C.H Kim, “Feature Extration Method for A Robot
Map Using Neural Networks,” AROB, Vol. 1, pp.
256-258, 2002.

Conclusion

In this paper, the adaptive occupancy grid mapping
algorithm is proposed. Unlike existing occupancy grid
mapping algorithm, our approach relies on the clusters. The clusters are the region that have the highprobability to be inconsistent with the sensor data.
Neural networks is used to make a cluster. According
to the cluster, we use optimal occupancy grid mapping
algorithm. As seeing in simulation result, we can map
more accurate and faster than the previous occupancy
grid mapping.

References[1] Thrun, S, “Learning Occupancy Grid Maps with
Forward Sensor Models,” Autonomous Robots,Vol.
15, pp. 111-127, 2003.

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Real Time Implementation of Visual Servoing Controlof SCARA Robot with Eight Joints
Dong Yean Jung, Hong Rae Kim

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

Abstract
Visual servoing is the fusion of results 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 from the often described use of vision inhierarchical task-level robot control systems. We present
a new approach to visual feedback control using imagebased visual servoing with the stereo vision in this paper.
In order to control the position and orientation of a robot
with respect to an object, a new technique is proposedusing a binocular stereo vision. The stereo vision enables
us to calculate an exact image Jacobian not only ataround a desired location but also at the other locations.
The suggested technique can guide a robot manipulator
to the desired location without giving such priori
knowledge as the relative distance to the desired locationor the model of an object even if the initial positioning
error is large. This paper describes a model of stereo
vision and how to generate feedback commands. The
performance of the proposed visual servoing system is
illustrated by the simulation and experimental results andcompared with the case of conventional method for dualarm robot made in Samsung Electronics Co., Ltd..

1 Introduction
Many of the control and vision problems are similar to
those encountered by active vision researchers who arebuilding "robotic heads". However the task in visual
servoing is to control a robot to manipulate its
environment using vision as opposed to just observing
the environment.
There are mainly two ways to put the visual feedback
into practice. One is called look-and-move and the otheris visual servoing. Visual servoing is the fusion of resultsfrom many elemental areas including high-speed image
processing, kinematics, dynamics, control theory, andreal-time computing. It has much in common withresearch into active vision and structure from motion,
but is quite different from the often described use ofvision in hierarchical task-level robot control systems.
The former is the method which transforms the positionand orientation of an object obtained by a visual sensor
into those in the world frame fixed to an environment©ISAROB 2005

167

and guides the arm of the manipulator to a desired
location in the world frame.[1,2] In this method, precise
calibration of a manipulator and camera system isneeded. On the contrary, visual servoing uses theJacobian matrix which relates the displacement of animage feature to the displacement of a camera motion
and performs a closed-loop control regarding the feature
as a scale of the state. Therefore, we can construct a
servo system based only on the image and can have arobust control against the calibration error because there
is no need to calculate the corresponding location in theworld frame.[2,3,4] A hand eye system is often used invisual feedback and there are two ways of arranging thesystem. One is placing a camera and a manipulator
separately; the other is placing the camera at the end-tipof the manipulator. The former motion strategy of themanipulator becomes more complicated than the latter.
In the latter, it is easy to control the manipulator using avisual information because the camera is mounted on the
manipulator end-tip. In this paper, we deal with the lattermethod. In the conventional works, some researches
have presented methods to control the manipulator
position with respect to the object or to track the featurepoints on an object using a hand eye system as theapplication of visual servoing.[3,4] These methods
maintain or accomplish a desired relative position
between the camera and the object by monitoring featurepoints on the object from the camera.[5,6]However, these have been all done by the hand eye
system with monocular visions and it is necessary tocompensate for the loss of information because theoriginal three-dimensional information of the scene isreduced to two-dimension information on the image. For
instance, we must add an information of the threedimension distance between the feature point and thecamera in advance or use a model of object stored in thememory. Besides, a problem that the manipulator
position fails to converge to a desired value arises
depending on the way of selecting feature points or when
the initial positioning error is not small. It is becausesome elements of the image Jacobian cannot becomputed with only the information of the image andsubstituting approximate values at the desired location
for them may result in large errors at the otherlocations.[7]This paper presents a method to solve this problem byusing a binocular stereo vision. The use of stereo vision
can lead to an exact image Jacobian not only at around a

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

desired location but also at the other locations. The
suggested technique places a robot manipulator to thedesired location without giving such priori knowledge as
the relative distance to the desired location or the modelof an object even if the initial positioning error is large.
This paper deals with modeling of stereo vision andhow to generate feedback commands. The performance ofthe proposed visual servoing system was evaluated by thesimulations and experiments and obtained results werecompared with the conventional case for a SCARA typedual-arm robot.

This relation can be expressed in a matrix form whichis called the image Jacobian. Let us consider n featurepoints p k (k = 1, L , n) on the object and the coordinatesin the left and right images arer

p = ( lx1 rx1 ly1 ry1 L lxn rxn lyn ryn )T(2)where each element is expressed with respect to thevirtual image frame ∑ p .
First, to make it simple, let us consider a case when
the number of the feature points is one. The relation
between the velocity of feature point in image I p& andthe velocity of camera frame c p& is given asIx cz = f ( cx + 0.5d )rx z = f ( x − 0.5d )ly z= f yry cz = f cy

c(3)components be w x , w y , w z then we can express thecamera velocity V as

V = [σx σ y σz wx wy wz ]T= [cv cwc ]T(4)

Then the velocity of the feature point seen from thecamera frame c p& can be writtencenvironment, which is expressed as c p = ( c x c y c z )T in
the camera frame. Then the following equation is
obtained (see Fig. 1).
Suppose that the stereo correspondence of featurepoints between the left and right images are found. In thevisual servoing, we need to know the precise relation
between the moving velocity of camera and the velocityof feature points in the image, because we generate a
feedback command of the manipulator based on thevelocity of feature points in the image.
lp& = I J c cp&

where I J c is the Jacobian matrix which relates the twoframes. Now let the translational velocity components ofcamera be σ x , σ y and σ z and the rotational velocity

Now let l p = ( l x, l y ) and r p = ( r x, r y ) be the projections
onto the left and right images of a point p in the©ISAROB 2005

p k ( r xk , r y k ) , respectively. Also define the current

I

Visual servo systems typically use one of two camera
configurations: end-effector mounted, or fixed in theworkspace.
The first, often called an eye-in-hand configuration,
has the camera mounted on the robot's end-effector. Here,
there exists a known, often constant, relationshipbetween the pose of the camera(s) and the pose of theend-effector.
We define the frame of a hand-eye system with thestereo vision and use a standard model of the stereo
camera whose optical axes are set parallel each other andperpendicular to the baseline. The focal points of two
cameras are apart at distance d on the baseline and theorigin of the camera frame ∑ c is located at the center ofthese cameras.
An image plane is orthogonal to the optical axis andapart at distance f from the focal point of a camera andthe origins of frame of the left and right images, ∑ l and∑ r , are located at the intersecting point of the two
optical axes and the image planes. The origin of theworld frame ∑ w is located at a certain point in the world.

cp k ( l xk , l y k ) andlocation of the feature points in the image I p as

2 Visual Servo System

c

l

d cp
dt
d c
=
Rw ( wp − wpc )
dt

p& =

(5)

= cRw {− w wc × ( W p − wpc ) }+ cRw (W p& − w p& c )Where c Rw is the rotation matrix from the camera frameto the world frame and w pc is the location of the originof the camera frame written in the world frame. As theobject is assumed to be fixed into the world frame,w
p& = 0 . The relation between c p& and V iscp& = cRw {− w wc × ( wp − wpc )}− cRw wp& c= − c wc × c p − c p& c

(1)c

⎡ − w y c z + wz c y − v x ⎤⎢⎥= ⎢ − wz c x + wx c z − v y ⎥⎢ − w cy + w cx − v ⎥
x
y
z⎦
⎣

168

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

⎡ 1
0
⎢− c
x
J= f ⎢
⎢
1
⎢ 0 −
c
x
⎣⎢

Therefore, substituting Eq. (6) into Eq. (3), we have thefollowing equation.
I

p& = J c p&
I

c

(7)

= JV

c

c c

x
x

x y
c
z

c 2
c

c 2
y
y
1
+
c 2
c
x
z2

⎛ c x2 ⎞ c y ⎤− ⎜1 + c 2 ⎟ c ⎥
⎜
x ⎟⎠
z ⎥
⎝
c c
c ⎥
xy
x
− c 2
−c ⎥
x
z ⎦⎥

The x , y and z axes of the coordinate frames areshown in Fig. 1.
We now introduce the positional vector of the featurepoint in the image of monocular vision using the symbolm
P = ( m x, my ) . This is the projection of the point

In Eq. (7) matrix J which expresses the relation
between velocity I p& of the feature point in the image
and moving velocity V of the camera is called theimage jacobian.
From the model of the stereo vision Eq. (1), thefollowing equation can be obtained.

expressed as c P = ( c x c y c z )T in the camera frame into
the image frame of the monocular vision, and has thefollowing relation.

2 cx ( l x − r x ) = d ( l x + r x )(8)y( x − x) = yd = yd

(9)m

x = f c x c z −1z( x − x) = f d

(10)

m

y= f

c

c

l

l

r

l

r

r

Above discussion is based on the case of one featurepoint. In practical situation, however, the visual servoing
is realized by using plural feature points. When we use
n feature points, image Jacobian J 1 , L , J n are given
from the coordinates of feature points in the image. By
combining them, we express the image Jacobian ( J im )asJ im = [ J1 LL J n ]T

p& = J im Vc(14-a)c −1(14-b)y z

Substituting Eq.s (14-a) and (14-b) into Eq. (13) yields
another expression of the image Jacobian for the
monocular vision.
⎡ 1
0
⎢− c
z
J=f⎢
⎢
f
⎢ 0 −c
x
⎢⎣

m
c
m
c

m m

x

x
y
x

1+

x y
f
m 2
y + f2
f2

−

x +f2

m 2

2

f
x y
−
f
m m

⎤
y⎥
⎥ (15)
⎥
m
− x⎥
⎥⎦
m

(11)
A disparity which corresponds to the depth of thefeature point, is included in J in the case of the stereo

Then, it is possible to express the relation of the moving
velocity of the camera and the velocity of the featurepoints even in the case of plural feature points, that is,I(13)vision, but s-term expressed in the camera frame c z is
included in J in the case of the monocular vision.
In the visual servoing, the manipulator is controlled sothat the feature points in the image reach their respective
desired locations.
We define an error function between the current
location of the feature points in image I p and the(12)

where we suppose that the stereo and temporalcorrespondence of the feature points are found.
In the case of the monocular, the image Jacobian J has
the following form.

desired location I pd as
E = Q ( I p − I pd )(16)

where Q is a matrix which stabilizes the system. Thenthe feedback law is defined as following equation

V = −G E(17)

where G corresponds to a feedback gain.
To realize the visual servoing, we must choose Q sothat convergence is satisfied with the error system can besatisfied with
∂E
∂ p&
E& =
=Q
= Q I P&∂t∂t
= QJ imV = − GQJ im E
I

Fig. 1 The coordinates system of vision model©ISAROB 2005

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(18)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The error between the desired location and the current
location of the feature points in cases of the monocularand stereo visions are shown in Fig. 4.
Next, we will show the results for the change of the way
to choose the feature points and set the initial error
image.
In Fig. 4, we can see that the result diverges in thecase of the monocular vision, but converges in the caseof the stereo vision. This is because the image Jacobian
is fixed at the desired location in case of the monocularvision. Therefore, a correct feedback command can not
be generated when the initial error is large. On the other
hand, the image Jacobian can be updated at every in thecase of the stereo vision, thus it is possible to generate acorrect feedback command which assures the stability
visual servoing.
In experiments, we used a SCARA type dual-arm
robot made in Korea with a stereo camera attached tothe end tip of the arm. The feature points are threecircular planes of 20 mm radius on three corners of aequilateral triangle, one side 87 mm and are placed on
the board. Precise calibration had not been done for thestereo camera attached to the end-tips.

Fig. 2 Block diagram of visual feedback system.

We use pseudo-inverse matrix of the image Jacobian
J im for Q to make Q J im positive and not to make an
input extremely large, that is,+T
T
Q = J im
= ( J im
J im ) −1 J im

(19)

Therefore, the feedback command is given as
V = − G J im+ ( I p − I p d )(20)

Fig. 2 shows a block diagram of the control scheme
described by Eq. (20). Note that the feedback commandu is sent to the robot controller and both thetransformation of u to the desired velocity of each joint
angle q& d and its velocity servo are accomplished in the
robot controller as show in Fig. 2.
Furthermore, as J im is a 4n × 6 matrix and pseudo+
is a 6× 4n matrix, a feedback
inverse matrix J im
command Eq. (20) of 6 degrees of freedom is obtained.

3 Experiments
Fig. 3 The experimental equipment set-up.

We have compared the visual servoing using themonocular vision with that using the stereo vision by theexperiment. Fig. 3 represents the experimental
equipment set-up. In Fig. 3 two DSP vision boards wereused, which had been made Samsung Electronics
Company in Korea based-on the TMS320C31 chips.
In the experiment, feature points of an object are thefour corners of a square whose side dimension is
300 mm . In the same condition, we used four featurepoints even in the stereo vision. Parameters used the
focal length, f = 16 mm , baseline d = 130 mm ,sampling time of 50 m sec , gain λ = 1 , desired location(a) Monocular

Pd = (100 100 500)T mm , desired orientation in Euler
angle(φ ,θ ,ψ ) = (0, 0, 0) rad,initialerrorc(−50, − 50, − 50) T
mm in the translation and(φ ,θ ,ψ ) = (20, 20, 20)rad in the orientation.
We select the four corners of a rectangle whose size is
200 × 200 mm as the feature points and set the(b) Stereo
Fig. 4 Positional error in x and y axes in the case of
the stereo and monocular vision.

translational error as (−150 − 150 − 450) mm and theother values are the same as before.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4 Conclusion
We proposed a new technique of visual servoing withthe stereo vision to control the position and orientationof an assembling robot with respect to an object. The
method overcomes the several problems associated withthe visual servoing with the monocular vision. By using
the stereo vision, the image Jacobian can be calculated at
any position. So neither shape information nor desired
distance of the target object is required. Also the stabilityof visual servoing is assured even when the initial error
is very large. We have shown the effectiveness of thismethod by simulation and experiments.
To use this visual servoing in practical tasks, there still
exist many problems such as the number of featurepoints to reduce noise or the quantization error and theway to choose feature points. Nevertheless, this method
overcomes the several problems in visual servoing withthe monocular vision.

(a) Left image

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

(b) Right image
Fig. 5 Position error in x and y axes.
Two stereo images were taken and transformed to thebinary images in the real time and in parallel by two
image input devices and the coordinate of thegravitational center of each feature point was calculated
in parallel by two transporters. We gave the stereo
correspondence of the feature point in the first sampling.
However, the stereo and temporal correspondence of thefeature points in the succeeding sampling were foundautomatically by searching a nearby area where there were
the feature points in the previous sampling frame. The
coordinates of the feature points were sent to a transporter
for motion control and it calculated a feedback command forthe robot. The result was sent to the robot controller by using
RS-232C, and the robot was controlled by a velocity servo
system in the controller.
The sampling period of visual servoing was about
50 m sec . Details were 16 m sec for taking a stereo
images, about 1m sec for calculating the coordinates ofthe feature points, 3m sec for calculating feedback
command, about 16 m sec for communicating with therobot controller. If we send a feedback input to the robot
controller without using RS-232C, the faster visual
servoing can be realized.
The desired location was (0, 0, 500)T mm and thedesired orientation in Euler angle, (φ ,θ ,ψ ) = (0, 0, 0)

References[1] Hashimoto, K., Edbine, T., and Kimura, H.,"Dynamic visual feedback control for a hand-eye
manipulator". In Proceedings of the IEEE/RSJ
International Conference on Intelligent Robots andSystems, pp. 1863-1868, 1992.
[2] Bernard, E., Francois, C., and Patrick, R., "Anewapproach to visual servoing in robotics". IEEE
Transactions on Robotics and Automation, pp. 313326, 1992.
[3] Weiss, L. E., Sanderson, A. C., and Neuman, C. P.,"Dynamic sensor-based control of robots withvisual feedback". IEEE Journal of Robotics andAutomation, pp. 404-417, 1987.
[4] G. Hager, S. Hutchinson, and P. I. Corke. "Atutorial on visual servo control". IEEE Trans.
Robotics & Automation, Vol. 12, No. 5, pp. 651670, 1996.
[5] V. Sundareswaran, P. Bouthemy, and F. Chaumette.
"Exploiting image motion for active vision in avisual servoing framework". International Journalof Robotics Research. Vol. 15, No. 6, pp. 629-645,
1996.
[6] Allen, P. K., Toshimi, B., Timcenko, A., "RealTime Visual Servoing".In Proceeding of the IEEE
International Conference on Robotics andAutomation, pp. 851-856, 1991.
[7] Hashimoto, K., Kimoto, T. Edbine, T., and Kimura,
H., "Manipulator control with image-based visual
servo". In Proceedings of IEEE International
Conference on Robotics and Automation, pp. 22672272, 1991.

degree and the initial error was (50, 50, 50)T mm fortranslation. The other parameters were the same as in thesimulation. The error of current and desired location of
the feature points are shown in Fig. 5. From theseexperimental results, we can see that the manipulator
converges toward a desired location even if thecalibration is not precise.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Tolerance of Permanent Magnet Biased Bidirectional Magnetic Bearings
and Its Robotic Applications
Yong Tae Kim

Uhn Joo Na

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
redistributed. Na and Palazzolo [4, 5] also investigatedthe optimized realization of fault-tolerant magnetic
bearing actuators, so that fault-tolerant control can berealized for an 8-pole bearing for up to 5 coils failed.
This paper introduces a fault-tolerant 4-active-polepermanent magnet biased, bi-directional magnetic
bearing such that the bearing can preserve the same
decoupled magnetic forces identical to the unfailed
bearing even after any one coil out of 4 coils fails.

Abstract
A fault tolerant scheme of a bi-directional magnetic
bearing is presented. The bearing continues to function
normally even though one coil among four radial coils
and one coil of two axial coils fail. The dynamic
properties and load capacity remain unchanged for thesuggested fault tolerant control scheme. A onedimensional circuit that represents the bi-directional
bearing is utilized to obtain the optimal bearing
parameters such as the radial pole face area, number ofcoil turns, and permanent magnet size. The results
identify advantages of the fault tolerant scheme and bidirectional
bearing
improvements
relative
toconventional magnetic suspension. Bidirectional
magnetic bearings find applications in robotic joints.

2 Magnetic Circuit Analysis
Figure 1 shows a schematic drawing of a permanent
magnet biased combo bearing. Four independent coils
are wound on each radial pole to supply control fluxes.
A pair of coils supplies axial control fluxes.

1 Introduction
P.A. 2Magnetic bearings find greater use in high speed, highperformance, applications such as gas turbines, energy
storage flywheels, and pumps since they have many
advantages over conventional fluid film or rolling
element bearings, such as lower friction losses,
lubrication free, temperature extremes, no wear, quiet,high speed operations, actively adjustable stiffness anddamping, and dynamic force isolation. Unlike
heteropolar bearings, homopolar magnetic bearings havea unique biasing scheme that directs the bias flux flow
into the active pole plane where it energizes the working
air gaps, and then returns through the dead pole plane
and the shaft sleeve. Some of the results on modeling,
design, and control of homopolar magnetic bearings areshown in literature. Meeks [1] utilized a permanent
magnet biased homopolar magnetic bearing to provide
smaller, lighter, and power-efficient operation. Faulttolerance of the magnetic bearing system is of great
concernforhighlycriticalapplicationsofturbomachinery since a failure of any one control
components may lead to the complete system failure.
Much research has been devoted to fault-tolerant
heteropolar magnetic bearings. Maslen and Meeker [2]introduced a fault-tolerant 8-pole magnetic bearing
actuator with independently controlled currents andexperimentally verified it in [3]. Flux coupling inheteropolar magnetic bearings allows the remaining coils
to produce force resultants identical to the unfailed
bearing, if the remaining coil currents are properly©ISAROB 2005

P.A. 2

P.A. 1

P.A. 3

P.A. 4

P.A. 4

Fig. 1 Schematic of a Bidirectional Magnetic Bearing
φl

Rl

ni1

R1

ni3

ni2

φ1

R2

φ2

R3

φp

ni4

φ3

R4

φ4

Rp
HcLpm

−nˆiz1

Rz1

φb

nˆiz2

Rz2

φz1

φz2

Fig. 2 Circuit for a Bidirectional Bearing
The permanent magnets are represented as the source
H c L pm and the total permanent magnet reluctance R p .

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The coercive force and the length of the permanent
magnet are H c and L pm , respectively. The reluctance inair gap j of the active pole plane is;gj
Rj =µ 0 a0where
g j = g 0 − x cosθ j − y sin θ j

B = ςA −1Φ
where

(1)

A = diag ([a0 , a0 , a0 , a0 ])The parameter ς represents flux fringing factor, andcan be empirically estimated. Magnetic forces developedin the radial pole plane are described as;(2)

The parameters µ 0 , a0 , and g 0 represent thepermeability of air, the pole face area of the active pole,and nominal air gap, respectively, and x and y are thejournal displacements. The axial air gap reluctances aredescribed as;
g zj
(3)
Rzj =
µ0 az 0where
g z1 = g z 0 − z , g z 2 = g z 0 + z(4)and where a z 0 and g z 0 are the axial pole face area
and the nominal axial gap, respectively, and z is therotor displacement along the axial direction. Applying
Ampere’s law and Gauss’s law to the radial magnetic
circuit leads to a matrix equation.
− R2
R2

⎡ R1
⎢0
⎢
⎢0
⎢
⎢1
⎢⎣

⎤
⎡ 0 ⎤
⎥ ⎡φ 1 ⎤ ⎢⎥⎥ ⎢φ 2 ⎥ ⎢ 0 ⎥− R 4 ⎥ ⎢⎢ ⎥⎥ = ⎢ 0 ⎥0R3φ⎢ H c L pm ⎥
R ⎥ 3
⎥
1
1
1 + 4 ⎥ ⎢⎣φ 4 ⎥⎦ ⎢ ~R R ⎥⎦⎢⎣ R R ⎥⎦
0
0
⎡
⎤
⎢
⎥
0
0
⎢
⎥ ⎡ i z1 ⎤
+⎢
⎥⎢ ⎥
0
0
⎢
⎥ ⎣i z 2 ⎦− R z 2 n~
R z 1 n~⎢⎥
⎣ R R ( R z1 + R z 2 ) R R ( R z1 + R z 2 ) ⎦0
0 ⎤ ⎡i ⎤⎡n − n1⎢0−n0 ⎥⎥ ⎢ i ⎥
n
⎢
⎢ 2⎥
+ ⎢0
− n ⎥ ⎢i ⎥0n⎢n ⎥⎢ 3 ⎥00⎢0⎥
R R ⎦ ⎣i 4 ⎦
⎣
0
− R3

Fϕ = − B T(8)

D = diag ( g j a0 /(2 µ 0 ))(9)and where ϕ is either x or y . Applying Ampere’s
law and Gauss’s law to the axial magnetic circuit leads
to a matrix equation.
⎡ R z1⎢ R z1
⎢1 +
RA
⎣

0
0

− Rz 2 ⎤0⎡⎤
H eq ⎥⎥ ⎡φ z1 ⎤ = ⎢ − Rl H c L pm⎥⎢−
1 ⎥ ⎣φ z 2 ⎦ ⎢⎥RA ⎦⎣ R A ( R p + Rl )⎦− n~ ⎤ ⎡ i ⎤⎡ − n~~⎥ z1+ ⎢− n0 ⎥ ⎢i ⎥⎢R⎦⎣ z2 ⎦
⎣ A

(10)

where
RA =

R p RlR p + Rl

+ Req

,

Req =

(5)

1
1
1
1
1
+
+
+
R1 R2 R3 R4,ni1 ni2 ni3 ni4+++R
R2 R3 R4
H eq = 1
1
1
1
1
+
+
+
R1 R2 R3 R4Equation (10) can be rewritten in vector form as;~~Rz Φ z = H + H xy I + NI z

(11)

where

R p Rl⎡0
H xy = ⎢⎣h1

Rp

R z1 Rz 2
Rz1 R z 2~
RR = R p +(1 +)Rz1 + Rz 2
Rz1 + R z 2
Rland φ , i , i , n , and n~ are fluxes, currents through jR p + Rl

j

∂D
B
∂ϕ

where the air gap energy matrix is;

where
RR =

(7)

j

+

0
h3

0⎤
, hi =
h4 ⎥⎦

n
Ri
1
1
1
1
+
+ )
RA ( +
R1 R2 R3 R4zj

th pole, axial currents, the number of radial coil turns,
and the number of axial coil turns, respectively. Equation(5) is rewritten in vector form as;

The flux density vector is then;Bz = ς z Az Φ z−1RΦ = H + H z I z + NI
(6)
The flux densities in the gaps are reduced by flux
leakage, fringing, and saturation of magnetic material.
The flux density vector is then;©ISAROB 2005

0
h2

where
Az = diag ([a z 0 , a z 0 ])

173

(12)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The parameter ς z represents the flux fringing factor in
the axial air gaps. Magnetic forces developed in the axialpole plane are described as;T ∂D z(13)
Bz
Fz = − Bz∂zwhere the air gap energy matrix is;Dz = diag ([ g z1a z 0 /( 2µ 0 ), g z 2 a z 0 /(2µ 0 )])⎡1⎢0
T4 = ⎢⎢− 1⎢⎣0

be linearized about equilibrium positions and the control
voltages by using Taylor series expansion. The
linearized magnetic forces are;(14)

⎡k pxx⎡ Fx ⎤⎢ F ⎥ = − ⎢k
⎢ pyx
⎢ y⎥
⎢ k pzx⎢⎣ Fz ⎥⎦⎣

The currents distributed to the radial poles are
generally expressed as a distribution matrix T andcontrol voltage vector v . The current vector is;

k pxy
k pyy
k pzy

k pxz ⎤ ⎡ x ⎤ ⎡k vxx⎢⎥
k pyz ⎥ ⎢⎢ y ⎥⎥ + ⎢k vyx
k pzz ⎥⎦ ⎢⎣ z ⎥⎦ ⎢⎣ k vzx

k vxy
k vyy
k vzy

k vxz ⎤ ⎡v x ⎤⎥k vyz ⎥ ⎢⎢v y ⎥⎥k vzz ⎥⎦ ⎢⎣ v z ⎥⎦

(20)

(15)

or
F = − K p Z + K vV

where⎡v x ⎤⎥,⎣v y ⎦[]

Tx = [t x1

tx2

t x3

t x4 ] ,T y = t y1 t y 2

t y3

t y4 ,

T = Tx Ty , v = ⎢

[

T

]

T

volts) after the 4-th radial coil and an axial coil failed
operation are;respectively. For example, the current distribution
scheme for unfailed radial poles is;
⎡1 0⎤
⎥
⎢
~ ⎢0 1⎥T =⎢− 1 0 ⎥⎥⎢⎣ 0 − 1⎦

− 10407.28 ⎤⎡− 939484.13 − 16547.43⎢
K p = ⎢ 3011.03− 968484.90
9227.66 ⎥⎥⎢⎣ − 13725.16
10856.53− 2699194.87⎥⎦

The fault-tolerant control scheme can be easily
implemented in a physical controller (DSP). The
controller consists of two independent parts, which are a
feedback voltage control law and an adaptive current
distribution mechanism. Though any control algorithm
for magnetic bearing systems appearing in the literature
can be utilized with the fault tolerant scheme, for sake ofillustration, a simple PD feedback control law is used tostabilize the system.

(17)⎡ t z1 ⎤⎥ , and v z is z control voltage. For⎣t z 2 ⎦where Tz = ⎢example, the current distribution scheme for unfailed
axial poles is;~ ⎡1⎤
Tz = ⎢ ⎥⎣1⎦,

1.64 ⎤⎡82.55 2.64⎢
K v = ⎢ 0.28 81.09 − 1.32 ⎥⎥⎢⎣ 0.32 12.08 179.82⎥⎦(16)

The currents distributed to the axial plane areexpressed as;
I z = Tz v z(21)

The flux coupling between the axial and radial planes
can be determined by the cross coupled stiffness
properties of Eq. (20). The linearized magnetic forces
calculated at the equilibrium points ( x0 = 2 mils, y0 = -1
mils, z 0 = 3 mils, v x 0 = 0.5 volts, v y 0 = 0.3 volts, v z 0 = 1

and v x and v y are x and y control voltages,

vcϕ = K pϕ + K d ϕ&(22)

ϕ ∈ ( x, y )(18)

While the feedback control law remains unalteredduring the failure the appropriate current distribution
matrix T can be continuously updated using an adaptive
current distribution mechanism. Failure status vectors
and the corresponding distribution matrices for the 5possible states including an unfailed vector can be

The remaining three currents, if one coil fails, areredistributed such that the same opposing poles, C-core
like, control fluxes still can be realized. The calculated
distribution matrix for the 4th coil failed operation is;©ISAROB 2005

(19)

The nonlinear magnetic forces of Fx , Fy , and Fz can

3 Fault Tolerant Control

I =Tv

1⎤
2⎥⎥
1⎥
⎥
0⎦

174

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

tabulated in a reference table and stored in the DSP
controller as a part of searching algorithm. The
distribution matrix corresponding to the failure vector is
implemented in the controller. By prior experience thisseries of actions for failure detection, searching for T ,and replacement by the new T can be implemented in
one loop time of a fast ( > 15K sec-1 ) DSP controller.
Any one coil out of 4 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
after failure.

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

References[1] C.R. Meeks, E. DiRusso, G.V. Brown,“Development of a Compact, Light Weight
Magnetic Bearing,” AIAA Paper No. AIAA-902483 (1990)[2] E. H. Maslen and D. C. Meeker, “Fault Toleranceof Magnetic Bearings by Generalized Bias Current
Linearization,” Magnetics – Transactions of theIEEE, 31, 2304 – 2314 (1995)[3] E. H. Maslen, C. K. Sortore, G. T. Gillies, R. D.
Williams, S. J. Fedigan, and R. J. Aimone, “A Fault
Tolerant Magnetic Bearings,” Journal ofEngineering for Gas Turbines and Power –Transactions of the ASME 121, 504-508 (1999)[4] U. J. Na and A. B. Palazzolo, “Optimized
Realization of Fault-Tolerant Heteropolar Magnetic
Bearings for Active Vibration Control,” Journal of
Vibration and Acoustics – Transactions of the
ASME 121, 504-508 (2000)[5] U. J. Na and A. B. Palazzolo, “Fault Tolerance ofMagnetic Bearings with Material Path Reluctancesand Fringing Factors,” Magnetics – Transactions of
the IEEE 36, 3939 – 3946 (2001)

4 Conclusion
A fault tolerant current distribution scheme isdeveloped for a bi-directional, permanent magnet biased,homopolar magnetic bearing. The bearing preserves thesame magnetic forces before and after failure eventhough one coil among four radial coils and one coil of
two axial coils fail. A one-dimensional circuit that
represents the bi-directional bearing is analyzed to obtain
the optimal bearing parameters such as the radial poleface area, number of coil turns, and permanent magnet
size. The results show advantages of the fault tolerant
scheme and bi-directional bearing improvements relative
to conventional magnetic suspension. Fault tolerance ofthe magnetic bearing actuator can be achieved at theexpense of additional hardware requirements andreduction of overall bearing load capacity.
These bidirectional magnetic bearings with fault
tolerant capability can be used as robot joints. Since
magnetic bearing supported robot arms can avoid oil
lubrication and dust generation, they can be used for
robots in clean environment, in vacuum chambers, or inspace. They also have some more advantages overconventional robot joints such as frictionlessmanipulation, force control, force sensing, active
vibration control.

©ISAROB 2005

175

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Detecting Method of Friction Force on Linear Actuators of a Parallel Manipulator
Based on the Gravitational Force
Haeng Bong ShinSe Han Lee

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

any other mechanical alignment error. The pre-stress cancause more friction problem. In particular, theconventional Stewart-Gough platform has 6 identicalactuators, and the actuators are required to be as more
identical as possible. However, it is impossible that they
are all identical perfectly. Friction force on each actuatoris expected to be detected and is compensated for better
control performance.

Parallel manipulators have been used to a variety of
applications, including the motion simulators andmechanism for precise machining. A Stewart-Goughtype parallel manipulator is composed of six linear joints,
which have wider contact areas than revolute ones, so
linear joints are more affected by frictional force. First,
the reference trajectories are computed from the modelof the parallel manipulator assuming that it is subject toonly the gravitational force and no friction exists. In theactual operation where friction exists, the control inputs,
which correspond to the friction forces, are obtained byforcing the actual joint variables to follow these
trajectories by proper control. It is shown that control
performance can be improved when the friction
compensation based on this information is added to thecontroller for position control of the moving plate of aparallel manipulator.

1. Introduction
Recently, there are carried out so many parallel
manipulator applications for machining machine, motion
simulator [1,2] and so on. In particular, a conventional
Stewart-Gough type parallel manipulator has a good
rigidity ration over the weight since it uses mainly linear
actuators where bending effect does not apply. In
addition, the errors from each actuator are notaccumulated and distributed since the actuators arearranged in parallel with closed loop form. Thanks to thecharacteristics, there is introduced the parallel
manipulator to applications of handling tool and carriage
in the machining machine which requires high rigidity.
Generally, on the contrary to the merits above, there is a
deficit of small workspace since the actuators have
limited stroke and are arranged in parallel with closed
loop form and they can interfere with themselves. In
addition, in case of the conventional Stewart-Goughplatform, there is significant friction force on eachactuator. A linear type actuator has wider contacting
surface than a revolute one and has more friction
problem. Meanwhile, ball-screw device for converting
rotation to translation motion may have an intentional
pre-stress because pre-stress can eliminate backlash and©ISAROB 2005

176

As a method for detecting friction force, when aconstant force is applied to the destination, the resultant
acceleration can tell us how much friction force is. Thatis hard to be performed due to a calibration problem
because it is hard to apply an exactly scaled force to thedestination. The gravitational force can be a candidate
for that because the gravitational force can be easily
assumed as constant force. Actually, the gravitational
force is most likely a constant and even free cost.
If the gravitational force is applied to an ideal parallel
manipulator without any friction effect, the end-effectorof the parallel manipulator falls down freely according
to the Newton’s law. Actually, the end-effector falls
down slowly than expected or even stops with thefriction force on each actuator. When the actuators arecontrolled in order to make the end-effector follow theideal free fall trajectory, the control input for theactuators can be assumed as the efforts for compensating
the friction force. For better tracking performance, thecontrol efforts corresponding to actuator’s position canbe stored and recalled for controlling the end-effector tofollow an arbitrary trajectory.
In this study, Chapter 2 describes characteristics of aconventional Stewart-Gough platform, Chapter 3 describes amethod for detecting friction force, Chapter 4 shows thevalidity of the method with experimental results, Chapter 5
makes conclusions finally.

2. Stewart-Gough parallel manipulator
Stewart-Gough has two plates, moving plate(end-effector) and fixed plate that are connected with 6
linear actuators in parallel. The end-effector has 6 dof(degree of freedoms) with 3 dof of position and 3 dot oforientation [4]. A parallel manipulator like theThe Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Stewart-Gough platform, has opposite characteristics to the aserial one that inverse kinematics for the parallel is easierthan that for a serial one, forward kinematics of the parallel
one is more difficult than that of a serial one and does noteven have analytical solutions. In addition, it is possible toperform accurate control since the actuation structure isclosed form and the error on each actuator is distributed and
it is also to obtain high rigidity over lightweight since theactuators run stress and tensional direction without bending.
Meanwhile the end-effector workspace is limited since theactuator’s stroke is limited and has closed form structure.
Meanwhile as a parallel manipulator uses a linear type
actuator and the contacting surface of the linear type actuator
becomes wider than a revolute type actuator, friction force
effect of the parallel manipulator is significantly increasing.
In addition, ball-screw device for converting rotation totranslation motion may have an intentional pre-stressbecause pre-stress can eliminate backlash and any othermechanical error. The pre-stress can cause more friction
problem. As a result, the parallel manipulator falls down
slowly or even is fixed due to the friction force without
any actuation force.
Figure 1 shows a conventional Stewart-Gough platform
built in the study. As an experimental setup, theStewart-Gough platform uses a ball-screw device at which ahigh pre-stress (75kgf) is applied. Due to the friction force,the end-effector of the parallel manipulator is fixed. It is wellknown that friction force is a negative factor for control
performance. If the friction force can be detected exactly andcompensated, the control performance will be improved.
z

γ
α

β

Moving plateyLinear actuator(rotary motorwith ball-screw)

Dynamic equations for a multi-degree manipulator aredescribed as a non-linear form equation as following&& + N (q, q& ) + G (q) = τJ (q)qwhere q∈Rn denotes joint variables on the manipulator (nstands for the numbers of the joints), J(q) denotes momentof inertia, N(q, q& ) denotes non-linear term corresponding tocentripetal and Coliolis force, G(q) denotes gravitational
force term, and τ denotes external torques. The gravitational
force term G(q) in (1) is dependent on the configurations, q
and is independent of initial velocity or acceleration. If there
is no external force, τ = 0, (1) becomes&& + N (q, q& ) = −G (q) ≠ 0
J (q)q(2)

Only the gravitational force in (2) is applied to the
manipulator. In case of a serial manipulator with a revolute
type actuator that has less friction force than parallel one, theend-effector of the serial one falls down slowly along thegravitational direction since the friction force cancels somepart of the gravitational force. If there is no friction force oneach actuator, the end-effector falls down freely. By solving(2), the joint variable, qg corresponding to the free fall
trajectory can be obtained.
Here is considering a case with friction force. If thefriction force is bigger than the external one, the friction
force is the same as the applied external force and theexternal force is canceled exactly. As described above,
friction force on the parallel manipulator is relatively large
and the external force (gravitational force in this case) fades
away due to the friction force. As a result, (2) becomes

L1

L2

where Ffriction (q& / |q& |) denotes friction force acting
opposite direction of the manipulator.
As if there is controlled the manipulator following thefree fall trajectory, the control efforts to each actuator are thesame as friction force on each actuator. The control input u
is carefully adjusted in order for the joint variable q to follow
the pre-computed trajectory qg.

Fixed base&& g + N(q g , q& g )q& g
J (q g )q= −G (q g ) + Ffriction (q& g / |q& g |)+ u

Fig. 1 Stewart-Gough type parallel manipulator.

3. Friction force detection using gravitationalforce
As a method for detecting friction force on a manipulator,
when and external force magnitude applied to a manipulator
is increasing, a force corresponding to the moment at where
the manipulator begins to move can be assumed a friction
force on the manipulator. This method is very simple,©ISAROB 2005

(1)&& + N (q, q& ) = −G (q) + Ffriction (q& / |q& |)= 0 (3)J (q)q

L6

L5

L4

L3

x

however, it is most likely impossible to generate force
exactly and it is required additional equipment for generating
force. In this study, the gravitational force that always exists
everywhere manipulator and even free cost will be used for
detecting friction force.

177

(4)

By letting the left side of (2) be the left one of (4), thefollowing is satisfied
Ffriction (q& g / |q& g |)+ u = 0(5)

As (5) shows that the control input, u, cancels the friction
force, Ffriction (q& / |q& |) , the control input will becorresponding to the friction force. A friction force Ffriction(q)corresponding to joint variable q can be obtained. Base on

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

that friction force, the friction force can be added into control
effort as following and better control performance is
expected.
u enhanced = u nominal + Ffriction (q)(6)

the control efforts are shown in Fig. 4. They arecorresponding to forces overcoming the friction forces.
There are shown friction forces of 250 N average. Theyshow that the biggest friction is on 3rd actuator and thesmallest one is on 4th actuator.
1.0

4. Experiments

Linear actuator

0.9
Position, Length (m)A Stewart-Gough platform is constructed for the study.
It consists of 6 linear actuators with 400W class BLDC
(BrushLess DC) motor and ball-screw device. Detailspecifications are on the following Table 1, and theexperimental setup is shown in Fig. 2. A computation
burden for the controlling the parallel manipulator isvery significant. The controller consists of 2 parts, highlevel and low level controller. The high level controller,
PC takes role of kinematics computation while the lowlevel controller, TMS320C31 DSP takes role ofcontrolling each actuator.

0.8
Moving plate

0.7
0.6
0.5

0

0.05

0.1
0.15
Time (sec)

0.2

0.25

Table 1 Workspace of the developed parallel manipulator.
RangesPay Load

200kg

X-translation±0.2m

Y-translation±0.2m

Z-translation

±0.1m

Roll

±25o

Pitch

±25o

Yaw

±30o

Fig. 3 Trajectories of linear actuator and moving plate
under gravitational force alone.
500
Friction force (N)Items

Actuator 1

Actuator 2

Actuator 3

Actuator 4

Actuator 5

Actuator 6

250
500
250

0min
Low level
controller
L5

L4

DSP

L1

L2

As a result of detecting the friction force of the parallel
manipulator, the resultant shows that the friction force oneach actuator is different with each other though theconfiguration of the parallel manipulator is symmetric. The
reason is that all the parts for the parallel manipulator is not
ideally uniform and fabricated. If the same gain is applied tothe each controller without considering each actuator’s
property, it is hard to expect to obtain uniform control
performance.

Base

Parallelmanip ulator

High level
controller

x6 Motor drivers

Fig. 2 Schematic of experimental setup.

4.1 Friction force detection
Before performing the experiments, inverse dynamics ofthe parallel manipulator is computed under external force
condition of gravitational force. As the external force isapplied vertically to the parallel manipulator and theconfiguration of the manipulator is symmetric, every
trajectory of each actuator is the same. For that reason, only
one trajectory of the actuator and vertical trajectory of theend-effector are shown in Fig. 3.
Each actuator connected to the real parallel manipulator iscontrolled to follow the pre-computed free fall trajectory and©ISAROB 2005

max
man/minmax/minActuator length (min : 0.7m, max : 1.0m)

Fig. 4 Control efforts of six linear actuators which follow
the ideal trajectories.

PC

L6

L3

Top

178

4.2 Friction compensation
In order to check the validity of the detected friction force,the friction force profiles are applied to the controller. Thereis introduced a simple PID controller for showing potentialof the friction force compensation.
u = K P (q ref − q) + K I ∫ (q ref − q)dt + K D (q& ref − q& ) (7)where K P , K I , K D , q ref denotes proportional gains, integral

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

gains for compensating the gravity, derivative gains, andreference trajectory respectively. The detected friction force
is added into the control effort finally shown in (6).

6

Y - trajectory (cm)4
Trajectory
endpoint
error

0

without
frictioncompensated-2-4with friction
compensation

-6

-4

2
4
-2
0
X - trajectory (cm)

x103

0.0

-1.0
1.0

actuator 1

actuator 2

actuator 3

0.0

-1.0
0

actuator 4

actuator 5
1Time (sec)(b)

actuator 6
1

1

Fig. 6 Control inputs for (a) the controller without
friction compensation, and (b) the controller withfriction compensation.

5. Conclusions
There is introduced a friction force detection scheme for aparallel manipulator in this study. This scheme uses thegravitational force for free cost and makes the compensation
algorithm be only a part of control algorithm without any
additional equipment. Because the scheme is included in thecontroller as a part of algorithm, the friction detection
scheme can be carried out at every initial action or at any
time for the request. The possibility to carry out the friction
detection at every initializing becomes a very significant
merit when the friction force is always subject to change
according to environmental temperature, humidity, andoperating conditions.

Reference(circle)2

Control force (N)Figure 5 shows 2 cases; one is that only feedback
controller takes role of compensating property of eachactuator (assuming that mechanical property and friction
force on each actuator is the same), the other is thatpre-computed friction force is added into the control input.
In order for all 6 actuators to move simultaneously, a circular
trajectory is introduced for reference trajectory. Figure 5
shows that the tracking performance for friction
compensation with feedforwarding pre-computed friction
force to the control input along the joint variable q, is better
than that for no friction compensation. In case of no friction
compensation, the reason for the irregular tracking pattern isthat each error on the actuator is represented in end-effector
non-linearly due to the non-linear kinematics of themanipulator.

1.0

By performing experiment of friction compensation withthe detected friction force, the validity of the friction
detection method is presented. Since the friction
compensation method can be implemented into main
controller with feedforward form, it does not require
additional complicated computation burden.

6

Fig. 5 Tracking performance of the moving plate
following reference circular trajectory during the
PID control without and with friction
compensation.

Acknowledgements
Figure 6 shows control inputs of both cases. Though theoutlines of both cases are similar to each other, in case offriction compensation, the control input difference between
max. and min. is decreasing little bit. The reason is expectedthat the integral part increases the control input to overcome
the friction force.

Control force (N)0.0actuator 1

actuator 2

actuator 3

0.0
-1.0
0

©ISAROB 2005

actuator 4

actuator 5
1Time (sec)(a)

actuator 6
1

References[1] G. Lebret, K. Liu, and F. L. Lewis, “Dynamic
Analysis and Control of a Stewart Platform
Manipulator,” J. of Robotic Systems, 10(5),pp.629-655, 1993.
[2] S. Chae, C. Chung, H. Yang, Y. Choi, and S. Lee,
“Adaptive Control of a 6 DOF Stewart Platform Based
Machine Tool,” MOVIC ’98, vol. 2, pp.461-466, 1998
[3] L. W. Tsai, Robot Analysis: The Mechanics of Serial andParallel Manipulator, John Wiley and Sons, 1999.
[4] D. Stewart, “A platform with six degrees of
freedom,” Proc. Inst. Mech. Engr., 180(1), pp.371 –
386, 1965.
[5] M. W. Spong, M. Vidyasagar, Robot Dynamics andControl, John Wiley and Sons, 1989.

x103
1.0

-1.0
1.0

This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

1

179

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Real Time Control of Feature Based Visual Tracking for Dual-Arm Robot
Jong Soo Kim, Hong Rae Kim

Sung Hyun Han

Dept. of Mechanical Design, Graduate School,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

Division of Mechanical and Automation Eng.,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

spheres and cylinders. However, the time extracting
complex features will become too long based on limited
hardware. Accordingly, visual servoing scheme which
utilizes many features effectively is required.
Furthermore exploiting the information carefully fromthe features will give robust and accurate control
performance [4], [5], [6].
Sanderson et al. proposed a feature-based approachand defined the Jacobian of ideal inverse interpretation
which was considered as the infinitesimal change of therelative position and orientation between the camera andthe 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 [10], [11], [12]. The
criterion addressed the choice of which feature should be
used to control each actuator, where the number of
selected features is equal to the number of the actuator.
Feddema et al. [3], [4], [5], [6] also studied the selection
method of the features to make the Jacobian goodcondition. Real time experiment of gasket tracking
showed that the proper selection of features is necessary
to minimize the effect of image noise.
Papanikolopoulos et al. [5], [6], [7] experimentally
examined many control algorithms including
Proportional-Plus- Integral, pole assignment and linear
quadratic gaussian. Some adaptive control schemes werealso examined in [8]. These approaches do not consider
to use the redundant features which are defined as thefeatures whose number is more than the degrees of
freedom of the robot manipulator.
Chaumette el al. [8] and Espiau el al. [9] derived theinteraction matrix, and introduced the concept of task
function. Chaumette [2] extended the task function
approach to the complex features. Jang and Bien [10]mathematically defined the “feature”, and derived thefeature Jacobian matrix. The authors [12] derived theimage 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, theparameters to improve the control performance are very
limited and the controllability of the redundant features
are not discussed.
The authors proposed a linearized dynamic model ofthe visual servo system and linear quadratic control
scheme for redundant features [11], [13].

Abstract
This paper presents how it is effective to use many
features for improving the speed and the accuracy of thevisual servo systems. Some rank conditions which relate
the image Jacobian and the control performance arederived. It is also proven that the accuracy is improvedby 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..

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 ofsensory 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 from the often described use of vision inhierarchical task-level robot control systems. Many of
the control and vision problems are similar to thoseencountered by active vision researchers who arebuilding robotic heads. However the task in visual
servoing is to control a robot to cope with its
environment using vision as opposed to just observing
the environment.
Most visual servoing problems can be considered as
nonlinear control problems with the gray level of eachtwo dimensional pixel array being an observation. The
difficulty of the problem is the size and the nonlinearity.
The size of the observation is larger than ten thousand
and they have nonlinear interaction with each other. A
few researches based on the stochastic models of thetwo-dimension observation are found, but most visual
servoing schemes uses the features of the image as theobservation. To manipulate objects with complex shapes,
it is important to deal with complex features such as©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

operation region Μ θ ⊂ R m . We restrict the robot motionin the operation region. Thus the robot Jacobian J robot
is invertible in the working area. It is useful to introduce
the feature manifold Μ , which is defined by

The controllability problem was discussed but theperformance improvement by utilizing the redundantfeatures was not presented.
This paper presents how the control performance ofthe feature-based visual servoing system is improved byutilizing redundant features. Effectiveness of theredundant features is evaluated by the smallest singularvalue of the image Jacobian which is closely related tothe accuracy in the world coordinate system. Usefulnessof the redundant features is verified by the real time
control experiments. To illustrate the accuracy of theredundant visual servo system, real time experiments on
the Dual-Arm robot with eight joints are carried out.
Translation and rotation step response with three, fourand five features are examined in this experiment.

Μ = {ξ ∈ R 2 n : ξ = ψ (θ ),θ ∈ Μ θ }(3)

The features on the feature manifold is called theadmissible features. If the features are admissible, thenthe robot Jacobian is invertible by definition. In equation(3), θ represents joint angle.
Differentiation of the system model yields

Camera Model

Kinematic Model

2 System Modeling and Formullation
The object image moves with the joint angle to theobject image, which is composed of the kinematic modeland the camera model as shown in Fig.1. Suppose that acamera 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 definedby the joint angle θ based on the kinematic structure of
the robot. The camera model is a map from the positionof the camera to the camera 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 system[XYZ ]T and in the image plane [xy ]T .
The perspective projection with f being the focal
length of the lens is given by[x y ] = [XT

Y ] ( f / z)T

Robot
Kinematics Camera

Object Position
Fig. 1 System Modeling••ξ = Jθ

(4)

where the 2n × m matrix J is defined by⎡ J im( 1 ) ⎤⎢⎥J ≅ ⎢ M ⎥ c J ARM⎢ J im( n ) ⎥
⎣
⎦

(5)

(i )

The matrix J im is given by [14], [12]

(1)

⎡ f
⎢−
z
(i )
J im ≅ ⎢ i⎢⎢ 0⎣

Suppose that there are n feature points, namelyT
p i = [X i Yi Z i ] (i = 1,...., n) , on an object and thecorresponding positions in the image plane areTξ i = [xi y i ] (i = 1,...., n) . Assume that the shape and thesize of the object are known and constant (i.e., the object
is a rigid body). Then ξ i for i = 1,...., n become
functions of the joint angle θ . Let us define a 2nTdimensional feature vector by ξ ≅ [ξ 1T Lξ nT ] . Then thesystem model for n feature points is defined by the map
ψ : R m → R 2 n from the joint angle θ to the feature
vector ξ as follows:ψ (θ ) ≅ ξ
(2)

0
f
−
zi

xi
Zi
yi
Zi

xi yi
f
yi2 + f 2
f−xi2 + f 2
f
xi yi
f

⎤
yi ⎥
⎥
⎥
− xi ⎥⎦(6)and called the image Jacobian [12]. c J ARM is the robot
Jacobian expressed in the camera coordinate system.
Since the vector c J ARM θ& ∈ R 6 is the linear and angularvelocities of the camera expressed in the camera(i )coordinate system, J im becomes the infinitesimalchange of the position of the camera. Moreover,J (i ) ≅ J im( i ) c J ARM becomes the infinitesimal change of thefeatures according to the infinitesimal change of the joint
angles.
The degenerated features are the features for which
the extended image Jacobian is not full rank. The
degenerate features should be avoided because thewhere 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©ISAROB 2005+

PositionObjectPosition
Featurein Camera Projection
ObjectCoordinate
Image
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

inverse map (the map from ξ to θ ) becomes singular.
Thus, when the number of joints is m ,rank J (θ ) = m∀θ ∈ Μ θThen

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 ofn = 4 , we have the following theorem.

1

x1

y1

1

x2

y2

1

x3

y3

det M = 1
1x2x3y2 • 1y3
1x3x4

y3 • 1
y4
1

x4
x1

y4 •
y1

1
1

x4
x1

y4
y1

1

x2

y2

ER = sup||∆ξ ≠ 0

M=

(11)

∆θ
∆ξ

=

1
β min ( J )(12)where β min ( J ) is the minimum singular value of J .
Then the sensitivity ER decreases strictly by increasing
the number 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 have(8)

And substituting this into (6) yields
(9)

⎡ J ⎤
J n +1 = ⎢ ( nn+1) ⎥⎣J⎦

where M i and N are defined by©ISAROB 2005

where

T T
4

For evaluating the performance of the feature-based
visual servo 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 featurepoint is an effective way to improve the performance.
Let the joint error be ∆θ = θ − θ d and the feature errorbe ∆ξ = ξ − ξ d . Define the worst joint/feature error ratio
ER , called sensitivity, as follows:

Theorem 1: Suppose that there are four points on aplane and the corresponding feature vector is admissible.
Then the extended image Jacobian is full rank if any
three feature points out of them are not collinear in theimage 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

xi

yi

0

0

x / f

f

0

0

xi

yi

xi yi / f

0

0

0

− r

− 1
0

0
1

r
0

0
0

0

0

0

0

p
q

0
1

0
0

0
0

0
0

− p
− q

0
r

− r
0

0 ⎤
0 ⎥⎥
0 ⎥
⎥
r ⎥
− r⎥
⎥
0 ⎥
0 ⎥
⎥
0 ⎦⎥

(13)

where J ( n+1) is the 2 × m image Jacobian
corresponding to the newly added feature point. It is
straightforward to see thatxi yi / f ⎤ ,⎥y i2 / f ⎦

2
i

0

⎡− 1
⎢ 0
⎢
⎢ p
⎢
1 ⎢ q
N =
r ⎢ 0
⎢
⎢ 0
⎢ 0
⎢
⎣⎢ 0

T
3

3 Analysis of Visual Servoing

Fig. 2 Singular Cylinder⎡f
Mi = ⎢⎣0T
2Thus M is invertible because any three feature points
are not collinear. On the other hand, if p 2 + q 2 ≠ 0 , thefirst six 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, theextended image Jacobian J is full rank.

FeaturePointsJ im( i ) = M i N

J = MN c J ARM ,obtain[M M M M ] . It is straightforward to see that
T
1

(7)

f − px i − qy i
f
=
Zi
r

we

β min ( J n ) ≤ β min ( J n +1 )

(14)

(10)

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 fullrank. Since we assumed that the features are notdegenerated, the equal sing should be dropped. Thus

182

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

adding extra feature points strictly increases theminimum 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 )direction). The camera is controlled to keep the featuresat the initial positions. Thus the initial values and thereference values are the same. The object motion isconsidered as a disturbance for the plots of the featuresin the image plane. On the other hand, the object motion
becomes the step change of the reference position for theplots of the camera motion in the world coordinate
system. Since Dual-Arm robot has only 6 degrees of
freedom, the orientation of the object changed slightly.
Thus, the reference orientation is [2.8, 0, -1.8] degrees
expressed in the Euler angles, say p,η ,φ .

(15)

where J d = J (θ d ) is the image Jacobian at thedesired point[16]. Note that z and θ are one-to-one in
the neighborhood of θ d . The dynamics of the featureerror on the tangent space of the manifold Μ is given byz& = J dT J (θ )θ&height :40 mm
8 mm

8 mm

120mm

60 mm
120mm

120mm

Fig. 3 Configuration of Feature Points(16)

Thus, for a simple continuous time control law
θ& = − Kz with a positive definite constant matrix K
yields an asymptotic stability if J dT J (θ ) is positive
definite. It is shown 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 arearranged to make 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 oneof five points at the center of the square, which hasheight 60mm. Dual-Arm robot holds the objects and acamera(Fig.4).
The
world
coordinate
systemω X − ωY − ω Z is at the base of the Dual-Arm robot. A
nominal camera position is almost in front of the planeon which the marks exist. To avoid the singularcylinder(Fig.2) the optical axis and the normal axis of
the object plane are not aligned. The distance is about
1000mm. The features are the x and y coordinates of
the center of the image of each mark. Computing theirminimum 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
Content

Remark

1 Arm

deg

180

deg

450

Z Axismm

150

R Axisdeg±180

Maximum Reachmm(350+260)

PayloadKg

2.5

High-speed

Max. Resultant Vel.

m/sec

5.4

1,2 Axis

Plane

mm

0.05

1,2 Axis

Z Axismm

0.02

M Axisdeg

0.05

WeightKg

200

Coincident Control
Axis No.

EA

8Axis
(4+4)

(17)
Position
Repeatabil
ity

183

Spec.

2nd Arm

WorkspaceThus accuracy of the position control of the camera in
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©ISAROB 2005

Unit
st

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fig. 5 Response in Image Plane for 3 Points(Vertical Step)

Fig. 8 Error in 3D for 3 Points (Vertical Stop)

Fig. 6 Response in Image Plane for 4 Points(Vertical Step)

Fig. 9 Error in 3D for 4 Points (Vertical Step)

Fig. 7 Response in Image Plane for 5 Points(Vertical Step)

Fig. 10 Error in 3D for 5 Points (Vertical Stop)©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fig. 5 has six curves which show the x and ycoordinates of the three feature points in the image plane.
The horizontal axis is the time. The curves disturbed
largely are the y coordinates and the others are the xcoordinates. They are almost stabilized in two seconds.
Fig.7 depicts the image coordinates of five points. All
responses in the image plane are similar to each other.
The plots in Fig.8 depicts the position errors of thecamera for three feature points (measured in the world
coordinate system). The error in ωY direction isdiverging. However, as shown in Fig.9, the response of
the camera position with four feature points is stabilized.
It is sluggish, and it takes more than 20 seconds tostabilize the disturbance. Fig.10 is the response with fivefeature points. It is improved very much for both speed
and accuracy. The steady state errors are smaller than
5mm for all directions

K. Hashimoto, T. Ebine, and K. Kimura,servoing
with
hand-eye
1996,“ Visual
manipulator― optimal control approach,” IEEE
Trans. on Robotics and Automation, vol. 12, no. 5,
pp. 766~774.
[5] K. Hashimoto. T. Ebine, K. Sakamoto, and H.
Kimura, 1993, “ Full 3D visual tracking withnonlinear model-based control,” in American
Control Conference, San Francisco, Calif., pp.
3180~3184.
[6] L. E. Weiss, A. C. Sanderson, and C. P. Newman,
1987, "Dynamic sensor-based control of robots
with visual feedback," IEEE J. Robotics andAutomation, vol. RA-3, no. 5, pp. 404~417.
[7] J. T. Feddema and O. R. Michell, 1989, "Vision
guided servoing with feature-based trajectory
generation,"IEEE Trans. Robotics and Automation,
vol. 5, no.5, pp. 691~ 700.
[8] K. Hashimoto et al., 1991,“ Manipulator control
and with image-based visual servo,” in IEEE Int.
Conf. Robotics and Automation, Sacramento, Calif.,
pp. 2267~ 2272.
[9] W. Jand and Z. Bien, 1991,“ Feature-based visual
servoing of an eye-in-hand robot with improved
tracking performance,” in IEEE Int. Conf. Robotics
and Automation, Sacramento, Calif., pp.
2254~2260.
[10] B. Espiau, F. Chaumette, and P. Rives, 1992,“ Anewapproachtovisualservoing
inrobotics,” IEEE Trans. Robotics and Automation,
vol. 8, no. 3, pp. 313~ 326.
[11] F. Chaumette, 1994, "Visual servoing using image
features defined upon geometrical primitives," inProc. 33rd Conf. Decision and Control, Lake
Buena Vista, Florida, pp. 3782~3787.
[12] J. T. Feddema, C. S. G. Lee, and O. R. Michell,
1987, "Automatic selection of image features forvisual servoing of a robot manipulator," inIEEE Int.
Conf. Robotics and Automation, Scottsdale, Ariz,pp. 832~837.
[13] N. Papanikolopoulos and P. K. Khosla, 1993,“ Adaptive robotic visual tracking: theory andexperiments,” IEEE Trans. Automatic Control,
vol. 38, no. 3, pp. 429~445.
[14] N. Papaniklolpoulos, P. K. Khosla, and T. Kanade,
1991, “ Vision and control techniques for robotic
visual tracking,” in IEEE Int. Conf. Robotics andAutomation, Sacramento, Calif., pp. 857~864.
[15] F. Chaumette, P. Rives, and B. Espiau, 1991,“ Positioning of a robot with respect to an object,tracking it and estimating its velocity by visual
servoing,” in IEEE Int. Conf. Robotics andAutomation, Sacramento, Calif., pp. 2248~2253.
[16] H. Longuet-Higgins and K. Prazdny, 1984, “ Theinterpretation of a moving retinal image,” Proc.
Royal Soc. London Ser. B, vol. 208, pp. 385~397.
[4]5 Conclusion
In this paper, it has been presented how the control
performance of the feature-based visual servo system isimproved by utilizing redundant features. Effectivenessof the redundant features is evaluated by the smallest
singular value of the image Jacobian which is closely
related to the accuracy in the world coordinate system. It
shows that the accuracy of the camera position control in
the world coordinate system was increased by utilizing
redundant features. Real time experiments on dual-armrobot were carried out to evaluate the improvement of
the accuracy and speed by utilizing the redundantfeatures. The results verifies that the minimum singularvalue of the extended image Jacobian plays an importantrole for performance improvement of the feature-based
visual servoing.

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education& Human Resources Development (NURI MECHANO 21).

References
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 andgrasping of a moving object with a robotic handeye system,"IEEE Trans. Robotics and Automation,
vol. 9, no. 2, pp. 152~165.
[1]©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Calibration and Control Experiments on Redundant Legsof a Stewart Platform based Machine Tool
Woo Song Lee

Han Sung Kim
School of Mechanical and Automation Eng.,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

Dept. of Mechanical Design, Graduate School,
Kyungnam University,
Masan, Gyeongnam, 631-701, Korea

axes; One (7th leg) is mounted along the X-axis and the other(8th leg) is along the Y-axis as shown in Figs. 1 and 2.
Since the six legs of a Stewart platform fully define theposition and orientation of the end-effector, the lengths ofthe two redundant legs cannot be arbitrarily determined.
When there exist some kinematic errors in the redundantlegs, the legs need to have some compliance not to break thesystem. To give some compliance to the redundant legs andto make the redundant legs act like linear spring, a passive
force control method is developed. In order to reduce
undesired internal forces between the redundant legs and the
Stewart platform, the initial lengths of the springs should be
accurately determined. For that purpose, the kinematic
calibration method of using constrained optimization issuggested. The experiment result of the calibration showsthat the suggested algorithm is more robust to measurement
noises that the previous ones [2-5].
This paper is organized as follows: First, a prototype
Stewart platform based machine tool with two redundantlegs is demonstrated. The passive force controller for the
redundant legs is suggested and the kinematic calibration of
the redundant legs is performed. Finally, cutting experimentresult is presented to verify the effectiveness of theredundant actuation method.

Abstract
In recent robotics research, a parallel-kinematic manipulator has been increasingly studied for possible use as amachine tool due to the advantages of high stiffness andaccuracy over serial-kinematic manipulators. In general, aspatial parallel manipulator has some limitations forincreasing the stiffness only with six actuators. In order tofurther increase the stiffness of a machine tool, the method
to add more than one additional actuator may be considered,although it may cause some more cost and restrict theworkspace to some extent. In this paper, a prototype Stewart
platform based machine tool with two redundant legs isdemonstrated. The passive force controller for the redundantlegs is suggested and the kinematic calibration of theredundant legs is performed. Finally, cutting experimentresult is presented to show the effectiveness of the redundantactuation method.

1

Introduction

It has been well recognized that the Gough-Stewart typeparallel manipulator, or referred to here shortly as the
Stewart platform, has some advantages over serial-type
manipulators in view of positioning accuracy and stiffness[1]. Among all the possible applications, the Stewart
platform interests many researchers especially in using it asa machine tool. In designing and evaluating a machine tool,
stiffness may be one of the most important factors to beconsidered, since the stiffness directly affects accuracy inmachining applications. Although a parallel manipulator isusually stiffer than a serial manipulator, it is made up of
several serial chains. For example, a Stewart platform
consists of 6 serial chains, which can be modeled as 6 linear
springs connecting base to moving platform. If there arelimitations to increase the stiffness of each serial chain, theremaining way to further increase the Cartesian stiffness is
to add more serial chains, i.e., springs in parallel.
With this regards, the redundant actuation method to add
more than one additional serial chain is suggested. In
general, the stiffness of a Stewart platform along the X- andY-axes is smaller that that along the Z-axis. Therefore, inthis work, two redundant legs are placed on the XY plane, inorder to further increase the stiffness along the X- and Y-©ISAROB 2005

2

System Configuration

The overall system of the Stewart platform based
machine tool system with two redundant legs is shown in
Fig. 1. The kinematic parameters of the manipulator are as
follows (refer to Fig. 2 and [6]):rb = 400 , rm = 150 ,li ,min = 801 , ∆li = 364 , for i = 1, 2,K,6
where rb and rm denote the radii of the base and moving
platforms, respectively, li ,min and ∆li denote the minimum
length and stroke of a leg, and the unit of length ismillimeter. The locations of the spherical joints of themachine tool with respect to each coordinate system can be
expressed bybi = rb [cos Λ i , sin Λ i , 0]T
M i = rm [cos λi , sin λi , 0]T , for i = 1, 2 , K , 6

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3

Passive Force Control

Since the six legs of the Stewart platform fully define
the position and orientation of the end-effector, the lengthsof the two redundant legs cannot be arbitrarily determined.
If all the kinematic parameter values were perfectly known,
the lengths of the redundant legs can be simply determinedby the inverse kinematics. However, since it is almost
impossible to know the exact kinematic parameter values,
the redundant legs must have some compliance, otherwise,
the manipulator may not move or very large internal forces
may be generated, which could break some parts.
For better stability, the following passive force controller
is suggested, which can provide two virtual linear springs to
the moving platform in cutting as shown in Fig. 3. Therefore,
the static and dynamic errors of the moving platform due tocutting force may be reduced. In this work, the passive force
controller with the trajectory estimator is suggested byControl Law: f = k p ( xˆ− x ) − k d x&
(1)
Trajectory Estimator: xˆ= x d +

1
fs
kf

(2)

where x d and x denote respectively desired and actual
positions and x& is the derivative of actual position withrespect to time. f s is the measured force from the load cellmounted at the end of a redundant leg. xˆ is the estimatedtrajectory based on the information of the force sensor.
k p and k d are the proportional and derivative gains,

Fig. 1 Prototype Stewart platform based machine toolwith two redundant actuators.

respectively, and k fcorresponds to the stiffness of aredundant leg. The block diagram of the suggested passive
controller is shown in Fig. 4. The reason for using the
trajectory estimator instead of the traditional stiffness
controller is that the back-drivable force of redundantactuators is relatively large.

Fig. 2 Kinematic configuration of the manipulator.

where Λ = [60 − φ , 60 + φ ,180 − φ ,180 + φ , − 60 − φ , − 60 + φ ] ,λ = [φ ,120 − φ ,120 + φ , − 120 − φ , − 120 + φ , − φ ] , φ = 10 and
φ = 16 .
The minimum length and stroke of a redundant actuator are
given byli ,min = 291 , ∆li = 364 , for i = 7 ,8 .
o

o

o

b

o

m

o

m

o

b

o

m

o

b

b

b

o

m

Fig. 3 Planar representation of the passive force controllers.

o

b

o

m

m

b

o

m

1/ k fxd

The locations of the spherical joints of the redundant actuatorswith respect to each coordinate system are given by
b7 = [−725, 0, 985]T , b8 = [0, 725,1045]Tkp
kd

M 7 = [−255, 0, 62]T , M 8 = [−255 sin 10o , 255 cos10o , 62]T©ISAROB 2005

xf

Redundant
Actuator

Fig. 4 Block diagram of the suggested passive force controller.

187

fs
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x

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4

different values for the kinematic parameter [5]. With thisregards, the optimization with inequality constraints issuggested for the kinematic calibration. The constrained
optimization problem for the kinematic calibration can bestated as follows: [8]Kinematic Calibrationm

Minimize: H = ∑ F k2

Subject to: δ li ≤ el , δ M i ≤ e m , δ bi ≤ eb

(7)

k =1

where the subscript k = 1, K , m denotes the number of
each measurement, the bounds of kinematic parameter
errors can be obtained from the information on tolerances ofparts and assembling errors, and the objective function is
given by~2
2
F k ≡ ( l i + δ l i ) 2 - li, k2 − δ M i − δ b i(8)~~− 2 ( l i T R k δ M i − l i T δ b i − δ M iT R k δ b i )Fig. 5 Kinematic error model.

4.1 Kinematic Error Model

4.3 Experiment Results

In Fig. 5, the outer vector loop represents the actualmodel to be estimated through calibration, and the inner
vector loop indicates the nominal model based on the CAD
data. The actual leg length and position vectors of thespherical joints can be written asli = l i + δ li , M i = M i + δ M i , bi = b i + δ bi
(3)
where the kinematic parameters for a nominal model are
expressed with an upper bar. δ li , δ M i and δ bi are thekinematic errors of a leg length and locations of sphericaljoints at the moving and base plates, which will be estimatedby the kinematic calibration.
Using the vector loop in Fig. 5, an actual leg can be
expressed by~(4)
l i = li + R δ M i − δ bi~
where l i is defined as a virtual leg by [6-8]~(5)li = x + R M i − bi
where x and R denote the actual position vector androtation matrix of the end-effector. The quadratic form of Eq.
(4) can be written as the following:~22
l i2 = ( l i + δ l i ) 2 = li 2 + δ M i + δ b i(6)~~+ 2 ( l i T R δ M i − l i T δ b i − δ M iT R δ b i )In this section, the effectiveness of the constrained
optimization is verified through the calibration experimentson two redundant actuators of the Stewart platform in Fig. 1.
Since the proposed passive force controller for redundantlegs is basically based on a position control, the accurate
kinematic information becomes one of the most importantfactors in control. Since the lengths of six non-redundantlegs are given, the position and orientation of the endeffector can be obtained from the forward kinematics, which
will be used for the measurement positions and orientationsin the calibration of redundant legs.
In order to show the effectiveness of the proposedconstrained optimization method, the results from theunconstrained and constrained optimization methods havebeen compared. The bounds of the kinematic errors are
assumed as
δl7 ,8 < 0.05 , δb7 ,8 < 50 , δM 7 ,8 < 1 [ mm] .

4.2 Kinematic Calibration Method

When there exist some measurement errors not to be
negligible, the calibrated kinematic values may be updated
to an undesirable direction. Although the exact information
about the kinematic errors cannot be known without thewell-organized calibration method, the bounds of thekinematic errors may be estimated. When the updated
kinematic parameters will go to an unexpected point due tomeasurement noises, it may be required or useful to impose
the inequality constraints on the kinematic errors.
Furthermore, even in absence of noise in the measurement,
the previous calibration algorithms may lead to up to 20©ISAROB 2005

It is noted that the bounds of the constraints are based on theinformation about tolerances of parts and assembling errors.
It can be seen that the kinematic errors from the constrained
optimization are obtained within the ranges of constraints.
In Fig. 6, the errors between the measured and thecalculated lengths before and after calibration are plotted at
the sixteen measured points. From these plots, it can be said
that the proposed calibration algorithm using constrained
optimization is more effective than the previous algorithm
using unconstrained optimization. The length errors fromthe constrained optimization are within ± 1 mm.
2

Before Calibration

50

Length Error of Leg 8[mm]Length Error of Leg 7[mm]64Method 1(without constraints)

3
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Fig. 6 Calibration results of the redundant legs.

188

12

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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5

Cutting Experiment

6

In order to show the effectiveness of the redundantactuation scheme, the dynamic errors in cutting have beenmeasured both for the non-redundant and redundant cases.
Some of the cutting conditions for the machining
experiment are as follows:
Tool: flat end-mill with 2 flutes and φ 10 [mm],Feedrate: 30 [mm/min],Spindle speed: 2000 [rpm],
Material: Al 2024,
Measurement device: resolution with 1[ µm ].
The cutting direction is along the X-axis, and using thelinear encoder, the dynamic error in the cutting operation ismeasured along the X- and Y-axes for both non-redundant
and redundant cases as shown in Fig. 7. In Fig. 8(a) and (c),the dynamic errors along the X- and Y-axes are plotted whenno redundant legs are used. In Fig. 8(b) and (d), the dynamic
errors are plotted when redundant legs are used. From Fig. 8,
it can be seen that the dynamic error along the Y-axis, i.e.,
perpendicular to the cutting direction is larger than thatalong the X-axis, i.e., the cutting direction. For the cutting
experiment with 2mm depth, the maximum dynamic error
along the Y-axis is reduced about from 120 µm (without
redundant actuation) to 60 µm (with redundant actuation).
However, it is noticed that the static error in the redundantcase is much larger than that in the non-redundant case. The
major source of relatively large static error in the redundantcase is the inaccurate kinematic information.

0.16
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(b) with redundant actuation-Fig.7.20(c)
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X-axis[mm](d) with redundant actuation-Fig.7.20(d)Fig. 8 Maximum dynamic error (depth: 2mm).

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189

Conclusions

The prototype Stewart platform based machine tool with
two redundant legs is developed. For the redundant legs, thepassive force controller is suggested, which provides virtual
linear springs to the cutting tool so as to increase thestiffness along the X- and Y-axes and to reduce the dynamic
error in cutting. The kinematic calibration method of using
constrained optimization is suggested, which is robust tomeasuring noise. The calibration experiment on the two
redundant legs shows that the constrained optimization
method provides more reasonable solution than theunconstrained one. Using the updated kinematic parametersof the redundant legs and the suggested controller, thecutting experiment is performed. It shows that the redundantactuation scheme can increase the overall stiffness andreduce the dynamic error. However, it may yield larger static
error if the kinematic calibration is not perfect. In the further
works, we will focus on reducing the static error.

Acknowledgements
This research was supported by the New University for
Regional Innovation program of the Ministry of Education &Human Resources Development (NURI MECHANO 21).

References[1] A. J. Wavering, “Parallel Kinematic Machine Research
at NIST: Past, Present, and Future,” Parallel Kinematic
Machines, pp. 17-32, Milano, Italy, June, 1999.
[2] H. Zhuang and Z. S. Roth, “Method for Kinematic
Calibration of Stewart Platforms,” Journal of Robotics
Systems, vol. 10, no. 3, pp. 391-405, 1993.
[3] Z. J. Geng and L. S. Haynes, “An Effective Kinematic
Calibration Method for Stewart Platforms, ” ISRAM, pp.
87-92, 1994.
[4] T. Roppenen and T. Arai, “Accuracy Analysis of a
Modified Stewart Platform Manipulator,” IEEE
International Conference on Robotics and Automation,
pp. 521-525, Nagoya, Aichi, Japan, 1995.
[5] C. Innocenti, “Algorithms for Kinematic Calibration of
Fully-Parallel Manipulator,” In J.-P. Merlet B.
Ravani(eds.), Computational Kinematics, pp. 241-250,
1995.
[6] H. S. Kim, Design and Control of a Stewart Platform
based Machine Tool, Ph. D. Thesis, Yonsei Univ., Korea,
2000.
[7] H. S. Kim and Y. J. Choi, “The Kinematic Error Bound
Analysis of the Stewart Platform,” Journal of Robotic
Systems, vol.17, no.1 pp. 63-73, 2000.
[8] H. S. Kim, B. J. Kim and Y. J. Choi, “Kinematic
Calibration for Redundant Legs of a Stewart Platform,”
Proceedings of the 32nd International Symposium on
Robotics, Seoul, Korea, 19-21 Apr., 2001.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A sociable and aﬀective artiﬁcial cohabitant
Naoko Matsumoto & Akifumi Tokosumi
Department of Value and Decision Science
Tokyo Institute of Technology
2-12-1 Ookayama, Meguro-ku, Tokyo Japan{matsun, akt}@valdes.titech.ac.jp
Abstract

learning system according to user actions. The popularity of the toy doll is evident in the fact that morethan one million units have been sold over the last ﬁve
years in Japan alone.

In this paper, we summarize our previous researchconcerning human-artifact relations conducted overthe past few years from an interdisciplinary perspective encompassing cognitive science, psychology, andhuman-robot interfacing. Based on our ﬁndings, we
discuss the cognitive signiﬁcance of a newly-emerging
sociable and aﬀective artiﬁcial cohabitant.
Keywords: attachment, aﬀective artifact, toy doll, cohabitant, social interactions

1

Introduction

Why do people feel strong aﬀection toward artiﬁcial things such as toy dolls, robots, some characters?
Japan is seeing a craze for talking toy dolls. We haveinvestigated this form of human-artifact relation overthe past few years from an interdisciplinary perspective encompassing cognitive science, psychology, andhuman-robot interfacing. In this paper, we summarize
our ﬁndings obtained through analyses on the texts of
fan letters sent to the toy company by the users of
Primopuel (produced by BANDAI. Co., Ltd), a talking toy doll. The purpose of this paper is to discuss
the cognitive signiﬁcance of a newly-emerging sociable
and aﬀective artiﬁcial cohabitant.

2

Figure 1. Primopuel3

The research methodology we have adopted is toanalyze the texts of fan letters sent to the toy company by Primopuel users. We analyzed 51 fan lettersmailed to the company and 271 electronic mail messages submitted to the manufacturer s web site. In
order to determine the underlying cognitive states of
the users from the textual data, we categorized propositions in the texts according to a classiﬁcation system([8]), which has a number of sub-categories, such as
descriptions of the toy as an artifact , personifying
descriptions of the toy , user actions toward the toy
as an artifact , user attachment behaviors , anduser actions toward others mediated by the toy .

The craze for an artiﬁcial cohabitantPrimopuel (Figure 1), produced by BANDAI. Co.,
Ltd., is very popular in Japan among middle-aged people. Primopuel has touch sensors, a sound sensor, atemperature sensor, and a calendar system, and a talking function (250-280 expressions) (e.g. I love you.
Good morning.
How s your life? ). The voiceof a 5-year-old boy was adopted as the voice for thetoy. Utterance selection is controlled based on an easy

©ISAROB 2005

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4

Characteristics of the toy and users

their perception of the toy (as toy only, as cohabitant
only, or as both toy and cohabitant).

In this section, we brieﬂy present some of research
results and outline our views concerning (i) the characteristics of aﬀective artifacts and (ii) the characteristics of user states experienced by users who regard
to the toy as a cohabitant artifact.

4.1

Toy story

4.1.1

Aﬀective cohabitant

Perception of the toy as a cohabitant is a key in
evoking user attachment. We suggest three factorsthat prompt users to regard the toy as a cohabitant;namely, (a) inferable states, (b) reactions, and (c) timesharing. First, approximately 85 % of the propositions in which users describe what they ﬁnd attractive
about the toy mention the toy s utterances (53 out of
63 propositions) rather that its appearance ([8]). For
instance, He (= the toy) says Good night to me
and I feel all warm inside or He asked me to make ascarf, so I made one for him. Users clearly make inferences about the toy s state from its utterances andregard it as a cohabitant. Second, the results of factor
analysis indicate that the toy s reactions to user behavior evoke in the users strong aﬀection toward thetoy. Third, factor analysis indicates that caring behavior and time-sharing with the toy also evoke useraﬀection ([5]).
4.1.2

N=322 (51 letters and 271 e-mails)
Figure 2. Users recognition about what the toy is
Another issue examined is whether the toy is regarded as an artifact or whether it is personiﬁed inany way? We extracted descriptions that are relevantto this issue. While 1,130 propositions (36.1 %) indicate the user regarding the toy as an artifact (e.g. Ichanged the batteries ), 809 propositions (25.8 %) suggest that the user are personifying the toy (e.g. ”He(= toy) seems to sleep well ”). These results lead us tothe conclusion that perception of the artifact as a cohabitant is an underlying cognitive state of users who
experience strong aﬀection for the artifact.
4.2.2

We have collected a total of 292 propositions that indicate attachment behaviors, where there is a clear relationship between positive emotions/evaluations andactions, such as this toy is so cute, I showed it to myfriend and this is really lovely, so I will buy anotherone. More concretely, attachment behaviors include
naming (”I named him Tatsu”), conversation ( I talkwith him ), inferring the toy s state ( He seems to
be cold ), social actions ( She proudly shows her Primopuel to her friend ), negative actions toward thetoy ( I ignore him.. ), and being together ( I took
him for a drive ). We have hypothesized that attachment behaviors function in strengthening attachment
emotions ([6]). This has been conﬁrmed by the results of factor analysis (e.g. User descriptions like I
ignore him (= toy) for a while, and then he seemed
to get angry. The way he gets angry is very lovely,
I can t resist it, indicate that attachment behaviors strengthen attachment emotions.) ([5]). There isalso an age diﬀerence in terms of self-cognition. While
young people more often evaluate the toy positively(young (0-39): 21.1 %, middle-aged (40-): 10.4 %),middle-aged people more frequently describe their attachment behavior (young: 10.7 %, middle-aged: 34.3

Sociable cohabitant

An interesting characteristic of aﬀective toys is theirsociable function. We have found that the toy s
character facilitates social behavior, which, in turn,strengthens user aﬀection ([5]). This ﬁnding prompts
us to regard the toy as being a sociable and aﬀective
artiﬁcial cohabitant.

4.2

Fan story

4.2.1

Perceiving the toy as cohabitant artifact

Users regard the toy as a cohabitant artifact, toward
which they can experience strong aﬀection. Counting
expressions related to Primopuel, we found 34 (66.7%) letters and 67 (24.7 %) e-mails including such expressions. Users described Primopuel as either a toy(toy, stuﬀed toy, toy doll) or as a cohabitant (family
including grandchildren, child, brother, partner, roommate, friend, idol, pet). Even within a single letter, it
is possible to observe mixed cognition towards the toy.
Figure 2 presents a breakdown of users according to©ISAROB 2005

Attachment behaviors eﬀect

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005%). Although young people tend to just describe attachment emotions, such as it s very cute, middleaged people also mention causes and attachment behaviors, such as it s very cute, especially its face,which makes me want to hold him tightly. This indicates that middle-aged people demonstrate greater
meta-self-recognition concerning their emotion states
and actions toward the toy rather than young people.
4.2.3

positive self-recognition can, in turn, lead these people
to have a sense of well-being in their physical/mental
states. Improved physical/mental states can facilitatesocial actions. Extending Norman s claim that attractive things work better as a heuristic of problem solving ([9]), our ﬁndings indicate that attractive things
can heighten one s sense of positive self-awareness.
Moreover, our results not only support previous research that shows that attachment fosters emotional
communication skills in human babies and higher cognitive skills ([1]), but they also highlight the eﬀects infacilitating social behavior.
This artifact which evokes human aﬀection may beseen as a new kind of sociable and aﬀective artiﬁcial
cohabitant, because users both regard it as a cohabitant and believe that it enhances their social actions.
This kind of sociable and aﬀective artiﬁcial cohabitant
is now emerging in our daily lives, particularly in situations where people lack rich social interaction, suchas elderly people living alone, single workers, being an
only child, in the nuclear family, and computerholics,
for individuals who have suﬃcient meta-self recognition.

Life-state improvements

The positive physical and mental states of users areoften attributed to the toy, with 8.9 % of the descriptions indicate positive changes in life state (e.g. talking with the toy makes me relax,
The toy gives me
warmth, energy, and vitality
Primopuel makes mylife enjoyable ) ([8]). Moreover, it was found thatusers believe the toy enhances interaction with family
members and/or with friends, as evidence by 16.4 % of
the letters and 10.7 % of the e-mails. (e.g. I give Primopuel to my neighbors as a present to let them know
just how cute he it ). One of ﬁve factors extracted
in our factor analysis, social action triggered attachment, relates to how attachment emotions can facilitate social behavior ([5]). This is consistent with our
cognitive Socially-supported Emotion Model (SEM)([7], [8]). In addition, we have observed that middleaged people more frequently experience shifts in theirinteractions with others (22.4 %) than young people(14.4 %) ([7]). Another ﬁnding is that descriptions ofnegative user life states before obtaining the toy arecorrelated with their sense of improved well-being (e.g.
I have lived alone since my husband passed away. I
felt a keen sadness in this house. Since getting the toy,
I can say Good night in bed, and often smile. )([5]). Taking these results together, clearly the users
with high meta-self-recognition can experience state
improvements due to the aﬀective artifact.

55.2

Our results indicate that one factor that strengthens user aﬀection for the cohabitant artifact is its
social eﬀects. That is, both attachment emotions
and social interaction are mutually strengthened eachother. This notion is consistent with the cognitive
Socially-supported Emotional Model (SEM) of emotional transmission that we have developed ([7], [8]);people strengthen their attachment to the toy by interacting or communicating with other people who also
have attachment to the cohabitant artifact. As Kitayama ([3]) claims subjective well-being is dependent
upon the cultural constructions of emotion, which issimilar to the notion of socially-transmitted emotions
that is incorporated within SEM.

Sociable and aﬀective artiﬁcial cohabitant

5.3
In this section, we discuss the newly-emerging sociable and aﬀective artifact cohabitant and our cognitivemodel SEM based on our ﬁndings described in Section
4.

5.1

Future work

Our research, which has investigated the craze for atalking toy doll, has certain advantages from analyzing
fan letters in obtaining insights into the daily psychological states of the users of the toy. It would not be
possible to gain such insights within the experimental
setting. We can extract the underlying cognitive states
relating to the natural relationships between the user
and the toy. However, further investigation is required
employing other research methods, such as interviews

A newly-emerging artifact

People with attachment emotions, that is, people
who have a strong positive aﬀection toward something
are able to perceive themselves more positively. This

©ISAROB 2005

Socially-supported Emotional Model
(SEM)

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and questionnaires. Moreover, this craze is very limited in being restricted to within Japan and in involving a particular talking toy doll, which points to theneed to conduct comparative studies with other formsof artifacts. It is hoped, however, that our research cancontribute to a better understanding of human-artifact
relationships involving strong human aﬀection.

6[5] Matsumoto N., Tokosumi A. (Submitted), Sense ofwell-being based on empathy for cohabitant character.
[6] Matsumoto N., Tokosumi A., Hirai Y. (2004a), Affection for cohabitant toy dolls. Computer Animation and Virtual World, 15(3-4):339-346.
[7] Matsumoto N., Tokosumi A. (2004b), Comparison
of aﬀection for toy dolls between middle aged people and young people (In Japanese). Proceedingsof the 4th Conference of the Toys for Well-Being
Association, Sibuya, Tokyo, Japan, July 4, 2004,
pp. 13-15.

Summary

In this paper, we have identiﬁed a newly-emerging
sociable and aﬀective artiﬁcial cohabitant in our daily
lives. This kind of artiﬁcial cohabitant may play arole in enriching daily life through improving physical/mental health and enhancing social interaction.

[8] Matsumoto N., Hirai Y., Tokosumi A. (2003), Toydoll as a cohabitant artifact (In Japanese). Cognitive Studies,10(3):383-400.

Acknowledgements[9] Norman D. (2002), Emotion and design: Attractive things work better. ACM Interactions JulyAugust:36-42.

The work presented here is part of the 21st Century
COE Program Framework for Systematization andApplication of Large-scale Knowledge Resources andﬁnancially supported by the Japanese Ministry of
Education, Culture, Sports, Science and Technology
within the 21st Century COE (Center of Excellence)
Program framework. We are greatly indebted toProfessor Furui for his continuous support. We would
also like to thank Dr. Terry Joyce and Yoko Hirai for
their helpful comments.

References[1] Greenspan S. I., Shanker S. G. (2004), The first
idea: How symbols, language and intelligenceevoked from our primate ancestors to modern humans. Da Capo Press.
[2] Haga Y. (2002), Contemporary toy business andtomorrow of robot toy (In Japanese). Journal of
the Robotics Society of Japan, 20(7):683-686.
[3] Kitayama, S., Markus, H. R., Kurokawa, M.
(2000), Culture, emotion, and well-being: Goodfeelings in Japan and the United States. Cognition
and Emotion, 14:93-124.
[4] Libin E., Libin A. (2002), Robotherapy: Deﬁnition, assessment, and case study. Proceedings of theEighth International Confrence on Virtual Systems
and Multimedia, Creative Digital Culture, pp.906915.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Child-robot interaction mediated by building blocks:
From field observation in a public space
Miki GOAN, Hiroyuki FUJII

andMichio OKADAATR Network Informatics Laboratories
Department of Ecological Communications
2-2-2 Hikaridai, "Keihanna Science City", Kyoto 619-0288, Japan
Phone: +81-774-95-2626 Facsimile: +81-774-95-2653
goamik@atr.jp
Keywords: field observation, communication, robot, childrenof the things in front of them by themselves. Vygotsky
revealed from this experiment that children have faintbut ceaseless mental activity, which is normally buried in
the parent’s supports[1]. He called their development ofsuch activities ‘The Zone of Proximal Development. [2]’HaroldGarfinkel,
who
wasoriginatorofEthnomethodology, implemented a series of ‘breachingexperiments’ in order to find a method to construct ‘seen
but unnoticed’ reality in daily life[3]. This attempt
disrupted the general ideas held in daily life byartificially making a situation that betrays ‘backgroundexpectancies’ such as rules or social common sense,
which are not explicitly visible because they arenaturally and tacitly shared by people.
A communication robot sometimes confuses our
natural human communications, since it looks neither
mechanical nor the same as a human. The authors intend
to clarify the process of development in humancommunications by investigating the behaviors ofchildren and their parents in front of the robot, which isthe very research theme of Vygotsky and Garfinkel. The
communication robot ‘Muu’ used in this study has
restricted capability in its functions so as to constructmeanings of things through communications with others(humans). The authors call this type of design method the‘minimal design of relationship.’

Abstract
This study attempts to describe children's behaviors
from the viewpoint of microscopic adjustment of actions
when they encounter an oddly shaped robot, called‘Muu.’ We investigated this through field observation
at a workshop in a children’s museum. Various aged
children and their parents participated in the workshop
together. They were instructed by an experimenter toplay with building blocks while talking with Muu. As aresult, it was found that the children and the robot could
establish rich communications with each other not when
the children evaluated Muu’s behavior but when Muu
evaluated the children’s works. This indicates that the
robot could become an ‘other’ that might interact withchildren mediated by the building blocks, whereas many
children and parents treated it as a ‘toy’, just as thebuilding blocks where considered merely ‘objects’ during
interaction.

1. Introduction
How do children behave toward robots, especially arobot that asks to communicate with them? Do theygather around the robot because of its novelty but thensoon lose interest? Or would such a robot become an
object to which the children attached themselves? This
study investigates how a robot should be put to practicaluse in a social organization from the viewpoint ofsystems engineering and also considers the development
processes in human communications.
A developmental psychologist, L. S. Vygotsky
attempted to investigate children’s mental processes
experimentally. In that study, the researcher intensively
confused the subjects (children) in their communications
and left them alone in a situation without a parent’s
support, which they usually received. Then the children
became upset and struggled to understand the meanings©ISAROB 2005

2. Method
2.1 Observation conditions
This experiment was implemented in the field at aworkshop in a children’s museum, ‘Kids Plaza OSAKA,’
for three days in June 2004. This paper reports the resultsof a two-day observation period under the same
experimental conditions.

2.2 Participants

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

left-rear of the subjects to record an elevated view ofthe experimental field.

A child and his/her attendants (usually parents ofthe child) were regarded as a team for this experiment.
Teams participated in the experiment by interacting with
Muu in order of their arrival. The total number ofparticipants was 69: 30 teams from 42 children and 27
attendants. The children’s ages varied from two to
twelve.

2.5 Analysis
In this study, the situation of talking with the robot
confused most children and attendants. However, a few
cases showed rich and natural human-robot
communication. This section focuses on three typicalcases of ‘good’ communication. In order to distinguishbetween the former cases and the latter ones, theConversation Analysis method was used as a qualitativeevaluation for the in-depth study of behavior in context.
An ethological analysis method was also exploited inhandling the video recordings of the children’s play: gazeand timing of movements were incorporated in an index
of social interaction.

2.3 Experimental setup
The experimental setup was located within thefacility and surrounded by partition walls. The setup iscomposed of a low table, 120 cm square, on which Muu
and a basket full of toy blocks were placed.
In this study, the behaviors of Muu are regulated asfollows.
Linguistic behavior: The Wizard of Oz method was used.
That is, Muu was controlled to speak the appropriate
words, selected from 150 prepared words, according to
the interaction with the subject. The subjects were notinformed of this fact. They seemed to guess that Muu
autonomously and spontaneously spoke by itself. The
contents of its speech were divided into different
categories: One is concerned with toy blocks, forexample, “Pile on a red block, please.” Another involves
evaluation of the child’s work, for example, “It’s cool,
isn’t it?” Another category is for compliments or chiming
in, for instance, “I see.”Non-linguistic behavior: Muu was controlled togenerate some slight rolling and pitching motion of its
body and to move about 20 cm forward and backward.
Muu was also controlled to move its body corresponding
to the expressions, “Hello,” “Good bye,” and so on.

3. Results and Discussions
This study focuses on the interactions of children
aged more than 5 years (23 teams). This policy is
supported by the fact that only the participant teams that
include children aged over 5 years showed spontaneous
speech directed toward Muu. Accordingly, this age isregarded as the threshold for children to begin interactingwith others independently of their parents, in spite ofbeing under the influence of parents.

3.1 Most observed interaction cases
In the type of interactions most often observed, thechildren built with the toy blocks to temporarily satisfy
Muu’s requests. In these cases, they seemed to have noclear of what they should build. After once following
Muu’s requests, the children piled on the toy blocks intheir own way without taking notice of Muu. Most
attendants ordered the children to speak to Muu when
they were independently playing with the toy blocks. If
the children still did not speak to Muu, the parents
induced them to speak about themselves to Muu bysaying words such as “Ask him how old he is.” Due tothe limitation in Muu’s capacity of speaking words (150words), inappropriate responses from Muu toparticipants’ words were sometimes observed. For
example, when the child received the same words from
Muu as in a previous situation, he/she pointed out thatfact by saying in amazement “Hey, you said the samewords before.” On the other hand, when Muu madeadequate responses, the parents complimented Muu withsuch expressions as, “You are cool”, “You are so cute”,“You must be so wise”, and so on.

2.4 Observation procedure
Observation was implemented in the following way.
(1) Let the children who want to interact with Muu
stand in a line in order of arrival.
(2) Induce the first child in the line and his/her
attendants to enter the test field, ask his/her name,and tell him/her while pointing at Muu “This is Muu.
It seems he wants to ask of you to build up the toy
blocks while speaking with him. Will you help him
to build up the toy blocks? Please ask him ‘Say,
what?’ if you cannot clearly hear Muu’s words.”(3) Recede to the side and begin to observe theinteractions among the child, attendant, and robot.
The time period for one interaction session was
limited to about 5 minutes. Two video cameras
captured the experiments, with the agreement of theparticipants. One camera was installed to the rightfront of the subjects and the other was set to the©ISAROB 2005

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style characterized by the subject piling on the blocks
one by one while asking about Muu’s intentions. For
example, his question to Muu at the beginning, “Hey!
Which block should I use first?” typically shows thisstyle. Gazing at Muu, conducting conversation, andpiling on the blocks are repeated in order. Subject B
asked questions while looking at Muu, and he received
some responses from Muu (Fig. 3). If he could not hear
Muu’s words, he again asked Muu with the word “Eh?”while gazing at it. When he guessed Muu’s words, heturned his eyes to the basket of blocks, found the blockthat Muu pointed out, and placed it in the way Muu
commanded. After that, he again looked at Muu andasked what to do next. This case of interaction continued
by repeating the steps of communication and piling on
blocks. In this case, the utterances of subject B and Muu
never overlapped, as if they were making a certain
communication rhythm; this was quite different frommost other observed interactions. Interestingly, hisfather’s utterance sometimes overlapped Muu’s. Subject
B made replies only to Muu’s words, not to his father’s.
However, he did not ignore his father’s intention. For
example, when he was asked to pile on a red block, helooked up at his father. This behavior is unprecedented
among his behaviors, in which he gazed at only Muu or
the toy blocks. At that time, their work of toy blocks was
piled so high that if they continued to pile on the blocks
as instructed by Muu, it might fall down. This implies
that the subject had to find a new way to overcome thesituation. After that, subject B began to tell his intentions
or suggestions to Muu, for example, “How about thisway?” The concept of subject B was consistent from thestart to the end, which was that he decided to build thework that Muu intended on behalf of Muu. He paid
attention to Muu’s intention and carefully piled on theblocks one by one. This behavior was not observed in theother subjects. The role of his father was to keep his eyeson his son and to give suggestions when his son had
some trouble with the work. As in case 1, the father’s
words were directed toward the concept of the work,
such as how to build with the blocks, and not toward
compelling his son to speak to Muu or to evaluate Muu’s
behavior such as “This robot is cool” or “This one issmart!”.

3.2 Rich and natural communication cases
In three cases, the subjects had such a clear conceptof their work that it was easily observable, and thesewere obviously different from most other cases observed.
Furthermore,
in
these
three
cases,
naturalcommunications were observed between the subjects andMuu. These three cases are described in detail below.
3.2.1 Case 1: Subject A (aged 6) and her father
In this case, it was observed that subject A began
speaking spontaneously. Although she would not talk to
Muu at the beginning of the experiment, through theencouraging advice and interventions of her father, shestarted communicating. The difference from most cases
is that, in case 1, the father built with the toy blocks inhimself and induced his daughter to join to build them,without compelling her to speak to the robot. At the
beginning of the interaction with Muu, subject A noticed
that Muu was slightly moving; however, she did not
spontaneously speak to Muu, but just repeatedly glanced
at Muu. On the other hand, her father himself began tobuild with the toy blocks, and informed her of what Muu
had said, for example, “He said feels so cool!” as ifinterpreting Muu’s words whenever Muu used theevaluating words for their works. Subject A repeated
Muu’s words such as “Feels so cool!” loud enough to let
her father and Muu hear the words. In the meantime, herfather suggested that she built the blocks closer to Muu,
saying “Closer is better for Muu.” Subject A accepted
the suggestion. Subject A and her father began to buildwith the blocks closer to Muu’s eye. Meanwhile, Muu
said the evaluating words for their work, “That’s cool!”
Subject A guessed that her work was highly evaluated,
however, her father insisted that the evaluation was forhis work, not for hers. Then, subject A looked at Muu fora while, read Muu’s expression, and finally wasconvinced that her father’s claim was right. After that,she gave up using ambiguous words that might be taken
the words for both her father and Muu. Instead, shebegan to frequently direct words to Muu asking for an
evaluation of her father’s work, such as “How do you
feel about this one?” and ”How about this?” (Fig. 2).

3.2.1 Case 3: Subject C (aged 11)
3.2.1 Case 2: Subject B (aged 7) and his father
In this case, it was observed that subject C came toclarify the concept of the work by referring to Muu’s
evaluation during interactions. This subject had the habitof talking to himself very loudly, as if addressing Muu.
At the beginning of the interactions, he repeatedmumbled expressions like “I can’t guess“, ”I’ve no idea”

Subject B accomplished two-person interaction with
Muu beside his father but without permitting his father’sintervention, in contrast to the case of subject A, whobegan to spontaneously speak to Muu with the support ofher father. The interaction in this case continued in a

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and so on, which expressed that he had no idea about theconcept of the work. Then, he received a comment fromMuu, “You are such a funny guy, aren’t you?” After a
moment, he muttered, “He said I am so funny, or
something like that,” and began to laugh. When hereceived the comment from Muu “A poor hand!”, heburst into laughter and broke the work down into manypieces, saying “A poor hand! Come on! What are you
talking about?”. After the accident, subject C silently
continued to build up the blocks. During the construction,although he received several comments from Muu suchas “I see” and “That’s cool,” he made nearly no reply,
except for rare giggling. The shape of the toy-block work
was gradually tuned, and at nearly the end of the given
time, it became clear that subject C intended to make a
toy-block robot (Fig. 4). At that time, Muu spoke
something but it was lost in the surrounding noise. Thenhe promptly asked Muu in a loud voice, “Eh? What?”;Subject C seemed eager to know Muu’s evaluation of his
work.

Fig. 1: Experimental setup

4. Conclusions
It was clarified that children and the communication
robot ‘Muu’ were able to establish rich and naturalcommunication with each other not when children
evaluated Muu’s behavior but when Muu evaluated thechildren’s works. In order to retrieve an appropriate
evaluation, it was necessary for the children to produce
an utterance in any way and to provide a trigger for Muu
to converse, for example, by talking to themselves in a
distinct tone, repeating Muu’s words, and so on. The roleof the communicative children’s parents was to arouse
their children’s interests in their work, to induce them tobegin speaking voluntarily, and to constructcommunications or conversations toward accomplishing
results. As future study, we are planning to provide anenvironment for human-robot interactions from a global
viewpoint, ranging from the development of the system
to a method for teaching subjects. Such an environment
will produce more substantial results and build upon theknowledge obtained in this study.

Fig. 2: Case 1 “How about this?”Fig. 3: Case 2 “Build with this block? This way?”

References[1] Takagi K (2001), The way of Vygotsky (in Japanese) .
Kaneko Shobo, Tokyo.
[2] Vygotsky LS (1978), Mind in society: The
development of higher psychological processes.
Harvard Univ. Press, Cambridge.
[3] Garfinkel H (1964). Studies in the routine grounds ofeveryday activities. Social Problems, 11: 225-250.

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Fig. 4: Case 3

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Social influence of overheard communication by life-like agents to a user
Satoshi V. Suzuki
CISS, IGSSE
Tokyo Institute of Technology
R1–42 4259 Nagatsuta, Midori-ku
Yokohama 226–8502 Japan

Seiji Yamada
National Institute of Informatics

Abstract

can be one of “interactive computing systems designed tochange people’s attitudes and behaviors” [5] with its many
modalities. In this study, we approach life-like agent technology from the framework of life-like agents as social
actors [6] applying the theories established in persuasion
studies [10].
In this article, first we review the studies of social response toward life-like agents, and clarify the problem inthe studies of inter-agent interaction. Second, the behavior rule called overheard communication is introduced andapplied to inter-agent interaction. Then, through results ofa psychological experiment, the influence and potential ofoverheard communication by life-like agents is discussed.

2–1–2 Hitotsubashi Chiyoda
Tokyo 101–8430 Japan

It is important to investigate influence of novel information technology, such as life-like agents, toward receiversof the information since some studies reveal that such novel
technology can “persuade” people, in other words, they
have strong power to change people’s attitude and behavior. In this study, considering social influence of life-likeagents’ embodied expression, to a user, the influence ofoverheard communication (OC) by life-like agents toward
online shopping Web site users was examined, since the
OC by people often changes attitude of receivers. An
experiment to compare the effect of OC by two life-likeagents (a persuader agent and a persuadee agent) with regular communication (RC) by one persuader agent were conducted. The result of this experiment implied that even
the OC by life-like agents could promote Web site users’
online shopping purchase likelihood more than the RC bythem. Moreover, attractiveness toward a persuader agent
evaluated by participants was positively correlated with
their purchase likelihood. This result suggests a new direction of studies of social influence from life-like agents,
especially from a viewpoint of embodied expression of lifelike agents, such as presence, gaze, appearance, and so on.

2 Related Works
2.1 Persuasion by Life-like AgentsSome studies of life-like agents as “social persuaders”
already exist, however, there is still no study which focuses on the social influence of existence of inter-agent
interaction. For example, André et al. [1] claimed the effect of inter-agent interaction in implementation of online
car dealer agent system, but they did not argue how effective inter-agent interaction was. Moreover, Takeuchi
and Katagiri [7] insisted that authorizing a life-like agent
by other agent could grab a user’s attention stronger than
non-authorizing situation. Their study compared two interagent interaction styles, not existence of inter-agent interaction and absence of inter-agent interaction, and they did
not mention the social influence by inter-agent interaction.
Therefore, the influence of existence of inter-agent interaction toward a user’s attitude change was investigated in thisstudy.

Keywords Life-like agents, embodied expression, overheard communication, social response to communication technologies, social influence

1 Introduction
Recently, there is much argument regarding influence ofsocial interaction between users and communication technology as the communication technology prevails into oureveryday life. Among such technology, a life-like agent(embodied conversational agent) has a possibility to interact with a user using embodied expression, and appearedin application softwares for presentation, Web navigation,and so on [4]. In particular, a life-like agent has potential
to change a user’s attitude. A life-like agent technology©ISAROB 2005

2.2 Influence of Gaze by a Life-like Agent
Among embodied expression by a life-like agent, where
the agent gaze plays a very important role in human-agent

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interaction. As people pay attention to someone’s eyes
when interacting with him/her [2], the agent’s “eyes” imply social meanings toward the user [6]. Although Reevesand Nass [6] claimed that the agent should gaze the user infront of a display since it would be the “etiquette” which
the agent should obey, especially if there is inter-agent interaction, it is natural that two or more agents talk withgazing at each other. For these reasons, the influence of theagent’s direction of gaze to distinguish to whom the agent
talk was considered.

Figure 1: Explanation of characteristics of an item in RC
condition

3 Overheard Communication by Life-like
AgentsSuppose you hear the reputation of a movie in which
you are not so much interesting in these two situations:
1. Your friend directly told to you that the movie wasvery interesting and you must watch it.

Figure 2: Explanation of characteristics of an item in OC
condition

2. You overheard that someone told to another one thatthe movie was very interesting and he/she must watchit.

4 Psychological Experiment

In some cases, the message from your friend may seem intrusive since your friend directly told you such an imposing
message in situation 1. On the contrary, in situation 2, you
may have interests in the movie because you did not receive
someone’s message directly and the message did not seemso intrusive. The persuasion style shown in situation 2, thata persuader tell another one the message, without tellingthe “true” persuadee, is known as overheard communication (OC) in persuasion studies [9, 10]. In this study, thepersuasion style that a persuader directly tell the persuadee
the message, represented in situation 1, is called regularcommunication (RC). Moreover, a life-like agent which behaves as a persuader is called a persuader agent, and a persuadee agent represents a life-like agent persuaded by apersuader agent in the situation of OC by life-like agents.
Considering the influence of human-agent interaction
and inter-agent interaction, and the effect of OC-style persuasive communication, we implemented OC by life-likeagents in the following manner:

4.1 Experimental Design and Prediction
In this study, we suppose two conditions for the psychological experiment. When a persuader agent explainscharacteristics of items, in the RC condition, it gazes toward a user in front of the screen; on the other hand, itgazes toward a persuadee agent in the OC condition. Considering the argument above, the OC by two life-like agentsshould promote a user’s attitude change more than the RC
by a life-like agent. Additionally, the OC by a persuader
agent should emphasize its attractiveness which it provides
more than the RC by it. Then, considering these hypotheses above, the following predictions should be determined:
P1 Participants in the OC condition will evaluate the pur-chase likelihood of items higher than those in the RC
condition.
P2 Participants in the OC condition will evaluate the per-• Let both the persuader agent and the persuadee agent
appear on the screen, because users will perceive thattwo distinguishable social actors exist and each ofthem behave at their own thought.

suader agent’s attractiveness which it provides higher
than those in the RC condition.

4.2 Procedure• The persuader agent always tell the persuadee agent a
message, because users will perceive the message ofthe persuader agent with distinguishing whom the persuader agent tell the message by where the persuader
agent gazes.

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Valid experimental data were collected from 24
Japanese participants (19 males and 5 females). The participants consisted of undergraduate students, graduate students, and office workers. Their age ranged from 19 to 29.

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They were randomly assigned to either RC condition or OC
condition. Each condition contained equal participants.
Participants are asked to look at the explanation of items
by a persuader agent on an online shopping WWW sitewith a note PC. In the RC condition, the persuader agent
introduced items directly gazing at participants (Figure 1);
in the OC condition, a persuadee agent appeared on thescreen and the persuader agent introduced items gazing at
the persuadee agent (Figure 2). The same item explanationphrases by the persuader agent were used in both condition.
To preserve natural conversation context, the persuadee
agent gave short responses to the persuader agent for each
item explanation phrase in the OC condition. As introduction of each item ended, participants answered how muchhe/she want to purchase this item by a 10-point scale. The
evaluation of purchase likelihood for the items which theparticipants had already possessed were omitted for analysis. The price of the items was considered so that the participant could afford to buy the item if he/she wanted it.
For all participants, the agent “James1 ” played a role ofthe persuader, and the agent “Cosmy2 ” played a role of thepersuadee. These agents did not change their roles among
participants. When participants finished the evaluation ofpurchase likelihood for 15 items, they answered the questionnaire about attractiveness of the persuader agent. The
attractiveness was evaluated on a 10-point scale for four
adjectives: kind, friendly, useful, and likable. After reporting the impression of this experiment, participants were debriefed, thanked for their participation, and dismissed. It
took around 30 minutes to finish the experiment for eachparticipant.

OC condition exceeded that in the RC condition, according to the result of two-tailed t-tests, significant difference
between the two conditions did not appear in the score, as
shown in Table 1. Then, the prediction P2 was rejected by
the result. Nevertheless, between the score of attractivenessand the score of purchase likelihood for each participant,there was a significant positive correlation. The values ofPearson’s product-moment correlation coefficient and thesignificance test of these values proved there were significant correlations between the two scores for each condition (in RC condition, r = .505, t(10) = 1.851, two-tailed
p < .10, and in OC condition, r = .615, t(10) = 2.463,
two-tailed p < .05).

5 Discussion and Future Works
The result shown in section 4.3 suggested that the OC
induced a user’s purchase likelihood of products on online
shopping Web site more than the RC. This result impliedthat life-like agents could play a role of a clerk and anothercustomer virtually. One reason why the OC by life-likeagents influenced user’s attitude is because the persuader
agent gazed at the persuadee agent when explaining featureof items and never gazed at the user. Therefore, the gaze ofthe persuader agent could be perceived as a important embodied expression to participants. In fact, one participant
in OC condition reported: “It is good to see the conversation between two agents from the viewpoint of a stranger,
since it may feel annoying if one agent directly talks tome.” Another reason is because the persuadee agent existed on the screen. Thus, the existence of the persuadee
agent could serve as a criterion for participants to consider
whether they should buy the items or not. There were twoparticipants who answered that he/she took the behavior of
the persuadee agent into consideration. This fact suggeststhat the influence of the existence of the persuadee agent
could not be ignored.
As for the impression of a persuader agent, there wasno significant difference between the OC condition and the
RC condition in three scores of impression. However, thescores of attractiveness is significantly correlated with thescores of purchase likelihood. This result indicated theinfluence of physical appearance of a persuader agent because we used the persuader agent with same appearance inboth the RC condition and the OC condition. Fogg [5] argued physical appearance of life-like agents as a importantfactor of persuasion of a user by life-like agents, and someparticipants answered that the persuader agents used in thisexperiment looked “too strong” or “not so cute.” However,participants’ evaluation of attractiveness of the persuader
agent was affected to their purchase likelihood both in the
RC condition and in the OC condition.

4.3 Result of Experiment
Mean and standard deviation values of all variables inthis experiment were shown in Table 1.
First of all, the mean value of purchase likelihood scoresin OC condition was significantly higher than that of RC
condition. Since the difference of variance between thesetwo variables was significant (F(11, 11) = 4.124, twotailed p < .05), Welch test was applied to confirming thesignificant difference between the mean values of them. As
a result, the significant difference between them observed(t(16.04) = 2.984, two-tailed p < .01, ES = 1.166). Thus,
the result supported the prediction P1.
However, there was no significant difference between
the two condition in the score regarding impression toward
the persuader agent. Despite the attractiveness score in the
1 This agent is available at http://www.cantoche.com/
english/gallery/msagent.htm.
2 This agent is available at http://www2.mic.atr.co.jp/
agent/.

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Table 1: Mean (standard deviation in parentheses) values of measured variables for each condition and results of statisticaltests
RC cond.
OC cond.
t-value
Effect Size(n = 12)(n = 12)(d.f. = 22) (ES)
Purchase likelihood scores4.127 (1.348) 5.421 (0.664) 2.984∗∗ F
1.166
Impression of the persuader agent
5.729 (1.760) 6.521 (1.694) 1.123
0.439
F For the significant difference of variances between the two conditions, the t-value by Welch test∗∗ : p < .01
was shown here (d.f. = 16.04).

In the OC by life-like agents, the persuader agent’s gaze
was functioned in the experiment. However, there are still
few studies which focus on the function of a body of a lifelike agent [8]. Thus, the influence of embodied expression
triggered by the appearance and behavior of life-like agentsis not clear so far. This influence should not only apply tohuman-agent interaction, but to human-robot interaction.
If we hope that technology pervade many aspects of ourlife, we should emphasize the social aspect of human-agent
interaction.
Besides, we did not mention the interactivity between auser and agents. First, this study does not consider the influence of the difference of a reaction by a persuadee agent
since we focus on the existence of inter-agent interaction.
Some persuasion studies reveal that the negative reaction ofothers toward a persuader gave people negative impression(for example, Axsom et al. [3]). The valence of reaction ofa persuadee agent can influence the decision of users, andshould be considered in future works.
In this study, we discussed the influence and potential ofoverheard communication by life-like agents. Particularly,
it is pointed out that embodied expression, especially existence of the persuadee agent and the direction of the persuader agent’s gaze, played an important role in the overheard communication by life-like agents. From the stanceof social influence of the embodied expression of life-likeagents to a user, the way to utilize life-like agents for thevoluntary attitude change of users should be explored in the
future.

[2] Argyle M, Cook M (1976), Gaze and Mutual Gaze.
Cambridge University Press, New York

References[10] Zimbardo PG, Leippe MR (1991) The Psychology of
Attitude Change and Social Influence. McGraw-Hill,
New York[3] Axsom D, Yates S, Chaiken S (1987), Audience response as a heuristic cue in persuasion. Journal of
Personality and Social Psychology, 53(1):30–40[4] Cassell J, Sullivan J, Prevost S, Churchill E (eds)(2000), Embodied Conversational Agents, The MIT
Press, Cambridge, MA, pp. 220–255[5] Fogg BJ (2003), Persuasive Technology: Using Computers to Change What We Think and Do. Morgan
Kaufmann Publishers, San Francisco, CA[6] Reeves B, Nass C (1996), The Media Equation: How
people treat computers, television, and new media
like real people and places. Cambridge University
Press, New York[7] Takeuchi Y, Katagiri Y (2002), Social dynamics in
Web page through inter-agent interaction. Transactions of the Japanese Society for Artificial Intelligence, 17(4):439–448[8] Takeuchi Y, Watanabe K, Katagiri Y (2004), Social identification of embodied interactive agent.
In: Proceedings of the 13th International Workshop
on Robot and Human Interactive Communication(ROMAN-2004), Kurashiki, Japan[9] Walster E, Festinger L (1962), The effectiveness of“overheard” persuasive communications. Journal of
Abnormal and Social Psychology, 65(6):395–402[1] André E, Rist T, van Mulken S, Klesen M, Baldes S(2000), The automated design of believable dialogues
for animated presentation teams. In: Cassell J, Sullivan J, Prevost S, Churchill E (eds), Embodied Conversational Agents, The MIT Press, Cambridge, MA,
pp. 220–255©ISAROB 2005

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Multi User Learning Agent based on Social Interaction
D. Katagami
H. OhmuraY. Yasumura
IGSSE
Advanced System
Factory ofTokyo Institute
Development Center
Engineeringof Technology
Yamaha Corporation Kobe University
Yokohama, 226-8502 Shizuoka, 438-0192
Kobe, 657-8501
Abstract

Learning Agent (MULA). MULA adopts an extended
classiﬁer system which uses a classiﬁer strength anda user proﬁle for each people. We veryﬁed MULA inexperiments of a user’s preference prediction task in areal world.

In this paper, we propose Multi User Learning
Agent(MULA) to interact with various types of people eﬀectively towards realization of Social Interaction.
MULA is equipped with two learning functions for Social Inteaction. One is direct learning function using individualization of the user parameters, and another is indirect learning function using past experience and the similarity between each users. MULA
adopts an extended classiﬁer system which uses a classiﬁer strength and a user proﬁle for each people. Each
person’s classiﬁer strength is enhanced by the interaction with the person and the past experience withsimilar type of people. We veriﬁed the eﬀect of MULA
by experimenting in interaction with six participants
using a pet-type robot AIBO.

1

2
2.1

Implementation of MULA
A learning based on past experience

A target here is to realize an adaptation to thecurrent user by using experience with another users.
Then, we realized MULA which incorporated past experience using the function explained based on themodel-based reinforcement learning (Dyna-Q algorithm [2]). In this paper, we deﬁne changing the action
by the inﬂuence of the others as ”social”.
[Direct learning by individualization of user parameters]
Although the technique [3] of preparing knowledge andthe rule set for each environment is common, the problem of eﬃciency, such as a problem of a memory anddignity attachment of the knowledge, occurs. Then,
we adopt the approach individually prepared for every
user about how to use knowledge (user parameters) instead of using two or more rule sets.
[Indirect learning using past experience]
Since it corresponds to two or more users, it is important to use past experience well. Here, the similarity
between users is used like collaborative filtering. Especially this may become the indicator for the next interaction in a stage with little experience with a current
user.

Introduction

In recent years, research of HAI (Human-AgentInteraction)[1] aiming at forming communication between an agent, such as a robot, and human is capturing the spotlight. However, the present HAI researchis in the stage of a designing one-to-one adaptations
between human and an agent, and the methodology
with two or more users is not established. In this research, we aim at realizing a social interaction performed by the social learning which learns from interaction with two or more users using a constructive
approach. Social Intearction is realized by two learning functions. One is direct learning function using
individualization of user parameters, and another isindirect learning function using past ecperience andthe similarity between each users. In particular, indirect learning uses experience with other users for thetarget user. We think it is one form of social learning to realize the suitable action from experience with
two or more users to the current user. We named anagent equipped with Social Interaction as Multi User

©ISAROB 2005

K. Nitta
IGSSE
Tokyo Instituteof Technology
Yokohama, 226-8502

2.2Social leraning using a interaction
with users

In order to realize above two functions we propose
MULA architecture (Fig. 1) and it’s learning algorithm (Fig. 2).

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direct RL
updateplannning
updaterA
realexperienceaFitnessF： It adopts as an evaluation value of the rulecreation by GA.
[individual variables]
This system prepares the variables of a classiﬁer (aprediction value p, a prediction error ε, and the degree of adaptation F ), without preparing a new rule
set for every user. We call it an individual prediction
value, an individual prediction error, and an individual ﬁtness, respectively. Moreover, we prepared theindividual evaluation value R which records evaluation of the past interaction. Each classiﬁer has anindividual evaluation value, for each user. When positive evaluation is received, the value increases, andwhen negative evaluation is received, it decreases. We
call these values individual variables.
[The procedure of the system]
MULA updates individual evaluation value R based on
evaluation of a user’s interaction, and the similarity is
calculated by it each time. MULA updates individual
variables p, ε, and F other than an individual evaluation value R like the usual XCS does. However, inindividual prediction value p, although individual prediction value pA is directly updated according to thesimilarity between user A and other users. All of otherusers’ individual prediction values pX are also updated
indirectly. The method how MULA learns the individual prediction value p of a classiﬁer is updated by theinteraction with users is explained in Fig.3.

calculatesimilarity

Policy/valuefunctionsSrA

simulated
experienceuser
information

user A

Figure 1: MULA architecture
Initialize p(s, a, i) and M odel(s, a, i) for all s, a, i
Do foreveruser identiﬁcation i ∈ I
simple Q-learning
U serP rof ileRi (s, a, i) ← ri
for j = 0 to j = n do
Similarity Sij ← U serP rof ile Ri , Rjq(s, a, j) ← q(s, a, j) +αSij (|ri +γmaxa q(s , a , i)−q(s, a, i)|)Figure 2: MULA algorithm
MULA speciﬁes a user ﬁrst. MULA updates directly individual user parameters of the user (it considers as User A temporarily) using simple Q-learningfrom an interaction (MULA receives remuneration rA
to act output aA to certain state sA ) with the user.
It is actual experience. Next, MULA calculates thesimilarity SAX of U serP rof ile between user A andanother users X using users’ information. MULA updates each U serP rof ile and individual variables of
all users indirectly using the interactive informationsA , aA , and rA and this similarity with user A. It is
indirectly experience.

2.3(1) MULA acquires sensor information SA（1001）by
interacting with user A.
(2) MULA generates Match Set [M ] from sensor information. And the action a chosen from an action selection method is outputted.
(3) MULA gets evaluation for the action from user A,
and updates prediction value p and other individual variables directly.
(4) MULA calculates all of the similarities SAX fromuser A’s updated proﬁle RA and other users’ proﬁles RX .

The outline of the system

Fig.3 shows the outline ﬁgure of the system proposed by this research. This system has adopted
XCS[4] as a fundamental learning mechanism, which
is one of most useful classiﬁer systems. XCS uses new
three parameters instead of strength of classiﬁer system for each classiﬁer.
predictionp： prediction of classiﬁer.
pt = pt−1 + α(|rt−1 + γmaxa pt−1 (s , a ) − pt−1 |)(5) MULA updates all other users’ inidividual prediction values pX using the similarities SAX indirectly.
We will explain about U serP rof ile, calculation of
the similarity in (5) and indirect update of individual
prediction in (6) as follows.
[User profile]
We regard the vector containing the individual evaluation value R which is an individual variable as user

(1)

prediction errorε： An error between prediction p
and actual measurement.

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Classifier Set [P]
SA

1001

IF

THEN

#001
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##11
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#001
11##

:
:
:
:
:
:

..
.

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01
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37
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15
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.

..
.

..

εA
.01
.14
.02
.29
.02
.19
.

..

FA

RA

97
8
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93
13

2
0
-3
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1

FA

RA

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.

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.

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Action Set [A]Match Set [M]
IF

UserＡ

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.

:
:
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:

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.

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.

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THEN

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.

Updateevaluation
valuesReward rA

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:

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p
p

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.

..

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.

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.

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ptj =

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.

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pA

01
01

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.01
.14
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.02
.

Action aA

Action Set [A]in order to be able to reinforce the inverse direcpt=0ition. In this paper, we simply deal with single-steptask in the experiment. Therefore update formula inFig. 2 can express formulas below. Here, the formulas
update users’ individual prediction value indirectly by
experience with user i.

Update individualuser parameters

FA

97
5

2
0

..
.

..
.

pjt−1 + αSij (rit−1 − pit−1 ) (S > 0)
pjt−1 − αSij (rmax − rit−1 − pit−1 ) (S ≤ 0)

(3)

Calculatethe similarity
A B C
A
0.5 0.2
B 0.5
0.1
C 0.2 0.1

RA



3

Update prediction

Experiments

3.1

0.5r A Calculate
0.2r A prediction

We investigate MULA’s eﬃciency in experiments ofa user’s preference prediction. In order to investigate
the agent’s eﬀect intelligibly, we veriﬁed based on thefollowing points.

Figure 3: MULA based on XCS• MULA predicts a user’s preference individuallyusing past experience with users and their user
proﬁles. We verify correlation between a user’s
true preference and a prediction value using pastexperience of a user and a user proﬁle.

profile. Therefore, the number of the individual evaluation values R in the proﬁle to user A is the same as
the number of classiﬁers (rules) which this system has.
This system uses user proﬁles of two users by calculation of the similarity in the following subsection.
[Calculation of the similarity]
We adopt the vector space method as one of the simplest methods of calculating the similarity. Corresponding to the output action, this system makes theinformation of user’s preference to an individual evaluation value, and acquires it for every classiﬁer. The
similarity S between user A and user B is then computed by the vector of individual evaluation value Ri
and Rj in the following formulas from the user proﬁleof each user which is used as the ingredient of the vector. f and g are two vectors (user proﬁles) to compare.

• The interaction (for example, evaluation andtaste) from human is always not optimal. And
it is changeable. We investigate changes of thesimilarity.
In this experiments, we introduced MULA into pettype robot SONY AIBO. All experiments are conducted in the following procedure based on the interaction between a user and AIBO.
(1) A subject pushes the button of AIBO and AIBO
outputs an action.

K

Rim Rjm(Ri , Rj )
m=1

= 
S(Ri , Rj ) =||Ri ||||Rj ||
K
K


m 2
(Ri )
(Rjm )2

Purpose of experiment and settings(2)(2) The subject gives one evaluation from two kinds,”praise(r = 100)” or ”scold(r = 0)”, by preference to the action of AIBO. It returns to (1).

[Indirect update of individual prediction value]
MULA introduces a social element into the update ofindividual prediction value p of each classiﬁer. It is
explained how to update this individual precidiction
by interaction between human and MULA as follows.
Other users’ individual prediction values pj are updated by the following formulas according to the similarity Sij . Here, ptj is a prediction value in step t touser j. α is a coeﬃcient. rjt is the reward from human j in step t. rm ax is maximum reward. Where(S ≤ 0), it decides ri , rmax as criteria initial value

The above procedure is considered as one procedure of
an interaction. In this experiment, the initial valuesof update parameters are α = 0.2, rmax = 100, pt=0 =50, Rt=0 = 0, S t=0 = 0.
We prepared 5 kinds of actions (e.g.
”bark”,”dance”,”greeting”) of agent, and 3 kindsof situation (”back button”,”head button”,”jawbutton”). According to Fig. 3, all user proﬁles are
created by classiﬁer set selected by the same act, thesimilarity SAX is created from them, and a suitable
act is predicted to the state.

m=1©ISAROB 2005

m=1

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100

60

Similarity

Prediction value

1

user auser buser cuser duser euser f

80

40

a-b
a-c
a-d
a-e
a-f

0.5
0
-0.5

20

-1
0
0

5

10

15

20

25 30
Trials

35

40

45

0

50

5

10 15 20 25 30 35 40
TrialsFigure 4: Changes of individual prediction values
Figure 5: The changes of the similarity between usera and the others4

Six subjects (a-f) participate in the experiments.
The experiments divided into two phase. All subjects
perform a proﬁle creation phase ﬁrst. They enter an
evaluation phase in order. In an evaluation phase, asubject is inﬂuenced by the subjects who performed
this phase before. For example, the 6th subject isinﬂuenced by ﬁve previous subjects.

3.2

In this paper, we attempted to realize social interaction by constructing MULA which performs social
learning from an interaction with two or more users.
Two learning functions realized social learning. One
is direct learning function using individualization ofuser parameters, and another is indirect learning function using past ecperience and the similarity between
each users. Especially indirect learning uses experience with other users for the target user. We think
it is one form of social learning to realize the suitableaction from experience with two or more users to a current user. By the experiment in real environment, we
veriﬁed that MULA can respond to dynamic change
of users’ preference, and perform eﬃcient learning.

Experimental results and discussions

Fig. 4 shows changes of individual prediction in
evaluation phase. The prediction value of user e and fis large compared with the initial value pt=0 = 50, anddirection of convergence ”praise(r = 100)” is in agreement, and you can see learning is promoted. In orderto check the eﬃciency of learning, the prediction valueof each action of user from b to f was investigated. The
action which prediction was successful and accelerated
learning was 74% and the action which prediction was
not successful was 9%. The correlation coeﬃcient ofuser’s actual preference and a prediction value is 0.73
on the whole. Therefore, MULA has been predicted
the suitable action to a user’s preference.

References[1] S. Yamada, T. Yamaguchi : Mutual Learning of
Mind Reading between a Human and a Life-like
Agent, The Fifth Paciﬁc Rim International Workshop on Multi-Agents, pp.138–150, 2002.
[2] R. S. Sutton: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming, Proc. of the 7thInternational Conference on Machine Learning,pp.216-224, 1990.

Moreover, the similarity is always changing with
the interaction between users and MULA dynamically.
According to the past experience between users, thesimilarity will change dynamically and it will be convergenced to the ﬁxed value in the situation of changeof the similarity between users (Fig. 5). Thus, MULA
calculates the optimal prediction value for the moment
according to the calculated similarity for every trial.
Therefore, ﬁnally exact anticipation like this experimental result was completed.

©ISAROB 2005

Conclusion[3] R. Parr and S. Russel: Reinforcement Learningwith Hierarchies of Machines, Advances in Neural Information Processing Systems 10, pp.10431049, 1998.
[4] S. W. Wilson : Classiﬁer Fitness Based on Accuracy, Evolutionary Computation, 3(2), pp.149175, 1995.

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Can robots get “membership” through social interaction?
Hiroyuki FUJII and Michio OKADA
Department of Ecological Communications,
Network Informatics Laboratories, ATR
Hikaridai 2-2-2, ”Keihanna Science City”, Kyoto
fujii h@atr.jp
Abstract2

To investigate how robots behave as social entities,
we analyzed the interaction of humans and “Muu,” anembodied artificial entity, to find the salient patternsof behavior that distinguish the robot from humans
in the course of interaction. Through exploratory observation, we found the manner of repetition and theaddress mismatch to be such salient patterns. We
discussed how the social display of such patterns as“marked” features plays a very important role in mutual interaction.
Keywords: human-robot communication, social
entity, quasi-interpersonal behavior

1

Chatting or playing is a form social interaction,
and, under limited conditions, we can perform this“social” interaction with robots. Humans tend to interact with an object (robot, car, computer, or animal) in nearly the same manner as with other humans.
Such behavior is referred to as “ quasi-interpersonal
behavior” (cf. Yamamoto (1994)[8] , and Takeuchi(1995)[7]).
A series of research using the “Media Equation [6]”paradigm showed that humans considered computers
to have social existence along with personality, despiteknowing that the computer is merely a machine thatcertainly does not have a personality. However, thebehaviors in interaction sequences with artificial entities have many features that are different from theinteraction behaviors among humans. The authors believe that these differences are linked to the definitionof the social entities of robots. To define the social
aspects of robots, it is necessary to identify the characteristic patterns of interaction sequences that let us
distinguish whether an interaction partner is a robot
or another human.
In this research, we try to describe such characteristic patterns by analyzing the social interaction between humans and “Muu.”

Introduction

Communication robots are expected to becocme anovel social entities that can interact socially with humans. For a communication robot to become an adequate participant in social interaction, it needs not
only to implement the mechanism for the skill or ability of communication individually, but also to acquire”membership” in a community in the course of social
interaction with other social entities.
Our research is based on the notion of categorizing the social membership of an artificial entity according to the degree of difference between the quasiinterpersonal behavior of humans interacting with theartificial entity and the natural interpersonal behaviorof humans interacting with other humans. Humans
tend to interact with a robot (or a computer or an animal) in nearly the same manner as they do with theother humans. Such behavior is referred to as quasiinterpersonal behavior.
We analyzed the interaction of humans and “Muu,”
a communication robot, to find the salient patterns of
behavior that distinguish the robot from human in thecourse of interaction. Consequently, we explored how
these salient patterns in interaction are linked to thecategorized ”membership” in a community.

©ISAROB 2005

Quasi-interpersonal behavior
in human-robot interaction

3

Analysis of Interaction
between Humans and “Muu”

In the authors’ research group, the “Muu project,”
we have attempted to observe the social interactions
between humans and “Muu” in various domains. The
communication robot “Muu” was developed for research on social interaction (Fig. 1). Muu was designed for human-robot communication mediated bysocial display such as contingent utterances, orienta-

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sentences were synthesized by CHATR (speech synthesizer system [1]) for use in the situation of playing withbuilding blocks with children (greetings, operations ofblocks, color of blocks, evaluations). One session was
about 5 minutes. Every interaction session was video
recorded, with the agreement of the participants. Aslong as the behavior of a participant followed the context of playing with building blocks according to the
instructions of the experimenter, Muu could behave
appropriately, to some extent, by using suitable timing and contents to initiate, continue, and finish an
intaractive session.

Figure 1: Muu : Embodied Artificial Creature

4

From observing the participants’ behaviors at theevent, it was clear that the adults acted in a more
awkward manner with Muu than did the children. To
clarify the pattern of such quasi-interpersonal behavior, we focus here on an interaction session between
an adult female and Muu. In that session, the participant seemed to be able to play with the building
blocks while talking with Muu for about five minutes.
For closer analysis, that session was decomposed into
a sequence of pairs of robot-human utterances. Thissession consists of 32 pairs of robot-human uttarances.
Then, an observer (one of the authors) identified thepoints at which some kind of “trouble” was assumed tooccur if that behavior were actually carried out withincommon human-human social interaction. The identified instances were found in six cases. These cases
were sorted into two categories, named “Manner ofrepetition” and “Address mismatch”.

Figure 2: Interaction with “Muu” observed at Kids
plaza Osakations of the body, and mutual coordination of bodyarrangement. Muu has a big fish-like eye, and hisrounded body is covered with soft urethane rubber.
Muu’s shape and behaviors are based on Lorentz’s“baby schema,” which was assumed to elicit the empathetic attitudes of participants.
In this paper, for analysis, we focused on the situation of participants playing with building blocks while
talking with Muu.

4.1

Manner of Repetition

Case 1 to 4 were included in a category named“manner of repetition.” These are the cases in which
the contingent responses of the participant with Muu(Fig. 3 b ) were out of the normal manner (Fig. 3 a )．
Case 1: Reply without adequate interval In
about one fourth of the Muu-human uttarance pairs(9/32), the start timing of the human’s uttarance followed the utterance of Muu too early. In these sequences, the intervals of two uttarances were 0.1 seconds or less. Commonly, the interval of two utterances
is distributed around 0.7 seconds (cf. Nagaoka et al.,2002 [5]). These replies of insufficient interval length
indicate that these behaviors were pre-fixed and notconducted in a manner of mutual coodination.

Experimental Settings This experiment was implemented in the field at a workshop in a children’s
museum, ‘Kids Plaza OSAKA,’ for three days in June
2004 (Fig. 2). In the experimental session, Muu
was presented to the participants as behaving autonomously and spontaneously. The utterances madeby Muu were selected from 150 prepared sentences by ahidden operator (Wizard of Oz [4] method). These 150©ISAROB 2005

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Figure 3: Manner of Repetition

Figure 4: Address Mismatch

4.2

Case 2: Repetition without modification Manytimes (7/32) the participant simply repeated the preceding uttarance of Muu. In the common humanhuman interaction, simple repetition (like a parrot) isa “marked” pattern. This pattern is sometimes considered as a display of teasing, so it could be a sourceof “trouble” in the interaction. These simple repetitions usually make continuation of utterance sequencesdifficult. The frequent occurrence of simple repetition
showed that Muu was a social entity that couldn’t recognize possible source of trouble in the course of social
interaction, and such a lack of ability in interaction
could be exposed in public.

Case 5 and 6 were included in a category named“Address mismatch (Fig. 4).”
These are the cases where the direction of gaze or
focusing differed (Fig. 4 b ) from the object predictedfrom the flow of utterance sequences (Fig. 4 a ).
Case 5:Gaze Aversion while replying The participant sometimes responded or talked to Muu without changing the direction of her face or gaze. In aface-to-face situation, a speaker usually orients his/herface or gaze to the listener, or at least moves the direction of gaze. This pattern of behaviors showed that
Muu was a social entity who didn’t have the ability tocare about the orientation of the face or gaze of the
speaker in a face-to-face situation, and the deficiency
could be exposed in public.

Case 3: Ignoring overlap Overlapping of the utterances of Muu and the human occurred two times,
but both times the participant continued her utterance and did not seem to care about having been interrupted. These patterns also means that these behaviors were pre-fixed and not conducted in a mannerof mutual coodination.

Case 6:Seeking other person The participant
sometimes sought out another person when she seemedunable to hear the uttarance of Muu. This pattern of
behaviors showed that Muu was a social entity that
didn’t have the responsibility for its own utterances,and the deficiency could be exposed in public.

Case 4: Ignoring no-responce of Muu The participant sometimes mentioned ”Lion” or ”Table” built
by the blocks, but these utterances did not seem torequire a response from Muu. She didn’t display an
attitude of concern about the non-response of Muu.
Rather she seemed to ignore the non-responce of Muu,
and these patterns showed that Muu was a social entity that wasn’t able to respond such spontaneous topics in social interaction.

©ISAROB 2005

Address Mismatch

5
5.1

General Discussion
Display for “marked” pattern

Our analysis showed that the awkward patterns in
the observed session of human-robot interaction to be

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pre-fixed and not conducted in a manner of mutual
coodination. These cases suggested that Muu behave
as a social existence that was unworthy of mutual interaction. These cases also suggested that Muu asa social existence was incapable of using the normalmanners of mutual interaction.
The “manner of repetition” and “address mismatch” are ”salient” or ”marked” patterns in social
interaction, awareness of such patterns is definitely an
important aspect of social entities in social interaction. These results suggested that for mutual interaction, communication robots have to be aware of such“marked” patterns in the couse of interaction and haveto display this awareness in approriate situations.

5.2“Robots” from the viewpoint“Membership Category Devices”[3] Goan, M., Fujii, H., and Okada, M., The first contact of children and robots: From the field observation in a public space, In Proceedings of Human
Interface Symposium 2004, pp.503-508 (2004)[4] Kelley, J.F.: An interactive design methodology
for user-friendly natural language office information applications; ACM Transactions on OfficeInformation Systems, Vol.2, issue 1, pp. 26-41,(1984).
[5] Nagaoka, C., Draguna, M., Komori, M., and Nakamura, T., The Influence of Switching Pauses on
Interpersonal Perception in Dialogues, In Proceedings of Human Interface Symposium 2002, (2002).

of[6] Reeves,B., Nass,C.: The Media Equation; Cambridge University Press, (1996).
[7] Takeuchi, S., About “Interpersonal behavior,”
Cognitive studies, Vol.2, No.4, pp.103-107, (1995).

How to define a social entity in social interaction
is not only a problem involving the ability of each individual. From the viewpoint of a socio-cultual approach, a social entity is also defined in social interaction by its “membership” in the community. In thesocio-cultural context that we commonly use in mutual interaction, the “membership categorization device” functions as a ”label” that has various attribute[2]. The fact that the participants could interact insome way with the robot in their first meeting with
it at the event suggested that the “robot” could be
categorized as having some kind of “membership” inthe surrounding society. Peoples can use some kind of
implicit schema of how to interact with the “robot” inhuman-robot interaction.
It is necessary to create not only a mechanism forsocial interaction but also the ”common sense” of therobot to evolve a social entity that can participate inmutual interaction with humans, to become a socialand cultural partner of people.

[8] Yamamoto, Y, “Interpersonal behavior” Cognitive
Studies, Vol.1, No.2, pp.95-99, (1994).

Acknowledgements
The research reported here was supported in partby a contract with the National Institute of Information and Communications Technology entitled, ‘Research on Human Communication.’

References[1] CHATR
URL:http://www.red.atr.jp/soft page/chatr.html[2] Gerfinkel,H.:
Studies in Ethnomethodology;
Prentice-Hall(1967)©ISAROB 2005

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Minimal Design for Human-agent Communication
Nobuyoshi MATSUMOTO, Hiroyuki FUJII, Michio OKADA
Department of Ecological Communications,
ATR Network Informatics Laboratories
2-2-2 Hikaridai,"Keihanna ScienceCity," Kyoto, 619-0288 JAPANnobu@atr.jp
Keywords: minimal design, quasi-interpersonal behavior

with a robot or a human, humans would communicate
with a robot based on the assumption that a robot is amachine. The behavior in such communication is called
quasi-interpersonal behavior. People are always looking
for non-human things subconsciously and it could be said
that communication is an exchange of incomplete
information with others. People often complement suchincomplete information automatically. In designing acommunication robot, it is also important to arouse suchabilities in humans who communicate with robots.
However, robots to date have been designed only tofeature improved functions, without a strong focus oncommunication between robots and humans.

Abstract
Robots have been anticipated to become both
workers and partners of humans from the earliest periodon their history. Thus, robots should become artificial
entities that can potentially socially interact with humanbeings in social communities. Recent advances intechnology have added various functions to robots:
Development of actuators and grippers show us infinite
possibilities for factory automation, and robots can now
walk movements and performance very smoothly. All ofthese functions have been developed as solutions torobots moves and performance However, there are manyremaining problems in communication between robots
and humans. These unsolved problems can be clarified byadopting the idea of subtractive methods.

"Life-likeness" can also be found on television,in video games, etc. People can get a feeling of“life-likeness” from other people, even non-living objects,
so describing a robot as life-like is quite feasible.
However, which may be why humans and robots cannot
maintain smooth communication. This problem cannot be
overcome via the methodology of building robots ascopies of living organisms.

In this paper, we consider minimal design ofthe robots from the viewpoint of designingcommunication. By minimal design, we meaneliminating the non-essential portions and keeping only
the most fundamental functions. We expect that due tothe simple and clean design of minimally designed
objects, humans can interact with these robots without
becoming bored too quickly. Because humans have "anatural dislike for the absence of reasoning" nature,
artificial entities built according to the minimal designprinciples have the ability to extract the human drive torelate with others. We propose a method of designing arobot that has "character" and is situated in a social
context from the viewpoint of minimal design.

A good communication robot cannot bedesigned by the present method of only enriching thefunctions of a robot. By such a method, the problem oflost "life-likeness" mentioned above is unconquerable, Inorder to realize interaction with people over a long period,
it is also an important condition that a robot ‘sappearance should be attractive to humans. Norman who
argued what is difficult to use in the book "The
Psychology of Everyday
Things," classified theelements of a design into instinctive design, behavioral
design and introspective design in a recent work
"Emotional Design." The ease of use referred inNorman's previous work was intended only forbehavioral design. Norman claimed thatbalancebetween instinctive design in connection with appearance,
and introspective design in connection with meanings ofappearance, and an action-design, is important. Moreover,he claimed that not only ease of use but also the attractive
features of a design, could satisfy people.

1. Introduction
The goal of robot research is not tomanufacture mechanical humans. What we are interested
in is communication between robots and humans.
Communication between robots and humans cannot
replace communication among people. Without using a
Turing test to determine whether they are communicating
MATSUMOTO, Nobuyoshi©ISAROB 2005

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In this paper, in order to design acontinuous-communication
robot
different toconventional robot research, we claim that subtraction
design and minimal design, in which subtraction issystematically performed based on a certain intention, areimportant. In minimal design, not to abbreviate elements
but to clarify the interface in communication, the design
should be completed after distinguishing what is essential.
In addition, we propose a minimal design to clarify what
the interfaces in communication are, such as the type ofaction or a situational setup, not only abbreviation as adesign.

2.2. Subtractive design
A design with function curtailment will becalled “subtraction design” as opposed to themethodology of function addition. As a robot designed byfunctional addition becomes more like a living thing thatreally exists, ironically the difference between the robot
and the living thing becomes even clearer. Such aproblem could be avoided by a design peculiar to therobot, not by copying the figure of a living thing. A clear
policy is needed to determine how far a design would be
reduced.

2. Communication Design
3. Benefit of Minimal Design
Communication is exchanging incomplete
information with others. Communication robots to datehave been intended to deal with problems of how toresolve incompleteness of information. Communication
is not enriched by large quantities of information, butshould be designed to clarify intentions or messages that
contain insufficient information. It is difficult to simulate
communication, since there is so much backgroundinformation necessary to realize communication andthat information is uncontrollable in engineering, making
it difficult to make a simulation that includes it.

We discussed, design by subtraction in thepreceding section. In this section, we will discuss
minimal design, which is we called the directional
simplification of subtraction design. In order toemphasize the main point of communication, it is betterto omit non-essential design elements. Which design
elements to omit are determined by goal setting. A
minimally designed robot is not complete in itself. It is
designed for communication with others, initiatinginterest in communication in a human. A minimal design
targets the principle design of communication.

2.1. Additional design
3.1. Designing derivative form of robots
To contrast the minimal design proposed inthis paper, we call the main guiding principle of robot
design the “additional design” here. In the design of aconventional communication robot, transfer equipment
and a structure for voice dialogs are added to a
fixed-position, industrial type factory robots. Although
there are robots that depend on wheels as a simple formof transfer equipment, bipedal robots have also received
much research attention inrecent years. Robot
researchers believed that their purpose was attained byenriching the functions of the robot. However, even if thenumber of functions increases and each function
approaches that of a living thing, in comparison with adog or a cat which are actually alive, people will lose
interest

Once the principle design elements are found,they will facilitate in designing a derivative form of arobot based on principle design. A design problem issolves by the calculation of elements, and suchcalculations comprise with exaggerations, omissions, andother modifications. They will help in the design offuture personalized robots.

4. Expression technique
The design of a robot is considered with thedesign of living organisms in mind. The expression
methods for living things can be a good reference forminimal design. Character designs in comics, cartoonsand video games are particularly inspired by nature. We
also have much to learn from the representation
techniques of performing arts. For example, Noh-play, aJapanese traditional performing art has no stage props,
only big drawings of a pine tree.

4.1. Expression technique in comics
The expression technique in comics provides
useful information for minimal design. In comics,
non-realistic descriptions, grossly exaggerated and withbold omissions are often used. Character design inexamples of design by addition

MATSUMOTO, Nobuyoshi©ISAROB 2005

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comics uses a lot of symbols and metaphors for living
things, comics the characters are drawn vividly. They
are expressed by character form, speech,bodymovements, and others in various communication
channels, Characters have strong features. A
communication robot must also be designed like a comic
character, having strong features.

A robot must be designed not to fit various
environments, but to tune in to the relationship between
itself and a user. In a classroom, it may be more
important for a robot not to break even if it drops from adesk than to climb one flight of stairs. It would also be
important that it could talk in small voice in a patients’room. A user may also wish to take a bath together with acommunication robot.

4.2. Expression technique in Noh PlayThe expression technique in Japanese Nohplays also provides useful information for minimal
design. Noh mask for the lead actor, which is called
Onna-Men(woman's mask) wears an empty expression.
The mask is not complete in itself, It takes meaning fromactor’s movements.

Summary
Until recent times, robots were merely tools for
production. Now, however people havecertainexpectations to robots as partners in communication, andrecently many communication robots have appeared.
Many of such robots are designed on the same principle
as factory robots: the idea of design by addition. Also
many robots have been created to imitate the figures ofliving things, as mechanical animals. Although suchrobots resemble living things differences do stand out.

A robot with a cute face, like that of a stuffed
toy, softens the user's heart as its first impression,
though this feeling does not last long. Such a preparedpretty face is unusable for long-term communication
design. Instead of preparing a pretty face, we suggest
designing robot movement to express the robot's feelings.
If the robot laughs, it will actually show it is pleased. If it
takes five seconds, however, it may show an artificial
smile. Feelings can be expressed by actions.

In this paper, we proposed a technique called“minimal design.” Minimal design is a design primarily
for maintaining communication. Also, we showed thesignificance of designing a robot with relation to the user
and the situation. In minimal design, in order toemphasize an important design element, thenon-essentials are omitted.
Although a minimal-design communication
robot is dissimilar to animals, it still recalls animal
characteristics in some ways. Since they are notphysically similar to animals, users don't expect too much.
In addition, we suggested a useful way for robots toexpress feelings similarly to characters in comics and a
Noh plays.
Minimal design does not express sharp
disagreement with improvements in elemental technologyof robotics. Technology can be used for making superior
communication robots with minimal design way. Thoughminimal design, a communication robot that never causesboredom can become possible.

Noh mask(Onna men)

5. in situ Design
Acknowledgements

In minimal design, a robot is designed inaccompaniment with a situation and the user's
preference,; communication depends on the situation. A
message can have various meanings in a given situation;
non-verbal expressions also affect communication.
However, even if communication is successful the firsttime in a controlled environment like an experimentallaboratory, there is no guarantee that the second time will
be successful. We expect there to be many further
problems in classrooms, patients’ rooms, or in homes.
MATSUMOTO, Nobuyoshi©ISAROB 2005

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The research reported here was supported inpart by a contract with the National Institute ofInformation and Communications Technology entitled,'Research on Human Communication'.

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References[1]Norman D. A., The Design of Everyday Things,
Perseus Books Group, 2002.
[2]Norman D. A., Emotional Design ; Why we love (orhate) everyday things, BASIC BOOKS, 2004.
[3]Reeves B. Clifford N., The Media Equation, CSLI
publication, 1996.
[4]Ekuan, K. The aesthetics of Japanese lunchbox, MIT
Press, 1998.
[5]Fushimi, K., Anno, M., Nakamura, G., Bi no kikagaku(Aesthetic of geometry), Chuo koron, 1979.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Development of a Speech-Driven Embodied Laser Pointerwith a Visualized Response Equivalent to Nodding
Hiroyuki Nagai∗1 , Tomio Watanabe∗1,∗2 and Michiya Yamamoto∗1∗1 Okayama Prefectural University
111 Kuboki, Soja, Okayama, 719-1197, Japan∗2 CREST of Japan Science and Technology Agency (JST)Abstract

synchrony of embodied rhythms.
In this paper, a new speech-driven embodied laser
pointer InterPointer is developed for supporting embodied interaction and communication in a lecture.
InterPointer is a pointing device in which the beam
pattern is changed from a point to an oval with a visualized response equivalent to nodding in the sametiming as InterActor to speech input.

InterActor is the speech-driven embodied entrainment character for activating human interaction andcommunication by embodied rhythms between speech
and body motions such as nodding in human face-toface conversation. In this paper, a new speech-driven
embodied laser pointer InterPointer is developed forsupporting embodied interaction and communication
for both a lecturer and audiences. InterPointer is apointing device in which the beam pattern is changed
from a point to an oval with a visualized response
equivalent to nodding in the same timing as InterActor
to speech input.

2

Development of a speech-driven
embodied laser pointer

2.1

1

Introduction

Figure 1 shows the concept of a speech-driven embodied laser pointer “InterPointer”. InterPointer isthe system for supporting interaction with lecturer
and audiences through visualized response equivalent
to nodding of listener on the basis of speaker’s speech.
Even if audiences’ reaction cannot be seen and it ishard to talk on the platform, a lecturer can make presentation with ease by using InterPointer that can feedback a typical nodding reaction. Audiences can also

According to the rapid progress of information andcommunication technology, it has become possible easily to communicate with distant people in everyday
life. In human face-to-face communication, not only
verbal message but also nonverbal behavior such as
nodding and body motions are rhythmically related
and mutually synchronized between talkers [1]. It is
to be desired that people can share embodied rhythmby the entrainment between human speech and nonverbal actions and motions in remote communication.
We have developed the speech-driven embodied interactive actor called InterActor which has both functions of speaker and listener by generating expressive
actions and motions coherently related to speech input. We demonstrated that the system can be eﬀective
for supporting human interaction and communication
between remote talkers [2].
It is diﬃcult for a lecturer on the platform to see
audiences’ reaction because they are separated in a
conference room, and they are not entrained. Therefore, it is diﬃcult for the lecturer to present the lecture like face-to-face communication. It is expected tosupport interactive communication by introducing the©ISAROB 2005

Concept of InterPointerInterPointerLecturer
Triangle

I can speak
rhythmically.

I hearyou clear.
It seems tolisten me.

Audiences

It's like listening
together.

Figure 1: Concept of InterPointer.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

enjoy good presentation, because they can share embodied rhythm with the lecturer and feel a sense of togetherness by seeing InterPointer’s nodding reaction.

2.2

Table 1: Beam patterns of InterPointer.
USB-I/O
cable is notconnected.

Outline of InterPointer

࡮

࡯

٤



Pattern

Figure 2 shows the outline of InterPointer. InterPointer utilizes the change of beam patterns of a laser
pointer LP-2000 (CABIN) for the visualization of nodding. LP-2000 can generate four kinds of beam patterns by laser light reﬂection in the mirror which isvibrated by two electromagnetic vibrators (Figure 3).
At the same time, slight vibration arises at the timingof pattern generation.

Nodding response
OFF
࡮

ON
࡮

࡯

٤



was connected to the button input part and it generated pattern switch signals. InterPointer had a connector in order to connect an USB-I/O cable and toreceive a nodding signal from PC.

User
Beam pattern
Nodding response
PC

USB-I/O

SpeechFigure 2: Outline of InterPointer.
Figure 4: InterPointer.

Mirror
LD
࡮
Point

DC3V

AAA

Electromagnetic
vibrator

AAA

٤
Oval


Cross

࡯
Line

Original cable called USB-I/O cable was developed
to connect USB with InterPointer as shown in Figure 5. USB-IO was built in the USB-I/O cable. The
control chip of USB-I/O (Morphy Planning) was a
CY7C63001A-PC (Cypress Semiconductor). It supported USB 1.1 (Low-speed, 1.5 Mbps) and was buspowered.

Figure 3: Outline of LP-2000.
Users can feel the nodding response of listener bychange of beam patterns. Response time of a nodding was set to 200 ms. InterPointer is controlled by
the USB-I/O from PC equipped with the microphone
input and the USB connector. Table 1 shows beam
patterns of InterPointer.

2.3

Development of InterPointerFigure 4 shows InterPointer. InterPointer has
installed the microcomputer PIC16FL84A (MICROCHIP) which can set up input and output for
pattern generation. The microcomputer PIC16FL84A©ISAROB 2005

Figure 5: USB-I/O cable of InterPointer.

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2.4

Listener’s interaction model

Lecturer---------

InterPointer is the laser pointer that can change
beam patterns in the equivalent timing to listeners
nodding, which is one of the typical patterns of nonverbal interaction, and it plays an important role insmooth interaction in human communication. As forthe prediction model of nodding on human face-toface communication, the MA (Moving-Average) model
which estimated the nodding y(i) as the weighted sumof the binary speech signal x(i) in each video frame of
1/30 second was introduced as follows [3].
ŷ(i) =J
a(j) x(i − j) + w(i)Speech-----------------------------------------------------------

AudiencePC-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Nodding
response-------------------------------------------------------------------------------------------------------------------------------------------------------------------------⍮⊛⽷↥ᮭߣߪ
࡮‫ߩޠ⃻⴫߿ࠕ࠺ࠗࠕޟ‬ᮨ୮ߦࠃࠆ
‫ޓ‬ᣂߚߥഃㅧᗧ᰼ߩૐਅࠍ㒐ߋ‫ޕ‬࡮৻ቯᦼ㑆⁛භ⊛ߦഃ૞⠪ߩᮭ೑ࠍ‫ޔߒ⼔଻ޓ‬ฬ⹷߿೑⋉ࠍ⏕଻ߔࠆ‫ޔࠅࠃߦߣߎޓ‬ᰴߥࠆഃㅧࠍଦߒ‫ޔ‬‫↥ޓ‬ᬺ߿ᢥൻࠍ⊒ዷߐߖࠆ‫ޕ‬DV camera(1)

Figure 6: Experimental setup of InterPointer.

j=1

The experiment was examined in the following
steps :
Steps 1 ; Subjects tried InterPointer, so that they
can get used to the system.
Steps 2 ; They were instructed to divide into a lecturer and an audience to perform paired
comparison by one-minute use for eachmode.
Steps 3 ; A lecturer and an audience swapped rolesin the same way.
Figure 7 shows the experimental scene of InterPointer and Figure 8 shows the example of change ofbeam pattern (200 ms). The contents of slides were theformulas of area, such as a triangle and a trapezoid, as
subjects can explain easily and straightforwardly. The
experiment was examined by 10 pairs of 20 Japanese
students. The outline of a system was not explained
to subjects beforehand.

a(j) : linear prediction coeﬃcient, w(i) : noise

3

Evaluation of the system

3.1

Experimental method

The eﬀectiveness of the InterPointer system was examined by sensory evaluation. Figure 6 shows the experimental setup of the system. One of paired subjects
was a lecturer who makes a presentation in front of ascreen, and the other was an audience for the presentation. The following four modes from (a) to (d) were
put to paired comparison in the experiment.
(a) Point (the beam pattern was a point, and doesn’tchange during the experiment)(b) Oval (the beam pattern was an oval, and doesn’tchange during the experiment)(c) Model (the beam pattern changes from Point toOval in the nodding timing by MA-model andreturns to Point after 200 ms)(d) Exponential (the beam pattern changes from
Point to Oval in the timing of exponential distribution and returns to Point after 200 ms)
The subjects were instructed to experience a presentation and choose better mode out of randomly selected two modes from (a) to (d). The exponential
distribution was generated as follows.
T = −3 ln (1 − λ), λ = [0, 1] = (0.1 ∼ 0.9)

(2)

The average of exponential distribution was set at 3
seconds which denotes the mean nodding interval.

©ISAROB 2005

Figure 7: Experimental scene of InterPointer.

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Table 2: Result of paired comparison of InterPointer.

IP

Figure 8:(200 ms).

Experimental result

πi
πi + π j(3)

πi = const. (= 30)

(4)

(a)
(b)
(c)

17
19

28

(d)

17

32

23

(c)
21
12
16

(d)
23
8
24

Ǜ Ǹ
67 8.54
37 3.87
71 9.48
65 8.11Conclusion

In this paper, a new speech-driven embodied laser
pointer was proposed, and the prototype of the system InterPointer was developed for supporting embodied interaction and communication for both a lecturer
and audiences. The eﬀectiveness of InterPointer wasdemonstrated by sensory evaluation.

Table 2 shows the result of paired comparison forthe lecturer and audience. The number in the tableshows that of subjects who preferred the line mode to
the row mode. Namely, this number is the number ofthe subjects who answered that it was more desirable
in the mode of each line. The Bradley-Terry model
was assumed to evaluate the preference of mode byInterPointer quantitatively, deﬁned as follows [4].

Pij =

(b)

(a) ࡮ (Point) (c) ࡮ψ٤ (Model)(b) ٤ (Oval) (d) ࡮ψ٤ (Exponential)

Example of change of beam pattern

4
3.2

(a)

Acknowledgements
This work under our project E-COSMIC has been
supported by CREST (Core Research for Evolutional
Science and Technology) of JST (Japan Science andTechnology Agency).

Referencesi[1] N. Kobayashi, T. Ishii, T. Watanabe, “Quantitative Evaluation of Infant Behavior and MotherInfant Interaction”, Early Development and Parenting, pp. 23-31, 1992.

πi : intensity of i,
Pij : probability of judgment that i is better than jHere, πi shows the intensity of preference of mode
by InterPointer. The model enables to determine
the preference based on the paired comparison. The
Bradley-Terry model assumed by using the result of
paired comparison is also shown in Table 2. To approve the matching of the model, the goodness-ofﬁt test and likelihood rate test were applied to thisBradley-Terry model. As a result, the model was not
rejected and π was validated.
By the result of paired comparison, the proposedmodel was highest. This indicates that the change
from Point to Oval by InterPointer is preferred to
Point by both lecturer and audiences. Much aﬃrmative opinions for InterPointer were also seen by thecomments after the experiment.

©ISAROB 2005

[2] T. Watanabe, “E-COSMIC : Embodied Communication System for Mind Connection”, Proceedings of the 13th IEEE International Workshop
on Robot and Human Interactive Communication(RO-MAN 2004), pp. 1-6, 2004.
[3] T. Watanabe, M. Okubo, M. Nakashige, R. Danbara, “InterActor : Speech-Driven Embodied Interactive Actor”, International Journal of HumanComputer Interaction, Vol. 17, No 1, pp. 43-60,
2004.
[4] R. A. Bradley and M. E. Terry, “Rank analysis
of incomplete block designs”, Biometrika, Vol. 39,
pp. 324-345, 1952.

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Development of Pointing Device to Use Vision for People with Disability
Masakazu Kubo*, Motohiro Tanaka*, Shunji Moromugi*, Youichi Shimomoto*,
Yasuhiko Ohgiya* and Takakazu Ishimatsu** Graduate School of Science and Technology, Nagasaki University, Nagasaki, Japan
Tel :+ 81-95-819-2508; Fax :+81-95-819-2508; E-mail: ishi@net.nagasaki-u.ac.jp

Abstract
Persons suffering from ALS disease present
symptoms of muscular deteriorations. Musculardeteriorations often impede communicating actions likeuttering voice or pushing buttons. In order to support
their communication, a device to use vision is proposed.
This device can be settled apart from the bed of thepatient. This means the device do not interfere with thenursing care. This device is developed for ALS patients
who have ability to move his head intentionally. The
head movement is measured by an original compact
vision system. Corresponding to the head movement,
the computer mouse on the computer display is moved.
This device enables fast input operations. This device isapplied to ALS patients. Applicability of this input
device is confirmed.
1. Introduction
Everybody should be able to enjoy his independent
life. However, patients suffering from ALS(Amyotrophic Lateral Sclerosis) are difficult to live
independent lives. They need intensive cares bycare-workers. As a typical symptom of ALS patients
their muscular functions deteriorate rapidly. In two or
three years after the onset of the disease, patients are
obliged to use artificial respirators since even thebreathing action becomes difficult. Once they start touse the artificial respirator, uttering voice becomes
impossible. In such a physical situation, they areusually confined to bed and possible physical actions
are limited to slight movements of head, fingers, lips oreyes. Therefore, their relationships with society are
extremely limited. However, recent development ofcomputer technology and network technology brought
great benefits to those peoples.
Using the computer and network system, they cancommunicate and talk with others in far places even if
they are confined to bed. Computer systems to support
their communication are already commercialized. But
difficulties remain at input devices for them.
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Considering the deteriorated muscular ability, some
input devices for ALS patients were developed. Those
input deices are categorized as a stand type and awearable type [1]. The stand type devices include touch
switches, laser sensors and vision sensors. These
devices are usually attached to the bed or table and used
to detect some body movements. The wearable typedevices introduce sensors like acceleration, angle, strain
and EMG (Electromyogram) sensors that are attachedto the patient’s body. These sensors measure physicalmovement or condition of the patient body and input
signal is activated based on the data measured.
Input devices to use a vision system havedistinguished advantages over the other devices that thedevices can be applied to ALS patients in a variety ofways [2], [3]. The vision device can be settled apartfrom the bed. This means care workers can be free fromannoying sensors, wires and mechanical parts aroundthe bed. Furthermore, the device can be applied todetect various body movements like lips, fingers,
eyebrow as well as eyeball. It is also important to notethat the vision devices have a supreme feature to reject
miss-operation by introducing tactful image processing.
Vision techniques only to process images of eyeball
were already reported since eyeball movement remains
possible even if the physical situations of ALS patients
become serious [2], [3], [4].
In this paper an input device to use vision for ALS
patients are proposed. An image processing technique isintroduced to extract intentional slight body
movements.
This
input
devicecorresponds
toatwo-dimensional pointing device [5]. This input device
requires the operator’s remaining ability to move his or
her head two-dimensionally. The input device measures
the two-dimensional head movement of the operator.
The operator is requested to wear an LED mark on his
forehead. The raster coordinates of the LED mark are
obtained by an image processing board. The board iscompactly composed of one FPGA chip and one chipCPU.
This input device is incorporated into the computer

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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system to support communication of the patient. In
order to test the applicability, this communication
system is applied to ALS patients. Test results revealed
the feasibility of the system.

LED

LED or reflector

Camera2. Configuration of Communication Device
Proposed communication systems are composed of
a computer, a remote controller and an input device asshown Fig.1. In the computer communication softwaredeveloped for the people with disability is installed.
And a remote controller is composed of an infrared
light remote controller. As an input device composed ofa camera and an image processing board is employed.
The input device is used to detect the body movementof the user. By processing the sequential image of thebody, intentional movement of the body is extracted.
Once the intentional movement is detected, the control
signal is transferred to the personal computer.

Image processing
board(FPGA+PIC)
Fig.2 Pointing device to use vision
Immediately after the movement is detected, thecommunication software in the computer responds. The
communication software has two main functions towrite texts and to control various home electric
appliances. Using these functions, the user can callfamily members at home, talk with them and enjoy TV
programs he wants.
3. Detection of Head Movement

Emergency
CallTV

At every frame the image processing board detects
raster coordinates of the brightest pixel in the image.
Firstly every image is converted to binary one byconsidering the maximal brightness obtained in thepreceding image. An example of resultant binary imageis shown in Fig.3. The proposed image processing
board detects the leftmost and the rightmost horizontal
coordinates of the bright cluster as UL and UR. Similarly,
the top and the bottom vertical coordinates VT and VBof the bright cluster are detected.

Air-Conditioner

ComputerRemote Controller
Image processing
board(FPGA+PIC)

uL

CamerauRuFig. 1 Configuration of communication device
This system is applied able to detect variety ofbody movements like lips, eyes, and fingers.
Considering the application to ALS patient withcapability to move his head two-dimensionally, an
efficient input device is recommended. In Fig.2, aproposed pointing device is shown where a LED lightreflector is attached to the forehead of the operator. A
compact image processing board detects the rastercoordinates of the LED light in the image. The image
processing board is composed of one FPGA (Altera
EPM9320ALC84-10) and one CPU chips (PIC16F876:
Microchip Co. Ltd.). The logic circuit implemented on
one FPGA chip executes the real time image
processing.
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223

VT
VB

bright cluster

vV
Fig.3 Image of bright cluster
Fig.4 shows the block diagram of the logic circuit
to perform the real-time image processing. From thevideo signal, the Sync. Signal generator extracts
horizontal synchronous signals H and verticalsynchronous signals V.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Maximum Brightness
Detector

allowable, the central coordinates of the bright cluster
are transferred to the computer. The computer uses theraster coordinates of LED light as the positioning dataof the computer mouse.

Digitizer

4. Evaluation of Pointing Accuracy and Field Test

A/D
converSync Signal
Generator

Pointing accuracy of the input device explained in
section 3 was evaluated by healthy examinees without
any physical disability. The data obtained by the
pointing device were used to locate the mouse pointeron the computer display in proportion to the head

Right-mostpixel detector
Left-mostpixel detector

movement. A camera (CCD sensor with 570H×480V

Most top
pixel detector

resolution) was settled 200cm apart from the examinee.
The examinee was requested to move his head so thatthe mouse pointer traced the rectangular shape in thecomputer display. One example of the test result isshown in Fig.6. The examinee moved his head 4cm inthe horizontal direction and 3 cm in the verticaldirection. The positioning error in the computer display

Bottompixel detector

H coordinate

HL ,HR memory

Y[pixel]V coordinate

1000

VT,VB memory

800
600

ONE CHIP CPU

400

Fig.4 Block diagram of image processing

200
Using these H and V signals, horizontal coordinate (Hcoordinate) and vertical coordinate (V coordinate) of
every pixel can be determined.
At the maximum brightness detector the video
signal is converted to 8-bit digital data and the brightest
pixel in the image is extracted. At Digitizer the video
signal converted to binary one based on the thresholdlevel. From the binary image, the value UL, UR, VT and
VB in Fig.3 are obtained. An image of the target LED isshown in Fig.5.

0
0

500

1000

X[pixel]Fig.6 Tracing test along rectangular

was less than 10 pixels, which corresponds to about one
percent in the horizontal and vertical direction on thedisplay. Five examinees tried to trace the rectangular on
the computer display. The positioning accuracy wasless than 10 pixels. The required time was shown in
Table 1.
Table 1 Time required to trace rectangularFig.5 Image of target LED
The data are transferred to one chip CPU. The one-chip
CPU checks the size of the rectangular. Once the size is©ISAROB 2005

224

User
No.

1

2

3

4

5

Time
Required

26
sec

36
sec

53
sec

38
sec

54
sec

The pointing device was applied to write a text. A
computer display of the communication software isshown in Fig.7. In the lower half of this display many

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

buttons were arranged. To each button one character or
one function was allocated. Moving the computer
mouse onto the target button and keeping it on thetarget button for a pre-specified interval ? T the desired
character is selected.
By selecting the desired characters sequentially,
desired text could be completed as shown in the upper
half of the display.

operate the communication system effectively using
proposed input device as shown in Fig.9.

A/D Conv.
FPGA

PIC

Fig.8 Image processing board

Fig.7 Computer display of communication system
Using this communication system, examinees
wrote the text “GOOD MORNING” under two valuesof ? T. Table 2 shows time required to write the text.
For ? T=0.5sec, all examinees finished to write the text
“GOOD MORNING” in 30 seconds. This means one
character takes around 2.5 second to select. For? T=1.0sec, around 40 seconds are required. The speed
was compared with that of conventional communication
system to use a touch sensor. Conventional system
always required more than three minutes.
Table 2 Time required to write “GOOD MORNING”
User
No.
Trial 1
ΔT =0.5 s
Trial 2
ΔT =1.0 s

1

2

3

4

5

26

28

22

29

26

34

39

33

43

35

Fig.8 shows a prototype of the proposed image
processor board, where one FPGA, one PIC (one chipCPU) and one A/D converter chips are mounted. Thisone PIC communicates with the computer via serial
communication line.
This system was applied to an ALS patient who
was difficult to move his hands. He succeeded to©ISAROB 2005

225

Fig.9 Operation by two-dimensional input device
5. Conclusions
A vision sensor is proposed as an effective input
device of a communication system. Different from theconventional devices like a touch sensor and a pushbutton, the proposed vision sensor could extract
operator’s intentional movement with enough accuracy
rejecting disturbing body movements. A distinct featureof this technique is that vision sensor is used as a
two-dimensional pointing device. This device enabled
fast operations of the communication system.
Communication systems to employ this vision device
were applied to ALS patients. The patients successfully
could communicate with their families and operate
electric devices by using the communication device.
In this paper we focused on the simple and robustalgorithm to use vision sensor for the ALS patients.
Considering various symptoms of the ALS patients,
also other algorithms need to be developed based on theremaining ability of the ALS patient.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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References[1] Y.Fukuda, M.Tanaka, S. Moromugi and T.
Ishimatsu, A Communication device for disabled slightmovement capabilities, International Journal of HWRS,
vol.4, No.4, December, pp.7-12, 2003.
[2] T. Ochiai, T. Ishimatsu, Computer Input device forhandicapped using vision sensor, in Proc.3rd Int.
symposium on artificial life and robotics Vol.2,
pp.634-637, 1997.
[3] O.Takami, T.Ishimatsu, and T.Shimomachi,
Development of the environmental control system byusing eyeball movements, Proceeding of the AsianControl Conference, pp.715-718, July 27-30, 1994.
[4] O.Takami, K.Morimoto, T.Ochiai, and T. Ishimatsu,
Computer Interface to use head and eyeball movement
for handicapped people, 1995 IEEE International
Conference of System, Man and Cybernetics,
Vancouver, Canada, October 22-25, 1995.
[5] H.Kawamura, N.Dohi, T.Oshima, “Experimental
Production of Light Mouse using PSD sensor”, Proc. of
5th Rehabilitation Engineering, Tokyo, Japan, 21-24
August, 1990, pp.233-236©ISAROB 2005
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Monitoring System of Body Movements for Bedridden Elderly
Rue Shao Dong* , Motohiro Tanaka*, Miki Ushijima * , Takakazu Ishimatsu**Department of Mechanical System Engineering, Nagasaki University, Nagasaki, Japan
Tel: +81-95-819-2508; Fax: +81-95-819-2508; E-mail ishi@net.nagasaki-u.ac.jp
Abstract: In this paper we propose a system to detect physical behavior of the elderly under bedridden status. This system
is used to prevent those elderly from falling down and being wounded. Basic idea of our approach is to measure the body
movements of the elderly using the acceleration sensor. Based on the data measured, dangerous actions of the elderly are
extracted and warning signals to the caseworkers are generated via wireless signals. A feature of the system is that thesenor part is compactly assembled as a wearable unit. Another feature of the system is that the system adopts a simplified
wireless network system. Due to the network capability the system can monitor physical movements of multi-patients.
Applicability of the system is now being examined at hospitals.
Keywords: dementia patient, monitoring system, vital sign, warning signal, wireless signal

1. INTRODUCTION

2. SYSTEM CINFIGURATION

Increase of the aged people is becoming a serious social
problem. In the care house for the elderly only a few care
workers are working for many aged people. Typically in thenighttime care workers are too busy to deal with various kindsof tasks, since only two or three care workers are staying atnight. Many of the care workers are doing their best but are
too tired. One serious problem at care-houses is the injury of
the aged caused by slipping and falling down on the floor.
Once they broke their legs and feet, it is not easy for them torecover sooner. Typically in the case of dementia patient, theyeasily forget and neglect the request not to leave the bed alone
by the caregiver. One simple answer to protect such cases is tofasten the patient onto the bed. But such kind of treatment
means neglecting the human right of the patient. Touch
sensors and pressure sensor is proposed to detect the escape orwake up motions of patient from the bed. But what requiredby the care workers is some technique to detect body
movements of the patient and forecast occurrence of the risky
actions of the patients. Of course, forecasting the risky actions
with reliability is not easy matters. But smart care workers say
every elderly represents individual physical behaviors. They
also says an alert signal can be generated before the risky
event occurs by prep aring individual criterion for each elderly.
Already some related researches are achieved, where
techniques to measure vital sighs, like sleeping stages,
heartbeat, respiration and snore [1], [2], [3], [4]. Image
processing is also one technique to measure such sighs, but thetechnique is not preferred because of the violation of theprivacy. Furthermore, the image processing costs a lot [5].
In this paper a monitoring system is proposed whichmeasures body movements of the bedridden patient. The datameasured is used to evaluate the current physical status of thepatient and the possibility of risky actions. Considering
practical application, the wearble sensor unit is compactly
achieved employing a two-dimensional acceleration sensor, aone-chip computer and a wireless communication module.
Due to the introduction of the simplified wireless network
system, the system is applicable to the multi-user condition.
The proposed system can recognize some kinds of physicalmovements like deep sleeping, a light sleeping, walking,standing up, slipping, falling down and jumping up.
Furthermore, based on the criterion about the time sequencesof these physical movement actions, the sensor sends an alert
signal to care workers. The care workers considering thefeatures of the physical behavior determine the criterion.
The proposed system is now under examination at hospitals.
©ISAROB 2005

A system configuration is shown in Fig.1 where patientsare wearing sensor units on their chest. On the sensor unit, an
acceleration sensor (ADXL202: Analog Device Co. Ltd.), a
one-chip CPU (PIC16F84A) and a wireless communication
module (iTRX315: itec Co.Ltd.) are implemented in acompact body as shown in Fig.2. The acceleration sensor
detects two-dimensional acceleration. Of course, since thesensor detects the acceleration caused by the gravity, thesensor gives information about the posture of the sensor.
Therefore, if a patient wears the sensor on his chest, thephysical behavior or posture of the patient can be readily
detected by the data obtained. A compact wirelesscommunication module on every sensor unit enables tocommunicate with the master computer. Furthermore, byintroducing an original communication protocol, every unit
can communicate with each other. In each sensor unit,
individual criterion is implemented considering the eachpatient’s situation. Based on the each criterion, the one-chipcomputer recognized the risky condition of the patient. Oncethe one-chip computer recognizes the situation of the patient isdangerous, it sends the risky condition to the master computer.
It should be noticed that the sensor unit could detect
extraordinary body movements like gastric spasm or
spasmodic asthma as well.

Master computer
receiverThree patients with sensor units

Fig.1 System configuration

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can detect the position of the patient in the facilities or detect
the absence of the patient.

In the sensor unit consumption of the electricity needs
to be suppressed since the compact battery is
indispensable for comfortable wearable sensor unit.
Therefore, the protocol of the communication wasdetermined.

3V
321

ADXL
6
202

iTR

4

C

Master computer1 1 1 9 8 7 6 54

Sensor unit 1

R

Sensor unit 2
Sensor unit 3
Sensor unit 4
Sensor unit 5

Fig.2 Electric circuit for sensor unit

Sensor unit 6

2. NETWORK SYTEM
Fig.3 Sensor units and master computer
In the proposed system an original network protocol isintroduced. The basic idea of the protocol is that every
communication module communicates with each othersynchronizing with the base synchronous signal emitted by themaster computer. A feature of our protocol is that even if asensor unit cannot detect the base synchronous signal emitted
by the master computer, another sensor unit that can detect thebase synchronous signal acts as an auxiliary master computer.
This feature provides us a great benefit under the situation that
only low power communication system is available.

3. EXPERIMENT
Every components of the sensor unit could be realized
in a compact body as shown in Fig.4. The reachable range ofeach wireless transmitter was around 10m.
Acceleration
sensorSuppose the base synchronous signal can be detected in therange of sensor unit 1 to 5 as shown in Fig.3. And sensor unit
6 is out of the range. As everyone notice the master computercan recognize that sensor units 1 to 5 are in the accessible
range and the sensor unit 6 is out of the range. In this situation,
the master computer asks every sensor unit if some unit canaccess the sensor unit 6. After that, each sensor unit tries tocommunicate with sensor unit 6 sequentially. If one of thesensor units is able to communicate with the sensor unit 6, thesensor acts as an auxiliary master computer for the sensor unit
6. Thereafter, the sensor unit can communicate with the mastercomputer via the auxiliary master computer. This means thateven if the maximum accessible range of the communication
module is less than 10 meters, by introducing relaying sensor
units the accessible range can be expanded as far as we like.
Therefore, our proposed system can be applicable to a hospitalor nursing facilities even if only low power wirelesscommunication is available. This feature is important since inmost of medical facilities only low power wirelesscommunication is allowed. Suppose this network system isrealized in a nursing facility. Using this network features, we©ISAROB 2005

battery
Wireless communication
moduleFig.4 Arrangement of components in the sensor unit
This sensor is attached to an examinee to check thefeasibility to recognize current status of body movements. An
examinee wearing the sensor on his chest is requested to (1)lie on the bed, (2) roll over, (3) wake up and (4) walk aroundthe bed. During the above body movements, the sensor
measured the acceleration. Fig.5 shows the acceleration data
toward the foot direction of the examinee. Fig.6 shows theacceleration data toward the leftward of the examinee.

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time sequences of the acceleration fluctuations are detected.

Acc.

action(1) action(2) action(3) action(4)2gg0

Fig.5 Sensor signal toward foot directiontime(1) Lie on the bed

Acc.

2g
g
0

action(1) action(2) action(3) action(4)time
Fig.6 Sensor signal toward leftward

Corresponding actions are shown in Fig.7. During t
he period of the action (1), where the examineelies on the bed, the acceleration toward foot directi
on was negative. This means the sensor unit on thechest inclined slightly upward. During the period of the action (2), the examinee repeated roll over tw
ice from rightward and leftward. During the periodof the action (3), because of wake up action the acceleration toward the foot direction increased tillaround 1.0g. During the period of walking around t
he acceleration toward the foot direction fluctuatedaround 1.0g.
4.

(2) Roll over

ALGORITHM TO RECOGNIZE
BODY STATUS

Denote the acceleration toward the foot direction as AF,
and the acceleration toward the leftward as AL. From the dataobtained in section 3, a simple criterion to recognize current
body status becomes as follows.

(3) Wake up(Status 1: Lying on the bed) when AF is around zero.
(Status2: Rolling over) when AF is around zero and AL isfluctuating.
(Status3: Waking up) when AF is increasing up to 1.0g.
(Status4: Walking) when AF is fluctuating around 1.0g.
In order to prevent the bedridden elderly from falling down
and being wounded, another criterion to determine degrees ofrisk can be proposed as follows.

(4) Walk around(3rd degree: risky level) when impulsive behaviors of theacceleration is detected.
(2nd degree: nearly risky) when frequent fluctuations of
the acceleration are detected.

Fig.7 Sequences of actions(1st degree: slightly risky:) when different pattern of the©ISAROB 2005

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The above criterion comes from one idea that before therisky action occurs some specified body movements’ occur.
The criterion should be determined based on the behaviorof the elderly.

5. DISCUSSIONS
We built a prototype composed of one master computer
and three sensor units. At every one second t h e mastercomputer checked accessibility of three sensor units. And at
every one second, every sensor unit responded that the sensor
unit was accessible with the current body status. In case one
sensor unit was not accessible by the master computer, theaccessible sensor unit acted as an auxiliary master computer.
In case that one sensor is not accessible with any other sensor
unit and the master computer, the master computer could
recognize the situation.

4. CONCLUSIONS
We developed a monitoring system of physical behavior of
the elderly under bedridden condition. The sensor can be effectively applicable to suppress the number of theaccidents caused by the slipping and falling down on the floor.
The system can be readily connected to the Internet system.
Due to the developing ability of Internet, our system can beexpanded to more advanced system like remote care of the patients in the far place. Furthermore, patients could be cared s
afely in the care house even if the caseworkers are busy.
We are now testing a prototype system at hospitals
in Nagasaki city. The results of the test will be demonstrated
sooner.

REFERENCES
[1]
[2]
[3]
[4]
[5]

T. Togawa, Home health monitoring, J.Med. Dent. Sci,
45, 151-160, 1998.
B.W.Carlson,et al., Evaluation of a Non-Invasive
Respiratory Monitoring system for sleeping subject,
Physiol.Meas., 20, 53-63, 1999.
H.Andoh, et.al., Network Health Monitoring System in
the Sleep, Proc. SICE Annual Conf.in Sapporo,
1421-1424,2004.
K.WEatanabe,et.al., Estimation of Sleep Stage Based on
Heart Rate Fluctuation and Body Movement, Proc. SICE
Annual Conf.in Sapporo, 2153-2156,2004.
R. Satoh, et al., Non-contact sensor to detect movementof dementia patient, ICCAS, 2001.

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Development of a training machine for elderly people with muscle activity sensor
Sung Jae Yoon*, Seok-Hwan Kim*, Motohiro Tanaka*, Shunji Moromugi*,
Yasuhiko Ohgiya*, Nobuou Matsuzaka** and Takakazu Ishimatsu**Department of Mechanical Systems Engineering, Nagasaki University, Nagasaki, Japan(Tel : +81-95-819-2508; E-mail: ishi@net.nagasaki-u.ac.jp)**Department of Physical therapy, Nagasaki University, Nagasaki, Japan Engineering, Japanrequired in the machine training is too hard for those aged
people. That could be a reason to keep aged people awayfrom the machine training. The third is that too muchmachine training might cause damage in user’s body, therefore
it is hard to ensure the safety in the training because aged thepeople’s body is somewhat weakened. Thus the training
machine that is designed for elderly people is needed. It
should be enough safe and comfortable to use for aged people
and also have high adaptability for wide range of the size andability of aged people.
In this paper we propose a training machine for the elderly
people. The machine is developed by introducing power

Abstract
An advanced training machine that can adjust load according
to muscle activity is proposed for elderly people. The training
machine allows users to have a safe and effective training
through exercise close to ordinal motion appears in daily life
such as stretching or stooping motion. The activation levelof user’s muscle is real timely monitored by a muscle activity
sensor during the exercise and the training load is adjusted
based on the data measured. The training load is exerted andcontinuously controlled by actuation of an electronic motor.
Keywords: Machine training, Elderly people, Preventive
approach, Load control, Muscle activity sensor

assisting technology.

1. INTRODUCTION

2. FEATURES OF THE TRAINING MACHINEPROPOSED

The welfare issues accompanying by the aging of society
are highly concerned in most of the advanced countries. It isstrongly hoped that the government provides adequate care
and accommodation to people when they become old.
However, it is extremely difficult to support all the people
perfectly because each aged person needs different care. In
addition to the fact, it costs huge and needs many hands toachieve it. Under this situation in Japan, that is one of themost aged society, the government started to focus on theapproach to reduce the number of aged people who neednursing care more than to offer perfect caring system for them.
The government promotes aged people to maintain enoughstrength to keep their independent life and allocate budgets for
activities to promote the preventive approach against
becoming bedridden status.
The importance of thepreventive approach is currently being recognized among
other aging countries as well.
As one of the effective preventive approaches the physicaltraining using machines is recommended. It is reported thatthe constant physical training is highly effective for aged
people to maintain their strength and quality of life. Some
care center introduced machine training in their program.
However, there are several problems. One is that thosemachines are designed for sports training not for training ofelderly people therefore users cannot use the machines
comfortably in some cases. The other is that the exercise©ISAROB 2005

An advanced training machine is proposed for keeping
elderly people healthy and energetic so that they can keep
living an independent life. The training machine let user towork on an exercise to maintain/recover physical abilities tosupport their body against the gravity. We focus on the leg
strength because the ability to support their body or to standup by themselves is very important both for aged people tokeep their quality of life high and for care worker’s to caretheir patients with less energy. This training machine has
three major futures as follows.
1) Leg pressing exercise
Usually sports training machines are designed to develop
specific muscles through a simple exercise such as knee
flexion, elbow extension and twist of main body. It iseffective for sports players to shape their body as they desire.
However, for elderly people, those machines are not always
comfortable because the purpose of the training is different
from that of athletes. It is not for strengthening their muscles
to compete against other players but maintaining or recovering
their physical function to do everyday movements such as
stretching or stooping of leg. The training machine proposedin this study is specially designed for aged people to train
stretching and stooping ability. By using this training
machine users can enforce their strength of both legs throughleg pressing exercise. In addition to the physical training

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effect, it also works for recovering sense of body motion orstrong desire to support their own body by themselves.
2) Adjust load with electronic actuator
Training load is generated by weights in the case of most of
conventional training machine. Users select preferable load
level before the exercise and train under the constant load
through the exercise. The proposed training machine uses anelectronic actuator. Therefore the training load can becontinuously adjusted during the exercise.
3) Feed back of muscle activity on load control
In the elderly training safety issue needs to be considered
carefully more than the case of sports training. Different
from the sound body of young athletes elderly people’s bodyis generally remarkably weakened. The proposed systemmonitors the activation level of user’s muscles and output
force of the leg. It is possible to select optimum load
simultaneously and achieve effective and safe training for
elderly people.

pre-defined load generated by the actuator. And during thestooping motion, user does not have to strengthen his leg, justenjoys stooping motion against a light pre-defined load
actuated by the motor.

(a) Stretch

3. MECHANICAL SYSTEM CONFIGURATION
Figure 1 shows the proposed system. The system iscomposed of a moving seat, an electronic actuator, step force
sensor, muscle stiffness sensor to detect user’s muscle
activation level, and a PC & FPGA based controller.
Muscle Stiffness
Sensor

Step Force
SensorPC(b) Stoop
Figure 2 Two states of leg pressing exercise
Table 1 Major specification of motor

Moving seat

FPGA baseController

Model

Honda DDW 2020 L size

Voltage

24 V

No load Maximum rpm

65 rpmRated rpm

40 rpmRated torque52 Nm

3.2 Human Machine Interface

Figure 1 Overview of the system
3.1 Moving Seat
While the target of the custom sports training machine is tosupport user to strengthen his muscle, the target of the
proposed training machine system is to support user tomaintain his physical ability. In this point, the moving seat isdesigned to provide user to stretch and to stoop with easy due
to the electronic actuator. Basically user’s weight is supported
by moving seat so that user can exercise without exerting all
his weight to leg. So, by just setting the reference load
according to one’s physical ability, user can enjoy exercise
with the proposed system.
Figure 2 shows two status of the system. During thestretching motion, user has to stretch his leg against the©ISAROB 2005

Figure 3 Muscle Stiffness Sensor
To detect user intentions of stretching, stooping andcontrolling the speed, the proposed system adapts two kinds of232

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sensors, one is a stepping force sensor and the other is a
muscle stiffness sensor. The step force sensor is used to detect
the force between stepping force to the foot plate and the loadthat is generated by an electronic actuator. With this sensor,
the proposed system controls the load on the foot plate in realtime base. Figure 3 shows the muscle stiffness sensor. The
muscle stiffness sensor is attached to thigh and detects muscle
activity as stiffness parameter that shows high similarity with
EMG sensor. It is developed as man-machine interface for the
power-assisting system. It was confirmed that there was highcorrelation between the data obtained from this muscle
stiffness sensor and the activity level of the muscle throughauthor’s studies. Figure 4 shows change of stiffness parameter
according to muscle activity change. The detail of this muscle
stiffness sensor is described in [1]-[3]. One advantage of thissensor is that it is easy to put on/off from the body. The
other advantage is its high robustness on measurement against
the disturbing contacting force from outside. By using thissensor user can work on the training comfortably and safely
with this training machine.

PC

FPGA based controller

Measurement

Analog data input

Muscle Stiffness Sensor

Analog data output

Step Force Sensor

Control Command(Torque, Speed)
Position data

Motor

System Operating(Stretch and Stoop)

Mechanical Structure

Figure 5 Configuration of a control system
Figure 6 shows the computer screen that is used to set up
the reference force and moving distance based on each user,and displays present state during the training. With thisprogram, each user’s training data can be classified andanalyzed for the better management.

Muscle Stiffness
Sensor
Cuff

Stiffness parameter

Weight4 weights

0.6
0.5
0.4
0.3
0.2
0.1
0

3 weights
2 weights
1 weight

Figure 6 Interface display

4. EXPERIMENT

0

5

10

15

Experiment is conducted to verify the effectiveness of the
proposed training machine system. In this experiment, muscle
stiffness sensor is used to detect muscle activity and to trigger
stretching or stooping motion. During the training, even if the
stepping force is changed, the load to leg is controlled tomaintain the reference value. Four kinds of load are tested toevaluate the controllability of the proposed system and its
HMI.
Because the proposed training machine adapted theelectronic actuator, it can impose load to both stretching andstooping motion. But in this experiment, the load is imposed
to only stretching motion, and the actuator executes stooping
motion automatically, so that user can enjoy the leg pressing
exercise.

Time [s]Figure 4. Stiffness parameter versus Muscle activation
3.5 PC & FPGA based Controller
As the controller, PC and FPGA (Flexibly Programmable
Gate Allay) based control circuit are adapted. PC interfaces
with the user to set up the target load and notices the present
state to user. FPGA based control circuit is used to archive
position data, muscle stiffness parameter, and stepping force
signal through A/D converter, and send control command to
the actuator through D/A converter. Figure 5 shows theconfiguration of the control system.

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Through the experiment, it can be verified whether thesystem can control the load to leg, the stepping force, in
real-time base. Muscle stiffness sensor can be used as
human-machine interface that detects muscle activity.

Load[kN] & Step Force[kN] &Position[cm] Stiffness Parameter

94

4.1 Control algorithm
Simple control algorithm is adapted for the proposedtraining machine system as followed:
In real-time base, the error between the stepping force andthe reference force is calculated by equation (1).
(1)∆F = FSTEP − FREF

46
34
22
10
0

10

20

30

20

30

20

30

(a) 30kgf
94

Load[kN] & Step Force[kN] &Position[cm] Stiffness ParameterFM 1 = FM 0 + C

58Time[s](2-2)∆F = 0 , FM 1 = FM 070-2where FSTEP is the stepping force measured by step force
sensor, and FREF is the reference force.
And, based on the error ( ∆F ), the control force FM is
calculated by Equation (2).
(2-1)
∆F > 0 , FM 1 = FM 0 − C∆F < 0 ,

82

(2-3)

where FM 0 , FM 1 are the previous and present control forces tothe foot plate, and C is the control offset. Based on the error( ∆F ), C is also chosen by equation (3).
If | ∆F |≥ 5 , C = 5
(3-1)
(3-2)
If | ∆F |< 5 , C = 1

82
70
58
46
34
22
10
-2

0

10

Time[s](b) 40kgf

4.2 Result
Load[kN] & Step Force[kN] &Position[cm] Stiffness Parameter

90

Stiffness Parameter

85
80
75
70
65
60
55
50
45
40
0

10

No loadTime[s]
30kgf

78
68
58
48
38
28
18
8
-2

20

0

10

Time[s]
40kgf

50kgf
Reference Position
Reference Load
Stiffness Parameter

Figure 7. Stiffness change during leg pressing motion underfour kinds of load [0, 30kgf, 40kgf, 50kgf]

Position
Step Force(c) 50kgf
Figure 8. Experiment data of leg pressing motion

When the posture of leg changes, correlated muscle makeschange in their activity. Figure 7 shows that even there is noload on the leg, stiffness parameter changes during the leg
pressing motion (stretching and stooping). And it also showsthat stiffness parameter increases according to the increase of
the load. It means that muscle stiffness sensor can be used as
HMI for the power-assisting devices.
In Figure 8, each (a), (b), and(c) shows graphs of stiffness
parameter, stepping force, position data, and reference value ateach load 30kgf, 40kgf, and 50kgf. In each case, it isdemonstrated that the system succeeds in controlling the
stepping force to the reference value.

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5. CONCLUSION
A new type of training machine is proposed that can help
elderly people maintaining their health. A feature of themachine is that a muscle stiffness sensor is incorporated tomonitor the muscle status and control the reference load of the
training. Through the experiment using a prototype, it isdemonstrated that the training load of this machine can becontrolled based on both the stepping force and the muscle
activity. It is expected that this training machine provides
effective and safe training opportunities for elderly people and

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be a great help for preventive approach against serious
situation of aging society by reducing people who needs daily
care.

REFERENCES
[1]

[2]

[3]

S. Moromugi, K. Izumi, T. Yoshimochi, T. Ishimatsu, T.
Tanaka and M.Q. Feng, “Device for Assisting Grasping
Function,” Proc. 17th Korea Automatic Control
Conference, International Conference on Control,
Automation and Systems, Muju, Korea, pp.1250-1254,
Oct., 2002.
S. Moromugi, Y. Koujina, S. Ariki, A. Okamoto, T.
Tanaka, M. Q. Feng and T. Ishimatsu, “Muscle Stiffness
Sensor to control assisting device for disabled,” Proc. 8thInternational Symposium on Artificial Life and Robotics,
Oita, Japan, pp.459-462, Jan., 2003.
S. Moromugi, A. Okamoto, S. H. Kim, H. Tanaka, T. Ishi
matsu and Y. Koujina, “Device for Assisting Grasping Fu
nction(2nd Report: Maneuverability Evaluation),” Proc.
18th Korea Automatic Control Conference, International
Conference on Control, Automation and Systems, Gyeon
gju, Korea, pp.2665-2669, Oct., 2003.

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Learning Algorithms and Uncertain Variables in Knowledge-Based Pattern Recognition*
Z.Bubnicki
Institute of Control and Systems Engineering, Wroclaw University of Technology
Wyb. Wyspianskiego 27, 50-370 Wroclaw, POLAND
e-mail: zdzislaw.bubnicki@pwr.wroc.pl~ D which means that
introduce also a soft property x ∈“ x approximately belongs to the set D” or “anapproximate value of x belongs to D”, with the certainty
index~ D ) = max h ( x).
v (x ∈(1)

Abstract
The paper is concerned with a class of intelligent
recognition systems described by a relational knowledge
representation with unknown parameters. The main idea
consists in the combination of two approaches:
application of uncertain variables and a learning processwith step by step knowledge updating. The recursive
recognition algorithm with a modification of a current
expert's knowledge at each step is presented. An example
illustrates the presented approach.

x∈DThe details concerning the uncertain variables and theirapplications may be found in [4,5] and in books [1,2].

2

Keywords: uncertain variables, uncertain systems,
learning systems, knowledge-based systems, recognition,
intelligent systems

Let an object to be recognized or classified becharacterized by a vector of features u ∈ U which may bemeasured, and the index of a class j to which theobjects belongs; j ∈ {1,2,..., M } = J . The set of theobjects may be described by a relational knowledge
representation R (u, j; x j ) where x j ∈ X j is a vector

1 Introduction
The paper is concerned with a class of intelligent
recognition systems described by a relational knowledge
representation with unknown parameters. For decision
making in such systems two different approaches havebeen proposed and developed [1,2]:
1. The application of uncertain variables described bycertainty distributions given by an expert.
2. The application of a learning process consisting in stepby step knowledge validation and updating.
The purpose of this paper is to present a new idea
based on the combination of these two approaches. At
each step of the learning process an expert's knowledge
is modified according to the current result of thelearning. Such an idea has been applied to uncertain
decision systems [3]. The application to patternrecognition requires to take into account a specificationof this problem. The uncertain variable is described by acertainty distribution given by an expert andcharacterizing his / her opinion on approximate values ofthe variable. In the description of the uncertain variable
x we introduce a certainty distribution h ( x) = v ( x ≅ x)
where x ≅ x means that “ x is approximately equal to x”
and v is a certainty index given by an expert. We*

Recognition problem based on uncertain
variablesof unknown parameters which is assumed to be a valueof an uncertain variable x j described by a certainty
distribution h x j ( x j ) given by an expert for all j ∈ J ..
The relation R is reduced to the sequence of sets

Du ( j; x j ) = {u ∈ U : (u , j ) ∈ R (u , j ; x j )}, j ∈ 1, M .

Now we can formulate the recognition problem
consisting in the determination of a class j maximizing
the certainty index that j belongs to the set of all
possible classes for the given u

D j ( x1 ,..., x M ) = { j ∈ J : u ∈ Du ( j ; x j )} .
Optimal recognition problem [2]: For the given
Du ( j; x j ) , h x j ( x j ) (i ∈ 1, M ) and u (the result of

measurement) one should find j * maximizing∆~ D ( x ,..., x )] =v[ j ∈v( j ).
j 1
M

Using (1) and (2) we obtain

This work was supported by Polish State Committee for Scientific Research under Grant No. 4 T11C 001 22©ISAROB 2005

(2)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

~ D ( j; x )] = v [ x ∈~ D ( j )] =v ( j ) = v [u ∈
u
j
j
xj

max

x j ∈D x j ( j )determine new Dˆ x j (n) , i.e. update the knowledge.

hx j ( x j )where D x j ( j ) = {x j ∈ X j : u ∈ Du ( j; x j )}.

4. Knowledge updating for u n = uˆ j n

(3)
(4)

Dˆ x j (n) = {x j ∈ Dˆ x j (n − 1) : u n = U − Du ( j; x j )} .

*To find j it is necessary to determine v( j ) according
to (3) and to maximize it with respect to j . Such a way

Put Dx j (n) = Dx j (n − 1) and ∆ x j (n) = Dx j (n) ∩ Dˆ x j (n) .
Without an a priori knowledge on x j (considered in the

may be applied under the assumption that x j and xi arenext section), the result of the estimation may be used in
the following way: Values x j n are chosen randomly

independent uncertain variables for i ≠ j .

from ∆ x j (n) and put into Du ( j; x j ) in place of x j .

3 Learning process

Then Du ( j; x j n ) are used to determine j n* in a way
described in the previous section.

Assume now the parameter x j has the value x j = c j
and c j is unknown. Assume also that we can use the

4 Learning system based on current
expert's knowledge

learning sequence (u1 , j1 ), (u 2 , j 2 ), …, (u n , j n ) wherej i is the result of the correct classification given by atrainer. Let us denote by u ji the subsequence of u i forwhich

At the n-th step, the result of the learning process inthe form of a set ∆ x j (n) may be used to present an

ji = j , by û ji the subsequence for whichji ≠ j , and introduce the following sets in X j :D x j (n) = {x j ∈ X j : each u ji ∈ Du ( j; x j )} ,expert's knowledge in the form of a certainty distribution
such thath jn (x j ) = 0
for everyh jn (x j )(5)

x j ∉ ∆ x j ( n) . Thus, the expert formulates his / herDˆ x j (n) = {x j ∈ X j : each uˆ ji ∈ U − Du ( j; x j )} . (6)∆D x j (n) ∩ Dˆ x j (n) = ∆ x j (n)current knowledge, using his / her experience and thecurrent result of the learning process based on theknowledge of the external trainer. In particular,
h j n ( x j ) = h ( x j , b j n ) , i.e. the form of the certainty

may be proposed as the estimation of c j [2]. The

distribution is fixed, but the parameter b j n (in general,

determination of ∆ x j (n) may be presented in the formb j n is a vector of parameters) is currently adjusted. Forof the following recursive algorithm for the fixed j :example,ifin
one-dimensional∆ x j (n) = [ x j min, n , x j max, n ] and h j n ( x j )The setIf j n = j (u n = u j n )parabolic form presented
b j n = ( d jn , a jn ) and

1. Knowledge validation for u n = u jn . Prove if[u n ∈ Du ( j; x j )] .

x j ∈Dx j ( n −1)

d jn =

If yes then D x j (n) = D x j (n − 1) . If not then one should

x j min,n + x j max,n2

in, a jn =

Fig.

case
has a
1,

then

x j max, n − x j min, n2h jn ( x j )determine new D x j (n) , i.e. update the knowledge.
2. Knowledge updating for u n = u jn

1

D x j (n) = {x j ∈ D x j (n − 1) : u n ∈ Du ( j; x j )} .
If j n ≠ j (u n = uˆ j n )3. Knowledge validation for u n = uˆ jn . Prove if[u n ∈ U − Du ( j; x j )] .

d jn − a jn

x j ∈Dˆ x j ( n −1)

If yes then Dˆ x j (n) = D x j (n − 1) . If not then one should©ISAROB 2005

d jn
Fig. 1237

d jn + a jn

xj.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

x j ≤ u T u ≤ 2x j ,For h j n ( x j ) the next result of the recognition j n* may
be determined in the way presented in Sec 2, i.e.

j = 1,2,..., M .

Assume that the parameter x j has the value x j = c j > 0

j n* = arg max v n ( j )
j∈J

(7)

and c j is unknown. It is easy to note that the sets
D (n) in (5) and Dˆ (n) in (6) have now the forms of

where v n ( j ) is determined by (3) with h j n ( x j ) insteadxj

xj

of h x j ( x j ) and u n instead of u. In general, as a result

the following intervals:of the maximization (7) one may obtain a set of theclasses D j n ∈ J . For h j ( x j , b j n ) we obtain the fixed1
D x j (n) = [ max u Tj i u j i , min u Tj i u j i ] ,2 iiform of the function v ( j, b j n ) :1
Dˆ x j (n) = (a j n , b j n )2vn ( j) =∆max

x j ∈Dx j ( x j )h j ( x j , b j n ) = v ( j, b j n )where a j n is the maximum value uˆ Tj i uˆ j i less thanand consequently, the fixed form of the final result, i.e.

min u iT u i , and b j n is the minimum value uˆ Tj i uˆ j i

j n* = j (b j n )greater than max u iT u i . The estimation of c j has the

or

the

set

of

the

classes

D j ,n +1 = D j (b j n ) .

The

same

approach

may beiiform

presented in the case of C-uncertain variables, with vc
instead of v (see [3]).
The block scheme of the learning recognition system
under consideration is presented in Fig. 2 where G is a
generator of random variables for the random choosing

1 Tu u ≤ x j ≤ uT u .
2
Applying (3), one obtains v n ( j ) as a function of d jn
illustrated in Fig. 3 for a jn = 1 :

G

0


1

for d jn ≤ u T u − a jn

2
 1 T
2
 − ( 2 u u − d jn ) + a jn
11for u T u − a jn ≤ d jn ≤ u T u

2
2

1
vn ( j) = 1
for u T u ≤ d jn ≤ u T u

2

T
2
 − (u u − d jn ) + a jnfor u T u ≤ d jn ≤ u T u + a jn


0


for d jn ≥ u T u + a jn .
Maximizationof vn ( j )vn ( j )Knowledge
validation
andupdatingj ∈ 1, M

unKnowledge - based
recognitio n∆ x j (n)j ∈ 1, M
Experth j n (x j )j ∈ 1, Mbe(4) for the given u T u are described by the inequality

jn*

un

may

parabolic form presented in Fig. 1. In this case the setsofthe computer recognition system or parts of the program
which may be used for computer simulations.

Objectanddetermined by a recursive algorithm presented in Sec. 3.
Assume that the certainty distributions h jn ( x j ) has aj n* from D. The blocks in the figure illustrate parts ofjn∆ x j (n) = D x j (n) ∩ Dˆ x j (n)Knowledge
representation
Du ( j; x j ), j ∈ 1, M

Fig. 2(8)

5 Example
Let the sets

Du ( j ; x j )be described by theinequalities©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Referencesvn ( j )[1] Bubnicki Z (2002), Uncertain logics, variables andsystems. Springer-Verlag, Berlin, London, N. York
[2] Bubnicki Z (2004), Analysis and decision making in
uncertain systems. Springer-Verlag, Berlin, London,
N. York[3] Bubnicki Z (2004), Application of uncertain
variables to learning process in knowledge-based
decision systems. Proceedings of the 9thInternational Symposium on Artificial Life andRobotics, Oita, Japan, Jan 28-30, 2004, Vol. 2, pp.
396-399[4] Bubnicki Z (2001), Uncertain variables and theirapplications for a class of uncertain systems.
International Journal of Systems Science 5:651-659[5] Bubnicki Z (2001), Uncertain variables and theirapplication to decision making. IEEE Trans. on
SMC, Part A: Systems and Humans 6:587-596[6] 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. 261264[7] Bubnicki Z (2004), Application of uncertain
variables and learning algorithms in a class of
distributed knowledge systems. Proceedings of 18th
IFIP World Computer Congress, Toulouse, France,
Aug 22-27, 2004, Vol. “The Symposium on
Professional Practice in AI”, pp. 111-120[8] Bubnicki Z (2004), Uncertain variables and systems– new problems and results. Artificial Intelligenceand Soft Computing – ICAISC 2004. Lecture Notes
in Artificial Intelligence. Springer Verlag, Berlin,
Vol. 3070, pp. 17-29[9] Bubnicki Z (2005), Uncertain variables and theirapplications in knowledge-based decision systems.
International Journal of Intelligent Systems (inpress)

1

1 T
u u
2

1 T
u u −1
2u T u + 1 d jnuT u

Fig 3.

For

M = 3 , u T u = 5 , d 1n = 2 ,d 3n = 6
we obtainv n (1) = 0.75 ,example,d 2 n = 5.2 ,forv n (2) = 0.96 and v n (3) = 0 . Thenj *n = 2 , whichmeans that for u T u = 5 the certainty index that j n = 2
belongs to the set of the possible classes has the
maximum value equal to 0.96.
The recognition algorithm in the learning system is asfollows:
1. Determine x j , min,n and x j , max,n using the estimation
algorithm with the knowledge updating
2. Find d jn , a jn using the formulas in Sec.4
3. Determine v n ( j ) using (8) with u = u n .
4. Find j *n maximizing v n ( j ) .

6 Conclusions
For a class of the recognition systems underconsideration, at each step of the learning process it ispossible to use the current expert's knowledge based on
uncertain variables. Numerical examples and simulations
show that the using of the expert's knowledge during thelearning process may improve the quality of thedecisions. On the other hand, the algorithm is more
complicated and requires using the correct classifications
given by a trainer. The presented approach may be
extended to other cases of uncertain recognition systems,
for which the applications of uncertain variables and thelearning algorithms were elaborated separately,
especially for systems with three level uncertainty andfor complex systems with a distributed knowledge[6,7,8,9].

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Association Rule Mining Using Genetic Network Programming
Kaoru Shimada

Kotaro Hirasawa

Takayuki Furutsuki

Graduate School of Information,
Production and Systems,
Waseda University, Japan

Graduate School of Information
Production and Systems,
Waseda University, Japan

Graduate School of Information
Production and Systems,
Waseda University, Japan

Abstract

However, the measure is not adequate for modelingall uncertainties of association rules. For instance, themeasurement does not provide a test for capturing thecorrelation of two itemsets. In order to improve thisframework, some measurements on the support andconfidence of association rules, such as chi-squared test
model have been recently proposed by Brin et al [3].
The chi-squared test method measures the significanceof associations via the chi-squared test for correlation
used in classical statistics. However, it is difficult to
use in case that the number of items included in association rules is increased.

A method of association rule mining using Genetic
Network Programming (GNP) is proposed to improve
the performance of rule extraction. The proposed system can evolve itself by an evolutionary method andmeasures the significance of the association via thechi-squared test using GNP. In this paper, we describe
the algorithms capable of finding the important association rules and present some experimental results.
GNP examines the attribute values of database tuples using judgement nodes and calculates the measurements of association rules using processing nodes.
The proposed method measures the significance of associations via the chi-squared test for correlation usedin classical statistics, where GNP evolves itself using
it as a part of the fitness value. Accordingly, the algorithms can extract the important association rules
efficiently. Extracted association rules are stored in apool all together through generations in order to find
new important rules. Therefore, the proposed method
is fundamentally different from all other evolutionary
methods in its evolutionary way.

Genetic Network Programming (GNP) [4, 5, 6] isa kind of evolutionary methods, which evolves arbitrary directed graph programs and includes judgement
nodes and processing nodes in the network. GNP isuseful because it can form not only the optimal structure effectively, but it also avoid the premature convergence. In this paper, we describe the algorithms capable of finding the important association rules using
GNP to improve the performance of rule extraction.
Attributes (items) in database correspond to judgement nodes in GNP, respectively. We are able to represent the connection of nodes as association rules andnodes are reused and shared with some other association rules because of GNP s feature. This methodmeasures the support, confidence and significance of
associations via the chi-squared test for correlation
used in classical statistics using GNP. GNP evolves
itself by an evolutionary method using chi-squared values as a part of the fitness value. Using genetic operation of GNP, we are able to obtain candidates of important association rules. Accordingly, the algorithms can
extract the important association rules efficiently. In
addition, extracted association rules are stored all together through generations and GNP evolves in order
to find new interesting rules. Therefore, the method
is fundamentally different from all other evolutionary
algorithms.

Keywords
Evolutionary Computation, Genetic Network Programming, Data Mining, Association Rule1

Introduction

Association rule mining is the discovery of association relationships or correlations among a set of attributes in a database [1]. Association rule in the formof If X then Y
is interpreted as database tuples satisfying that X (antecedent) are likely to satisfy
Y (consequent) . Association rules are widely usedin marketing, decision making, and business management. Agrawal et al. have built a support-confidence
framework for mining association rules from databases[2]. This model measures the uncertainty of an association rule with two factors: support and confidence.

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2

Genetic Network Programming

In this section, the outline of Genetic Network Programming (GNP) [4, 5, 6] is explained. GNP is one of
the evolutionary optimization techniques, which uses
network structures as solutions. The basic structureof GNP is shown in Fig.1. GNP is composed of
two kind of nodes: judgement node and processing
node. Judgement nodes correspond nearly to elementary functions of Genetic Programming (GP). Judgement nodes are the set of J1 , J2 ,. . . , Jm , which work as
if-then type decision making functions. On the other
hand, processing nodes are the set of P1 , P2 ,. . . , Pn ,which work as some kind of action/processing functions. The practical roles of these nodes are predefinedand stored in the library by supervisors. Once GNP isbooted up, the execution starts from Start node, then
the next node to be executed is determined according
to the connection from the current activated node.

Figure 1: The basic structure of GNP individual

Figure 2: Gene structure of GNP (node i)transaction T is a set of items such that T ⊆ I. Associated with each transaction is a unique identifier,called T ID. We say that a transaction T contains X,
a set of some items in I, if X ⊆ T . An association rule
is an implication of the form X ⇒ Y , where X ⊂ I,
Y ⊂ I, and X ∩ Y = ∅. X is called the antecedentand Y is called the consequent of the rule. In general,a set of items is called an itemset. Each itemset has
an associated measure of statistical significance called
support. If the fraction of transactions in D containing
X equals s, then we say that support(X) = s. The rule
X ⇒ Y has a measure of its strength called confidence
defined as the ratio of support(X ∪ Y )/support(X).
An example is shown below using Table 1. Let item
universe be I = {A, B, C, D} and transaction universe
be T ID = {1, 2, 3, 4}. In order to extend our research
not only to market baskets problems but also to others, we indicate the items of the transaction by a 1
as shown in Table 1. In Table 1, itemset {A, C} occurs in two transactions of T ID = 1 and T ID = 3.
So, its frequency is 2, therefore, its support, that is,
support((A = 1) ∧ (C = 1)) becomes 0.5. Itemset{A, C, D} occurs in the transaction of T ID = 3. Its
frequency is 1, and its support, i.e., support((A =1) ∧ (C = 1) ∧ (D = 1)) becomes 0.25. Therefore,
support((A = 1) ∧ (C = 1) ⇒ (D = 1)) = 0.25, andconf idence((A = 1) ∧ (C = 1) ⇒ (D = 1)) = 0.5.

The genotype expression of GNP node is shown in
Fig.2. This describes the gene of node i, then the setof these genes represents the genotype of GNP individuals. N Ti describes the node type, N Ti = 0 when
the node i is judgement node, N Ti = 1 when the node
i is processing node. IDi is an identification number,for example, N Ti = 0 and IDi = 1 mean node i is J1 .
Ci1 , Ci2 ,. . . , denote the nodes which are connectedfrom node i firstly, secondly,. . . , and so on depending on the arguments of node i. di and dij are thedelay time. They are the time required to executethe processing of node i and delay time from node i
to node Cij , respectively. All programs in a population have the same number of nodes, and the nodes
with the same node number have the same function,
respectively. The following genetic operators are usedin GNP. Mutation operator affects one individual. All
the connections of each node are changed randomly by
mutation rate of Pm . Crossover operator affects twoparent individuals. All the connections of the uniformly selected corresponding nodes in two parents
are swapped each other by crossover rate Pc . GNP
evolves the fixed number of nodes and these operators
only change the connections among the nodes.

3

Association Rules
Table 1: An example of database
T ID
1
2
3
4

The following is a formal statement of the problem of mining association rules [1, 2] . Let I ={i1 , i2 , . . . , il } be a set of literals, called items or attributes. Let D be a set of transactions, where each

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A
1
0
1
0

B
0
1
1
1

C
1
1
1
0

D
0
1
1
1

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Calculation of χ2 value of the rule X ⇒ Y
is described as follows [3]. Let support(X) = x,
support(Y ) = y , support(X ∧ Y ) = z and the number of database tuples equals N . If events X and Y
are independent then support(X ∧ Y ) = xy. Table 2is the contingency of X and Y : the upper parts are
expectation values under the assumption of independence, and the lower parts are observational. Now,
let E denote the value of expectation value under theassumption of independence and O the value of observational. Then the chi-squared statistic is defined asfollows:
χ2 =X
AllCells(O − E)2
E(Yes/No). Fig.3 shows a basic structure of GNP. P1
is a processing node and is a starting point of association rules. Each Processing node have an inherent
numeric order (P1 , P2 ,. . . , Pn ) and basically are connected from a judgement node. Yes-side of judgement
node is connected to another judgement node. Judgement nodes can be reused and shared with some otherassociation rules because of GNP s feature. No-sideof judgement node is connected to the next numberedprocessing node. We now demonstrate this using an
example. In Table 1, the tuple T ID = 1 satisfies
A = 1 and B 6= 1, therefore the moving is from P1
to P2 in Fig. 3. If the examination of the connection
from the stating point Pn is ended, then GNP examines T ID = 2 likewise. Thus, all tuples in database
will be examined. The total number of tuples moving
to Yes-side at each judgement nodes are calculated forevery processing node, which is a starting point forcalculating association rules. All GNP individuals aresearched parallel at the same time. If Yes-side connection of judgement nodes continue and the number of
their judgement nodes becomes a cutoff value (maximum number of attributes in extracted association
rules), then Yes-side connection is transferred to thenext processing node obligatorily.

(1)

We calculate χ2 using x, y, z and N of Table 2.
χ2 =N (z − xy)2
xy(1 − x)(1 − y)(2)This has 1 degree of freedom. If it is higher than a cutoff value (3.84 at the 95% significance level, or 6.63 at
the 99% significance level), we reject the independence
assumption.
Table 2: The contingency of X and P
Y
X
¬X
P
col

Y
N xy
Nz
N (y − xy)
N (y − z)
Ny¬Y
N (x − xy)
N (x − z)
N (1 − x − y + xy)
N (1 − x − y + z)
N (1 − y)rowNx

N (1 − x)N

Figure 3: GNP for association rule mining

4

Association Rule Mining Using GNP
4.2

In this section, a method of association rule mining using GNP is proposed. Let Ai , Bi be attributes(items) in a database and its value is 1 or 0. The
method extracts the association rule as follows:(Aj = 1) ∧ · · · ∧ (Ak = 1) ⇒ (Bm = 1) ∧ · · · ∧ (Bn = 1)
(briefly, Aj ∧ · · · ∧ Ak ⇒ Bm ∧ · · · ∧ Bn ).

4.1

In Fig.3, N is the number of total tuples, and a,b, c and d are the numbers of tuples moving to Yesside at each Judgement node. Table 3 shows the measurements of association rules. The proposed methodmeasures the significance of associations via the chisquared test for correlation used in classical statistics.
For example, if we change the connection of P1 nodefrom ’A = 1’ node to ’B = 1’ node (judgement node)in Fig.3, then we are able to calculate the support ofB, B ∧ C and B ∧ C ∧ D in the next examination. As aresult, we obtain chi-squared statistics and repeat thislike a chain operation. We can define important association rules as the rules which satisfy the following:

GNP for Association Rule Mining

Attributes in the database correspond to judgement
nodes in GNP, respectively. We are able to represent
the connection of nodes as association rules. GNP examines the attribute values of database tuples using
judgement nodes and calculates the measurements ofassociation rules using processing nodes. The measurements include support and confidence. Judgement
node determines the next node by a judgement result©ISAROB 2005

Extraction of Association Rules

χ2 > 6.63
support ≥ supmin

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(3)
(4)

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two offspring. Each judgement node is selected as acrossover node with the probability of Pc . Two parents exchange the genes of the corresponding crossovernodes. 40 individuals are divided into 20 pairs of parents and replaced with new 40 individuals.
Mutation-1 : Mutation-1 operator affects one individual. The connection of each judgement node ischanged randomly by mutation rate of Pm1 . Top 40 individuals reproduce new 40 individuals by Mutation1.
Mutation-2 : Mutation-2 operator also affects one individual. The connection is changed to barter thejudgement nodes. For example, in Fig.3, if ’B = 1’
node is bartered with ’D = 1’ node in position, thenwe examine A ⇒ D, A ∧ D ⇒ C, A ∧ D ∧ C ⇒ B
and so on. Mutation-2 is executed using the rate of
Pm2 at each judgement node. New 40 individuals are
reproduced by Mutation2. Table 4 shows samples ofPc , Pm1 and Pm2 . All the connections of processing
nodes are changed randomly in order to extract rules
efficiently.

supmin is the threshold minimal support given by supervisors. The extracted important association rules
are stored in a pool all together through generations
in order to find new important rules. When an important rule is extracted, the overlap of the attributes ischecked and it is also checked whether an important
rule is new or not. If the rule is new, it is stored in thepool with its support, conf idence and χ2 . Therefore,
the method is fundamentally different from all other
evolutionary algorithms.
Table 3: Association rules

association rules
A⇒B
A⇒B∧C
A⇒B∧C ∧DA∧B ⇒CA∧B ⇒C ∧D
A∧B∧C ⇒D

4.3

support
b/N
c/N
d/N
c/N
d/N
d/N

confidence
b/a
c/a
d/a
c/b
d/b
d/c

Genetic Operators

Table 4: Conditions of crossover and mutation

Fitness evaluation function of GNP is defined asX{χi 2 + 10(n(iante ) − 1)F =GNP-M GNP-L
Crossover Probability (Pc )
15/78
10/78
Mutation-1 Probability (Pm1 )
25/78
15/78
Mutation-2 Probability (Pm2 )
16/78
12/78
(Note: 78 corresponds to the number of Judgement nodes)i∈I+ 10(n(icon ) − 1) + αinew } (5)The components are as follows:
I : a set of the number of important association rules
which satisfy (3) and (4) in a GNP (individual)n(iante ) : the number of attributes at the antecedentof rule i. n(icon ) : the number of attributes at theconsequent of rule i. χi 2 : chi-squared value of rule i.
αinew : additional constant defined as(αnew （i is new）
αinew =
(6)0（i has been extracted already）

5

We have performed experiments and estimated theperformance of our algorithms. All the experiments
were run on synthetic data. The synthetic databaseincludes 26 attributes (Aj , j = 1, 2, . . . , 26). The
number of tuples are 200, support(Aj = 1) = 0.7(j = 1, 2, . . . , 5) and support(Aj = 1) = 0.5 (j =6, 7, . . . , 26). Evaluation is studied in the case of
free consequent (Simulation 1)
fixed consequent (Simulation 2)
in order to analyze the performance of rule extraction.
In simulations, the population size is 120. The number of processing nodes is 10, and 26 different kindof judgement nodes (’Aj = 1’, j = 1, 2, . . . , 26) areused, each by three. We use (3), (4), (5) and (6)(αnew = 150). Table 4 shows the two conditions ofcrossover and mutation. In addition, we consider theRandom GNP model, which does not evolve but repeats random initialization every generation.

At each generation, individuals are replaced with newones by selection and reproduction rules. Each individual is ranked by fitness evaluation value and selected by ranking. New individuals are generated bycrossover and mutation. These operators are executed
at a part of judgement nodes and a part of processing
nodes of GNP genes, respectively. We demonstrate
the rule concretely using the case of 120 individuals at
each generation. The individuals are ranked by fitness
values and the top 40 individuals are selected. They
are reproduced three times and three genetic operators are executed to them as follows:
Crossover : crossover we used is the uniform crossover,and it is executed between two parents and generates©ISAROB 2005

Simulation Results

5.1

Simulation1

We have performed two experiments as follows:
1) supmin = 0.2, n(iante ) ≤ 5, n(icon ) ≤ 5

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 5: Number of association rules of free consequent in the pool (Simulation 1)
supmin = 0.2

1000
900
800
700
600
500
400
300
200
100
0

100th generation
GNP-M GNP-L Random
905
903
808
898.9
868.6
794.4
889
833
781
1000
1305
181
300.5
918.5
136.1
148
549
111

1000th generation
GNP-M GNP-L Random
922
922
922
921.9
921.3
922.0
921
920
922
4821
3696
1323
2834.53029.21213.6
1568
1121
1139

3500
Number of association rules

Number of association rules

supmin = 0.1
6 or more
attributes

Max
Ave
Min
Max
Ave
Min

25th generation
GNP-M
GNP-L
Random
748
741
616
714.8
708.3
604.3
658
678
593
97
131
57
55.3
68.8
31.8
20
36
20

GNP-M
GNP-L
RandomGNP-M
GNP-L
Random

3000
2500
2000
1500
1000
500
0

0

200

400
600
Generation

800

1000

0

200

400
600
Generation

800

1000

Figure 4: Number of association rules of free consequent (supmin = 0.2)

Figure 5: Number of association rules of free consequent (supmin = 0.1, 6 or more attributes)2) supmin = 0.1, n(iante ) + n(icon ) ≥ 6, n(iante ) ≤ 5,n(icon ) ≤ 5
The number of changing the connections of processing
nodes is 5. Table 5, Fig.4 and Fig.5 show the number of important association rules in the pool. The
system can extract the important association rules inthe database effectively. Each figure shows the meanvalue over ten simulations. Fig.6 and Fig.7 show fitness curve. Fig.8 and Fig.9 are the results of the number of association rules at each generation. These show
the different important association rules in 120 individuals at each generation. GNP-M suits the extraction of the rules including 3-5 attributes, while GNP-L
will be convenient for 5-7 attributes rules. The results
show that our method works effectively by its fitness
curve and the number of extracted rules at each generation. It is found that the proposed evolutionary
method is effective in association rule mining. In addition, it is also found that we can set a condition to
extract rules, for instance, the number of attributes in
the rules.

cates support((A = 1) ∧ (B = 1)), and GNP calculates
support((A = 1) ∧ (B = 1) ∧ (A26 = 1)) at the sametime, because support(A26 = 1) is known. Then we
can obtain the measurements of the rule A∧B ⇒ A26 .
Experiments is performed by Pc = 15/75, Pm1 =25/75, Pm2 = 16/75. (judgement node ’A26 = 1’ isnot used.) Fig.10 shows the number of important association rules. Especially, Fig.11 shows the numberof rules satisfying n(iante ) ≥ 7. The results also showthat our method works effectively. It is also foundthat the system can extract the interesting rules easily,
which are made up of 7 or more antecedent attributes.

5.2

6In this paper, a new method of association rule mining using Genetic Network Programming (GNP) has
been proposed. The proposed system can evolve itself
by an evolutionary method and measures the significance of associations via the chi-squared value. An
efficient algorithm for identifying association rules ofimportance was designed. We have performed experiments and estimated the performance of our algorithms. The results showed that our method extracts
the important association rules in the database effectively. In addition, it is found that we can set a condition to extract rules, for instance, the number of
attributes in the rules. In a future, we plan to extend
the proposed method to the one applicable to large
databases.

Simulation2

We have performed experiments with one specificconsequent attribute (A26 ) supposing supmin = 0.05
and n(iante ) ≤ 8. (such as A1 ∧ A2 ∧ A3 ∧ A4 ∧ A5 ⇒A26 will be extracted.) As we suppose that the
support(A26 = 1) = 0.5, the method is fairly simplified. Each judgement node examines the ’A26 = 1’.
For example, in Fig.3, the number of tuples ’b’ indi-©ISAROB 2005

Conclusions

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

600
Number of association rules

4000

500
Fitness

400
300
200

GNP-M
GNP-L
Random

100

3500
3000
2500
2000
1500
1000

0
0

200

400
600
Generation

800

1000

0

200

400
600
Generation

800

1000

Figure 10: Number of association rules of fixed consequent (supmin = 0.05)

Figure 6: Fitness curves of free consequent (supmin =
0.2)
1400

Number of association rules

400

1200
1000
Fitness

GNP
Random

500

0

GNP-M
GNP-L
Random

800
600
400
200
0
0

200

400
600
Generation

800

350
300
250
200
150
100
GNP
Random

50
0

1000

0

Figure 7: Fitness curves of free consequent (supmin =0.1, 6 or more attributes)

200

400
600
Generation

800

1000

Figure 11: Number of association rules of fixed consequent (supmin = 0.05, 7 or more attributes)

Number of association rules

250
200

[2] R. Agrawal, R. Srikant : “Fast Algorithms for Mining Association Rules ”, Proc. of the 20th VLDB
Conf. Santiago , Chile, pp.487-499 (1994)

150
100
GNP-M
GNP-L
Random50[3] S. Brin, R. Motwani, and C. Silverstein. “Beyond
market baskets : generalizing association rules tocorrelations ” In Proc. of ACM SIGMOD, pp.265276 (1997)

0
0

200

400
600
Generation

800

1000

Figure 8: Number of association rules of free consequent (supmin = 0.2)

[4] H. Katagiri, K. Hirasawa, and J. Hu : “Genetic
network programming - application to intelligent
agents -”, In Proc. of IEEE International Conf. on
Syst. , Man and Cybernetics, pp.3829-3834 (2000)

Number of association rules

140
120
100

[5] H. Katagiri, K. Hirasawa, J. Hu and J. Murata:“Network Structure Oriented Evolutionary Model- Genetic Network Programming”, in Proc. of Genetic and Evolutionary Computation Conference,
pp.219-226 (2001)

80
60
40

GNP-M
GNP-L
Random

20
0
0

200

400
600
Generation

800

1000

[6] K. Hirasawa, M. Okubo, H. Katagiri, J. Hu and J.
Murata: “Comparison between Genetic Network
Programming (GNP) and Genetic Programming(GP)”, in Proc. of Congress of Evolutionary Computation, pp.1276-1282 (2001)

Figure 9: Number of association rules of free consequent (supmin = 0.1, 6 or more attributes)

References[1] C. Zhang , S. Zhang , Association Rule Mining:models and algorithms , Springer (2002)©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Evolution of Metaparameters for Efficient Real Time Learning
Genci Capi1, Masao Yokota1 and Kenji Doya2
1

Faculty of Information Engineering
Fukuoka Institute of Technology
3-30-1 Wajiro-Higashi, Higashi-ku, Fukuoka, 811-0195, Japan
2

CREST, Japan Science and Technology Agency (JST)
ATR, Computational Neuroscience Laboratories“Keihanna Science City”, Kyoto, 619-0288, Japan
capi@fit.ac.jp
for a human expert to tune them usually by trial and error.
But tuning multiple metaparameters is quite difficult due
to their mutual dependency, e.g., if one changes the noise
size, one should also change the learning rate. In addition
hand tuning of metaparameters is in a marked contrast
with learning in animals, which can adjust themselves tounpredicted environments without any help from asupervisor.

Abstract
In this paper, we propose a new method based on
evolutionary computation for setting the metaparametersof reinforcement learning in order to match the demandsof the task and reduce the learning time. The basic idea isto encode the metaparameters of the reinforcement
learning algorithm as the agent’s genes, and to take themetaparameters of best-performing agents in the next
generation. We considered a complex task where the
Cyber Rodent robot has to survive and increase its
energy level. The results show that appropriate settingsof metaparameters found by evolution have a great effecton the learning time and are strongly dependent on eachother.

The specific questions we ask in this study are: 1)
whether GA can successfully find appropriate
metaparameters subject to mutual dependency and 2)
how the metaparameters and initial weight connections
effect the learning time. In our method, the basic idea isto encode the metaparameters of the RL algorithm as theagent’s genes, and to take the metaparameters of bestperforming agents in the next generation.

KEY WORDS
Actor-critic Reinforcement Learning, Genetic Algorithm,
metaparameters.

In order to answer these questions, we considered asurviving behavior for the Cyber Rodent (CR) robot([11]), which is a two wheeled robot with a wide-angle
camera. The robot must recharge itself by capturing
active battery packs distributed on the environment. In
order to see the effect of metaparameters and initial
weight connections on learning time we consideredlearning with: 1) arbitrarily selected metaparameters andrandom initial weight connections; 2) evolved initial
weightconnections
andarbitrarily
selected
metaparameters; 3) evolved metaparameters and random
initial weight connections; 4) evolved metaparameters
and initial weight connections.

1. Introduction
Reinforcement learning (RL) ([1], [2],[3], [4]) provides asound framework for autonomous agents to acquire
adaptive behaviors based on reward feedback. The theory
of RL has been successfully applied to a variety ofdynamic optimization problems, such as game programs([5]), and resource allocation.
In RL, the learning capabilities are strongly
dependant on a number of parameters, such as learning
rate, the degree of exploration, and the time scale ofevaluation. The appropriate settings of metaparameters
depend on the environmental dynamics, the goal of thetask, and the time allowed for learning. The permissible
ranges of such metaparameters are dependant on
particular tasks and environments, making it necessary©ISAROB 2005

Results show that appropriate settings of metaparameters can be found by evolution. The learning time
is significantly reduced when the agent learned using the
optimized metaparameters and the initial weight
connections.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005••

The energy received by capturing the battery pack(by changing the charging time);
The energy consumed by the agent for 1m motion.

Based on the energy level and the distance to thenearest active battery pack, the agent can select among
three actions: 1) capture the active battery pack; 2) search
for a battery pack or 3) wait until a battery pack becomes
active. In the simulated environment, the batteries have along reactivation time. In addition, the energy consumedfor 1m motion is low. Therefore, the best policy is tocapture any visible battery pack (the nearest when there
are more than one). When there is no visible active
battery pack, the agent must search in the environment.

Fig. 1. Cyber Rodent robot.

2. Cyber Rodent Robot
The CR robot is a two-wheel-driven mobile robot asshown in Fig. 1. The CR is 250 mm long and weights 1.7
kg. The CR is equipped with:
•
•
•
•
•
•
•
•
•

Omni-directional C-MOS camera.
IR range sensor.
Seven IR proximity sensors.
3-axis acceleration sensor.
2-axis gyro sensor.
Red, green and blue LED for visual signaling.
Audio speaker and two microphones foracoustic communication.
Infrared port to communicate with a nearby
agent.
Wireless LAN card and USB port tocommunicate with the host computer.

Fig. 2. Environment.

4. Actor-Critic RL
We applied an actor-critic RL. The agent can selects
among three actions: 1) Capture the active battery pack;2) Search for an active battery pack; 3) Wait for adetermined period of time. The wait behavior isinterrupted if a battery becomes active or after a predetermined period of time. Both networks receive as
input a constant bias input, the CR battery level and the
distance to the nearest active battery pack (bothnormalized between 0 and 1).

3. Task and Environment
In the second environment, the CR robot has to survive
and increase its energy level. The environment has 8battery packs, as shown in Fig. 2. The positions ofbattery packs are considered fixed in the environment
and the CR robot is initially placed in a random positionand orientation.

4.1 The Critic

The agent can recharge its own battery by capturing
active battery packs, which are indicated by red LED
color. After the robot captures the battery pack, it canrecharge its own battery for a determined period of time(charging time), then the battery pack becomes inactive
and its LED color changes to green. The battery becomes
active again after the reactivation time. Therefore, in thisenvironment the following parameters can vary:••

The critic has a single output cell, whose firing rate is
calculated as follows:3m
Oc = ∑ bi xi + ∑ ci y i
i =1
i =1
where m is the number of hidden neurons,31
y i = g ( ∑ a ij x j ) , g ( s ) =
−s .
j =1
1+ e
The TD error is calculated as follows:

The number of battery packs;
The reactivation time;©ISAROB 2005

247

(1)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

 0 if the start state)r [t + 1] = ,kr[t + 1] + γ ν [t ] otherwise

The actor weights are adapted as follows:ei [t + 1] = ei [t ] + α 1 rˆ[t + 1]( q[t ] − p[t ]) x i [t ]f i [ t + 1] = f i [ t ] + α 1 rˆ[ t + 1]( q[ t ] − p [ t ]) z i [ t ] (7)d ij [t + 1] = d ij [t ] + α 2 rˆ[t + 1]z i [t ](1 − z i [t ])(2)using the reward

rt +1 = ( En _ levelt +1 − En _ levelt ) / 50.

sgn( f i [t ])(q[t ] − p[t ]) x j [t ]TD reduces the error by changing the weights, as
follows:bi [t + 1] = bi [t ] + ρ1rˆ[t + 1] xi [t ]ci [t + 1] = ci [t ] + ρ1rˆ[t + 1] yi [t ](3)aij [t + 1] = aij [t ] + ρ 2 rˆ[t + 1] yi [t ](1 − yi [t ]) sgn( ci [t ]) x j [t ],5. Evolution of Metaparameters
In our implementation, a real-value GA was employed inconjunction with the selection, mutation and crossover
operators (Table 1).
The fitness function in the surviving behavior isconsidered as follows: CRlife
if aget dies learn _ t
Fitness =  Enlevel + 1
if agent survives
learn _ tbattery fully recharged
 En max +CRlife

where ρ1, ρ2 are the learning rates.

4.2 The actor
The agent can select one of three actions and so the actor
make use of three action cells, pj, j=1,2,3. The captured
behavior is considered pre-learned ([6]). When the searchbehavior is activated, the agent rotates 10 degrees
clockwise. The agent does not move when the wait
behaviour becomes active. The output of action neurons
is calculated as follows:3z i = g ( ∑ d ij x j ) ,
j =1

(4)

3
n
pi = g ( ∑ eij xi + ∑ f ij z i ) ,
i =1
i =1

(5)

where g(s) =1
1 + exp

where CRlife is the CR life in seconds,
max_learning_time is the maximum learning time, Enlevelis the level of energy if the agent survives but the battery
is not fully recharged and Enmax is the maximum level ofenergy. The maximum learning time is 7200 s. The valueof Enmax is 1 and Enlevel varies between 0 and 1. Based on
this fitness function the agents that can not survive get abetter fitness if they live longer. The agents that survive
get a better fitness if the energy level at the end of
maximum learning time is higher. The agents thatlearned to fully recharge their battery faster get thehighest fitness value.
In order to see the effect of metaparameters andinitial weight connections on learning time, we
considered the following cases: (a) Evolution of metaparameters, initial weight connection of actor and critic
networks, and the number of hidden neurons; (b)Evolution of meta-parameters with randomly initialized
weight connections; (c) Evolution of initial weight
connections with arbitrary selected meta-parameters; (d)Learning with arbitrary selected meta-parameters andrandomly initialized weight connections.

−s and n is the number of hidden

neurons
A winner-take-all rule prevents the actor fromperforming two actions at the same time. The action is
selected based on the softmax method as follows:P ( a, s t ) =e3pi ( a , s )

∑ (e

τ

pi ( a , s )

,

(6)

τ )

i =1

where τ is the temperature of the algorithm.

Table 1. GA functions and parameters.
Function Name
Arithmetic Crossover
Heuristic Crossover
Simple Crossover
Uniform Mutation
Non-Uniform Mutation
Multi-Non-Uniform Mutation
Boundary Mutation
Normalized Geometric Selection©ISAROB 2005

6. Results

Parameters
2
[2 3]
2
4
[4 GNmax 3][6 GNmax 3]
4
0.08

In order to determine the energy level after each action,
we measured the battery level of CR when the robot
moves with a nearly constant velocity of 0.3m/s. The
collected data are shown in Fig. 3. The graph shows that
there is a nonlinear relationship between time and energy
level. Therefore, we used the virtual life time todetermine the energy level after each action. Except
capturing the battery pack, the search and wait actions
increased the virtual life time.

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First, learning took place with arbitrarily selected
metaparameters (Table 2) and random initial weight
connections. The agent continued learning for nearly5500s until the battery was fully recharged (Figure 4).
Then, using the same metaparameters and neural
structure, we evolved the initial weight connections.
Initially, 100 individuals are created and the evolution
terminated after 20 generations. Figure 4 shows thatlearning time is reduced.

energy level

1

When the metaparameters are evolved and initial
weight connections are randomly selected, the learning
time is significantly reduced (Figure 4). This result is
important because it shows that metaparameters haslarger effect on learning time compared to initial weight
connections. The searching interval and GA results,
when both metaparameters and initial weight connections
are optimized by GA, are shown in Table 3. The critic
and actor networks have 1 and 3 hidden neurons,
respectively. The initial value of weight connections arevery near with their respective values after learning. In
addition, the optimized value of cooling factor is low.
Therefore, the agent starts to exploit the environment andmake greedy actions soon after the learning starts,
recharging its own battery in a very short time.

evol. metaparam and init weight0.6

evol. metaparam / rand. init weight
evol. init weight /arbitrary metaparam
arbitrary metaparam/rand init weight

0.4
0

1000

2000

3000

4000

5000

6000

time [s]Fig. 4. Energy level of the best agent for different
combinations of learning and evolution.

7. Conclusion
In this paper, we presented a method to optimize themetaparameters in RL based on evolutionary approach.
Based on the simulations and experimental results weconclude:z Evolutionary algorithms can be successfully applied
to determine the optimal values of metaparameters.
z Meaparameters play important role on the agent
learned policy.
z Optimized metaparameters can significantly reduce
the learning time.
In the future, we are interesting to see if evolution
can also be applied to shape the reward function usedduring the learning process.

Table 2. Values of metaparameters
Parameters
Values
Initial Weights
Randomly between [-0.5 0.5]
ρ1, ρ2, α1, α20.2; 0.1; 0.8; 0.3
τ0
10
γ
0.9
2600
2400

energy level

0.8

2200

References:

2000[1] Barto AG(1995), Reinforcement learning. In M. A.
Arbib (Ed.), The handbook of brain theory and neuralnetworks, (pp. 804–809). Cambridge, MA: MIT Press.
[2] Doya K (2000), Reinforcement learning in continuous
time and space. Neural Computation, 12:215–245.
[3] Doya K, Kimura H, Kawato M (2001),
Computational approaches to neural mechanism oflearning and control. IEEE Control Systems Magazine,
21(4):42–54.
[4] Sutton RS, Barto AG (1998), Reinforcement learning.
Cambridge, MA, MIT Press.
[5] Tesauro G (1994), TD-Gammon, a self teachingbackgammon program, achieves master-level play.
Neural Computation, 6:215–219.
[6] Capi G, Uchibe E, Doya K (2002). Selection of neuralArchitecture and the environment Complexity. AdvancedIntelligence, Vol. 6, pp. 311-317, Editors: K. Murase andT. Asakura.

1800
1600
1400
0

1000

2000

3000

4000

tim e[s]Fig. 3. Battery level during CR motion.

Table 3. Searching space and GA results.
Optimized
Searching
Results
parametersinterval
Initial Weights[-1 1]
ρ1, ρ2, α1, α2[0 1]
0.9210; 0.3022;
0.9256; 0.6310
τ0[1 10]
5.3718
γ
[0 1]
0.794

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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©ISAROB 2005

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250

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

 "#      0 ""   ## 0      5 
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 &             * "* '<# 4      " #    "  #  *   $           "   #        )6+ /                  6 4     
"   #        "       #       *  4      =      "  " 8 #  9#  =     ©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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"-*?12# "1*B1?# "1*C1C# "1,929# "1,*2*#
"1,,2,# "2,212# "2,-2-# "3,222#   
    3            "6 6 6 #    1*©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Learning Control of Manipulator with a Free Joint
T.Goto, H.Lee, K.Abe
Dept. of Elec. & Comm. Eng.
School of Eng.,Tohoku Univ.
Sendai, Aoba05. 980-8579

Hiroyuki Kamaya
Dept. of Elec. Eng.
Hachinohe National College of Technology
Hachinohe. 039-1192

Abstract

On the other hand, some researchers have applied
the cognitive and learning capability of human to complex control systems. Even though, a human operator
fails to control complex system in the beginning, but
after enough training, he, she can ﬁnd a way to control it satisfactory. It is apparent that he, she does
not use a mathematical model. This fact shows thathuman can ﬁnd a satisfactory control law by a trialand-error without the knowledge of the mathematicalmodel to control object. For example, it is reported
that the joint angle control of 2PUAM can be achievedby human operator[4]. This indicates the capability oflearning the behavior which can not be achieved byusing continuous feedback.
In order to solve such diﬃcult control object, realization of RL algorithm reproducing human abilities
is desirable. Therefore, the investigation of humanlearning mechanism from the perspective of RL is basically necessary. In this paper, a 2PUAM is selected
as the learning problem. And learning experiments by
RL and human are tested. In the RL experiment, Qlearning is implemented. For the experiment of humanmanual control, a 2PUAM simulator is developed. It
includes a policy evaluation module. This module automatically approximates the Q-value function according to the action series of human. By using these experimental results, the comparison between RL agent
and human learning is described.

In this paper, reinforcement learning approach tomotion control of 2-link planer underactuated manipulator is described. This manipulator has one passive
joint and is diﬃcult to control. The experiments oflearning to control this manipulator by RL and human are executed. Using the experimental results, theassociations between RL and human learning are considered.

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]. RL has been applied to many research areas. Motion learning is one of such areas.
For the robot, in order to adapt to a dynamic environment, motion learning is one of key issues. Therefore, many algorithms for motion learning have been
intensively discussed for years[2]. Many control objects are tested in acrobot, inverted pendulum, walking robot etc., and most of these tasks are nonlinear. They have some equilibrium points which canbe stabilized by continuous feedback control. In thesecases, by using the information of these equilibrium
points as prior knowledge, more distinguished motion
learning algorithms can be designed, e.g. a hierarchical RL algorithm composed of linear controllers andan adequate reward function[2]. However, there areseveral typical nonlinear systems which are not able
to apply such hierarchical algorithms. 2-link planer
under-actuated manipulator (2PUAM) is one of thosesystems. This system has been widely studied by control engineers[3]. But only a few researches have been
done from view point of learning. On design of RL
algorithm for 2PUAM, setting of the reward is diﬃcult, and in addition, the state space is multidimensional and continuous. Therefore, the approximationof value function is needed to solve the local optimal
problem.

©ISAROB 2005

2

Model of manipulator

In this paper, a 2PUAM is used for the learning
task. It has only one active joint and one passive joint,and neither gravity nor friction torque acts on it. It
is one of the simplest forms of underactuated manipulators. The equation of motion of the manipulatoris:
M11 (θ)θ¨1 + M12 (θ)θ¨2 + c1 (θ, θ̇) = τ̃ ,M21 (θ)θ¨1 + M22 (θ)θ¨2 + c2 (θ, θ̇) = 0,

254

(1)
(2)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3.1

where θ1 and θ2 are angle of each joint, M is inertia matrix, c denotes centrifugal term, and right sidesof boss Eq. (1), (2) are the input torque. Therefore
Eq. (2) means the dynamic constraint caused by thezero torque at the passive joint. This manipulator has
two main characteristics. The ﬁrst is that the inertia
matrix includes the passive joint angle θ2 as usual[3],then, Eq. (2) is nonintegrable. It is called secondorder nonhoronomic constraint. The second principal
characteristic is that this manipulator is not bound by
gravity or friction, i.e., arbitrary angle become equilibrium points. However, it is not controllable by a continuous feedback. To stabilize an equilibrium point,
this manipulator must be controlled by discontinuousor time variant feedback control.

In motion learning such as manipulator, continuous
state variables are dealt with. Thus, tile coding is
employed[1] here. In tile coding, the receptive ﬁeldsof the features are grouped into exhaustive partitionsof input space. Each partition is called a tiling, andeach element of the partition is called a tile. When theagent observes its environmental state s, and selects
an action a, the Q-value function is calculated asqi (j, a)φsi (j)(3)
Q(s, a) =i,jwhere i(i = 1, 2, . . . , m) is the number of tiling, j(j =1, 2, . . . , n) is the number of tile. q is the parameter
vector of each tiling and φ is binary feature vector.
If the state is inside the tile of each tiling, the corresponding feature has the value 1, otherwise the feature
is 0.

Goal Area
p1

Objective Arm Positionp2

3.2
Start Point

Environment of Learning

In learning the task of 2PUAM called ”manipulatortask”, the actuator installed in active joint is regarded
as an agent. As shown in Fig. 1, by selecting an input
torque, τ ∈ (−0.1, 0, 0.1) the RL agent or the subjects
must drive the end eﬀector to the goal area and bringto halt or keep between ±1(degree/s) angular velocity. Because of the 2PUAM’s mechanism, it would be
two objective positions, the upper position p1 and thelower p2 .

3

δ = r + γQ(s , a ) − Q(s, a)(5)

for all i and jqi (j, ā)ηi (j, ā)← qi (j, ā) + αδηi (j, ā),← γληi (j, ā),

(6)
(7)

where α(0 < α ≤ 1) is the learning rate, γ(0 ≤ γ ≤ 1)
is the discount factor, η is the replacing eligibility trace
function, and λ(0 ≤ λ ≤ 1) eligibility factor.
During learning, at time step t, the agent will selectan action according to some strategies. In the experiments of this paper, Max-Boltzmann distribution[6]rule is employd. In Max-Boltzmann distribution, an
action with maximal Q value is chosen with probability pmax , and an action according to the Boltzmann
distribution is chosen with probability (1−pmax ). The
probability of selecting action ai in state s is

Reinforcement Learning

At each time step t(∈ 0, 1, 2, . . .), the agent observes
its environmental state, st ∈ S and selects an action,at ∈ A(st ). As a consequence of the action, the agent
receives a scalar reinforcement signal, referred reward,
rt ∈ R. One time step later, the agent observes a newstate, st+1 .The aim of the agent
∞is to maximize theexpected discounted reward E{ t=0 γ t rt }, where γ isthe discount factor. In this paper, Sarsa(λ)[5] is employed to learn estimates of optimal Q-value functions
that map state-action pairs (s, a) to optimal return on
the action taken in the current state.

©ISAROB 2005

Sarsa(λ)

In
Sarsa(λ),
onexperiencing
transition< s, a, r, s , a >, the following updates are performed in order:
 s
φi (j) for ā = a
ηi (j, ā) =(4)
0 for ā = aFigure 1: Environment of Learning.

2.1

Function ApproximationeProb(ai | s) = Q(s,ai )Tke

Q(s,ak )T(8)where temperature T adjusts the degree of randomnessof action selection.

255

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3.3

Learning Parameters

In manipulator task, the observable parameters of
the agent are the angles and angular velocities of thejoints, θ1 , θ2 , θ˙1 , and θ˙2 . Thus, the state space in thistask is a bounded rectangular region in four dimensions. In this environment, the state space is divided
into 21×21×11×11 tiles, and 10 tilings are used. The
remaining parameter of tile coding and Sarsa(λ) algorithm are α = 0.1/m, λ = 0.9, τ = 0.1, and Q0 = 0.
The parameter of Max-Boltzmann, pmax is linearly increased from 0.9 to 1.0. The constant physical parameters are; the mass of the arms, m1 = m2 = 1.0; thelength, l1 = l2 = 0.2; the length from joint to the center of each arm, r1 = r2 = 0.1. The time step t = 0.03.
The action is chosen after every one time step. A trial
ends whether 10000 steps are elapsed, or the goal isreached.

4

Figure 3: Motion of 2PUAM.

and converged. This result shows that it is possible tolearn manipurator task by using RL algorithm. Fig. 3
shows the best trajectory acquired by RL agent. First,
the agent inputs the torque crock wise (CW) in orderto make angle of free joint θ2 take the value at which
the arm forms in lower objective position p2 . Then theagent manages to keep the value θ2 , and drives the end
eﬀector to the goal area with low speed.

Manual Control Experiment

Experiment of manual control is conducted with thecooperation of 5 subjects. They have no knowledge
about the dynamic response. they observe the manipulator’s states from visual data, and input torqueτ ∈ (−0.1, 0, 0.1) given by a joystick. The time limit isset at 2000 steps, and the number of trial is set at 20
trials a day. The subjects do the same task for a week.
In this experiment, the Q-value is recorded according
to Sarsa(λ). The other details of this experiment are
roughly same as RL’s.

Group A

2500
2000
1500
1000
500

Steps

0

0

20

40

60

80

100

120

140

80

100

120

140

Group B
2500
2000
1500

5
5.1

1000

Experimental Results

500
0

RL Experiment

0

20

40

60

TrialsFigure 4: Result of learning by Subjects
10000

Group A

8000

Group B

Steps

6000

4000

2000

0
0

500

1000

1500

Figure 5: Motions of 2PUAM controled by Subjects.

2000

TrialsFigure 2: Result of learning by Tile Coding Sarsa(λ).

5.2

The result of RL experiment is shown in Figs. 2 and3. In Fig. 2, the number of steps indicates how long
the agent takes to achieve the goal. It is clear from Fig.
2 that the steps required to achieve the goal is reduced©ISAROB 2005

Manual Control Experiment

From the result of Manual Control Experiment , thelearning pattern of subjects is divided into Group A
and Group B. Fig. 4 shows the typical learning curve256ǰ(rad)2ǰ(rad)1

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

p1
p2




the goal faster by increasing input. It partly makes
them possible to shorten the time passing through the
trajectory. However, because of 2PUAM’s mechanicalproperty, it is impossible to slow down the angular velocity of second joint near the end of this trajectory.
Therefore it causes the failure of the ﬁrst approach. In
this case, Group B searches another way to the goal ina way of changing the objective position, e.g., from p2
to p1 . Therefore, the failure of Group B temporarily
increases during 20 to 40 trials. In an alternating succession of such approach, they are enable to ﬁnd thebetter trajectory like the right side of Fig. 5. In thistrajectory, it is possible to decelerate each joint speed
near the goal. Hence, it is thought that Group B cometo achieve the goal much faster.







Input





















TrialsFigure 6: Input and Trajectory(Group A).

ǰ(rad)2ǰ(rad)
1

6
p1
p2




In this paper, RL approach for motion control of
2PUAM was proposed. And the associations between
RL and human learning were investigated. As theresult, some noticeable characteristics about humanlearning process were found. In particular, it was realized that the structure of human learning in 2PUAM
has two processes. One is ﬁnding the trajectory to thegoal, and another is shortening the time of passing
through the found trajectory.
As future works, we aim to apply these characteristics into the machine learning process.







Input



















Summary

TrialsReferences

Figure 7: Input and Trajectory(Group B).

[1] R. S. Sutton and A. G. Barto, Reinforcement Learning:
An Introduction, MIT Press, (1998).

of each group. As shown in Fig. 4, each group ﬁrst
achieve the goal within a day. However, the failures
and required steps of Group B are notably reduced
from 60 to 80 trials.
Fig. 5 shows the best trajectories, and Fig. 6，Fig.
7 show the time series of each joint angle and input
torque. It is clear from Fig. 5 that Group A s isvery similar to RL s. That of Group B is also sameas Group A at the beginning. However, at the end,
Group B changed their policy and achieve the goalmuch faster than Group A.

5.3[2] J. Yoshimoto, S. Ishii, M. Sato, Application of reinforcement learning based on on-line EM algorithm
to balancing of acrobot, Systems and Computers inJapan, 32Vol. 5, 12-20, (2001).
[3] G. Oriolo and Y. Nakamura, Free-Joint Manipulators:
Motion Control under Second-Order Nonholonomic
Constraints, Proc. of IROS’91, 1248-1253, (1991).
[4] M. Yagai, T. Ishihara, H. Inooka, Manual Control of
the Positioning of Two-link Arm with a Free Joint,
SICE Tohoku chapter workshops, 206-1, (2002)(inJapanese).
[5] G. A. Rummery and M. Niranjan, On-line Qlearning using Connectionist Systems, Technical Report CUED/F-INFENG/TR 166, Engineering Department, Cambridge University, (1994).

Discussions

As the result of these experiments, some noticeable
points of human learning in 2PUAM environment arefound. The ﬁrst is that, after once the subjects ﬁnd a
trajectory to the goal, such as the left side of Fig. 5, allsubjects try to trace the same trajectory and achieve©ISAROB 2005

[6] M. Wiering and J. Schmidhuber, HQ-learning, Adaptive Behavior, 6-2, 219-246, (1997).

257

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Evolutionary Simulations based on a Robotic Approach to Emotion
Tomoko KATO

Takaya ARITA

Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
E-mail: tomo@create.human.nagoya-u.ac.jp, arita@nagoya-u.jp

Abstract

robots, which is based on Dietrich Doerner's theory on humanemotions described in Section 2. Also, we have shown byconducting a robotic experiment with 1000 human subjects thathuman beings can identify a set of basic emotions in robots
synthesized based on the definition [1].

This paper reports on the current state of our efforts tosynthesize emotion in robots from a selectionist perspective inorder to explore the origin and the adaptations of emotion and torealize efficient robotic systems. We have defined a minimalmodel of emotion in robots based on a behavioristic theory on
human emotions, and have shown by conducting a robotic
experiment that human beings can identify a set of basic
emotions in robots. In this paper, we conduct evolutionary
simulations based on the definition of emotions to verify theevolutionary adaptivity with the scenario of the origin and theevolution of human emotions in mind. The simulations show thatrobots are evolved to move effectively in the environments withvarious physical conditions by changing their emotions
corresponding to the type of the condition they encounter.

Defining emotions as
behavior modulations
Modifying
Implementing emotions
in robotic behavior

Verifying the definition
based on evaluation test

Verifying evolutionary adaptivity

based on evolutionary
simulation

Keywords: Emotion in robots, Artificial life, Behavior
modulation, Evolutionary psychology

GOAL 2
Realizing a practical frameworkof emotional robots

1 Introduction

Fig. 1 Study scheme.

The function of emotion has been studied by many researchers.
Typical explanations are based on flexibility of behavior
response to reinforcing signals, communications which transmit
the internal states, or social bonding between individuals, which
could increase fitness in the context of evolution. We believe thatthe possession of emotion is an adaptive “trait” also to robots,
which is parallel with a selectionist view that emotion in humans
is the product of adaptive evolution. The first purpose of ourstudy is to explore the origin and the adaptations of emotion bysynthesizing an emotion system “as it could be” in robots (Fig.
1). The second purpose is to realize an efficient robotic system inwhich robots perform tasks flexibly and effectively by using
their emotional system. Although there is a possibility that ouremotion system is rapidly becoming obsolete as our life
environment has changed at a much faster rate than evolution,
the emotion system in robots can be optimized according togiven tasks by evolutionary algorithms.
We have already proposed a minimal model of emotion in©ISAROB 2005

GOAL 1
Understanding the evolutionary
significance of emotions

Based on these, this paper conducts evolutionary simulations
to verify the evolutionary adaptivity with the scenario of theorigin and the evolution of human emotions in mind. Each robot
moves with the emotion based on 4 modulator values(activation, externality, precision and focus) from the start point
to the goal point in the environments with various physicalconditions like hazard, poorly-lit and so on. The fitness function
is defined as the time for completing the task. In the firstexperiments, each individual contains modulator values directly
in its genome, and is evolved in an environment with one type ofconditions. In the next experiments, each robot has a simple
neural network in which input is the type of conditions of thearea and output is modulator values. Each individual contains
the synaptic weights. Robots are evolved to move effectively bychanging their emotions corresponding to the type of the area
they encounter in the environment.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Tab. 1 Modulation patterns of six emotions [4].

2 Emotions as Behavior Modulations
Our model of emotion in robots is based on the Dietrich
Doerner's theory on human emotions. His claim is, in short, thatemotions are seen as behavior modulations [2][3][4]. The theory
identifies four different modulators that describe goal-directedbehavior at any given time, activation, externality, precision andfocus.
a) Activation indicates the amount of nonspecific activationthat is involved while pursuing a goal. It has the extremesof hypoactivation (a lack of energy) and hyperactivation (asurplus of energy).
b) Externality indicates the proportion of time spent inexternal activity. It has the extremes of introversion(devoted mainly to information processing) andextroversion (devoted mainly to action).
c) Precision indicates how much care or precision a goal ispursued with. it has the extremes of imprecision andprecision.
d) Focus indicates the amount of attention that is allocated tothe current task rather than to the surveillance of the
background. It has the extremes of broadened senses andof narrowed senses.
Following analogy [5] might help our understanding of
emotions as behavior modulations. For example, a television
can be adjusted for brightness, contrast and so on. These
adjustments determine a “behavior modulation” for the
television such as how bright, how much contrast and so on.
The point here is they are all independent of the TV program
actually showing. Similarly, human behavior can be modulatedby emotions as behavior modulations. The volume control andso on in televisions correspond to activation, externality,
precision, and focus in the emotion systems. Fig.2 shows a
simple overview of this concept. Emotion system modulates
behavior selected by another system (behavior generator). Each
modulation pattern is associated with a particular emotion asshown in Tab. 1.
AdjustEmotionModulators

systemModulation pattern in terms of modulators

BhaviorsystemGenerator

SensorEnvironment

Externality

Precision

Focus

None

0.50

0.50

0.50

0.50

Anger

1.00

1.00

0.00

1.00

1.00

0.00

1.00

1.00

Contentment

0.25

0.25

0.75

0.00

Excitement

0.75

0.75

0.25

0.00

Sadness

0.00

0.00

1.00

1.00

b)
c)
d)

Activation indicates how long each behavior cycle is. The
robot uses a certain amount of energy to move in one
behavior cycle, which also depends on this modulator
value.
Externality indicates how fast it moves through the path.
Precision indicates how far its sensory range to obstacles isand how often it updates the sensory information.
Focus indicates how often it deviates from the route. If it
concentrates on its task, it moves to the goal directly along
the route (the dashed line in Fig.4). If not, it deviates fromthe route. It also indicates how far its sensory range to lightresources and the goal is.
direction of movement

precision*externality

energy decrease
activation
sensing
activation*externality

focus

obstacle
precision

light

Fig. 3 Modulations of the robot behavior.

3 Evolutionary Simulations
3.1 Environmental setting
We conducted evolutionary simulations based on our definitionof emotions so as to verify the evolutionary adaptivity. In thesimulations, a robot moved “emotionally” from the start point to
the goal point in the environments with various physicalconditions 1(Fig. 4) including a hazard area (Zone A) where therobot suffers serious damage every second, a narrow path (Zone
B) where it is difficult for the overly cautious robot to movethrough, a poorly-lighted area (Zone C) where the visual rangeof the robot is limited, and a light-rich area (Zone D) where therobot can fill in its energy if it finds a light source.

Actuator
Environment

Fig. 2 An overview of the concept of behavior modulations.
We have defined following four modulators to synthesize
emotional behaviors in the robot used in the simulations.
©ISAROB 2005

Activation

Anxietya)

Behavior modulation

Congnition

Emotion1

259

Plain area without any particular physical conditions is also in the environment.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Zone B

distribution of the individuals

50

damage45
40

distribution

35
30

Zone A

25
20

Zone C

angry2

15
10

Plain area

5
0
1

behavior
modulators

1801

1601

1401

1201

1001

distribution

behavior
modulators

1801

1601

1401

1201

1001

801

1
1801

50
45
40
35
30
25
20
15
10
5
0

normal3
distribution of the individuals

distribution

1

201 401 601 801 1001 1201 1401 1601 1801

50000
40000
fitness

e xte rnality

30000
20000

activation
precision

10000

Hamming distance between four modulators of individuals and modulation
patterns of six emotions (Tab. 1) are used to classify the emotion of individuals.
The smaller the suffix number is, the closer to the basic six emotions.

0.8
0.6
0.4
0.2
0

1801

1601

generation

1

1401

1201

1001

601

401

201

1

0

behavior
modulators

generation angry3
fitness and behavior modulators
1.2
fitne ss
focus
1

801

1601

1801

1601

1401

1201

generation

normal4
wall

behavior
modulators

1401

1201

1001

801

601

401

1

201

1001

801

601

201

1

401

fitness

601

fitnessFig. 7 The results of the simulation in a poorly-lit area.

Fig. 5 The results of the simulation in the environment without
any particular physical conditions: a sketch of the robot
trajectories in 2000 generation (upper left), the distribution of theindividuals 1 (upper right), and the average fitness and the
behavior modulators of 50 individuals (lower).

©ISAROB 2005

401 601 801 1001 1201 1401 1601 1801
generation

generation

fitness and behavior modulators
focus
fitne ss 1.2
1e xte rnality
0.8
pre cision
0.6
0.4
0.2
activation
0

47850

201

fitne ss 1.2
1
pre cision
0.8
focus
0.6
0.4
0.2
activation
0

401

distribution

normal4

angry2 normal3 generation

48350

sadness3

fitness and behavior modulators
e xte rnality

50000
40000
30000
20000
10000
0

0

48850

normal4 disutribution of the individuals

1

10

49350

50
45
40
35
30
25
20
15
10
5
0

poorly-lit

20

49850

801

Fig. 6 The results of the simulation in a hazard area.

40
30

601

generation

distrubution of the individuals

50

201

3.2 Experiments with a single type of zones
Each individual contained 4 modulator values directly in its
genome, and was evolved in an environment with one type ofconditions by a genetic algorithm. The fitness function was
defined as the level of the energy when the robot completed thetask. Fig. 5-9 shows the result of the experiments. It is shownthat particular modulation patterns representing the typicalemotions emerged corresponding to the given environments.
For example, the individuals with the anger-like modulation
pattern were adaptive in the environment with a hazard area.
Also, it is shown that expression of sadness and excitement inaction was adaptive in the environment with poorly-lit andlight-rich area respectively. Environments with a narrow pathevolved the individuals expressing somewhat angry behavior.

50000
40000
30000
20000
10000
0
1

Fig. 4 An environment with various physical conditions.

angry1 generation
fitness and behavior modulators
e xte rnality
fitne ss1.2
10.8
activation focus
0.6
0.4
0.2
pre cision
0
401

start

201

goal

fitness

Zone D

201 401 601 801 1001 1201 1401 1601 1801

Fig. 8 The results of the simulation in the environmentswith a narrow path.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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distribution of individuale xcite me nt3

distribution of the individual

40

distribution

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distribution

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801

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behavior
modulators

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1001 1201 1401 1601 1801 2001

generation

Fig.11 The results of simulation in the environment with
mixed types of zones in the same setting as in 3.2.
The results of both experiments are shown in Fig. 10 and Fig.
11 respectively. We see from Fig. 10 that the robot was evolved
to adjust its behavior modulators properly corresponding to thetype of the area where it moved. The fitness of the robot with the
evolved neural network in the last generation was 4.46*104 andthe fitness of the robot with evolved fixed modulator values in
the last generation was 1.74*104, which clearly shows theeffectiveness of the robot behavior with dynamically changing
its emotion corresponding to the environmental change.

4 Conclusion
We have conducted evolutionary simulations based on thedefinition of emotions in robots and have shown that particular
modulation patterns representing the typical emotions emerged
corresponding to the particular physical conditions. It has also
been shown that the robot with a simple neural network moved
effectively by changing their emotions corresponding to theenvironmental change. These results show that possession ofemotion is adaptive for robots, which is parallel with aselectionist view that emotion in humans is the product ofadaptive evolution. We are now conducting more practicalevolutionary simulations in which robots change their emotions
according to their actual sensor values and their internal states.

fitness

1
501
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1501
2001
2501
3001
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5000004000003000002000001000000
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3.3 Experiments with mixed types of zones
In the next experiment, the robot has a simple three-layer
neural network (three input neuron, five hidden neurons andfour output neurons), in which input was the explicitly
expressed type of conditions of the area and the output was themodulator values. Each individual contained the synaptic
weights, and evolved to move in the environment shown in Fig.
4 effectively by changing their emotions corresponding to thetype of the area they were in. We also conducted anotherexperiment using the same environment but evolving directly
four modulator values as shown in 3.2 to evaluate and compare
the results.
poorly-lighted area(C)plain are achange of behavior modulators
z one
A
B
C
D
e xte rnality
1.2
1
0.8
activation
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pre cision
0.4
0.2
focus
0

[1] Tomoko Kato and Takaya Arita, "A Robotic Approach to Emotion from a

1
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2501
3001
3501
4001
4501
5001
5501
6001
6501
7001
7501
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401

100000Fig. 9 The results of the simulation in a light-rich area.

wall(B)

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1401

1201

1001

201

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generation e xcite me nt4
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1.2
fitne ss activation
1
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pre cision
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e xte rnality 0.4
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0

300000conte ntme nt4

10conte ntme nt3 generation
fitness and behavior modulators fitness

040000020

01050000030

handlungs- regulation oder die interaktion von emotion, kognition undReferences
Selectionist Perspective", Proceedings of the 8th International Symposium on
Artificial Life and Robotics, pp. 601-604, 2004.
[2] D. Doerner, H. Schaub, T. Staeudel and S. Strohschneider, “Ein system zur
motivation”, Sprache & Kognition, Vol. 4, pp. 217–232, 1988.

step[3] D. Doerner and K. Hille, “Artificial souls: Motivated emotional robots”,

Fig. 10 The results of simulation in the environment with mixed
types of physical conditions: a sketch of the robot trajectories ingeneration 6000 (upper left), the average fitness of 50
individuals (upper right), the change of the behavior modulators
corresponding their location in generation 6000 (lower).
©ISAROB 2005

Proceedings of the International Conference on Systems, Man andCybernetics, pp. 3828-3832, 1995.
[4] K. Hille, “Synthesizing Emotional Behavior in a Simple Animated Character”,
Artificial Life, Vol. 7, No. 3, pp. 303-313, 2001.

261

2001

behavior
modulators

light

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Models of individuals for constructive approachto dynamic view of language and society
T. Hashimoto
hash@jaist.ac.jp

T. Satostakashi@jaist.ac.jp

A. Masumi
a-masumi@jaist.ac.jp

School of Knowledge Science
Japan Advanced Institute of Science and Technology (JAIST)
1-1, Tatsunokuchi, Ishikawa, Japan, 923-1292
Abstract

or many individuals and dynamics of the systems areoften induced by activities of individuals.
The dynamic view of language is to understand language as an activity of meaning creation by languageusers at the situation of using it such as communication. This is on the tradition of the viewpoint of language as processes insisted by Tokieda[2] and the individualistic subjectivism view of language addressedby Bakhtin[3].
The dynamic view of society conceptualizes society and social structures as dynamically changing.
Blumer[4] argues that humans are subjective entities
actively interacting with objects and that societies
consisting of such humans are dynamically and transitionally changing. Heyek asserted the spontaneousorder of society, in which individuals actively gather
and develop knowledge and social structures spontaneously emerges as results of subjective actions of suchindividuals[5].
While such thinkings have been developed so long,the constructive approach can contribute to work out
further such dynamic concepts. In order to performthe development, we need appropriate models dynamic
individuals, which can be used in constructive studies.
In this paper, we introduce three models of individuals developed for studying linguistic and social systems
using nonlinear dynamical systems, based on the dynamic viewpoint of language and society. Analyzing
features of the models and results of simulations using
the models, we advocate the advantage of the models
and of taking the dynamic viewpoint for understanding the cognitive, linguistic, and social systems.

In order to understand dynamic nature of our world
and ourselves, we propose three models of individuals that can be used in multi-agent systems for dynamic linguistic or social simulations. They are developing word-web, simple recurrent network with selfinfluential connection (SRN-SIC), and coupled NZ
maps. We briefly summarize the architecture of thesemodels and show some dynamic behavior in multiagent simulations composed by these models1 .
Keywords: Developing Word-Web, Simple Recurrent Network with Self-Influential Connection (SRNSIC), Coupled NZ Maps, Constructive Approach, Dynamic View of Language, Dynamic View of Society

1

Introduction

One of the main aims in complex systems studies
including Artificial Life is to understand dynamics of
our world dynamically, namely, not eliminating the essential dynamics. The theory of nonlinear dynamicalsystems and chaos is a useful tool for the aim. The
constructive approach also has power to produce deep
understanding of dynamical nature of the world[1].
This approach is a methodology to understand an objective system by constructing it and operating thesystem constructed. Artificial Life is originally constructive studies of biological systems. The constructive approach has expanded its applicable domains tocognitive, linguistic, and social systems. In these domains, the important step for understanding such systems constructively is to make a good model abstracting individuals engaged in cognitive/linguistic/social
activities, since such systems usually consists of one

2

The first model to be introduced is the Developing Word-Web model[6], which is suited for the study

1 Due to the space limitation, the description of the models
and the results are concise. Please refer cited papers for details.

©ISAROB 2005

Developing Word-Web

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3of dynamics and evolution of language[7]. It consists of a word-web, relationships among words, as
individual’s linguistic knowledge. The web develops
through conversational interaction with other individuals. Namely, the web, implemented as a matrix, is
updated by uttering and accepting a sentence. The
update algorithm is a modification of a word relation
calculation method proposed in corpus linguistics inorder to update incrementally for using in conversation, not in corpus.
Simulating conversations between some individuals using the Developing Word-Web model, we havefound that they come to acquire prototypical categorical structures, which are thought of as the important
structure of categories in natural languages[8]. Figure
1 depicts a change of the relationship among words atsome point in a simulated conversation. Two corresponding words, before and after accepting a sentence
containing a word used in a new usage, are connectedby arrows. Words form clusters as shown by circles.
Since words in a cluster move in a coherence way, thewhole structure does not change drastically. A word
indicated by a dotted circle moves in completely different way from other words in a cluster. This word
is used in a new usage. Thus, the structure can adapt
to a new usage.

The second model is Simple Recurrent Networkwith Self-Influential Connection (SRN-SIC)[9], which
is a modification of Elman network[10] by adding an
additional recurrent connection between output andinput layers (Fig. 2). The system’s own past output
affects its behavior through the additional connection.
This is a model of an individual having internal dynamics that is indispensable to humans as cognitive
systems.

0.10
principal compornent
2ndsecondprincipal
component

We are engaged in a simulation of a social system consisting of individuals implemented by SRNSIC playing the Minority Game (MG)[11] which is amany players’ game in which players selecting a minority choice from two alternative choices win. The individuals play the MG continuously with given past minority side, and intermittently change their behavioral
rule through learning the past time series of the minority side. Thus, this simulation is equipped with a interaction loop between micro (individuals) and macro(society) levels.
By analyzing the time series of the minority side,
we observed itinerant change of dynamical state of thetime series (Fig. 3). The dynamical states of the gamechange frequently among fixed points and various periodic cycles via aperiodic motions. It is a similar dynamics to chaotic itinerancy, a spontaneous transition
among attractors[12].
This itinerant dynamics at the macro level is interpreted as a continual change of social structures, since
the fact that the system with many individuals is ina low dimensional dynamical state means the system
has some sort of order, that is, structurized.

0.20


first principal compornent
1st principal
component

Figure 1: The dynamics of cluster structure. This is ascatter diagram of a word relationship matrix dealt withthe principal coordinate analysis. The horizontal and vertical axes are the first and the second principal coordinates,
respectively

This result indicates that the model shows the coexistence of stability and adaptability of a categorysystem. Further, we have shown that the simulated
language system realizes the coexistence of commonality and individuality. These features are intrinsic
for mutual understanding in linguistic communicationand creative development of natural language systems.

©ISAROB 2005

L

Elman-type neural network with an additional recurrent
connection (dashed line) between the output and the input
layers. The circles are neurons and arrows are connections.

−0.10

0.00

hidden layerLnonlinearfunction

Figure 2: The basic architecture of SRN-SIC. It is a

0.00

−0.10

L

input layer
Own past output
External stimuli Hidden layer’s
past activity

0.10

−0.20
−0.20

output layerLcontext
layer

0.20



Simple Recurrent Network with SelfInfluential Connection (SRN-SIC)

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Minority SideThe Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

1.0
0.8
0.6
0.4
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284000284100284200284300284400284500284800284900285000Minority Side

Step
1.0
0.8
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0.0
284500284600284700
Step

Figure 3: An example of itinerant dynamics at the macro level between learning. The horizontal and the vertical axes are
the time steps and the minority sides converted to real numbers, respectively.

4

Coupled NZ Maps

feature of symbols, representing something andmanipulated in accordance with some rules. Although
an order in the transition among the attractors, representing rule of symbol manipulation, is not found,the model has a potential to represent a process ofsymbol formation.

The third model is a coupled chaotic maps, called
NZ map[13]. The map changes its function shape anddynamical state by changing a parameter (Fig. 4). The
coupled system change autonomously the parameter of
each map. This dynamic change of functions induce
dynamic behavior of the coupled system. It is known
that the system shows chaotic itinerancy. This modelof an individual also reflects the dynamic perspectiveof cognitive system[14, 15].

q=0.09Dist(n)

q=0.5



q=0.9







C

 

 

.

 

C

–
4

–
F

 

  

nFigure 5: Transition among embedded attractors. The

Figure 4: The change of the shape of maps and dynamics

horizontal axis is time step. The vertical axis is a distance measure from embedded patters. A sinusoidal input
sequence is given. When the orbits stay at a pattern, it
is labeled. The labels C, F, 4 are embedded patterns andC̄, F̄ , 4̄ are their reversed patterns.

of a NZ map for different values of a parameter. Each box
shows a unit interval, the horizontal and vertical axes are
the value of a variable at time t and t + 1, respectively.
The dotted and the solid lines shows the shape and the
dynamics of the map, respectively.

This model is used for modeling symbol
formation[16]. In this model, symbols are represented with attractors and manipulation rules ofsymbols by transition among attractors. We canembed some orthogonal patterns into the system. We
have confirmed that this system shows a transition
among the patterns according to input sequences(5).
This means that the model may show two important©ISAROB 2005

4

5

Conclusion

We have introduced three models of dynamic individuals. Each model and multi-agent system consisting of the models show dynamic behavior such as: category formation and its change by Developing WordWeb model, dynamics of social structure by SRN-SIC,

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and transition among embedded patterns induced byexternal inputs by the coupled NZ maps. We insist
that these models can be used for progressing the dynamic view of language and society.
The importance of dynamic nature of humans as
cognitive systems are recently recognized[14, 15]. Several mathematical and computational models havebeen proposed. This paper summarize our effort, especially for understanding the dynamics of linguistic
and social systems.
Fukaya and Tanaka studies sense-making process,which shares the dynamic view of language, throughobservations of conversations and other communicational activities[17]. Although Developing Word-Webmodel is not enough for a complete model of thesense-making process, the constructive study using themodel can develop the dynamic view of language[6].
Actually, we are studying the evolution of language[7],especially the evolution of lexical (categorical) structure, using this model.
Shiozawa advocated that the micro-micro loop is a
key notion to understand the dynamics of social structure [18]. However, no effective actual simulation models has been proposed to implement both the loop andindividuals acting in such dynamic loop. We can construct a dynamic social simulation using SRN-SIC andshow that the micro-macro loop actually plays an important role to form and maintain the dynamics ofsocial structures[19].

[4] H. G. Blumer, Symbolic Interactionism: Perspectiveand Method, Prentice-Hall, 1969.
[5] J. Gray, Hayek on Liberty, Basil Blackwell, 1984.
[6] T. Hashimoto, “ The constructive approach to the
dynamical view of language”, A. Cangelosi and D.
Parisi (Eds.), Simulating the Evolution of Language,Springer, pp.307–324, 2002.
[7] T. Hashimoto, “What is problem of language evolution? – from constructive perspective” (in Japanese),
The Proceedings of 18th Annual Meeting of JapaneseSociety of Artificial Intelligence, 2004.
[8] J. R. Taylor, Linguistic Categorization – Prototypes in
Linguistic Theory, Oxford University Press, 1995.
[9] T. Sato and T. Hashimoto, “Dynamic social simulation with multi-agents having internal dynamics”, New
Frontiers in Artificial Intelligence, Springer, 2005 (inprinitng).
[10] J. L. Elman, “Finding structure in time”, Cognitive
Science, 14(2), 179–211, 1990.
[11] D. Challet and Y. C. Zhang, “Emergence of cooperation and organization in an evolutionary game”, Physica A, 246, 407–418, 1997.
[12] K. Kaneko and I. Tsuda, “Chaotic itinerancy”, Chaos,13(3), 926–936, 2003.
[13] H. Nozawa, “A neural network model as a globallycoupled map and applicatoins based on chaos”, Chaos
2 (3), 377–386, 1992.

Acknowledgements[14] T. van Gelder, “The dynamical hypothesis in cognitive science”, Behavioral and Brain Sciences 21, 615–
665, 1998.

This research is supported by the following grants:
Canon Foundation Europe Fellowship; a Grantin-Aid for Scientific Research (No.12780269 andNo.15700183) from the Ministry of Education, Culture, Sports, Science and Technology of Japan andby the Japan Society for the Promotion of Science;
and JAIST research grant (grant for in-house research
projects).

[15] I. Tsuda, “Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems”,
Behavioral and Brain Sciences, 24(5), 793–847, 2001.
[16] T. Hashimoto and A. Masumi, “Learning and transition of symbols in chaotic dynamical system: toward
dynamical model of symbolic individual”, The Proceedings of 2nd. International Symposium on Emergence and Evolution of Linguistic Communication,2005 (forth coming).

References[17] M. Fukaya and S. Tanaka, Kotoba no hImidukeroni (A Theory of hSence-Makingi of Language) (inJapanese), Kinokuniya Shoten, 1996.

[1] K. Kaneko and I. Tsuda, “Constructive complexity andartificial reality: an introduction, Physica D, 75, 1–10,
1994.

[18] Y. Shiozawa, Fukuzatsusa no Kiketsu (Consequencesof Complexity) (in Japanese), NTT Shuppan, 1997.

[2] M. Tokieda, Kokugo-gaku Genron (The Principles ofJapanese Linguistics)(in Japanese), Iwanami Shoten,
1941.

[19] T. Sato and T. Hashimoto, “Dynamic social simulation with multi-agents having internal dynamics”, submitted to Artificial Life.

[3] V. N. Vološinov, Marxism and Philosophy of Language,
Harvard University Press, 1986.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Grasping the Distributed EntiretyPeter Sapaty 1

Masanori Sugisaka 2

Nikolay Mirenkov 1

Minetada Osano 1 Robert Finkelstein 3

1

University of Aizu
Aizu-Wakamatsu, Fukushima-ken 965-8580 Japan+81-242-37-2604, +81-242-37-2553 (fax){sapaty, nikmir, osano}@u-aizu.ac.jp
2

Oita University
700 Oaza Dannoharu, 870-1192 Japan+81-97-554-7831, +81-97-554-7841 (fax)msugi@cc.oita-u.ac.jp
3

Robotic Technology Inc.
11424 Palatine Drive, Potomac, Maryland 20854+1-301-983-4194, +1-301-983-3921 (fax)RobertFinkelstein@compuserve.com

Abstract. A flexible WAVE-WP ideology andtechnology, aimed at creation of new and penetration into
other distributed systems of different natures and at different
levels, are described. Supported by a high-level spatial
programming language, the technology allows us to grasp
large distributed systems as a whole, study and manage their
behavior, and direct evolution. Programming examples
combining swarm behavior of an unmanned group with ahierarchical command and control are presented, providing
integrity, flexibility, and openness within the same solution.
Other application areas of the paradigm are discussed too.
Keywords: parallel distributed processing, spatial
navigation, WAVE-WP language, cooperative robotics,
swarm behavior, distributed command and control, open
systems, over-operability.

1 Introduction
To understand the mental state of a handicapped person,of what the life and soul really mean, problems of economy
and ecology, or how to win market or battlefield, we must
consider the system as a whole – not just as a collection ofparts. The situation complicates dramatically if the systems
are dynamic and open, spread over territories, compriseunsafe or changing components, and cannot be observed intheir entirety from a single point.
Usually the whole can be comprehended and decisions
made only by a single human brain -- that is why we still
have queens, kings, presidents, prime-ministers andcommanders-in-chief. However, due to physical limitations,the brain cannot perceive the entire system in detail, anduses simplifications. Hierarchical systems are common too,
where decisions on different levels are made by single
brains on restricted information, with averaging andabstracting on higher levels. This is usually inherited byautomatic systems using computers and computer networks
mimicking human hierarchies. The hierarchical systems
with predetermined partitioning onto levels often become
static and clumsy, and may not operate efficiently in©ISAROB 2005

266

changeable environments.
In this paper, as an alternative to existing manned or
unmanned control systems, we are describing flexible
WAVE-WP (or World Processing) ideology andtechnology aimed at creating new or penetrating othersystems and their organizations. Supported by a highlevel spatial programming language, WAVE-WP allows
us to grasp large systems as a whole, study their behavior
and direct evolution in the way required. On theimplementation layer, the technology widely uses selfspreading mobile cooperative program code dynamicallycovering and matching distributed systems. This often
allows us to get solutions orders of magnitude morecompact than by other approaches. The internal systemorganization, including partitioning into components andspecific command and control, can be a function of theenvironment and the mission scenario in WAVE-WP; it
may change at runtime while preserving the overallsystem operability and goal orientation.
Practical applications (with code examples) will bepresented in relation to collective behavior of a robotic
group, which integrates a swarm-based movement withhierarchical command and control.
The proposed approach allows us to grasp andmanage the integrity and wholeness of large dynamic
systems in highly parallel and fully distributed mode,
often contrary to the human experience, also to thesystems modeling human behavior, with a real potential
to outperform them.

2 The WAVE-WP Spatial Automaton
The WAVE-WP automaton [1,2,3] effectively
inherits the integrity of traditional sequential
programming over localized memory, but for working
now with the real distributed world, while allowing its
parallel navigation in an active pattern flow andmatching mode, as a single spatial process. The
automaton may start from any point of the distributed
world to be controlled, dynamically covering its parts or

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the whole, and mounting of a variety of parallel anddistributed knowledge and control infrastructures.
Implanting distributed “soul” into the system organization,
the automaton increases system's integrity, capability of
pursuing local and global goals, assessing distributed
situations, making autonomous decisions, and recoveringfrom indiscriminate damages. Many spatially cooperating orcompeting parallel WAVE-WP automata may evolve on thesame system body serving, say, as deliberative, reactive,
and/or reflective spatial processes.
One of the distinguishing features of WAVE-WP is therepresentation of distributed worlds it operates in. Physicalworld (or PW) is continuous and infinite in WAVE-WP.
Existing at any its point, and possibly performing a job, isconsidered as residing in a node having physical coordinates.
Such a node, reflecting only occupancy at the point,
vanishes with the termination of all occupancies in it.
Virtual world (or VW) is discrete and interlinked in WAVEWP, being represented similar to WAVE [4] by a distributed
Knowledge Network (KN). Its persistent nodes may containestablished concepts or facts, and (also persistent) links(oriented and non-oriented, connecting the nodes) may
reflect different relations between the nodes.
The same model can also operate with the united (orPVW) world, in which any element may have features of theboth worlds. A variety of effective access mechanisms tonodes, links and their groups, say, by physical coordinates,
electronic addresses, by names, via traversing links, etc.
(classified as tunnel and surface navigation) abound in themodel, using both selective and broadcasting access modes.
Solutions of any problems in this formalized world in
WAVE-WP are represented as its coordinated parallelnavigation (or exploration, invasion, grasping, coverage,
flooding, conquest, etc.) by some higher-level forces, orwaves [2,4] These bring local operations, control andtransitional data directly into the needed points of the world.
The obtained results, together with the same or otheroperations may invade the other world parts, and so on.
During the world navigation, which may be loose andfree or strictly (both hierarchically and horizontally)
controlled, waves can modify the very world they evolve inand move through, as well as create it from scratch(including any distributed structures and topologies). Waves
may also settle persistent cooperative processes in world’s
different points, subsequently influencing its further
development and evolution in the way required.

wave
advance
move

Æ
Æ
Æ

constant
variable
act
rule

Æ
Æ
Æ
Æ

{ advance ; }{ move , }constant | variable | { move act } |[ rule] ( wave )information | physical-matter
nodal | frontal | environmental
flow-act | fusion-act
forward-rule | echo-rule

Fig.1. Syntax of WAVE-WP language.
In this description, braces set up zero or more
repetitions of a construct with a delimiter at its right;
square brackets identify an optional construct; semicolon
allows for sequential, while comma for parallel
invocation of program parts; and parentheses are used forstructuring of WAVE-WP programs (or waves).
Successive program parts, or advances, develop from allnodes of the set of nodes reached (SNR) by the previous
advance, whereas parallel or independent parts, moves,
constituting the advances, develop from the same nodes,
while splitting processes and adding their own SNRs to
the resultant SNR of the advance.
Elementary acts represent data processing, hops inboth physical and virtual spaces, and local control. Rules
establish non-local constraints and contexts over spaceevolving waves like, for example, the ability to create
networks, also allowing WAVE-WP to be used as aconventional language. Variables, called spatial (asbeing dynamically scattered in space), can be of the three
types: nodal, associated with virtual or physical nodes
and shared by different waves; frontal, propagating withwaves as their sole property; and environmental,
accessing elements of internal and external environments
navigated by waves.
This recursive navigational structure of the language
allows us to express highly parallel and fully
decentralized, albeit strongly controlled and coordinated,
operations in distributed worlds in a most compact way –in the form of integral space processing andtransformation formulae. These resemble data processing
expressions of traditional programming languages, but
can now operate in and process the whole distributed
world.

4 Implementation Basics
3 WAVE-WP Language
The system language expressing full details of this newcontrol automaton has been developed. Having recursive
space-navigating and space-penetrating nature, it canoperate with both information and physical matter. The
language can also be used as a traditional one, so no
integration with (and/or interfaces to) other programming
models and systems may be needed for solving complex
distributed knowledge processing and control problems.
Very compact syntax of the language, as shown in Fig.1,
see also [1,2], makes it particularly suitable for directinterpretation in distributed environments, being supported
by effective program code mobility in computer networks.

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On the implementation layer, the automaton widely
uses high-level mobile cooperative program code selfspreading and replicating in networks, and can be easily
implemented on any existing software or hardware
platform. As the automaton can describe direct
movement and processing in physical world, its
implementation may need to involve multiple mobile
hardware, with or without human participation. A
network of (hardware or software) communicating
WAVE-WP language interpreters (or WIs), which can bemobile if installed in manned or unmanned vehicles,
should be embedded into the distributed world to becontrolled, in most sensitive points, see Fig. 2.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

platoon), making them move in a swarm.

Spatial scenario 1
WAVE-WP
interpreters

WI

WI

x

Sensitive
pointsWIDistributed
physical &virtual World

WI

WI
WI

WI

WI

Creating distributed
infrastructures
Spatial scenario 2

yFig.2. A network of WAVE-WP interpreters.

Fig. 3. Distributed group of mobile units: initial order.

During the spatial execution of system scenarios in
WAVE-WP, individual interpreters can make local
information and physical matter processing, as well as
physical movement in space. They can also partition,
modify and replicate program code, sending it to otherinterpreters (along with local transitional data), dynamicallyforming
track-based
distributed
interpretation
infrastructures.
The automaton can exploit other systems ascomputational and control resources too, with or without
preliminary consent, i.e. in a (remotely controlled) virus-likemode. For example, many existing network attacks(especially DDoS) may be considered as a possible
malicious, simplified and degenerated implementation of theautomaton. WAVE-WP can also effectively integrate withother advanced systems managing distributed resources, like,for example, J-UCAS [5], within their orientation on rescue
and crisis relief missions.

5 Programming Example: Integration of Swarm
Behavior with Distributed Command and Control
Effective integration of swarm behavior [6] with strict
command and control for robotic teams may help fulfill
complex objectives and survive in dynamic environments.
Different forms of group behavior can coexist within
WAVE-WP model of parallel and distributed processing
and control. Written in a higher-level spatial language, withconsiderable code reduction, the combined system scenarios
can start from any component, covering at runtime thewhole system that may be dynamic and open.
A distributed organization has been programmed which,for example, makes all units of a scout platoon move in a
swarm, but at the same time regularly redefining the
topologically central unit and creating a fresh, most efficient,neighborhood-based hierarchical infrastructure covering allunits. It fuses targets seen by the units, distributing themback to all units for an individual selection and impact.
We will consider here only some very simplified
examples of the WAVE-WP code expressing different
distributed operations of the platoon, along with their unity.
5.1 Swarm Movement
The initial, casual, distribution of mobile units in space
may be as shown in Fig. 3. The simple program below
activates all units in the group (say, unmanned scout©ISAROB 2005

268

Each unit randomly chooses next step within a given
global direction, if the planned new position is not too
close to other units, otherwise the next step is being
redefined unless a suitable move is found. The program(let us call it swarm-move) may start from any node,
making all units fully autonomous and independent, andcommunicating only locally with other units:
Flimits = (dx0_dy-2, dx8_dy5);
Frange = r5; direct # all;repeat([Fshift = Flimits ? random;(direct # Fshift;
direct ## Frange)== nil;
WHERE += Fshift];)

A possible snapshot of the group during the work ofthis distributed program is shown in Fig. 4.
Thresholddistance toother units
Globaldirection of
movement

Fig. 4. Moving in a swarm with a threshold distance
between units.
5.2 Finding Topologically Central Unit
Let us consider now the finding of a topologically
central unit of the group for a certain moment of time (asthe units may be constantly moving and changing
positions to each other). Starting from any unit, this canbe done by the following program (calling it findcenter):
Faver = average(direct # all; WHERE);
Ncenter =min(direct # all;(Faver, WHERE) ? distance _ ADDRESS) : 2

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

units move in the swarm, and a new hierarchicalinfrastructure being rebuilt each time and frequently used
for parallel and distributed vision and impacting targets.

5.3 Creating a Hierarchical Infrastructure
Let us create a hierarchical infrastructure starting fromthe central unit found and covering all other units. It can bemost efficient if based on a physical neighborhood principle,with the next layer nodes lying from a current node, say,within a certain physical range. This can be accomplishedby the following program (calling it infra-build):
Frange = r20;
repeat(direct ## Frange;
grasp((all #) == nil; [create(-infra # BACK)]))Central unit
Distributing
all collected
targets to allunits

Collecting
targets fromall units
infra

infra

infra

infra

Fig. 6. Hierarchical fusion and distribution of targets.

An example of such an infrastructure built over a swarmof Fig. 4 is shown in Fig. 5.

Next level
range

To achieve this, we will also need removing of theprevious infrastructure each time before creating of anew one from, possibly, a new central unit, which can beeasily done by the following program (symbolicallycalled infra-remove):infra

direct # all; all #; LINK = nil

infra

The united program, combining all the previous
programs within a single distributed scenario, which canoriginally be injected from any mobile unit, will be as
follows.

Central unit

infra

infra

Fig. 5. Creating a neighborhood-based infrastructure fromthe most central unit.
Such an infrastructure can be effectively used for
different purposes within the distributed command andcontrol, with a possible one discussed below.
5.4 Hierarchical Fusion and Distribution of Targets
Starting from the same root node, the createdhierarchical infrastructure can be repeatedly used forcollecting targets discovered by the sensors of all units(ascending the hierarchy in parallel), with subsequent
distribution of the collected list of targets back to all theunits (descending the hierarchy, in parallel too, with thetarget list replicated in nodes). The units may be allowed tochoose suitable targets individually, impacting them by theavailable means. All this can be achieved by the following
spatially-recursive program (named collect-distribute):
F1={(+infra#; ^F1), ?detect};F2={(+infra#; ^F2), Fseen?selectImpact};
repeat([Fseen=(^F1); Fseen!=nil; ^F2])

The work of this program is explained in Fig. 6.

The named constituent programs, discussed before,
can participate in it directly by their full texts, or by calls
to them if represented as procedures. The program allows
the regularly updated infrastructure to be used for fusingdistributing targets for some period of time (here 300sec.), after which it finds the topologically central unit
and new infrastructure from it again, after removing theprevious infrastructure, and so on. All units continue
moving in a swarm (with the details given before)
independently of the infrastructure updating, targets
collecting, distributing, and impacting processes.
As can be seen from the programming examples
above, WAVE-WP is a completely different language
from conventional terms, allowing us to express complex
operations and control directly in distributed dynamic
spaces, with programs often orders of magnitude morecompact than in other known languages.

6 Other WAVE-WP Applications

5.5 The Combined Scenario
All these programs can be effectively combined within asingle scenario, with the center constantly migrating when
©ISAROB 2005

swarm-move,repeat(find-center; direct # Ncenter;[infra-remove]; [infra-build];
orparallel(collect-distribute,
TIME += 300
)
)

269

The technology has numerous practical applicationsin other areas too, summarized in [1-4]. Some exemplary
projects are as follows.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

• Distributed knowledge representation and processing.
Dynamically creating arbitrary knowledge networks in
distributed spaces, which can be modified at runtime,
WAVE-WP can implement any knowledge processing andcontrol systems in parallel and fully distributed way. A
program package had been developed for basic problems ofthe graph and network theory, where each graph node could
reside on a separate computer.
• Operating in physical world under the guidance of
virtual world. Operating in the unity of physical and virtualworlds, the WAVE-WP model can effectively investigate
physical worlds and create their reflection in the form of
distributed virtual worlds. The latter can guide further
movement and search in the distributed PW, and so on.
• Intelligent network management. Integrating traditional
network management tools and systems, and dynamicallyextracting higher-level knowledge from raw data via them,
WAVE-WP establishes a higher, intelligent layer allowing
us to analyze varying network topologies, regulate network
load and redirect traffic in case of line failures orcongestions. It also can be used for essentially new,universal and intelligent network protocols.
• Advanced crisis reaction forces. Smaller, dynamic
armies, with dramatically increased mobility and lethality,
represent nowadays the main direction in the developmentof advanced crisis reaction forces, which may effectively
use multiple unmanned units. The WAVE-WP technology
can quickly assemble a highly operational battle force from
dissimilar (possibly casual) units, setting intelligent
command and control infrastructures over them.
• Distributed road and air traffic management.
Distributed computer networks working in WAVE-WP,
covering the space to be controlled, can be efficiently used
for both road and air traffic management. The model
provides simultaneous tracking of multiple objects in PW bymobile intelligence spreading in VW, via computer
networks.
• Autonomous distributed cognitive systems. Whilecognitive systems include reactive and deliberative
processes, they also incorporate mechanisms for selfreflection and adaptive self-modification. The WAVE-WP
paradigm allows for the description of interacting
deliberative, reactive, and reflective processes on a semantic
level, representing the whole mission rather than individual
robots. This provides new, important degrees of freedom forautonomous robotic teams.
• Distributed interactive simulation. The technology
allows for highly efficient, scalable distributed simulation ofcomplex dynamic systems, like battlefields, in open
computer networks. Due to full distribution of the simulated
space and entities operating in it, there is no need tobroadcast changes in terrain or positions of entities to othercomputers, as usual. Each entity operates in its own part ofthe simulated world, communicating locally with otherentities. Entities can move freely through the simulated
space (and between computers) if needed.
• Intelligent global defense and security infrastructures.
WAVE-WP can also be used in a much broader scale,especially for the creation of intelligent international
infrastructures widely using automated and fully automatic
control and advanced robotics. The global system may
effectively solve problems of distributed air defense, wheremultiple hostile objects penetrating the air space can be©ISAROB 2005

270

simultaneously discovered, chased, analyzed, anddestroyed using computerized radar networks as acollective brain.

7 Conclusions
The WAVE-WP technology allows for a morerational and universal integration, management andsimulation of large complex systems. This is being
achieved by establishing a higher level of their visionand coordination, symbolically called “over-operability”[2] versus (and in supplement) to the traditional“interoperability”.
Distributed system creation and coordination
scenarios in WAVE-WP are often orders of magnitude
simpler and more compact than usual, due to high leveland spatial nature of the model and language.
This helps us to effectively grasp and manage large,
dynamic and open systems and solutions in them as awhole, often avoiding tedious partitioning into pieces(agents) and setting their communication andsynchronization.
These and other routines are effectively shifted to theautomatic implementation by dynamic networks of
WAVE-WP interpreters. Traditional software or
hardware agents may have sense within this approach
only when required, during the spatial development ofparallel system scenarios.
A detailed description of the WAVE-WP model andits extended applications can soon be available from [7].

References[1] P. Sapaty, N. Mirenkov, M. Sugisaka, and M. Osano,“Distributed Artificial Life Using World Processing
Technology”, Proc. of the Fifth Int. Conference on
Human and Computer (HC-2004), September 1-3,
2004, The University of Aizu, Japan, 2004.
[2] P. S. Sapaty, “Over-Operability in Distributed
Simulation and Control”, The MSIAC's M&S
Journal Online, Winter 2002 Issue, Volume 4, No. 2,
Alexandria,
VA,
http://www.msiac.dmso.mil/journal/WI03/sap42_1.html.

[3] P. Sapaty, M. Sugisaka, “WAVE-WP (World
Processing) Technology”, Proc. 1st International
Conference on Informatics in Control, Automation
and Robotics, 25-28 August 2004, Setubal, Portugal.
[4] P. S. Sapaty, “Mobile Processing in Distributed andOpen Environments”, John Wiley & Sons, ISBN:
0471195723, New York, February 1999, 436 p.
[5] “Joint Unmanned Combat Air Systems (J-UCAS)”,
www.darpa.mil.
[6] R. Finkelstein, “Swarm Intelligence: Application to
the 4/DRCS and the Scout Platoon Mission”, White
Paper, Robotic Technology Inc., USA, 2004.
[7] P. S. Sapaty, “Ruling Distributed Dynamic Worlds”,
John Wiley & Sons, ISBN: 0471655759, New York,
June 2005, 256p., www.wiley.com.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

M ot ion con t r ol of b ip ed r ob ot b en d in g t h e k n ee
K oji Umez a ki

M a s a nor i S u gis a ka

D e pt . of E l e c t r i c a l a nd E l e c t r oni c E ngi ne e r i ng, O i t a Uni ve r s i t y.
7 0 0 D a nnoha r u O i t a 8 7 0 - 1 1 9 2 J a pa n.
E - ma i l : ms ugi @ c c . oi t a - u. a c . j p

Abstr act
2．Str ucture of biped robot

I n t his r es ea r c h, i t ex per i me nt a b out t hestab le wa l k b y l eveli ng b y a sta ti c wal k.
The r ob ot ma nuf a c t ur e d t his i s t he bi pe drob ot wi t hout t he por ti on of t he uppe r ha l fof t he b ody, s uc h a s a n a r m, of onl y a
lower ha lf of the body.
Then, wal k operat i on us i ng centerof
gr a vit y move me nt wi t h a cr ot c h a nd a n
a nkl e wa s cr ea t e d, a nd a ngl e c ha nge of t heser vo mot or i n t hi s me c ha nis m on ei t her
si de wa s c o mpa r e d. The la r ge st e p a nd
wa l k wa s a b l e t o b e ob ta i ne d b y ca rr i e do u t mo v e a b l e o f t h e k n e e t h i s t i me .
A fut ur e pr ob l e m i s t o ra is e t he bi pe drob ot’ s sta b il i t y fr o m now on, a nd t oena ble it to perform va rious opera tion.

2．1

Size and degree of freedom of biped robot are
shown in a Table1.
Table1 Specific and degree of freedom
sizeheight449[mm]×width 200[mm]×length 120[mm]degree of
hip joint 3×2
freedomknee joint 1×2
ankle joint 2×2(Total 12)

2．2

1．Intr oduction

Str uctur e and system of biped r obot

The biped robot manufactured for the first timeat this laboratory had attached the upper half of the
body. However, load was applied and damaged tothe servomotor of knee joints for weight. Then,biped robot which removed fixation of knee joint
was manufactured last year. However, in order tomitigate the burden of a knee joint, the upper halfof the body is removed.
The biped robot manufactured this time is shownin Fig.1.

In these years, the humanoid robot which canharmonize with a life of human attracts attention
many biped robots are studied developed. Thosemany think entertainment nature as important. In
future it live together and cooperates with humanin activity of human’s ever day, and it is thoughtthat the demand of a robot with the practicality
which can perform work and support increases.
In this research, in order to perform a walk on
step or slope, it amide at performing walk motion
which separated the leg from the floor certainly.
The walk preformed this is static walk. Withstatic walk, it progresses in front little by little,
maintaining “static balance” in every moment.
Therefore, in static walk even if it stops walk
operation on the way it is possible to maintainbalance and to continue stopping at a state as it is.
On the other hand, dynamic walk carries out
maintaining “dynamic balance” on the assumptionthat it moves. If dynamic walk is stopped during
walk operation for movement which will be therequisite, balance will be broken down and it will
fall.

©ISAROB 2005

Specific of biped r obot

Fig.1

271

Photograph of developed robot

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3．Exper iment of walking cycle
3．1

Table2 Robot walking cycle
double stance phase37%
single stance phase
63%

The exper iment method

The motion which a robot is made to perform
was created using the manufactured biped robot. In
creation to each servomotor was determined bytrial and error. The flow of motion creation isshown in Fig.2.

Fig.3 shown that the knee joint on either side isperforming symmetrical change. However, at a hip
joint and ankle joint on either side, the instruction
value is not change symmetrical. This is because it
will have fallen if the center of gravity is moved toright-and-left symmetry. The following thing canbe considered as this reason. It is that the intervalof a leg on either side becomes narrow for a robot’s
weight , when performing walk operation.
Moreover, in walk operation, it turns out that theknee joint is performing operation to bend andoperation to lengthen by a unit of two times.

a step of a basic position(Home position)
generation of basic motion(movement of the center of gravity,
bending exercises, step)
it carries out basedon basic motion.
generation of a series of motion

3．3

The rate of double static phase and single static
phase in human walking cycle is shown in Table.3.

motion of a biped robot

Fig.2

3．2

The creation of the motion

Table3 Human walking cycle
double stance phase20%
single stance phase80%

Walk oper ation of biped r obot

angle change[°]

The rate of double stance phase and single stancephase in a biped robot’s walking cycle is shown in
Table2. Angle change of the instruction value ofthe servomotor of each joint is shown in Fig.3.
Double stance phase is a period which stands
both legs, arriving at the ground and switches a leg.
Moreover, single stance phase is a period which
stands by one leg and carries idling leg in front
from back.
20
10
0
-10

0

5

10

15

20

Human walking cycle, the hip joint is operation
to bend and operation to lengthen by a unit of one
times. Moreover, the knee joint and the ankle joint
are performed by a unit of two times.

3．4

R：hip
R：ankle
L：hip
L：ankle-20time[s]angle change[°](a) the thigh and an ankle
100
80
60
40
20
0

R：kneeL：knee
0

5

10

15

20

time[s](b) a knee
Fig.3

©ISAROB 2005

Walking analysis of human

Servomotors value of each joints

272Comp ar ison of a r obot a nd huma n being
in a walking cycle

From Table2 and Table3, biped robot walking
cycle is understood the rate of double stance phaseis long as compared with human thing. This isbecause walk operation of biped robot is static
walk. Usually, by comparison of dynamic walkwhich human is performing this thing for time tomovement of the center of gravity as a reason.
Dynamic walk is taken out with the leg near at handsimultaneously with movement of the center ofgravity. However, static walk is performing
separately operation issued before movement andthe leg of the center of gravity.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4．Up r ise of step
4．1

Relation between a step and the sole

4．2

(a)

Biped robot shows the angle change of each
servo motor in operation which reaches a step in
Fig8. It is the hip joint and the ankle joint on eitherside which are shown in Fig8. Moreover, linkmechanics of biped robot is shown in Fig.9.
angle change[°]In order for me to make it go up a step, bipedrobot’s sole and position of a step were considered.
The model and size of a sole of biped robot are
shown in Fig.4 and Table4.

Motion which go up to step.

(b)

(c)

20
R：hip
R：ankle
L：hip
L：ankle

10
0
-10
-20
0

servomotor
Fig.4

The model of sole

Table4
part
(a)
(b)
(c)

The size of sole
size [mm]
65
100
125

Fig.8

Servomotors value of each joints (a step)

ankle joints

Fig.9

Link mechanics of biped robot

There servo motors are mainly used for movementof the center of gravity on either side. Fig shows
moving the center of gravity finely, when moving
the center of gravity to the leg before being on a
step. This is because movement of the center ofgravity on either side and center of gravity of order
are moved separately. From this, it seldom needs tomaintain balance by movement of the center ofgravity of order by the case of the flat ground at thetime of movement of the center of gravity on eitherside. However, in the case of a step, I thought that
it was required to maintain balance at movement of
the center of gravity of order.

step
A pace less than 100[mm]I thought that there may be no center of gravity on
step, and it might fall back for this. Moreover,when a pace is larger than 100[mm], it is become
like Fig6. I thought that there may be center ofgravity on step and it could be a step.
servomotor
step©ISAROB 200515

knee joint

servomotor

Fig.6

10
time[s]hip joints

The following thing was considered from this.
However, it is the case where both legs are placed
just before a step. When a pace less than 100[mm],it become like Fig.5.

Fig.5

5

A pace larger than 100[mm]273

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

5．Conclusion
Walking motion which separated biped robot’s
leg from the floor completely was able to be madeto perform this time. It become possible to make a
step reach by this, and operation was generated.
However, many condition are required in order tooperate.

6．Challenges for the futur e
The environment which can be operated forthe influence of friction with the sole and afloor is limited. Moreover motion is generated
by trial and error this time. Therefore, theposition of the center of gravity cannot be
grasped. In order to act, the center of gravity
is important. From now on, it is necessary toconsider move transition of the position of
the center of gravity. As for the rest, stable
operation is aimed at by using technology,
such as a sensor and image processing.

Reference[1]
Nikkei mechanical ・ Nikkei design joint
compilation“Robolution”
Nikkei BP Co.
p.40〜41 2001
[2]

Ryuichi Nakamura, Hiroshi Saito“Basic Kinematics” Isiyaku publication
Co. Ltd p.334〜342 2002

©ISAROB 2005

274

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Artificial Life-Based Search Technique on the Solution of SingularConfigurations Concerning Screw Parameters in Helicoidal Robots
Ignacio Juárez Campos
Nachito Robotics Group
Privada de Antimonio 56, Col. Industrial 58130
Morelia, Michoacán, México
+52 443 3172656
ijcunam@att.net.mx

Facultad de Ingeniería Mecánica, UMSNH
Ciudad Universitaria, Edif. W, Santiago Tapia S/N,
Col. Felicitas del Río 58120
Morelia, Michoacán, México
+52 443 3167207

ABSTRACTThe screw-based robot manipulators belonging to the
SBMF6 and SBMF7 present closed-form equations
describing joint variables as functions of end-effector
coordinates; the mentioned closed-form equations
depends on the so called screw parameters that, in someconditions, can not be determined because present
singularities. It means that the inverse kinematics solvedby this technique is not robust enough. The paper presents
an auxiliary technique that will be helpful in finding thecorresponding inverse kinematics. The mentioned
technique is an artificial life-based search method thatwill be used only for those cases in which singularities arepresent and closed-form equations fail.
Key words: Artificial Life, Artificial Intelligence,
Genetic Algorithms, Robotics, Inverse kinematics

2. GOALTo present an auxiliary technique employed to find thescrew parameters when traditional or closed-form
solutions fail. The mentioned screw parameters arenecessary because the corresponding inverse kinematics is
expressed in terms of them. This adjacent technique is an
artificial life-based search method (ALBSM) because it
uses the mechanisms employed by living species to be
successful [5].
3. GENERAL SCREW DISPLACEMENT
Suppose that two poses of an object at time t = 0 andat time t = t f are given and it is necessary to find the

1. INTRODUCTION

screw motion that interpolate them, then the descriptionof computing pose interpolating screw parameters can bedone as follows: it is a known fact that three non-collinear
points P1, Q1 and R1, belonging to a work piece, arebrought to positions P2, Q2 and R2 by a screw
displacement (φ , d ) about an axis with direction ê

The SBMF6 and SBMF7 are sets containing
manipulators having a particular mechanical architecture
capable of transporting 3-dimensional work pieces bymeans of the helical or screw-based motions. This
singular way of transferring work pieces from one pose
(position and orientation) to another one is based on thewell-known Chasles’ Theorem and the Rodrigues’
Formula [1], [2], [3], [4], [5], [6]. The SBMF6 possesses
the PPSP, RRSP, SPRP and RPSP arrangements, while
the SBMF7 contains the PPSPP, RRSPP, SPRPP and
RPSPP manipulators; all these manipulators are named as
Helicoidal Manipulators. The elements contained by theset SBMF6 have 6 degrees of freedom, while elements
belonging to the SBMF7 posses 7 degrees of freedom. In
all cases, Helicoidal Manipulators were designed
according to the screw transformation that says that apiece described by three non-collinear points P, Q and R
is brought from an initial to a final one by means of ascrew displacement [1], [2], [3], [4], [5], [6]. In order totransfer a work piece from the initial pose to the final one,it is necessary to find the corresponding joint
displacements; it is the duty of the inverse kinematics
which is expressed by a set of closed-form equations
depending on the screw parameters found by Rodrigues in1840 [6]. These parameters can not be found for some
initial and final positions and orientations of the workpiece. This is a singular configuration dealing with thescrew motion. In this case, it is necessary the aid of and©ISAROB 2005

auxiliary method to find the corresponding screw
parameters. In this case, a search technique, based on theway species find the solution to the survival problem, wasselected.

passing through a point defined by s 0 [1], [2], [3], [4],[5], [6]. They are called “screw parameters”. (1), (2), and(3) present the mentioned functions and are known as theRodrigues’ formulae [6].

p 2 − p1 = (tan (φ / 2))eˆ ⊗ ( p 2 + p1 − 2s 0 ) + deˆ (1)q 2 − q1 = (tan (φ / 2))eˆ ⊗ (q 2 + q1 − 2s 0 ) + deˆ (2)r2 − r1 = (tan (φ / 2))eˆ ⊗ (r2 + r1 − 2s 0 ) + deˆ(3)

Where pi , qi , ri are the position vectors describing Pi,
Qi and Ri when i=1, 2. Symbol ⊗ represents the crossproduct.
It is possible to find the corresponding four screw
parameters in terms of the three mentioned points byusing (4), (5) and (6).

283The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

φ tan  eˆ =2
{[(r2 − q2 ) − (r1 − q1 )] ⊗ [( p2 − q2 ) − ( p1 − q1 )]}=[(r2 − q2 ) − (r1 − q1 )]T [( p2 − q2 ) + ( p1 − q1 )]{eˆ g = a1 a 2 a3
So then, (10) must be used to obtain the screw axis.

}eˆ g =(4)

 p1 + p 2 + (eˆ ⊗ ( p 2 − p1 ) / tan (φ / 2 )) − 
s0 = 
− eˆ T ( p 2 + p1 ) eˆ


1
2

(

)

∑a

(a1

a2

a3 )

T

(10)

2
i

φ g , representing the rotation

angle, is constituted by one genetic structure, (11).

d = eˆ T ( p 2 − p1 )4.
ANALYSISSINGULARITIES

OF

(6)

φ g = b1

(11)

The third part of (8),s 0 , contains four genetic

MATHEMATICAL
structures, (12).

Consider (7) which is the denominator of (4). It is notpossible to find the screw axis and the rotation angle
when (7) becomes null. As a result, the other two screw
parameters, (5) and (6), can not be found because they
depend on the screw axis and the rotation angle.

s 0 = c1 c 2 c3 c 4

den = [(r2 − q 2 ) − (r1 − q1 )] [( p 2 − q 2 ) + ( p1 − q1 )]T
s0 = The mentioned denominator becomes zero when
position
vectorsand[(r2 − q 2 ) − (r1 − q1 )]
rest
perpendicular.
In
some
cases
it
[( p 2 − q 2 ) + ( p1 − q1 )]is desirable for the pick and place operation to haveparticular initial and final position and orientations, butthey could make (4) singular, so then, inverse kinematic
equations, depending on screw parameters, can not befound. This is the main reason to use a method whoserobustness ignores limitations dealing with thissingularity [5].


 c1  
1  
 c2   c4
32 
ci  c3  
∑
i =1


(13)

The last part of (8), d eˆ , is a scalar parameter related
to the linear displacement along the screw axis, (14).

d eˆ = d1

5. THE ARTIFICIAL LIFE-BASED SEARCHMETHOD (ALBSM)(14)

So then, equation (8) can be expressed by (15).

solution(n) = a1 a 2 a3 b1 c1 c 2 c3 c 4 d1

5.1 GENOTYPE(15)

The member n, belonging to the species of sets ofscrew parameters and representing a particular solution,must contain the most basic information, responsible forthe determination and transmission of hereditarycharacteristics corresponding to the four screw parameters(8).

solution (n) = eˆ g φ g s 0 d eˆ(12)These four elements help to find the position vector
describing the point where the screw axis passes, (13).

(7)

5.2 LENGTH OF BASIC GENETIC STRINGSThe length of the genetic vectors or strings depends on
the length of the domain of the search space and therequired precision [5]. The chromosomes corresponding

(8)

to

ai ; i = 1,2,3 , b1 , ci ; i = 1,2,3 , c 4yd1 by means of binary strings are given by (16)-(20),represents the screw axis which contains

taking in account that b*,* = 0, 1 .

three elements due to the fact that it is a Cartesian vector(9).

©ISAROB 2005

3

The second part of (8),T

Where ê g

1
i =1

(5)
Where eˆ s 0 = 0(9)ai = bK 1−1,ai ...b2,ai b1,ai b0,ai

284

(16)

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b1 = bK 2−1,b1 ...b2,b1b1,b1b0,b1(17)ci = bK 3−1,ci ...b2,ci b1,ci b0,ci(18)

c 4 = bK 4−1,c 4 ...b2,c 4 b1,c 4 b0,c 4(19)

d1 = bK 5−1,d 1 ...b2,d 1b1,d 1b0,d 1(20)

S (n )([w1 , s ](n ), [w2 , s ](n ), [w3 , s ](n )) (27)With this in mind, it is possible to define the scalarg S , wi as follows,

The problem consists in finding K1, K2, K3, K4 andK5 in (16)-(20) satisfying the search space and theprecision requirements [5].

• If [wi , s ](n ) < [wi , s ] then g S , wi =

[wi , s ](n ) .
[wi , s ][sum(n ) =The mapping from binary strings into real numbers canbe done by (21)-(25).

j = 1,2,3

K 5 −1 d
d1 = ( ∑ bi ,d 1 2 i ) KMAX5
 2 −1
i =0





(28)

[ ]

eval (n ) =(22)

Obviously,

the

exact

9 + sum(n )18solutioncorresponds

eval (n ) = 1 , belonging to the member n, Fig 1.

(29)
to

(23)
Maximal score

K 4 −1 c
c 4 = ( ∑ bi ,c 4 2 i ) KMAX4 2 −1
i =0

 i =1, 2,3

S , wi]

The final evaluation function, eval (n ) ∈ 0, 1 , isdefined by (29).

K 2 −1K 3−1
 2 
c j = −1 + ( ∑ bi ,cj 2 i ) K 3
;
 2 −1
i =0∑  ∑ g

S = P ,Q , R

j = 1,2,3 (21)

 2π 
b1 = ( ∑ bi ,b1 2 i ) K 2

 2 −1
i =0[wi , s ] .
[wi , s ](n )Then, the proximity of the work piece corresponding
to solution n is expressed by sum(n ) ∈ − 9, 9 , (28).

5.3 PHENOTYPES

K 1−1
 2 
a j = −1 + ( ∑ bi ,aj 2 i ) K 1
;
 2 −1
i =0• If [wi , s ](n ) ≥ [wi , s ] then g S , wi =(24)

sgn ([wi , s ]) = sgn ([wi , s ](n) )[wi , s](25)

Where c MAX and d MAX are the upper limits of thesearch spaces.

sgn ([wi , s ]) ≠ sgn ([wi , s ](n) )5.4 EVALUATION FUNCTION

Minimal score

The evaluation function takes in account the final poseacquired by the work piece. If the object is close to therequired final pose, then the corresponding solution isevaluated with a high score, otherwise, it will have a poor
effectiveness. Each solution, generated by the ALBSM,
provides a final pose described by P2(n), Q2(n), R2(n).
The comparison between the required final pose (P2, Q2,
R2) and (P2(n), Q2(n), R2(n)) will permit evaluate
solution n. It is important to take in account that whatever
points S and S(n) are represented by sets of threecoordinates:
S ( w1 , s , w2 , s , w3 , s )
(26)

[

][

][

Fig. 1 Evaluation function for the coordinate [wi , s ](n )belonging to the member n
5.5 THE ALBSM FLOWCHART

Fig. 2 presents the flowchart representing the different
stages followed by the ALBSM.

]

And,©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

7. CONCLUSIONS

Randomly generate a population of potential solutions

Evaluate Population Members

The ALBSM has demonstrated its effectiveness infinding the solution of singular configurations. However,this method should be only used for those cases in which
it is not possible to find the screw parameters by othermeans. It is necessary to take in account that the ALBSM
is an iterative one; therefore, it spends more time to findthe solution in comparison to the closed-form equations.
The main advantage resides in its robustness because thismethod does not use the equations that could become
singular. All the manipulators, belonging to the SBMF6
and SBMF7, have their own inverse kinematics, but theseequations depends on the screw parameters, so then, the
ALBSM can be applied to all the screw-based
manipulators.

End?

Rank-Order Population Members Based on Fitness
Mate Best Members of Population
Mutate Resulting Members
Substitute old population by new one

Fig. 2 Flowchart representing the different stages of the
ALBSM

8. REFERENCES

6. EXPERIMENTATION[1] Ignacio Juárez Campos, Beatriz Juárez Campos,
Oracio García Lara. (2004) The Inverse Kinematics of
the SPRP Screw-Based Manipulator. Proc. of the 10th
IASTED International Conference on Robotics andApplications, RA 2004, August 23-25, Honolulu,
Hawaii, USA, pp. 34-40.

It is necessary to transfer a work piece from the initialand final positions described in table 1.
Objectcoordinates(2, 2, 0)
P1(3, 2, 0)Q1(2, 3, 0)
R1(2, 2, 0)
P2(1, 2, 0)Q2(2, 1, 0)
R2
Table 1. Initial and final poses[2] Ignacio Juárez Campos et al. (2004) The Inverse
Kinematics of the PPSP Helicoidal Robot
Manipulator. Proc. of the 10th IASTED International
Conference on Robotics and Applications, RA 2004,
August 23-25, Honolulu, Hawaii, USA, pp. 22-27.
[3] Ignacio Juárez Campos, Oracio García Lara, Beatriz
Juárez Campos. (2004). The Inverse Kinematics of
the RPSP Screw-Based Robot Manipulator. Proc. of
the 4th Int. Conf. on Advanced Mechatronics,
ICAM’04. October 3-5, Asahikawa, Hokkaido,
Japan.

For the mentioned initial and final poses presented intable 1, (4) becomes singular. The screw parameters,
shown in table 2, were found with the aid of the ALBSM.
Screw Parametersê(0 0 0.995)Tφ

179.898ºs0(1.997[4] Ignacio Juárez Campos, Oracio García Lara, Beatriz
Juárez Campos. (2004). Trajectory Planning for the
PPSP Helicoidal Robot Manipulator. Proc. of the 4thInternational Symposium on Robotics andAutomation ISRA 2004. August 25-27, 2004.
Querétaro, Qro, México, pp. 287-294.

2.001 0.003)

Td0
Table 2. Screw parameters resulting from the ALBSM[5] Ignacio Juárez Campos, Beatriz Juárez Campos.
(2004). Síntesis Evolutiva de un Mecanismo deTornillo, Proc. 9th SOMIM Conf. on MechanicalEngineering, Veracruz, Ver, Mexico. pp 35-40.

The exact values of the screw parameters are shown intable 3. The comparison of both tables, It is concluded
that the ALBSM provides a suitable result.
Screw Parametersê(0 0 1)

φ

180º

s0

[6] O. Bottema, B. Roth (1979) Theoretical Kinematics(North Holland Publishing Company, Amsterdan,
New York, Oxford.

T

(2

2 0)

T

d
0
Table 3. The exact screw parameters©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Human-following Robot Using the Particle Filter
in ISpace with Distributed Vision Sensors
Tae-Seok Jin, Kazuyuki Morioka, and Hideki Hashimoto
Institute of Industrial Science, the University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
tracking the movement of many moving objects because
systems implementing multiple hypotheses and multiple
targets suffer from a combinatorial explosion, rendering
those approaches computationally very expensive for realtime object tracking.
Our intelligent environment is achieved by distributing
small intelligent devices which don’t affect the present
living environment greatly. Color CCD cameras, which
include processing and networking part, are adopted as
small intelligent devices of our intelligent environment. We
call this environment “Intelligent Space (ISpace) ” [3].
Intelligent Space is constructed as shown in Fig.1. In thispaper, how to represent feature vectors of multiple objects
using particle filter is described. Then, the technique toachieve the tracking by using color information is
described.

Abstract
We present a method for representing, tracking andhuman following by fusing distributed multiple visionsystems in intelligent space, with application to pedestrian
tracking in a crowd. In this context, particle filters provide
a robust tracking framework under ambiguity conditions.
The particle filter technique is used in this work, but inorder to reduce its computational complexity and increase
its robustness, we propose to track the moving objects bygenerating hypotheses not in the image plan but on the
top-view reconstruction of the scene. Comparative results
on real video sequences show the advantage of our method
for multi-object tracking. Simulations are carried out toevaluate the proposed performance. Also, the method isapplied to the intelligent environment and its performanceis verified by the experiments.

DIND(Human Tracking)

Keywords: Multi-vision sensors, Tracking, Intelligence
Space, Mobile robot, Particle filter

DIND4

DIND3

(Robot Control)(Robot Control)

1. Introduction

DIND2

Video object tracking in dense visual clutter, although
being notably challenging, has many practical applicationsin scene analysis for automated surveillance, such as thedetection of suspicious moving objects (pedestrians orvehicles), or the monitoring of an industrial production[1][2][3][4]. The quality of an object tracking system isvery much dependent on its ability to handle ambiguous
conditions, such as occlusion of an object by another one.
To cope with such ambiguities, multi-hypotheses
techniques have been developed [5]. In the standard
techniques using multi-hypotheses for the state estimationand tracking, the Kalman filter is used under the premisethat the noise distributions are Gaussian and the system
dynamics are linear [6]. However, when tracking human
movements, non-linear and non-stationary assumptions
make it suboptimal to use. In this context particle filter
algorithms are attractive because they are both simple andvery general. The particle filter algorithms track objects bygenerating multiple hypotheses and by ranking themaccording to their likelihood. They suppose that the correct
hypothesis is retained [7][8]. Many tracking filters havebeen proposed using this approach, defining the states as
being each static posture or position of the objects andmodeling a motion sequence by the composition of thesestates with some transitional probabilities [9][10]. Thosestate-of-the-art techniques perform efficiently to trace themovement of one or two moving objects but theoperational efficiency decreases dramatically when
©ISAROB 2005

(Robot Control)

DIND1(Robot Control)

Following Human

Physical Agent(Mobile Robot)

z

x

Human

y

Intelligent Space

Fig. 1. Intelligent environment by distributed Cameras.

2. Vision Systems in Intelligence Space
2.1 Basic Scheme
Fig.2 shows the system configuration of distributed
cameras in Intelligent Space. Since many autonomous
cameras are distributed, this system is autonomous
distributed system and has robustness and flexibility.
Tracking and position estimation of objects ischaracterized as the basic function of each camera. Eachcamera must perform the basic function independently atleast because over cooperation in basic level between
cameras loses the robustness of autonomous distributed
system. On the other hand, cooperation between manycameras is needed for accurate position estimation, controlof the human following robot[4], guiding robots beyond
the monitoring area of one camera[5], and so on. These are
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advancedfunctions of this system. This distributed
camera system of Intelligent Space is separated into twoparts as shown in Fig.2. This paper will focus on the
tracking of multiple objects in the basic function.

computer with a video capture board. It is necessary to
extract only the moving objects robustly in order tosimplify the matching process. Background subtraction is
simple and efficient to recognize the moving objects in
fixed camera image. Following process based on
background subtraction is performed to extract the object
region.
Fig.3 shows the example of results of this object
extraction process mentioned above. Fig. 3(a) is the raw
image captured by the CCD camera. Extracted objects,
which are human and robot, are shown in Fig. 3(b), 3(c).
It is clear that this can extract the multiple objects
simultaneously. When the image of the size of 320X240
pixels is captured and Pentium III 866 MHz PC is used,
this process is performed at the speed of 28 to 30 frames
per second. In this process, a lot of processing time is not
required. Matching process of the objects between
successive frames is based on the information acquired
from these extracted objects.

Fig. 2. Configuration of distributed camera system.

2.2 Previous Research for Moving Object Tracking
Various tracking methods of moving objects using avision system have been investigated. These can beseparated in two major compartments. One is the methodof matching and clustering of feature points extracted froman input image. For example, optical flows are extracted
in a image, and tracking is achieved by clustering ofthem[11]. The other is the method that the knowledge on
objects is given to the system as an object model inadvance and the model and an input image are compared.
For example, the 3D ellipse model is used for humantracking in [12]. The former has the merit that various
feature points can be extracted according to image
processing, because the whole of the captured image canbe always observed. However, matching of feature points
between successive frames become difficult andcomputational cost increases, according to number of thefeature points in the complicated scene and by the effect ofnoise. The other hand, in the latter method, only
comparison between the model and input image is required.
Tracking of moving objects is achieved by comparing thereal image with the model. Computational cost is lower
than the former. However, tracking systems have toprepare the models of the objects in advance. For example,
human tracking for surveillance system needs humanmodels[8] and vehicle tracking for ITS needs vehicle
models[9]. Tracking cannot be achieved without object
models. It is impossible to build a model of every object
which exists in our daily life.

(a)

(c)

Fig. 3. Captured image and extracted objects.

3.2 Target regions encoded in a state vector Using
Particle filter
Particle filtering provides a robust tracking framework,
as it models uncertainty. Particle filters are very flexible inthat they not require any assumptions about the probability
distributions of data. In order to track moving objects (e.g.
pedestrians) in video sequences, a classical particle filter
continuously looks throughout the 2D-image space todetermine which image regions belong to which moving
objects (target regions). For that a moving region can be
encoded in a state vector.
In the tracking problem the object identity must be
maintained throughout the video sequences. The image
features used therefore can involve low-level or high-levelapproaches (such as the colored-based image features, asubspace image decomposition or appearance models) tobuild a state vector.
A target region over the 2D-image space can be
represented for instance as follows:r = {l, s, m, γ }(1)where l is the location of the region, s is the region size, mis its motion and γ is its direction. In the standard
formulation of the particle filter algorithm, the location l,of the hypothesis, is fixed in the prediction stage using only
the previous approximation of the state density. Moreover,the importance of using an adaptive-target model to tackle
the problems such as the occlusions and large-scalechanges has been largely recognized. For example, the3. Processing Flow
3.1 Extraction of Objects
Our system uses many low-cost cameras to improverecognition performance. Position and viewing field of allcameras are fixed. Each camera is connected to a normal©ISAROB 2005

(b)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

lenses has not been incorporated in this article. Under thisapproach, the processing time is dependent on the region
size.

update of the target model can be implemented by theequation
rt = (1 − λ ) rt −1 + λ E[rt ](2)where λ weights the contribution of the mean state to thetarget region. So, we update the target model model duringslowly changing image observations.

SCENE 3Dzyx

4. Tracking moving objects
IM A G E 2D

4.1 State-space over the top-view plan
In a practical particle filter implementation, theprediction density is obtained by applying a dynamic modelto the output of the previous time-step. This is appropriate
when the hypothesis set approximation of the state density
is accurate. But the random nature of the motion model
induces some non-zero probability everywhere in statespace that the object is present at that point. The tracking
error can be reduced by increasing the number ofhypotheses (particles) with considerable influence on thecomputational complexity of the algorithm. However in thecase of tracking pedestrians we propose to use the top-view
information to refine the predictions and reduce the statespace, which permits an efficient discrete representation. In
this top-view plan the displacements become Euclidean
distances. The prediction can be defined according to thephysical limitations of the pedestrians and their kinematics.
In this paper we use a simpler dynamic model, where theactions of the pedestrians are modeled by incorporatinginternal (or personal) factors only. The displacements
Mttopview follows the expression−1
M ttopview = A(γ topview )M ttopview
+N

(a)

(b)
Fig. 4. (1) the approximation of Top-View plan by image plan with amonocular camera, (2) size estimation

4.3 Tracking experiments
Some experiments are performed to verify thistracking method. Fig.5 shows the experimental
environment and objects that should be tracked by thismethod. Three objects, which are human, a mobile robot
and a chair, exist in this environment. In this experiment,
the system does not have object models for these objects inadvance. A mobile robot and a chair are static at the
beginning and human is walking between them afterward.
Since only one camera is used for this experiment,
occlusion between human and the other objects is supposed
to happen as shown Fig. 5(a). Fig. 5(b) shows theclustering result of the feature vectors obtained in a given
time, when three objects exist in the space as shown in Fig.
5(c).

(3)

where A(.) is the rotation matrix, γ topview is the rotation
angle defined over top-view plan and follows a Gaussianfunction g (γ topview ; σ γ ) , and N is a stochastic component.
This model proposes an anisotropic propagation of M :the highest probability is obtained by preserving the same
direction. The evolution of a sample set is calculated bypropagating each sample according to the dynamic model.
So, that procedure generates the hypotheses.

y4.2 Estimation of region size
The size of the search region represents a critical point. In
our case, we use the a-priori information about the target
object (the pedestrian) to solve this tedious problem. We
assume an averaged height of people equal to 160 cm,
ignoring the error introduced by this approximation. Thatmeans, we can estimate the region size s of the
hypothetical bounding box containing the region of interestr = {l, s, m, γ } by projecting the hypothetical positions
from top-view plan in Fig. 4. A camera calibration step isnecessary to verify the hypotheses by projecting thebounding boxes. So this automatic scale selection is anuseful tool to distinguish regions. In this way for eachvisual tracker we can perform a realistic partitioning(bounding boxes) with consequent reduction in thecomputational cost. The distortion model of the camera's©ISAROB 2005

occlusion

x
O

human

1m

camera(a)
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5. Conclusion
In this paper, the basic function of the vision system
in Intelligent Space was described. The vision sys tem ofIntelligent Space needs real time processing, tracking ofmultiple objects, extension to cooperative multiple cameras
network and overcoming partial occlusion. To realize them,
it is required that model based method and feature based
method are combined efficiently. Then, new tracking
strategy was proposed based on extracting the objects bybackground subtraction and creating color appearance
model dynamically with particle filter. This strategy
achieved real-time and robust tracking of multiple objects.
Especially, correct matching had been kept after theocclusion among objects happened in the experimental
results.
As a future work, representation method of objects
that are close to achromatic color will have to beinvestigated. Next, recognition of the wide area using the
distributed cameras should be performed. It will need thatdifferent cameras share information about clusters and thefeature space. Then, sharing method of the information thateach camera acquires will be investigated.

(b)
0. 16

0. 14

0. 12

0. 1

Initial Color Models

Human

7
tn
e
n
o
p
m 0. 08
o
c
r
o
t
c
e
v

Chair

0. 06

0. 04

Robot
0. 02

0

0

0.05

0.1

0.15

0. 2
0.25
vector component 1

0.3

0. 35

0.4

0. 45

(c)
Fig. 5. Experiment: moving area and models.

References

Fig. 6 shows the captured image by a camera in thisexperiment. Experimental result of multiple objects
tracking is shown in Fig. 7. X axis and Y axis represent X
and Y pixel coordinate of captured image respectively.
Central pixels of each object are plotted. Althoughocclusion between human and other objects was observed
during tracking of walking human, matching and trackingof each object achieved without fail. In this case, thissystem doesn’t have the complex object models, however
tracking of multiple objects was performed in low
processing time.

[1] A. W. Senior, “Tracking with Probabilistic Appearance
Models," in Proc ECCV workshop on Performance Evaluation of
Tracking and Surveillance Systems, pp 48-55, June 2002.
[2] M. Bierlaire, G. Antonini and M.Weber, “Behavioural
Dynamics for Pedestrians," in K. Axhausen (Ed.), Moving throughnets: the physical and social dimensions of travel, pp. 1-18,
Elsevier. 2003.
[3] Joo-Ho Lee, Hideki Hashimoto, “Intelligent Space -concept
and contents”, Advanced Robotics, Vol.16, No.3, pp.265-280,
2002.
[4] Kazuyuki Morioka, Joo-Ho Lee, Hideki Hashimoto, “Human
Centered Robotics in Intelligent Space”, IEEE International
Conference on Robotics and Automation(ICRA’02), Washington
D.C., USA, May 2002.
[5] K. Choo, and D.J. Fleet, “People tracking using hybrid Monte
Carlo filtering," In Proc. Int. Conf. Computer Vision, vol. II, pp.
321-328, Vancouver, Canada, 2001.
[6] B. Anderson, and J. Moore, Optimal Filtering, Prentice-Hall,Englewood Cliffs, 1979.
[7] A. Doucet, N. de Freitas, and N. Gordon, Sequential MonteCarlo Methods in Practice, Springer Verlag, April 2001.
[8] G. Kitagawa, “Monte Carlo Filter and Smoother for NonGaussian Nonlinear State Space Models," Journal ofComputational and Graphical Statistics, Vol. 5(1), pp. 1-25, 1996.
[9] K. Nummiaro, E. Koller-Meier, L.J. Van Gool, “Object
Tracking with an Adaptive Color-Based Particle Filter," DAGMSymposium Pattern Recognition, pp. 353-360, 2002.
[10] J. Vermaak, A. Doucet and P. Perez, “Maintaining MultiModality through Mixture Tracking," International Conference on
Computer Vision, ICCV2003, Nice, France 2003.
[11]Peter Norlund and Jan-Olof Eklundh, “Towards a Seeing
Agent”, Proceedings of First International Workshop on
Cooperative Distributed Vision, pp.93-120, 1997.
[12]Nakazawa Atsushi, Kato Hirokazu, Hiura Shinsaku, Inokuchi
Seiji, “Tracking Multiple People using Distributed Vision
Systems”, Proceedings of the 2002 IEEE International Conferenceon Robotics & Automation, pp.2974-2981, Washington D.C, May
2002.

Fig. 6. Multi-Objects detection and tracking in ISpace.

Fig. 7 Experiment results: Multi Objects tracking.

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Outwit game – a dynamical systems game for market dynamics
M. Konno
m-konno@jaist.ac.jp

T. Hashimoto
hash@jaist.ac.jp

School of Knowledge Science
Japan Advanced Institute of Science and Technology (JAIST)
1-1, Tatsunokuchi, Ishikawa, Japan, 923-1292
Abstract• The perfect competition should not be assumed
for a model of markets.

A dynamical systems game model of market, called
Outwit Game, is proposed. In the game, individual
behavior in a market is abstracted as orienting majority and orienting minority. We suppose that individuals’ profit changes with a course of these two actions.
Through computer simulations, we show that an index
representing both micro and macro dynamics show a
power law distribution.
Keywords: Outwit Game, Dynamical Systems
Game, Market Dynamics, Micro-Macro Loop, PowerLaw Distribution

1• The perfect rationality should not be assumed fora model of individuals.
• The model should include dynamic and strategicinteraction among individuals.
In order to focus on the dynamical change of market,
we adopt a framework of dynamical systems game[2].
It is an extension of the game theory for describing
changes of game environment such as payoff matrix,
options by players.

Introduction

2

In this paper, we propose a game model, called Outwit Game (OG), for capturing dynamics of market
by considering individuals behavior in a market. In
neo-classical economics, the assumptions of the perfectcompetitive market and the perfect rationality makethe market model static. However, any market, in
reality, dynamically changes. Therefore, in order tounderstand the real market, we need a model that includes dynamics of market and individual behavior.
The real market is composed of interactions amongindividuals in a game theoretic situation. Namely, others’ action affects decision making and consequences ofactions by an individual and vice versa. In addition
to the interactions among individuals, there exist interactions between individuals’ actions, that is, micro
dynamics, and change of a market, that is, macro dynamics. This circular interacting causation is called“micro-macro loop” by Shiozawa[1]. We think thatthe essence of market dynamics consists of endogenous change induced by individuals’ actions, interaction among individuals, and the micro-macro loop.
Constructing a game theoretic model of a market,
we put importance on the following points:©ISAROB 2005

Individuals’ Actions in Market

At first, we consider the properties of individual
behavior in a market. Keynes[3] likens a market toa beauty contest, in which the prize goes to a parson
who is voted at most and the voters for the winnerearn. In this beauty contest, people try to vote for aparson who is thought of as the most beautiful by mostof the people, not for a parson they themselves think
as the most beautiful. In financial market, people try
to invest a company that many investors invest. Thisis an action pursuing a trend and orienting a majority.
Investors do not always follow trends. They invest
in a company that is not in a trend at present and may
be in future, invest to a new business, or sell stocks
they have before the upward trend ceases. Namely,
people try to make a trend by themselves. This action is not to do the same action as others and favors
minority.
In the market, people change over these two actions,orienting majority and orienting minority and gain or
loss. Switching two actions may be a trigger of changeof trends. The market change is often induced by thetrend changes. Although the people favor majority or

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3.2

minority intentionally, the consequence of an action isdetermined by the market. This is an uncertainty ofmarket.

3

In a game theoretic model, players pursue their own
profit. In SOG, the player must predict the number ofmajority and decide the move at the next step according to the prediction in order to pursue his/her profit.
Namely, they decide their move as½
Majority (Ñ M (t + 1) > N M (t))
l(t + 1) =, (1)Minority (otherwise)

Outwit Game

Based on the above discussion, we introduce a game
abstracting the real market. In the game, individuals’
actions have features of orienting majority and minority, that changes with the state of market. This typeof game is named as Outwit Game (OG).
We define two versions of the OG, Simple Outwit
Game (SOG) and Monetary Outwit Game (MOG).
The MOG is relatively complex version than the SOG.
In the MOG, so as to approach to the dynamics of realmarket, two forms of profit, capital gain and realized
gain, are considered. These two games share the essential part. We analyze the SOG at first and reportin this paper. The MOG is not treated in this paper.

3.1

where l(t + 1) is the side of majority or minority andÑ M (t + 1) is the predicted number of majority at thenext step. Note that the players cannot certainly bea majority/minority when they want to be so. They
must predict which move is the majority/minority as
well. Accordingly, SOG has double uncertainty in theprediction of the number of majority and its move.

4

Definition of Simple Outwit Game

1. Each player selects one of two alternative moves
at each time step t.
2. The majority and minority sides are decided fromall players’ moves.
3. Each player is grouped into the majority or theminority according to his/her move and given the
payoff p(t) defined by Table 1.

Ñ M (t + 1) = P N (N M (t), mM (t), m(t)) .

payoffMinorityp(t) =N M (t) − N M (t − 1)Nm̃M (t + 1) = P m (N M (t), mM (t), mi (t), Ñ M (t + 1)) .
(3)
Every time step, the players adjust the prediction
function according to success and unsuccess of the predictions.

p(t) = 0

In Table 1, N M (t) and N M (t − 1) are the numberof majority players at the present (t) and the previous
step (t − 1), respectively, and N is the number of all
players. Note that N M (t) and N M (t − 1) change withtime, the players have the possibility both to gain andto loss when they keep in the majority side. If they
are in the minority side, they are always risk-free.

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(2)

This expression represents that a player produce a predicted number of majority Ñ M at the next step t + 1,
based on the number of majority, N M (t), the move of
the majority side, mM (t), and the move of the player
itself, m(t), at the present step t.
Further, using the same information at the present
and the output from the prediction function, P N , theplayer try to predict the move of the majority m̃M (t +1) at the next step using the other prediction function
P m,

Table 1: The payoff matrix of Simple Outwit Game

Majority

Players’ Model for Simulation ofSOG

We analyze the characteristics of SOG using computer simulations. In the simulation, we use a modelof players with prediction and learning. Because of thespace limit, we briefly explain the players’ model.
Each player has two kind of prediction functions forthe number and the move of majority from the present
information. At first a player predict the number ofmajority in the next step,

The procedure of the SOG is the followings:

movePlayers’ Action

5

Simulation Results

We analyze the dynamic behavior and statisticalproperties of the game. The total number of players296

is 51, two moves are -1 and 1.

(a)

5.1

S(t)

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23744.4(b) 1

23744.2

move

23744

Dynamics of Game

23743.823743.6

To see the dynamics of SOG, we observe the transition of the players’ payoffs. The most of players cangain the payoffs averagely. Three examples of the transitions of accumulated payoffs,pi (t0 ) ,(c)-1
7000227000247000267000281700030700020

(d)

20

10

M

move

(4)

M

t
X

23743.2
700020N (t)-N (t-1)

Si (t) =23743.4

700022700024700026700028700030700022700024700026700028700030

0

-10

-1

t0 =0

700020700022700024700026step

are depicted in Fig. 1, where pi (t) is the payoff of thei-th agent at the time step t. We find three types of
the transition: rapid increasing in a long range, slow
increasing, and decreasing.

700028700030700020step

Figure 2: Transitions of (a) the accumulated payoff, S(t)of a player, (b) the moves of the player, (c) the majority’s
move, and (d) the number of change of majority per one
step, N M (t) − N M (t − 1), at the period of time step (thex axis) between 700020 and 700030.

35000
30000

S(t)

25000
20000

5.2

15000
10000

We observe some statistical characteristics of SOG.
Figure 3(a) shows a distribution of the length that theplayers continue to take the same move. The straightline is a fitting taken at the range longer than 30.
This observation value obeys a power law distribution
in the range of longer length, while in the range ofshorter length the value is much larger than the extrapolation of the power law fitting in the longer range.
This means that the players mostly change their moveshortly, but some players often do not change theirmoves very long periods.
Figure 3(b) is a distribution of the length that the
majority continues to take the same move, namely aversion of global value or market dynamics of Fig. 3(a).
This value also obeys a power law.

5000
0
0

200000400000600000800000

1e+06

step

Figure 1: The transition of the accumulated payoff Si (t)of tree typical players. The x axis is time step, the y axis
is the accumulated payoff. The arrow indicates a player
depicted in Fig. 2.

The increasing of the accumulated payoff meansthat the players are able to predict the transition of
the majority and it’s move to some extent. We observe more closely the dynamics of the game in orderto know how they obtain the payoff (Fig. 2). Figure2(a) depicts the accumulated payoff of a player whose
accumulated payoff grows rapidly in the period from630000 to 730000 steps in Fig. 1 (indicated by an arrow). The player gains some payoff every three steps.
The time series of the player’s moves is period three(-1, -1, 1) as shown in Fig. 2(b). Figure 2(c) is thetime series of the majority’s move. This shows period three dynamics (-1, 1, -1). Namely, the player’s
belonging group changes as (majority, minority, minority). Finally, we draw the change of the number ofminority from last step, that is, N M (t) − N M (t − 1), in
Fig. 2(d). This value also shows period three dynamics as (positive, negative, negative). Accordingly, theplayer belongs to the majority when he/she can gain
and to the minority when he/she may loss. In otherwords, he/she can appropriately outwit.

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Power Law Distribution of Players’
and Market Dynamics

6
6.1

Discussion
Power Law Distribution in Market

We found the power law distributions in the lengthof consecutive time steps that individuals take thesame move and that the majority is in the same move.
The former can be thought of as a characteristic ofindividual action, that is, a micro dynamics, and thelatter as that of market, that is, a macro dynamics. If
we interpret the latter index as the length of trends,
this result means that infinitely long trends can occur in a market. Namely, there is possibilities of large

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005(a)3) In the OG, to win the game, players should select the majority/minority appropriately responding
to changes of game situations, while in the MG to be
the minority is always to win.

1e+08

1e+07

1e+06

frequency

100000

10000

6.3

1000

SOG as Tragedy of a Common

100

The SOG has a characteristic of tragedy of a common. If a player in the SOG change from the majority
to the minority in order to avoid a loss, the other players in the majority suffer a loss. For example, when nplayers are in the majority, suppose a player changes
to the minority. Since the number of the majority
decreases to n − 1, the players remaining in the majority lose their payoff. But the player changed to theminority eludes a loss. Namely, an action pursuing individual’s own profit conflicts with profit of the whole.

10

1
1

10

100
length

(b)

1000

10000

1e+07

1e+06

frequency

100000

10000

1000

100

10

1
1

10

100

1000

length7

Figure 3: The distribution of the consecutive length of thesame move taken by (a) the players and (b) the majority
side. The x and y axes are the length and the frequency,
respectively. The straight lines are fitting by pow functions
at ranges that length is (a) longer than 30, and (b) 1 to100.

We propose a new dynamical systems game theoretic model of financial market. This game is named
the Outwit Game. The simplest version of the game,the Simple Outwit Game, is analyzed using computer
simulations. We show that indices corresponding to a
micro and macro dynamics obey the power law. Thus,
it can be said that the Outwit Game reflect some nature of market dynamics. The analysis of the Outwit
Game should be progressed for showing the utility ofthis game model for understanding market dynamics.

stock bubbles, long term inflations and deflations infinancial markets.
Power laws have been found in several indices in real
financial markets. We have not, however, made correspondence between such power laws and the powerlaw distribution in our game model. The reason why
such power law is observed in our game model is also
an open problem.

6.2

Acknowledgements
The authors are grateful to Makoto Nishibe andSusumu Egashira for their fruitful suggestions.

Comparison with Minority Game

References

A game that can be considered as a model of market is the Minority Game (MG)[4]. The Outwit Game
(OG) is thought of as a modification of MG. The differences between the OG and the MG are the followings:
1) In the OG, two kind of actions, orienting majority and minority are taken into consideration, while
the MG takes only the action orienting minority.
2) The OG has a payoff matrix that changes withtime (explicitly includes a time variable t), while thepayoff matrix of M G is fixed1 .

[1] Y. Shiozawa, “On micro-macro loop” (in Japanese),
Keizai Ronsou (Kyoto University), 164(5), 1–73,
1999.
[2] E. Akiyama, K. Kaneko, “Dynamical systems gametheory and dynamics of game”, Physica D, 147, 221–
258, 2000.
[3] J. M. Keynes, The General Theory of Employment,Interest, and Money, Macmillan Cambridge University Press, 1936
[4] D. Challet and Y. C. Zhang, “Emergence of cooperation and organization in an evolutionary game”,
Physica A, 246, 407–418, 1997.

1 As a simple modification of the MG, the number of majority
can be given to the minority. Although the relative value ofthe payoff changes with time in this modification, the essential
payoff structure that the minority is always win does not change.

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Conclusion

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Artificial Ecosystem on the Resource Conservative Tierra Structure
Shuichi Matsuzaki*, Hideaki Suzuki**, Minetada Osano**Graduate School of Computer Science and Engineering, Aizu University
Aizu-Wakamatsu City, Fukushima 965-8580 Japan**ATR Network Informatics Laboratories
2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto 619-0288 Japan

realize such biological behaviors in computer architectures [2,3]. A Tierran digital creature that consists of aself-replicating program and a CPU create its replicant(copy of itself), and also evolve it through mutations. For
example, one class of mutants called “parasites” appears
in Tierra experiments, and these parasites will
self-replicate by partly depending on other programs. In
addition, Network Tierra, a following study of Tierra,modeled digital creatures that are organized by several
distinct programs, and showed that the programs candifferentiate into more specialized units [3,4]. Having
shown such evolutionary behaviors in artificial systems,
a kind of extraordinary evolution of the natural
ecosystem can be reproduced by the Alife systems, while
another type of evolutionary phenomenon in which it
will continuously affect creatures with far tinier changes
is hardly shown in such systems.
In principle, natural creatures belong to a particulartrophic level, and their evolutions are affected by mutualinteractions (such as predator-prey interactions) in orderto stabilize an entire food web (the resource-recycling
system). In terms of this, an aspect of the preservation of
space/matter resources, which is a type of abiotic rule,causes creature interaction. In contrast to such a system,
resources become freely available (with looser limits)within the above-mentioned systems that Tierra shows.
From these points, we consider that a strong restrictionof the resources may produce one kind of evolutionary
behavior, while a weak one causes another kind.
In this paper, we present an Alife system with aremodeling of the Tierran structure. The remodelingfocuses on the appearance of a spontaneous interactionof digital creatures and the global systems resulting fromthe interaction. A theory proposed by Suzuki et al. [5]formed the underlying concept of this study: the theory

Abstract
In this paper, we present an Artificial Life system witha remodeling of Tierra. This system provides a rule for asystem environment, which is induced by the natural
resource property: “matter resources” will be conserved
and recycled through biochemical reactions. Because ofthat point, fundamental components (as matter) of digitalcreatures being conserved (never being added/deleted)are introduced into this system. Consequently, thesystem forces the creatures to recycle their program
ingredients in order to self-replicate. We have developed
two distinct ancestor programs called “Plants” and“Animals,” which they can recycle resources byexecuting photosynthesis/preying. Our experimental
results demonstrate that particular interactions, which aresimilar to a food web, appeared as a result of therecycling actions, and such interactions stabilized the
population equilibrium among groups of Plants andAnimals. In addition, co-adaptive evolutionary behaviors
caused by predator-prey interactions are also observed.
Keywords: Tierra, self-replication, resource conservative

system

1. Introduction
The Cambrian explosion, an example of Adaptive
radiation with the sudden appearance of many animal
body plans, produced most types of higher taxonomic
categories of actual animals [1]. It is assumed that therich environmental resources of “space” and “matter”
mainly caused this outstanding evolutionary event.
Tierra is an artificial life (Alife) system designed to©ISAROB 2005

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programs (thus, this part of the Tierra program has been
almost fully renewed). We have developed the following
two types of ancestor program with distinct copy
procedures.

of “Symbol Resource Conservation”. This theory
suggests that the component symbols (or symbol
ingredients) in which matter is equivalent be conservedin each elementary reaction. A set of Tierran instruction
words, represented by bits, composes a self-replicating
program. Those bits are regarded as a fundamental
matter of the system. The instruction words, however,can be copied/overwritten by elementary reactions(computations). Hence, the Tierra Operating System(OS; namely, a higher-level manager) prohibits
overwriting on “active instruction words” (componentsof active programs). Nevertheless, the total number ofactive instruction words explodes in the RAM unless areaper perpetually eliminates creatures. If no elimination
occurs, the system will not be able to function due tomemory overflow. Without conserving matter, systems
with a conserved (limited) space will experience thisproblem in principle. Consequently, we have assumed
that the instruction words of Tierra should be given morerational characteristics as matter. Our model introduces
an environmental rule whereby each instruction word isconserved, and because of this rule, creatures recycle thewords to self-replicate. We show here two types ofcreature designed with distinct manners of recycling thewords.
In the next section, we introduce the digital creaturesand some important rules of the system. Following that,
we show several of our experimental results, and thenprovide concluding remarks.

(1) Plant Ancestor
In principle, creatures in this model can replicate one
instruction word within one replicating cycle (thus,
self-replication will be accomplished by finishing all thecycles).
An ancestor program called a “Plant” synthesizes
no-instruction words during each replication cycle, andthen produces instruction words. Figure 1 shows anactual example of a replication cycle, and each of its
actions (1-7) is described as follows:
1. Identify an instruction word that a creature will
replicate during this cycle.
2. Obtain an arbitrary no-instruction word (Element A)
by searching entire memory space.
3. Obtain another arbitrary no-instruction word
(Element B) by searching entire memory space.
4. Produce an instruction word (Product. It is equivalent to what is identified in 1) by using andarranging 12 bits within Elements A and B.
5. Produce an arbitrary instruction word (By-product)
by using and arranging six bits that are not usedin 4.
6. Move the Product to an address allocated for a
replicant.
7. Move the By-product to the address where Element
B was.

2. The Model
2.1. Self-Replicating Programs
Our model characterizes a “word” with the following
two aspects: on the one hand it is represented by six bits
(each order corresponds to a type); on the other hand,each of its appropriate sets represents a self-replicating
program. The system produced by this model conserves
every bit as the ingredient of the words. All–possiblecombinations of six-bit sets are classified into two
categories: instruction words and no-instruction words(the CPU will decode instruction words into particular
computations, but will not decode no-instruction words).
These words are necessary for digital creatures to
use/reuse in their self-replication, and such actions areincluded within a copy procedure of our ancestor©ISAROB 2005

Figure 1．An example of the replication cycle for Plants.

In brief, in order to replicate each word, ingredients of
two no-instruction words (obtained in 2 and 3) will be

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recycled to ingredients of two instruction words (4 and
5). Since this action will increase the number of instruction words in a system, Plants are considered as a
producer of instruction words in a system.

2.2 System environments(1) DeathA function of OS for eliminating a creature removes a
CPU from the system and changes the state ofcomponent words into the non-active one. This function
will eliminate a creature in which at least one of thefollowing conditions is fulfilled:(2) Animal ancestor
In each replicating cycle, Animals first identify an
instruction word that they will replicate during this cycle(the same manner as Plant’s), after which they will begin
searching this instruction word by checking each address
outward in both directions (within the range of 350).
Then, Animals will obtain the instruction word andfinish the cycle if they can satisfy the following
conditions in parallel:(a) If the creature finds the instruction word;(b) If its “CPU state” equals True.
The condition of (a) will be satisfied when the search
reveals a corresponding instruction word. In the next
condition, the CPU state represents a kind of Boolean
register (True/False) equipped in the CPU, and the state
existing at the every beginning of replication cycles isfixed to False. It can only be changed with “templatematching,” which will be executed in parallel with thesearch. The search template, which has been installed on
the Animal programs, will look for a corresponding
template matching itself (such a manner is similar to thatof Tierra). On matching the search template with anothertemplate, the CPU state will switch to another state: True
→False or False→True.
To take the case of Animal ancestor, a matching of its
search template with its own “membrane template”(templates which exist at beginning and end of eachprogram) will occur, and the Animal changes its CPU
state (False→True). Having changed its CPU state toTrue, the Animal satisfies the condition of (b), and it willobtain a word to replicate when it satisfies the conditionof (a) after this. However, by matching its searchtemplate with another membrane template afterward, it
will change its CPU state again (True→False), and it
will not obtain words.
Animals can obtain not only non-active instruction
words (not a component of programs), but also active
instruction words, enabling them to take component
instruction words away from the other self-replication
programs. Since such behavior of Animals can eliminate
other creatures, it is regarded as predation in our model.

©ISAROB 2005•

By losing an instruction word necessary to
self-replicate, the OS will eliminate the creature.
Predations and some mutations (wrong cases) canremove/modify instruction words, and they mightmake a self-replicating program an invalid one.

•

By failing to execute any of its own instruction
words within 3,000 steps, the OS will eliminate it.
An instruction pointer might jump incorrectly toother programs by errors/mutations, but will rarely
return to its own program.

•By failing to replicate an instruction word in 100
cycles, the OS will eliminate this creature at 10%.

(2) Resource Management
The OS sets the following rules relating to an
existence of words.
•
If the number of active instruction words amounts
to 80% of the total, the OS will prohibit creaturesfrom obtaining any more words. Without this rule,creatures use most of the words for their own
self-replication, giving rise to result that none ofthem will be able to obtain enough further words toself-replicate.

•301

If a non-active instruction word has not been usedby any creatures during those first 100 steps, theOS will change it into an arbitrary no-instruction
word. As we have already seen, since Plants will
synthesize instruction words from no-instruction
words, the number of instruction words will
unilaterally increase. In this respect, this rule will
make up a balance of the two word types. In
comparison with the natural world, this rule isregarded as a kind of dissimilation: the process ofgradually changing materials of dead bodies into
other materials by chemical reactions.

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number of instruction words will also decrease after
that. Thus, further replications of Animals become
ever more difficult. However, having boosted thenumber of no-instruction words, the population of
Plants will start to increase again. As a result of
these interactions, the population equilibrium
between Animals and Plants will stabilize.

3. Experiments
3.1. Basic Experiment
Here we present experimental results of a system inwhich Plant and Animal ancestors are initially inoculated.
First, Fig. 1 shows variables for populations of Animals
and Plants.

Next, we describe another experimental result that
illustrates predator-prey interactions. In the system, theinteraction by which predators attempt to gain the
instruction words of prey is regarded as a predation.
Before every predation occurs, the predator checks thatits search template can match a membrane template of
the prey. If these templates match, the prey will beprotected from the predator. Therefore, It is consideredthat a diversity of matching patterns of the membrane
template is a measure of a creature’s ability to protect
itself against enemies (by making a template longer, themembrane template will come to match more types ofshort templates representing the search templates in mostcases). For this reason, we use the Matched Template
Number (MTN) as the measure that indicates a prey’s
ability to protect its entire program against predation.
The MTN of each creature represents the total number oftemplate matchings in which its membrane templates
match templates of “all possible four-letter words” (thesearch template basically contains four-letter words).
Figure 3 shows variables of the MTN (maximum: 32) ofeach group.

400
350

Population

300
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100
50
0
0

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200,000

300,000

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PlantsAnimals

Figure 2. Populations of Animals and Plants.

In the early steps the system explodes its population of
Plants and brings the population almost to its maximum
possible size. The system then starts increasing its
population of Animals, decreasing the Plant population
inversely. Although the population equilibrium between
them fluctuates during the several-hundred-thousandsteps of the experiment, it does gradually stabilize after awhile. With respect to these results, we have considered
the following:
1. The words are randomly given initially (the total
number is fixed). Plants can obtain sufficient
no-instruction words, and they increase. On theother hand, Animals can rarely gather enoughinstruction words for self-replication because there
are few available instruction words around them.
2. By increasing the population of Plants, an increasein the total number of instruction words will follow,
after which the Animal population will start toincrease. However, having decreased the number of
no-instruction words, further replications of Plants
becomes increasingly difficult.
3. A decrease in the total production of instruction
words will follow a decrease in the Plant population(and an increase in the Animal population).
Through the function of the OS (see 2.2(2)), the©ISAROB 2005

Matched Template Number (MTN)

18
17
16
15
14
13
12
11
10
0

100,000

200,000

300,000

400,000

500,000

600,000

Execution steps

PlantsAnimals

Figure 3. Matched Template Number.

The MTN of each group keeps increasing almost
through the experiment. In addition, the MTN of Plants
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is higher than that of Animals in most cases. An
increasing MTN actually indicates a lengthening oftemplates, and places the heavier load on
self-replication; however, it has been observed
perpetually in the experiment, that is to say, this resultshows mutations in creatures for adapting to predations
by other Animals.
As we have seen above, the experimental results in
Figs. 2 and 3 indicate that this system produces twovarieties of creature interaction that influence creature
behaviors. We finally summarize the consideration givenin the experiment.
In this resource-conservative system, a particular
relationship between Animals and Plants in which
each group indirectly cooperates with each otherhas appeared through their reactions of theresource-recycling process.
In local reactions, competition among individual
creatures has been observed, causing creatures tomutate in order to adapt to each other.

Figure 4．Results of an experiment with a simple replicator.
Both of these graphs, Tierra (upper) and our model (lower),show the balance of power among creature groups. The Tierra
experiment evaluates the balance between simple replicators(black) and normal creatures (other colors). The experimentconducted with our model evaluates the balance among three

3.2. Experiment with a Simple Replicatorgroups: simple replicators (black), Plants (light gray), andAnimals (gray/dark gray).

It is considered as one of the advantages of resource-conservative systems that they may limit an explosion of trivial replicators with a simple structure. Thesesystems will preserve the digital creatures from being
expelled by such trivial replicators. From this point, wehave planned an experiment in which simple replicators
are deliberately inoculated. We have conducted theexperiments not only in our model, but also in Tierra, tocompare the results. We prepared a simple replicating
program with 20 instruction words that were allcomposed by Tierran instruction words. Therefore, thisprogram can perform within both of those models.
Figure 4 shows the results of the experiments where
the simple replicator is added in 3,000 steps.

In the Tierra experiment, the group of simple
replicators drastically expands, reducing groups ofnormal creatures. Then, it occupies an entire space, andthe others disappear. In our model, the group of simple
replicators expands slightly at the beginning, but it
hardly changes at all afterward.
Since the simple replicators are actually capable ofself-replicating almost six times faster than the ancestor
creatures, the Tierra result is considered natural at thispoint. In contrast, the results from our model willcontradict that, with the following principles:
Reducing the number of Plants will make theobtaining of instruction words difficult.
Only a few kinds of word are necessary for a
simple program to self-replicate, and they will be
consumed within a short period. Then, only thesewords will become insufficient, and this will
restrain the simple program from self-replication.
A simple program has few templates, and seldom
avoids predation (by Animals).

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Japan.
[4] Ray, T. S., & Hart, J. (1998). Evolution ofdifferentiated multi-threaded digital organisms.
In: Adami, C., Belew, R.K., Kitano, H., and Taylor,
C.E. (eds.): Artificial Life VI: Proceedings of the
Sixth International Conference on Artificial Life,MIT Press, Cambridge, MA 295--304[5] Suzuki H., Ono N., & Yuta K. (2003). Several
Necessary Conditions for the Evolution of Complex
Forms of Life in an Artificial Environment,
Artificial Life Vol.9, No.2, 537-558

These results demonstrate the robustness of ourmechanism of our system compared with that of Tierra
against the simple replicating program, and owing to it
this system keeps such replicators under control. In
general, any creature that cannot be placed in a cycling
rule of this system will be weak, even if it canself-replicate faster than the others.

4. Conclusion
We have proposed an artificial system that had been
designed based on the Tierra structure. The system ruledthat any program component regarded as matter isconserved. We also introduced two self-replicating
programs (Animals and Plants) in which processes torecycle the components are preprogrammed. We then,demonstrated the evolutionary performances of such
digital creatures. First, particular creature interactions
caused by the recycling processes were observed thatstabilized the population equilibrium existing among
creature groups. Next, we showed predator-prey
relationships through local inter-creature competition. As
a result of such competition, creatures mutated theirprograms in order to adapt to each other. Finally, therobustness of this system against a trivial replicator wasproved by a comparison with Tierra.

Acknowledgements
The second author's research work was supported inpart by the National Institute of Information andCommunications Technology and Doshisha University's
Research Promotion Funds.

References[1] Sole R, V. Salazar-Ciudad I., & Garcia-Fernandez J.
Landscapes, (2000). Gene Networks and Pattern
Formation: on the Cambrian Explosion. Adv.
Complex Systems 2, 313-337.
[2] Ray T, S. (1994). An Evolutionary Approach toSynthetic Biology: Zen and the Art of Creating Life,
Artificial Life Vol.1, 179--209.
[3] Ray, T. S. (2001). Overview of Tierra at ATR.
Technical Information, No.15, Technologies for
Software Evolutionary Systems. ATR-HIP. Kyoto,©ISAROB 2005

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Rule ecology dynamics for studying dynamical andinteractional nature of social institutions
T. Hashimoto
hash@jaist.ac.jp

M. Nishibenishibe@hucc.hokudai.ac.jp

School of Knowledge Science,
Japan Advanced Institute of
Science and Technology (JAIST)
1-1, Tatsunokuchi,
Ishikawa, Japan, 923-1292

Graduate School of Economics
and Business Administration,
Hokkaido University
Kita 9 Nishi 7, Kita-ku,
Sapporo, Japan, 060-0809

Abstract

even only one, from our daily life.” As we can easily
imagine, each institution has different importance or
influence for individuals’ behavior.
Aoki and Okuno-Fujiwara[2] discusses that diversity of institutions exists in different social systems.
But, as quoted above, there are great diversity of institutions in a society. Aoki and Okuno-Fujiwara[2]point out the important feature of institutions, thatis an institutional complementarity. When an institution provides a reason to exist another institution,these two institutions are refereed to as in the relationship of institutional complementarity. Since only
one of such institutions cannot change independently,
Aoki and Okuno-Fujiwara insist that they are stable.
However, social institutions often changes and many
institutions interact with each other. Accordingly, achange of an institution induces changes of other institutions. The chain of change may go on to all institutions.
This chain of change through interaction among
institutions is like an ecological system of biological
species. In this paper, we conceptualize such dynamic
and interactional nature of institutions as an ecology of rules. Here we use a term ‘rule’ as a more
abstract version of institutions. We propose a newframework to formalize the ecology of rules and its
dynamics as an extension of evolutionary game theory. This framework is named “Rule Ecology Dynamics (RED)1 ”. This framework can integrate two gametheoretical treatment of institutions, one treats institutions as game rules, and the other as equilibria ofgames[3].

In this paper, we address a new concept to view asocial system as consisting of diversified institutions
interacting with each other and dynamically changing
through actions of individuals in a society. A mathematical framework for the concept is formalized. The
framework, called Rule Ecology Dynamics (RED), is
an extension of multi game dynamics, in which players
play plural games simultaneously, by introducing timedependent weights for the plural games and a “metarule”. The meta-rule is a rule to determine the changeof rules. We show simulation results of two kind ofmeta-rules , average payoff type and inverse standard
deviation type. We discuss that this framework is a realization of rule dynamics and it has certain relevanceto describe real phenomena of institutional dynamics.
Keywords: Rule Ecology Dynamics, Formation andChange of Institutions, Replicator Equation, MultiGame Dynamics, Evolutionary Game Theory, RuleDynamics

1

Introduction

Social institutions are rules for individuals’ behavior and cognition broadly accepted and used in asociety. In our society, there exist various institutions/rules ubiquitously. Seiyama[1] describes suchsituation as “We are living with institutions, suchas family, commuter passes, trains, universities, telephones, postal services, E-mails, meetings, lectures,laws, norms, conventions and so on. There is no one
that is not institutional. We engage in our daily life
postulating such institutions. They are omnipresent.
It is very difficult to pick out non-institutional aspect,©ISAROB 2005

1 The previous version of the framework was called “MetaEvolutionary Game Dynamics” since it is an extension of game
dynamics (replicator dynamics) introducing a meta-rule[3].

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2

Rule Ecology Dynamicsgwhere
PM wg is the gweight of the g-th game, satisfying
g=1 w = 1, λ is an evaluation of the g-th game,PMg gand λ̄ =g=1 w λ is the weighted average of theevaluations of games, τ is a time coefficient for thechanging velocity of the games relative to that of thestrategy populations, x = ({xi1 ···iM }) (ig = 1 · · · rg )is the strategy profile that is a vector of population
share of each strategy, and ug (x) = ({ugi1 ···iM (x)})
is the combined payoff or the payoff profile that is avector of the payoff of each strategy.
The equation (5) is a meta-rule to give evaluation ofeach game. The variables of the evaluation function is
the strategy profile x and the combined payoff ug (x).
This setting makes an interaction loop among individual behavior, the rules and the meta-rule closed.
Namely, the change of rules depends on the consequences of the behavior under the rules.

In our framework, rules and individual behaviors
are modeled by games and strategies of players in thegames, respectively. We start the formalization of the
RED from the replicator equation that governs the
dynamics of population of each strategy[4]:ẋi = xi (ui − ū)(i = 1 · · · N ) ,(1)

where xiP
is a population share of the i-th strategy andNsatisfies i=1 xi = 1, ui is a payoff of the i-th strategy,PNū = i=1 xi ui is the average of the payoffs, and N is
the number of strategies.
To represent various compositional rules, multiple
games that all players play are brought in, as proposedin Multi Game Dynamics[5]:ẋi1 ···iM=xi1 ···iMMX(ugi1 ···iM − ūg )

(2)

3

g=1

(g = 1 · · · M ) ,We show simulation results of the system for tow
settings of the meta-rule. The meta-rule should beconsidered appropriately for the objective system to
be modeled.
The first example is the average type meta-ruleX
λgA (x, u) =xi1 ···iM ugi1 ···iM .
(6)

where xi1 ···iM is a population share of a strategy (i1 , i2 , · · · , iM ), which means that a player
plays the strategy i1 at the game 1, the strategy i2 at the game 2 and so on, satisfying
Pr1 Pr2PrM
g
i1 =1
i2 =1 · · ·iM =1 xi1 ···iM = 1, where r is thegnumber of options at the game g, ui1 ···iM is a payoffof the strategy (i1 , i2 , · · · , iM ) at the game g, ūg is theaverage payoff at the game g, and M is the number ofgames.
We introduce a weight for each game to treat thedegree of influence or importance of each rule. The
weight of each game change with time through individual behavior. The change of the game weights isgoverned by a meta-rule. A meta-rule is a more basic and stabler rule than focal rules. It is for determine which rules are relatively desirable or relatively
strong in influence for individual behavior. Introducing a meta-rule is a representation of the hierarchicalstructure of interaction and stability of rules. Thus,
the meta-rule corresponds to the constitution or social values and norms, which are relatively not easy tochange. In our framework, it is modeled as an evaluation function of games. The RED is defined by thefollowing three equations:ẋi1 ···iM=xi1 ···iMMXwg (ugi1 ···iM − ūg ) ,i1 ,···,iMThis is a model of a market economics and each ruleis thought of as describing a market. We suppose in
the market economics that the more profit a market
or a rule gives for individuals/organizations, the more
important the market/rule is, as in stock markets.
The simulation result is depicted in Fig. 12 . In
this case, a phenomenon like globalization is observed.
Namely, the system is monopolized by a strategy andthen games in which the monopoly strategy wins develop their weights.
The second example of the meta-rule is to describe
the egalitarianism that is the doctrine of the equalityof mankind and the desirability of political, economic
and social equality. This meta-rule is formalized as
the inverse of standard deviation of agents’ payoffs:
λgIV (x, u) =

τ ẇ
λg

©ISAROB 2005

=
=

g

g

w (λ − λ̄) ,λg (x, ug ) ,X n¡¢2 o−1xi1 ···iM ugi1 ···iM − ūg. (7)i1 ,···,iM(3)2 In the following simulations, we simplify the RED (Eq.(3)-

(4)
(5)

(5)) as follows: The number of options of all games are thesame, rg = N (g = 1 ∼ M ) and each strategy is limited totake the same one at the all games and thus the strategy shareis indicated as xi1 ···iM ≡ xi .

g=1
g

Simulation Result

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1

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Figure 1: An example of dynamics of RED with theaverage type meta-rule (Eq.(6)). The y axes are thestrategy distribution for the top graph and the weights
of games for the bottom, respectively. The x axis istime steps. The system size is N = 6, M = 7.

Figure 2: An example of dynamics of RED with thethe inverse standard deviation type type meta-rule(Eq.(7)). The y axes are the strategy distribution for
the top graph and the weights of games for the bottom,
respectively. The x axis is time steps. The system size
is N = 3, M = 10.

The dynamics of the strategy share and the games’
weights are shown in Fig. 2. This case gives revolutionary changes of predominant rules. Namely, no one
game and no one strategy can dominate the systemstably and temporarily prevalent games and strategies change continually. It is a rather paradoxical situation that egalitarianism induces destabilization of
established rules and revolutions.

4

ical phenomena are found in evolution, development,
learning, adaptation and emergence. We call such dynamic phenomena rule dynamics.
The social rule is also an example having undecomposability between operators and operands. Social
structures such as institutions and norms are formed,
maintained and changed with time. The remarkabledynamic nature in such systems are self-modification.
Individuals behave under some institutions and change
the institutions.
Some efforts to study and to describe rule dynamics
have been launched [7, 8, 9, 10]. The RED proposed
here is also a framework to describe the rule dynamics,
especially observed in social systems. Actually, RED(Eqs.(3)-(5)) can be written in a matrix form as

Discussion – RED as Framework for
Rule Dynamics

When we try to understand some object, it is often
described by a set of states and a system of static functions. The functions are rules to govern the change ofstates. In other words, the rules are operators andthe states are operands. Describing with static functions implies that the decomposability between rules
and states is presupposed. The decomposability may
occasionally not be able to be presupposed. This undecomposability between rules and states, or between
operators and operands is one of the main featuresof complex systems[6]. In complex systems, rules areoften not static but dynamically changing. The typ-

©ISAROB 2005ẋ = (GT (t)x − x · GT (t)x) x ,(8)PM
where GT = g=1 wg Ag is a total game that is theweighted some of all games and Ag (g = 1, · · · , M )is the payoff matrix of the g-th component game. It
is clearly seen that RED is a replicator equation withtime dependent interaction matrix GT (t).
The RED can be resolved into a multi-population
replicator system[11] of the individual strategies and

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the rules:ẋi1 ···iMτ ẇgbe a tool of designing institutions from the viewpointof evolutionary economics.
= [(Aw)i1 ···iM − x · Aw] ẋi1 ···iM , (9)£¤= λg − λ̄ wg(10)Acknowledgements
This research is partly supported by Canon Foundation Europe; and a Grant-in-Aid for Scientific Research (No.15700183) from the Ministry of Education,
Culture, Sports, Science and Technology of Japan andby the Japan Society for the Promotion of Science.

where the element of a matrix A is (A)i1 ···iM g =ugi1 ···iM . This representation clearly show that RED
is interactions between the individual strategies andthe rules.

5

Conclusion

References

We have proposed a new model for studying the dynamics of social institutions. The model called Rule
Ecology Dynamics (RED). The key concept of RED
is that rules in a society is diverge and ubiquitous, interact with each other and change through behavior
of individuals acting under the rules. The ecologicalinteraction of rules is described by a modification of
the replicator equation. The notable point is to incorporate not only the interactions among individuals
but also those of rules. In RED, an interaction loop
between individual behavior and rules is realized byintroducing a meta-rule. Accordingly, RED is a mathematical model of the micro-macro loop proposed by
Shiozawa[12] to understand economic dynamics.
The formation and the change of social institutions
is a representative of rule dynamics phenomena, which
is a key feature of complex systems. We show that ourmodel is a framework to describe rule dynamics. We
also show that RED describes the interaction (game)between individual behavior and rules.
By setting two kind of meta-rules, the average type
and the inverse standard deviation type, we show thesimulation results that show actually the dynamics
of rules and support certain effectiveness of RED to
study the dynamics of institutions.
We need to promote the relevance of the RED for
describing the real phenomena of rule dynamics. We
can suggest a concrete example of the rule dynamics tobe described by RED. It is changes of monetary credit
systems in Argentina where a local currency, GRT,
has been used. Since the crash of national currency,
Peso, caused by the governmental default in 2002, thecredit of the national and the local currencies, mechanisms to establish and support the currencies, relationship between them, users’ impression about them
have changed. All of these factors constitute institutions and interact with each other ecologically. Thisphenomena may be able to be modeled with RED.
Through such effort, the framework of RED come to©ISAROB 2005

[1] K. Seiyama, Seidoron no kouzu (The plot of institution
theory) (in Japanese), Soubunsha, 1995.
[2] M. Aoki, M. Okuno-Fujiwara (Eds.), Comparative Institutional Analysis: A New Approach to Economic
Systems (in Japanese), University of Tokyo Press,
1996.
[3] T. Hashimoto, Y. Kumagai, “Meta-evolutionary game
dynamics for mathematical modelling of rules dynamics”, Advances in Artificial Life, W. Bnzhaf, et al.
(Eds.), Springer, pp. 107-117, 2003.
[4] P. Taylor, L. Jonker, “Evolutionary stable strategies
and game dynamics”, Mathematical Biosciences, 40,
145–156, 1978.
[5] K. Hashimoto, “Multi-game dynamics” (in Japanese),
Suri-Kagaku, 495, 75-83, 2004.
[6] T. Hashimoto, “Complex systems”, Knowledge Science(in Japanese), K. Sugiyama et al. (Eds.), Kinokuniya
Shoten, pp.126–131, 2002.
[7] N. Kataoka and K. Kaneko, “Functional dynamics I:
articulation process”, Physica D, 138, 225-250, 2000.
[8] N. Kataoka and K. Kaneko, “Functional dynamics II:
syntactic structure”, Physica D, 149, 174-196, 2001.
[9] G. Masumoto, T. Ikegami, “Lambda game system –an approach to a meta game–”, Advances in Artificial
Life, J. Kelemen, P. Sosik (Eds.), Springer, pp.695-699,
2001.
[10] J. Namikawa, T. Hashimoto, “Dynamics and computation in functional shifts”, Nonlineality, 17, 13171336, 2004.
[11] J. W. Weibull, Evolutionary Game Theory, MIT
Press, 1995.
[12] Y. Shiozawa, Fukuzatsusa no kiketsu (Consequencesof Complexity) (in Japanese), NTT Shuppan, 1997.

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Interactive musical editing system for supporting human error
and offering personal preferences for an automatic piano- Preference database for crescendo and decrescendo Eiji Hayashi

Yasuya Takamatsu

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

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

In this research, we analyzed the data of some
performances to investigate pianists’ performance
preferences. The main object of the analysis was data
about crescendos and decrescendos. We considered theparameters that would express preferences. Also, we
structure of the database which stored preferences tohave taken out and developed the automatic editing
system[11].
In this paper, the preference database and adeveloped interactive editing system are described.

Abstract
We have developed an automatic piano that canaccurately control the motion of both the keys and thepedal, based on performance information data that a user
has elaborated for the piece of music. However, thissystem cannot perform a new piece of music by sight, as
in a simulation of a human’s expressive performance,
because the user must first elaborate his or her desiredinterpretation of a new piece of music. Therefore, wehave developed a program that can memorize and use
knowledge databases and the user’s preferences
concerning an interpretation of a piece of music.
In this paper, we describe the interactive music
editing system, the pianist’s analysis of preferences
regarding crescendos and decrescendos, and the resulting
preference database that was developed.
Key words: automatic piano, knowledge database,music interface, user’s preference, computer music

1. Introduction
In recent years, with the progress of electronic
technology, electronic musical instruments aredeveloping rapidly. However, when comparing theperformance of a pianist and the performance of a player
piano in the present situation, the timbre andinterpretation by the electronic instrument are not a good
representation of the live performer. When working with
an electronic piano, the user must arrange the respective
tones at a given tempo, dynamic, and so on. In the caseof piano music, there can be over a thousand notes ineven a simple score, which means a very large amount oftime is needed for editing the input.
We are attempting to develop a performance
information editing support system to edit music more
efficiently[1]～[9]. The system has the function to change
performance information automatically and the function
to provide the user music knowledge and so on (seeFigure 1). We structured the database to store knowledge
about the musical grammar, the score, and the user’s
preferences[10]. Moreover, we have developed an
automatic editing system based on musical rules.
©ISAROB 2005

Figure 1. View of the automatic piano

2. Format of Performance Information
The performance information to be edited is shownin table 1 and table 2.
The automatic piano that we have developed uses a
music data type structure that is similar to MIDI. We
defined performance information, dividing it into two
categories, the notes and the pedals.
There are six note information parameters: the key(note), velo(velocity), gate, step, bar, and timing
involved in producing a tone. The velocity is dynamics,
given by the value of 1～127. The gate is the duration of
the note in milliseconds. The step is the interval of time309

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until the next note, and it also exhibits tempo. The bar isthe vertical line placed on the staff to divide the music
into measures.
There are four pedal information parameters: the key(indicating the kind of pedal, “Damper” or “Shifting”),
velo (the pedaling quantity), time (the duration of
applying the pedal), and bar.

3.2 Editing Support Process
The procedure of the editing by the system is shownin Figure 4.
Temporary music data (TMD) is the data of a pieceof music without expression. For example, the TMD of apart of the first movement of Beethoven’s Sonata No. 8,
which is shown in Figure 5, is shown in Figure 6.
Because expression has not been added, the necessary
editing of the TMD is extensive.
Therefore, the TMD is translated into original music
data (OMD; This data may or may not be automaticallytranslated using databases at the beginning of editing,and after that, the user can start to edit it. The data
structure is similar to that of the TMD.) by the automatictranslation of the system and is provided for the user (seeFigure 7). The automatic translation program uses a
Score Database, Musical Rules Database, and Preference
Database (the details of the databases are described later).
The user adds editing to the OMD and makes slightadjustments. When editing, the system watches over thedata the user enters and music knowledge is provided.
Concurrently, a phrase in the music is discovered. Whenthe phrase with the same pattern exists in the music, it isautomatically translated. After editing, the system
extracts the expressions and the preferences which are
peculiar to the user from the OMD. These expressions
are stored in the Preference Database, which is then used
when editing other music, which results in improved
editorial efficiency.

Table 1. The parameters of note information
Parameter
Key
Velo Gate Step Time BarUnit
m sec m sec m sec Reference 21～108 1～127-

Table 2. The parameters of pedal information
Parameter
Key
Velo Time Bar
Unit
m sec
Reference Damper or Shift 0～127

3. Editing Process with Databases
3.1 System Architecture
The structure of the system is shown in Figure 2. The
user edits music via the user’s interface on the computer
display, which is shown in Figure 3. Also, the user canaccess a database that has musical grammar, the user’s
preferences, and so on. As a result, editorial work is
reduces and efficient editing becomes possible.
User’s Interface (see Figure 3)

Temporary Music Data

Edit
Reference

Automatic translation Musical Rules Database

Databases

Score Database

Computer

User

Automatic translation

Play

Original Music Data
Edit
Automatic Piano

MIDI

Preference Database
Preference extraction

The watch of the phrase and the entry data

Figure 4. The editing procedure

Figure 2. Structure of the editing system

Figure 5. Bars 1~2 of Sonata No. 8

Figure 6. Temporary music data

Figure 7. Original music data
Figure 3. User’s interface on the computer display©ISAROB 2005

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line is the regression line of the notes with a crescendoor a decrescendo. Figure 8-1 is the result of drawing a
regression line except for the note of the melody fromthe performance data. Figure 8-2 is the result
including the note of the melody. The graph may
appear confusing at first, but inspection will revealthat the graph in Figure 8-2 expresses a crescendo
better than that in Figure 8-1.
The considering which was compared by presenting
a sound to this actually in MIDI is included. Figures
8-3 and 8-4 can be more easily understood. The
straight line with 3 colors in the graph is the notes
played with the right hand, the notes played with theleft hand, and the notes of the melody. Therefore,
when we considered the right hand, the left hand, andthe notes of melody and drew the regression line, wewere able to show a linear crescendo (or decrescendo).
Figure 8-4 shows that a crescendo (or decrescendo) isstrongly influenced by the expression mark, thedynamics mark, and so on. In the range of our analysis,
this rule applied to any case.
Based on these results, we developed a Preference
Database that includes crescendos and decrescendos. An
overview of the Preference Database is shown in Table 6.

3.3 Details of the Databases
3.3.1 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 bar, and also ina bar symbol was 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).
Recently, we added “field of the note value” and “field of
the pitch of melody” to the “Element table”. The
overview of the Score Database is shown in Table 3.

3.3.2 Musical Rules Database
This database has the architecture of the musicalgrammar necessary to interpret symbols in musicalnotation. This database is composed of five tables
containing “Dynamics marks”, “Articulation marks”,“Symbol of the Changing Dynamics or the Changing
Tempo (Symbol that affects the speed of a note or theincrease or decrease of the volume.)”, “Time signature”
and “Tempo marks”.
Dividing a music symbol according to every usage
allows efficient information processing by the system.
The overview of the Musical Rules Database is shown in
Table 4.

Table 3. Structure of the Score Database
Table

Summary

Element The position of the note, the role of the note and so on.
The range where the position, the symbol of the symbol show
Symbolan effect
Same
The position of the phrase with the same pattern

Table 4.

Structure of the Musical Rules Database

Table
Summary
ExampleDynamics
This specifies the dynamics of the note.
ff , mpmark
Articulation This changes the length and the strength of one staccato ,
mark
note.
fermataChanging This changes the strength and the speed of therit., cresc.
mark
note in the specific range gradually.
3
TimeDefining both the number of beats in a measure4
signatureand the type of note that fills one beat.
Tempo
This specifies the tempo of the music.
Allegro.
mark

3.3.3 Preference Database
This database contains the expressions of the user's
characteristic performance. The expressions show the
relationship between tempo and dynamic. The basic data
structures used in this database are shown in Table 5.
The “Edit” selection in the user’s interface of Figure
3 gives the user access to the parameters for expression.
A user can edit his or her parameters, and the respective
databases will automatically change at least one of theirparameters.
The main theme of this paper is the development ofthat part of the Preference Database dealing with
“Crescendo and Decrescendo”. We analyzed theperformance data of the pianist to make it, to pick up thefeaturing and expressed parameter. Those analysis results
are described in the following section.

Table 5. Structure of the Preference Database
Table
Summary
Phrase vector The vector* of all notes of the phrase.
Symbol vector The vector* of the note which had a symbol.
Gate rate
The ratio of Gate to Step.(Gate/Step)*The ingredient of the vector is Velo and Step.
Table 6. Preference Database for crescendo and decrescendo
Record

3.4 Analysis of Crescendo and Decrescendo
We analyzed the pianist’s performance data in termsof crescendos and decrescendos to clarify the editorial
tendency of the performance information. The graphs of
the analysis results are shown in Figure 8-1 to 8-4. The
horizontal axis of each graph is time (in milliseconds),
and the vertical axis is velocity (1 to 127).
These graphs are a result of using the music of
Beethoven's “Moonlight” piano sonata. The straight©ISAROB 2005

Summary

ChangeNumKind of Symbol; Cresvendo or Decrescendo
Kind of the dynamics mark which is before andDyNum
behindKind of the articulation mark which is before
PsNum
and behind
Unit
Cresc and Decresc are connecting or no.
NoteValue First note value of the Cresc or Decresc.

311

Slope

Slope of the regression line

Intercept

Intercept of the regression line

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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4. Example of the Automatic Translating
4.1 Case by Musical Knowledge
The TMD of the first movement of Beethoven's
Moonlight Sonata, which is shown in Figure 9, is shownin Figure 10.
Figure 11 is the OMD, which is derived from theTMD, changed automatically using the Musical Rules
Database and Score Database. When observing Figures
10 and 11, OMD was changed from the TMD that
Velocity was constant into the data which the expression
which is certain degree of was added to.

Fig. 8-1

Except for the note of the melody

Fig. 8-2

Include for the note of the melody

MelodyRight
Left

MelodyRight
Left

4.2 Case by User’s Preference
A result of applying the developed Preference
Database for crescendo and decrescendo to the OMD(Figure 11) is shown in Figure 12, which can be seen todiffer from Figure 11. With sufficient editing by such an
automatic translation, the performance can eventually
approach the performance of the user's preference.
Eventually, the editorial efficiency, too, will improve.

Fig. 8-3 Crescendo from “pp”

Fig. 8-4 Crescendo from “p”

Figure 8. The results of analysis

5. Conclusion
We developed a Preference Database specific to auser. Specifically, it stored the preferences regarding
crescendos and decrescendos. Also, we developed ainteractive musical editing system that translates
performance information automatically using thedeveloped Preference Database. Then we input
information about the music to the Score Database.
The added information included the note value andnote of melody. As a result of this improvement, thesystem became able to extract the user's preference
regarding every melody or note value.

Figure 9 Bars 16～18 of Moonlight Sonata

Figure 10. TMD

Figure 11. OMD

References[1] Hayashi, E. et al, “Behavior of piano-action in a grand piano.I”,
Journal of acoustical Society of America, Vol.105, pp.3534-3544,
1999.

Figure 12. Result of applying the user’s preferences[2] Hayashi, E. et al, “Development of a Piano Player”, Proc. of the1993 International Computer Music Conference, pp.426-427, 1993.
[8] Asami, K and Hayashi, E. et al, “An intelligent supporting systemfor editing music based on grouping analysis in automatic piano”,
IEEE Proceedings of RO-MAN’98, 672-7, 1998.

[3] Hayashi, E. et al, “Development of Moving Coil Actuator for an
Automatic Piano”, International Journal of Japan Society for
Precision Engineering, Vol.28 No.2, pp164-169, 1994.

[9] Hayashi, E et al, “Interactive musical editing system for supporting
human errors and offering personal preferences for an automatic
piano”, Proc. of the 7th International Symposium on Artificial Lifeand Robotics, Vol.2, pp 513-516, 2002.

[4] Hayashi, E. et al, “Development of piano player 1st. Analysis ofbehavior of piano-action until string-striking of hummer”, Journalof the Japan Society of Mechanical Engineers, vol.60 No.579,
pp.325-331, 1994. (in Japanese)[10] 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)[5] Hayashi, E. et al, “Development of piano player 2nd. Study on
repetition of same key based on Analysis of behavior of
piano-action”, Journal of the Japan Society of MechanicalEngineers, Vol.61 No.578, pp.339-345, 1995. (in Japanese)[11] Arimatsu, M., Takamatsu, Y. and Hayashi, E., “Development of
Performance Information Edit Support System for an Automatic
Piano”, The bachelor's degree paper of Kyushu Institute ofTechnology, 2004. (in Japanese)[6] Hayashi, E. et al, “Behavior of piano-action in a Grand Piano for an
Automatic Piano”, Proc. of 1st Pioneering International Symposium
on Motion and Vibration Control in Mechatronics, pp.80-85, 1999.
[7] Asami, K and Hayashi, E. et al, “Intelligent Edit of Support for an
Automatic Piano”, Proc. of the 3rd International Conference on
Advanced Mechatronics, pp.342-347, 1998.

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The role of population structure in language evolution
Yoosook Lee∗ Travis C. Collier Edward P. Stabler Charles E. Taylor
Dept. of EEB
Dept. of EEB
Dept. of Linguistics
Dept. of EEB
University of California, Los Angeles
Los Angeles, CA 90095
Abstract

dividuals have an equal probability of learning fromeach other and the fitness contribution of language
is evaluated using the frequency among the entire
population[20, 21]. We are extending this study tostructured populations, which are generally more realistic and offer rich opportunities for diversification.
Our work focuses on the convergence properties ofa population of learners acquiring a language fromone another under different connectivity connectivity
conditions, called topologies. This approach is motivated in part by studies indicating that whom a person learns language from can heavily influence one’s
language [22, 23, 24, 25].

The question of language evolution is of interest to
linguistics, biology and recently, engineering communicating networks. Previous work on these problems
has focused mostly on a fully-connected population.
We are extending this study to structured populations,which are generally more realistic and offer rich opportunities for linguistic diversification. Our work focuseson the convergence properties of a spatially structured
population of learners acquiring a language from one
another. We investigate several metrics, including
mean language coherence and the critical learning fidelity threshold.

1

Breaking the symmetry that a fully-connected population provided makes finding an analytical solutions
much more difficult, though perhaps not impossible.
Therefore, we are using simulations to explore theconvergence properties of variety of distinct topologies. We compare the topologies on several metrics,
including mean language coherence and critical errorthreshold. Our results show that topology has a large
effect on overall convergence and can create stable
multi-language solutions.

Introduction

The question of linguistic divergence is of interest to linguistics[1, 2, 3, 4, 5], biology[6, 7],and engineering communicating networks[8, 9, 10, 11]. For
linguists the question is: “What causes languages tochange[12, 13, 14], and why do humans have so manydifferent languages?[15, 16, 17]”. From an engineering
point of view, how to achieve convergence to a single language in a distributed adaptive system[18, 19]
is an important issue, as in adversarial conditions,where we would like to maintain high coherency among“friendlies” with minimal understanding from the adversary.
More generally, the dynamics of language evolution
provides insight into convergence to a common understanding where distributed learning is a goal. At a theoretical level, these issues are fundamentally similar.
The evolution of language takes on special importancefor robotics and artificial life because it provides a superb platform for studying the emergence of united
behavior from distributed, separate agents.
Previous work on these problems has focused
mostly on a fully-connected population where all in-

The multi-language solutions are a third distinct
phase of local convergence between no-convergence(“Tower of Babel”) on the one hand, where all languages are represented in roughly equal frequencies,and global convergence (“Lingua Franca”), where asingle language and its close variants predominate. In
a multi-language solution, the average individual belongs to a neighborhood predominated by a single language, but no single language dominates across theentire population.
In our paper these simulations are described anddiscussed. Among our conclusions is that local convergence has important implications for developing systems such as sensor networks where adaptive communication between agents in a heterogeneous environment is desirable.

∗ yoosook@ucla.edu©ISAROB 2005
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2

Methods

and the complexity of the grammar, but in the simplified system the learning fidelity is reducible to a transition probability function between grammar Gi andgrammar Gj equal to q for i = j, and (1 − q)/(n − 1)for i 6= j.

Our system is constructed with each individual possessing a parameterized grammar, in the principles
and parameters tradition, which can be encoded asa sequence of symbols. However, for the baseline tests
we report here, each grammar consists of a single symbol and all grammars have the same expressive powerand equal distance from each other. This is a necessary simplification to make our results comparable tothe analytic results from Komarova et. al.[20].

u u
@
¡
@u
u¡

u u u

for each individual i in a population P
set a random language Li of iend forfor each individual i ∈ P
compute fitness fi of iend fordo until number of updates is met
select an individual k ∈ Pselect a random neighbor j of individual kreplace the neighbor j with an offspring of individual k
the offspring becomes an individual j
if the offspring is mutant( mutation rate = µ)get a random language for Lj
else
Lj = Lk
end ifupdate fitness of the individual j
end doDCBAThe algorithm of our program is as follows:

u u
u u

r

r

fully r r fully
connected connected

Figure 1: Topologies: (A) Fully-connected, denoted FC. The
number of connection for each individual nc is N − 1. (B) Linear, nc = 2. (C) A von Neumann lattice with r = 1, denoted
VN, nc = 4. (D) Bridge, which has multiple fully-connectedsubpopulations and a fixed number of connections between subpopulations.

One important metric is the dominant grammar
frequency. We measure this directly each time stepby counting the abundance of each grammar. Whichgrammar is the dominant one may change each time
it is measured; in other words, the dominant grammar
is whichever grammar happens to be at the highest
frequency at the time.
The linguistic coherence, denoted as φ, is measured
using the following equation:

Each individual exists within a topology defining a
set of neighboring individuals. We explore four different topologies: fully-connected (FC), linear, von Neumann lattice (VN), and bridge, illustrated in Figure
1.
The fitness of an individual has two parts: the basefitness, denoted as f0 , and a linguistic merit proportional to the probability that the individual could successfully communicate with its neighbors. In the simplified system, linguistic merit is proportional to thenumber of neighbors which share the same grammar.
In the fully-connected topology, each individual of agiven grammar will have the same fitness, but thisdoes not hold for other topologies.
Specifically, the fitness of individual i, fi , is f0 plus
the sum over each neighbor j of the similarity between
i’s grammar and j’s grammar.

N

φ=

c
1X
(aij + aji )
2 j=1

(2)

Various different “levels” of coherence exist as defined by the set of individuals in nc the second summation occurs over. Local coherence, φ0 , only sums
over the neighbors of each individual and is proportional to mean fitness (equal if f0 = 0). φ1 is thecoherence measured over the set of neighbor’s neighbors, and generally, φi is measured using the set of(neighbor’s)i neighbors. Global coherence, φ∞ , corresponds to summation is over the entire population. In
the fully-connected topology, all of these convergencelevels reduce to the same value.
For the experiments, we used a population size Nof 500, except for the von Neumann lattice which was
a 22×22 torus giving a population size of 484. The
similarity of between languages a was set at .5, thebase fitness f0 was 0, and the number of different possible grammars n was 10. All relevant parameters are
summarized in Table 1.

nfi = f 0 +

n

c
1 X1X
(aij + aji )N i=1 2 j=1(1)

Each time step, an individual is chosen proportional to its fitness to reproduce. Reproduction canbe thought of as the chosen individual producing anoffspring which inherits the parent’s grammar and replaces one of the parent’s neighbors. The offspringlearns the parent’s grammar with a certain learning
fidelity, q. This learning fidelity is properly a functionof the specifics of the learning method the child uses

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n (# grammars)
N (pop size)# subpops
subpopsize# connections# time steps

FC

500

105

Topologies
Linear
VN
0.5
0
10
500
484

106

5 × 105

1

Bridgeq1=0.8360.9

q2=0.925

0.8

0.7

Dominant Frequency

Parameter

500
2
250
10
105

0.6

0.5

0.4

0.3

0.2

Table 1: Parameters. Note that 10 connections of bridge topol-

0.1

ogy are randomly selected from each subpopulation.
0
0.65

The experiments, or runs, are done for a set numberof time steps that varies with topology. The goal is
to make each run long enough that the system willvery probably reach an equilibrium. A set of 5 replica
runs, varying only the random number generator seed,
were done at each q value between 0.65 and 1 at 0.01
intervals.

3

0.7

0.75

0.8
0.85
Learning Fidelity

0.9

0.95

1

Figure 2: The dominant(◦) and average(×) grammar frequency at the last time step of a set of fully-connected
runs, overlaid with symmetric (horizontal line) and asymmetric(curved line) analytic solutions for a = 0.5, n = 10, f0 = 0.

When q1 < q < q2 for a specific q2 , both the symmetric X± and asymmetric X0 solutions exist and arestable. For q > q2 however, only the asymmetric solution where one grammar dominates the populationis stable. This q2 value is the point where X0 = X− ,giving:Analytic Model

For the fully-connected topology given a uniform
similarity a between n different grammars, and thelearning fidelity of q, three equilibrium solutions forgrammar frequency were derived by Komarova et.
al.[20]:X0 = 1/n(3)√(a − 1)(1 + (n − 2)q) ∓ D(4)
X± =2(a − 1)(n − 1)q2 =n2 (f0 + a) + (n + 1)(1 − a)n2 (f0 + a) + 2n(1 − a)(6)

Komarova et. al. provide much more detail andproofs[20]. We plot these solutions and compare themto experimental results in Figure 2.

where

4

D = 4[1 + a(n − 2) + f0 (n − 1)](1 − q)(n − 1)(a − 1)+(1 − a)2 [1 + (n − 2)q]2

The empirical results for the fully-connected topology well match the expectation from the analytic results arrived at by Komarova et. al.[20], as shown in
Figure 2. In the region where only the symmetric solution is stable (q < q1 ), the average grammar frequency is 1/n. The dominant grammar frequency appears high because it is the upper end of a distributionof grammar frequencies which has a non-zero variancedue to the finite population size.
In the bi-stability region (q1 < q < q2 ), a discrepancy between the analytic and empirical results presumably derives from a lack of runs settling at the symmetric solution. With a finite population, the basin of
attraction of the symmetric solution in this region isvery weak. Choosing which individual reproduces each

Below a specific learning fidelity q1 , D is negative
and there is no real solution for X± . Therefore, forq < q1 , only the symmetric solution X0 exists andno grammar dominates. Solving for q when D = 0
determines the critical leaning fidelity threshold q1 ,which corresponds to the error threshold in molecular
evolution.
4 − 2f0 (n − 1)2 − 3n − a(2n2 − 7n + 6)
(1 − a)(n − 2)2
3p2(n − 1) 2 1 + f0 [1 + a(n − 2) + f0 (n − 1)]+(1 − a)(n − 2)2
q1 =

(5)

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Results

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1

0.8

Convergence

1
0.9
q=0.9

0.8

0.4

0.2

0.6

0

0.5

1

q=0.85

Convergence

Dominant Frequency

0.7

0.4
0.3
q=0.8

0.2

0.8

1

6
5
4
Time Step [× 104]

7

8

9

10

Convergence3

Figure 3: Time-series of fully-connected single runs. The dashline(--) is X+ for q = 0.9, the upper dot-dash line(·-) is X+ for

0.8

0.85

0.9

0.95

1

0.8

0.85

0.9

0.95

1

0.8

0.85

0.9

0.95

1

0.9

0.95

1

B: Bridge

0.7

0.75

C: Linear

0.6

0.4

0.2

0

q = 0.85, the lower dot-dash line is X0 . When q = 0.8, only theX0 is stable.

0.8

0.4

0

2

0.75

0.2

0

1

0.7

0.6

0.1

0

A: Fully−Connected

0.6

0.7

0.75

Convergence

1time step is stochastic. This combined with stochastic
learning errors appear to be sufficient perturbation to
make the symmetric solution unstable empirically inthis region.
The time series of single runs with three different
learning fidelities in the fully-connected topology are
shown in Figure 3. These learning fidelities correspondto the three different regions of stable solutions. The
run in the region where symmetric and asymmetricsolutions are possible shows the very weak attractionof the symmetric solution. Even starting with every
individual in the population being initialized to thesame grammar, a dominant frequency of 1, runs at
this learning fidelity settle into a similar pattern (datanot shown).
For topologies other than fully-connected, convergence provides a more clear picture of system dynamics than dominant frequency. Global coherence φ∞correlates very closely with dominant frequency, but
local coherence φ0 corresponds directly to the linguistic contribution to fitness and is directly operated onby the evolutionary process.
Figure 4 shows the coherence values by taking the
average values at the end-points of the 5 replica runs at
each q value. The learning fidelity threshold, or errorthreshold, for the emergence of a dominant grammar
is where indicated by the inflection point in the coherence curve. The emergence of a dominant universal
grammar among the entire population is reflected in
the global coherence curve.
The bridge topology with 2 subpopulations of size
250 and 10 random connections between subpopula-

0.8

D: Von Neumann lattice

0.6

0.4

0.2

0

0.7

0.75

0.8
0.85
Learning Fidelity

Figure 4: Linguistic coherence. The solid line(–) for φ0 , thedash line(--) for φ1 , and the dot-dashed line(·-) for global coherence φ∞ .

tions (Figure 4 B) is indistinguishable from two isolated fully connected populations. It demonstrates avery similar learning fidelity threshold, q1 ≈ 0.84, as
the fully-connected run shown in panel A. However,global convergence is never achieved reliably in excess
to the probability that both subpopulations individually converge to the same grammar by chance. The
up-tick in the φ∞ line at q = 0 is not statisticallysignificant due to this effect. Additionally, φ1 is extremely close to φ0 while φ∞ only rises to approximately 0.5, indicating that there is a large degree ofseparation between many individuals.
For the linear topology shown in Figure 4 C, φ0 and
φ1 slowly rise over the entire range shown, and thetrend extends all the way from q ≈ 0.2 where learning
error makes each offspring essentially random (datanot shown). Since φ1 trends upward along with φ0 ,we can conclude that extended “patches” of individuals with the same grammar form. Near q ≈ 0.99,
φ1 begins to approach φ0 , and φ∞ shows a slight uptick. However, even with perfect learning fidelity, φ∞is only slightly different from the symmetric solution©ISAROB 2005
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1/n. There appears to be no possible global convergence learning fidelity threshold for this topology.
In contrast to the linear topology, the toroidal von
Neumann lattice topology (VN) shows a clear learning fidelity threshold for all three coherence metrics
at q ≈ 0.96. Below this threshold, the VN topology
behaves similarly to the linear topology with an expected decrease in φ0 and φ1 due to the doubling ofneighbors.

5

The fully-connected topology provides the scenario
with the lowest critical learning fidelity, but it also
requires the most intensive communication. A topology with much more limited connectivity such as alattice or clustered graph may still globally converge
with much more limited communication. For many engineering situations such as adaptive sensor networks,
this is an important consideration.
Language in this study is sufficiently abstract thatthese results apply to many situations where agents
adapt by learning from one another and convergenceis desirable. In an adaptive sensor network setting, it
may be beneficial for sensor nodes to adapt their communicative coding and recognition/detection systems
based on the specific topology of deployment and theactual inputs to the network. An evolutionary strategy where nodes adopt successful schemes from theirneighbors with a fitness bonus for agreement is a general option with great promise. Such a system maps
directly onto the linguistic systems we present.

Discussion

Empirical results using agent-based simulations
closely match the analytic results produced by Komarova, Nowak, and others for the fully-connected
topology. However, a relatively small population size
combined with stochastic scheduling and learning errors lead to sufficient perturbations that empirical results show less stability than the pure math would suggest.
The instability of the fully-connected model atlearning fidelities just above the critical threshold q1
tempts the conclusion that human languages exist in
this “edge of chaos” region. Humans exist in complex social connectivity networks, which are probably
closer to the bridge topology. The instability of humanlanguage is more probably related to changing connectivity and topology than a specific learning fidelity.
Topologies other than fully-connected can behave
quite differently. The bridge topology for the parameters we have tested quickly and stably converges to
two independent grammars, one for each subpopulation, above a critical learning fidelity. A linear topology fails to converge to a single dominant grammar,
but does converge to many “patches” that increase insize as q increases. Both of these cases correspond tostable multi-language solutions which do not exist in
the fully-connected topology.
Additionally, the bridge topology has parametersthat quite likely change its dynamics. At a higher
number of connections between subpopulation and/or
a higher number of smaller subpopulations, there is
probably a global coherence threshold.
The lack of apparent learning fidelity threshold forthe linear topology and similarity to the behavior of
the VC topology below its threshold suggests that
there is a critical connectivity value that a regular lattice must exceed before global convergence is possible.
This result, while only hinted at here, would fit very
well with percolation theory. Percolation theory may
also provide insight into what parameterized randomgraph topologies a learning threshold exists for.

6

Summary

We demonstrated the role of topology is criticalin determining the degree of linguistic coherence andthe learning fidelity threshold through empirical studies informed by the theoretical results for an idealized population. The reality of complex population
structure makes evident the importance of topology
in studying the dynamics of language acquisition andlanguage evolution. Further investigation on various
topologies with different parameter settings may provide a more in depth understanding of language evolution and diversification.

Acknowledgements
This work was supported by the UCLA Center for Embedded Network Sensors, the Defense Advance Research Projects
Agency (DARPA), administered by the Army Research Office under Emergent Surveillance Plexus MURI Award No.
DAAD19-01-1-0504, and DARPA MURI award administered by
the US Airforce No. F49620-01-1-0361. Any opinions, findings,
and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect theviews of the sponsoring agencies.

References[1] S. Kirby. Fitness and the selective adaptation of language.
In J. R. Hurford, M. Studdert-Kennedy, and C. Knight, editors, Approaches to the Evolution of Language: Social andCognitive Bases, Cambridge, 1998. Cambridge University
Press.

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[2] K. Smith. Natural selection and cultural selection in theevolution of communication. Adaptive Behavior, 10(1):25–
44, 2002.

[20] N. L. Komarova, P. Niyogi, and M. A. Nowak. Evolutionary
dynamics of grammar acquisition. Journal of Theoretical
Biology, 209(1):43–59, 2001.

[3] G. Jäger. Evolutionary game theory and linguistic typology: a case study. In Proceedings of the 14th Amsterdam
Colloquium. ILLC, 2003.

[21] M. A. Nowak, N. L. Komarova, and P. Niyogi. Evolutionof universal grammar. Science, 291:114–118, 2001.
[22] S. Pinker. The Language Instinct. HarperPerennial, New
York, NY, 1995.

[4] M. A. Nowak, D. C. Krakauer, and A. Dress. An error
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[23] A. Senghas and M. Coppola. Children creating language:
How nicaraguan sign language acquired a spatial grammar.
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[5] N. Grassly, A. von Haesler, and D. C. Krakauer. Error,
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[6] S. Nowicki, W. A. Searcy, M. Hughes, and J. Podos. The
evolution of birdsong: male and female response to song innovation in swamp sparrows. Animal Behaviour, 62:1189–
1195, 2001.

[24] J. Kegl, A. Senghas, and M. Coppola. Creation throughcontact: Sign language emergence and sign languagechange in nicaragua. In M. DeGraff, editor, Language Creation and Language Change:Creolization, Diachrony, andDevelopment, pages 179–237. MIT Press, Cambridge, MA,
1999.

[7] W. J. Sutherland. Parallel extinction risk and global distribution of languages and species. Nature, 423:276–279,
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[25] L. Steels. Social language learning. In M. Tokoro and
L. Steels, editors, The Future of Learning, pages 133–162,
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[8] T. C. Collier and C. E. Taylor. Self-organization in sensor
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linguistic diversity. In J. N. D. Floreano and F. Mondada,
editors, ECAL99, pages 704–708, Berlin, 1999. SpringerVerlag.
[10] L. Steels and F. Kaplan. Stochasticity as a source of innovation in language games. In C. Adami, R. Belew, H. Kitano, and C. Taylor, editors, Proceedings of Artificial LifeVI, pages 368–376, Cambridge, MA, June 1998. The MIT
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[11] T. Arita and C. E. Taylor. A simple model for the evolution of communication. In L. Fogel, P. J. Angeline, and
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485. The MIT Press/Bradford Books, 1993.

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Truth Table Language for Generating Self-replicating Systems
Hajime Harada and Yukihiko Toquenaga
Integrative Environmental Sciences
University of Tsukuba
Ibaraki 305-8572, Japan
E-mail: hajime@pe.ies.life.tsukuba.ac.jp
Abstract

lucky variants in species which later prevail within thesystem. Thus, making perfectly self-replicating ancestral species becomes the key to constructing artificial
self-replicating systems. Additionally redundant androbust language should be used for self-replication inorder to protect against genetic perturbation so as toprevent the core of the self-replication processes fromgenerating variant species which are not capable ofself-replication.
What happens when not all ancestral species are
self-replicating, but some of them reproduce in hypercycles? Hypercycle[9] is recognized as the common
theory of replication cycles, in which each member
catalyzes the replication of the next in the cycle, andmay be dynamic in the sense that members of eachhypercycle change both temporally and spatially. The
coexistence of multiple dynamic hypercycles as an integrated whole can realize a self-replication system.
Such a system may be robust and hence resistant toexternal perturbation, similar to immune systems realized in biological systems. However, several studies have argued that hypercycles seem to be adversely
affected by noise or perturbations, causing errors in
replication, and that they are unstable in regards topersistence at higher phases[10, 11].
In this article, we propose the Truth Table Language (TTL) for generating a self-replication systemthat starts with many self-irreplicapable species and apair of self-replicating species. It has a simple structure, but shows some interesting behavior against external noise.

In Artificial Life studies, a self-replicating system
is considered to have mostly perfect copying capacity.
However this feature is sensitive external to perturbation. We proposed a simple self-replication systemwith self-irreplicable individuals. The system consistsof truth table relations for generating individual notations. The system incorporating 1-D spatial configuration showed cyclic behavior. Within the cycle, thesystem acquired replication redunduncy, or offspring
was of the same species irrespective of which neighboring individual was chosen as a mate. Another feature of this system was its robustness; when one ormore individuals were altered by noise effect, the system absorbed the perturbation. Creating a Truth Table Language (TTL) and a self-replicating system isfor the purpose of orienting to a platform for bioinformatic research. Dealing with TTL systems as study
subjects, we may further propose new techniques foranalyzing TTL systems, which can be used as feedback
into bioinformatics.
keywords: Artificial Life, truth table, redundancy,
cyclic behavior, bioinformatics.

1

Introduction

Most self-replicating systems coined in Artificial
Life studies start from one or several ancestral species
or individuals that are capable of perfectly copying
themselves. In the field of Artificial Life, many experiments, such as artificial chemistry[1, 2], Tierra[3],Avida[4], random access language[5], machine andtape dynamics[6], and binary string system[7, 8], havebeen performed in order to study self-reproduction
systems.
However, these studies focused on perfect replication in self-reproduction. In such systems, perturbation by mutation and other evolutionary operations,which work against the self-copying process, generate©ISAROB 2005

2

Model

For self- or other-replication, we used bitmatching rules applying 16 possible rules in the truth
table (Fig. 1). The system consists of individuals,
each of which is coded by a specified length of a binary bit-string (Fig. 2A). For example, in the case of319

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

a 6-bit length, there were 64 types of individuals inthe system. Pairs were then formed among the individuals for producing offspring. Each individual wasable to select one individual to pair with, so that thedirection went from one individual to another. Individuals in a pair were defined as “Self” and “Mate” toclarify the direction of producing offspring; the Self individual produced offspring with the Mate individual.

- - 0 0 1 -1 1 1 1 1 0- - 1 1 1 -Figure 2: Individual production schema

Figure 1: Truth Table. (A):2-bit input.(B):16 possiblecombinations

locus-wise translation. At the locus point, Self’s locus
bit was the focus for deciding the linking order for
extracting four bits. When the focal locus bit was“0”, the four bits neighboring the locus were linked
in a clockwise sequence, which started from the Self’s
lower bit, through the Mate’s lower and upper bits,
and finished at the Self’s upper bit. When the Mate’s
bit was “1”, four bits were linked in a counterclockwise
sequence. This method, which linked four bits to one
sequence, is not biased, and can extract all sorts of
16 rules on the truth table. An extra step was applied
only to the loci in the head and tail positions. In these
positions, the bit sequence of an individual was defined
as a circular structure, so that the bits of head and tailloci were defined as adjacent bits for the execution of
the above extraction methods. When the head locus
was focal, the tail locus was considered adjacent, andvice versa. Thus, the production of a new individual
was completely deterministic and depended solely on
the bit-patterns of the two parent individuals.

The bit pattern of an offspring was determined bylocus-wise bit-matching between the two aligned bitstrings of the parents (Fig. 2B), and each individual
had a head and tail position in the bit pattern. The
length of individuals in the system was the same, sothat the alignment of two individuals’ bit patterns was
simply a matter of aligning each individual’s head or
tail position. After alignment, each locus had a position in one of four possible patterns (00, 01, 10, or 11)(Fig. 1A).
At this point, we considered the bit pattern as
input for producing a 1-bit pattern of the offspring’s
locus. When an offspring’s 1-bit pattern is determinedusing a pair’s 2-bit pattern on one locus, there are
16 patterns possible (there are 2 single-bit patterns,
and there are four 2-bit patterns for one locus. Thus
the combination is 24 = 16). These input and output
patterns are just as a 2-state and 2-input truth table(Fig. 1B).
The bit-pattern at a locus (00, 01, 10, or 11) was
then decoded with one of the 16 possible truth tablefunctions for 2-bit patterns. To make the truth table,
we needed to extract four bits, which were used for
deciding the new 1-bit pattern (Fig. 2B). The patternconsisted of four bits, surrounding the target locus,
used to determine which truth table was applied for©ISAROB 2005

1 1 0 0 1 0

3

TTL Systems

In this system, exactly one offspring was generated at each reproduction event. The parent individual was then replaced by the newborn offspring, and

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

hence, the total number of individuals was kept constant. As each individual had a fixed length, the number of pairings between Self and Mate individuals was
finite. For example, the number of pairings (sorts)
between 6-bit individuals was 4, 096(26 × 26 ), becauseof the existence of a one-way production direction for
Self individuals. As can be seen by the absence of agrayscale line running diagonally from the top left tothe bottom right of the diagrams in Figure 3, perfectly
replicating individuals did not, for the most part, exist within the system. Only the pairs of each 00...0s
and 11...1s individuals were able to produce offspring
identical to the Self and Mate individuals’ bit strings.
A few pairs were able to reproduce an individual identical to Self, though these were not perfect copies inthe sense that Mate’s bit string was not identical tothe offspring’s, and vice versa.

111110100001100101111100111111

10
00

1111

Results

The results obtained by incorporating 1-D spatial
restrictions are as follows. The composition of individuals in the loop at the state of convergence was not estimated from these initial compositions. Neverthelessthe production process by the parents is predictable.
Although the initial configuration strongly influenced
the system, the diversity of emerging individuals wasmaintained.
The cyclic behavior of individual production was
revealed either when all individuals in the loop had
the same bit-string, or when a Self individual was able
to reproduce an individual with a bit-string matching that of the Mate, irregardless of whether or not
it matched the Self’s bit-string. Within the cycle,
the same species was reproduced as an offspring irrespective of which neighboring individual was chosen
as Mate (Fig. 5).
Another feature of the system with 1-D restrictions is its robustness. When one or more individuals
were replaced by randomly selected individuals of adifferent species, the system absorbed the perturbation. Individual replacement is the same as noise injection directly onto a bit-string (Fig. 6). In Figure 6,
after the first replacement occurred, the inserted individual reproduced a bit-string different from that ofthe previous series. Replacement did not seem to havea wide range of effects in the loop domain. The loop
structure has absorbing capabilities. This behavior

111111Figure 3: The matrixes of individual combinations:Individuals ordered as binary digits are suggested asgrayscale. (00...0): white, (11...1): black. Each matrix shows the individual length: (A)3, (B)4, (C)5,and(D)6.
If there was no spatial structure (Fig. 4A), a
small number of bit-patterns (species) were able topersist. After a transient phase, the system dynamics converged into cyclic behavior; each location in theloop was occupied by a specific series of individual
species. The word “convergence” does not mean astable state in which an individual is producing an
identical individual with same bit-string, but rather
that the group, which consists of specific individuals,
is sequentially producing individuals of each other, inwhich case the dynamics of production by the groupare closed. Thus, we incorporated a 1-D spatial constraint; individuals were arranged in a loop and a reproducing individual interacted with one of its one-

©ISAROB 2005

11

11

001

11111

101

000000000000

101

11111

11

100

00000

11

Figure 4: Spatial settings. (A): non-spatial. (B): 1-D
spatial restriction

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step neighbors (Fig. 4B).

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influences the phenomenon of early convergent generations. Thus, the loop of individuals achieved a stable cyclic transition with redundant reproduction, andproved to be robust in regards to external perturbation.
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show neither the informational relation between thefunction of the TTL system nor the biological meanings between an artificial string and a biological string.
If these bioinformatic tools suggest the homology ofsome genes, they may find some “biased” patterns affected through production within a TTL system (Fig.
7).
What is the sequence pattern? How does the pattern emerge? The bioinformatic tools are considered
to “· · · look for patterns and make predictions without a complete understanding of where biological data
comes from and what it means[12](p.4).” However thesupreme goal of genome sciences should be to solve thealgorithms that realize homologous or similar sequence
patterns in the comparisons between extracted DNA
data.

Discussion

The proposed TTL system with 1-D spatial structure showed robustness in regards to external perturbation and redundant reproduction as a whole. However, it was not our intent that these phenomena
emerge during the process of individual production.
The structure of each individual as well as the TTL
system applied to individual production are simple,
and are not intended to intrinsically replicate perfectly.
We may be able to extract one generation fromthe results of the 1-D restriction system, and consider
it as a coded biological string, such as a DNA string, bytransposing the string into a DNA sequence and analyzing it with bioinformatic tools. If meaningful results
are produced, we must ask ourselves how they should
be interpreted. These bioinformatic tools merely suggest the homology between the strings and the character data coded by the base (A, T, G, and C). They

©ISAROB 2005

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Figure 6: Noise effect on 1-D spatial restriction.

Figure 5: The result of selection behavior. (A): First
cycle. (B): Second cycle. Despite random individual
selection, each cycle has the same individual production.

5

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6

Future Direction

The TTL system is limited in fixed individual
length for individual production, and so requires system improvements. Creating the TTL and a selfreplicating system is not our final goal. In this paper,
we proposed the present self-replicating system as aplatform for bioinformatic research.
• Generally, one can only infer the evolutionary
events from real biological data. However, thepresent system includes true trees and lineages
for specific evolutionary events, and all histories

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

mussen S, editors, Artificial Life II, pages 371–
408. Addison-Wesley, Redwood City, CA.
[4] Adami C. (1998). Introduction to Artificial Life.
TELOS, Springer–Verlag, New York.
[5] Rasmussen S, Knudsen C, Feldberg R. (1992).
Dynamics of programmable matter. In Langton CG, Taylor C, Farmar JD, Rasmussen S, editors, Artificial Life II, pages 211–254. AddisonWesley, Redwood City, CA.
[6] Ikegami T , Hashimoto T. (1995). Active mutation in self-reproducing networks of machines andtapes. Artificial Life, 2(3):305–318.
[7] Banzhaf W. (1993). Self-replication sequenceof binary numbers. foundation I and II: Generaland strings of length n=4. Biological Cybernetics,
69:269–281.

Figure 7: An example of BLASTN results with
TTL system output (extended neighborhood selection:
length 1→2). http://www.ncbi.nlm.nih.gov/BLAST/[8] Dittrich P, Banzhaf W. (1998). Self-evolution in aconstructive binary string system. Artificial Life,
4:203–220.

for the “life”. Such a system may prove to beadvantageous in analysing evolutionary events inmore detail.

[9] Eigen M, Schuster P. (1977). The hypercycle. a principle of natural self-organization. parta: Emergence of the hypercycle. Naturwissenschaften, 64:541–565.

• There are biases in the usage of truth tables similar to the codon bias in DNA data. These biases
suggest that the TTL system does not merely output data at random. Thus, it is the meaningfulexperiments that we try to analyze these outputs,
which have been generated randomly by initial
settings.

[10] Barbieri M. (2003). The Organic Codes : An
Introduction to Semantic Biology. Cambridge.
[11] Szathmáry E, Smith JM. (1997). From replicators to reproducers : the first major transitions
leading to life. Journal of Theoretical Biology,
187:555–571.

Dealing with TTL systems as study subjects, we
can evaluate the conventional techniques for analyzing
molecular data. We may further propose new techniques for analyzing TTL systems, which can be usedas feedback for bioinformatics.

[12] Gibas C, Jambeck P. (2001). Developing Bioinformatics Computer Skills. O’REILLY.

References[1] Fontana W. (1992). Algorithmic chemistry. In
Langton CG, Taylor C, Farmar JD, Rasmussen S,
editors, Artificial Life II, pages 159–209. AddisonWesley, Redwood City, CA.
[2] Dittrich P, Ziegler J, Banzhaf W. (2001). Artificial chemistries — a review. Artificial Life,
7(3):225–275.
[3] Ray TS. (1992). An approach to the synthesis oflife. In Langton CG, Taylor C, Farmar JD, Ras-

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Effectiveness of Emerged Pheromone Communication in an Ant Foraging Model
Yoshiyuki Nakamichi
Graduate School of Human Informatics
Nagoya University

Takaya Arita
Graduate School of Information Science
Nagoya University

Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

nakamiti@create.human.nagoya-u.ac.jp

arita@nagoya-u.jp

Abstract

doesn’t function at all until the following two rules autonomously emerge in accordance with each other: “In
what kind of situation should the agents secrete somekind of pheromone?” and “How should they act when
they detect some kind of pheromone?”.
To pursue this issue, some models have been proposed, including the pioneering model by Collins andJefferson in which the mechanism for the evolution ofpheromone communication was implemented [5] anda simple model by Kawamura and Ohuchi in which
pheromone communication emerged in the environment where two colonies of artificial ants competed
for food resources [6]. Based on these studies, we developed an ant foraging model, in which there canbe several kinds of pheromone [7]. This paper reports our current state of the research based on thecomputer experiments using a simple model for evolution of pheromone communication, in which neuralnetworks of ant agents evolve based on the result of
foraging.

The collective behavior of social insects has been apuzzling problem for scientists for a long time. In particular, it is well known that ants solve difficult problems, for instance selecting the shortest pathway bycommunicating with each other via pheromone. How
is it possible for such simple creatures to coordinate
their behaviors and to solve problems as a whole? This
paper focuses on the emergence of the pheromone communication system based on an ant foraging model inwhich neural networks of ant agents evolve according
to the result of foraging. The computer experiments
show that the ant agents using emerged communication with one type of pheromone are more adaptive
than the ant agents not using pheromone communication or the ant agents using human-designed communication with 2 types of pheromone. This paper also
discusses the reason for this superiority of the evolved
pheromone communication.
Key words: ant colony, pheromone communication,
swarm intelligence, artificial life.

2
1

Introduction

This section offers the brief summary of our ant
foraging model which was proposed in our previous
paper [7]. The model is based on multi-agent modelingand is inspired by foraging ants in nature. Foraging
ant agents move around the X × Y grid environment
in which there are food resources, pheromone and anest. Each colony consists of Na ant agents and theirobjective is to look for and carry to their nest as many
food resources as possible.
Ant agents could secrete several types of pheromone
in the same environment. The type of pheromone
is identified by the subscript v (v = 1, 2, · · ·). They
have no effect on each other. Each ant agent candrop pheromone on the ground by dropping action.
Dropped pheromone gradually evaporates and diffusesin the air. Ant agents can detect diffusing pheromone
only. Dropped pheromone and diffusing pheromone atposition (x, y) are represented by Tv (x, y) and Pv (x, y)
respectively. Then the diffusion process is defined by
the partial differential equation as follows.

Complex and adaptive behavior of population
emerges in social insects like ants and bees, though
individuals seem to follow a relatively simple set ofrules. The last decade has seen an explosion of research in fields variously referred to as swarm intelligence or collective intelligence. Especially in ants,
pheromone communication is the key to understanding
their swarm intelligence. The fundamental question
regarding pheromone communication is: How have thepheromone communication systems emerged? This
topic is not only a biological one, but, at the sametime, a cross-disciplinary one [1, 2, 3, 4].
As a first step towards the origin of pheromone
communication, we focus on the adaptive propertyof pheromone communication in population of agents,
depending on the kind of pheromone communication.
There can be two computational approaches to thisissue, one is to define, a priori, both the meaning ofpheromone and the rules of behavior for agents, andthe other is to let them establish communication autonomously through learning or evolution. The latter
is effective especially when the specific knowledge forsolving the problems is limited or unknown. However,
it is quite difficult to establish communication withouta priori knowledge because pheromone communication©ISAROB 2005

The ant foraging modelTv∗ (x, y) = (1 − γeva )Tv (x, y) +∆Tvk (x, y) =

(

Qp

0
324

Na
X

∆Tvk (x, y) (1)k=1

if k-th ant agent on the grid(x, y) put the pheromone v (2)otherwise

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

where the function Pv# indicates the average intensity
among neighbor grids.
The output value Oj (j = 1, · · · , 5 + 2V ) of j-th
output neuron is calculated by using following regularequations.
!
3+4VX
Oj = f(9)
wij Ii − θjPv∗ (x, y) = Pv (x, y) + γdif (Pv (x − 1, y)+Pv (x, y + 1) + Pv (x, y − 1) + Pv (x + 1, y)−5Pv (x, y)) + γeva Tv (x, y)(3)
where the parameters γeva and γdif are the evaporation rate and diffusion rate of pheromone per a time
respectively. Also, the letter “*” in superscript meansthat this represents intensity of pheromone at timet + 1, and Qp is the intensity of pheromone dropped
by an ant agent.
All ant agents in each colony are homogenous, andhave the same mechanism for action decision. Eachagent performs actions in the following order. 1) The
ant agent senses whether a food resource exists on thegrid, senses whether the grid is a part of nest, andrecognizes whether it is carrying a food resource. 2)
The ant agent might drop a certain type of pheromone
depending on the output of the neural network. Eachant agent can use V types of pheromone. In case V =2, ant agents can use pheromone v = 1 and pheromone
v = 2 in the experiment. 3) When there is a foodresource, if the ant agent carries no food, it picks it up,
and if the ant agent has a food resource and is on thenest, the ant agent drops it. 4) If the ant agent did not
perform the previous action, it selects and perform one
action from 5 options according to the output of theneural network: “wait (do nothing)”, “move forward”,“turn backward”, “turn left”, “turn right”. If the ant
agent selects an action that is impossible to do, it waits
instead.
All ant agents have the same simple two-layer neural network with 3 + 4V sensory input and 5 + 2V
output neurons. Action selection is stochastically decided according to the probability from output valueof a neural network.
The output value Ii (i = 1, · · · , 3 + 4V ) of i-th sensory input neuron of an ant agent is defined as follows.

1 if food is present
I1 =(4)0 otherwise
I2 =

1 if the ant agent is on the nest
0 otherwise

(5)

I3 =



1 if the ant agent has food0 otherwise(6)

I4+4(v−1)∼7+4(v−1) =i=1

f (x) = 1/ (1 + exp(−x))

where wij and θj are the weight and the threshold of
the neural network. The values of 5 outputs (O1∼5 )correspond to “wait”, “move forward”, “turn backward”, “turn left” and “turn right” actions respectively and the value of 2V outputs (O6+2(v−1) andO6+2(v−1)+1 ) correspond to “dropping pheromone v”
and “doing nothing about pheromone v” actions.
The genetic algorithm (GA) is adopted to evolve
the neural networks of ant agents. A set of weights
and thresholds describing a neural network is directlyencoded to each genotype. The population size is N
and all genotypes are initialized to random values of[−0.5, 0.5]. A fitness function is defined to be 1 plus
the number of food resources that have been stored in
the nest. New generation with N individuals is generated by the roulette wheel selection and mutation.
The mutation operator is defined as adding a random
real number of [−0.5, 0.5] to the value in each locus
with the mutation probability Pm . These processes
are repeated until the final generation gmax .

3

1 if the Pvs is satisfied(7)
0 otherwise©ISAROB 2005

1
Pv (x0 ,y 0 )−Pv# (x,y)
1 + exp −T

Computer experiments

The parameters of ant foraging and GA were set
as (X, Y , Na , tmax , Qp , γeva , γdif , T ) = (50, 50, 40,
1000, 1, 0.1, 0.1, 0.002) and (N , gmax , Pm ) = (20,
2000, 0.05). The size of the nest was 5 × 5 and it
was located in the center of the environment. 72 foodresources were put on a randomly selected 3 × 3 areaat the time t = 0. Each grid in the area had 8 foodresources. Under these conditions, several trials wereconducted for each V (V = 0, 1, 2).
Fig. 1 shows the moving average of food resources
stored in the nest. It increased sharply to the range
between 7 and 10 by 3000 generations in all graphs,
and from then on, transitions differed depending on
the parameter V . In the case of V = 0, most trials
showed that each moving average remained at around
7. In the case of V ≥ 1, transitions were classified into
two typical classes, and thus, it would be supposedthat there are at least two evolutionary pathways. One
was the case that moving average remained between
7 and 10, which is similar to the case of V = 0. The
cases 1a and 2a correspond to this class. The otherwas the case that moving average increased beyond
10. The cases 1b and 2b correspond to the class. The
moving average had a tendency of swinging between
15 and 25 in 1b and it gradually increased beyond 7
in 2b. We see that the case 1b outperformed the othercases.

Here, the position of an ant agent is assumed to (x, y),and its neighbor position is (x0 , y 0 ) ∈ {(x − 1, y), (x +1, y), (x, y − 1), (x, y + 1)}. Pvs (x0 , y 0 ) is the probability to fire input neuron of pheromone sensory input
neurons. This stochastic firing function is used whenobtaining the discrete values of I4+4(v−1)∼7+4(v−1) ,which depends on the gradient of density of pheromone
and is defined as follows.
Pvs (x0 , y 0 ) =

(10)

(8)

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The number of food resources stored in the nest

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
25

start

20

sense the environment and its own state15

if there is food

0

10

true

drop pheromone 1(food pheromone)truedrop pheromone 2(nest pheromone)

false
5

if there is the nest
false

0
0

5000

10000

15000

20000

Generation

if thereis food while the ant
has no food

The number of food resources stored in the nest(a) V = 025

truepick a foodresource up

false
20

15

true

if there is thenest while theant has food

1b10

drop a foodresourcefalse

5

if the ant has food

1a

true

false

0
0

5000

10000

15000

mova to the nest
according topheromone 2

20000

move to food according to pheromone 1

Generation

The number of food resources stored in the nest(b) V = 1
25

end

20

15

2b

Figure 2: Behavior of human-designed ant agents.

2aTable 1: Average number of stored food resources.

10

5

0
0

5000

10000

15000

20000

Ant agent
0 (V = 0)
1a (V = 1)
1b (V = 1)
2a (V = 2)
2b (V = 2)
human-designed

Generation(c) V = 2
Figure 1: Moving average of the number of stored foodresources.

It was found from the analysis of neural networkthat the ant agents in case 1b behaves as follows.
Overall, the ant agents move forward with probability of about 90% and it turns right with probability of
about 10%. However, the ant agents turns right withprobability of about 40% only in cases in which they
exist on the falling gradient of pheromone. Therefore,
we can assume that the pheromone moderately indicates the presence of both food and a nest. The ant
agents without food hardly secrete pheromone. The
ant agents with food secrete pheromone with the probability of 40% and they on the nest secrete pheromone
by the probability of 20% even if they aren’t carrying
food. Therefore, the pheromone moderately indicatesthe presence of both food and a nest.
We conducted additional experiments on the ant
agents with human-designed pheromone communication (Fig. 2) in order to evaluate the effectiveness of emerged pheromone communication. The ant
agents used two types of pheromone (food pheromone
and nest pheromone) and were expected to do shuttling behavior between the nest and the food locations efficiently by using them. In the experiments, human-designed ant agents and the evolved ant
agents (in the 20000th generation in all cases shownin Fig. 1) performed the foraging task 1000 times
each. It is clearly shown from Table 1 that evolved©ISAROB 2005

Average
8.8
18.9
28.0
14.4
24.7
12.4

pheromone communication outperformed the humandesigned pheromone communication, in other words,
some clever pheromone communication beyond humandesign emerged through evolution.

4

Superiority of emerged pheromone
communication

The results of the first experiments are summarized
as follows. The ant agents using pheromone (V ≥ 1)
could be clearly more adaptive than the ant agents
without pheromone (V = 0). In other words, emergent
pheromone communication could be adaptive. However, the results also show that it is not necessarily
true that the more the number of available pheromone
type increases, the more the ant agents could be adaptive. This might be because of the difficulty in establishing the meaning of pheromone or communication
protocol based on multiple types of pheromone. We
believe that more sophisticated and powerful mechanisms of learning or evolution could make pheromone
communication work better.
The results of the second experiments show thatemerged pheromone communication outperformed
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Nest

Ant agent

Nest pheromone

Food

Food pheromone(a) The ideal state(b) An ill-balanced state(c) An ill-balanced state

Figure 3: The ideal and the ill-balanced states in the case of 2 types of pheromone.

human-designed pheromone communication. Why
did emerged pheromone communication outperform
human-designed one?
To answer why emerged
pheromone communication performed better, we observed the behavior of ant swarms and the spatial distribution of pheromone across the environment.
In the case of human-designed ant agents, it seems
an ideal state that equal amount of two types ofpheromone exist in the environment (Fig. 3 (a)).
However, we found that ill-balanced states (Fig. 3(b) and (c)) frequently happened, which mean inefficient concentration of the ant agents around the foodor the nest. This unexpected result shows the difficulty in controlling the behavior of ant agents by using pheromone communication, which was caused by
its emergent property.
In contrast, ant agents hardly crowded the foodor the nest in case 1b. The results shown in previous section suggests that the ant agents in case 1b
behaved as follows. Ant agents secrete one type ofpheromone with high probability both when they arein the nest and when they have food. So, in this case,the pheromone moderately indicates the presence ofboth food and the nest in the emerged pheromone
communication. Ant agents have evolved to have atendency to move to the places with more intensive
pheromone. Therefore, ant agents move around the
peripheries of both food and the nest. These ant
agents don’t behave optimally, because it has no wayof distinguishing food and nest by using only one typeof pheromone. However, it is a significant fact that we
hardly observe the ill-balanced states which appeared
frequently in the case of human-designed ant agents.
Above discussion is summarized as follows. Whilethe uniquely existing type of pheromone acquired amoderate meaning of the presence of both food and thenest through evolution, the diversity of pheromone distribution and the diversity of the ant agents’ collective
behavior got to be maintained at proper levels, which
realized the robust foraging behavior of ant agents.

pheromone communication or the ant agents using human-designed communication with 2 types ofpheromone. The reason for the superiority of theevolved pheromone communication is the diversity ofpheromone distribution and the diversity of the ant
agents’ collective behavior got to be maintained atproper levels, which realized the robust foraging behavior of ant agents.

5[7] Y. Nakamichi and T. Arita, “An evolutionary
simulation of the origin of pheromone communication,” in Proceedings of the 5th International
Conference on Simulated Evolution and Learning,pp. 1–6 (STP–1–93), 2004.

Acknowledgments
This work was supported by a Grant-in-Aid for the
21st Century COE “Frontiers of Computational Science”.

References[1] T. Arita, Artificial Life: A Constructive Approachto the Origin/Evolution of Life, Society, and Language. IGAKU-SHUPPAN, revised 2nd ed., 2002.
[2] M. Dorigo, Optimization, Learning and Natural
Algorithms. PhD thesis, Politecnico di Milano,
1992.
[3] E. Bonabeau, M. Dorigo, and G. Theraulaz, “Inspiration for optimization from social insect behaviour,” Nature, vol. 406, pp. 39–42, July 2000.
[4] D. Payton, M. Daily, R. Estkowski, M. Howard,
and C. Lee, “Pheromone robotics,” Autonomous
Robots, vol. 11, no. 3, pp. 319–324, 2001.
[5] R. J. Collins and D. R. Jefferson, “Antfarm: Towards simulated evolution,” in Artificial Life II(C. G. Langton, C. Taylor, J. D. Farmer, and
S. Rasmussen, eds.), pp. 579–601, 1992.
[6] H. Kawamura and A. Ohuchi, “Evolutionary
emergence of collective intelligence with artificial pheromone communication,” in Proceedings of
IECON-2000, pp. 2831–2836, 2000.

Conclusion

This paper describes the ant foraging model forthe evolution of pheromone communication and discusses the result of the computer experiments. The
computer experiments show that the ant agents using
emerged communication with one type of pheromone
are more adaptive than the ant agents not using©ISAROB 2005

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Foraging Behavior of Ant-Like Robots with Virtual Pheromone
Ken Sugawara1 and Toshiya Kazama2
Department of Liberal Arts, Tohoku Gakuin University, Sendai, JAPAN
2
Graduate School of Information Systems, Univ. of Electro-Communications, Tokyo, JAPAN1

Abstract

munication but also for indirect communication. In insect world, pheromones are used for various purposes:
alarm, aggregation, sex attractant, recruitment, defense, trail-making, and so on.

In multi-robot system, communication is indispensable for eective cooperative working. To apply chemical method to the communications of multi-robot system is challenging topic, but it is not easy to treat
real chemical materials for the robots because of sometechnical diculties. In this paper, we show virtual
pheromone system in which chemical signals are simulated with the graphics projected on the oor, and inwhich the robots decide their action depending on thecolor information of the graphics. We examined theperformance of this system through the foraging task,
which is one of the most popular tasks for multi-robot
system.

2 Properties of Pheromone
Important characteristics of pheromones as a chemical signal are described as follows:

Marking

Most pheromones do not disappear immediately andremain in the eld for some time. It means agentscan share an information based on the pheromones
even in the absence of signal source agent. Foraging
ants, for example, move from the food source to theirnest laying "recruit pheromone" while food remains at
the source, and they can nd out the food eciently
thanks to this property.

1 Introduction
Recently, research in the area of multi-robot systems has been very active, and many researchers
are currently studying the behavior of these types ofsystems1, 2]. One of the most important aspects ina multi-robot system is the ability of robots working
cooperatively. Working together, they complete tasks
that a single robot cannot.
For eective working, mutual communication between units is indispensable. Many researchers haveintroduced the direct communication, and in mostcases, physical media such as light, sound, radio wave
are used for the communication. Those media are also
employed in biological system: for example, reies
make use of light as a mating signal, and crickets use
the sound as the signal. However, as we know, not only
physical signals but also chemical signals are used forthe communication. It is well-known that some insects
use the chemical signals, which are generally calledpheromone. They show very interesting behaviors depending on the properties of pheromones. One of themost popular pheromone-controlled behaviors is foraging of ants. Here, pheromone released by organism
enables individuals to communicate with each other.
Pheromones are available not only for direct com-

©ISAROB 2005

Di usion

Most pheromones are volatile and spread over a large
area. This characteristic is used for long range communication, for generating chemical gradient eld, andso on.

Evaporation

Pheromone disappears by evaporation because of its
volatility. This characteristic leads to the erase ofneedless/useless information. In case of ant societies,
less food remains at the food source, less pheromone
the ants lay. As a result, they can avoid useless energy
consumption for foraging.

Diversity

There is a lot of materials which are used as
pheromones, and moreover, some of them are used in
combination. In ant societies, for example, each individual can distinguish its colony's mates and ones ofother colonies based on the dierence of mixture rateof some pheromones.

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3 Virtual Dynamic Environment forAutonomous Robots (V-DEAR)

tween the environment and the robots.
In this paper, we will show you the performance of
this system through the foraging task by the interacting robots. This is one of the most popular tasks inthe study of multi-robot system, but almost all of themtreat physical communication methods4, 5, 6, 7, 8].

As described above, communication by chemicalmaterial such as pheromone has some interesting characteristics that physical communication system does
not have, but few researches treat real chemical materials as a communication media for physical robot
system3]. We can consider following reasons. At thisstage, it is not easy to treat chemical material comparing with the physical medium, and it is also not easy
to get proper chemical sensors. Moreover, chemicalmaterials, especially gas, are invisible and it is quitedicult to observe how they spread and aect robots'
behaviors.
Here we propose "Virtual Dynamic Environment
for Autonomous Robots (V-DEAR)" for real robot experiment. In this system, pheromones are replacedwith graphics projected on the ground. Robots decide their actions following the color information of
the projected CG. As virtual pheromones are represented as CG, we can avoid the problems describedabove. In addition, we can easily control the rate ofdiusion, evaporation, diversity, etc. of the virtual
chemical materials.
Fig. 1 shows the schematic and photo of this system. This is composed of LC projector to project the
CG and the CCD camera to trace the position of therobots in the eld. The robot moving on the eld has
sensors on the top to detect the color and brightnessof the eld, and determine its actions autonomously
based on the condition of the CG on the oor.

4 Experiment
4.1 Robot for Experiment
Robot

Each robot has the ve fundamental behaviors as follows: "searching," "attracted," "recruiting," "homing," and "avoidance" (Fig. 2). When a robot insearching state nds food, it stays there for a shorttime and picks up some of the food. After that, it
moves to home leaving a pheromone. When arriving
at home, it lays the food there and goes searching foodagain. The robot which detects the pheromone follows
the trail, if it has no food.

Figure 2: Fundamental robot behaviors. "Recruiting","searching," "attracted","homing," and "avoidance."
The robot used in this experiment is shown in Fig.
3. It has touch sensors extended to 8-directions todetect collision, a pair of infrared sensors to return
to home, three color-sensors to detect eld condition,bottom sensors to detect "Home", and 1 LED to indicate its state.

Figure 1: Virtual Dynamic Environment for Autonomous Robots (V-DEAR). Schematic (left) andphoto (right).

Pheromone

Dynamics of pheromone is described as_ =  + Dr2 ; k (1)where is the concentration of pheromone,  is injection concentration, D is a diusion coecient, andCombining the position information of the robots
acquired from CCD camera and the projected CG byprojector, we can realize the dynamic interaction be-©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

this experiment, we chose homogeneous and localized
distributions.
In homogeneous distribution, 24 food points areprojected on the eld. When a robot in searching state
detects the color of food point, the robot turns on the
LED on the top and changes its state to homing state.
When the V-DEAR system detects the LED, it erases
the corresponding food point and starts to draw thevirtual pheromone following the robot's homing route.
In localized eld, the behavior of robots is sameas the case of homogeneous distributed eld, but thequantity of food is assumed to be in nite, i.e. the foodpoint is not erased.

Figure 3: Robots detect the eld condition by sensors
on the top.
k is the rate of evaporation.

Fig. 4 shows the basic behavior of the foraging
robots. On discovering a food, the robot turns on
a LED on the top and moves towards the nest. The
V-DEAR system detects the LED and projects a CG
pheromone trail during the LED is turned on. Whenthe robot arrives at the nest, it turns o the LED
and changes into the searching state. If it nds thepheromone trail, it follows the trail.
Here you can see the trail pheromone, or a bandof light is projected following the homing route of therobot (a), and a robot traces a band (b) in Fig. 4.

4.3 Results
Fig. 5 is the snapshot and the trajectories ofthe foraging by a robot in the homogeneously fooddistributed eld, where Fig. 5(a) and (b) are thecase of (D = 0:05 k = 0:1) and (D = 0 k = 0),
respectively. In case that the evaporation rate of thepheromone is high, the pheromone hardly remains. On
the other hand, when the evaporation rate and the diffusion coecient are too low, the pheromone remains
clearly in the eld. Here the trajectory shows that the
robots are trapped by their own pheromone and tend
to do useless searching.
Fig. 6 is the snapshot of the foraging by two robots
in the locally distributed eld, where g. 6(a) and(b) are the case of (D = 0:15 k = 0:01) and (D =0:15 k = 0:002),respectively. In case that the evaporation rate of the pheromone is high, the pheromone
hardly remains. However, the evaporation rate is low,a stable trail is formed between the food point andtheir nest. In this case, the robots can search andcarry the foods by tracing the laid pheromone eectively, which can be observed from the trajectory.
Fig. 7 shows the relation between the evaporation
rate and the number of collected foods. Fig. 7(a)is the result of homogeneously food-distributed eld,and (b) is the result of locally food-distributed eld.
The parameter in these graphs is the number of robots(13).
These graphs clearly show that less pheromone isleft in the eld, more foods are collected in homogeneous eld, and that the performance becomes better
when a pheromone trail is formed continuously in localized eld.

Figure 4: Basic behavior of the foraging robot. (a)On discovering food, the robot lays chemical trail
while returning to nest. (b) The robot detecting thepheromone follows the trail.

4.2 Field for Experiment
Field

The eld for this experiment was an 90  90 cm blacksurface with the wall at the boundary. A nest is represented with a yellow circle on the ground and IR-LED
array is placed there.

Food distribution

We can consider various types of food distribution. In©ISAROB 2005

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5 Conclusion
In this paper, we treat indirect communication bychemical signal between the robots, and investigate
a foraging behavior by multi-robot system. Here we
proposed a Virtual Dynamic Environment for Autonomous Robots (V-DEAR), where we can simulate
chemical system such as pheromone, and discussed theforaging by the experiment.
This work was partially supported by a JapaneseGrand-in-Aid for Encouragement of Young Scientists
from the Ministry of Education, Science and Culture(No.15760291).

Figure 5: Snapshot of experiment in homogeneously
distributed eld. (a)D = 0:05 k = 0:1, (b)D = 0 k =0.

References
1] Y.U.Cao, A.S.Fukunaga and A.B.Kahng, "Cooperative Mobile Robotics:Antecedents and Directions," Autonomous Robots, 4, (1997) pp.7-27.
2] L.E.Parker, "Current Research in Multi-Robot
Systems", Journal of Arti cial Life and Robotics,
vol. 7 (to appear).
3] A.T. Hayes, A. Martinoli, and R.M. Goodman,"Distributed Odor Source Localization", IEEE
Sensors, Vol. 2, No. 3 (2002) pp.260-271.
4] L. Steels, "Cooperation between distributed
agents through self-organization," Proc. of theFirst European Workshop on Modeling Autonomous Agents in a Multi-Agent World, (1990)
pp. 175-195.
5] A. Drogoul and J. Ferber, "From Tom Thumbto the Dockers: Some Experiments with Foraging
Robots," Proc. Simulation of Adaptive Behavior,(1993) pp.451-459.
6] T. Balch and R.C. Arkin, "Communication
in Reactive Multiagent Robotic Systems," Autonomous Robots, 1, (1994), pp. 27-52.
7] R. Beckers, O.E. Holland and J.L. Deneubourg,"From Local Actions To Global Tasks: Stigmergy
and Collective Robotics," Articial Life IV, MIT
Press, (1994) pp. 181-189.
8] M.J.B. Krieger, J.B. Billeter, "The call of duty:
Self-organized task allocation in a population of
up to twelve mobile robots", Robotics and Autonomous Systems, Vol.30 (2000) pp.65-84.

Figure 6: Snapshot of experiment in locally distributed eld. (a) D = 0:15 k = 0:01 (b) D =0:15 k = 0:002.

Figure 7: Relation between the evaporation rate andthe number of collected foods. Parameter is the number of the robot. (a) Homogeneous eld. (b) Localized
eld.

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Information-theoretic approach to embodied category learning
1

Gabriel Gómez1 , Max Lungarella2 and Danesh Tarapore3
Artificial Intelligence Laboratory, University of Zurich, Switzerland
2
Dept. of Mechano-Informatics, the University of Tokyo
3
Autonomous Systems Lab., EPFL, Lausanne, Switzerlandgomez@ifi.unizh.ch, maxl@isi.imi.i.u-tokyo.ac.jp, danesh.tarapore@epfl.ch

Abstract

sense – the number of degrees of freedom (dimensionality) of the sensory space.
This paper aims at quantitatively underpinning thishypothesis (see also [1, 2, 5, 6, 7, 8]). We present theanalysis of data collected by a simulated robotic agent
by means of correlation, mutual information, geometric separability index, and isometric feature maps. We
conclude that an information-theoretic approach tothe study of categorization leads to a better (quantitative) understanding of how embodied interaction
simplifies category learning despite the high dimensionality of the sensorimotor data sets. This approach
may also shed light on the characteristics of the objects being categorized, and reduce the time requiredby the agent to learn.

We address the issue of how statistical andinformation-theoric measures can be employed toquantify the categorization process of a simulated
robotic agent interacting with its local environment.
We show how correlation, entropy, and mutual information can help identify distinct informational structure which can be used for object classification. Further, by means of the isometric feature mapping algorithm, we analyze the weights of a neural network
designed to find clusters based on these distinct information theoretic characteristics of the object’s shape,size and color. We conclude that an understanding of
the information-theoretic implications of categorization could help design robots with improved categorization and better exploration strategies.

2
1

Experimental Setup

introduction
We simulated a two-wheeled robot moving ina closed environment cluttered with randomly distributed, colored objects. Objects in the environment
were red cubes and cylinders.
The robot was equipped with 11 proximity sensors(d0−10 ) and a pan-controlled image sensor or camera unit (see Fig. 1b). The proximity sensors had aposition-dependent range (see caption of Fig. 1). The
output of each sensor was affected by additive white
noise, and was partitioned into a space having 32 discrete states, leading to sensory signals with a 5 bit resolution. To reduce the dimensionality of the input
data, we divided the camera image into 24 vertical
rectangular slices (i0−23 ). We computed the amountof “effective” red color in each slice as R=r-(b+g)/2.
For the control of the robot we opted for the Extended Braitenberg Architecture [3] (see Fig. 2). To
pick up the regularities induced by the sensorimotor
coordination we used a Kohonen feature map [9].
Before being projected onto the Kohonen map,the 28-dimensional input vector consisting of the ac-

In our daily lives, we are exposed to a barrage ofmultimodal sensory stimulation (e.g., visual, tactile,
proprioceptive). Perceptual categorization can be conceptualized as the ability to identify regularities in thiscontinuously changing stream of sensory information,and to treat similar, but not necessarily identical objects and events, as being somehow equivalent. Recently, evidence has been accumulating showing thatperceptual categorization is not a mere mapping froma set of sensory nodes to a set of category nodes – aspreviously assumed – but is instead the result of a process of sensorimotor coordinated interaction between
an embodied agent and its local environment. Suchinteraction has been hypothesized to be one of the
major information theoretic implications of embodiment, because it allows an agent to actively generate
constraints in its sensory input ([1, 2, 3, 4]). Such constraints, in turn, simplify the problem of learning categories by inducing spatio-temporal correlations, andby reducing drastically – in an information theoretic©ISAROB 2005

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d4

d6

d5

d0

and the right and left motor activation). The reason
for this re-ordering operation was to avoid having theKohonen map discriminate between objects, based on
the direction of circling.
The 28-dimensional output vector of the input selector was normalized to unit length, and each of
its elements was projected onto each of the neurons of the Kohonen map (that is, the input layer
was fully connected to the map layer). The Kohonen map consisted of 576 neurons, arranged in theform of two-dimensional lattice with Nr =24 rows and
Nc =24 columns. The network’s 24x24x28=16128 initial synaptic weights were chosen from a random set.
The dependence of the learning-rate parameter η on
discrete time n was chosen to be η(n) = η0 e−n/τ1 ,where η0 =1.0 was the initial value of the learning parameter, and τ1 = 2.2log Nr Nc . That is, the learning rate decreased exponentially over time. Anotherfeature of the Kohonen map was that the size of theneighborhood of each neuron shrank over time. The
dependence on discrete time n was σ(n) = σ0 e−n/τ2 ,where σ0 =9.0 and τ2 = 2.0log Nr Nc .

d1

d9

d10
camera

m0

m1

d2

d3

d7

(a)

d8

(b)

Figure 1: (a) Bird’s eye view of the robot and its ecological niche. The trace represents a typical path of
the robot during an experiment. (b) Schematic representation of the agent. The distance sensors havea range that depends on their position. If rl is thelength of the robot, then the range of d0 , d1 , d9 , andd10 is 1.8 rl, the one of d2 and d3 is 1.2 rl, and the oneof d4 , d5 , d6 , d7 , and d8 is 0.6 rl.

3

At the outset of each experimental run the behavioral mode of the robot was set to “exploring.” In thismode the robot roams through the environment at aconstant speed while avoiding obstacles. Upon detection of a salient red object, the robot approaches it under guidance of the visual system (“tracking” mode).
As soon as the object is close, the robot starts circling around it keeping it in the center of its visual
field by adjusting the camera’s pan-angle (“circling”
mode). Concurrently, a habituation signal starts increasing. While the robot circles the object, the valuesof the input signal (24 image sensors,a pair of side distance sensors, and two motor activations) – after being
appropriately re-organized by the input selector – areprojected onto the Kohonen feature map; the synaptic
weights of the network are updated, and the learningrate parameter decreased. The robot keeps circlingaround the object for a while and then resumes the
exploration of its environment. The trace of a typicalexperimental run is shown in Fig. 1a. The entire experiment comprised 78 such experimental runs, eachrun consisting of approximately 6000 data samples.
All samples were stored into a time series file for subsequent analysis.
The categorization error as the percentage of misclassified objects per unit of time is shown in Fig. 3.
Clearly, the network learned to discriminate between

Figure 2: Block diagram of the control architecture.

tivations of 24 red channels, the activations of two
out of four proximity sensors located on the sameside of the robot (that is, either the pair d2 ,d9 , or
the pair d3 ,d10 ), as well as the left and right motor activation values (ml ,mr ), was preprocessed by
the “input selector” (see Fig. 2). If the agent circled an object counter-clockwise – i.e., the object
was on its left – the input vector of the Kohonen map was [i0 , i1 , . . ., i23 , d2 , d9 , ml , mr ]T . However, if the agent circled an object clockwise – i.e.,the object was on its right – the input vector was[i23 , i22 , . . ., i0 , d3 , d10 , mr , ml ]T . That is, the vector(i0 ,. . . ,i23 ) was fed to the Kohonen map in reverse order (i23 was the first element, followed by d3 and d10 ,©ISAROB 2005

Behavioral specification

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Categorization Error (%)

Categorization Error (%)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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(f [xi ] + f [xi ∗ ] + 1) mod 2

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Figure 3: Categorization error (vertical axis) versus
time (horizontal axis). (a) cubes; (b) cylinders.

cubes and cylinders: The classification error after
timestep 77 dropped to almost zero.

4

Methods

In this section, we describe the statistical and information theoretical measures employed in this paper.
The correlation Corr(X, Y ) quantifies the amount oflinear dependency between two random variables X
and(e.g., two sensory channels), and is given by
P YPx∈Xy∈Y p(x, y) (x−mX )(y−mY ))/σX σY , wherep(x, y) is the second order (or joint) probability density function, mX and mY are the mean, and σXand σY are the standard deviation of x and y computed over X and Y . The entropy of a random variable X is a measurePof its uncertainty, and is defined as H(X) = − x∈X p(x) log p(x), where p(x)is the first order probability density function associated with X (in a sense entropy provides a measure for the sharpness of p(x)). Similarly, the joint
entropy between
X and Y is defined as
P variablesP
H(X, Y ) = − x∈X y∈Y p(x, y) log p(x, y). For entropy as well as mutual information, we assumed thebinary logarithm. Using the joint entropy H(X, Y ),we can define the mutual information between X andY as M I(X, Y ) = H(X) + H(Y ) − H(X, Y ).

5

Data Analysis and Results

Correlation
While the robot circled the object, we observed negative correlation between the left and right motors.
The circling behavior also induced negative correlation between pairs of proximity sensors located on thesame side of the robot, that is, between (d2 ,d9 ), (d0 ,d2 )and (d2 ,d4 ) when the robot circled the object in the
counter-clockwise direction. (see Fig. 4).
There is a high discrepancy in the (d0 ,d2 ) sensor correlation between cubes (-0.3083) and cylinders(0.7109). In the case of cylinders, due to their smoothsurface, the sensors d2 as well as d0 will always be incontact with the object. Due to the corners of the
cubes, as well as the length of its edge, this positive
correlation will not be evident.

To get a better grasp on the regularities in the sensory space, we computed also the Geometric Separability Index (GSI) introduced in [10]. Geometric separability is a generalization of linear separability, andquantifies how close regions in the sensory space belonging to the same object are to each other. The
GSI is computed by checking for every sensory pattern
whether the nearest pattern (in terms of the Euclidean
distance) is part of the same class. It is calculated as©ISAROB 2005,

where f [xi ] = 0, if xi belongs to one class, andf [xi ] = 1, if xi belongs to the other class (f is also
called the category function); xi is the i-th sensory
pattern (N-dimensional vector), and xi ∗ is the nearest neighbor of xi . If the nearest pattern in sensory
space always belong to the same class of the currently
perceived object the GSI is 1. High values of the GSI
thus indicate that the sensory patterns belonging to
the two categories are quite separated in the input
space and easy to discriminate, while value close to
0.5 indicate that the sensory patterns corresponding
to the two categories completely overlap.
In addition to these three measures, we also used
the isometric feature mapping, or Isomap, algorithm
described in [11]. Isomap solves the problem of dimensionality reduction by using local metric information to learn the underlying global geometry of a data
set. It discovers – given only the unordered highdimensional input – the low-dimensional representations of the data. Although the classical techniques
for dimensionality reduction such as Principal Component Analysis (PCA) and Multi Dimensional Scaling(MDS) are simple to implement and efficiently computable, many data sets contain non linear structuresthat are invisible to PCA and MDS. Isomap combines
the major algorithmic features of PCA and MDS withthe flexibility to discover nonlinearity in the input
data.

10
0

N

30
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0

follows:

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in Fig. 6 and Fig. 7. The diagonals of the plots represent the entropy of the sensory stimulation.
While the robot circled the object, we observed highmutual information between pairs of proximity sensors
located on the same side of the robot, that is, between(d2 ,d9 ), (d0 ,d2 ) and (d2 ,d4 ) when the robot circled theobject in the counter-clockwise direction.

Cylinders-0.31890.71090.0540Cubes-0.4995-0.3083-0.4476

Correlation(d2 ,d9 )(d0 ,d2 )(d2 ,d4 )Table 1:

d10

d10

d9

d9

d8

d8

d7

d7

d6

d6

d5

d5

d4

d4

d3

d3

d2

d2

d1

d1

d0

d0
d0

d1

d2

d3

d4

d5

d6

d7

d8

d9

d10

d0

d1

d2

d3

(a)

d4

d5

d6

d7

d8

d9

d10

(b)

Table 2:

Figure 4: Distance sensor correlation matrix obtained
from the pair-wise correlation between pairs of distance sensors (indexes d0 to d1 0) for (a) cubes and(b) cylinders . The behavioral state is “circling”. The
higher the correlation the larger the size of the square.

There is a high discrepancy in the sensor d0 entropy
between cubes (0.0464) and cylinders (0.1752). This
discrepancy is also observed in (d0 ,d2 ) mutual information between cubes (0.0252) and cylinders (0.1474).In
the case of cylinders, due to their smooth round surface, the sensor d0 will always be in contact with theobject. However the sharp corners of the cube prevent
this contact from occurring as frequently. We also observe a high discrepancy in (d2 ,d4 ) mutual information
between cubes (0.1956) and cylinders (0.0506).

While the robot was circling around a cube, strong
correlations between the output of all the 24 red channels (i1 to i24 ) could be observed. The average correlation amounted to 0.5628 (see Fig. 5a). For cylindricalobjects, the average correlation between the same redchannels (i1 to i24 ) was 0.1321.

d10

d10

d9

d9

d8

d8

d7

d7
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d7

d8

d9

d10

d0

d1

d2

d3

d4

d5

d6

d7

d8

d9

d10

(b)

Figure 6: Distance sensor mutual information matrix
obtained from mutual information between pairs of
distance sensors (d0 to d10 ), in one particular experimental run. The behavioral state is “circling” (a)cubes and (b) cylinders. The higher the mutual information the larger the size of the square.

24

(b)

Figure 5: Image sensor correlation matrix obtained
from the pair-wise correlation between pairs of image
sensors (indexes 1 to 24) for (a) cubes and (b) cylinders. The behavioral state is “circling”. The higher
the correlation the larger the size of the square.

While the robot was circling around a cube, highmutual information between the output of all the 24
red channels (i1 to i24 ) could be observed. The average mutual information amounted to 0.8166 bits (seeFig. 7a). For cylindrical objects, the average mutual
information between the same red channels (i1 to i24 )was 0.2676 bits.

Entropy and mutual information
The pair-wise mutual information between the 24
image sensors, and the 11 proximity sensors is shown©ISAROB 2005

d1

d0

2

Cylinders (bits)
0.1752
3.04390.91030.14750.05060.0858

Cubes (bits)
0.04643.02251.02920.0252
0.19560.1919Mutual Information(d0 ,d0 )(d2 ,d2 )(d9 ,d9 )(d0 ,d2 )(d2 ,d4 )(d2 ,d9 )335

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24

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Isometric feature mapping
To get a grasp on the high-dimensional spacespanned by the sensory data, we used the Isomap algorithm. By applying Isomap to the 28-dimensional
input space, we were able to compress it to a twodimensional space. In Fig. 8, the clusters have been
enclosed in boxes. The X axis shows positive correlation with a linear combination of the following factors: a) the difference between distance sensors d2 -d9
or d3 -d10 : cubes are clustered by higher values of thisdifference and cylinders by the lower values; b) the difference of right and left motor mR -mL : the cubes are
clustered according to higher values of this differencecompared to the cylinders; and c) the sum of the image sensor values focused on the object being circled:
cubes display a larger number of image sensor activations than cylinders.The total correlation considering
all these factors was 0.931.
Activation of the sensor d2 orients the robot toward objects (attraction); activation in d9 , on theother hand, makes the robot turn away from objects(repulsion). For cubes, due to their flat surfaces andsharp corners, attraction prevails on repulsion. This
results in cubes being associated with larger valuesof the differences d2 -d9 , and d3 -d10 . In the case of
cylinders, however, attraction prevails on repulsion,
causing the cylinders to be associated with low valuesof the difference d2 -d9 or d3 -d10 . Due to the sudden
turns the robot has to make when circling cubes, thedifferencemR -mL is higher for cubes than for cylinders. When circling cubes, the robot is closer to it
than for cylinders. The reason behind this is thatin the case of cylinders, the robot starts circling theobject, when one of its distance sensors d4 or d5 detects the object ahead. However, in the case of cubes,
the distance sensors d4 or d5 do not detect the cube.
Rather, it is detected by the center distance sensor d6
which has a much shorter range. This causes the robot
to be much closer to the encircled object in the caseof cubes (larger number of image sensor activations)than in the case of cylinders (smaller number of image
sensor activations).

2
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24

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4

6

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(b)

Figure 7: Image sensor mutual information matrix obtained from the pair-wise mutual information between
pairs of image sensors (indexes 1 to 24), in one particular experimental run. The behavioral state is “circling” (a) cubes and (b) cylinders. The higher themutual information the larger the size of the square.

Geometric Separability Index (GSI)The GSI identifies the regularities founded by the
Kohonen map in the sensory space, allowing us to predict the Kohonen map’s learning performance. A GSI
of 0.9989 when the robot has learnt to classify the
cubes and cylinders. Sensory motor interactions indicates the presence of clusters in the data, which the
Kohonen map has extracted. Indeed, the “circling”
behavioral mode by inducing constraints in the input
space, made cubes and cylinders discernible because
data points belonging to one object were close to eachother in the data space (correlation). The data points
belonging to different objects are far apart.
1.5
Cube Cluster − Upper
Cluster1

Y Axis0.5
Cube Cluster −Lower Cluster(in black)

0

−0.5

CylinderCluster (right side)
Cube
Cluster −Upper
Cluster−1
CylinderCluster (left side)

−1.5
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−1.5

−1

−0.5
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0

0.5

1

6

1.5

Does sensorimotor coordinated activity induce discernible regularities or structure in the sensorimotor
space? As shown by the statistical and informationtheoretic analyses of the recorded sensory and motor
data, appropriate coordinated motor activity leads toFigure 8: Dimensionality reduction of the 28dimensional sensory input vector. Applied to N=576
vectors with K=7, Isomap learned a two-dimensional
embedding of the data’s informational structure.

©ISAROB 2005

Further discussion and conclusion

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Acknowledgementsa characteristic “fingerprint” of the sensorimotor interaction – that is, spatio-temporal patterns in the sensorimotor space reproducible across multiple experimental runs (for a similar result see also [7]). Suchpatterns can be identified, and exploited to simplify
subsequent discrimination tasks.

Gabriel Gómez would like to thank the EU-Project
ADAPT (IST-2001-37173) for funding. Max Lungarella would like to thank the University of Tokyo
for funding and Yasuo Kuniyoshi for support and encouragement.

It is important to note that the categorization for
the robot is based on a 28-dimensional vector of sensorimotor values, where each of the sensors take 32 values (each sensor has a 5 bit resolution). Indeed, thisresults in a very large space of potential sensorimotor configurations (2832 ). The circling behavior of therobot, allows the agent to generate constraints in its
sensory input that lead to a drastic dimensionality reduction of the sensory space. This greatly reduces thesearch space of the robot. In other words, the circularbehavior, akin to the object rotation behavior found in
human infants [12], or exploration strategies observedin adults exploring objects with their hands [13], induces spatio-temporal correlations among the sensory
patterns. These correlations are a further indicationof the fact that the sensorimotor coupling leads to a
reduction of the degrees of freedom in the input space[2].

References[1] R. Pfeifer and C. Scheier. Sensory-motor coordination: The metaphor and beyond. Robotics and Autonomous Systems, 20:157–178, 1997.
[2] C. Scheier, R. Pfeifer, and Y. Kuniyoshi. Embedded
neural networks: Exploiting constraints. Neural Networks, 11:1551–1569., 1998.
[3] R. Pfeifer and C. Scheier. Understanding Intelligence.
MIT Press, 1999.
[4] M. Lungarella, T. Pegors, D. Bulwinkle, andO. Sporns. Methods for quantifying the informational
structure of sensory and motor data. Neuroinformatics (submitted), 2004.
[5] M. Lungarella and R. Pfeifer. Robots as cognitive
tools: information-theoretic analysis of sensory-motor
data. In Proc. of 2nd Int. IEEE/RSJ Conf. on Humanoid Robotics, pages 245–252, 2001.
[6] O. Sporns. Generating structure in sensory data
through coordinated motor activity. In Proc. of Joint
Conf. on Neural Network, page 2796, 2003.
[7] D. Tarapore, M. Lungarella, and G. Gómez. Quantifying patterns of agent-environment interaction.
Robotics and Autonomous Systems (accepted for publication), 2005.
[8] R. Te Boekhorst, M. Lungarella, and R. Pfeifer.
Dimensionality reduction through sensory-motor coordination.
In Proc. of the Joint Int. Conf.
ICANN/ICONIP, pages 496–503, 2003.
[9] T. Kohonen. Self-organization and associative memory. Springer, Berlin, 1988.
[10] C. Thornton. Separability is a learners best friend. In
Proc. 4th Neural Computation and Psychology Workshop: Connectionist representations, pages 40–47,
1997.
[11] J. Tenenbaum, V. de Silva, and J. Langford. A global
geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000.
[12] H. A. Ruff. Infants’ manipulative exploration of objects: effects of age and object characteristics. Developmental Psychology, 20:9–20, 1984.
[13] S. J. Lederman and R. L. Klatzky. Haptic explorationand object representation. In M. Goodale, editor, Vision and Action: The Control of Grasping, pages 98–
109. New Jersey: Ablex, 1990.

The high values of correlation that we have observed in the “circling” behavioral state allow us toinfer that while in this state, the agent generates redundancy in the sensory signals, which can picked upby the agent itself. In turn, such redundancy is a prerequisite for learning, and provides a basis for some
sort of “self-cognition.” That is, the agent can acquirea notion of its own emergent behavior.
We further note that while it is possible, at leastin simple cases, to characterize exploration strategies
by means of information theoretic and statistical measures, the proposed measures are by no means equivalent, but complement each other. For instance, in contrast to linear correlation, mutual information takes
into account also nonlinear dependencies between various stochastic variables. The geometric separability
index, on the other side, discerns sensory input basedon how linearly separable it is, that is, based on where
it falls respect to a hyperplane. Isomap, finally, tries toisolate nonlinear degrees of freedom in the data set. It
would be of interest to understand if the agent could
perform such “analyses” on its own. By characterizing its interactions with the environment, it would
form the basis on which to build its own individual
experiences, its memory. In future work, we also plan
to study more complex agent-object interactions, andto experiment with more sophisticated robots.

©ISAROB 2005

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On the Diversity of HIV using Cellular Automata Approach
Hiroshi UEDA1

Yukio IWAYA1

Toru ABE2

Tetsuo KINOSHITA2

1 Research Institute of Electrical Communication, Tohoku University

2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan{uep, iwaya}@fir.riec.tohoku.ac.jp
2 Information Synergy Center, Tohoku University

2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan
beto@isc.tohoku.ac.jp, kino@riec.tohoku.ac.jp

Abstract

physical, chemical, life, traffic and economical processes.
Advantage of CA model is that can simplify complicatedinteraction into local interaction.
Since it is considered to that the immune system responses based on local interaction between immune cells
and antigens [5], many CA models about HIV infection
has been proposed [6, 7, 8, 9, 10, 11, 12].
But until now, no work has focused on diversity of HIV
with CA model. In this study, we proposed CA model considering diversity of HIV and investigated its behavior.

Diversity of HIV (Human Immunodeficiency Virus) invivo has been reported. In this study, we propose a CA
(Cellular Automata) model about interaction between the
immune system and HIV, and examine the effect of this diversity. Originality of our CA model is that it has not only
four states (HIV, Virgin, Dead, Infect) but diversity of HIV
and T-Cells. Growing maximum value of the diversity, we
performed computer simulation with the CA model. The
results demonstrated that growth of the diversity have aneffect on cell population and simulations steps. Additionally, we observed CA state corresponds to infection, incubation period and development of AIDS. In on our CA
model, it is demonstrate that the diversity of HIV is the
major factor which HIV can escape the immune response.

2

We consider two-dimensional CA, the cells of whichbeing arranged in a square lattice.
Each cell has
four states (HIV, Virgin, Dead, Infect) and Type i (i =0, 1, 2, · · · , Tmax ¡ 1) as follows:

Keywords: Cellular automata, HIV, Immune system† HIV[i]: A state being HIV type i which is from outside the body, other lymphocyte or host cell. Although
there exists only one HIV strain (HIV[0]) at the beginning of infection, it mutates into strain HIV[1],
HIV[2], · · · , or HIV [Tmax ¡ 1] in each proliferate process.

1 introduction
From the late in the 1980s, many active researches about
infection of HIV (Human Immunodeficiency Virus) anddevelopment of AIDS (Acquired Immunodeficiency Syndrome) are current in progress. Medical works have beenreported diversity of HIV which is due to its high mutation rate and variability in HIV-specific T cell epitope sequences [1, 2, 3], but its functionality to develop HIV infection against the immune response is not clear in vivo.
Nowak and May have proposed a mathematical model considered with diversity of HIV strains[4]. According themodel, there exists antigenic diversity threshold of HIV.
On the other hand, many scientists and engineers haveproposed CA (cellular automata) model to investigate©ISAROB 2005

Model

† Virgin[i]: Uninfected healthy host cell which can only
response and eliminate against In f ect[i].
† Dead: Nothing exists.
In f ect[i] was eliminated.

After moving HIV [i] or† Infect[i]: A state corresponds to host cell (Virgin[⁄])infected by HIV [i].
HIV [⁄],Virgin[⁄] and In f ect[⁄] imply any type of HIV,
host cell and infected cell respectively. Tmax stands for anti-

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genic diversity of HIV and the immune system in the CAmodel. In fact, this is the originality of our model.
Parameters of the model are maximum value of thediversity of HIV, Tmax and small initial infection fracture pHIV . The initial CA configuration is composed of
Virgin[⁄] with pHIV of HIV [0]. Type i and the placement
chosen randomly.
In one time step the entire lattice is updated in a synchronized parallel way, according to the rules described below. The updated state of a cell is dependent on the statesof its Moore neighbors in a square lattice.
We determine this process will continue until all theHIV [⁄] and In f ect[⁄] or Virgin[⁄] is eliminated.

350

Number of Cells

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Tmax(Diversity of HIV)

Rule 1 Moving of HIV: If HIV [⁄] has at least one Virgin[⁄]or Dead neighbor, it becomes Dead with probability(the number sum of Virgin[⁄] and Dead)/8. This rule
represents that HIV moves to the cite nothing exists or
infects to another virgin host cell.

Figure 1: Average number of HIV [⁄] cells versus Tmax .

Number of Cells

Rule 2 Being infected of virgin host cell with HIV: If
Virgin[⁄] has at least one HIV [i], it becomes In f ect[i]with probability (the number of HIV [i])/8. This rule
represents that virgin host cell is infected with HIV.
Rule 3 Mutation and emergence of HIV: (a) If Dead has
at least one In f ect[⁄], it becomes HIV [⁄] since HIV
mutates before emergence, with probability (the number of In f ect[⁄])/8 . This rule represents that HIV
emerges from infected host cell. (b) If Dead has atleast one HIV [i], it becomes HIV [i] with probability(the number of HIV [i])/8. This rule represents thatmoving of HIV from another cite.

10000
9000
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Dead

0

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Figure 2: Average number of Virgin[⁄] and Empty cells
versus Tmax .

Rule 4 Update of infected cell: (a) If In f ect[⁄] has at least
one Virgin[i], it becomes Virgin[i] with probability(the number of Virgin[i])/8. This rule represents that
infected cell is eliminated by type i activated virginhost cell. (b) Otherwise it becomes HIV [i] since HIV
eliminate infected cell.

been reported. From these results, it seem to suggest that
antigenic diversity of HIV has great effect on the model.
Fig. 3 shows the relation between average simulation
steps and Tmax . The simulation steps increased with Tmax .
Long time incubation period of HIV has been known. This
result corresponds to HIV infect phenomenon in vivo.
Main points are described below:
First, since the decrease of Virgin[⁄] depend on HIV infection in the model, the situation that immune system cannot response was assumed to be due to increase of antigenic
diversity of HIV (Fig. 1, 2). In detail, if no responsible immune cells exist on its Moore neighbors, HIV can escape
the immune response in the CA model.
Second, Fig. 3 points out that long time steps wereneeded to stop simulation in the situation high antigenic
diversity of HIV.
Fig. 1-3 indicate only average value of 1000 times runs.

We performed computer simulations of the model, using periodic boundary condition, on a 100 £ 100 lattice,
pHIV = 0.05 with Tmax from 1 to 35. For each Tmax , weperformed 1000 times trial runs.

3 Results and Discussion
Fig. 1 and 2 show the relation between average number of cells and antigenic diversity of HIV, Tmax . HIV [⁄],Empty increased with Tmax . In addition, decrease of
Virgin[⁄] was also observed. Mutation of HIV in vivo has©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

1800

Simlation Steps

1600
1400
1200
1000
800
600
400
200
0

0

5

10

15

20

25

30

35

Tmax(Diversity of HIV)

Figure 3: Average simulation steps versus Tmax .

Since the CA model is not deterministic, the simulation
result of one trial run (namely whether HIV is control ornot) is not depend on parameters. Fig. 4-6 show the timeevolution of CA state. In early stage (Fig. 4), a volume of
In f ect[⁄] was observed. This state seem to correspond toinitial infection. In contrast, only a few In f ect[⁄] cells wasobserved in fig. 5. This state seem to correspond to incubation period in which being controlled of HIV [⁄]. Moreover, since CA is filled with Dead in fig. 6, this state seemto correspond to break down of the immune system, that is
AIDS.

Figure 4: Snapshot of CA state at step 1. Red: In f ect[⁄],Black: HIV ⁄, White: Virgin[⁄], Gray: Dead. Parameters
are: Tmax = 24, pHIV = 0.05

These results depend on that interaction between HIV
and the immune system is sum of local interactions. We
are currently performing studies to determine CA model
to compute Moore neighbor. Therefore, it is interesting tochange condition of neighbor.
Further studies to analyze development of antigenic diversity of HIV and pattern which is formulated by CA are
required.
In summary, the present work shows that the the CAmodel taking account for antigenic diversity of HIV can
examine interaction between HIV and the immune system
successfully.

4 Conclusion
In this study, we modeled the interaction between HIV
and the immune system with CA approach to examine theeffect of antigenic diversity of HIV. We make a point that
CA approach which is based on local interaction is very
useful to investigate the interaction between HIV and theimmune system.
Our CA model contrasts with deterministic model because simulation result of each trial runs is different at same
parameters. Since all the HIV carrier don’t have an onsetof AIDS; variety of time in incubation period has reported,
this property of CA model is correspond with phenomenon
in vivo.
Originality of out CA model is that takes into consideration of antigenic diversity of HIV. We have taken antigenic
diversity of HIV as parameter. Simulation results suggestthat increase of antigenic diversity of HIV has great impact
on the immune system.
By taking account for antigenic diversity in CA model,
we make a point that HIV which has diversity can escapefrom local immune response and proliferate.

©ISAROB 2005

Acknowledgments
A part of this research was supported by Research Grantof Kiban (B): No.6300011 from Ministry of Education,
Science and Culture, Japan.

References[1] A. S. De Groot et al., “Engineering immunogenic
consensus T helper epitopes for a cross-clade HIV
vaccine,” Methods, vol. 34, no. 4, pp. 476–487, 2004.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Figure 5: Snapshot of CA state at step 463. Parameters aresame to Fig. 4

Figure 6: Snapshot of CA state at step 1908. Parameters
are same to Fig. 4[2] D. Turner et al., “Variability in the PR and RT
genes of HIV-1 isolated from recently infected subjects,” Antiviral Chemistry & Chemotherapy, vol. 15,
pp. 255–259, September 2004.

[9] F. Kougias and J. Schulte, “Simulating the immune
response to the HIV–1 virus with cellular automata,”
Journal of Statistical Physics, vol. 60, no. 1/2,
pp. 263–273, 1990.

[3] P. J. R. Goulder et al., “Evolution and transmission of
stable CTL escape mutations in HIV infection,” Nature, vol. 412, pp. 334–338, July 2001.

[10] P. Sloot, F. Chen, and C. Boucher, “Cellular automata
model of drug therapy for HIV infection,” in ACRI
(S. Bandini, B. Chopard, and M. Tomassini, eds.),
vol. 2493 of Lecture Notes in Computer Science,
pp. 282–293, Springer, 2002.

[4] M. A. Nowak et al., “Antigenic diversity thresholds
and the development of AIDS,” Science, vol. 254,
pp. 963–969, Novemver 1991.

[11] R. B. Pandey and D. Stauffer, “Metastability withprobabilistic cellular automata in an HIV infection,”
Journal of Statistical Physics, vol. 61, no. 1/2,
pp. 235–240, 1990.

[5] N. K. Jerne, “The immune system,” Scientific American, vol. 229, no. 1, pp. 52–60, 1973.
[6] R. B. Pandey and D. Stauffer, “Immune response via
interacting three dimensional network of cellular automata,” Le Journal de Physique, vol. 50, pp. 1–10,
January 1989.

[12] T. Tomé and J. R. D. de Felício, “Probabilistic cellular automaton describing a biological immune system,” Physical Review E, vol. 53, no. 4, pp. 3976–
3981, 1995.

[7] R. B. Pandey, “Cellular automata approach to interacting cellular network models for the dynamics ofcell population in an early HIV infection,” Physica A,
vol. 179, no. 3, pp. 442–470, 1991.
[8] R. M. Z. dos Santos and S. Coutinho, “Dynamics of
HIV infection: A cellular automata approach,” Physical Review Letters, vol. 87, no. 16, pp. 168102–1–
168102–4, 2001.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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54

©ISAROB 2005
345

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

An Investigation into State-Change Complexities in Synchronization
Algorithms for Cellular Automata
Kouhei Matsumoto

Hiroshi Umeo

Univ. of Osaka Electro-Communication

Univ. of Osaka Electro-Communication

Neyagawa-shi, Hatsu-cho 18-8

Neyagawa-shi, Hatsu-cho 18-8

Osaka, 572-8530, Japan

Osaka, 572-8530, Japan

Abstractdeveloped so far. The synchronization algorithms being compared in this paper are seven optimum- andlinear-time algorithms developed by Balzer [1], Fischer [2], Gerken [3], Mazoyer [8], Minsky [9], Waksman[17] and Yunés [18] . The state-change complexity inseveral synchronization algorithms on two-dimensional
arrays are also studied in the last section.

This paper presents a comparative study of statechange complexity in optimum- and linear-time synchronization algorithms for a large-scale of cellular automata on one- and two-dimensional arrays. We implement most of the synchronization protocols developed so far and investigate their state-change complexities on a computer.

1

Introduction

C1

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In recent years cellular automata (CA) have been
establishing increasing interests in the study of modeling real phenomena occurring in biology, chemistry,
ecology, economy, geology, mechanical engineering,
medicine, physics, sociology, public traﬃc, etc. Cellular automata are considered to be a nice model ofcomplex systems in which an inﬁnite one-dimensional
array of ﬁnite state machines (cells) updates itself insynchronous manner according to a uniform local rule.
The synchronization in cellular automata has been
known as ﬁring squad synchronization problem since
its development, in which it was originally proposed by
J. Myhill to synchronize all parts of self-reproducing
cellular automata [9]. The ﬁring squad synchronization problem has been studied extensively for morethan 40 years [1-18].
The complexity of synchronization algorithms hasreceived much attention, and those synchronization algorithms have been studied from viewpoints of timecomplexity, number of internal states of the automata,and number of transition rules. Vollmar [15, 16] introduced a concept of state-change complexity as ameasure of energy consumption in the cellular spaces
and showed that any optimum-time synchronization
algorithms operating on one-dimensional array needΩ(n log n) state-changes for synchronizing n-cells.
In this paper, we give a comparative study of statechange complexity in the synchronization algorithms©ISAROB 2005

2

mFigure 1: Cellular automata.

2

Firing squad synchronization problem on one-dimensional cellular automaton

In this section we give informal deﬁnitions of cellular automata, ﬁring squad synchronization problem
and a measure of state-change complexity for synchronization algorithms on one-dimensional cellular automata.

2.1

One-dimensional cellular automaton

Figure 1 (above) shows a ﬁnite one-dimensional cellular array consisting of n cells, denoted by Ci , where1 ≤ i ≤ n. Each cell is an identical (except the border cells) ﬁnite-state automaton. The array operates
in lock-step mode in such away that the next state ofeach cell (except border cells) is determined by both

346

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

1000000

its own present state and the present states of its left
and right neighbors. All cells, except the left end cell,
are initially in a quiescent state at time t = 0. The
quiescent state has a property that the next state of aquiescent cell with quiescent neighbors is the quiescentstate again.

100000State-Changes [S(n)]2.2Firing Squad Synchronization Problem

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. At time t = 0 the left
end cell (general ) is in the fire-when-ready state, which
is an initiation signal to the array. The ﬁring squadsynchronization problem is stated as follows. Givenan array of n identical cellular automata, including
a general on the left end which is activated at time
t = 0, we want to give the description (state set andnext-state function) of the automata so that, at somefuture time, all of the cells will simultaneously and, forthe first time, enter a special firing state. The set ofstates and the next-state function must be independent of n. Without loss of generality, we assume thatn ≥ 2. The tricky part of the problem is that the samekind of soldier having a ﬁxed number of states must be
synchronized, regardless of the length n of the array.

2.3

Optimum-time synchronization algorithms

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Figure 2: State-changes in optimum-time synchronization algorithms.

seven-state synchronization algorithm: Gerken I, respectively, thus decreasing the number of states required for the synchronization. In 1987, Mazoyer[8] developed a six-state synchronization algorithm
which, at present, is the algorithm having the fewest
states. The state change complexity for those algorithms is investigated on a computer, and the next
theorem is established.
[Theorem 2] Each optimum-time synchronization algorithm developed by Balzer [1], Gerken [3], Mazoyer[8] and Waksman [17] has an O(n2 ) state-change complexity, respectively.
Figure 2 shows a comparison between statechanges of the optimum-time synchronization algorithms. Gerken [3] has shown that his 155-state algorithm has Θ(n log n) state-change complexity.

3.2

Linear-timerithmssynchronizationalgo-

The ﬁring squad synchronization problem was ﬁrst
solved by J. McCarthy and M. Minsky [9] who presented a 3n-step algorithm. Fischer [2] gave a 15state implementation for the 3n-step synchronization
scheme. Both of the algorithms were based on thedivide-and-conquer scheme that were realized with
1/1- and 1/3-speed signals interacting each other.
These signals look like threads in the time-space diagram and the 3n-step synchronization algorithm basedon the scheme is said to be thread-like algorithm.
Yunés [18] presented a seven-state implementation
based on the thread-like algorithm. In his construction, the width of the threads is larger than the pre-Waksman [17] presented a 16-state optimum-timesynchronization algorithm. Afterward, Balzer [1] andGerken [3] developed an eight-state algorithm and a©ISAROB 2005

0

Cells [n][Theorem 1][16] Ω(n log n) state-change is necessary
for synchronizing n cells in (2n − 2) steps.

3.1

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Vollmar [15, 16] introduced a state-change complexity in order to measure the eﬃciency of cellular algorithms and showed that Ω(n log n) state changes are
required for the synchronization of n cells in (2n − 2)steps.

State-change complexity in onedimensional synchronization algorithms

10000

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State-change complexity in onedimensional synchronization algorithms

3

BalzerWaksmanGerkenI
MazoyerGerken II

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

vious design. We can get the state change complexity
for the 3n-step thread-like synchronization algorithms
with ﬁnite-width threads.

problem is to determine a description (state set andnext-state function) for cells that ensures all cells enter the fire state at exactly the same time and for theﬁrst time. The set of states must be independent ofm and n. Maeda and Umeo [5] developed a simple
and eﬃcient mapping scheme that enables us to embed any one-dimensional ﬁring squad synchronization
algorithm onto two-dimensional arrays without introducing additional states. We see that any conﬁguration on a 1-D CA consisting of m + n − 1 cells can bemapped onto 2-D m × n arrays. Therefore, when theembedded 1-D CA ﬁres m + n − 1 cells in T (m + n − 1)steps, the corresponding 2-D CA ﬁres the m × n array
in T (m+n−1) steps. We can design state-eﬃcient synchronization algorithms based on the mapping. Thosealgorithms are stated as follows:[Theorem 3] Any 3n-step ﬁnite-width thread-likesynchronization algorithm has an O(n log n) statechange complexity.
[Theorem 4] Each linear-time 3n-step synchronization algorithm developed by Fischer [2], Minsky and
MacCarthy [9], and Yunés [18] has an Ω(n log n) statechange complexity, respectively.
Figure 3 shows a comparison between state-changesof the 3n-step synchronization algorithms with ﬁnitewidth threads.
70000
Minsky:Yunes:
Fischer:

60000[Theorem 5][5,14] There exists a 6-state ﬁring squadsynchronization algorithm that can synchronize any
m × n rectangular array in 2(m + n) − 4 steps.

state changes [S(n)]50000[Theorem 6][4,5,14] There exists a 13-state ﬁring
squad synchronization algorithm that can synchronize
any m × n rectangular array in optimum m + n +max(m, n) − 3 steps.
In Fig. 4 (left), we show snapshots of the 13-state
optimum-time generalized ﬁring squad synchronization algorithm. Shaded squares in Fig. 4 (right) meancells that change their states in the computation. Figure 5 shows a comparison of the state-change complexity of the algorithm in the case where the initial
general is located on C1 ,Cn/4 and Cn/2 . Figure 6 illustrates the state-change complexities between those
two linear- and optimum-time algorithms for rectangular arrays.

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cells [n]Figure 3: State-changes in 3n-step synchronization algorithms.

4

State-change complexity in twodimensional synchronization algorithms

Figure 1 (below) shows a ﬁnite two-dimensional (2D) cellular array consisting of m × n cells. Each cellis an identical (except the border cells) ﬁnite-state automaton. The array operates in lock-step mode insuch a way that the next state of each cell (except
border cells) is determined by both its own present
state and the present states of its north, south, east
and west neighbors. All cells (soldiers), except thenorth-west corner cell (general), are initially in thequiescent state at time t = 0 with the property thatthe next state of a quiescent cell with quiescent neighbors is the quiescent state again. At time t = 0,
the north-west corner cell C1,1 is in the fire-whenready state, which is the initiation signal for synchronizing the array. The ﬁring squad synchronization©ISAROB 2005

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Figure 4: Snapshots of the generalized 13-states synchronization algorithm.

348

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

6000

[5] M. Maeda and H. Umeo: A design of two-dimensional ﬁring squad synchronization algorithms and its implementation.
Proc. of Cellular Automata 2001, pp.102-107(in Japanese),(2001).

"General Position 1""General Position n/2"State-Changes(S[n])5000"GeneralPosition n/4"

4000[6] K. Matsumoto, T. Sogabe and H. Umeo: State-change complexity for ﬁring squad synchronization algorithms on onedimensional cellular automata. Proc. of IEEJ, 3-099, pp.143144(in Japanese), (2004).

3000

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[7] K. Matsumoto and Hiroshi Umeo: A State-Change Complexity in Synchronization Algorithms for Cellular Automata.
SICE Annual Conference 2004 in Sapporo (SICE2004), P57, (2004).

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[8] J. Mazoyer: A six-state minimal time solution to the ﬁring
squad synchronization problem. Theoretical Computer Science, vol. 50 (1987), pp. 183-238.

Cells[n]Figure 5: Comparison of state-changes in the generalized 13-states synchronization algorithm.

[9] M. Minsky: Computation: Finite and infinite machines.
Prentice Hall, (1967), pp. 28-29.
[10] 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.

6-state_algorithm:
13-state_algorithm:

S(m,n)
1200000[11] P. Sanders, R. Vollmar and T. Worsch: Cellular automata:Energy consumption and physical feasibility. Fundamenta Informaticae, 52(2002) 233-248.

1000000
800000600000400000200000[12] 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).

0

10

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30

Column [n]

40

50

60

70

80

90

100 10

20

30

40

50

60

70

80

90

100

Row [m][13] H. Umeo, M. Hisaoka and T. Sogabe: A comparative
study ofoptimum-time synchronization algorithms for onedimensional cellular automata -A survey-. Proc. of the 6th International Conference on Cellular Automata for Researchand Industry, LNCS 3305, Springer-Verlag, pp.50-60(2004).

Figure 6: State-changes in the two-dimensional synchronization algorithms.

5[14] H. Umeo, M. Maeda and N. Fujiwara: An eﬃcient mapping
scheme for embedding any one-dimensional ﬁring squad synchronization algorithm onto two-dimensional arrays. Proc. of
the 5th International Conference on Cellular Automata for
Research and Industry, LNCS 2493, Springer-Verlag, pp.6981(2002).

Conclusions

We have implemented most of the synchronization algorithms developed so far and investigated theirstate-change complexities on a computer. A comparative study of the state-change complexity in optimumand linear-time synchronization algorithms on oneand two-dimensional cellular arrays has been presented.

[15] R. Vollmar: On Cellular Automata with a Finite Number ofState Change. Computing, Supplementum, vol. 3(1981), pp.
181-191.
[16] R. Vollmar: Some Remarks about the “Eﬃciency” of Polyautomata. International Journal of Theoretical Physics, vol.
21, No. 12(1982), pp. 1007-1015.
[17] A. Waksman: An optimum solution to the ﬁring squad synchronization problem. Information and Control, vol. 9 (1966),
pp. 66-78.

References[18] J. B. Yunés: Seven-state solution to the ﬁring squad synchronization problem. Theoretical Computer Science, 127 (1994),
pp.313-332.

[1] R. Balzer: An 8-state minimal time solution to the ﬁring
squad synchronization problem. Information and Control,
vol. 10 (1967), pp. 22-42.
[2] P. C. Fischer: Generation of primes by a one-dimensional realtime iterative array. J. of ACM, vol. 12, No. 3 (1965), pp.
388-394.
[3] Hans-D., Gerken: Über Synchronisations-Probleme bei Zellularautomaten. Diplomarbeit (1987), Institut fur Theoretische
Informatik, Technische Universitat Braunschweig, pp. 50.
[4] M. Hisaoka, M. Maeda and H. Umeo: An implementationof two-dimensional optimum-time ﬁring squad synchronization algorithms. Proc. of Annual Conference IEICE, D-1-9(inJapanese), (2004).

©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A State-Eﬃcient Implementation of Synchronization Algorithms for
Two-Dimensional Cellular Automata-Extended AbstractMasato Teraoka† , Masaya Hisaoka† , Masashi Maeda† and Hiroshi Umeo††Univ. of Osaka Electro-Communication
Neyagawa-shi, Hatsu-cho 18-8
Osaka, 572-8530 Japan

Introduction

cells) is determined by both its own present state andthe present states of its north, south, east and westneighbors. All cells (soldiers), except one general cell,
are initially in the quiescent state at time t = 0 withthe property that the next state of a quiescent cellwith quiescent neighbors is the quiescent state again.
At time t = 0, any one cell on the array is in thefire-when-ready state, which is the initiation signal forsynchronizing the array. The ﬁring squad synchronization problem is to determine a description (state
set and next-state function) for cells that ensures all
cells enter the fire state at exactly the same time andfor the ﬁrst time. The set of states must be independent of m and n. We call the synchronization problem
normal, when the initial position of the general is restricted to north-west corner of the array. We consider
a generalized ﬁring squad synchronization problem, inwhich the general can be initially located at any position on the array. As for the normal synchronization
problem, several algorithms have been proposed, including Beyer [1], Grasselli [2], Kobayashi [3], Shinahr[7], Szwerinski [8] and Umeo, Maeda and Fujiwara [9].
Umeo, Maeda and Fujiwara [9] presented a 6-state
two-dimensional synchronization algorithm that ﬁres

2

...

3

...

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m...

Figure 1 shows a ﬁnite two-dimensional (2-D) cellular array consisting of m × n cells. Each cell is an
identical (except the border cells) ﬁnite-state automaton. The array operates in lock-step mode in such a
way that the next state of each cell (except border

4...

Firing Squad Synchronization Problem on Two-Dimensional Cellular
Automata

3

...

...

2

2

n

...

1
1

...

The synchronization in cellular automata has been
known as ﬁring squad synchronization problem since
its development, in which it was originally proposed by
J. Myhill to synchronize all parts of self-reproducing
cellular automata [5]. The ﬁring squad synchronization problem has been studied extensively for morethan 40 years [1-10]. The present authors are involved
in research on ﬁring squad synchronization algorithms
on two-dimensional (2-D) cellular arrays.
In this paper, we ﬁrst propose a new linear-time
generalized synchronization algorithm that can synchronize any m × n rectangular array in m + n +max(r + s, m + n − r − s + 2) − 4 steps with the general at an arbitrary initial position (r, s) of the array.
The algorithm is based on a state-eﬃcient mapping
scheme for embedding a restricted class of generalized
one-dimensional optimum-time synchronization algorithms onto 2-D rectangular arrays. The embedding
can be implemented with providing two additional
states. We show that the linear-time 14-state solution
developed yields an optimum-time synchronization algorithm in the case where the general is located at thenorth-east corner. Due to the space available, we omit
proofs of the theorems.

...

1

...

Figure 1: A two-dimensional cellular automaton.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

t=0
C1

C2

C3

Cn

C4

Cn+1

Cr+s-1

Cr+n-1

g4

C1 2

Cr 4

C21

gm+n-1
Cm n

t = n+k-2

Cm s

Cr 3
Cm 4
Cr 2
Cm 3
Cr 1
Cm 2

t = n+k+max(k,n)-3

Cm 1

(a)

Figure 2: Correspondence between 1-D and 2-D cellular arrays.

(b)

.
.
,
gm+n−1 = {Cm,n }.
The objective of our correspondence is to embed
conﬁgurations of 1-D generalized synchronization algorithms onto 2-D arrays.
Property Q: We say that a generalized ﬁring algorithm
has a property Q, where any cell, except the generalcell Ck , keeps a quiescent state in the area A of thetime-space diagram shown in Fig. 3(a).

Linear- and Optimum-Time Firing
Squad Synchronization Algorithms

Now we consider a generalized ﬁring squad synchronization problem, in which the general can be initiallylocated at any position on the array. Before presenting
the algorithm, we propose a simple mapping scheme
for embedding one-dimensional generalized synchronization algorithms onto two-dimensional arrays.
Consider a correspondence between 1-D array oflength m + n − 1 and 2-D array of size m × n, shown in
Fig. 2. Each black square corresponds to initial general cell on the array. The 2-D array of size m × n isdivided into m + n − 1 groups gk , 1 ≤ k ≤ m + n − 1,that is deﬁned as follows:

For any 2-D array M of size m × n with the general at Cr,s , where 1 ≤ r ≤ m, 1 ≤ s ≤ n, there
exists a corresponding 1-D cellular array N of length
m + n − 1 with the general at Cr+s−1 such that the
conﬁguration of N can be mapped on M, and M ﬁres
if and only if N ﬁres. The transition table for N consists of four parts, one is a transformation rule set(Type (I)) that is for the inner cells of 2-D array andthe other two sets (Type (II) and Type (III)) are forthe state transition of cells C1,1 and Cm,n . The fourthpart is a new set of transition rules (omitted) for thetransmission of general state in the diagonal direction.
Let δ1 (a, b, c) = d be any transition rule of M, wherea, b, c, d ∈ {Q − {w}}. Then, N has seven Type (I)transition rules, as shown in Fig. 4. The ﬁrst rule(1) in Type (I) is used by an inner cell that does notinclude border cells amongst its four neighbors. Rules

gk = {Ci,j |(i − 1) + (j − 1) = k − 1}, i.e.,
g1 = {C1,1 },g2 = {C1,2 ,C2,1 },g3 = {C1,3 ,C2,2 ,C3,1 },.

©ISAROB 2005

1 2 3 4 5 6 7 8 9 10111213
0 QQQ R QQQQQQQQQ
1 QQ < G > QQQQQQQQ
2 Q < QGQ > QQQQQQQ
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4 W ] QGQQQ > QQQQQ
5 W ] ] GQQQQ > QQQQ
6 W ] ANQQQQQ > QQQ
7 W ] H ] HQQQQQ > QQ
8 W ] H ] R > QQQQQ > Q
9 W ] HQ R H > QQQQQW
10 W ] R H R H N > Q Q Q [ W
11 W ] R H R ] H H > Q [ [ W
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15 W ] R H H Q W Q A A R [ W
16 W ] R H Q [ W ] Q A R [ W
17 W ] R ] [ [ W ] ] [ R [ W
18 W ] H W H [ W ] H W A [ W
19 W ] [ W ] [ W ] [ W ] [ W
20 W W W W W W W W W W W W W
21 F F F F F F F F F F F F F

Figure 3: Time-space diagram for optimum-time generalized ﬁring squad synchronization algorithm andsnapshots for a 12-state implementation of the generalized ﬁring squad synchronization algorithm withthe property Q on 13 cells with a general on C4 .

any m × n arrays in 2(m + n) − 4 steps. The algorithm
is slightly slower than the optimum ones, but the number of internal states is considerably smaller. Beyer [1]and Shinahr [7] presented an optimum-time synchronization scheme in order to synchronize any m × narrays in m + n + max(m, n) − 3 steps. To date, thesmallest number of cell states for which an optimumtime synchronization algorithm has been developed is
28 for rectangular array, achieved by Shinahr [7]. On
the other hand, Szwerinski [8] proposed an optimumtime generalized 2-D ﬁring algorithm with 25,600 internal states.

3

1

t = n-1

t = 2k-2

Cr s

C23

C2 2

1

Area A

gr+n-1
Cr n

g1

C1 1

gr+s-1

C2 4

C1 3

Cn-1

t = k-1

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C2 s

C1 4

g2

gn+1
C2 n

C1 s

g3

C0

1
1

gn
C1n

C-( k-1 )Cm+n-1

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Type (I): Inner cell

For any state a,b,c,d 2 {Q-{w}}such that δ1(a, b, c) = d :
a
a b c
c

d

(2)

w
a b c
c

d

(3)

a
a b c
w

d

(4)

a
w b c
c

d

(5)

a
a b w
c

d

(6)

a
w b c
w

d

(7)

w
a b w
c

d

(1)

[Theorem 2] Let M be any s-state generalized synchronization algorithm with the property Q operatingin T (k, ) steps on one-dimensional  cells with a general on the k-th cell from the left end. Then, basedon M, we can construct a two-dimensional (s + 2)state cellular automaton N that can synchronize any
m × n rectangular array in T (m, m + n − 1) steps.
The one-dimensional generalized ﬁring squad synchro-

Type (II): Upper-leftcorner cell

For any state b, c, d 2 {Q-{w}}such that δ1(w, b, c) = d :
(1)

w
w b c
c

d

Type (III): Lower-rightcorner cell

For any state a, b, d 2 {Q-{w}}such that δ1(a, b, w) = d:a(1) a b w
dw

nization algorithm with the property Q can be easily
embedded onto two-dimensional arrays with a small
overhead. Fig. 3(b) shows snapshots of our 12-state
optimum-time generalized ﬁring squad synchronization algorithm with the property Q.
[Theorem 3] There exists a 12-state one-dimensional
cellular automaton with the property Q that can synchronize  cells with a general on the k-th cell fromthe left end in optimum  − 2 + max(k,  − k + 1) steps.
Based on the 12-state generalized 1-D algorithm
given above, we obtain the following 2-D generalized
synchronization algorithm that synchronizes any 2-D
array of size m × n in m + n − 3 + max(r + s − 1, m +n − r − s + 1) = m + n + max(r + s, m + n − r − s + 2) −4
steps.

Figure 4: Construction of transition rules for 2-D
linear-time ﬁring squad synchronization algorithm.

[Theorem 4] There exists a 14-state 2-D CA that cansynchronize any m × n rectangular array in optimumm + n + max(r + s, m + n − r − s + 2) − 4 steps withthe general at an arbitrary initial position (r, s).

(2)-(5) are used by an inner cell that has a bordercell as its upper, lower, left, right, lower left, or upper
right neighbor, respectively. Here the terms upper,
right etc. on the rectangular array are interpreted ina usual way, shown in Fig. 2, although the array is rotated by 45◦ in the counterclockwise direction. Rules(6)-(7) in Type (I) are used by an inner cell that hasborder cells in its left-lower and right-upper neighbors,
respectively.
t
Let Sit , Si,jand Sgti denote the state of Ci , Ci,j atstep t and the set of states of the cells in gi at stept, respectively. Then, we can establish the following
lemma.

Two additional states are required in our construction (details omitted). Szwerinski [8] also proposed an
optimum-time generalized 2-D ﬁring algorithm with
25,600 internal states that ﬁres any m × n array inm + n + max(m, n) − min(r, m − r + 1) − min(s, n − s +1) − 1 steps, where (r, s) is the general’s initial position. Our 2-D generalized synchronization algorithm ismax(r + s, m + n − r − s + 2) − max(m, n) + min(r, m −r + 1) + min(s, n − s + 1) − 3 steps larger than the
optimum algorithm proposed by Szwerinski [8]. However, the number of internal states required to yieldthe ﬁring condition is the smallest known at present.
Snapshots of our 14-state generalized synchronization
algorithm running on a rectangular array of size 7 × 9with the general at C4,5 are shown in Fig. 5.
Our linear-time synchronization algorithm is interesting in that it includes an optimum-step synchronization algorithm as a special case where the generalis located at the north-east corner. By letting r = 1,s = n, we get m+n+max(r+s, m+n−r −s+2)−4 =m+n+max(n+1, m+1)−4 = m+n+max(m, n)−3.
Thus the algorithm is a time-optimum one. We have:[Lemma 1] The following two statements hold:1. For any integer i and t such that 1 ≤ i ≤ m + n −r − s + 1, r + s + i − 3 ≤ t ≤ T (m + n − 1, r + s − 1), Sgti = 1 and Sgti = Sit . That is, all cells in gi atstep t are in the same state and it is equal to Sit ,where the state in Sgti is simply denoted by Sgti .
2. For any integer i and t such that m+n−r−s+2 ≤i ≤ m + n − 1, 2m + 2n − r − s − i − 1 ≤ t ≤T (m + n − 1, r + s − 1),  Sgti = 1 and Sgti = Sit .

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

[Theorem 5] There exists a 14-state 2-D CA that cansynchronize any m × n rectangular array in m + n +max(m, n) − 3 steps.
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Conclusions

We have proposed a state-eﬃcient mapping scheme
for embedding a restricted class of generalized onedimensional synchronization algorithms onto 2-D
rectangular arrays, then based on the scheme, a
new linear-time generalized synchronization algorithmwith fourteen states has been presented that can synchronize any m × n rectangular array in m + n +max(r + s, m + n − r − s + 2) − 4 steps with the general
at an arbitrary initial position (r, s) of the array. It isshown that the linear-time 14-state solution developed
yields an optimum-time synchronization algorithm inthe case where the general is located at the north-east
corner.

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[1] W. T. Beyer: Recognition of topological invariants by iterativearrays. Ph.D. Thesis, MIT, (1969), pp. 144.

Step:121

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[2] A. Grasselli: Synchronization of cellular arrays: The ﬁring
squad problem in two dimensions. Information and Control,
vol. 28(1975), pp. 113-124.

3

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[3] K. Kobayashi: The ﬁring squad synchronization problem
for two-dimensional arrays. Information and Control, vol.
34(1977), pp. 177-197.

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[6] H. B. Nguyen and V. C. Hamacher: Pattern synchronizationin two-dimensional cellular space. Information and Control,
vol. 26(1974), pp. 12-23.

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[7] I. Shinahr: Two- and three-dimensional ﬁring squad synchronization problems. Information and Control, vol. 24(1974),
pp. 163-180.

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Figure 5: Snapshots of the 14-state linear-time generalized ﬁring squad synchronization algorithm on rectangular arrays.

[10] H. Umeo, M. Hisaoka, K. Michisaka, K. Nishioka and M.
Maeda: Some generalized synchronization algorithms andtheir implementations for a large scale cellular automata.
Proc. of the Third International Conference on Unconventional Models of Computation, UMC 2002, LNCS 2509,
Springer-Verlag, pp.276-286(2002).

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36[9] H. Umeo, M. Maeda and N. Fujiwara: An eﬃcient mapping
scheme for embedding any one-dimensional ﬁring squad synchronization algorithm onto two-dimensional arrays. Proc. of
the 5th International Conference on Cellular Automata for
Research and Industry, LNCS 2493, Springer-Verlag, pp.6981(2002).

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Step:181[8] H. Szwerinski: Time-optimum solution of the ﬁring-squadsynchronization-problem for n-dimensional rectangles withthe general at an arbitrary position. Theoretical Computer
Science, vol. 19(1982), pp. 305-320.

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[4] M. Minsky: Computation: Finite and infinite machines.
Prentice Hall, (1967), pp. 28-29.
[5] 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.

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A Study of the Basic Concept of Information in a Complex System
Yasuo Kinouchi

Takashi Komiyama

Department of Information Systems
Department of media and cultural studies
Tokyo University of Information Sciences
kinouchi@rsch.tuis.ac.jp
komiyama@rsch.tuis.ac.jp
1200-2 Yatoh-cho, Wakaba-ku, Chiba, 265-8501 Japanponents according to their relationships. (3) Each component has inputs, inner states, and outputs.

Abstract
Neither the basic concept of information nor its
fundamental properties have yet been clarified. A basic
concept of information and its fundamental properties are
given from the original viewpoint that information isboth a kind of method of natural recognition and a kindof conceptual structure. The information properties that
contribute to constructing actual complicated systems arealso clarified.
Key words: basic concept of information, conceptual structure, and complex system

The property that information can be copied by usinga small amount of energy is based on the model being
expressed as a conceptual structure and having feature(1). Feature (2) represents the property of receiving information with sensitivity or susceptibility. The inputs
and outputs in feature (3) correspond to transmitting information between components, while the inner states
represent the memory or recording functions of information. In addition, the information quantity proposed by
Shannon can be defined as a measure of the variety ofstimuli from other components at the input interfaces of acomponent. By using this model, the properties of information can be clearly explained, and the main previous information concepts proposed by Wiener, Yoshida,
and Noguchi can be systematically integrated.

1. Introduction
In our modern society, which is a very complicatedsystem, information fulfils an important role, along withenergy and materials. In the world of living things, fromhumans to bacteria, complicated mechanisms operate
based on information. Pure information systems, such as
computer systems and the Internet, are the most complicated systems that human beings have ever produced.
There are now many complex systems based on information. However, Wiener, Shannon, Noguchi, Bateson andYoshida showed their concepts of information, they are
different from each other. The basic concept of information and its fundamental properties have not yet beenclarified. [1]-[6]Moreover, because information can be described asa structure, and a structure generally has the properties offacilitating easy generation of new components by combining multiple components and of enabling resolution of
one component into multiple components, information
systems combining computers and networks can be madeextremely complicated. In addition, these properties are
drastically accelerated through the digitization of information in computers and networks.

In this paper, we first clarify the basic concept of
information and its fundamental properties. We thenshow that the fundamental properties of information
greatly contribute to constructing actual complicatedinformation systems, such as computer systems and network systems.

2. Previous views of information
2.1 Wiener and Shannon’s concepts of information
In the middle of the twentieth century, Wiener advocated using the name “cybernetics” for the entire fieldof control and communication theory, whether in machines or in animals. Information thus fulfils a very important role in cybernetics, and Wiener wrote that “Information is a name for the content of what is exchanged
with the outer world as we adjust to it, and make ouradjustment felt upon it. The process of receiving and ofusing information is the process of our adjusting to thecontingencies of the outer environment, and of our living
effectively within that environment.” [1][2] Thoughfeeding is an important exchange activity with the external world of animal, this is clearly not included. Hence,
information does not include exchanges of energy ormaterials.

We work from the original viewpoint that information is a kind of method of natural recognition. From thisviewpoint, as well as from modeling of a physical phenomenon as a conceptual structure composed of components and clearly defined relations, an information-based
phenomenon can also be modeled as a kind of conceptual
structure. This structure has the following features, differing from the model of a physical phenomenon:(1) The components and the relations between components in the structure must be expressed in forms that do
not depend on their physical properties. (2) Each component one-sidedly determines the effects of other com-

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cinctly defined as any difference which makes a difference in some later event. This definition is fundamental
for all analysis of cybernetics systems and organization.
… What is a difference? A difference is a very peculiar
and obscure concept. A difference is an abstract matter.
…What we mean by information– the elementary unit of
information –is a difference which makes a difference,and it is able to make a difference because the neuralpathway along which it travels and is continually transformed are provided with energy.” [5]At the same period, Shannon established the basisof communication theory, in which communication functions by utilizing an information source, a transmitter, achannel, a receiver, and a destination. He discussed only
the “quantity” of information without mentioning its“meaning,” and he advocated that that quantity of information is measured by a decrease in entropy. [3] Since
an information source and a transmitter are assumed withregard to information transmission in Shannon’s model,
his concept of information has a narrower meaning than
Wiener’s.

Bateson defined information by referring to the information transfer in a neuron as “the difference whichmakes the difference,” as shown above. On the key concept of “difference,” however, he said only that it is abstract. It has not yet been clearly defined. Consider asystem receiving multiple external stimuli. In this case,there are two viewpoints on determining whether thesestimuli differ or resemble each other. One viewpoint is tomutually compare the multiple stimuli. The other viewpoint is to compare the effects that each stimulus has on
the system. Whether the stimuli mutually differ or resemble each other depends on the properties selected forthe comparison, which are limitless in their diversity. We
think that the difference necessary for defining information must be based on the differences in the effects of thestimuli on the system. These differences are not abstract
and can be treated very concretely. Bateson’s concept of
information must be reexamined from this viewpoint. In
addition, Bateson’s indication that neural pathways areprovided with energy means a kind of physical condition
for copying or transmitting information by using very
little energy.

2.2 Noguchi’s concept of information
In 1974, Noguchi, a Japanese economist, gave thewidest definition of an information concept, which was
based on the capability of copying. He wrote that “Thebasic concept of information is the capability to copy it
by using a very small amount of energy while leaving theoriginal information in the same condition afterward.
Although the copying capability is an important property
of information, more precisely, something that can becopied is called information itself.” [4]The capability of copying as the definition of information can be intuitively understood from the routine
work of copying paper documents with copy machines.
However, information is not the only thing with theproperty of having the capability to be copied. For example, consider an iron bridge over which cars pass. It is
possible for a similar purpose to be achieved by building
an iron bridge of equal composition or structure in another place. In addition, it is possible for cars to passover a wooden bridge, instead of an iron bridge. In general, the equivalent purpose can be achieved by any
similar “structure” of stone, iron, or wood, which can beconsidered a kind of copying. However, a bridge with asimilar structure cannot be copied by using only a verysmall amount energy, and thus, Noguchi thought that a
copied bridge was not information. From a different
viewpoint, consider the world of micromachining. The
copied machines in this case also are not information,even though various structures can be copied with a verysmall amount of energy. Therefore, the capability of
copying by using a very small amount of energy in Noguchi’s definition should be reexamined in terms ofstructure. In addition, we must consider the fact that
where Wiener regarded as system receiving information
as important, Noguchi regarded the copied content asimportant.

In addition to Bateson, the Japanese philosopher,
Yoshida, has advocated the basic concept of information
based on “difference”. Moreover, he used the word “informational phenomena” in his argument. [6] Usually
we express and model natural phenomena related tochemistry or physiology by using the terms chemical
phenomena or physiological phenomena. It is very interesting that natural phenomena related to information canbe similarly expressed and modeled in terms of "informational phenomena."

2.4 Watanabe’s view
The Japanese physicist, Watanabe, who is investigating a mathematical model of information propagation,
claims that the ”sensitivity” of the receiver is very important, because the effect of information is greatly different as received by each person, even if it is the same
information. [7] As described above, both Wiener andBateson mentioned the importance of the component
receiving information, but Watanabe clearly and intuitively conceptualized the importance by using the word
“sensitivity.”

2.3 Bateson and Yoshida’s concepts of information
Bateson showed a view of information based on“difference” in his book, Steps to an Ecology of Mind, as
follows: “The technical term ‘information’ may be suc-

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system, a machine, a dog, a cell, or an atom. On the other
hand, the conceptual system does not really exist but is asymbolic construct, such as mathematics, physical laws,
or the plan of a bridge. We classify the conceptual system expressing a model of nature as shown in Fig. 2.

3. Informational phenomenon and informational structure

In this section, we give our view of information in
the widest sense.

3.1 Information and informational phenomenon
as a method of natural recognition
We clarify information in the widest sense as aphenomenon reduced to a physical phenomenon. However, information cannot be clarified, even if we microscopically observe the mutual relationships between materials. It cannot be expected that information or functions of information are recognizable from the relationships between material particles under gravity. As shownin Figure 1, there are chemical phenomena in which materials react in chemical relations, and physiologicalphenomena overlaying the chemical phenomena. In addition to these phenomena, we recognize a phenomenon
related to information as an “informational phenomenon,”as shown by the dashed ellipse. We thus recognize
information as a kind of method of natural recognition,
so that chemistry and physiology may also be methods ofnatural recognition. As an example of recognizing acomplicated system, there are macroscopic observation
or a method of coarse graining. Though it differs fromthese methods, a physical phenomenon of some kindplainly be grasped by recognizing its nature as an information phenomenon of information. Also, the basic concept of information can be clarified by modeling thisinformational phenomenon.

(1) Conceptual structure: A subset of a conceptual system,
in which the relationships among components must be
defined clearly. (An example is a model using physicallaws.)(2) Conceptual structure not depending on physicalproperties: A subset of a conceptual structure, in which
the relationships among components must be expressedin forms that do not depend on physical properties. (Aphysical property means a property of a physical phenomenon in the widest sense, including chemical andphysiological property, and so on. An example is a modelusing mathematical expressions.)(3) Informational structure: A subset of a conceptual
structure not depending on physical properties. An informational structure represents or models informational
phenomenon and has the following properties:
a. Each component one-sidedly determines the effects
of other components according to their relationships.
(This property represents the sensitivity of a receiver.)b. Each component has inputs, inner states, and outputs.
Each component determines its outputs and inner states
as functions of inputs and previous inner states.
In an informational structure, transmission, memory,and processing of information are carried out as follows.
The relationship between components that send out or
receive changes corresponds to “transmission.” In somecases, these changes occur as physical stimuli originatingfrom light, sound, or electric current. In other cases, theyoccur as a result of transferring material, such as DNA ordocument. The setting or retaining of the inner states of acomponent corresponds to “memory,” and the conversion
from input states to output states in the component corresponds to “processing.”
The digital computer is an example of an informational structure modeling an informational phenomenon.
Each logical circuit in a digital computer certainly works

3.2 Modeling informational phenomena
The technical terms used here are defined as follows
in order to discuss the idea of an informational phenomenon. We define the term “system” as “a sets ofcomponents or elements standing in interrelation,” according to the definition of Bertalanffy in his book, General system theory. [8] A “system” is classified into two
categories: a real system, and a conceptual system. The
real system is entities that really exists, such as a solar©ISAROB 2005

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according to a very complex physical phenomenon basedon the motion of electrons, where these actions are described by chains of ON or OFF values, whose effects
are one-sidedly determined by adjoining circuits and as awhole are not dependent on physical properties.

development is easy for the conceptual structure, as themathematics is developable. The informational structure
also has the property that the result of development becomes easy to implement as a real system, based on theproperties of easy replacement and copying, and the lackof dependence on physical properties. Since these four
properties work comprehensively, the construction of acomplex system becomes very easy by applying them.
By digitizing the informational structure, this tendency
can be further accelerated. Representative examples include today's computer systems and communication
network systems.

3.3 Reexamination of the previous information concepts based on the informational structure
Recognizing information as a kind of process according to the view of Wiener and Bateson corresponds
to recognizing the relations in an informational structure
whose components are an animal, a neuron, and an environment. Components receiving various stimuli in a realsystem distinguish whether it is “information” by using
the “difference” advocated by Bateson and Yoshida. In
an informational structure, the “difference” is converted
to a quantity that does not depend on physical properties.
Under conditions that functions of the component
receiving information are not changed in the informational structure, we can replace input stimuli or entities
to the component by different ones in the corresponding
real system. This capability of replacement corresponds
to the capability of copying as the basic concept of information, as advocated by Noguchi, because a copy is akind of replacement that maintains the same effects on
the component receiving information.

5. Conclusion
The basic concept of information and its fundamental
properties have been clarified from the original viewpoint that information is both a kind of method of natural
recognition and a kind of conceptual structure, called an“informational structure.” Relations, actions, and statesin this informational structure model “information” itself
and its functions. This model can account for previous
information concepts, such as those of Wiener, Shannon,
Bateson, and Noguchi. The properties of information
showed here greatly contribute to constructing complex
systems.

Acknowledgments

The information quantity of Shannon showswhether a component receiving information recognizes
the outside of the component to a certain degree of fineness. The input of information with a quantity of n bits
enables the component to recognize the outside as aspace composed of 2n pieces.

We thank Prof. Shuji Hashimoto of Waseda University and Mr. Shoji Inabayashi of Pacific Technos Corp.
for useful suggestion. This work was supported by Frontier Project of the MESC.

References
4. Contributions of information properties toconstructing complex systems[1] N.Wiener(1948), Cybernetics: or control and Communication in the Animal and the Machine, MIT Press[2] N.Wiener(1954), The Human Use of Human Beings - Cybernetics and Society, Da Capo Press[3] C.E.Shanonn(1948), Mathematical theory of communication, The Bell system technical journal Vol.27,
pp379-423,623-656, (1948)[4] Yukio Noguchi(1974), Economical theory of information(in Japanese), Toyo keizai sinpohsya[5] G.Bateson (1972), Steps to an ecology of mind, The University of Chicago Press[6] TamitoYoshida (1990), Informational science of self-organization(in Japanese), Shinyohsya[7] Tadashi Watanabe and Takeo Ebisu(2003), A Mathematical
Model for Information Propagation( in Japanese), Journal ofTokyo University of information sciences, Vol. 6 No.2, pp27-38[8] L.V. Bertalanffy(1968), General System Theory–Foundations, Development, Applications, George Braziller

As shown above, an informational structure has thefollowing three properties.
a. An informational structure is not dependent on
physical properties.
b. The actions of an informational structure function in
uni-directionally.
c. A real system represented by an informational structure can be simply replaced with different materials, orcomponents while still maintaining all functions. Then
information can be copied easily.
In addition, the next important property should also
be applied.
d. Development is easy in an informational structure,and the result of the development is easily implemented
as a real system.
Here, "development" means the generation of a newcomponent from multiple components and the resolutionof a component into multiple components. Generally,©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Quantitative analysis of self-organizing multiagent intera tionwith entropy and mutual information

Koji Nishikawa Hidenori Kawamura Masahito Yamamoto Azuma Ohu hi
Graduate S hool of Information S ien e and Te hnology, Hokkaido University
Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido, 060-0814, Japanfkoji, kawamura, masahito, ohu hig omplex.eng.hokudai.a .jp

behavior is required in designing systems.
Histori ally, resear hers analyze system's overallbehavior with the indi ators su h as a task a hievement ratio and a payo . These indi ators are important for the point that agents need to a omplish atask. However they are inadequate to make lear theentity of omplex systems and it is diÆ ult to say why
and how the task is a hieved or how we get highers ore. Sin e they are spe ialized in the target systemand the analysis is also spe ialized. Therefore analyses that quantify the entity of systems universally are
required. In these respe ts, there are resear hes that
omprehend the dynami s of systems and agents withthe on ept of entropy [1℄, [2℄, [3℄, [4℄. In these resear hes they onsider about the behavioral diversity
and the onstraint with varian e of entropy, and attempt to quantify the system.
In this paper, we propose the approa h that analyzes a multiagent system quantitatively by fo using
on the dynami s of a system and the intera tion among
multiagents as the entity of systems. In parti ular, wequantify the system's overall behavior and the intensity of intera tions among agents and analyze systems.

Abstra t

In the resear h of a multiagent system, the indiators su h as a task a hievement ratio and a payo
have been used for analyzing a system. These indiators are important for the point that agents needto a omplish a task. However they are inadequate
to make lear the entity of phenomena that o ur inomplex system, be ause they are spe ialized in thetarget system and the analysis is also spe ialized. In
these respe ts the approa hes that analyze a system
quantitatively are begun to investigate in re ent years.
In our resear h, we propose the approa h that analyzes a multiagent system quantitatively by fo using
on the dynami s of a system and intera tion among
multiagents. We use two indi ators, i.e., entropy andmutual information, for analyzing a system. Entropy
estimates the behavior of an agent and mutual information estimates intera tions between two agents. For
verifying these propositions, we ondu t veri ation
experiment in the simple slime mold model. The result shows a relationship between agents' behavioral
patterns and two indi ators, therefore our approa husing entropy and mutual information is available foranalyzing a multiagent system.
Keywords.

2

Multiagent system, Quantitative analysis,

entropy, mutual information, intera tion

1

The intera tions among multiagent in selforganization systems are onsidered to be inter hange
of information about the state of agent. The dis ussion of the relationship between these information andentropy is done histori ally [5℄. In parti ular, de reaseof entropy is great issue, and it is said that gain of information auses de rease of entropy andemerge onstraint and gain of information onstantly keeps up these onstraint.

Introdu tion

The resear hers who onstru t multiagent systems must deal with the omplex behavior of selforganization. Self-organization in multiagent systems
emerges from agents' behavior, whi h is independently
autonomous but orporately stru tured by intera tions among agents and the environment. Resear hers
want to reveal these intera tions be ause the oheren eof the agents' lo al behavior and the system's overall©ISAROB 2005

Analysis of multiagent intera tion

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Therefore it is onsidered that information and entropy are important to apture the entity of multiagent intera tion.
The on ept of entropy is de ned three ontexts,
i.e., thermodynami s, statisti al me hani s and information theory; parti ularly information theory de nition is alled information entropy or Shannon's entropy [6℄. Though informational de nition does notinherit two other de nitions, there are ommon entities be ause of the sameness of these formulas. With
using information entropy, analysis of multiagent systems are studied in thermo dynami s perspe tive [1℄and nonequilibrium thermodynami s perspe tive [2℄.
Therefore we use informational theoreti al approa h
to quantify the entity of multiagent system.
Our analysis method uses two indi ators entropy
and mutual information, where mutual information is
the indi ator of the value of information. In our approa h we an analyze
1. agent's behavior, e.g., stability, onstraint or omplexity with entropy and2. the intensity of intera tions between two agentswith mutual information.
When one omputes these indi ators, what to observe as states is the great issue. Then onsider aboutan agent, it is the autonomous individual that takes input from environment through its sensors and outputs
through these input and de ision-making pro ess. At
this time, agent's de ision-making is followed its internal state, whi h an be variously designed. For apturing the essential states of agent that is not spe ialized
in systems, agent's internal state depends systems andis not just as well. Whereat we use agent's input andoutput as the state of agent.
3

Figure 1: The environment of slime mold model
suddenly hange their behavior and aggregate to a single multi- ellular body. During this aggregation proess, they emit a hemi al signal alled AMP to guidethe olle tive movements. As they move, they follow
the AMP gradient in the environment.
The whole pro ess is a self-organization. Though
all amoebas a t with lo al information around themand without any other guidan e su h as oordinating
the aggregation, they aggregate.
In the slime mold model, amoeba agents are pla edon the grid world as Figure 1. An agent and environment model are des ribed as follows.
Agent model

An agent a ts as des ribed below at ea h step.
1. Put AMP on the urrent grid.
2. Sense the density of AMP on the forward 3 grids
in 8 neighbors.
3. Move to the grid that has the most AMP.
Environment model

In the environment, AMP is de ned on the gridas parameters T (x; y) and P (x; y). T (x; y) denotes
the amount of AMP on the grid (x; y), and P (x; y)denotes the density of AMP over the grid (x; y). An
agent an sense the density of AMP over the grid.
As time passes, AMP evaporates and di uses. By
evaporation and di usion, T (x; y) and P (x; y) hanges
into T (x; y) and P (x; y) at ea h stepT  (x; y ) = (1(1)
eva )T (x; y ) + T
exists on grid (x; y) (2)T = f Q0 if an agentotherwise

Experimental setup

We experiment with these on epts using a simple
model of self-organization, slime mold model. In thisse tion we des ribe the experiment in the slime moldmodel and how one measures entropy and mutual information.
3.1

Slime mold model

The group behavior of slime mold ells is the famous
fo us for models of self-organization [7℄. Normallythey move around as individual amoebas throughout
their substrate, performing a simple random walk.
But when the environmental situation worsens, they

©ISAROB 2005P  x; yP x; y;ydif fP x
P x;y
P x; yP x; yP x; y geva T x; y( )= ( )+( 1 )+( +1 )+ (1) + ( + 1)
5 ( ) +
( )

359

(3)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

where Q denotes the amount of AMP put by an agent
in 1 step, and eva and dif denotes the evaporation
rate and the di usion rate of AMP, whi h de nes theproperty of AMP.
3.2

Table 1: The distribution of agents' entropy and mutual information in the slime mold model (in 100 trials)HIV (I )V (H )
rId
3
a. 0.21 5:2  10 0.53 1:5  10 2 -0.31
b. 0.048 3:1  10 4 0.34 4:5  10 3 -0.41. 0.23 4:9  10 3 0.54 1:1  10 2 -0.34
d. 0.056 6:8  10 5 0.39 3:3  10 4 -0.11

The parameter of slime mold model

In the slime mold model, the AMP's property thatis de ned by evaporation and di usion rate a e ts
the pattern of agents' self-organizational pro ess. We
ompare these various patterns and the distributionof entropy and mutual information in our experiment,
and verify our proposition.
The environment is 50  50 grid world, and ontained 50 agents. We hange AMP's property to vary
information's property among agents in respe t of time
length and a ura y, and the value of evaporation anddi usion rate is set to 4 di erent values, i.e.,eva = 0:1, dif = 0:1
eva = 0:1, dif = 0:3
eva = 0:5, dif = 0:1
eva = 0:5, dif = 0:3.
We experiment with these setups at 1000 steps.

4

Table 1 shows entropy and mutual information inea h setup, a  d, where I and V (I ) denote the average and varian e of mutual information for randomly
sele ted 100 pairs of agents. H and V (H ) denote theaverage and varian e of entropy for all agents. rId
denotes orrelation oeÆ ient between mutual information and Eu lidean distan e of pairs of agents. The
results show that the average of mutual informationis larger in the setting a and than b and d, and
it is onsidered that agents intera t more intensive inthe setting a and . The average of agents' entropy issmaller in the setting b and d, and the states of agentswill be stable.
On the other hand, we look the states of agentsin visible by Figure 2  5. In the setting a and ,many agents form lusters by self-organization. On the
ontrary many agents a t independently in the settingb and d. These behavioral patterns are same as we see
the di eren e of mutual information in ea h setup on
the point of the intensity of intera tions. Moreover,the value of rId shows tenden y that the loser two
agents are, the intensive they intera t.
In addition, we look the pro ess of forming luster
to look the stable of the system. In the setting a and, be ause of the intensity of intera tions, the agents
form various luster one after another in 1 experiment.
While in the setting b and d, when the agents form aluster, they seldom go out from this luster and keep
it up be ause the AMP dropped by the agent out ofthem is weak and not enough to draw them in. Thesebehavioral patterns are also same as the analysis byentropy, i.e., the states of agents are more stable inthe setting b and d than a and .

a.

b.
.

d.

3.3

Measuring entropy and mutual information

Computing entropy and mutual information requires that we measure
1. the set of an agent X 's states x 2 fx1;    ; xn g(i.e. input and output) and2. the probability p(x) of being those states,
where the agent's input is the dis rete density of AMP
that exist on agent's forward 3 grids, and output is theagent's moving dire tion toward its fa ing dire tion.
Measuring those states is observation of the agent at
ea h step. To measure the probability, we take Monte
Carlo approa h. By ounting those states in wholestep of 1 experiment, we estimate the probability.
With using these variables, entoropy of an agent X
and mutual information among an agent X and Y isde ned in following equations,
H (X ) =p(x) log p(x)(4)

X
X X ( ) log ( )( : )=x

I X Y

p x; yx©ISAROB 2005yp x; ypxpy()()

Experimental results

5(5)Summary

In this paper, we proposed the quantitative analysis of multiagent intera tion with entropy and mutual360

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3: The look of the
Figure 2: The look of the Figure
system1000 step in thesystem at 1000 step in the setting b.at The
form
setting a. The agents form small lusters, agents
andsomesome lusters.
agents a t independently.

Figure 4: The look of the Figure 5: The look of thesystem at 1000 step in the system at 1000 step in thesetting . The agents form setting d. Many agents a t
big luster.
independently.
information. We ondu ted veri ation experiment in
the slime mold model to quantify the intera tions in
self-organizational pro ess. The results show the relationship between agents' behavioral patterns and ouranalysis, and validity of our proposal method.
Referen es[1℄ H.Van Dyke Parunak, Sven Brue kner, \Entropy
and Self-Organization in Multi-Agent Systems",
Pro eedings of the Fifth International Conferen e
on Autonomous Agents, pp.124-130, 2001
[2℄ Stephen Guerin, Daniel Kunkle, \Emergen e ofConstraint in Self-Organizing Systems", Journal of
Nonlinear Dynami s in Psy hology and Life S ien es, Vol. 8, No. 2, 2004

©ISAROB 2005

361

[3℄ Mikhail Prokopenko, Peter Wang, \Evaluating
Team Performan e at the Edge of Chaos", In Proeedings of the 7th RoboCup-2003 Symposium,
2003[4℄ Tu ker Bal h, \Hierar hi So ial Entropy: An Information Theoreti Measure of Robot Team Diversity", Autonomous Robots, Vol. 8, No. 3, 2000
[5℄ Christoph Adami, \Introdu tion to Arti ial Life",
Springer-Verlag, New York, 1998
[6℄ Claude E. Shannon, Warren Weaver, \The Mathemati al Theory of Communi ation", University of
Illinois Press, 1949[7℄ Mit hel Resni k, \Turtles, Termites, and TrafJams: Explorations in Massively Parallel Miroworlds", MIT Press, 1997

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Transforming Information to Knowledge and Intelligence*
Yi. X. ZHONG
University of Posts & Telecommunications
Beijing 100876, P.R.CHINA
e-mail: yxzhong@ieee.org

Abstract: Intelligence is regarded as the most precious wealth among all kinds of capabilities. It
would be of great interest and significance if the formation mechanism of intelligence could beunderstood. It is our discovery that intelligence is activated from knowledge and the latter in turnis refined from information and the transformations of information to knowledge and further tointelligence and that is the secrets of intelligence formation. These discoveries will be reported in
the article as what could be followed for building intelligent machine. As an interesting by-product,
the three existing approaches to AI research have also been unified in the article.
Key Words: Information, Knowledge, Intelligence, Transformation from Information to
Knowledge and to Intelligence
1, Introduction
Based on the observations and results
accumulated during his research, the author of
the article would like to give some of theanswers toward the questions above. Due tothe space limitation we have for the article, theanswers will be concise and brief.

As the most attractive, powerful, and unique
attributes to human beings, intelligence has
been received more and more attentions fromscientific circles. It would be a great scientificbreakthrough in history if human intelligence,or even part of it, can steadily be transferred tomachines.

2, Model of Human Intelligence Process
To begin with, a model of intelligence process
performed by humans is necessarily given as abasis of the discussions that will be carried onin the article.
In Fig.1 below shows amodel of human intelligence process in theboxes of which are human organs (the sensors,nerve system, brain and actuators) while
outside the boxes are the functions the organs
perform (information acquisition, informationtransferring, information cognition & decision

What is the essence of intelligence we should
understand in the context? What is thetechnicallyfeasiblemechanism
for
intelligence formation? Would it be reallypossible for humans to make machines
intelligent? How to make machines intelligent,
if possible? And what if intelligent humans
can eventually utilize and manage theintelligent machines?

----------------------* The work is supported in part by National Project 863-2001AA114210-13©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

making, and strategy execution) and alongside
Definition 1 Epistemological Information ofan object is a description, given by the subject(observer or user), concerning the object’s
states and the manner including their forms,
meanings and utilities and are respectively
termed as the Syntactical Information,
Semantic
Information
andPragmatic
Information, see [1].
Definition 2 Knowledge: Knowledge
concerning a category of objects is thedescription, made by subjects, on various
aspects of the states at which the objects may
stay and the law with which the states mayvary. The first aspect is the form of the states
and law and that is termed the formal
knowledge, the second aspect is the meaningof the states and law that is termed the content
knowledge and the third aspect is the value ofthe states and law with respect to the subject
that is termed the value knowledge. All thelatter three aspects constitute a trinity ofknowledge [2].

The model tells how human beings solve theproblems they face in the real world and how
the intelligent strategies and accordingly theintelligent actions are produced fromknowledge and information.
3, The Transformations
The mechanisms embedded in the process ofintelligence formation consist of a number oftransformations that will be explained in thefollowing sub-sections. Due to the limitationof the space we have for the paper, only the
nucleus, the transformations of information toknowledge and then to intelligence are dealt
with in the paper.
3-1 The Transformation: from Information
to Knowledge
The first step is to deal with thetransformation from information to knowledge
and here the information is the so-called
epistemological information as is shown in
Fig.1. The related concepts can be given
below.

K ⇐ ∩{I E }The definitions 1 and 2 indicate that thetransformationfromepistemologicalinformation to knowledge can be implemented
through inductive algorithms:(1)More specifically, the formal knowledge canbe refined from syntactic information, content
knowledge can be refined by semantic
information, and utility knowledge can be
refined by pragmatic information throughinduction/deduction as indicated below:

where the symbol ∩ in (1) stands for induction
operator; {IE} the sample set of theepistemological information; and K theknowledge produced by {IE}. In some cases,
there may need some iterations between
induction and deduction and the deduction
itself can be expressed as

K new ⇐ ℜ{K old , C}(3)

K V ⇐ ∩{I pr }(4)

K C ⇐ ∩{ℜ( I y , I pr , C )}

(5)

(2)

where C stands for the constraint for
deduction. Fig.3 shows the general process oftransformationfromepistemologicalinformation to knowledge.

©ISAROB 2005

K F ⇐ ∩{I sy }where the symbols KF ， KV and KC
respectively stand for formal, content andvalue knowledge while Isy and Ipr for syntactic

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intelligence and is therefore also called
intelligent strategy.

and pragmatic information. The generalalgorithms related to （3）,（4）and（5）canbe referred to [2].

The transformation from knowledge andinformation to strategy can be expressed as

Knowledge, in accordance with its maturity inthe process of growth, can roughly be further
classified into three categories: theexperiential
knowledge,
theregularknowledge and the knowledge in common
sense.

I S : ( P, E , G; K )(6)

where IS denotes the strategy, P the problem tobe solved, E the constraints given byenvironment, G the goal of problem solving, K
the knowledge related to the problem solving
and S the space of strategies. Theoreticallyspeaking, for any reasonably given P, E, G
and K, there must exist a group of strategies
such that the problem can be solved
satisfactorily and among the strategies there
will be at least an optimal one guaranteeing
the optimal solution.

Definition 4 Empirical Knowledge: The
knowledge produced by induction yet without
verification is named the empirical knowledge.
It may also be called the potential knowledge,or pre-knowledge, sometimes.
Definition 5 Regular Knowledge: The
regular knowledge can be defined as matured
knowledge. It is the normal stage ofknowledge growth.

The specific form of the transformation will
be dependent on the properties of the problem
faced and the knowledge possessed. For
empirical knowledge, the form of thetransformation can obviously be implemented
via learning/training and testing. In fact, this is
the mechanism of neural network’s learning[3].

Definition 6 Knowledge in Common Sense:
There exist two sub-categories: axiom andreflection with or without conditions. Learningand reasoning process are not needed in the
category. Knowledge can also be called the
popular knowledge.

As for the regular knowledge, thetransformation can be implemented via aseries of logic inferences. More specifically,
for the given problem, constraints, goal andthe related knowledge, it is possible to form atentative strategy for the selection of rules for
applying to the problem and producing a newstate of the problem. Diagnosing the new state
by comparing it with the goal and making
analysis based on the related knowledge, thetentative strategy can thus be improved or
maintained. A new rule can then be selected toapply to the new state and new progress may
be made. This process will be continued until
the goal is reached, the constraints are met. In
the meantime, the strategy is also formed.

3-2 Transformation: from Knowledge toIntelligence
The task for decision-making in Fig.1 is tocreate an intelligent strategy, based on theknowledge and information, as the guidelines
for problem solving intelligently. The strategy
is the embodiment of the related intelligence.
Definition 7 Strategy: A Strategy for problem
solving is sort of procedure, produced basedon the related knowledge and information,
along which the given problem could besatisfactorily solved, meeting the constraints
and reaching the goal. It is the embodiment of©ISAROB 2005

S

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Evidently, this is the mechanism of strategy
formation in the so-called Expert System [4].

As is seen in last section, however, all thethree approaches have well been unified into
one same mechanism of intelligence formation,that is, the transformations from information
to knowledge and further to intelligence. In
views of the mechanism of intelligence
formation, there is a unified theory ofintelligence, realizing the unification among
the three approaches. This is shown in Fig.2.

In the case of common-sense knowledge, themechanism of intelligent strategy formation
can be implemented by directly linking the
input pattern and the strategies. As long as the
input pattern is recognized the strategy forcontrol can immediately be determined basedon the common-sense knowledge direct
related to the problem without any inferences
needed. This is the typical feature of strategy
formation sensor-motor category [5].

It is clearly enough to see from Fig.2 that thereal difference among the three traditional
approaches lies only on the categories ofknowledge in use while the core mechanismsof cognition and decision-making are always
necessary.

Summary: As it is indicated in Fig.1, there
are four categories of functional units in theentire information process (also intelligenceprocess). The units of information acquisitionand execution are two kinds of interface
between intelligent system and the external
world: the former acquires the ontologicalinformation from the external world while thelatter exert strategic information to theexternal world. On the other hand, the units of
information cognition and decision-making
are the two kinds of inner core of theintelligent system: the former createknowledge from information and the latterproduce strategy from the knowledge. Only by
the synergetic collaboration among all thefour functions could make intelligencepractical.

As stated in section 3-2 and shown in Fig.2, if
the knowledge to be used must be refinedfrom information directly and instantly (this is
the first category of knowledge, or
experiential knowledge), the implementationof cognition and decision-making will have toemploy the training and testing procedure byusing, for example, the artificial neuralnetworks approach. This is so-called
Structuralism because Artificial Neural
Networks are designed by following the
Biological Neural Networks in principle.
If the knowledge to be used can be obtained
from somewhere and not necessary to berefined from information (this is the second
category of knowledge, or regular knowledge),the cognition is simply be performed by thesystem designers while the decision-making
can be implemented via logic inference. Thisis the approach emphasizing on the functionsof the system without considering thestructure.

4. Unified Theory of Intelligence: A
By-Product
It is interesting to note that in a long history of
Artificial Intelligence development there havebeen three strong approaches to the research inliterature, the structuralism also called asconnectionism, the functionalism also termed
as symbolism, and the behaviorism or
senor-motor approach, that seem distinctive toeach other.

©ISAROB 2005

When the knowledge to be used is the third
category, the common sense knowledge about

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the relationships between the input patterns
and output actions, there is of course no needfor else knowledge. This is behaviorism
approach.
After all, the three approaches in Artificial
Intelligence research are by no means incontradiction.
They,
Artificial
NeuralNetworks Approach, Expert Systems Approach,and Senor-Motor Approach, are wellcomplementary to each other and no one cantake the other’s place.

Fig.1 Model of Human Intelligence
Process

This, the author believes, is an important
conclusion resulted from the studies in thecore transformations in intelligence creation.
References[1] Y. X. Zhong, Principles of Information
Science ， BUPT Press ， 2002 （ The Third
Edition）[2] Y. X. Zhong，A Framework of Knowledge
Theory, China Engineering Science, Vol.2,
No.9, p.50-64, 2000
[3] J. M. Zurada, Introduction to Artificial
Neural Systems, West Publisher, New York，
1992[4] N. J. Nilsson, Principles of Artificial
Intelligence, Springer-Verlag, Berlin, 1982
[5] R. A. Brooks, Intelligence without
Representation, Artificial Intelligence, 4,139-159, 1991

©ISAROB 2005

Fig.2 Intelligence Theory Unification

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Establishment of sound-corresponce laws of word-initial consonants
between Finnish/Uralic and Malayo-Polynesian/Proto-Austronesianlanguages: Towards making comparative analysis of word-initialconsonant frequencies
1

Koji Ohnishi, 2Shigeki Akiyama, 3Yusuke Murayama, and 4Masaki Goda

1,3

Dept .of Biology and 2Dept of Mathematics, Faculty of Sciences,and 4Faculty of Engeneering, Niigata University, Niigata, 950-2181, Japan.
1

E-mail: ohnishi@sc.niigata-u.ac.jp

suggested an interesting phenomenon, which

Abstract
In order to make comparative analysis of

however needs more evidence based on detailed

word-initial sound (or consonant) frequencies

comparative and statistical analyses.

among different languages or language families, it
is most important for us to find basic knowledge
concerning

consonant-correspondence

laws

1.

Introduction

among related languages. However, we have very

Comparative linguistics has had shown thatfew knowledge about such laws useful forsound-correspondence laws are most importantcomparing distantly related language families. In

for comparative and evolutionary analyses ofthis paper, Finnish-Austronesian sound-corres-languages. Whether Zipf’s law can be found or

pondenceusing

not in the distribution of word-initial consonants

comparative dictionaries, and Finnish (Uralic)-

is an interesting problem of theoretical linguistics.

Austronesian sound correspondence laws wereIn order to begin comparative analyses of word

established for most of word-initial Finnish (and–initial sound-(or consonant-)frequencies between

proto-Uralic) consonants, as shown in Tables 1

Uralic (URA) and Austronesian (AN) languageand 2. This establishment provides us with a very

families,

important tool not only for evolutionary Uraliccorrespondence laws between these two language

language studies, but also for comparative

families. This approach is based on the findings ofanalyses of word-initial sounds (or consonants)

Austronesiian origins of various Eurasiatic andamong distantly related Eurasian languages.

Pacific-rim languages (Ohnishi, 1999).

laws

were

analyzedbyWord-initial consonant frequencies in Finnish©ISAROB 2005367

we attempted to elucidate sound-The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2. Establishment of word-initialsound-correspondence laws
between URA and AN

analyzedinrepresentative

URA andAN

languages.

By letting xN denote the number ofpages of the words(in A. Wuolle “Finnish–English

For finding sound-correspondences in cognate

and Emglish-Finnish Dictionary”, Helsinki, 1978)

words (“evolutionarily related words”= “homo-for the N-th most frequent consonant in word-

logous words” ) between URA and AN, cognates

initial position, the relationship between N and xN

were extensively searched by using “Uralisches

was found as shown in Fig. 1, suggesting a simple

Etymologisches

relation, ln xN = a - k N,

Woeterbuch”,“Austronesiani.e., xN = A e -kN,

Comparative Dictionary” and others. which are

where a, A, k are constants (> 0). Further analyses

listed in the “References” portion of Table 1.

are needed for confirming this phenomenon.

Extensive comparative search for cognates

4.

between Finnish (Finn.) (and other Uralic

N = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20}

languages such as Hungarian (Hung.), etc.) and= {k, #, s, t, p, v, h, l, m, r, n, j, o, i, y, u, b, d, f, g}

Austronesian languages has revealed that many

XN

References: See references in Table 1.

basic Urklic words have their own cognates inAustronesian, especially in Malayo-Polynesian(MP) subfamily consisting of Western MP(W.MP), Central MP (C.MP) and Oceanic (OC).
Based on these cognate relations listed in Table 1,
sound-correspondence-laws between Finn.(, URA

N

and prpto-URA) and AN (mostly MP) for word-ln XN

initial consonants were analysed and elucidated,
as summarized in Table 2. Very beautiful soundcorrespondence laws have now been establishedfor nearly all Finnish word-initial consonants,
which means that Uralic language family have
evolved from a branch of MP subfamily of theN

AN language family.

Fig.1. Relationship between N and xN, where xN,
denotes the No. of pages (proportional to the No. of

3. Analyses of word-initialconsonant frequencies

words) in Finnish–English Dictionary for the N-th mostIn the next step of our research, sound-xN = {61.1, 57.2, 48., 46.3, 45.3, 44.2, 34.8, 27.7, 23.7,

frequencies©ISAROB 2005

inword-initial

positionsfrequent word-initial consonants. “#”(in N={})= None.

were

21.3, 12.2, 11., 10.3, 9.9, 9.6, 7., 1.2, 1.1, 1., 0.8}

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Table 1.
consonants.

©ISAROB 2005

Sound-correspondence laws of Finnish/Uralic word-initial consonants to Austronesian
List of Uralic-Austronesian cognate words.

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Table 1. (Continued. )©ISAROB 2005

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Table 1. (Continued. )©ISAROB 2005

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Table 1. (Continued. )Table 2. Word-initial sound-correspondence laws of consonants between Finnish/Uralic and Austronesian©ISAROB 2005

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Effect of action selection on the emergence of one-way communication
using Q-learning
Masanobu Nakanishi, Katsunari Shibata
Dept. of Electrical and Electronic Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan

Abstract
In this paper, the effect of action selection in thelearning of one-way communication between two
agents using Q-learning is examined. The ratio of
successful learning becomes larger when the receiveragent’s action selection is greedy and the transmitter
agent’s action selection is not completely greedy butwith a small random factor. From the analysis of thelearning process, it is known that inappropriate mapping from states to signals in the transmitter agent
sometimes breaks the mapping from signals to action
severely in the receiver agents. Accordingly, thetransmitter agent needs to find an appropriate mapping through exploration, while the receiver agent
decides its action after the mappings is fixed in thetransmitter. Accordingly, no exploration is necessary
in the receiver agent.

Input：
Relative position of the male
Output：Communication
signalFemaleMale

Fig.1 Learning of one-way communication“female” are assumed in a discrete environment. The
male can move, but does not have sight. On the other
hand, the female can’t move, but can transmit some signals to the male. The female’s input is the relative position of the male, and its output is a communication signal.
The male’s input is the communication signal and its
output is a physical action. If the male touches the female,a reward is given to the both agents. The meaning of thecommunication signal is not given to either agent at all
beforehand. Therefore, a transmitter agent has to learn
what communication signal should be transmitted and a
receiver agent have to learn to generate appropriate actions from the signal. If some common language can bebuilt up between the male and the female, the contact canbe repeated efficiently.

1. Introduction
Communication plays a very important role in the
supplement of insufficient observation, collision avoidance and cooperative action in multi-robot andmulti-agent systems. In order to learn a purposive communication autonomously, evolutionary method[1] orreinforcement learning[2][3] has been used. Autonomous
acquisition of one-way communication to supply thereceiver’s insufficient observation has been examined[1][2]. However, it was not examined what kind of
information should be transmitted, and whether the optimal communication can be acquired in any cases. Then,
we also focused on the learning of one-way communication that supplies the receiver’s insufficient observation.
For simple analysis, the number of agents is limited totwo, those are a transmitter agent and a receiver agent.
We have examined the reason why state confusion occurred in some simulations[4]. In this paper, the effect ofaction selection in the learning of one way communication between two agents using Q-learning that the authors discovered through the simulations is examined.

2-2 Learning method for the both agents
For the learning of the both agents, Q-learning isused. In Q-learning, state-action pairs are evaluated, andthe action value is called Q-value. An agent chooses anaction with the probability calculated from the Q-values.
It is usually applied on a discrete action space.
The algorithm of Q learning is as follows.
(1) The agent observes a state.
(2) The agent selects and executes an action.
(3) The agent observes the state after the transition.
(4) A reward rt +1 is received from the environment.
(5) Q-value is modified as
Q (s t , a t ) ← (1 − α )Q(s t , a t )(1)⎤⎡+ α ⎢ rt +1 + γ max Q ( s t +1 , a ) ⎥a⎦⎣

2. Learning of one-way communication
2-1 Task description
In this paper, the learning of one-way communicationthat supplies the receiver’s insufficient observation is
focused on. The simulation environment was decided
referring to [1][2]. Fig.1 shows the image of one-way
communication learning. Two agents called “male” and©ISAROB 2005

Input：
Communication signal
Output：Physicalaction373

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

where α is a learning rate (0 < α ≤ 1)
γ is a discount factor (0 ≤ γ < 1)(6) t -> t + 1 , and the flow returns to the step(2).
For the female, the state s is the male’s relative position, and the action a is the communication signal. For
the male, the state s is the communication signal, andthe action a results in a state transition. α is 0.1, andγ is 0.9 here.

realized even in greedy selection[5].
In this simulation, the number of the trials until thetemperature coefficient reaches the minimum is varied
for each agent in the case of Boltzmann selection, andsuccessful learning ratio was observed.
When the temperature coefficient T reaches the minimum value at the N -th trial, the temperature at thek -th trial is calculated ask

2-3 Action selection
2-3-1 Boltzman selection
When the state is x , the probability of the action
a is calculated as,exp(Q( x, a) / T )p(a |x) =∑ i∈A exp(Q( x, i ) / T )T (k ) = 0.005 N= 0.005

if (k < N )otherwise(3)

N is varied from 200 to 1000 with the interval of 200
in each agent. The total number of trials is 1000. Furthermore, the greedy selection is also employed. The
average successful ratio over 1000 simulation runs foreach combination of exploration ways of two agents isshown in Table 1.
From this figure, it is known that the successful ratio
is high when the male’s action selection is greedy, andthe female’s one is Boltzmann selection with the temperature decreased fast. On the other hands, the successful ratio is low when the female’s action selection is
greedy.
Next, in order to examine the effect of the small exploration factor remaining due to the lower bound of thetemperature, greedy selection was employed when thetemperature reaches the minimum value 0.005. In theprevious simulation, the action which does not have themaximum Q-value sometimes selected because of theprobabilistic selection after the temperature coefficientreaches the minimum. On the other hand, in this simulation, the action that has the maximum Q-value is always
selected after the temperature coefficient reaches theminimum.
The results are shown in Table 2. The successful ratio
is higher than in Table 1 when the male’s action selection
is greedy, and the female’s selection is Boltzmann selection. However, when the female’s action selection is
greedy, and the male’s action selection is Boltzmann selection, the successful ratio is lower than in Table 1. The
reason why the successful ratio is high when the maletemperature coefficient is decreased faster than the female can be thought that the male’s action selection becomes greedy in the early stage of the total trials.

(2)

where A is a set of actions, and T is a temperature
coefficient. An action is selected almost randomly when
T is large. As opposite to it, when T is close to 0, a
little difference of Q-value has a great influence on theaction selection, in other words, the action selection isalmost greedy. In the following simulations, the initialvalue of T is 1.0, and it is gradually decreased exponentially to 0.005. In the rest of trials, it was fixed at 0.005.
The reason is that when it becomes smaller than 0.005,
the computation on our computer becomes impossible.
2-3-2 Greedy selection
In greedy selection, there is no probabilistic factor andthe action with the maximum Q-value is always selected.

2-4 Flow of the learning
The agents act in accordance with the following cycle.
(1) The female detects the male’s state.
(2) The female’s Q-value at t -1 is modified.
(3) The female transmits a signal to the male.
(4) The male receives the female’s signal.
(5) The male’s Q-value at t -1 is modified.
(6) The male makes an action.
(7) If the both agents touch each other, the trial finishes,
and they get a reward. In that case, they learn their
Q-values at t according to Eq.(1) withmax Q ( s t +1 , a ) = 0 and the flow returns to (1). If
the trial finished, t =0, otherwise t ->t +1.
When t =0, the step(2)or(5) is not executed.

The others

3. Simulation
A simulation environment is shown in Fig.1. In thisenvironment, the number of states, signals and actions
are decided to be the same to match the condition of theboth agents. All the initial Q-values are 1.0 here. When
all the initial Q-values are set to be high, which is called
Optimistic initial value, the effect of exploration can be©ISAROB 2005

１

１

２

２

The others

7８Female state:8 action:8
Male state:8 action:8
Fig.2 Simulation environment
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corresponding Q-values converged to 1.0. In Fig.3, themale’s maximum Q-value became large in the early stageof learning, but it decreased drastically. On the other
hand, in Fig.4, the male’s Q-learning is slightly oscillating around a comparatively small value. It can be said
that, in greedy selection, reconstruction of the signal-action pair occurs often and drastically, but in the
Boltzmann selection, the learning is more stable.
Then, the reason why the difference of successful ratio and the change of Q-value due to the female’s exploration factor in action selection is examined. An example
to show the influence of one agent’s learning to the otheragent’s learning is shown in Fig.5. The failure of the female’s learning means that the female selects the same
signal in two or more states. For example, when the female transmitted the signal 1 in the both state 1 and 8,
the male continues to select the action 8 as long as thesignal 1 is received and the action for the signal 1 is notswitched by the greedy selection of the both agents. Accordingly, signal-action mapping is broken not only inthe state 1 but in the state 8, and learning has to be done
again from scratch.
On the other hand, in the case of the failure of the
male’s learning, the male selects the same action for thedifferent signals even though the female assigned signals
appropriately. For example, although the female transmits the signal 1 at the state 1 and transmits the signal 2
at the state 8, the male selects the action 8 in either caseof the signal 1 or the signal 2. Then, for the male,
Q-value of the action 8 on the signal 1 decreases, and forthe female, Q-value of the signal 1 on the state 1 also
decreases. However, in this case, it gives no influence on
the Q-values at the state 8. So, insufficient learning of
the female breaks the generation of the right action also
in other states in the male, while insufficient learning of
the male breaks the signal only at the state in the female.
That is supposed to be the reason why the successful
ratio becomes larger when the male’s action selection is
greedy, and the female’s action selection is not completely greedy, but with a small random factor.
Finally, in order to investigate how the learning progressed in the both agents, the number of steps since
which the action with the maximum Q-value was notswitched was observed. The result shows that the action
with the maximum Q-value began to be fixed from thestate that is close to the goal, and then, the range where
the action was fixed spreads to the far states from thegoal gradually. Furthermore, the time when the female
fixed its signal is slightly earlier than the number of steps
when the male fixed its action. That must be also because
the female’s insufficient learning sometimes breaks themale’s proper mapping from the signal space to the action space.

Table 1: Success ratio according to the number oftrials when the temperature reached 0.005

The female’s number oftrials until T=1000

The male’s number of trials until T=0.005
greedy

200

400

600

800

1000

Greedy

87.1

44.7

38.7

30.2

19.8

9.8

200

100.0

82.0

77.0

61.1

32.2

7.6

400

99.9

80.1

78.8

61.0

36.0

9.9

600

99.1

64.8

62.4

57.4

34.9

9.0

800

81.9

41.0

36.4

32.1

21.8

7.6

1000

62.8

6.1

6.9

5.6

2.7

0.4

Table 2: Success ratio according to the number oftrials when the action was changed to greedy selection after the temperature reached 0.005.

The female’s number oftrials until T=1000

The male’s number of trials until T=0.005
greedy

200

400

600

800

1000

greedy

87.1

84.0

67.2

40.5

20.3

9.8

200

90.5

86.9

75.9

47.9

22.4

10.4

400

90.5

90.9

83.4

65.2

28.7

11.9

600

90.4

87.9

85.9

77.8

53.0

14.4

800

83.8

84.4

85.8

79.3

54.5

18.6

1000

62.8

61.5

56.8

36.7

6.8

0.4

Then, in order to investigate the reason of the differenceof successful ratio depends on the action selection of theboth agents, the change of Q-values in the learning process is observed. The change of Q-values when the female’s action selection is greedy from the beginning isshown in Fig.3. The change when Boltzmann selection
was employed and the temperature coefficient was fixed
at 0.005 from the beginning is shown in Fig.4. Fig.(a)shows the change of female’s Q-values when the male isin the state 8 that is located 1 step before the goal, andFig.(b) shows the change of male’s Q-values for the action 8 that takes the agent from the state 8 to the goal, but
is not rational for the other states.
From Fig.3,4, it is known that only one Q-value ishigher after learning progressed in some degree, and thesignal with the highest Q-value in the female corresponds to the signals with the highest Q-value in the
male. It is also known that the signal with the maximum
Q-value was switched several times. Finally the female
decides to select a signal in the state 8, and the male decides to select the proper action from the signal. The both

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1

Q-value

0.75
0.5
0.25
0
0

500

1

m̲q[8][1]m̲q[8][2]m̲q[8][3]m̲q[8][4]m̲q[8][5]m̲q[8][6]m̲q[8][7]m̲q[8][8]0.75Q-valuem̲q[8][1]m̲q[8][2]m̲q[8][3]m̲q[8][4]m̲q[8][5]m̲q[8][6]m̲q[8][7]m̲q[8][8]

0.5
0.25
0
0

1000

Iteration(Number of trials)(a) Female's Q-valueo̲q[1][8]o̲q[2][8]o̲q[3][8]o̲q[4][8]o̲q[5][8]o̲q[6][8]o̲q[7][8]o̲q[8][8]Q-value

0.75
0.5
0.25
0
500

1000

State 8Signal 1

Action 8

Signal 1

0
500

1000

Signal 1

Signal 1

State 8Signal 2

Signal 2

Fig.4 Change
Changeinofthe
theQ-vQ-values
when
female’s
Fig.7
alue whenthethefemale’s
actionaction
selection
is Boltzmann
selection on
selection
is Boltzmann-selecti

Reference[1] G.M. Werner & M. G. Dyer : Evolution of Communication in Artificial Organizing System, Proc.of Artificial life II, 1-47(1991)

Male

State 1

Action 8[2] N.Ono, T. Ohira, and A. T. Rahmani: Emergent
Organization of Interspecies Communication in
Q-Learning Artificial Organism,Advances in Artificial
Life, pp. 396-405 (1995)(b) When the mapping in the male is not appropriate
Fig.5 The influence of one agent’s learning tothe other’s learning[3] K. Shibata and K. Ito: Learning of Communication
for Negotiation to Avoid Some Conflicts of Interests-Learning of Dynamic Communication Using a Recurrent Neural Network- Trans. of SICE Vol.35，No.11，
1346-1354 (1999) (in Japanese)

4. Conclusion
In this paper, the effect of action selection in thelearning of one-way communication between two agentsusing Q-learning was examined. The successful ratio
became higher when the receiver agent’s action selection
is greedy, and the transmitter agent’s is not completely
greedy but has a small random factor.
By observing the change of Q-values and the numberof the steps when the both agents determine their actions,
it is found that insufficient learning in the female may
break learning fatally.
©ISAROB 2005

0.25

Acknowledgement
This research was partially supported by the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Reearch, #14350227 and #15300064.

Male(a) When the mapping in the female is not appropriate
Female0.5(b) Male's Q-value

Fig.3
when thethe female’s
female’sFig.6Change
Changeofinthe
theQ-values
Q-value when
action selection
ingreedyselectionaction selection is Greedy-selection

Signal 1

0.75

Iteration(Number of trials)(b) Male's Q-value

State 1o̲q[1][8]o̲q[2][8]o̲q[3][8]o̲q[4][8]o̲q[5][8]o̲q[6][8]o̲q[7][8]o̲q[8][8]0Iteration(Number of trials)

Female1000

1Q-value10500
Iteration(Number of trials)(a) Female's Q-value[4] Masanobu Nakanishi, Masanori Sugisaka & Katsunari Shibata: Occurrence of State Confusion in the
Learning of Communication Using Q-leaning,Proc. of
The 9th AROB (Int'l Sympo. on Artificial Life andRobotics), Vol. 2, pp. 663-666, (2004)[5] R.S. Sutton and A.G.Barto,: Reinforcement Learning,
The MIT Press,1998

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DESIGN OF A ROBUST ADAPTIVE CONTROLLER FOR A CLASS OF
UNCERTAIN NONLINER SYSTEMS WITH TIME-DELAY INPUT
Thi-Minh-Huong NGUYEN, Thi-Nga DINH

Thi-Ngoc NGUYEN, Van-Truong TRAN

Abstract

tracking problem, an ultimate bound of tracking error isinvestigated under the specific reference model.

Center for Automation Technology – CAT,
Hanoi, Vietnam

Center for Automation Technology – CAT
thiminhhuong_nguyen@yahoo.com

This paper presents a systematic analysis and asimple design of a robust adaptive control law for a classof nonlinear systems with modeling errors and atime-delay input. The theory for designing a robust
adaptive control law based on input-output feedback
linearization of nonlinear systems with uncertainties anda time-delay in the manipulated input by the approach ofparameterized state feedback control is presented. The
main advantage of this method is that the parameterizedstate feedback control law can effectively suppress theeffect of the most parts of nonlinearities, including
system uncertainties and time-delay input in the
pp-coupling perturbation form and the relative order ofnonlinear systems is not limited.
Keywords: nonlinear, robust control, adaptive control,
time-delay

2 Preliminaries and problem formulation
Consider the uncertain single input single output(SISO) nonlinear system with time-delay input:x,(t)=f(x(t))+Df(x(t))+[g(x(t))+Dg(x(t))]u(t-d) (1)y(t)=h(x(t)), where x ∈ Rn is the state variable, u ∈ R is the
manipulated input, d>0 is the time delay in the
manipulated input, y ∈ R is the output .f(•), g(•), Df(•)
and Dg(•) are smooth vector fields on Rn, and h(•) is asmooth function. The nominal system is then defined asfollows:x,(t)=f(x(t))+g(x(t))u(t)(2)y(t)=h(x(t)), i.e. assume that Df(•) = 0, Dg(•) = 0, and d=0 in (1).
There has been a great deal of research in recent years
over the development of a complete theory for explicitly
linearizing the input-output map of the nominal system(2) using state feedback. Here we introduce somenotations from differential geometry, namely the Liederivative, which is frequently used in this paper. For
more general treatment in this area, readers are advised
to look in [1], [5]. Given a scalar function h(x(t)) and avector field f(x(t)) on Rn, one can define a new scalarfunction Lfh(x(t)), called the Lie derivative of h(x(t))
with respect to f(x,t):∂h( x(t ))Lfh(x(t))=
f(x(t))(3)∂x (t )
Thus, the Lie derivative Lfh(x(t)) is the directional
derivative of h(x(t)) along the direction of the vectorf(x(t)). Higher order Lie derivative can be definedrecursively as:
Lf0h(x(t))=h(x(t))
(4)
n
∂
k
k-1
L f(x(t))= ∑[L fh(x(t))]fj(x(t)), k = 1, 2, 3, …
j =1 ∂x (t )(5)
if g(x)is another smooth vector field on Rn, then one
can define the Lie derivative of h(x(t)) with respect totwo different vector fields:n∂[Lkfh(x(t))]gj(x(t)),
LgLfk h(x(t))= ∑j =1 ∂x (t )k=1, 2, 3, …
(6)
Furthermore, the minimum eigenvalue of a hermitianis denoted as λ min(•) and λ max(•), where as thetranspose of the vector or of a matrix is written as (•)Tand || • || denotes the Euclidean norm.

1 Introduction
Recent developments in the theory of differential
geometry provide useful methods for a class of nonlinear
systems. The central concept of this approach is toalgebraically transform the nonlinear system dynamics
into an equivalent linear system, such that theconventional linear control techniques can also be
applied [1, 2]. Generally, the feedback linearization
techniques require the accurate mathematical model forthe plant to achieve exact linearization of the close loop
system. However, for many real processes, there exist
inevitable uncertainties in their constructed structure
models. Therefore, design of a robust controller for anonlinear system is important subject.
In this paper, we present a systematic analysis and asimple design of an adaptive control law for a class ofnonlinear systems with modeling errors and a time-delay
input. The so-called matching conditions [3] forcontrolled nonlinear systems and the Smith predictor are
not necessary. System uncertainty is considered as thenon-vanishing case in the desired operating condition.
Using the parameterized coordinate transformation, theoriginal nonlinear system can be transformed to a classsingular perturbation problem having distinct fast andslow dynamics. An adjustable parameter can be detuned
to satisfy the desired control specification. When the
lumped nonlinearity, including uncertainty andtime-delay input, is constrained to a closed bounded set
and satisfy the local Lepschitz condition [4], its effect on
the output trajectory can be effectively suppressed using
the proposed technique. More precisely, to treat the©ISAROB 2005

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An important property of a nonlinear system is its
relative degree. For a linear system defined in transfer
function form, the relative degree is usually defined as
the order of the denominator minus the order of anumerator. Amore general definition is used for
nonlinear systems:
Definition 1: The system (eqn.2) is said to have aconstant relative degree r [6], if there exists a positive
integer 1<=r<= ∞ ,such that
LgLfkh(x(t))=0,k< r-1(7)
LgLfr-1h(x(t))!=0 for all x ∈ Rn and t ∈ [0, ∞ ) (8)Throughout this paper, we assume that the nominal
system (2) possesses a relative degree r. Based on theassumption, it has been shown that [1] there exits aneighborhood U of the operating point xs such that the
mapping:
p: U-> Rn(9)defined aspi(x(t))=Ei(t)=Lfi-1h(x(t)), i=1,2,3,. . .,r(10)pk(x(t))=Nk(t), k=r+1,r+2,. . .,n(11), and satisfying:
Lgpk(x(t))=0,k=r+1,r+2,. . .,n(12), is a diffeomorphism onto image. To obtain a linear
input-output relation of (2), start with the external
dynamics:∂p dx ∂h dx
E,1(t)= 1== Lfh(x(t))=p2(x(t))=E2(t)(13)∂x dt ∂x dt…
r −1∂p dx ∂L f dx=
= Lfr-1h(x(t))
E,r+1(t)= r −1
∂x dt
∂x dt
=p 2(x(t))=Er(t)
(14)
r −2
∂p r dx ∂L f dx=
E,(t)=∂x dt∂x dt=Lfrh(x(t)) + LgLfr-1h(x(t))u(t)(15)
Seta(E(t)), N(t)=LgLfr-1h(x(t))u(t) | −x 1= p ( E (t ), N (t )) (16)Thus the following linearising feedback law can bederived from (16), (17) and (21):u(t)= φ (t ), v(t )) = (LgLfr-1h(x(t))-1(-Lfrh(x(t))+v(t))=a-1(E(t),N(t))[-b(E(t),N(t))+v(t)](24), where v(t) is a new external control to be designed forthe purpose of tracking signals. Note that the control lawof (24) makes the state vector N(t) completely
unobservable at the output. Since we are interested inachieving stable state tracking, it is required that N(t)(7)
remain bounded for the bounded E(t). However,weobserve that E(t) can be thought as an external input
vector with respect to the dynamics of N(t). Since E(t) is
expected to track arbitrary time functions, it is clear thatthe boundedness of N(t) is entirely depend on the vector
field q(E(t),N(t)). It can be easily verified that theequation:
E,(t)=q(0,N(t))(25), is referred to as the zero dynamics [1]. The system inwhich the zero dynamics is asymptotically stablereferred to as the minimum phase system. Stable tracking
requires a stronger stability criterion for the dynamics
E,(t)=q(E(t),N(t))(26)
be bounded input bounded state (BIBS) stable. In
addition to the relative degree assumption, a further
property of the zero dynamics required. This is
illustrated in the following assumptions:
Assumption 1: The zero dynamics of eqn.25 isexponentially stable. Moreover, the function q(E(t),N(t))
is Lepchitz uniformally in N(t).
Remarks:(i)Since the zero dynamics is exponentially stable byassumption therefore by a converse theorem of
Lyapunov [7], there exists a Lyapunov Vo(N(t))which
satisfies the following properties:
K1ºN(t)º2<= Vo(N(t))<=K2ºN(t) º2(27)∂Vo( N (t ))q(0,N(t)) <=-K3ºN(t)º2(28)∂N (t )
(16)b(E(t), N(t))=LgLfr h(x(t))u(t) | −x 1= p ( E (t ), N (t ))(17)(15) can be written as:(18)
E,r(t) = b(E(t),N(t))+a(E(t),N(t))uNext, under eqn.12 the integral dynamics is
considered as:∂p k
N,k(t) =( f ( x(t )) + g ( x(t ))u (t ) = Lfpk(x(t))+∂xLgpk(x(t))u(t) = Lfpk(x(t)), k=r+1,r+2,r+3, …
(19)
Thus, in short, the state space description of thesystem in the new co-ordinates as follows:(20)
E,i(t)=Ei+1(t), i=1,2,3, …
(21)
E,r =b(E(t),E(t))+a(E(t),N(t))u(t)N,(t)=q(E(t),N(t))(22)y(t)=E1(t)(23), where qi(E(t),N(t)) = Lfpk(x(t)) | −x 1= p ( E (t ), N (t )) , k =r+1,r+2, … , n
Generally, (20), (21) are called the external dynamics
of the system, and (22) is called the internal dynamics ofthe system. The proper choice of a linearizing control
law is now apparent from (21). As b(E(t),N(t)) isbounded away from zero, its inverse is well defined.

∂vo( N (t ))<=K4ºN(t)º(29)∂N (t ),, where K1, K2, K3 and K4 are some appropriate
positive constants.
(ii) Due to the fact that q(E(t),N(t)) is Lepschitz inE(t) there exists a positive constant L such thatºq(E(t),N(t))- q(0,N(t))º<=LºE(t)º, N(t) ∈ Rn-r
(30)
, and Lis called a Lepschitz constant of q(E(t),N(t)) [1].
Now, we are ready to design the tracking controller
for the system (1) to minimize the trajectory error and tostabilize the close loop control system in the presence ofsystem uncertainties and time-delay input.

©ISAROB 2005

3 Results of research
In this part, the robust control objective is to design aparameterized feedback linearizing control law such thatthe desired output trajectory of the close loop system isachieved and the effects of system uncertainty attenuatedwhile maintaining the boundedness of all signals inside
the control loops. For simplicity, all uncertain
components can be lumped and the uncertain nonlinear
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

system can be reduced tox,(t)=f(x(t))+g(x(t))u(t-d)+∑(x(t),u(t-d))y(t)=h(x(t))(31), where ∑(x(t),u(t-d))=Df(x(t))+Dg(x(t))u(t-d).
Applying the nominal change of co-ordinates ofequations (10)-(12) and the nonlinear state feedback of(24) to the system (1) yields
E,1(t)=E2 +∂h( x(t ))( ∑( x(t ), u (t − d )))∂x(t )

The following assumptions are needed to achieve thedesired output trajectory:
Assumption 2:
The desired trajectory output and its first r derivatives
are all uniformly bounded, and satisfy:º(yd(t) yd(1)(t) .... yd(t))º <=Bd(41)
, where Bd is the some positive constant.
Remark:(iii) The fact that the model reference must be ofrelative order of r, to avoid the use of differentiators in
the controller. Furthermore, if the desired trajectory andits derivatives are uniformly bounded, one can make theoutput tracking error, y(t)-yd(t), as minimum as possible
and the whole system states locally stable. This is further
verified in the subsequent theorem:
Define: ei(t)=Ei(t) -yd(i-1)(t), i=1,2,. . .,r(42)
Then equations (24) and (37)-(39) become:e,(t) = Ae(t)+B(v(t)-ydr(t)) + DA(x(t),u(t),u(t-d)) (43)N,(t) = q(E(t),N(t)) + DΦ(x(t),u(t-d))(44)e1(t)=Ce(t)(45)
Define the tracking error with parameterizationei*(t)=ppi-1ei(t), i=1,2,. . .,r(46), for a positive constant pp<=1. Then we obtain thefollowing equations:ppe,*(t)=Ae*(t) + pprB(v-yd+ppDA*(x(t),u(t),u(t-d)(47)N,(t)=q(E(t),N(t)) + DΦ(x(t),u(t-d))(48)
e1(t)=Ce*(t)(49), where DA*=[DA1
ppDA2 . . . pp r-1Ar]T
Now, we propose the control law v(t) of the following
form:

(32)

…
E,r-1(t)=Er(t) +∂Lrf− 2 h( x (t ))∂x (t )∑ ( x(t ), u (t − d ))(33)

E,r(t)=v(t)+∂Lrf−1∑ ( x(t ), u (t − d )) + g ( x(t )(u (t − d ) − u (t )) (34)∂x (t )
N1,(t)=q1(E(t),N(t)) +∂p n∑ ( x (t ), u (t − d ))∂x(t )(35)∂p r +1∑ ( x(t ), u (t − d )) (36)∂x (t )Taking advantage of the identities in the nominaltransformations and by some derivations, it can be easily
verified the equations (32)-(36) can be transformed into:
E,(t)=AE(t)+Bv(t)+DA(x(t),u(t), u(t-d))(37)
N,(t)=q(E(t),N(t))+DΦ(x(t),u(t-d))(38)y(t)=CE(t)(39), where E=[E1,E2,. . ., En]T, N=[N1,N2,. . ., Nn-r]T,DA = [DA1, DA2, … , DAr]T = ∂h( x (t )) ∂k (t ) ∑( x(t ), u (t − d ))



...

 r−2

 ∂L f h( x (t )) ∑( x(t ), u (t − d )) ∂x (t ) r −1 ∂L f h( x(t ))∑( x(t ), u (t − d ) + g ( x (t ))u (t − d ) − u (t )

(
)
x
t
∂



 ∂p r +1
 ∂x (t ) ( ∑( x(t ), u (t − d )))
 ∈ R ( n − r )×1, DA∈Rrx1 , DΦ= ...
 ∂p n (∑( x(t ), u (t − d )))  ∂x (t )
0 1 0 . 0 
0 0 1 . 0 
A= . . . . .  ∈ R r×r ,
0 0 0 . 1 
0 0 0 0 0 
B=[0 0 ... 0] T ∈ Rr×1, C=[0 0 ... 0] T ∈R1×r
Consider that the output y(t) will track the output yd(t)of the reference model. The desired model reference isz,(t)=A0z(t)+B0 ysp(t)(40)
where z(t) is the state variable, A 0,B0,C0 are matrices
and vectors with appropriate dimensions. Ysp(t)is theexternal input, and yy(t)is the desired output trajectory.
N,n-r(t)=qn-r (E(t),N(t)) +©ISAROB 2005v (t ) = Φ 0 (e * (t ), y d(r )(t )) = y d

(r )

r

(t ) − pp − r ∑ a i e * i (t )t =1

= y d ( r ) (t ) + Γ(a, pp)e(t )
(50)
-r
-1
, where Γ (a,pp) = [-pp a1,. . ., -pp ar]
Note that a1,a2,. . .,ar are chosen such thatsr + arsr-1 + . . . +a1(51)
is a Hurwitz polynomial and s is the Laplace operator.
By some derivations we obtain a standard singularly
perturbed system of the form:
ppe,*(t)=Ace*(t)+ppDA*(x(t),u(t),u(t-d))(52)
N,(t)=q(E(t),N(t))+DΨ(x(t),u(t-d))(53)
0 1 0 0 0

0 0 1 0 0 is companion matrix.
, where Ac =  . . . . .

0 0 0 0 1
 − a1 − a 2 . . − a r Moreover, we define P as a symmetric positive definematrix satisfying the Lyapunov equation:
AcTP + Pa c = -I
(55)
, where I is an identity matrix.
Intuitively, to reduce the effects of the nonlinearity
including state and input perturbations to the tracking
error e(t) while maintaining the boundedness of N(t), we
can choose a large gain in the control law v(t) eqn.50.
However, the input error between the delay in put u(t-d)
and the desired control law u(t) will be large due toinappropriate high gain feedback control. Therefore how
379

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

to choose a suitable tuning parameter pp in the present
control law, which guarantees the overall systemstability and good performance, is the main key issue ofin this paper.
Owing to state feedback control the resulting closed
loop system of (52) and (53) may include state delay andinput delay. Hence, the bound information of the delayedstate x(t-d) and current state x(t) is a priori. This isaddressed in the following assumption.
Assumption 3:
Consider that W(e*(t))=ppe*(t)Tpe*(t) is a positive
define function, where e *(t) is the parameterized variable
as shown in eqn.46, and P satisfies eqn.55. If there exists
W(e*(t-d))<=ε2 c*ºe*(t)º(56)
where c* is the ratio between maximum and minimum
eigenvalues of matrix P.
Remarks:(iv) This assumption is based on the stability theoremof Razumikhin [7]. Under the continuous inversion ofparameterized co-ordinate transformation (equations(10), (11), (42), (46)), i.e.:xl(t) = pk-l(Ei(t),Nj(t)) = pk-l (ei(t)+ydi-1(t),Nj(t))= p k-l(pp1-iei*(t)+ yd(i-1)(t)Nj(t)), i=1, 2, …
The result of (56) represents that if the current statex(t) is bounded, the delayed state x(t-d)is uniformly
bonded. This assumption had been used in the literaturefor state-delayed systems [8].
From the assumptions above, the error can be
expressed further as follows:³u(t-d)-u(t)³= ³Φ(x(t-d),v(t-d) - Φ(x(t),u(t)))³=³-a-1(E(t-d),N(t-d))b(E(t-d),N(t-d))+yd(t-d)a-1(E(t-d), N(t-d)) –yd(t)a-1(E(t),N(t))+Γ(a,pp)
a-1(E(t-d),N(t-d))e(t-d)-a-1(E(t),N(t))e(t))³(57)
After taking derivation of Lepschitz Jacobian near theorigin, one can show that:³u(t-d)-u(t) ³<=pp-r(c1ºE(t-d)-E(t) º+ c2ºN(t-d)-N(t) º+c3<=k*/ppr(58), where c1,c2,c3 and k*are positive constants.
Based on the ongoing analysis, a simple design
procedure for better output tracking in the presence ofplant uncertainties and time delay is proposed. The basic
principle is to design the parameterized state feedback
control law for getting a good system response and totune the parameter pp for achieving robust stability andperformance specification.
The control structure is shown in Figure 1, and thedesign procedure consists of five steps:
ysp (t)yd (R)y(t)φ

4 Conclusions
The theory for designing a robust adaptive control
law based on input-output feedback linearization of
nonlinear systems with uncertainties and a time-delay inthe manipulated input by the approach of parameterizedstate feedback control has been presented. The main
advantage of this method is that the parameterized state
feedback control law can effectively suppress the effectof the most part of nonlinearities, including system
uncertainties and time-delay input in the pp-coupling
perturbation form and the relative order of nonlinear
systems is not limited.

References[1] Kravaris, A., et al (1987), Nonlinear state feedback
synthesis by global input/output linearization, AIchJ.
[2] Sastry, S., et al, Adaptive control of linearizable
systems, IEEE Trans., AC 34, 1123-1131.
[3] Narebdra, K.S, et al (1986), robust adaptive control in
the presence of bounded disturbances. IEEE Trans. On
Aut. Control AC, 306-315.
[4] Bethtash S. (1990), Robust output tracking for
nonlinear systems, Int.J.Control,1381-1407.
[5] Campion, G. and g. Bastin (1990) Indirect adaptive
state feedback control of linearly paramiterized
nonlinear systems.Int.J of adaptive control and Signal
processing, 4,345-358.
[6] Peterson, B. B., et al (1982), Bounded error adaptive
control. Trans. On IEEE Aut. Control, AC-27.
[7] Reed,J.,S, et al ,(1984)Instability analysis and robust
control of robotic manipulator, IEEEJ.of robot andAutomation, 15.
[8] Lioa, T., et al (1990) Adaptive robust tracking of
nonlinear systems and with an application to a robotic
manipulator. System and control Letters, 15, 339-348.

yd(t)

Model
φ0

Step 1: Select the controlled output and calculate theco-ordinate transformation based on the nominal system
as shown in equations (10)-(12).
Step 2: Transform the nonlinear system into an
equivalent linear system as shown in equations(20)-(23).
Step 4: Calculate the desired reference model basedon the specific specification as shown in equation (40).
Step 5: Adjust the tuning parameter pp to satisfy theperformance specification.

u(t)

x(t)

Plan
r

Figure 1: Structure of the controller©ISAROB 2005

380

P(x(t))

E(t)

x(t)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Optimization of Camera Positions for Taking All Indoor Sceneries by GA
Yusuke TOMINARI
Faculty 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

Takumi NAKAGAWA
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

Abstract

RJQVQ#Haruo TAKEDA
Office of Strategic Planning
Systems Development Laboratory
Hitachi, Ltd
Kawasaki, JapanRJQVQ$CG images consisting of all indoor sceneries are usually used in movies, virtual reality applications, etc.
This paper addresses a GA-base method to find the optimal camara positions to synthesize a complete indoor
image from multiple photographs. For this objective,
we use a efficiency crossover operation. Our method is
evaluted by computer simluations that find the camera positions which can take all walls in the building.
The experimental results show that our method cantake all walls in the building by the small number ofcameras.

RJQVQ#RJQVQ$QXGTNCR
Figure 1: Overlap to compose a seamless image

Keyword: Genetic Algorithm, CG, Composition of aseamless image

1

needs to have appropriate overlap areas between theneighboring photographs to make a seamles image asshown in Figure.1.

Introduction

Recently, Computer Graphics (CG) are used inmany fields such as movies, games and so on. ManyCG engineers have developed sophisticated techniques
to produce photo-realistic CG images. In particular, movies use many CG images to synthesize scenes
which are hard to film. This field reruires an easy
way to get a complete in image a building. Some CG
engineers synthesize the complete indoor image frommultiple photographs.
If has two problems to synthesize a complete indoor
image from multiple photographs; how to determine
the minimum number of cameras to take all indoor
scenery, and how to determine the optimal camera
positions and view angles. In addition a photograph©ISAROB 2005

2

Definition of the problem

The final goal is to compose a seamless image fromthe minimum number of photographs with suitable
overlaps, and the seamless image takes all indoor scene
in the building. For this objective we derive the optimal camera positions by GA under the following assumptions and constraints.

2.1

Assumptions

To simplify the problem, we suppose the following
four assumptions.

381

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

• There is no furniture in the room.

3.2

Genetic operations• Building is described as two dimensional floor
map.

3.2.1

Parents Selection and Crossover

The numbers of cameras in an individual are affectedby the crossover operation. To reduce the number ofcameras effectively, it is desired that a camera takemore area of the walls. For this reason, the selectionof parents is operated as follows;• Cameras are allowed to put on the descrete position. The interval of positions is given by user.
• The view angle of camera is 48 degree, that is
as the same as that of standard lens of 35mm
camera. And the focus range is given by user.

2.2

1. “Parent-A” is chosen in order of the fitness value
defined by Equation (1) in Section 3.2.2.

Constrains

2. A temporary individual A’ is made from the“Parent-A” . The individual A’ is almost the sameas the “Parent-A”, but some cameras taking awall that has the smallest area are removed. The
wall taking by the removed cameras is called as
the “WorstWall-A”.

To compose a seamless image from multiple photographs, the image has to satisfy the following two
constraints.
• The seamless image has to take a complete viewin the view building. In other words, the seamless
image has to take all walls in the rooms.

3. “Parent-B” that takes the largest area of the“WorstWall-A” is chosen from the population.
Camera is called as the “BestCamera-B”.

• Each photographs has to have suitable overlap areas between neighboring photographs as shown in
Figure.1. Since the wall taken by more than two
cameras is redundant, it is not used as the overlap.

2.3

4. A temporary individual B’ is made from the“Parent-B”. The individual B’ only includes the“BestCamera-B”.

Optimal camera position

The “BestCamera-B”are inserted into Individual A’
in one by one as shown in Figure.2 . Finally, newoffspring is created.

We define the optimal camera position that satisfies the constraints under the assumptions by the minimum number of cameras. However the minimum number of cameras is not generally decided by the probabilistic search such as GA. Therefore we decide thesmallest number of cameras through computer simulations. Concretely, if N cameras put on the appropriate
positions can satisfy the constraints, and if N −1 cameras can not satisfy them, N is the minimum numberof cameras. This N is decided through all computer
simulations.

3

Genetic Algorithms

3.1

Genetic Coding

3.2.2

The fitness function is defined by Equation （1） where
j is ID of walls. N is the number of walls in floor map.

Pj =fg = Σj (Pj + Qj ),(1)

lja + ljb − λ× 100(%),
Lj

(2)


 100 Pj =100(%) andλ > (1 − α)M
Qj =
,

0
other

(3)

In Equation (2), la and lb denote the length on thewall j taken by the camera A and the camera B, respectively. λ denotes the length of the wall j taken
by both of the camera A and the camera B (overlap).
Lj is the length of the wall j. These parameters arealso illustrated in the Figure 3 . When the wall j iscompletely taken by the camera A and the camera B,
Pj becomes 100.
Qj becomes 100 if the wall j is completely taken,
and the photographs include desirable overlap to compose a complete image. The parameter α (0 ≤ α ≤ 1)
and M are given by user.

A camera has three parameters; the horizontal position, the vertical position and the angle from thehorizontal axis. These three parameters are arranged
as an individual. Xi and Yi denotes the horizontal
and vertical coordinates of the camera i, respectively.
Xi and Yi have descrete values as described in the assumption. θi denotes the angle of from the horizontal
axis. The range of θi is ±180 degree by one degree
step.

©ISAROB 2005

Fitness Function

382

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

5QTVGFKPFKXKFWCNUD[HKVPGUUQTFGT

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YKVJVJGNCTIGUVCTGCYKVJVJGUOCNNGUVCTGC

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56'2q(a) The angle of all cameras are changed from 0
to 360 degree at random. The angle with thehighest fitness value is set to the new angle
for each camera.

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



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

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

qq q

q q(b) After above operation, the individual thathave more than 90% of the elite’s fitness values are selected. The angle of these cameras
is changed within ±10 degree by one degree
step, then the angle with the highest fitness
value is selected.

Figure 2: CrossoverThus the maximum of fitness value for each wall is
200 when all the walls are taken, and it has appropriate
overlaps between photographs.
3.2.3

2. Change of the camera position

Mutation

At first, the camera is provisionally moved to theeight neighbors of the current position. Then thecamera is moved to the position with the highest
fitness value.

In GA, the contents of an individual are randomlychanged by the mutation. It means that the direction
and the position of the camera are changed by themutation in this application. However, this mutation
causes low fitness value at the last stage of evolutional
process. Therefore we use the following two operations
as the mutation.
If a camera takes only one wall, this camera isdeleted from the individual to reduce the number ofcameras. If a photograph taken by camera A is completely included the other photograph, the camera A
is deleted from the individual.

4

Experiments and Discussions

4.1

Experimental conditions

For the experiments, we prepare a floor map basedon the actual building in University of Miyazaki.

4.2
3.2.4

.⁜
#TGCUVCMGPD[ECOGTC

Figure 3: Parameters to calculate fitness

56'2
⁞⁜⁔The parameter of GA

Local searchParameters of GA for the experiments are describedin below. These parameters are derived from the preliminary computer simulations.

Since GA is a kind of the probabilistic search, it dose
not guarantee to find the optimum solution. To improve the quality of solution, local search operation is
employed. The local search is operated as follows;• Maximum number of generations: 100

1. Change of the camera direction©ISAROB 2005

• Number of individuals: 100

383

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

an elite individual after 100 generations. As shown in
Figure.4, all cameras take more than two walls withsuitable overlap. In addition the number of cameras
was decreased into 8. In this case, the fitness of the individual was arrived at the maximum value 4000. As aresult, it is showed that our method could take multiple photographs including desirable overlap to synthesize a seamless indoor image of the building. Figure.5
shows changes of fitness value. As shown in Figure.5,
the fitness reached the maximum value after 200 (sec)execution.

5#TGCUYGTGVCMGPD[ECOGTC

Recent progress of image processing technology
leads many applications in real world. In particularcomposition of a seamless image from the multiple
photographs taken from many viewpoints attracts attention in the movie fields. We propose a method tocomposition a seamless image by the minimum number of photographs. For this objective, we employed
the genetic algorithm to find an appropriate layout of
the cameras. To reduce the computation time we allowed the cameras to place on the discrete positions,and employ a powerful local search method to find thebetter camera layout. Simulations results showed that
our method could derive the suitable camera layoutthat makes be possible to compose of a seamless image.
Future works remain as the experiments in the floor
map containing some funiture, and we try to apply our
method to 3-D buildings.

#TGCUHQTEQORQUKVGKOCIG

Figure 4: An example of camera layout at 100-th generation
fitness

4000
3800

fitness

3600
3400
3200
3000
2800
2600
2400

0

Conclusion

50 100 150 200 250 300 350 400 450 500
time (sec)Figure 5: Change of fitness

References• Number of offspring generated by Crossover: 100• Initial number of cameras: 12[1] H.Kitano,Genetic Algorithm,
1993.

• Camera position interval: 0.5 (ｍ)[2] M.Tanaka and M.Sakawa, Genetic Algorithm, Assakura shoten, 1999.

• Walls in the floor map: 20[3] H.Takeda, M.Yamasaki, T.Moriya, T.Minakawa,
F.Beniyama and T.Koike,A video-based virtual
reality system, ACM VRST’99 (ACM Symposium on Virtual Reality Software and Technology), pp.19-25, 1999.

• Number of simulations: 30• Desirable overlap length M: 0.03 (ｍ)• α:0.3

4.3[4] J.H. Holland, Adaptation in Natural and Artificial
Systems, University of Michigan Press, 1975.

Experiments Results[5] D.E. Goldbreg, GeneticAlgorithm in Search, Optimization and Machine Learning, Addison Wesley, 1983.

In the 0-th generation, some walls are taken by morethan two cameras, and some walls are not taken by
any cameras. Figure.4 shows the camera position of©ISAROB 2005

Sangyou tosho,

384

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Real-Time Structure Preserving Image Noise Reduction For Computer Vision On
Embedded Platforms∗
Walter Nistico, Uwe Schwiegelshohn, Matthias Hebbel, Ingo Dahm
Department of Electrical Engineering
Computer Engineering Institute
University of Dortmund
Otto-Hahn-Str. 4, D-44221, Dortmund, Germany
name.surname@udo.edu

Abstract

as we will show, the computational cost of such filters
makes them not suitable for real-time applications onembedded platforms.

A camera is often the main sensor of autonomous
robots. As those embedded platforms offer minor computing power only, it is a challenge to provide a fastand robust image processing. This paper describes
a new real-time structure preserving noise reduction
operator based on the so-called SUSAN filtering approach. We use a correlation function to determine
which part of a pixel’s neighborhood has to be includedin the smoothing process. Thus, the smoothing process takes place only inside homogeneous regions of
the image, without blurring edges and bi-dimensionalfeatures which are needed for object recognition.

1

2

Conventionally, noise reduction is done by cutting
the high frequency components of an image. Thus, allrelevant information of the high frequency part of thespectrum of the image is lost. This is why non-linear
operators try to preserve image structures by selectively determine which part of a pixel’s neighborhood
has to be included in the smoothing process.

2.1

Introduction

SUSAN Noise Filtering

The SUSAN noise reduction filter achieves this byapplying a correlation function to calculate the similarity of the brightness of a pixel to be filtered with aneighborhood defined by a fixed convolution mask [2]:¡ I(p)−I(p ) ¢2
0
−
t
(1)
c(p, p0 ) = e

With the recent development of autonomous mobile applications, embedded platforms have becomea viable and convenient option for robotics (e.g.
Sony ERS-7).However, embedded robotic platforms
are severely limited in terms of processing resources,
due to the space and power constraints which are aconsequence of autonomous operation. Furthermore,
low power consumption cameras can exhibit significant amounts of noise in the images they capture [1].
Linear noise reduction convolution operators, such as
Gaussian smoothing filters, are relatively inexpensive
in terms of computing power. Yet, they significantly
alter crucial features for computer vision such as edges
and corners. Non-linear operators which selectively
determine what part of the convolution kernel to include in the smoothing process, exhibit satisfactory
performance in terms of image structure preservation
while effectively reducing noise [2]. On the other hand,

In Equation 1 c(p, p0 ) represents the Gaussian correlation function, where the so-called nucleus p0 =(x0 , y0 ) is the pixel which is going to be filtered,p = (x, y) is a pixel which belongs to the convolution mask around the nucleus, and t is the brightness
threshold which controls the width of the Gaussian.
In the spatial domain, the SUSAN filter also makes
use of a Gaussian weighing. Accordingly, the overall
equation of the filter is given by Equation 2:¡ I(p)−I(p ) ¢2
2
0
P
− r −
t
I(p) · e 2σ2
I 0 (p0 ) =∗ This work was supported by the Deutsche Forschungsge-

p6=p0

2

P

meinschaft DFG under program of emphasis SPP 1125

©ISAROB 2005

Noise Reduction Filtering

p6=p0

385

e

− r 2−2σ¡ I(p)−I(p ) ¢2
0

t

(2)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

well for i = 1, 2) in the domain of the function, which
has the only negative effects of an increase of granularity of the threshold value (which can then assume only
values modn (t) = 0) and require an additional shift operation of ∆I by i positions. The multiplication can be
avoided, by using c(p, p0 ) = −rect2τ (p, p0 ) ∈ {0, −1}
whose domain is represented in signed byte format respectively as 00000000 and 11111111, hence the correlation weighing can be performed with a simple bitwise
logical operation AN D hI(p), c(p, p0 )i. The remaining
multiplications can be avoided by approximating theoriginal Gaussian spatial weighing with a special maskthat is illustrated by Figure 2. Then, no spatial weighing multiplications are required, because the neighbors
whose weight is 0 in the mask are simply not includedin the numerator and denominator sums n and d. As aresult, let M = {(1, 1); (1, −1); (−1, 1); (−1, −1)} represent the neighbors considered by the algorithm, theequation of the new filter is:

Here, I(x, y) is the brightness of a pixel before filtering, I 0 (x, y) is the filtered brightness , and σ determines the spatial Gaussian weighing.In case the denominator of such a function for a given pixel is zero,
it means that its whole neighborhood is uncorrelatedwith it, and hence it’s treated as pulse noise. This isdealt with by using as an estimate for the pixel brightness the median of its 8 closest neighbors [3].

2.2

Going Real-Time

An efficient implementation of such a filter is toprecalculate the non-linear correlation function for achosen t in a look-up-table L. Using the Sony ERS-7
as a reference implementation, the total size of sucha table is 2044 byte. In the following, this will bereferred to asˆ µ¶2 !
∆I(x, y)(3)
Lcorr. [∆I] := exp −tP

Further, it has to be specified the spatial scale ofthe smoothing σ, which can be also precalculated andstored in the convolution mask of neighbors:(∆x)2 + (∆y)2
Lmask [∆x][∆y] := exp −
2σ 2
µ

¶

0

I (p0 ) =

(5)

p∈M

In order to speed-up the division operation, we firsthave to note that the denominator d of such a function
can assume only 5 possible values: d ∈ {0, 1, 2, 3, 4},which represents how many neighbors are ”similar” tothe nucleus for a given pixels. The statistical incidenceof each case is dependent on the threshold τ of the cor-(4)

Now for each pixel in the mask of neighbors, applying Equation 2 can be done at the cost of 2 lookups, a division and several multiplications and additions(depending on the mask size).
If the denominator of Equation 2 equals zero, theeight closest neighbor’s brightnesses have to be put inan array which has to be sorted, and the 2 medianvalues averaged. Due to efficiency reasons, we recommend the use of the insertion sort algorithm [4].
In all other cases, the division is the most complex
and expensive operation in terms of execution time,
followed by the multiplication [5, 6].
In order to avoid one source of multiplications involved in the original algorithm, we chose to replace
the correlation function with a rect2τ (p, p0 ): having
a sharper cutoff, the optimal threshold τ is somehow
more dependent from the amount of noise present in
the images ([2]), however having a binary co-domain
for the function means that we can represent it with integer values, and use the smallest data type offered by
the processors for that purpose, which is the byte, reducing the size of the look up table to 14 of the original,
for a total of 511 byte. This can be further reduced, incase of processors with very limited cache amounts, byintroducing a quantization n : 1 (with n = 2i , works©ISAROB 2005

I(p) · rect2τ (I(p) − I(p0 ))P
rect2τ (I(p) − I(p0 ))p∈M∀(x,y)

relation function: obviously, for τ = 0 ⇒ d(x, y) =∀(x,y)

0, while for τ = 255 ⇒ d(x, y) = 4. Using a sample
of 100 images captured from the on-board camera of
our robot (Aibo ERS7) from real-world situations, wehave measured that for values of τ ≥ 6 case d(x, y) = 4
totally dominates with more than 80% of occurrences(which become > 91% for d(x, y) = 10): following thecriterium of making the common case fast, we have replaced the division with a series of nested conditional
branches, such that in the most common case only
one conditional branch is performed, followed by thesecond most common with 2, and so on.

The following case differentiation shows, what oper-

386

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

0
1
0

ations should performed according to the denominatorvalue d in order to achieve optimized processing speed:
d=4:
d=3:

d=2:
d=1:
d=0:

3.1

1
2
1

2
4
2

1
2
1

Running TimeWe measured the running time of each filter over
100 images captured by the camera of the robots: on
the robot Sony Aibo ERS7 the camera has a resolutionof 208 × 160 pixel, 24 bit per pixel. In all cases, thefilters were running in normal conditions of operations,thus in parallel with other system threads. While the
SUSAN RealTime execution time is dependent on thebrightness threshold used, we have empirically derived
the optimal for the noise present in the images (τ =14), and we have verified that its speed is only ≈ 1%
slower than the best case which is (τ = 255).

ERS7 576MHz, 208 × 160 pixel image
Max
Average(ms) Min
Filter10
9.8
9.904
SUSAN RT τ = 14
50.65 50.95
50.796SUSAN τ = 12
6
5.75
5.864
Gaussian σ 2 ≈ 0.64

As can be seen, the SUSAN RT is ≈ 5 times fasterthan the original algorithm, and in tests where thelatter used the same mask, our approach was still ≈ 3times faster. Compared to the gaussian, it’s ≈ 1.7
times slower, which is a very good result for a nonlinear filter. A more detailed comparison can be foundin [7].

Performance Analysis

To better evaluate the performance of our new approach, we have separated our tests into two categories: structure preservation in noisy images, andrun-time measurements. The latter is particularly important for our application domain, since the filter ismeant to be used on a robot with limited computational resources in a dynamic and competitive environment, where it has to quickly react to the changing
situations. As a reference, we have compared our filter,that here will be referred to as ”SUSAN RealTime”,
with the following operators:

3.2

Noise Reduction

To evaluate the noise reduction and structure
preservation capabilities, we have followed a similar
approach as in the original SUSAN article ([2]), butwith a notable exception: the results will be measured
after a single application of each filter, this because in
our domain the total execution time is critical, making unfeasible a repeated iteration until the error variance reaches 0. For each filter which requires to determine a threshold, we have used in all tests the value
which provided the overall best score: using different
thresholds in different tests would yield higher scores,
but this doesn’t reflect the real usage, since several
kinds of noise are present simultaneuosly inside real
images, and the amount of edges and bi-dimensional- the original SUSAN, presented with the smallest
mask suggested by its authors (3 × 3), in order tominimize its running time;- the Gaussian, with mask size also 3 × 3, in themost popular and computationally efficient variant, as shown in Figure 2.

©ISAROB 2005

0
1
0

Figure 2: Masks of neighbors, integer approximationof the original gaussians. Left: SUSAN RealTime
mask. Right: Gaussian with σ 2 ≈ 0.64.

replace division by a shift-right instruction by
two positions: I 0 (x, y) = n(x, y) >> 2
approximation:
add nucleus to numerator and divide by four I 0 (x, y) =(n(x, y) + I(x, y)) >> 2
replace devision by a shift-right instruction by
one bit I 0 (x, y) = n(x, y) >> 1
nothing to divide
perform median filtering: just discard the
maximum and minimum, then average the remaining.

Figure 1: Calculating the median. {a, b, c, d} is theset of neighbours, m(x, y) stands for the minimum andM (x, y) for the maximum of elements x and y, the 2
final values have to be averaged.

3

1
0
1

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

different CPU architectures. We proved that this filter combines the benefits of non linear structure preserving operators with processing times comparable tolinear ones. We presented a practical application for
the RoboCup Four-Legged Soccer League. Furthermore, we showed that the limits of this approach (thelack of scalability in terms of noise filtering due to thespatial constraints and the rippling behavior in thehigh frequency range) cannot be overcome with traditional approaches as well within the typical processing constraints of real-time applications on embedded
platforms. Compared to existing algorithms, the suggested approach provides an excellent ratio between
image quality and runtime behavior. As a final conclusion, this efficient algorithm for image processing
helps to save processing power which can be used forless time-critical applications like AI or task scheduling.

structures is varying continuously depending on theobjects present in the scene.

Edge Preservation, 10% of Noise, final gradient
Gaussian Pulse Uniform
Filter
54.44
53.45
55.51
SUSAN RT τ = 24
54.35
51.56
55.09
SUSAN τ = 19
34.03
33.54
34.72
Gaussian σ 2 ≈ 0.64

Table 1: The reference image is a perfect step edge of
300 pixel length, with gradient of (∆ = 55). Higher
numbers mean a better score.

Corner Preservation, 10% of Noise, final σGaussian Pulse Uniform
Filter
4.68
6.07
4.39
SUSAN RT τ = 24
4.33
10.93
3.77
SUSAN τ = 19
4.78
10.77
4.60
Gaussian σ 2 ≈ 0.64

Table 2: The reference image is 102 × 102, with auniform brightness of 112, which contains 100 squaresof size 5×5 and brightness 135, so the feature strengthis (∆ = 25). Lower numbers mean a better score.

References[1] J. Bunting, S. Chalup, M. Freeston et.al., “Returnof the NUbots! The 2003 NUbots Team Report,”
Tech. Rep. N.N., Newcastle Robotics Laboratory,
The University of Newcastle, Australia, 2003.

For all the experiments we have made, it must be
noted that the SUSAN RealTime is performing better than the original in presence of pulse noise: thisis a consequence of having a smaller mask, so thatthe chances of finding two similar noise spikes inside it
are lower, and because of the sharper correlation function. The gaussian filter performed badly, reducing
the strength of edges and corners; the original SUSAN overall provided the best filtering, however the
new SUSAN RealTime scored very close to it, andproved to be very well balanced in all test conditions.

4[2] S. M. Smith and J. M. Brady, “SUSAN - A New
Approach to Low Level Image Processing,” Int.
Journal Computer Vision, vol. 23, no. 1, pp. 45–
78, 1997.
[3] M. Singh and P. K. Bora, “Two-Dimensional Linear Prediction Based Median Filtering,” 2002.
[4] R. Sedgewick, Algorithms, ch. 9, pp. 120–125.
Addison-Wesley, 2nd ed., 1984.
[5] D. A. Patterson and J. L. Hennessy, Computer Organization & Design. The Hardware/Software Interface, ch. 4. Morgan Kaufmann, 1994.

Results

We have used the SUSAN RealTime filter in
the image processor of our RoboCup team Microsoft Hellhounds (which is part of the GermanTeam that won RoboCup 2004 in Lisbon) to takepart at several RoboCup competitions: GermanOpen
2004, AustralianOpen 2004, AmericanOpen 2004, andJapanOpen 2004. The average running time of thewhole image processor, on the Sony ERS7 robot, was
17ms for a frame processing rate of 28.5 fps, which
was more than adequate to track fast moving objects.
The performance of the suggested approach is analyzed both in terms of noise filtering and structure
preservation as well as in terms of running time on©ISAROB 2005[6] J. L. Hennessy and D. A. Patterson, Computer Architecture - A Quantitative Approach, ch. 3, 5, Appendix H. Morgan Kaufmann, 3rd ed., 2003.
[7] W. Nistico, U. Schwiegelshohn, M. Hebbel, and
I. Dahm, “SUSAN RealTime - Structure Preserving Noise Reduction For Computer Vision,” Tech.
Rep. 1004, University of Dortmund, 2004.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Model-less Visual Servoing Using Modified Simplex Optimization
K. Miura, K. Hashimoto, H. Inooka
Graduate School of Information Sciences
Tohoku University
Aramaki aza aoba01,
Aoba-ku, Sendai, 980-8579, Japan
Abstract

In this paper, a Newton-like optimization algorithm
is adopted for quicker convergence. The simulations
are carried out in the case where the kinematic modelof the robot is unknown, assuming that the joint limits
are known. The intrinsic parameters of the camera arealso unknown. The improvement of the convergence
speed is discussed with the simulation results.

In this paper, we present a robot positioning taskwith respect to a static target by visual servoing. The
vision system is uncalibrated and the kinematic modelof the robot may be totally unknown. The displacements of the robot in joint level are generated inreal time in order to minimize an objective function.
The objective function includes the quadratic error
between the current and the desired target images.
A simplex method is used to minimize the objectivefunction, and a Newton-like method is also used near
the convergence. We successfully validate this methodwith simulations under the graphic library OpenGL.

1

2
2.1

introduction

Simplex method and its modificationfor visual servoing
Conventional simplex
Nelder and MeadmethodbySimplex method is an unconstrained optimization
technique. Note that it is diﬀerent from the linear
programming technique method also called ”simplex”.
This method was originally proposed by Spendley,
Hext, and Himsworth [7] and was developed later byNelder and Mead [5].
This section deals with the methods for solving theminimization problem:

Most of the previous works on visual servoing assume that the kinematic model of the robot and thecamera intrinsic parameters are known. Most of thesemethods could work with weak calibration, but theywould fail if the robot and the vision system were fully
unknown.
Uncalibrated and model-less visual servoing has
been addressed by Hosoda et al. [1], and Jägersand[2]. They use an on-line estimation of the Jacobian between the joint velocities of the robot and the feature
velocities in the image plane. Their approach assumes
the on-line identiﬁcation of a large number of parameters. In the presence of noise, this type of approach
may lead to a badly estimated Jacobian matrix if the
motions of the robot do not guarantee identiﬁabilityof the parameters.
In previous research, we proposed a novel approachfor uncalibrated and model-less visual servoing using a
modiﬁed simplex iterative search method is proposed.
We successfully demonstrated the algorithm with a industrial manipulator[4]. However, it usually requires
a lot of iteration to complete the positioning task accurately.

©ISAROB 2005

J. A. Gangloﬀ, M. F. de Mathelin
LSIIT UMR CNRS 7005
Louis Pasteur Strasbourg University
Bd. S. Blant,
67412 Illkirch Cedex, France.

Find X = {x1 , x2 , . . . , xn } which minimize F (X) (1)The geometric ﬁgure whose vertices are deﬁned by aset of n+1 points in an n-dimensional space is called asimplex. For example, in two dimensions, the simplex
is a triangle, and in three dimensions, it is a tetrahedron.
The basic idea in the simplex method is to compare
the value of the objective function at the n+ 1 verticesof a simplex and move the simplex gradually toward
the optimum point during the iterative process, known
as reﬂection, contraction, and expansion.
If X h is the vertex corresponding to the highest
value of the objective function among the vertices ofa simplex, we can expect the point X r obtained by
reﬂecting the point X h in the opposite face to have a389

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

smaller value. Mathematically, the reﬂected point X r
is given by;
X r = α(X o − X h ) + X o

In real-time, in other words, this is equivalent to modifying the Nelder and Mead simplex method by adding
a line search procedure during the reﬂection, expansion, or reduction process. With this technique, thecontraction process is omitted and faster convergenceis expected.

(2)

where X o is the centroid of all the points X i except
i = h, and α is the reﬂection coeﬃcient (α > 0).
In the case f (X r ) gives the smallest cost between
all the vertices, one can generally expect to see thefunction value decrease further by expanding X r to
X e;
X e = γ(X r − X o ) + X o

3Assuming that m features are detected in the image, let deﬁne f (X) as the m-dimensional vector of thefeature errors, where X is the vector of size n deﬁning the position of the robot (e.g., X are the joint
angles). Hence, the feature error vector f (X) = 0 if
X = X ∗ , the desired position of the robot. Therefore,
the positioning task using visual servoing is achievedby minimizing the following objective function:(3)

where γ is called the expansion coeﬃcient (γ > 1).
If the reﬂection process gives a worst value, X r is
contracted to X c ;X c = β(X h − X o ) + X o(4)where β is called the contraction coeﬃcient (0 ≤ β ≤1).
If the contracted point still has worst value, thecontraction process will be a failure, and in this case
we apply a reduction process, i.e. all X i are replacedby;1
newX i = (X i + X l ) i = 1, 2, . . . , n + 1
2

F (X) =with

2.2

F (X l ) ≤ ε2

(8)

(9)

Indeed, the feature error value f (X ∗ ) = 0 minimizes
F (X).
A Newton-like algorithm is given by:

(5)

X k+1

=

X k − αk (Jk Jk )−1 Jk f (X k )(10)where, 0 < αk is the step size in the descent direction,
and J(X) is the n × m Jacobian matrix of the featureerrors:Jij (X) =(7)∂fj (X)∂xi(11)

and Jk = J(X k ).
If the Jacobian matrix is unknown, it must be estimated on-line. Piepmeier et al. [6] presented a moving target tracking task based on the quasi-Newton
optimization method. The Jacobian of the objectivefunction is estimated on-line with a Broyden’s update
formula (equivalent to a LMS algorithm). This approach is adaptive, but cannot guarantee the stabilityof the visual servoing scheme in presence of large errors in the image or if the motions of the robot do notguarantee identiﬁability of the parameters.
It should be pointed out that the Newton optimization algorithm has local properties of convergence, i.e.,
it will converge toward the nearest local minimum ofF (X)Modified simplex method for realtime visual servoing

We slightly modify the Nelder and Mead simplex
method for the particular case of visual servoing witha robot. The main idea is to use a simplex like optimization algorithm to move the robot from an initial
position to a goal position, so that the robot is performing the optimization. Since the vision system acquires images continuously and assuming that joint
angles are also measured, the cost function can becomputed along the trajectory of the robot while it
is moving from a vertex to its reﬂection point. Therefore, the optimum could be selected along that vertex.

©ISAROB 2005

f (X) =

1f (X) f (X)2⎞⎛
f1 (X)⎟⎜..
⎠⎝.
fm (X)

with X l , the vertex where the objective function isminimum. Then, we restart the reﬂection process.
The method is assumed to reach convergence whenever some stopping criteria have been met, e.g.:

n+1
n+1
  {F (X i ) − F̄ }2
 F (X i )≤ ε1 , with F̄ =
(6)
n+1
n+1
i=1
i=1
or

Newton-like optimization method390

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4

Simulation

4.1

Experimental procedure

The model of robot manipulator is a 6DOF industrial manipulator, and the target object is a cylindricalwhite cup in Fig. 1. The task consists in bringing theend-eﬀector ﬁtted with a camera (eye-in-hand conﬁguration) at the vertical of the object. To compare
several optimization runs, we use the same starting
positions.
Three procedures are compared with respect to thenumber of iterations and the accuracy of the convergence:
Scheme1: A positioning task is carried out only withthe simplex optimization.
(a)start position
Scheme2: During simplex iterative search, on-line Jacobian estimation using a Broyden’s update formula is carried out using the information at thesimplex vertices. Once the simplex optimization
arrives near the minimum, the process switches
to the Newton-like optimization with Eq. 10.

Figure 1: Start and goal position of the robot andtheir images taken at the end-eﬀector

form factor of its image (form factor = 1 when
this angle is zero). This angle can be decomposedin two elementary angles (e.g. roll and pitch).

Scheme3: The process switches to the Newton-like optimization without on-line Jacobian estimation
during the simplex optimization phase. The Jacobian matrix is estimated with small movement
around the current position.

We also use the sum-of-square-diﬀerence (SSD) of
each pixel intensity between the current and the reference image of the object as an error. Note that SSD
does not need feature extraction.
For this task, we propose the following objective/cost function:

Furthermore, to accelerate the convergence, a hybrid scheme combining an optimization algorithm with
an image-based partial visual servoing loop is proposed. The idea is to bring the target object to thecenter of the image, during the optimization process.
Thus the target will not be lost during the iterative
search, which is a well known problem in visual servoing (cf. E. Malis [3]). The manipulator has six joints,
and the image-based centering scheme is assigned to
two joints near the end-eﬀector. Three joints are controlled by the simplex and Newton-like optimization,
and a joint is ﬁxed.

4.2(b)ﬁnal position

F (X) =

1f (X) f (X)2(12)

where,
f1 (X)
f2 (X)
f3 (X)

Objective function

s
−1
S

llong
= W2
−1
lshort
2= W3(Pcur (i, j) − Pref (i, j))
= W1

i,j

The objective functions are usually selected as sumof square of feature errors. A possible objective function could have the following features:

where s and S are the actual and the desirable size ofthe object in the image, llong is the longest distancefrom the center of the object to its contour, and lshortllong
is the shortest one. Therefore lshortis the form factorof the object image. Pcur and Pref are pixel intensitiesof the current and the reference image, respectively.
Finally, W1 , W2 , and W3 are the respective weights offeature errors in the cost function.

• The distance between the end-eﬀector and the object is given by its size in the image.
• The angle between the object axis (cylinder axis)
and the optical axis of the camera is given by the©ISAROB 2005

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5

Results and Discussion

6

In this paper, we used the modiﬁed simplex optimization techniques for a positioning task by visual
servoing. This method does not need a model of therobot and does not require the estimation of Jacobianmatrices. Thus, a robot never goes in the wrong direction due to the bad estimation. Moreover, the objective function does not need to be diﬀerentiable.
Since the simplex method took more iterations near
the minimum, the Newton-like method with on-line
Jacobian matrix estimation was also executed in orderto have quicker convergence. We successfully demonstrated the proposed scheme with simulations.
Improvements are needed for stable convergence.
Jacobian matrix estimation was not always correct because of the large motions of the robot between thevertices of the simplex that do not guarantee suﬃcient
excitation for the identiﬁcation of the parameters.
For the future works, it is important to ﬁnd theobjective function without noise, which may cause abadly estimated Jacobian matrix.

Simulations were carried out comparing several
schemes. All schemes were executed with modiﬁed
simplex optimization until rough convergence. Termination tolerance in Eq.6 and Eq.7 were decided experimentally.
Scheme1 converged, but it took more iteration near
the minimum. The modiﬁed simplex roughly converged with 16 iterations, and the process took 27 iterations near the minimum. The vertices of the simplex
often fell into a small local minimum, and the simplex
restarted the process again.
Scheme 2 did not converged. The estimated Jacobian was not close enough to the true Jacobian.
Though this type of estimation method guarantees theaccuracy in a small area, the estimation used the image data at the vertices of the simplex in a large area.
A solution is to have a large forgetting factor λ, however, it may lead to a badly estimated Jacobian matrix
in the presence of noise.
Scheme 3 converges quicker than simplex near theminimum with 36 total iterations. The trajectory
was also very simple, as shown in Fig. 2. However,this scheme was not always robust when the objectivefunction had large noise at the initial position of the
Newton-like optimization process.
All these experiments make it clear that the optimization with only the simplex method takes many iterations near the minimum. Newton-like method withon-line Jacobian estimation converged faster, since the
Newton optimization algorithm has better local properties of convergence. However, it has a risk of a badly
estimated Jacobian matrix in the presence of noise.
10

Cost

10

References[1] K. Hosoda, and M. Asada, Versatile Visual Servoing
without Knowledge of True Jacobian, Proc. of theIEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Munchen, September 1994.
[2] M. Jägersand, O. Fuentes, and R. Nelson, Experimental Evaluation of Uncalibrated Visual Servoing for Precision Manipulation, Proc. of the 1997 IEEE Int. Conf.
on Robotics and Automation, New Mexico, April 1997.
[3] E. Malis, F. Chaumette, and S. Boudet, 2 1/2 D Visual
Servoing, IEEE Transactions on Robotics and Automation, Vol. 15, No. 2, pp. 238–250, 1999.

6[4] K. Miura, J. A. Gangloﬀ, and M. F. de Mathelin,
Robust and Uncalibrated Visual Servoing without Jacobian using a Simplex Method, Proceedings of the2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, September 2002.

4

Switch from Simplex to Newton

1010Conclusion

2[5] J. A. Nelder, and R. Mead, A simplex method for function minimization, Computer Journal, Vol. 7, No. 4,
pp. 308–313, 1965.

0[6] J. A. Piepmeier, G. V. MacMurray, and H. Lipkin,
Uncalibrated Dynamic Visual Servoing, IEEE Transactions on Robotics and Automation, Vol. 20, No. 1,
pp.143–147, 2004.

0

10

20

30

40

Number of iteration[7] W. Spendley, G. R. Hext, and F. R. Himsworth, Sequential application of simplex designs in optimization and evolutionary operation, Technometrics, Vol. 4,
pp. 441–461, 1962.

Figure 2: Trajectory of the optimization process©ISAROB 2005

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Impulsive noise reduction using M-transform and wavelet
with applications to AFM signals
Hiroshi Harada
Su-Kyun Jeon
Hiroshi Kashiwagi
Kumamoto University
Kumamoto University
Kumamoto University
hiroshi@mech.kumamoto-u.ac.jp jsk4544@hotmail.com kashiwa@gpo.kumamoto-u.ac.jp
Japan
Japan
Japan
2

Abstract
The authors propose here a new method for reducing both impulsive noise and white noise by use of
M-transform and wavelet shrinkage. M-transform is
a new signal transformation proposed by the authors,
and any periodic time signal can be considered as theoutput of a filter whose input is an M-sequence. By
using the properties of M-tarnsform, it is shown thatboth impulsive noise and white noise can be eliminatedby use of first M-transform and then wavelet shrinkage. This method is applied to the signal obtainedin Atomic Force Microscope(AFM) signal which usually contains many impulsive noise and withe noise.
The result of experiments show that this method canbe widely applied to practical situations for reducing
both impulsive and white noise.

1

M-transform is a new signal processing techniqueproposed by the authors[2]. This transform is based on
the pseudo-orthogonal property of a pseudo-random
M-sequence. Just like in case of Fourier transformwhere any time signal can be expressed as a sum ofsinusoidal signals by use of Fourier transform, any periodic time function can be considered to be a weighted
sum of M-sequences.
Let {ai} (ai = 1 or 0) be an n-th order M-sequence.
Then, we provide a new sequence {mi } as is definedin Eq.(1).
mi = 1 − 2ai (0 ≤ i ≤ N − 1)(1)Here, N = 2n − 1 is a period of the M-sequence.
A matrix Mi of N ×N degree is defined by the next
equation.
mi , mi−1, · · · , mi−N +1
 mi+1 , mi , · · ·, mi−N +2 Mi = 
(2)
 .

 .

mi+N −1 · · · · · · , miIntroduction

In this paper, the authors propose a new method
for impulsive noise reduction by using M-transformand wavelet shrinkage. Various filters such as Wavelet
shrinkage have been proposed for the removal of white
noise included in a signal[1]. However, these methods
are based on assumption that the removed noise is a
Gaussian white noise. Therefore, when a white noise
and an impulsive noise are included in the observed
signal, the impulsive noise can not be removed.
The authors have recently proposed a new signal
processing technique called M-transform[2]. By using
M-transform, both impulsive noise and a white Gaussian noise are converted into a small-amplitude random signal. By combining M-transform and Wavelet
shrinkage, a new method is proposed here to remove
both impulsive noise and white noise simultaneously
in Atomic Force Microscope(AFM) signal. From theresults of computer simulation, the proposed method
is shown to be very efficient to remove both impulsive
noise and white noise in AFM signal.
©ISAROB 2005

M-transform

Let Xi be an arbitrary periodic discrete time signal
represented as
Xi

=

x(i)

=

4

(x(i), x(i + 1), · · · , x(i + N − 1))T (3)x(i∆t)

where ∆t is a sampling period. Then, M-transform
A of the signal Xi is uniquely determined as
Xi
A

=

Mi A

(4)

=

(MiT Mi )−1 MiT Xi(5)

The definition of M-transform is shown in Fig.1.
Any periodic time signal Xi can be considered as
the output of a filter whose input is an M-sequence.
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Here, Lj is a length of the wavelet coefficient at levelj. Since wavelet transform is a linear transformation,(j)
the wavelet coefficients We (k) of the white noise become also white noise. On the other hand, when theoriginal signal u(i) does not contain high-frequency(j)component, the coefficients Wu (k) become 0. Thus,
the white noise can be removed by removing the co(j)efficient Wx (k) below a threshold level λ. For thethresholding, Donoho[1] used the following equation.

Figure 1: M-transform
Impulsive noise is converted into small-amplitude random signals through M-transform. Let P be a periodic
time signal, in which a single impulse is included.
P (i) =

(0, 0, · · ·, pl , 0, · · ·, 0)T


(j)
(j)

 sgn(wx (k)(|wx (k)| − λ) · · ·
0
(j)
(j)
Wx (k) =· · · (if |wx (k)| > λ)(j)
0 · · · · · · · · · (if |wx (k)| ≤ λ)(6)Here, l means a position of the impulse and pl isthe amplitude of the impulse. Substituting Eq.(6) into
Eq.(5), M-transform Ap of the signal P is given as
Ap

=

αp(i)

=

(αp (0), αp(1), · · · , αp(N − 1))
1(mi+l − 1)pl
N +1

Here, sgn(x) is a function satisfying,(7)

sgn(x) =λ=σ(j)©ISAROB 2005

(13)

Impulsive noise reduction method

x(i)
Xn

(9)

= u(i) + e(i) + p(i)= (x(0), x(1), · · ·, x(N − 1))T

(14)
(15)

Then, M-transform A of the noisy signal Xn is calculated by Eq.(5).

Here, L is a length of the signal. Computing alevel JL orthogonal wavelet transform of the observed(j)signal x(i), the wavelet coefficient Wx (k) is given by
the next expression.
(j)p
2 log LThe noise reduction method for both impulsive
noise and white noise in AFM signal by using Mtransform and wavelet shrinkage is as follows[4].
Let x(i) be a time signal which includes both impulsive noise p(i) and white noise e(i).

In the method called wavelet shrinkage, the noise
included in the signal is removed according to the following procedures[1]. Let u(i) be a discrete time original signal and e(i) be a Gaussian white noise. Then,the observed signal x(i) is given by the next equation.

A = Au + Ae + Ap

(16)Here, Au , Ae and Ap are the M-transform of theoriginal signal, white noise and impulsive noise, respectively. As mentioned above, both impulsive noise(j)Wx (k) = Wu (k) + We (k)(j = 1, · · ·, JL ; k = 0, 1, · · ·, Lj − 1)(12)Noise reduction is completed by reconstructing the0(j)signal by using the coefficient Wx (k) processed by
Eq.(11).

Wavelet Shrinkage(i = 0, 1, · · ·, L − 1)

1 (x > 0)−1 (x < 0)This method is called soft-thresholding. If the standard deviation σ of the Gaussian noise is alreadyknown, the threshold level λ can be determined by

4x(i) = u(i) + e(i)(8)Since the amplitude pl is a constant, it is clear thatthe impulsive noise is converted into a small amplitude M-sequence. M-transform of a time signal isequivalent to calculating the cross-correlation between
the time signal and M-sequence. Since white noise
does not correlate with an M-sequence, M-transformof a white signal also becomes a random signal having small amplitude. Thus, these two different kindsof noise become the small-amplitude random signal
through M-transform.

3

(11)

(10)

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p(i) and white noise e(i) are converted into smallamplitude random signals through M-transform. So,
if we apply the wavelet shrinkage method to the Mtransform α(i) intead of the time signal x(i), it is possible to remove both impulsive noise and white noise.
The method for noise reduction proposed in thispaper is as follows. First, M-transform A of the AFM
signal x(i) is calculated. Then, the wavelet shrinkage
method is applied to the M-transform A. After the
wavelet shrinkage, the filtered signal is transformed
into time domain through the inverse M-transform andthe noise reduction procedure is completed.
Fig.2 shows the procedure of the proposed noise
reduction method.

Figure 3: Structure of AFM

2. AFM does not need a special operating environment, and be able to measure the surface profile inatmosphere, in liquid, and in the vacuum.
3. Unlike the scanning electron microscope, AFM
can measure surfaces of the conductive material, ceramics, the polymeric material, and the biological material.
4. AFM can measure various kinds of force suchas van der Waals force, repulsive force and adhesive
force.
In order to shorten the measuring time, it is necessary to increase the scanning speed of a probe that
traces the surface of the test piece. However, when
the scanning speed becomes faster, impulsive noise islikely to occur, and it becomes impossible to measure
the precise surface profile. Those impulsive noises cannot be removed by using ordinary low-pass filter or anonlinear median filter.
The AFM used in this paper is SPI3700 manufactured by SEIKO Instruments Inc., Japan, and themeasuring range of the AFM is from 1µm × 1µm to150µm×150µm. The maximum resolution of displacement is 0.2nm in th x-y place and 0.01nm in z axis
direction.
An example AFM signal which includes impulsive
noise is shown in Fig. 4. Here, the sample used forthe measurement was a silicon plate which was polished like a mirror finished surface. In this sample,
the scanning speed is 1Hz and the measure range is
25.57µm × 25.57µm. In Fig. 4, 127 × 127 pixels ofa measured image of 256 × 256 pixels are displayed.
From this figure it is clear that a lot of impulse noises
are included in the AFM signal, and it is not possible
to measure accurate surface profile of the sample.
The proposed noise reduction method is applied to

Figure 2: Procedure of the proposed noise reduction
method5

Application of the proposed method
to atomic force microscope signal

Atomic force microscope(AFM) can measure thesurface profile very precisely by using the atomic force
operating between a probe and the sample surface. A
schematic configuration of AFM is shown in Fig. 3.
AFM mainly consists of three parts, a cantilever, adisplacement sensor and scanning element. The cantilever converts the atomic force that is received bya probe into displacement. The displacement sensor
detects the deflection of the cantilever. The scanningelement is used to move the sample in high accuracy
in three-dimensional space. AFM has the following
features.
1. AFM possesses the spatial resolution at an
atomic level.
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the AFM signal. Since the size of the image shown in
Fig. 4 is 127 × 127, the degree of M-sequence is chosen
to be 7 and the characteristic polynomial f(x) is
f(x) = x7 + x4 + x3 + x2 + 1(17)

The level j of the Wavelet transform is j=3.
The result of the noise reduction is shown in Fig. 5.
Although, there remain some impulsive noises, mostthe impulsive noises were removed from the original
AFM signal.
Figure 6: Original signal and noise reduced signal
An example of a column of AFM signal containing
original signal and noise reduced signal is shown in
Fig. 6. The dotted line is the original signal, and thesolid line is noise reduced signal by using M-transformand wavelet shrinkage. From this figure, we see theproposed method is very effective for impulsive noise
reduction.

6

Conclusion

In this paper, the authors propose a new method
for impulsive noise reduction by using M-transformand wavelet shrinkage. From the results of computer
simulation, it is shown that the proposed method isvery efficient to remove both impulsive noise and white
noise in AFM signals.

Figure 4: AFM signal which includes both impulsive
noise and white Gaussian noise

References[1] Donoho, D.L., ”De-noising by soft-thresholding”,IEEE Trans. Information Theory, Vol.41(1995), No.3, pp.613-627.
[2] Kashiwagi, H., M.Liu, H.Harada and T.Yamaguchi, ”M-transform and its application to System Identification”,Trans. of SICE, Vol.E-1(2002),
No.1, pp.289-294.
[3] Harada, H., H.Kashiwagi, T.Andoh and K.Kaba,”Impulsive noise reduction by use of M-transform”,Trans. of SICE, Vol.39(2003), No.7, pp.688690.(in Japanese)
Figure 5: AFM signal after impulsive noise and white
noise reduction by using M-transform and wavelet
shrinkage©ISAROB 2005

[4] Harada, H., H.Kashiwagi, K.Kaba and T.Yamaguchi, ”Impulse noise reduction by using Mtransform and wavelet shrinkage”, Proc. ICSS heldin Wroclaw, Poland,(2004), pp.313-319.
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DNA Computing Approach to Evolutional Reasoning Algorithm
by Using Restriction Enzyme
Yusei Tsuboi, Zuwairie Ibrahim, and Osamu Ono
Institute of Applied DNA Computing
Graduate School of Science & Technology
Meiji University
1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa, 214-8671 JAPANe-mail: tsuboi@isc.meiji.ac.jp

attribute-value pairs contained in the set of represented
objects, plus a tag node for each object. The objects
representing double-stranded DNAs (dsDNAs) will beformed via parallel self-assembly, from encoded
single-stranded DNAs (ssDNAs) representing theattribute attribute-value pairs (nodes), as directed bysplinting ssDNAs representing relations (edges) in the
network. The computational complexity of theimplementation is estimated via simple simulation, whichindicates the advantage of the approach over a simple
sequential model. According to this report, if suchreasonable computation is realized in vitro, a huge
number of DNA molecules will be needed in advance as
the size of the graph increases. Thus, we have togenerates massive initial pools in the first implementation
step and then filter the candidate solutions which satisfy
the given conditions. To successfully decrease the initialpools, there are a few different initial pool generation
methods, parallel overlap assembly (POA) introduced by
Stemmer [3], the mix and sprit method introduced by
Faulhammer et al. [4], with their own advantages anddisadvantages. In addition to these methods, the adaptive
DNA computing algorithm (ADCA) with a feedback
structure was introduced by Yamamoto, et al. [5] in 2004.
We strongly support the ADCA because this algorithm
requires only simple and reliable operations: annealing,
cutting by a restriction enzyme, polymerase chain
reaction (PCR) and gel electrophoresis. The ADCA isapplied to a shortest path problem for a mobile robot. The
simulation results indicated to extremely reduce thenumber of DNA molecules needed as compared with asimple Adleman’s model.
In this paper, we propose an evolutional reasoning
algorithm which uses the SMC and the ADCA. We will
review the ADCA in section 2 and the SMC in section3.
Section 4 explains the evolutional reasoning algorithm byusing a restriction enzyme, as experimental protocols.
Section 5 presents the discussion and conclusion.

Abstract
In this paper, we describe how to encode SMC andpropose a new reasoning algorithm as experimental
protocol of DNA computing techniques, which areenhanced by a concept derived from ADCA. When the
SMC is encoded into DNA sequences, the enzyme
recognition site involves in each encoded ssDNA
representing nodes and edges. The reasoning algorithm
shows an object is reasoned out. The ssDNAs are mixed
and anneal to complementary sequences under definedconditions in a test tube. In order to retrieve a correct
dsDNA which means an object reasoned, a following
cycle is repeated. First, the restriction enzyme such as
EcoRⅠcuts a specific part of dsDNAs, reading enzyme
recognition sites of the sequence. Second, the cut
products are annealed. Finally, the generated products areanalyzed by gel electrophoresis. Even if once annealing
process runs, the cutting process enables to evolutionarily
reuse the same DNA molecules for computation. This
repetition stops, when correct strands remain at theanalysis process. If the strands do not remain in spite ofseveral repetitions of the cycle, we can say “No”. As an
output, DNA chips will display the name of the object
reasoned.
Keywords: DNA computing,Algorithm, AI application

1

Semantic

Network,

Introduction

In 1994, L. Adleman’s [1] ground-braking 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 memory machinesthat are machines with very large memory that
implements rather simple search operations.
In 2004, a semantic model was proposed anddescribed theoretically for DNA-based memories by
Tsuboi, et al [2]. This model, referred to as ‘semantic
model based on molecular computing’ (SMC) has the
structure of a graph formed by the set of all attribute-©ISAROB 2005

2

Encoding Scheme

Many works on DNA computing have employed theencoding scheme presented by Adleman. In Adelman’s
experiment, each of edges and nodes within the small

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Hamiltonian path graph presented by him is represented
by ssDNAs of 20 nucleotides respectively. These codes
were constructed at random; the length 20 is enough inorder to ensure that the codes are “sufficiently different”.
The edge u→v from node u to node v is described to be
Watson-Crick complementary to the node sequences
derived from the 3’ 10- mer of the node u and from the 5’
10-mer of the node v. For instance, the codes of the node1, 2 and the edge 1→2 specified below

EcoRⅠrecognition site

5’

α

β

3’

3’

α

β

5’
Cut by EcoRⅠ

3’
5’

node1:
5’TATCGGATCGGTATATCCGA3’
node2:
5’GCTATTCGAGCTTAAAGCTA3’
edge1→2: 3’CATATAGGCTCGATAAGCTC5’

5’

α

3’

α

β

β

Figure 1 A dsDNA is cut by enzyme EcoRⅠ

As for an encoding scheme of ADAC, let us supposethat we have code α, β and complementary code α , βwhich are parts of recognition site of a restriction enzyme
EcoR I. α and β are respectively assigned ‘AATTC’ and‘G’. That is to say, α and β are respectively assigned‘TTAAG’ and ‘C’. A dsDNA involving codes α, β, α , β3Semantic Model Based on MolecularComputing (SMC)

Figure 2 describes an SMC formed by the union of aset of some objects. It is made of three relations: object,
O; attribute, A; and attribute-value, V. This list
representation is denoted as follows:is cut at the set part by EcoRⅠas shown in Figure 1.
α and β code are embedded between the sequences
derived from the 3’ 10- mer of the node u and from the 5’
10-mer of the node v. α and β are attached to the
5’end of the edge u→v. In this way, the codes of the node1, 2 and the edge 1→2 are modified below,{<O, Ai, Vji> | i=1, 2,…, m; j=1, 2,…, n}A tag as a name of an object is set to an initial node in
the graph. Both the attribute and attribute-value are also
node1:
5’TATCGGATCG | α β | GTATATCCGA3’
set to another node following by the tag node. The nodes
node2:
5’GCTATTCGAG | α β | CTTAAAGCTA3’
denote either a name of the object or both the attributeedge1→2: 3’CATATAGGCTCGATAAGCTC| α β |5’and the attribute-values. In short, one path from an initialnode to a terminal node means one object named on thetag. The model represents an object, as reasoned out by
the combinations between the nodes connected by theedges. An SMC contains all attributes common to every
object as well as each attribute-value. Attribute layers
consist of attribute-values, lined up. If an object has novalue of a certain attribute, the attribute value is assigned‘no value’.
Attribute layer(Object 1)(A1, V11)(Object 2)(Am, V1m)(A2 V12)(A1, V21)

Object (Concept)(A2, V22)(Am, V2m)(Object 3)(A1, V31)(A2, V32)(Am, V3m)(Object 4)(A1, V41)(Object 5)(A1, V51)(Object 6)(Object 7)(A1, V61)(Object 8)(A1, No value)(A2, V42)(Am, V4m)(A2, V52)(Am, V3m)(A2, V62)(Am, V6m)(A2, No value)(Am, No value)(Object 9)

Figure 2 A semantic model based on molecular computing (SMC), whichcollectively models a set of objects, given a total number of attribute layers, m.

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4

to retrieve a correct dsDNA which means an object
reasoned, the generated products are analyzed from DNA
length by gel electrophoresis. If they remain, say “Yes”:
the object is reasoned out, otherwise, we should consider
about two cases. The one is “No”: the object is notreasoned out. The other is that the number of DNA
molecules prepared in advance is too few to form thecorrect dsDNAs. To distinguish the two cases, a following
cycle is repeated. First, the EcoR Ⅰ cuts the all theanalyzed products, reading the enzyme recognition site of
the sequences. Second, the cut products are annealed
again. Figure 5 illustrates ligation & hybridization
process after the cutting. Finally, the generated products
are analyzed as well. Even if once annealing process runs,
the cutting process enables to evolutionarily reuse thesame DNA molecules for computation. This repetition
stops, when correct strands remain at the analysis process.
If the strands do not remain in spite of several repetitionsof the cycle, we can say “No”.

Methodology

In this section, we propose a reasoning algorithm byusing a restriction enzyme, which are enhanced by aconcept derived from the ADCA.
4.1 DNA Representation of SMC
Each of the nodes and edges within an SMC may be
represented by a DNA library. In the DNA library, a row
shows attributes and a column shows attribute-values.
DNA sequence is designed by these relations so that it
might not be overlapping with the other sequences at
random. Firstly, with Adleman’s encoding scheme, except
for initial and terminal edge, each nodes and edges is
assigned ssDNA oligonucleotide of length 20. As for tag
nodes, the sequences are also assigned by random 20
bases. The initial and terminal edges are respectively
represented by the size which suits the end of the DNA
pieces exactly. Here, an important thing is that every
sequence is designed according to these relations toprevent mishybiridization via other unmatchingsequences. Next, we innovate the concept of ADAC
encoding scheme in the Adleman’s encoding scheme. The
code α, β α and β of the enzyme recognition site of EcoR

4.3 Output
DNA Chips, output in readable format isaccomplished by attaching the cloned, coding sequences
to an array. Thus, each spot would represent an object.
Readout occurs directly form sensing fluorescent tags
attached to a tag sequence of the correct strands, asprobes.

Ⅰinvolves in each encoded ssDNA representing nodes
and edges, except for the initial node, the terminal nodeand the terminal edge. Figure 3 shows DNA
representation of one of the object within the SMC.

(Object 1)

5’3’

4.2 Algorithm(Object 1) →(A1, V31)

The reasoning algorithm shows an object is reasonedout by DNA computing techniques. Semantic informationof some reference objects is stored in a semantic memory
as knowledge bases. The algorithm reveals that whichreference object several input objects are classified intowith DNA molecules all at once. Figure 4 explains overall
procedures of DNA operations required for solutions.
A set of DNA representing reference objects and an
input object fragments, formed by the combinations ofoligonucleotides, are synthesized as follows:(A2, V42)

α β

α β

α

α

β

(Am, V2m)

3’
5’

β

(A1, V31) →(A2, V42)

(Am-1, V5, m-1)→(Am, V2m)

Figure 3 DNA representation of one of the objects

Synthesis

Hybridization & Ligation

Cuttingz Reference object
The ssDNA of each edge and tag node in the network issynthesized as a set of knowledge based molecules.

Gel Electrophoresisz Input object
Attribute-values are extracted from an input object
according to determined attribute Ai. Using the attributes
and the attribute-values, an ssDNA is synthesized as a setof input molecules with the sequence defined by the DNA
library.

Answer {Yes, No}

DNA ChipThe ssDNAs are mixed and anneal to complementary
sequences under defined conditions in a test tube. In order©ISAROB 2005

(A1, V31)

Figure 4 Overall procedures of DNA Operations

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α 3’

5’ β

3’

α 3’

5’ β

α

β

α 3’

5’ β

α

5’ 3’

β

DNA computing. It is expected that the proposed
algorithm would extend many AI applications, knowledge
bases, pattern matching, etc. As a future work, to achieve
reliable
performance,someparametersofreactions-temperatures, concentrations of oligonucletides,
times of reactions, etc. will be experimentally tested.

α

5’ 3’

β

5’

Reference

Hybridization & ligation

5’ β
3’

α

β

α

β

α

α 3’

β

α

β

α

β

[1] L. Adleman, “Molecular computation of solutions to
combinatorial problems”, Science 266, pp.583-585,
1994.
[2] Y. Tsuboi, Z. Ibrahim, and O. Ono, “Semantic model
for artificial Intelligence based on molecular
computing”, Proc. of The 8th International
ConferenceonKnowledge-BasedIntelligent
Information and Engineering Systems, Lecture Notes
in Artificial Intelligence (LNAI), Vol. 3213, pp. 32-39,
2004.
[3] W. Stemmer, “DNA shuffling by random
fragmentation and reassembly: in vitro recombination
for molecular evolution, Proc. of Natl. Acad. Sci.,
pp.10747-10751, 1994.
[4] D. Faulhammer, A. R. Cukras, R. J. Lipton, and Laura
F. Landwever, “Molecular computation: RNAsolutions to chess problems, Proc. of Natl. Acad. Sci.,
pp1385-1389, 2000.
[5] O. Ono, Y. Tsuboi, T. Yamamoto, Z. Ibrahim, and S.
Watanabe, “Computation algorithm based on adaptive
DNA molecules by using polymerase chain reaction”,
Pcoc. of 20th Fuzzy System Symposium pp. 83-86,
2004 (in Japanese).
[6] R. Daton, M. Garzon, R. C. Murphy, J. A. Rose, D. R.
Franceschetti, and S. E. Stevens Jr, “Reliability andefficiency of a DNA-based computation”, Phys.
Review Letters 80, pp.417-420, 1998.
[7] R. Deaton, C. R. Murphy, M. Garzon, D. R.
Franceschetti, and S. E. Stevens Jr.,”Good encodings
for DNA-based solutions to combinatorial problems”,
Proc. of DNA Based Computers DIMACS Series in
Discrete Mathematics and Theoretical Computer
Science, Vol.44, pp.247-258, 1999.
[8] J. A. Rose, R. Deaton, D. Franceschetti, M. Garzon,and S. E. Stevens Jr., “A statistical mechanicaltreatment of error in the annealing biostep of DNA
computation”, Proc. of Genetic and Evolutionary
Computation Conference, pp.1829-1834, 1999.
[9] J. SantaLucia, H. Allawi, and P. Seneviratne,“Improved nearest-neighbor parameters for predicting
DNA duplex stability”, Biochemistry, Vol.35, No.11,
pp.355-356, 1996.
[10] R.Penchovsky, and J. Ackermann, “DNA library
design for molecular computation”, Journal ofComputational Biology, Vol.10, No.2, pp.215-229,
2003.

5’

Figure 5 Hybridization & ligation process after cutting

5

Discussion & Conclusion

We have been discussed an evolutional reasoning
algorithm by using DNA computing techniques. This
paper provides a necessary DNA computing-chemical
process including experimental operations. An adopted
protocol is very simple. Standard genetic engineering
techniques such as annealing, ligation, cutting and gelelectrophoresis are required. Several experiments havebeen conducted to assess the performance of DNA-based
databases realized by biochemical reactions. Within the
SMC, attributes and attribute-values are represented byrandom (0-20) oligonucleotides {A, T, C, G}. There are
two issues to consider length and sequences design ofDNA. The one is that if a set of knowledge increases,
oligonucleotides, length 20 are not fully assigned to eachof nodes and edges in the network. Such length limits torepresent a lot of objects. This issue would be resolved
simply by assigning longer oligonucleotides length. The
other is that in practical sequences design, we have toconsider an effective way to select proper sequence to
avoid mismatched, error hybridization will have to bedevised. Word design strategies for DNA-based
computation have been investigated so far. Substantial
progress has been reported on this issue [6]-[10]. We
expect that this issue will be satisfactorily resolved in thenear future. Thus, these issues are very crucial to obtain acorrect answer. In the light of these issues, we will haveto design best sequences with adequate length, when alaboratory experiment is done.
The SMC is one of the models for applying DNA
computing techniques to the research field of artificial
intelligence. Its application has many incredible
advantages, whereas the reasonable performance
demands a huge number of molecules. The proposed
algorithm will enable to minimize useless molecules
synthesized. In addition, it repeats cutting, annealing andanalysis processes to reach the solution only, whichinterests us in terms of an intelligent mechanism based on©ISAROB 2005

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A Model for Coevolution
K. Makino
Dept. of Mech. and Ctrl. EngTokyo Institute of Technology
makino@mylab.ctrl.titech.ac.jp

K. Nakano
School of Bionics
Tokyo University of Technology
nakano@cc.teu.ac.jp

keywords : coevolution, differentiation of species, genotypic gene, phenotypic gene
Abstract
In the natural world, many kinds of animals andplants have been evolving mutually influenced. This
study aims to investigate how the differentiation of
species occurs through coevolution. We have constructed a model by computer simulation, which iscompared to coevolution of bees and flowers. By theuse of the model, the process of breading have been
examined. The influence on correspondence between
genotypic genes and phenotypic genes has been examined as well.

1

pollena hammer orchida dummy wasp

a hinge

a

b

c

d

Figure 1: A hammer orchid and a wasp.
of bees and flowers is introduced. The characteristics
of creatures are decided by the phenotype that is described by the genotype. In this model, a bee flies toa flower whose color is its favorite. If the shape of thebee fits to that of the flower, the bee can get pollen.
It can also get nectar (that is, energy), if the nectarof the flower is its favorite one. After bees’ gathering
the nectar and transferring the pollen, the bees andthe flowers bear their children. Probability of matingof a flower increases according to the number of times
it received pollen. The number of mating of a bee isdecided by the amount of the energy that it gathered.
This is how we model the cooperative relation between
bees and flowers. Secondly, we discuss the differentiation of species through coevolution and show that suchdifferentiation occurs in our model. In addition, it isconfirmed that differentiation of species is influenced
by the relation between genotype and phenotype andby the condition of mating.

Introduction

For the evolution of creatures, mutual interaction isimportant. This is called coevolution. Competitive relation between gnus and lions, and cooperative relation
between bees and flowers are good examples of coevolution. The aim of this research is to construct a model
for coevolution and to investigate the mechanisms ofdifferentiation of species through coevolution[1].
We start with introducing an example of coevolution, that is, the relation between hammer orchids andwasps. A hammer orchid has a sort of lure, which resembles a female wasp in its shape and pheromone(Fig. 1-a). A male wasp catches the dummy by mistake (Fig. 1-b). It tries to fly with the dummy andtouches the pollen, because the flower is connected tothe stem with a hinge (Fig. 1-c). It transfers pollen
from flower to flower (Fig. 1-d). This is the example
we focus on to construct a model for coevolution.
Every creature has genes (genotype) as biologicalinformation. Different genes do not necessarily describe different observable characteristics (phenotype).
We introduce genotype and phenotype to our model.
For simplicity of the argument, it is assumed that there
are only two kinds of creatures in the world, one is“FLOWER” and the other is “BEE”. First, our model©ISAROB 2005

a wasp2

Model of Bees and Flowers

In this section, the detail of our model is described.
The model consists of bees and flowers. Hereafter wedenote BEE’s and FLOWER’s for those in the model.
Since an accurate model is not necessary for our purpose, there is neither a queen bee nor the difference
between a male and a female. BEE’s and FLOWER’s

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have genes. The genes have genotypic and phenotypic
expression. The detail is described in section 2.1.
In section 2.2, the behavior of BEE’s and
FLOWER’s are explained. There are two phases. One
is pollinating phase, where BEE’s fly to FLOWER’s to
get nectar and transfer pollen. The other is breeding
phase, where BEE’s and FLOWER’s bear children. A
sequence of a pollinating phase and a breeding phaseis called a cycle.

0000

0011

Phenotype and Genotype

0100

1000

0101

0110

1001

1010

0111

1011

1101

1110

or

yyyy

1100

: genotype

White : phenotype A

Gray: phenotype BBlack: phenotype C

Figure 2: Relation between genotype and phenotype(relation rule 1).

BEE’s and FLOWER’s have genotypic genes. The
same observable characteristic does not necessarily
correspond to the same genotypic gene. Sometimes,a few genotypic genes express the same characteristic.
This characteristic expression is called phenotype. A
BEE has three genotypic genes each of which is composed of 4 bits. Totally they are represented by 12
bits x1 x2 x3 x4 |x5 x6 x7 x8 |x9 x10 x11 x12 , and corresponding phenotypic genes are X1 X2 X3 . In the same manner, genotypic genes of a FLOWER are represented byy1 y2 y3 y4 |y5 y6 y7 y8 |y9 y10 y11 y12 , and phenotypic genes
are Y1 Y2 Y3 . Y1 , Y2 , and Y3 describe a color, shape, andnectar, respectively, where each of them takes a value
out of A, B, and C. X1 , X2 , and X3 are the BEE’s
favorite color, fitting shape, and favorite nectar, respectively. Each of them also takes a value out of A,
B, and C. DNA and a characteristic in a real creaturecorrespond to genotype and phenotype, respectively.
In our model, we define the correspondence between
genotype and phenotype, and investigate how coevolution happens through interactions. One example ofsuch correspondence is shown in Fig. 2 as relation rule
1, and another in Fig. 3 as relation rule 2. Though inthe model, both coding of genes and the relation rule
are quite different between bees and flowers, the samecoding and the same rule are applied to BEE’s and
FLOWER’s. The essence of coevolution still exists inthe model. In Fig. 2, each of the genotypic genes is
assigned to a phenotypic gene at random. In Fig. 3,
similar genotypic genes are assigned to the same phenotypic gene, that is, genes with zero and single 1’s are
assigned to phenotype A, with two 1 to B, and withthree and four 1’s to C. The lines indicate the relation
between two genotypic genes where 1 bit is inverted.
Table 1 shows an example of relation between genotypic and phenotypic genes adopting relation rule 1 inFig. 2. There are a BEE with ABA and a FLOWER
with ABB. The BEE likes the color of the FLOWER
and fits to it in the shape, and can take the pollen andthe nectar, but the nectar is not nutrient for the BEE.

©ISAROB 2005

0010

1111
xxxx

2.1

0001

0000
A

0011

0001

0010

0100

1000

0101

0110

1001

1010

0111

1011

1101

1110

1100

B

C

1111
xxxx

or

yyyy

: genotype

White : phenotype A

Gray: phenotype BBlack: phenotype C

Figure 3: Another relation rule (relation rule 2).
It is assumed that creatures with the same phenotypic genes constitute one species.

2.2

Pollinating and Breeding Phase

The details of the behavior of BEE’s and
FLOWER’s in pollinating phase and breeding phaseare described as follows.
Pollinating phase The pollinating phase consistsof 3 steps. Every BEE acts as step 1 ∼ 3.
Step 1 A BEE flies to a few FLOWER’s (fiveFLOWER’s for example) that have its favorite
color and spends some energy. If the energy becomes zero, the BEE dies. The BEE finishes flying to any FLOWER, if there is no FLOWER
with its favorite color.
Step 2 If the shape of the FLOWER fits to that of
the BEE, it gives the FLOWER pollen which it
has taken from another FLOWER and takes newpollen from the FLOWER. The BEE does nei-

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

BEE
FLOWER

genotypic genes
0000 0001 0110
1000 1001 0001

phenotypic genes
ABA
ABB

Figure 4 illustrates that BEE’s fly to five
FLOWER’s with their favorite color one by one. Anda flowchart of this phase is shown in Fig. 5.

Table 1: An example of genes of a BEE and a
FLOWER in the case of relation rule 1.

The BEE in the breeding phase :A BEE canbear children in proportion to its energy. Whether
two BEE’s are able to mate or not, is not decided
by phenotype but by genotype. First, two BEE’s arechosen at random. Secondly, a similarity of their genotypic genes is examined. Finally, the BEE’s bear achild through mating, if their genotypic genes are similar. Mutation can happen at this point. The similarity is evaluated by the Hamming distance between
their genotypic genes. The maximum of the difference,
where mating is possible, is defined as MPD (Maximum Permitted Difference). In other word, two BEE’s
can bear a new BEE, when different bits between their
genotypic genes are less than MPD. This parameter isimportant in the following simulations. Mating is performed in every gene but not in every bit. In addition,
the BEE’s give certain energy to the child.

ther give nor take pollen if it dose not fit to the
FLOWER in the shape.
Step 3 Presume that BEE’s can recognize whether
the nectar is nutrient for it or not. If the nectar is nutrient, the BEE gathers the nectar (someenergy), and the FLOWER looses a part of thenectar. Therefore, a BEE can not gather the nectar from the FLOWER which lost all the nectar.
The nectar is recovered at the end of the cycle.
FLOWERBEE

The FLOWER in the breeding phase :A
FLOWER and the transferred pollen bear a new
FLOWER as the BEE’s.
Maximum number of
FLOWER’s is limited. That is, randomly chosen
FLOWER’s bear new FLOWER’s until the maximum
number is reached.
Figure 4: BEE’s fly to FLOWER’s.
In addition, BEE’s and FLOWER’s are given a lifetime. They die of the lifetime, after breeding phase.
Pollination phase start3

yes

Does BEE fly to FLOWER’s at given times?
noDifferentiation of Species though CoevolutionnoIs there a favorite colored FLOWER?

After the process described in the previous section,
BEE’s and FLOWER’s with new genes appear. The
creatures with the same phenotypic genes constitute a
sub-species. Most of the sub-species can not survive
because the population is small, but a few of themsurvive where the population increases to make new
species. Fig. 6 shows an example of the differentiation of species through the coevolution. In initial condition, there are only two species, BEE’s with AAA
and FLOWER’s with AAA. It is assumed that a new
sub-species of FLOWER’s with BAA appeared aftersome cycles. The sub-species survives by chance. If
BEE’s with BAA appear and increase after some cycles from the appearance of FLOWER’s with BAA,
BEE’s with BAA and FLOWER’s with BAA mutu-

yes
End

The BEE flies to it.

Step 1
no

Does the BEE fit to the FLOWER in the shape?
yes
The BEE give the FLOWER the carried pollen.
And the BEE gets new pollen.

Step 2
no
Does the FLOWER have some nectar?
yes
Does BEE like the nectar?

noyes
BEE gets nectar.

Step 3Figure 5: Flowchart of pollinating phase.

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AA

ally cooperate with and survive. Also, the BEE’s with
BAB and the FLOWER’s with BAB survive.

AAA

BAB

number of FLOWER's

number of BEE's

mating
CAA

500
400
CAA

300

BAA

200
100
AAA

0

BAA

cooperation

CAB

800
AAB

600

BAC
CAA
BA

400
200

AAA

100

200

300

0

400

a. BEE

100

200

300

400

cycle

cycle

BAA

BAA

0
0

cooperation

BEE
FLOWER cooperation

CAC

1000

600

b. FLOWERBABFigure 7: Time evolution of the population of every
species at every cycle with relation rule 1 and MPD =
4.

AAA

AAB

Figure 6: Differentiation of species though coevolution.

4

the original one (AAA), have appeared. The BEE’s
with AAA can not gather enough nectar to mate, because the maximum number of FLOWER’s and theamount of the nectar that a FLOWER generates arelimited. If FLOWER’s that has different color appear,
the BEE’s that like the color can gather more nectar
than the BEE’s of other species. Then, the new BEE’s
and the new FLOWER’s increase. For this reason, increase and decrease of species are repeated. On theother hand, FLOWER’s that have the same color andshape as the original one but have different nectar appear. The FLOWER’s can survive, because they attract the BEE’s and receive pollen. However, even if
the FLOWER’s increase, many BEE’s that fly to them
can not gather the nectar, because it is not their favorite one. The BEE’s decrease, because the energy
is not enough to bear children. It is considered thatthe model is stable at the condition that the numberof the FLOWER is small.

Simulation

The basic parameters are chosen as in Tab. 2.
At the initial point, there are 10 BEE’s and 10
FLOWER’s. Their genotypic genes are 0000 0000
0000. Every BEE flies to 5 FLOWER’s. First, a basic
simulation is shown in section 4.1. As the result, it has
been confirmed that differentiation of species occurs.
Secondly, the influence of MPD on differentiation of
species is analyzed in section 4.2. Finally, in section
4.3, another relation table is adopted.
item
Initial energy of a BEE
Initial energy of a FLOWER
Energy that is necessary to fly to a FLOWER
Energy that a BEE gets from a FLOWER
Energy that a FLOWER gives to a BEE
Energy that two BEE’s give to a new BEE
Lifetimevalue
5 [point]
10 [point]
1 [point]
2 [point]
2 [point]
5 [point]
5 [cycle]

4.2

First, the simulation has been performed in the caseof MPD = 0. The results are illustrated in Fig. 8. It
is confirmed that differentiation of species does notoccur.
Next, the simulations under the condition of MPD= 2 and MPD = 3 have been tried to investigate how
MPD influences the differentiation Fig. 9 and Fig. 10
show that different species is easier to occur, as MPD
increases.

Table 2: Definition of basic parameters.

4.1

Basic Simulation

This simulation is performed with a relation rule inFig. 2 and MPD = 4 [bit]. The results are shown in
Fig. 7-a, b. These figures show time evolution of the
population BEE’s and FLOWER’s of every species at
every cycle. There are many species at the same time.
It means that differentiation of species has occurred.
In addition, two interesting phenomena are observed. One is that two new species of BEE’s with
BAA and CAA, that have different favorite color from

©ISAROB 2005

Influence on the MPD

4.3

Influence of Relation between Genotype and Phenotype

Figure 11 shows the result when relation rule 2 inFig. 3 is adopted. In this case, differentiation of
species does not occur. The reason is considered as

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CCA

400
300

AAA

200
100
0

BAA

800
600

AAA

400
200
0

0

100

200

300

500
400

CAA

300
200

BAA
AAA

100
0

0

400

100

200

300

400

100

200

300

0

200

AAA

100
0

800
CAA

BAA
BAA

600
AAA

400
200

100

200
cycle

a. BEE

300

400

100

200

300

300

400

500
400
300

AAA

200
100
200
cycle

a. BEE

cycle

800

AAB

600
AAA

400
200
0

100

400

b. FLOWER

BAA

1000

0
0

200

b. FLOWER

0

0
0

100

cycle

number of FLOWER's

300

number of BEE's

number of FLOWER's

number of BEE's

1000

BAA

AAA

200

400

600

400

BAA

400

Figure 10: Time evolution of the population of every
species at every cycle with relation table 1 and MPD
=3

CAA

500

600

a. BEE

Figure 8: Time evolution of the population of every
species at every cycle with relation rule 1 and MPD =0, that is, their genotypic genes must be exactly thesame in order to mate.
600

AAB

cycle

b. FLOWER

CAA

CAA

800

0
0

cycle

cycle

a. BEE

number of FLOWER's

500

CAC

1000

600
number of BEE's

number of FLOWER's

number of BEE's

AAC

1000

600

300

400

0

100

200

300

400

cycle

b. FLOWER

Figure 11: Time evolution of the population of every
species at every cycle with relation rule 2 and MPD =4.

Figure 9: Time evolution of the population of every
species at every cycle with relation rule 1 and MPD =2.

species.
To investigate the behavior of more complicatedbio-systems composed of many kinds of animals andplants is one of our future works.

follows: when the genotypic genes have a close relation
with phenotypic genes, two creatures belonging to different species can not mate with each other. Therefore
the sub-species that appears from original one can notsurvive for a long time.

Reference5

Conclusion[1] K. Makino and K. Nakano, “The 66th National
Convention of IPSJ,” Information Processing Society of Japan ,Vol. 2, pp. 15-16, 2004.

We constructed a model for coevolution of bees andflowers. Using the model, we confirmed that the differentiation of species occurs through coevolution. Influence of various parameters to the process of evolution
is investigated. The differentiation of species is influenced by the relation between genotype and phenotype. It easily occurs when relation rule 1 (randomly
constructed) is adopted. Next, it is easier to occur,
as MPD (Maximum Permitted Difference) is larger.
It has been found that lifetime of creatures, numberof FLOWER’s which a BEE flies to, getting and consuming energy, and maximum number of FLOWER’s
do not make much influence on the differentiation of©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Simultaneous State and Parameter Estimation of Nonlinear Models∗
by Evolution Strategies Based Particle Filters
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

grals in Bayes’ rule is approximated by a weighted sum
based on the discrete grids with associated weights
sequentially chosen by the importance sampling. A
common problem in the particle filter is the degeneracy phenomenon, where almost all importance weights
tend to zero after some iteration and a large computational effort is wasted to updating the particles withnegligible weights. In order to resolve this difficulty,
several modifications have been proposed such as resampling particle filter (SIR) [5] that introduces a resampling steps. Applying the concept of Evolution
Strategies [7], we also developed the Evolution Strategies based prticle (ESP) flter [8]. In this paper, the
ESP filter is applied to simultaneous state and parameter estimation of nonlinear state space models.
Numerical simulation studies have been conducted toexemplify the applicability of this approach.

Recently, particle filters have attracted attentions
for nonlinear state estimation. In this approaches, aposterior probability distribution of the state variable
is evaluated based on observations in simulation using
so-called importance sampling. We proposed a new filter, Evolution Strategies based particle (ESP) filter tocircumvent degeneracy phenomena in the importanceweights, which deteriorates the filter performance, andapply it to simultaneous state and parameter estimation of nonlinear state space models. Results of numerical simulation studies illustrate the applicabilityof this approach.
Keywords.
Nonlinear filtering, particle filters,
Bayesian approach, evolution strategies, importancesampling, selection.

1

Introduction

2

The problem of state estimation of dynamic systems
using a sequence of their noisy observations has beenan active research area in control system sciences formany years. We focus here on Bayesian estimation approaches, that is, inference on the unknown state canbe performed according to the posteriori probability
density function (pdf), which is obtained by combining a prior pdf for the unknown state with a likelihood
function relating to the observations. When observations come sequentially in time, recursive state estimation is often interested, where the evolving posterior
pdf is evaluated recursively in time. However, in many
realistic problems, state space models include nonlinear and non-Gaussian elements that preclude a closed
form of expression for the posteriori pdf, and hence
many approximations have been proposed such as theextended Kalman filter (EKF) and Gaussian sum filter [6]. Recent progress of computing ability allowed
to the rebirth of Monte Carlo integration and its application of Bayesian filtering, or Monte Carlo filters.
A class of Monte Carlo filters, known as “particle filters” [4, 2] is discussed here. In this approach, the inte-

Consider the following nonlinear state space model.
xk+1yk= f (xk , vk )= g(xk , wk )

(1)
(2)

where xk and yk are the state variable and observation, respectively, f and g are known possibly nonlinear functions, vk and wk are independently identically distributed (i.i.d.) system noise and observationnoise sequences, respectively. We assume vk and wk
are mutually independent. The main objective here
is to find the best estimate of the state variable xk insome sense based on the all available data of observations y1:k = {y1 , y2 , . . . , yk }. We can solve the problem
by calculating the posteriori pdf of the state variable
xk of time instant k based on all the available data ofobservation sequence y1:k .
The posteriori pdf p(xk |y1:k ) of xk based on theobservation sequence y1:k satisfies the following recursion:Zp(xk |y1:k−1 ) = p(xk |xk−1 )p(xk−1 |y1:k−1 )dxk−1 (3)∗ 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 2005

Particle Filtersp(xk |y1:k ) =406

p(yk |xk )p(xk |y1:k−1 )p(yk |y1:k−1 )(4)The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

with a prior pdf p(x0 |y0 ) ≡ p(x0 ) of the initial statevariable x0 . Here normalizing constantZp(yk |y1:k−1 ) = p(yk |xk )p(xk |y1:k−1 )dxk

The particle filter with these steps is called “Sequential
Importance Sampling Particle Filter” (SIS).
It is known that the SIS filter suffers from the degeneracy phenomenon, where all but one of the normalized importance weights are very close to zeroafter a few iterations. By this degeneracy, a large
computational effort is wasted to updating trajectories whose contribution to the final estimate is almost
zero. In order to prevent this phenomenon, several
modifications have been introduced. Among them, resampling process, which eliminates trajectories whose
normalized importance weights are small, is a common approach. It involves generating new grid pointsx∗k (i) (i = 1, . . . , n) by resampling from the grid approximation (5) randomly with probability

depends on the likelihood p(yk |xk ), which is determined by the observation equation (2).
Since a closed form solution of recursions (3) and(4) is not admitted except in very restrictive cases including linear Gaussian state space models, where theKalman filter [1] can be applied, some approximations
should be introduced such as the extended Kalman filter (EKF)[6] and particle filters [4, 2]. Since EKF uses
a linearization technique based on a first order Taylor expansions of the nonlinear system and observation equations about the current estimate and approximates the posteriori pdf to be Gaussian, it can neverdescribe the true non-Gaussian density well. Particle
filters approximate the true posteriori pdf p(xk |y1:k )(i)by a large set of n  1 particles {xk , (i = 1, . . . , n)},where each particle has an assigned relative weight,
Pn
(i)
(i)
(i)
{wk ,(i = 1, . . . , n)}, wk > 0, i=1 wk = 1 as follows:
n
X
(i)
(i)
p(xk |y1:k ) ≈wk δ(xk − xk )
(5)

(j)

N̂ef f = Pn(i)q(xk |y1:k )

.

(i)

3

When the importance density q(xk |y1:k−1 ) is chosen
to factorize such that
(7)

(i)

we can obtain samples xk by augmenting each of(i)the existing samples xk−1 sampled from the importance density q(xk−1 |y1:k−1 ) with the new state sam(i)pled from q(xk |xk−1 , y1:k ).
Noting that the posteriori pdf can be rewritten using Bayes’ rule asp(yk |xk , y1:k−1 )p(xk |y1:k−1 )p(yk |y1:k−1 )∝ p(yk |xk )p(xk |xk−1 )p(xk−1 |y1:k−1 )(8)and inserting (7) and (8) into (6), the weights are recursively updated as
(i)

(i)

(i)

©ISAROB 2005(i)

∝ wk−1(i)p(yk |xk )p(xk |xk−1 )
(i)

(i)

q(xk |xk−1 , y1:k ).

σj0= σj exp(τ 0 N (0, 1) + τ Nj (0, 1))x0j= xj + σj0 Nj (0, 1)(12)where N (0, 1) and Nj (0, 1) denote a realization of normal random variable and normal random variables
sampled anew for counter j with zero mean and unit
variance, respectively, and σj denotes the mean step
size. The factors τ and τ 0 are chosen dependent on the
population size. The µ individuals of higher fitness arechosen deterministically out of the union of µ parentsand λ offspring ((µ + λ)-selection) or λ offspring only((µ, λ)-selection) to form the parents of the next generation in order to evolve towards better search region.
It can be seen that SIR and ES have similarities; boththe importance sampling process in SIR filter and mutation process in ES give perturbation to the parentp(xk |y1:k ) =wk

Evolution Strategies Based Particle
Filter

Evolution Strategies (ES) is one of the Evolutionary
Computation approaches, computational models simulating natural evolutionary processes to design andimplement computer-based problem solving systems(see the extensive surveys, for examples[3]. It has
been applied to continuous function optimization in
real-valued n-dimensional space via selection and perturbation processes called mutation. Mutation process
is realized by the additive process.

(6)

q(xk |y1:k ) = q(xk |xk−1 , y1:k )q(xk−1 |y1:k−1 ),(11)with the associated normalized weight wk is less thana predefined threshold Nthres < 1. Particle filter withthis resampling process is called “Sampling Importance Resampling Particle Filter” (SIR).

(i)p(xk |y1:k )

1

(i) 2
i=1 (wk )

i=1

(i)

(10)

and the weights are reset to wk∗ (i) = 1/n, when

where δ(·) is Dirac’s delta function (δ(x) = 1 for x = 0
and δ(x) = 0 otherwise).
Here, the particles are generated and associatedweights are chosen using the principle of “importance(i)sampling”. If the samples xk in (5) were drawn from(i)
an importance density q(xk |y1:k ), then the associatednormalized weights are defined by
wk ∝

(j)

Pr(x∗k (i) = xk ) = wk

(9)

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(i)

(i)

Since the form of the theoretical pdf p(θ|y1:k ) is notknown for unknown parameter case, we replace θ by
θk at time k, and simply include θk in an augmented
state vector xk = (xk , θk )T , where θk evolves as

individuals xk−1 with extrapolation by f (xk−1 ), andboth resampling process in SIR filter and selection process in ES select offspring among the perturbed individuals. However, there is a difference between them,i.e., resampling in SIR is carried out randomly and theweights are reset as 1/n, while the selection in ES isdeterministic and the fitness function is never reset.
Hence, by replacing the resampling process in SIR by
the selection process in ES, we can derive a new particle filter as follows.
(i)
Based on the particles {xk−1 , (i = 1, . . . , n)} sampled from the importance density q(xk−1 | y1:k−1 ), we(i,j)
generates ` samples {xk , (j = 1, . . . , `)} according(i)to the importance density function q(xk | xk−1 , y1:k ).

θk+1 = θk + ηkand ηk is a normal random disturbance with zero-mean
and very small variance. Then approximation of thetrue posteriori pdf is given byp(xk |y1:k ) ≈

(i,j)

(15)

(i)

(i)

(i)

(i)

q(xk |xk−1 , y1:k )

(13)

(i)

(i)

= qx (xk |xk−1 , θk−1 , y1:k )
(i)

(i)

(i)

×qθ (θk |xk−1 , θk−1 , y1:k ) (16)
(i)

(i)

with importance densities for xk and θk , qx (xk |xk−1 ,

(i,j)

(i)

(i)

(i)

(i)

θk−1 , y1:k ) and qθ (θk |xk−1 , θk−1 , y1:k ), and the associated normalized weights are evaluated by
(i)

wk

(i)

(i)

(i)

(i)

p(yk |xk , θk )(i)

∝ wk−1
×

(i)

(i)

(i)

(i)

(i)

(i)

(i)

qx (xk , θk |xk−1 , θk−1 , y1:k )(i)p(xk , θk |xk−1 , θk−1 )(i)qθ (θk |xk−1 , θk−1 , y1:k )

.

(17)

Then, the SIS, SIR and ESP filters are defined asabove.

(i,j)

ate the weights {wk , (i = 1, . . . , n, j = 0, . . . , `)}
(i,j)
(i)
by (13), and then choose n sets of (xk , wk ) withlarger weights from the ordered set of n(` + 1) par(i,j)(i,j)
ticles {xk , wk , (i = 1, . . . , n, j = 0, . . . , `)}, we
can obtain another ESP filter. Since this ESP filter
uses the selection corresponding to (n + n`)-selection
in ES, we can call this filter as Evolution Strategies
based particle filter Plus (ESP(+)).

4.1

Numerical Examples

Numerical simulation are carried out to exemplify
the applicability of the proposed ESP filter. First, weconsider the following nonlinear state space model
xk =

θxk−1
xk−1
+
+ 8 cos (1.2k) + vk
2
1 + x2k−1= f (xk−1 , θ) + vk
x2
yk = k + wk
20

Simultaneous State and Parameter
Estimation by Evolution Strategies
Based Particle Filter

(18)
(19)

where vk and wk are i.i.d. zero-mean normal random variables with variance 10 and 1, respectively, andvalue of the parameter θ is known to be 25. The nor(i)mal distribution with mean f (xk−1 ) and variance 10

The proposed ESP filter is applied here to simultaneous state and parameter estimation of nonlinear
systems. Consider the nonlinear state space model (1)with unknown parameter θ and (2), where a posteriori pdf p(xk , θ|y1:k ) should be approximated to estimate state and parameter simultaneously, Applicationof Bayes’ rule (4) provides(i)is chosen as the importance density q(xk |xk−1 , y1:k ).
Sample paths of the estimates by the proposed ESP(,)(n = 100, ` = 4) and EKF as well for comparison
are given in Fig.1. Proposed ESP filter works well in
nonlinear state estimation, while the estimate by EKF
cannot follow the true state.

p(xk+1 , θ|y1:k+1 ) ∝ p(yk+1 |xk+1 , θ)p(xk+1 |θ, y1:k+1 )×p(θ|y1:k+1 )©ISAROB 2005

(i)

(i)

wk δ(xk − xk )If particles xk in (15) were drawn from an importancedensity

From the set of n` particles and weights {xk , wk ,(i = 1, . . . , n, j = 1, . . . , `)}, we choose n sets with(i)(i)
the larger weights, and set as xk , wk (i = 1, . . . , n).
This process corresponds to (n, n`)-selection in ES.
Hence, we call this particle filter using (n, n`)-selection
in ES as Evolution Strategies based particle filter(i,0)
Comma (ESP(,)). When we add the particles xk =
(i,j)
(i)
f (xk−1 ), (i = 1, . . . , n) in addition to n` xk , (i =1, . . . , n, j = 1, . . . , `) sampled from the importance(i)
density function q(xk |xk−1 , y1:k ) as above and evalu-

4

n
X
i=1

(i,j)
Corresponding weights wk
are evaluated by
(i)
(i,j)
(i,j)
)p(xk−1 | xk−1 )(i) p(yk | xk
(i,j)
wk ∝ wk−1(i)(i,j)
q(xk | xk−1 , y1:k )

(14)

408

15

15

10

10

5

5

0

0

x

x

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

-5

-5

-10

-10

-15

-15

0

20

40

60

80

100

0

20

40

k

60

80

100

k

(a) Estimate by ESP(,)(a) A sample path of the state estimate
25

30

20

20

15

theta

x

40

10

10

0

5

-10

-20

0
0

20

40

60

80

100

0

k

20

40

60

80

100

k

(b) Estimate by EKF(b) A sample path of the parameter estimate

Figure 1: Sample paths of state estimates (dashed line:
estimate, solid line: true state)

Figure 2: Simulation results in simultaneous state andparameter estimation by ESP(,)

References

Next, we consider the unknown parameter casewhere the true value of θ = 25 in (18) is not known.
Here, only the results by ESP(,) with the impor(i)
(i)
(i)
(i)
tance densities qx (xk |xk−1 , θk−1 , y1:k ) ∼ N (f (xk−1 ,
(i)

(i)

(i)

(i)

[1] B. D. O. Anderson and J. B. Moore: Optimal
Filtering, Prentice-Hall, 1979.
[2] S. Arulampalam, S. Maskell, N. Gordon and
T. Clapp: A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking,IEEE Trans. on Signal Processing, SP-50, No.2,
pp.174–188, 2002.
[3] T. Bäck, Evolutionary Algorithms in Theory andPractice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, 1996.
[4] A. Doucet, N. de Freitas and N. Gordon (Eds.):
Sequential Monte Carlo Methods in Practice,Springer-Verlag, 2001.
[5] N. Gordon, D. Salmond and A. Smith: A novel
approach to nonlinear/non-Gaussian Bayesianstate estimation, IEE Proc. on Rader and Signal
Processing, 140, pp.107-113, 1993.
[6] A. H. Jazwinski: Stochastic Process and Filtering
Theory, Academic Press, 1970.
[7] H.-P. Schwefel: Evolution and Optimum Seeking,
J. Wiley, 1995.
[8] K. Uosaki, Y. Kimura and T. Hatanaka: State estimation of nonlinear stochastic systems by evolution strategies based particle filter. Proc. 9thInt. Symposium on Artificial Life and Robotics,
pp.406–409, 2004.

(i)θk−1 , 10) and qθ (θk |xk−1 , θk−1 , y1:k ) ∼ N (θk−1 , 0.01)
are shown in Fig.2 since the EKF does not work as
before. Though the estimate approach to the true
ones, the convergence speed is slow and the filter leaves
much for improvement. For examples, better choice ofdesign parameters n, Nef f and ` and choice of evolution operations should be pursued since the estimation
performance, of course, depends on the choice of them.

5

Conclusions

The novel particle filter, which is developed by recognizing the similarity and the difference between theimportance sampling and resampling processes in the
SIR filter and mutation and selection processes in ES
and substituting (µ, λ)-selection in ES into resampling
process in SIR, is applied to simultaneous state andparameter estimation of nonlinear state space models.
It works stably and provides small mean square errors
compared to EKF filter. Application of other evolution operations such as crossover and modification of
mutation will have the potential to create much higher
performance particle filters.

©ISAROB 2005

409

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Evaluating a solution of Tour Planning Problem based on
the partially exhaustive exploration Monte Carlo Method
Masaki Onodera∗∗

Hidenori Kawamura∗Azuma Ohuchi∗

Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan{onodera, kawamura, ohuchi}@complex.eng.hokudai.ac.jp

Abstracttion using a few samples. Furthermore, we suggest the
Tour Planning Problem as an example including such
uncertainty. We apply the proposed method to thisproblem.
The remainder of the paper is organized as follows:
In section 2, the Tour Planning Problem is formulated. In section 3, the partially exhaustive exploration Monte Carlo Method is proposed. In section
4, we apply the proposed method to the Tour Planning Problem and discuss the effectiveness. Finally,
concluding remarks are given in Section 5.

Researches on a planning, which includes the uncertainty that a result to a single solution cannot be determined uniquely, have been done. In such problems,a solution needs to be evaluated based on the uncertainty. In this paper, as the evaluation function basedon such uncertainty, expected value estimated using
the Monte Carlo Method is suggested. Moreover, thepartially exhaustive exploration Monte Carlo Method
is proposed to improve the accuracy of the estimation. As an example of the problem, the Tour Planning Problem is suggested and the proposed method
is applied to this problem. From experimental results,
it has been confirmed that the accuracy is improvedby the proposed method. The proposed method canestimate the expected value as accurately as the standard Monte Carlo Method using the fewer samples.
There is a probability of improving the effectivenessof searching because more solutions can be evaluated.

2
2.1

Introduction

Researches on a planning, such as the automatic
decision-making by the software or the combinatorial
optimization problem containing probabilistic parameter, have been done [1][3][4]. These problems include
the uncertainty that a result cannot be determineduniquely. In short, in such problems, a single solution
may produce different results. Therefore, a solution
needs to be evaluated based on the uncertainty. In thecase where all possible results and each probability are
known, the simplest evaluation function includes the
expected value. However, if the number of possible
results is large, it is difficult to calculate the expectedvalue actually because of the time constraints.
In this paper, we suggest the expected value estimated using the Monte Carlo Method (MCM) [2] asthe evaluation function based on the uncertainty andpropose the partially exhaustive exploration Monte
Carlo Method to improve the accuracy of the estima-©ISAROB 2005

Overview

The purpose is to plan a tour, which maximizes
the total score within the time limit, when there are
multiple tourist facilities. In traditional model, it isassumed that the each time cost involved with transferring between the facilities or staying at each facility
is constant. We introduce the probability distributions
as each time cost. It cannot be defined that whether
a tour exceeds the time limit or not because each time
cost is not defined uniquely.
We compared two evaluation functions about a tourin the preliminary experiment. One is the expectedscore. The other is the score, which is got when it isassumed that each cost is the average of given probability distribution. From experimental results, it isconfirmed that using the expected value can search
for a tour, which rarely exceeds the time limit andgets high score averagely. Therefore, we formulate theTour Planning Problem as the combinatorial optimization problem aiming at maximizing the expected score.

Keywords: Monte Carlo Method, uncertainty, Tour
Planning Problem

1

Tour Planning Problem

2.2

Formulation

A set of node, which corresponds to a tourist facility, is denoted by V , and a set of time is denoted by
T.
V
T

410

=
=

{v1 , · · · , vn },(1){tr |tr = t1 + (r − 1) · δt, r = 1, 2, · · ·}, (2)The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

where δt is unit time. The vi has the opening timeoi and closing time ei (oi , ei ∈ T ). A set of time cost
involved with moving from vi to vj and staying at vi
is denoted by Wij and Wi (vi , vj ∈ V ), respectively.

Equation (5) represents the expected score. The matrix (6) represents a tour. If aui = 1, the u-th nodethat a client visits is vi . Equation (7) represents thetotal score. In this model, the score for the case of exceeding the time limit is set to zero because we assume
the time limit as the departure time of the airplane or
the train that a client will take. Therefore, if to exceed
the time limit is allowed somewhat, it is possible thatnot zero but the function of a certain penalty is used assuch score. Equation (8) is a score of vi in the time t.
Equation (9) represents a direct product consisted of
combination of time cost. As (10) shown, b (∈ B) represents the combination of arbitrary costs. Where ci
and cij are arbitrary time costs determined according
to pi (wik ) and pij (wijk ). The value of (11) is occurrence probability of b. In short, p(b) is a probability
that arbitrary ci and cij are selected at the same instant. Equation (12) represents the time that a clientarrives at ξ-th node. The value of (13) is the total
number of node that a client visits. The value of (14)represents the number of u-th node that a client visit.
Equations (15)-(17) are the constrained conditions.

Wi = {wik |wi,k+1 = wik + δt, k = 1, · · · , mi }.
(3)
Wij = {wijk |wi,j,k+1 = wijk + δt, k = 1, · · · , mij }. (4)The vi hasP the probability distribution denotedby pi (wik )( k pi (wik )=1, pi (wik )≥0), which indicates the probability of staying at vi for wik .
Moreover,Pthe probability distribution denoted bypij (wijk )( k pij (wijk )=1, pij (wijk )≥0), which indicates the probability of taking wijk to move from vi to
vj , is defined. Given starting point vs , goal vg , departure time Ds and time limit Dg , the objective of Tour
Planning Problem is to maximize
X(S(A, b) · p(b)) = E [S(A, b)] .
(5)b∈B= (aui )u=1,···,n,i=1,···,n (aui ∈ {0, 1}). (6)PL−1
u=2 sa(u) (tu ) : tL ≤ Dg
(7)=
0
: tL > D g .

si : oi ≤ t ≤ ei=
(8)
0 : otherwise .

A
S(A, b)si (t)

B = Wa(1) × · · · × Wa(L)×Wa(1),a(2) × · · · × Wa(L−1),a(L) .

3
3.1

=

L
Y

(9)

pa(u) (ca(u) )

u=1

×
tξ

=

L =
a(u)

=

L−1
Y

pa(u),a(u+1) (ca(u),a(u+1) ).

(11)

u=1

Ds +

ξ−1
X

ca(u) +

ξ−1X

ca(u),a(u+1) . (12)

u=1

u=2
n X
n
X

i=1

3.2

aui .

(13)

(aui · i).

(14)

u=1 i=1
n
X

= 1.

(15)

1.

(16)

u=1
n
X
i=1

©ISAROB 2005

au+1,i

≤

n
X

aui .

Partially
exhaustive
Monte Carlo Method

exploration

This method uses the feature that the b with thehigh occurrence probability can be enumerated without exploring all combinations of time costs. As above,
the p(b) is calculated as a product of pi (ci ) and pij (cij ).
Therefore, a certain threshold is set when the p(b) is
calculated. If a product is below the threshold in thecourse of calculation of p(b), the rest of all the calculation about the combination is omitted. By this tree
pruning, the cost for enumeration can be reduced.
The procedure to estimate the expected score aboutan A is as follows. First, a certain threshold is determined and the set B is divided into two sets. One

subject toa1,s = aL,g
n
X
aui
≤

Estimation of expected score

Because it is difficult to search for the optimum
solution, we search the approximate solution by theheuristic search. However, if the expected score is calculated actually, it takes time granted that heuristic
search is used. Therefore, we propose the expectedscore estimated by the MCM as the evaluation function. Some combinations of ci and cij , which is b, isselected randomly according to pi (wik ) and pij (wijk )as samples. Then, the MCM estimates the expectedscore. The number of samples is denoted by M . The
M has to be set a low value because of the time constraints. Therefore, we propose the partially exhaustive exploration Monte Carlo Method to improve theaccuracy of the estimation using a few samples.

b = (ca(1) , · · · , ca(L) , ca(1),a(2) , · · · , ca(L−1),a(L) ). (10)p(b)

Proposed method

(17)

i=1

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

is the set of b, which has the higher p(b) than thethreshold. The other is the set of other b. The former
is denoted by B (1) and the latter is denoted by B (2) .
The number of samples used for estimation about B (1)is M1 and about B (2) is M2 . M is the sum of M1 andM2 . Secondly, all b contained in B (1) are exploredexhaustively. Therefore, M1 equals |B (1) |. The E1 ,which is answer of exhaustive exploration about B (1) ,is calculated.
X
E1 =(S(A, b) × p(b)) .
(18)

Figure 1: The calculation of the occurrence probability
Table 1: The three kinds of setups about clientvs vg Ds
Dg
Client 1 13 0 840 1380
Client 2 00 480 1080
Client 3 0 13 540 1260b∈B (1)

Thirdly, the MCM estimates the expected score about
B (2) . The M2 samples are generated randomly according to pi (ci ) and pij (cij ). They are denoted by ξq .
However, the p(ξq ) may be higher than the threshold.
Such sample cannot be used. Therefore, the numberof the samples, which can actually be used, may beless than M2 . The number of samples, which can beused, is denoted by M2true . The E2 , which is expectedscore estimated about B (2) , is calculated.
E2 =

4.2

To compare the accuracy of estimation, the expected score is estimated about one thousand solutions each ten thousand times by each MCM. Then, toevaluate the accuracy, a error rate to a true expectedscore is calculated. Three kinds of client are preparedas shown in Table 1. In the setting of client 1, somenodes will be closed depend on the time. In the settingof client 2, some nodes do not stay open at first. In thesetting of client 3, more time can be used than othersettings. Furthermore, two kinds of settings about M ,M =200 and M =500 are used. In this paper, we used
the M as an indicator of the calculation cost. Because
it is thought that the computation time depends onthe feature of the problem or efficiency of programs,
we fixed not the computation time but M.

M2true1
M2trueX
q=1

S(A, ξq ). (M2true ≤ M2 )(19)

Finally, the expected score E is calculated.
E = E1 +Xb∈B (2)p(b) · E2 .

(20)

In the preliminary experiment, it is confirmed thatthe larger there is deviation between the occurrence
probabilities, the more this method is effective. When
there is not deviation so much, the effect cannot be
expected. However, because it is difficult to define that
the Tour Planning Problem is contained in which, it
is necessary to investigate.

4
4.1

4.3

Results of the first experiment

Figure 2 - 7 show the average error rate. The “1.0”in figures represents the standard MCM. About clients
1 and 2, the proposed method could estimate the expected score more accurately than the standard MCM.
In the case of M =500 of client 1, accuracy was improved about one to two percent. In view of the factthat the Standard MCM can estimate the expectedscore comparatively accurately, it is thought that theaccuracy was improved considerably. In other words,
the proposed method can estimate the expected score
as accurately as the standard MCM using the fewer
samples. Furthermore, the accuracy differs dependingon the balance between the threshold and the number of samples M . Therefore, the threshold is very
important. About client 3, there is not so much of adifference between the proposed method and the Standard MCM. Because client 3 can visit more nodes, thecombination of time cost increases. Each p(b) becomes
low and almost all p(b) becomes lower than a threshold. As a result, the proposed method is not so different from the standard MCM. Next, client 1 that theaccuracy is most improved is considered selectively.

Experiment
Experimental setup

We design the model based on fourteen tourist facilities in Sapporo city, Japan. The pij (wijk ) is determined as follows. First, standard time cost involved
with moving from vi to vj , which is denoted by µij , iscalculated using the store-bought map software. Secondly, ij is calculated as µij divided by K. This K
is parameter to set the variation of the time cost. Finally, pij (wijk ) is determined by dividing a normal distribution N (µij , ij ) according to δt as Figure 1. The
pi (wik ) is determined in the same way as pij (wijk ).
The standard time cost µi is determined referring tosome guidebooks. Each oi and ei is the actual value.
In this experiments, δt = 5, and K = 15 are used.

©ISAROB 2005

Comparison with the standard MCM

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Figure 2: The average
error rate about client
1 (M =200)

Figure 8: The averageof true expected scoresof 500 tours

Figure 3: The average
error rate about client
1 (M =500)

Figure 9: The average of probabilities ofbreach to time limit

The selected tours rarely exceed the time limit. The
computation time of the proposed method is similar
to that of the standard MCM within four seconds.

5
Figure 4: The average
error rate about client
2 (M =200)

Figure 5: The average
error rate about client
2 (M =500)

Figure 6: The average
error rate about client
3 (M =200)

Figure 7: The average
error rate about client
3 (M =500)

4.4

In this paper, we suggest the expected value estimated using the MCM as the evaluation function
based on the uncertainty. Moreover, the partiallyexhaustive exploration Monte Carlo Method is proposed to improve the accuracy of the estimation. Furthermore, the Tour Planning Problem is suggested
as an example of the problem including the uncertainty. From results of experiments, it has been confirmed that the accuracy is improved by the proposed
method. In other words, the proposed method canestimate the expected value as accurately as the standard MCM using the fewer samples. There is a probability of improving the effectiveness of searching because more solutions can be evaluated. Moreover, areasonable tour, which rarely exceeds the time limit
and gets high score averagely, can be searched by using
the expected score as the evaluation function. As the
future works, we have to design the means to adjust athreshold automatically. Moreover, we have to discussa case that it is difficult to estimate an expected value.

Comparison of effectiveness of searching

To discuss the effectiveness of searching by eachmethod, we search for a tour 500 times actually about
the setting of client 1 and compare the tours, which are
selected finally. We use the simulated annealing (SA)because of the ease of implementation. The parameters are determined in the preliminary experiments.
A tour is expressed as permutation of the visited node
number.

4.5

Acknowledgment
Our special thanks are due to Mr. Koichi Kurumatani, National Institute of Advanced Industrial Science and Technology for considerable cooperation andvaluable advice.

Results of the second experiments

References

Figure 8 shows the average of true expected scoresof 500 tours selected by 500 trials of SA. There is not
so much of a difference between the two methods. It isthought that a tour, which finally is selected, is muchthe same because the expected score is estimated comparatively accurately whichever method. However, as
stated in section 4.3, the proposed method can estimate the expected score as accurately as the standard
MCM using the fewer samples. As a result, more solutions can be evaluated. Therefore, there is a probability of improving the effectiveness of searching by
the proposed method. Figure 9 shows the average ofprobability that a selected tour exceed the time limit.

©ISAROB 2005

Conclusion[1] E.L.Lawler, et al. The TRAVELING SALESMAN
PROBLEM, October, 1986.
[2] Arnaud Doucet, et al. Sequential Monte Carlo
Methods in Practice, Springer-Verlag, January
2001.
[3] M. Wellman, et al. Designing the Market Game fora Trading Agent Competition, IEEE INTERNETCOMPUTING, March/April (2001. 8) :p43–51[4] Michael P. Wellman, et al. Path Planning under
Time-Dependent Uncertainty*, the Eleventh Conference on Uncertainty in Artificial Intelligence,
August, 1995.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A Comprehensive Evaluation of the Methods for Evolving a Cooperative Team
Yasuyuki Suzuki
Takaya Arita
Graduate School of Information Science
Nagoya University
yasu@create.human.nagoya-u.ac.jp
arita@nagoya-u.ac.jp
Abstract

18 typical combinations available to execute. Then, in
Section 3, we describe the ultimately simplified soccer game played on one-dimensional field as a testbed
for comprehensive evaluation for these 18 candidate
methods. Section 4 reports on the results of the comprehensive evaluation of these methods, and Section 5summarizes the paper.

This paper focuses on the techniques of evolutionary computation for generating players performing
tasks cooperatively. However, in using evolutionary
computation for generating players performing tasks
cooperatively, one faces fundamental and difficult decisions including the one regarding the so-called “credit
assignment problem”. We believe that there are somecorrelations among design decisions and therefore acomprehensive evaluation for them is essential. We
first list three fundamental decisions and possible options in each decision in designing methods for evolving a cooperative team. We find that there are 18
typical combinations available to execute. Then we
describe the ultimately simplified soccer game playedon one-dimensional field as a testbed for comprehensive evaluation for these 18 candidate methods. The
results are analyzed in this paper.

1

2

Three fundamental decisions are necessary when
one designs an evolutionary computation method forgenerating players performing tasks cooperatively, andthere may be not a few combinations of the options inthese decisions.
The first decision is: How many evolving populations are there? The answer is derived by considering
whether or not the population structure depends on
the number of the teams in the game or the numberof the player roles in the game (Figure 2). Supposethat the game is played by 2 teams consisting of 3
players. We can assume an evolutionary computation
with 2 populations corresponding 2 teams, with 3 populations corresponding 3 players, or with 6 populations
corresponding to 2 teams and 3 players. So, the typical options for the number of the populations are 1,
R, T and T · R (T : Number of teams in the game, R:
Number of the player roles in the team).
The second decision is: What does each individual (genome) represent? Typical options are a player
and a team. In case of that each genome represents aplayer, there can be two further options: all players in
the team share one genome (“homogeneous players”)
and all players are represented by different genomes(“heterogeneous players”). Also in case that eachgenome represents a team, there can be two further
options: whether or not the roles of the players represented in each genome are fixed. In case that theroles of the player is fixed, for example, if a part of agenome represents a defender in the game, this partalways represents a defender.
The third decision is: How is the fitness function
evaluated? One option is that fitness is evaluated fora team as a whole. In this case, if each genome represents a player, each player in a team is supposed tohave the same fitness. The other option is that the fit-

Introduction

Some problems can be efficiently solved only byteams consisting of cooperative autonomous players.
Many researchers have developed methods that don’t
require human designers to define specific behaviorsof players for each problem. The work reported here
focuses on the techniques of evolutionary computation, which has been regarded as one of the mostpromising approaches to solve such complex problems.
However, in using evolutionary computation for generating players performing tasks cooperatively, one
faces fundamental and difficult decisions including the
one regarding the so-called “credit assignment problem” [1]. For example, if we can only evaluate theglobal performance of each team, how do we divideup the team’s performance among the participating
players? We believe that there are some correlations
among design decisions and therefore a comprehensive evaluation for them is essential, although not a
few researchers have proposed evolutionary methods
for evolving teams performing specific tasks.
The rest of the paper is organized as follows. In
Section 2, we list three fundamental decisions and possible options in each decision in designing methods forevolving a cooperative team. We find that there are©ISAROB 2005

Methods for Evolving a Team

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ness is evaluated for each player directly or indirectly.
Direct evaluation of players in a cooperative team issometimes a very difficult task, as in general altruistic
behavior is important or essential in the establishment
and maintenance of cooperation in population. Somemethods for indirect evaluation has been proposed [2].
We adopt a method as this option in which the fitness of a player is defined as the decrease in the fitnessof the team when the player is replaced by a predefined “primitive player” which has a minimum set of
behavior rules.
Therefore, there could be 18 combinations available
to execute for evolving players performing tasks cooperatively as shown in Table 1.

c) T populationsa) 1 populationb) R
populationsof a player with a ball means dribbling. Players moveto either of the neigboring cells, but when a player
moves to the cell with a player, it skips the neigboring player (it cannot skip more than one player). In
this case, if both are in opposite teams and one ofthem has a ball, the ball moves to the other player
with a certain probability (Psteal ). If there is an opponent player between the passer and the receiver, theball-passing becomes failure with a certain probability (Pcut ), and in this case the ball moves to the cell
where the oppenent player resides. The success rate
for shooting is anti-proportional to the length between
the player’s position and the goal irrespective of thepresence of the oppsite players. In case of scoring agoal, the game restarts with initial player-location. In
case of the failure, the game restarts after the ballmoves to the opposite player nearer to the goal post.
We expect two types of altruistic behavior which
could lead to the emergence of cooperation in thegame. One is putting a ball up to the other player
in his/her team instead of dribbling the ball or getting a shot at the goal. The other type is making a
run in the opposite direction but not toward the goal.
The former type of altruistic behavior is analyzed in
4.3.

Games

d) T R
populations

4

Evaluation

4.1
Figure 1: 4 options for the population structure. a)
The population represents all player roles in all teams.
b) Each population represents one player role in allteams. c) Each population represents all player roles ineach team. d) Each population represents one playerrole in each team.

3

Each player selects next action deterministically
based on the positional relationship of players and theball. In the recognition of each player, opponent players are not distinguished. So, to be precise, genetic
information of each player decides the next action of
the player based on one of 48 patterns, in which eachpattern is associated to one of the four actions: running/dribbling to the right, running/dribbling to theleft, feeding (passing) the ball to the player of his/her

Ultimately-Simplified Soccer GameThe ultimately-simplified soccer game is defined asa testbed for comprehensive evaluation for these 18
candidate methods. It is a 2 vs. 2 player game playedon one-dimensional cellular field as shown in Figure 2 (field[1-20]). Players are homogeneous except
their starting positions (Left team: player1 (field[8]),player2 (field[5]), Right team: player1 (field[13]),
player2 (field[16])), and each player makes a run, dribbles a ball, makes a shot on goal or put a ball up to theplayer of his/her team. One of the action is decided
to take based on the relative locaton of all players andthe ball (72 patterns). Action is taken in turn alternatively between 2 teams. Each step in the game iscomposed of 4 actions by all players.
Multiple players can’t be in a cell. The ball is always in a cell where a player resides. Moving action©ISAROB 2005

Expression of the Players

G
O
A
L

G
O
A
L

ԘMoving/DribblinglԙPassingIf a player with a ball moves tothe cell with a opponent player,
he/her loses the ball with aprobability of Psteal.

The success rate for ball-passing is
1 - (Pcut . number of opponent players).
ԚShootingl

The success rate for shooting is 1 - 0.8 .

Figure 2: The ultimately simplified soccer game.
415

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 1: Classification of the methods for evolving a team.
Population structure
depends on
Number of
populations
T?
R?

Each genome represents

a playerNo1Noa team
Yes

R

No

T

a playera playerYes

a teamYes

T ·R

a player

Code nameby direct evaluation
1-PHe-PDby indirect evaluation
1-PHe-PIa team (same fitness in a team)
1-PHe-T
homogeneous players
a team (same fitness in a team)
1-PHo-T
fixed player-roles
a team
1-TFi-T
unfixed player-roles
a team
1-TUn-Tby direct evaluation
R-PHe-PDheterogeneous players a player by indirect evaluation
R-PHe-PIa team (same fitness in a team)
R-PHe-Tby direct evaluation
T-PHe-PDheterogeneous players a player by indirect evaluation
T-PHe-PIa team (same fitness in a team)
T-PHe-T
homogeneous players
a team (same fitness in a team)
T-PHo-T
fixed player-roles
a team
T-TFi-T
unfixed player-roles
a team
T-TUn-Tby direct evaluation
TR-PHe-PD
heterogeneous players a player by indirect evaluation
TR-PHe-PIa team (same fitness in a team)
TR-PHe-T(T : Number of teams in a game, R: Number of player roles in a team)heterogeneous players

team, making a shot on goal. Therefore each player isrepresented by 96 bits genetic information.

4.2

Unit of fitness evaluation isa player

1/T-PHe-PD
1/T-PHe-PI

Evaluation Setting

1/T-PHe-T

1-PHo-T

1/T-PHo-T

T-PHo-T

1/T-TFi-T

The evaluation is conducted through two steps: anevolution step and an evaluation step. In the evolution step, populations are evolved for 2000 generations using 18 methods independently. Each population has 40 individuals in all methods. The roundrobin tournament of the ultimately-simplified game of
200 steps is held to evaluate the fitness in each generation. The parameters Psteal and Pcut are set to
0.8 and 0.4 respectively in both steps. In case of<team-evaluated> option, fitness is calculated as thegoals the team acquired minus the goals the opponent
team acquired. In case of <direct-player-evaluated>
option, fitness is calculated as the goals the player acquired minus the opponent team’s goals divided by 2.
Then tournament selection (selecting repeatedly theindividuals with higher fitness as a parent by comparing randomly picked 2 individuals), crossover with a
60% probability and one-point mutation with a 3%
probability are adopted as genetic operators. In caseof <indirect-player-evaluated> option, we use a primitive player designed a priori as follows. In case of theplayer keeps a ball, if he (or she) is behind the otherplayer he passes the ball to the other player, otherwise
he makes a shoot. In case of the player doesn’t keep aball, if he is behind the other player, he moves back,otherwise he moves toward the goal. In the evaluation©ISAROB 2005

1/T-TUn-T
R/TR-PHe-PD
R/TR-PHe-PI
R/TR-PHe-T

0.4

0.5

0.6

0.7

0.8

Figure 3: The average winning ratio of the best 10
teams evolved by each of 18 methods.
step, the best team is selected in each of the last 50
generations in the evolution step, and selected 50£18
teams conduct the other round-robin tournament of
the game of 1000 steps.

4.3

Evaluation Results

Figure 3 shows the winning ratio of the teams
evolved by 18 methods, each of which is the averageof winning ratio of the best 10 teams from 50 teams in
the all-play-all tournament described above. Table 2(the left-hand in the results) also shows it. Each pairof bars shows the results of the strategies with same
options in genome representation and fitness evalua416

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

tion except the population structure option (Upper
white bars: <1/R-population> options, Lower black
bars: <T /T · R-populations> options).
It is shown that the top 3 methods inthis evaluation are <1-population, team-represented
with fixed player-roles, team-evaluated>, <T · Rpopulation, heterogeneous-player-represented, teamevaluated>, and <1-population, homogeneous-playerrepresented, team-evaluated>. Their winning ratios
are 74.6%, 74.1% and 73.5% respectively. An additional evaluation using the team consisting of 2 primitive players showed that its winning ratio was 16.0%.
This ratio could be a measure for the performance ofthe methods.
Regarding to population structure, <1/R-populations> options performed better than <T /T · Rpopulations> options in general. This might be because of the ill-balanced evolution, over-specialization
or “round and round going”. Adoption of an asymmetric game as a testbed would make this tendency weaker. Regarding to genome representation,<homogeneous-player-represented> option performed
well in general. Also, <team-represented with fixed
player-roles> option performed well, though <teamrepresented with unfixed player-roles> option performed badly. Regarding to fitness evaluation, <teamevaluated> option performed well in general as the factthat 5 methods among top 6 methods adopt this option has shown. The performance of <indirect-playervaluated> option depended largely on the other options.
We have observed interesting separation of roles
among 2 players in the teams with high winning ratio. For example, in some teams the forward player
tended to play near the goal and the backward player
tended to move in order to intercept the ball, and insome teams both players seemed to use man-to-man
defense.
Next we examined the relationship between altruistic behavior which could lead to cooperative behavior
and the winning ratio. Here we focus on the following behavior pattern. A player with a ball makes apass for the other player, who receives the ball without being intercepted and then successfully shoots agoal immediately or after dribbling. We termed thisseries of actions as “assisted goal”. Table 2 shows theassist ratio, which is the ratio of assisted goals among
all goals, and winning ratio of the teams evolved by 18
methods. We see from this table that good performing teams have a tendency to also have a high assist
ratio. In contrast, it is not necessarily the case thatteams with a high assist ratio have a tendency to havea high winning ratio. This means that above-defined
assisting behavior is a necessary requirement for theteams to perform well.
It is a remarkable fact that <indirect-playerevaluated> option made the assist ratio much higher©ISAROB 2005

Table 2: Average winning ratio and assist ratio.
Code nameResults

Winning ratio Rank
1-PHe-PD
0.673
7
0.609
11
1-PHe-PI
0.699
51-PHe-T
1-PHo-T
0.735
3
1-TFi-T
0.746
1
0.571
16
1-TUn-T
R-PHe-PD
0.607
12
R-PHe-PI
0.713
4
R-PHe-T
0.639
10
T-PHe-PD
0.574
15
0.536
17
T-PHe-PI
T-PHe-T
0.603
14
0.683
6
T-PHo-T
0.654
9
T-TFi-T
T-TUn-T
0.536
18
TR-PHe-PD
0.666
8
0.607
13
TR-PHe-PI
TR-PHe-T
0.741
2Assist ratio
0.146
0.289
0.310
0.390
0.342
0.336
0.109
0.503
0.402
0.080
0.391
0.242
0.226
0.260
0.214
0.077
0.388
0.416

Rank
15
10
9
5
7
8
16
1
3
17
4
12
13
11
14
18
6
2

compared with the winning ratio. As this option, we
adopted a method in which the fitness of a player isthe decrease in the fitness of team when the player isreplaced by the primitive player. This method should
generate the strong interaction between 2 players because it tends to make large decrease when the player
is replaced. Therefore the teams generated by the indirect evaluation method have a higher assist ratio despite having the relatively low winning ratio.

5

Conclusion

This paper has focused on the methods for evolving a cooperative team by conducting a comprehensive evaluation for 18 methods. We have found thatsome methods performed well and at the same timethat there are complex correlations among design decisions. Also, further analysis has shown that cooperative behavior can be evolved and can be a necessary
requirement for the teams to perform well in even sucha simple game. Future work includes more detailed
analysis of cooperative behavior and extension of theultimately-simplified soccer game.

References[1] Thomas Haynes, Sandip Sen, Dale Schoenefeld andRoger Wainwright, “Evolving a Team”, Working
Notes for the AAAI Symposium on Genetic Programming, pp. 23-30, 1995.
[2] Thomas Miconi, “A Collective Genetic Algorithm”, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp.
876-883, 2001.
417

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A Fast Algorithm in Finding Communities of Book Network
Shijun Wang, Changshui Zhang
State Key Laboratory of Intelligent Technology and Systems, Department of Automation,
Tsinghua University, Beijing 100084, China
wsj02@mails.tsinghua.edu.cn, zcs@mail.tsinghua.edu.cn
edges and vertices that act as boundary between
communities.
Wu et al. [8] present a method which is based on
notions of voltage drops across networks that are both
intuitive and easy to solve. However, their algorithm has
to specify the number of communities beforehand.
Zhong Su et al. [9] present a recursive density-based
clustering algorithm for web document clustering based on
DBSCAN [10]. In order to cut off the bridge between two
clusters, their algorithm varies Eps and MinPts whenevernecessary.
In this paper we present a new method named NDBC
that can discover communities within networks ofarbitrary size in a very short time. The key idea of our
method is that we combine the clustering algorithm
DBSCAN with the structural information of complex
networks in finding communities. We apply our method tofinding communities of book network. The result showsthat NDBC is very efficient because we just need scan thevertices of the data set once. Moreover, it does not requirea predetermined cluster number to operate. Our method
achieves the same goal as Zhong Su [9] but need only one
constant predefined in the algorithm.
The outline of this paper is as follows. In Sec. 2 weintroduce the book network. In Sec. 3 we show the key
idea of DBSCAN and its drawback in finding
communities. In Sec. 4 our algorithm NDBC is described.
The dataset and experimental results are listed in Sec. 5. In
Sec. 6 we give our conclusions.

Abstract
In this paper, we present a new fast algorithm named
NDBC in finding communities within large network
systems. The algorithm is developed based on DBSCAN
and makes use of the structural information of complex
networks. We test NDBC on the book network which isconstructed by the readers’ borrowing behaviors. The
experimental result shows that it can quickly findcommunities of a big network which contains thousands
upon thousands vertices.
Keywords: complex network, community, booknetwork

1. Introduction
Many systems in the real world can be abstracted ascomplex networks. The examples of complex network
include the Internet, science collaboration network, neuralnetworks, metabolic networks, food web, etc. [1]. Recent
research on complex networks has revealed a number ofdistinctive statistical properties that most networks seem toshare [1] [2] [3], such as power law distribution of degree,
high clustering coefficient and short average path length.
“It is widely assumed that most social networks show“community structure”, i.e., groups of vertices that have ahigh density of edges within them, with a lower density ofedges between groups.” [4] How to discover communities
in large network systems within a short time is aninteresting problem.
A recent influential algorithm that has been used isbased on the idea of betweenness, proposed by Girvan andNewman [5] [6]. The betweenness of an edge is defined as
the number of shortest paths that traverse it. Their method
iteratively removes edges which lie between two clusters
and has highest betweenness from the network. In thismethod, the time involved to discover the community
structure of the graph scales as O (n3 ) , with n the numberof vertices in the network.
In the computer science literature, there are a numberof fast heuristics, such as “FM-Mincut”. Flake et al. [7]use a maximum flow/minimal cut algorithm to define the©ISAROB 2005

2. Book Network
In the book network, the vertices are the books, andtwo vertices have a common edge if the corresponding
books have been borrowed together by the same person.
That means if two books occur in someone’s bookborrowing records, then the two books are associated. Ifthese two books are borrowed together by more than one
person, then the weight of link between them areaccumulated. So the book network is a weighted andundirected network.
If two books are borrowed together by N persons
separately, we define the distance between those twobooks is 1/ N . Set D as the book set. The Eps418

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

neighborhood
of
a
book
p
is
as
N Eps ( p ) = {q ∈ D |dist q( p, ≤) 1/MinTime}definedwhere

4.1. Clustering Coefficient
The DBSCAN algorithm just considers the distances ofsamples from each other, but in a complex network, there
is also structural information between vertices besides
distance information. The edges between vertices form the
topology of a network.
In order to describe the connections in the environment
closest to a vertex, we often use the so-called clustering
coefficient [1]. “For the network with undirected edges,
the number of all possible connections of the nearest
neighbors of a vertex µ ( z1( µ ) nearest neighbors)
equals z1( µ ) ( z1( µ ) − 1) / 2 . Let only y ( µ ) of them be present.

Eps = 1/ MinTime . MinTime is the least times that two
books are borrowed together.
A clustering CL of book set D with respect to Eps,
MinPts is a set of density-connected [10] sets with respect
to Eps, MinPts in D. For any points in a space, where apoint corresponds to a book, the more books that co-occur
with it in some reader’s borrowing records, the higher its
density is.

3. DBSCAN

The
clustering
coefficientof
thisvertex, C ( µ ) ≡ y ( µ ) / ⎡ z1µ ( z1µ − 1) / 2⎤ , is the fraction of⎣⎦existing connections between nearest neighbors of thevertex.” [2] The clustering coefficient C of the network is
the average of C ( µ ) over all vertices of a network. The
clustering coefficient is the probability that two nearest
neighbors of a vertex are nearest neighbors also of one
another.
In simple terms the clustering coefficient of a book in
the book network tells us the likelihood a book’s
neighbors are borrowed together.

The clustering algorithm DBSCAN [10] based on
sample density is designed to discover clusters of arbitrary
shape as well as to distinguish noise.
“The key idea of a density-based cluster is that for eachpoint of a cluster it’s Eps-neighborhood for some given
Eps > 0 has to contain at least a MinPts minimum numberof points, i.e. the “density” in the Eps-neighborhood ofpoints has to exceed some threshold.” [11]In order to find communities of book network, we first
tried DBSCAN to the book network. The result shows thatDBSCAN often leads to a single, giant cluster which is notdesirable. The reason why all these books are connected
together lies in the inherent nature of DBSCAN. As
pointed above, DBSCAN is based on sample density. For
many popular books and reference books of various
categories, the probability that they are borrowed together
is very high. For example, book A is a science fiction andbook B is a reference book of physics. If A and B areborrowed together many times, then the weight between
them is high. So A and B serve as a bridge between
science fictions and references of physics and these twoclusters are connected together. This is illustrated in Fig.
1.

4.2. NDBC
Most complex networks exhibit a large degree ofclustering [1] [2] [4]. In a network with high clustering
coefficient, though two communities may be connected bya bridge, the vertices on the bridge belonging to different
communities have few common neighbors. Based on thephenomena observed above, we can cut off the bridge
between these two communities by neighborhood check.
From the statistics of clustering coefficient of booknetwork, illustrated in Fig. 4, we can see that theclustering coefficient of book network is very highcompared with random network. This means the neighborsof a book are often connected. So based on the highclustering coefficients of most complex networks, we
extend DBSCAN by considering the superposition degreeof neighbors between a seed vertex and it’s subsequent
seed vertex in the expansion progress of a cluster.
If vertex A as a seed belongs to community C1 andvertex B as A’s neighbor belongs to community C2,
suppose that there are enough neighbors of A at given
Eps, then at the expansion of this cluster from seed A,
vertex B’s relationship with A’s neighbor is checked. If B
is connected with most of A’s neighbors, then B is
assigned the same label with A; otherwise, B is treated asa noise. A constant called superposition is defined as a
threshold in order to check the superposition degree ofthese two vertices’ neighbors. Because most of A’s

Figure 1. Bridge between two communities

4. NDBC: Neighbor Density Based Clustering
algorithm
To solve this problem, we propose a clustering
algorithm called NDBC that attempts to overcome thedrawback of DBSCAN in finding communities byutilizing the high clustering of complex network.

©ISAROB 2005

419

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

neighbors belong to C1 and most of B’s neighbors belong
to C2, the number of common neighbors of A and B issmall. By means of checking two vertices’ neighborsuperposition degree, the bridge between two different
communities is truncated.
The algorithm is listed below:

2003. In this period there are 573,862 book borrowing
records in which 142,081 books appear.
The average clustering coefficient for the book network
is 0.6188 with 170 isolated vertices. The distribution ofclustering coefficients for the book network is shown in
Fig. 4.

Algorithm NDBC(DB, MinTime, MinBook)
For each v ∈DB do
If v is not yet assigned to a cluster then
Expand(v, MinTime, MinBook);
Assign them to a new cluster or noise;

Expand(v, MinTime, MinBook)
Find v’s neighbors NSet with weight greater than
MinTime;Find vertices whose neighbors have few
superposition with v’s neighbors, and delete themfrom NSet;
If the size of NSet is greater than MinBook
Expand the cluster from vertices of Nset;

Figure 4. Distribution of Clustering
Coefficients

Figure 2. Neighbor Density Based Clustering
algorithm

5.2. Experimental Results

4.3. Analytical Evaluation
The runtime of NDBC is

To find the communities which DBSCAN can’t, weapplied our community-finding method to the same booknetwork from Library of Tsinghua University.
In Table 1 we show several communities found by
NDBC. By examining the names of books belonging to
the same cluster, we can see that the clusters found by
NDBC are more reasonable than that found by DBSCAN
since similar books are indeed grouped together.

O(n * runtime of aneighborhood query ) : n objects are visited and exactly
one neighborhood query is performed for each of them[11]. Thus, the overall runtime depends on theperformance of the neighborhood query. Fortunately, all
the interesting neighborhood predicates are based on
adjacency matrix – like distance predicates – which can be
efficiently supported by sparse matrix when the network
contains many vertices. So the runtime of NDBC is very
short.

Table 1: Some clutering results using NDBC.
Cluster ID
Book Name
77

5. Experiment5.1. Data Set

71

b1010768x;03-04-08 15:35;p10583956
b10107939;03-04-08 10:18;p10824522
b10126235;03-04-08 09:28;p10809545
b10255977;03-04-08 14:05;p10633716

Figure 3. Book borrowing records
We first analyze the data set under consideration. Our
experiments draw on data collected from Library of
Tsinghua University, China. Fig. 3 is an example ofTsinghua University Library’s book borrowing records.
The first column is ID of book and the third is ID ofreader. It consists of book borrowing records from
15:35:00 March 4, 2003 through 11:21:00 November 12,©ISAROB 2005

122…

Basic algebra
Sheaves on manifolds
Algebraic geometry
Lectures on algebraic topology
Geometric integration theory…
A physicist's guide to Mathematics
Classical mechanics
Quantum mechanics
Computational fluid dynamics
The universe in a nutshell
Modern physics…
Thinking in C++
C++ Primer
The design and evolution of C++
The Annotated STL Sources (Using SGI STL)…
…

In Fig. 5 we illustrate 3 clusters of the result from theapplication of our algorithm to the book network. It shows
community structures clearly.
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

72367

In this paper we present a new method that allows thequick discovery of communities within a big network. The
traditional clustering method DBSCAN fails in the
discovery of communities of complex networks. In orderto overwhelm the shortcoming of DBSCAN, we introduce
structural information of complex networks in finding
communities. By utilizing the high clustering coefficientsof complex networks, our method succeeds in cutting of
the bridge between two communities. Moreover, we do
not have to specify the number of communities we wish todivide the network into.
We test the algorithm by applying it to book network.
The experimental result shows its validity.
A possible defect of our method is that in order todivide the popular books coming from different categories,
we select a high MinTime. Unfortunately, the books which
are rarely borrowed together with popular books within
the same category are treated as noise. In some sense, we
just find the cores of the book network communities.
In spite of possible shortcoming we believe that thealgorithm we have presented is valid and fast when trying
to quickly find communities within large complex
networks.

6970
11

12

6969

12

7117

59116

5588
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58608
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5590

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14

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Quantum Physics

5304

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29

Signal Processing

12 12
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13014612135820

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Machanics of Materials

113027

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15

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24344

125585109964

24345

97121
26702

96526

52350

40388

Figure 5. 3 Clusters of Book Network
Table 2 and Figure 6 show the clustering result andefficiency comparison between the two clustering
algorithms (Runtime of Table 2 does not include the timereading data from disk). We see that using NDBC, we
obtain more clusters for the book network and generate
clusters with more even distribution than DBSCAN. In the
result of DBSCAN, we can see from Fig. 6 that a cluster
contains books far more than that of other clusters. The
runtimes of these two algorithms are approximate.
Table 2. Comparison of NDBC and DBSCANNDBCDBSCAN
Number of Books
Runtime (Sec)Eps/MinPts
Number of Clusters

14208140.125/10
14214208130.125/10
69

References[1] R. Albert and A. Barabasi, Statistical mechanics of complex
networks, Reviews of Modern physics, 2002, pp. 74.47.
[2] S.N. Dorogovtsev and J.F.F. Mendes, Evolution of networks,
Adv. Phys., 2002, pp. 51, 1079-1187.
[3] Steven H. Strogatz, Exploring complex networks, Nature
410, March 2001.
[4] M. E. J. Newman, The structure and function of complex
networks, SIAM Review, 2003, pp. 45, 167-256.
[5] M. Girvan and M. E. J. Newman, Community structure insocial and biological networks, Proc. Natl. Acad. Sci. USA 99,
2002, pp. 8271-8276.
[6] M. E. J. Newman and M. Girvan, Finding and evaluating
community structure in network, Phys. Rev. E, 2003.
[7] Gary Flake, Steve Lawrence, and C. Lee Giles, Efficient
identification of web communities, In Sixth ACM SIGKDD,
Boston, MA, August 20-23 2000, pp. 150-160.
[8] Fang Wu and Bernardo A. Huberman, Finding communities
in linear time: a physics approach, cond-mat/0310600, 2003.
[9] Zhong Su, Qiang Yang, Hongjiang Zhang, Xiaowei Xu andYuhen hu, Correlation-based document clustering using web
logs, 34th Annual Hawaii International Conference on System
Sciences, 2001, pp. 5022.
[10] M. Ester, H.P Kriegal, J. Sander, and X. Xu, A densityBased algorithm for discovering clusters in large spatial
databases with noise, KDD 96, 1996.
[11] M. Ester, H.P Kriegal, J. Sander, and X. Xu. Density-Based
Clustering in Spatial Databases: The Algorithm GDBSCAN and
its Applications, Data Mining and Knowledge Discovery ,Kluwer Academic Publishers, Norwell, MA, June 1998, pp.
169-194.

Figure 6: Number of Books in every Cluster
Our algorithm seems to find two types of communities:
pure community and complex community. The purecommunity just contains books belonging to the same
subject; the complex community often contains books
belonging to several subjects. From the result ofclustering, we find that the books relating to computer
programming often appear in the same cluster with somebooks of other subjects. The phenomenon reveals thatmany students studying in different subjects are interested
in programming. It shows that the Computer Science isclosely related with other subjects.

6. Conclusions©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Autonomous evolutionary machine vision systems.
Jeffrey Johnson and Valerie Rose
Department of Design and Innovation
The Open University
Milton Keynes, MK7 6AA, England
j.h.johnson@open.ac.uk and vrose@ukonline.co.uk
There are many approaches to machine vision,
including algorithmic knowledge-based
programming, neural systems that learn fromexamples, and combinations of both. Many
practical systems are based on algorithms orprocedures making opportunistic use of special
features of particular objects and scenes.
Although this may give acceptableperformance for a given application, there isusually a poor ability to generalise to othersimilar scenes, and no ability to generalise todifferent environments. A system designed toinspect industrial parts is unlikely to beincorporated in a mobile planetary robot.

Abstract
We propose a radical new approach to machine
vision based on biological principles in thecontext of a multilevel architecture of
representation and reconstruction.
1. Introduction
Machine vision is a notorious bottleneck inrobotics and automated systems. We seek amethod of creating very flexible machine
vision systems that can evolve in particularenvironments to recognise anything that an
operator has indicated as being ‘interesting’ inthat environment. For example, Figure 1 shows
an object that a house-tidying robot might
encounter during its everyday duties.

It is common for human programmers todesign vision systems so that data are
optimised for the particular problem andclassification technique being used. The
generality is that machine vision systems arehand-crafted to give the best results for aparticular application, but are brittle andperform poorly outside their narrowspecification, and lack any ability to adapt.
We seek machine vision systems that can:••••use point-and-learn training
work for cluttered scenes
adapt to changes in objects and scenes
adapt to any scene or environment

To achieve this we propose a multilevelarchitecture in which machine vision systems•

Figure 1.Contouring an object•

Our intention is that non-programmers cantrain our vision systems by ‘pointing’ at an
object in a scene, e.g. drawing a contour roundit, with the system to evolving the ability torecognise such objects automatically. Our
approach is based on new combinatorial
structures supporting an architecture thatallows vision systems to generalise and adapt
to recognise new classes of objects. Thisarchitecture is based on new combinatorial
mathematics in the science of complex
systems [1].

©ISAROB 2005••

evolve appropriate retinal
configurations
evolve connectivities to represent
spatial relationships
abstract their own higher level
constructs
levels are integrated by new relational
mathematics

The key feature of the architecture is theability of the system to abstract its own
constructs from data in a multilevel algebraic
representation. This allows the system to learn
objects that may change through time, and to

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danger of an infinitive regress: To test RRB it isnecessary to test RRB,i for each xi. But to test
RRB,i it is necessary to reduce xi to its parts,and test them. And so on. Where can it all end?
In robotics and machine vision the answer tothis question is easy. Top-down reductionism
ends when the pattern recognisers are
grounded in sensor data. In other words thesensors ‘ground’ everything the machine canknow about its environment (Figure 3).

adapt to learn radically new objects and scenes
without the need to change the underlying
program. These requirements are very
demanding and beyond any existing machine
vision systems.
2. The Fundamental Structuresa b c d e f g h
i j k l m n o pcross-piece

PRB(x1, x2, …, x16) = True ?

pillara b c d e f g h
i j k l m n o p

Figure 2. An image of an arch.

PRBi isgrounded

The fundamental idea behind our architectureis that of n-ary relations. To illustrate thisconsider the image of an arch in Figure 1. As
we view it, we see two pillars supporting acrosspiece. The crosspiece, for example, ismade up of the pixels marked a, b, c, d, e, f, g,h, i, j, k, l, m, n, o, and p. These sixteen pixels
are assembled by a 16-ary relation, RRB, into a
rectangular block.

sensorFigure 3. Reductionist grounding prevents
infinite regress in pattern recognition
When PRB(x1, x2, …, x16) = True for a particular
set of features, {a, b, c, …}, we give theresulting object a name, here C, and writeσ(C)= 〈 a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p;
RRB〉. σ(C) is called a simplex and the elements〈a〉, 〈b〉, 〈c〉, etc are called its vertices. The partsor features of an object can be said to lie at alower level in its representation than the object
itself. If the parts are drawn within an Euler
circle (ellipse), the name of the object can be
drawn as the apex of a hierarchical cone, as
illustrated in Figure 4.

The construct ‘rectangular block’ defines a setof objects which will be written RB = {x |x isa rectangular block}.In order to be
operational, this requires a pattern recogniser,
PRB, which is able to say of any candidate formembership, x, that x is a rectangular block, PRB(x) = True, or that x is not a rectangular
block, PRB(x) = False.
Generally pattern recognisers need to refer to aset of features of the object. In this case there
are sixteen features, {x1, x2, …, x16}, ht e pixels
used to make up the block. Each of these xi
needs to be of the right type, so the overall
pattern recogniser requires a set of sub-patternrecognisers, PRB,i, with the requirement that
PRB,i(xi) = True.

C

RRB
a b c d e f g h
i j k l m n o p

Now it can be seen that the pattern recognition
involves two types of decision:

Figure 4. A hierarchical cone.

(i) for each xi, it is necessary that xi is of theright type, here a dark pixel. PRB,i (xi) = True.
(ii) given that all the xi are pixels, it isnecessary to established that they areassembled properly so that the relational
structure holds with PRB(x1, x2, …, x16) = True.

The relation RRB and all its reductionist subrelations will be called a construct. Clearly, inorder for a construct to be operational, it must
be grounded. Generally constructs are named,
and they define sets of named objects. E.g., we
can use the name ‘rectangular blocks’, andwrite rectangular blocks = {x |x is arectangular block }, which is an intensional
definition. Alternatively we can write
Rectangular Blocks = {RB1, RB2, …, RBn},

Clearly (i) comes before (ii). There is no point
applying expensive pattern recognition
procedures to objects which are of the wrong
type to form the pattern. However, there is©ISAROB 2005

PRBi(xi) = True ?

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here. At this level of representation are theusual greyscale histograms.
The next level of representation must be
characterised by sets of pixels structured byrelations – nothing else is possible!So, Level 2
in the representation will consist of sets ofpixels under n-ary relations. To illustrate this,consider the pixel configurations shown in
Figure 6. To establish them at the lowest levelin the representation, these will be called
retinal constructs.

where each of RBi is the name of a particular
rectangular block.
Figure 5 shows the two stages of assembly of
the arch; which is defined as structured set ofblocks. The blocks are defined as structured
sets of pixels; and the pixels are grounded inreality through the camera sensor.
ARCH

Level 3 – object

In Figure 6(a) there is a central sensor, such as
light-sensitive rod, responding to relative
darkness, surrounded by six other sensors,numbered 0, 1, 2, 3, 4 and 5. There are 26 = 64
configurations of light-.dark for these six
satellite sensors. The configurations have been
designed to have a topology corresponding
more closely to packed cells than the usual
Cartesian grid. Also they are designed using
the ‘next but one’ neighbours according to
Simon’s three pixel principle [3][4].

CA

B

Level 2 – blocks

Level 1 – pixels(sensor-grounded)0

Figure 5. Multilevel construct aggregation

5

The pillars named as A and B in the image arealso structured sets of pixels, as shown in
Figure 5. The intermediate constructs A, B andC can be assembled by a 3-ary relation, Rarch toform the construct called an ARCH. Thus wehave σ(ARCH) = 〈 A, B, C; Rarch 〉. In this way
we build primitive structures from atomic
constructs (pixels), we build intermediate
constructs from these at a higher level in themultilevel representation, and so on, until we
recognise objects within scenes at the highest
level. At every level we use named constructs
to reference the objects abstracted.

central sensor

124satellite sensors

3(a) hexagonal array of pixel sensors

This example illustrates a major problem inmachine vision. The notions of ‘pillars’ and‘crosspieces’ are social constructs insideprogrammers’ heads. Vision systems arehighly dependent on programmers’ ways of
construing the visual universe. It is well known
that this can be very different between
different people [2], and there is no guaranteethat a given programmer will choose the mostappropriate constructs. Much better to have thevision system abstract these constructs foritself.

0

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3. Low level pixel configurations
In the proposed multilevel architecture, let the
pixels define a base level, Level 1. (Lower
level sub-pixel constructs are possible, (e.g.
Johnson and Picton, 1985), but not discussed©ISAROB 2005(b) the 64 retinal configurations
Figure 6. Hexagonal pixel constructs

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frequency for the circles than the diamonds,
reflecting their natural response to vertical left
and right edges respectively. Similarly,
configurations 7, 30, 51 and 57 favour the
diamond shape by responding well to obliqueedges.

These configurations are examples of masks orfilters which are widely used in machine
vision. As such they have been designed by the
programmer (me!) and have the problem ofsubjective selectivity. Although I find these
configurations attractive for a number ofreasons, how can I be sure that they are themost appropriate for any particular objects inany particular environment?

In principle, a conventional multilayer
perceptron neural network will classify suchdata well, assuming convergence. Note that inTable 1, twenty nine of the sixty four possible
configurations respond to the eighty shapes,
leaving thirty five retinal configurations that
do not respond to these shapes. Training the
network with all sixty four configurations as
inputs increases the computation and thepossibility of non-convergence.

diamond(a) circle and diamonds (b) line segments
circleFigure 7. Examples object classes
The sixty four retinal configurations in Figure
6 were used to analyse eighty hand-drawn
shapes, forty ‘circles’ and forty ‘diamonds’,
similar to those shown in Figure 7. Each dark
pixel in the shapes was analysed by inspecting
its surrounding pixels and assigning to it oneof the sixty four retinal configurations. As afirst level of analysis, the numbers of eachconfiguration were counted, giving a 64element vector for each configuration. The
vectors of the configurations with non-zerofrequencies are given in Table 1.

Figure 5. A single level vector neuralclassifier
Table 2 gives the configuration counts for theline segments shown in Figure 4(b). The
response of these objects to the retinal
configurations is completely different to thatfor the shapes. These response vectors can also
be used for robust classification between thesteep and shallow line segments. It is
encouraging that a single layer neural classifier
can discriminate these line segments, since it isbelieved that animal vision uses suchprimitives.

4. Single Level Neural Classification
Inspection of Table 1suggests that thefrequency vectors alone are sufficient forclassification of the simple circle and diamondshapes, and indeed they are. For example,
configurations 14 and 31 have much higher

diamond3 4 6 7 12 14 15 24 28 30 31 32 33 35 39 46 47 48 49 51 53 55 56 57 59 60 61 62
63
2 1 1 22 1 1 21 2 25 24 0 1 2 21 0 1 21 1 2 25 1 21 18 24 2 0 17 24 978
2 1 1 25 1 2 21 1 14 26 1 0 1 18 1 1 24 1 2 18 0 17 25 27 0 2 23 12 885
1 0 1 27 2 4 23 0 27 17 3 0 0 25 2 0 25 2 2 28 0 22 28 19 0 4 26 25 1256
2 0 0 30 1 3 17 2 26 29 0 0 0 22 5 0 28 1 3 28 0 20 21 31 0 3 20 25 1292
circle3 4 6 7 12 14 15 24 28 30 31 32 33 35 39 46 47 48 49 51 53 55 56 57 59 60 61 62
63
0 0 2 14 0 8 20 0 25 19 6 0 1 11 31 0 12 0 22 17 0 8 10 14 19 21 6 21 1322
0 0 0 18 2 18 10 0 6 18 16 0 0 8 33 0 14 2 16 18 0 4 17 13 14 32 15 4 1253
0 0 2 13 1 11 37 0 10 12 10 0 0 12 28 0 11 1 14 16 0 8 27 15 11 23 24 7 1375
0 0 1 18 1 14 10 2 14 18 12 0 1 8 23 0 15 1 22 14 0 5 10 12 20 19 9 13 1083

Table 1.
©ISAROB 2005

Frequencies of retinal configurations in the shapes of Figure 4.
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However, these classifier soon break downwhen the number of objects to be classified
gets large, as is required for recognising acomprehensive set of line segments.
steep lines
0 1 4 5 8
9 0 13 2 0
3 0 11 1 1
8 1 17 1 0
7 0 11 0 0

The answer to the second question must be thata single level of classification is not adequate.
If it were, objects and scenes could bepresented to a network as an input vector, todeliver recognition of classified objects. Even
if this were possible in theory, it would be
impractical because combinatorial explosion
mean that the necessary input vectors would
have astronomic numbers of elements.

9 32 36 40
0 13 21 2
0 12 27 0
0 16 15 2
0 11 28 0

5. Interpreting the data as constructs

shallow lines
0 1 4 5 8 9 32 36 40
21 7 5 1 7 4 5 1 1
32 4 0 0 4 5 0 0 030 10 1 1 10 18 1 0 1
28 11 1 3 13 10 3 0 1

In the previous section it has been seen how
some retinal configurations can be associatedwith constructs such as ‘oblique’, ‘vertical’,‘left and ‘right’ edges. These are humanconstructs that can be imposed on the data.
The machine, of course, does not share these
constructs explicitly in its representation. Thus
there is a co-relation between our concept of a‘round edge’ and, say, the pixel configuration
49, , taking a relatively high value forcircular objects.

Table 2. Line segments frequencies ( Fig 4)
The approach to pattern recognition illustrated
here map the object to a vector of numbers
counting the frequency of ‘interesting’ featuresof the objects, interprets the vector as a pointin multidimensional space, and classifies thepoints according to some notion of ‘similarity’.
In terms of our objects it begs two questions:1.
2.

Put like this is becomes possible to understandwhy conventional approaches to machine
vision have failed so comprehensively. As
programmers we seek appropriate descriptors
or constructs to represents objects to berecognised. We look at an object, and abstract
properties such as ‘roundness’ and‘straightness’, that our language conveniently
has terms to describe. We then seek machinebased abstractions that match these linguistic
constructs.

where do the ‘interesting’ features
come from?
is a single level of processing
adequate to discriminate objects incomplex scenes?

In answer to first question, in our illustrative
application, the ‘interesting’ features were
designed in by the programmer. Delegating theselection of ‘interesting’ features to a
programmer inevitably means that the system
will be limited in its ability to recognise
objects, and unable to adapt to recognise
objects that are very different from the design
specification.

But as animals we constantly recognise objects
for which there is no explicit name. For
example, most readers will recognise the shape
as being one of those in Figure 9, eventhough this shape has no explicit common
name. Since I want to talk about it I will givethe name of ‘double-square shape’. Then I cansay things like ‘the double square shape isbetween the cross and the square in Figure 9,
and even begin to reason about double-squareshapes. However, if such a shape were to berecognised within a machine, it can simply be
named implicitly by the data structures,
possibly, its position in memory.

Figure 9. Shapes with equivalent verticaland horizontal lengths
It is easy to show that this kind of single levelof classification is inadequate in general for
object recognition in vision. For example, theobjects in Figure 9 all have the same length ofvertical and horizontal edges. Conceivably thecorners would have different retinal
configurations, but the numbers would besmall, and robust discrimination between theobjects by a single vector of retinal
configurations is unlikely.

©ISAROB 2005

Freeing ourselves from serendipity
abstractions in a particular programmer’s head,and designing machines to form their own
constructs is here seen as the way forward. To
some extent this is what multilayer neuralnetworks do, and some researchers assert thateach neuron is processing a construct.
However, that approach is relatively blunt,since the constructs are always implicit.

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5. Multi-Level Pattern Recognition

Level N high level constructs

Figure 10 shows one of the diamond shapes of
Figure 7 with its numbered retinal pixel
responses letter-coded as follows: A (7), B(14), C (15), D(24), E (25), F (30), G (35),H(47), I (49), J (51), K (53), L (55), M(56), N(57), O (61), P (62), dot (63), and X (allothers). Note that on the bottom edge thesequence ACH () recurs along thebottom left edge. This can be written as asimplex, 〈 A, C, H; R horizontal 〉, in the terms of
Section 2. This can be considered to be aconfiguration of retinal configurations, andexists at a higher level of aggregation.

Level 3 intermediate constructs

Level 2 intermediate constructs

Level 1 retinal configurations(a) Iterated assembly up the representation
diamond

ACH
ACH
ACH

L LGG
L LGG
L LGG

(b) iterated assembly through cones
Figure 11. Multi-level aggregation

6. Spatial Relationships

Figure 10. 〈 A, C, H; R horizontal 〉 as an
emergent construct

The configurations in machine vision
inherently involve spatial relationships.
Conventional approaches to machine vision
often take a highly geometric approach tospatial relationships, based on the Cartesiangeometry of the pixel grid. It is interesting toconsider whether Cartesian geometry is a
product of the human mind, or part of thefundamentals of its workings.

The ACH configuration emerges from thediamond shape, Let it be denoted σABC. Thenpairs of such configurations can form thestructure 〈 σACH,i , σACH,j ; R above_left 〉, at yet
another level of aggregation. Let this structure
be denoted by the symbol σACH,. Then these
too can be aggregated to form a structure thateventually aggregate into structures involving
all seven of the ACH sequences. From ahuman perspective this could be called astraight edge. From a machine perspective thisis a learned or evolved structure, physicallyembedded in the machine that has emerged
because there is advantage in it doing so.

©ISAROB 2005

From our perspective it is much easier, inprinciple, to represent multilevel spatial
structure through multilevel tessellations andconnections between levels. In other words, we
propose to proceed on the basis of spatial
relationships being hard-wired into thesubstrate of the vision system. This idea is
illustrated in Figure 12, where four objects
have been recognised, and the spatial

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knows nothing of this a priori. In many places
the mug has highlights, making it visually veryvariable. In the first instance we do not assume
that that our system will have top-down
context knowledge such as ‘if the scene
contains an ellipse, then it contains a cylinder’.

relationship between them is established (andcomputed) by located connections between thesite of response and higher level processingthat recognises the object.
diamond

Although the examples in this paper have been
pre-segmented binary images, the methods
developed here are highly applicable togreyscale and coloured images. The relational
method can be very powerful when, forexample, the satellite pixels are compared tothe centre and classified as light/darker. Thisapproach can lead to areas with varying
greyscales but homogeneous greyscale
gradient. This approach has been successfully
applied in scientific measurement systems. The
details are beyond the scope of this paper, but
further details can be found in [3][4].
8. The architecture
Figure12. Hard-wired spatial relationships
The research described in this paper, in thecontext of our objectives leads us to thefollowing principles:

7. Segmentation
One of the most difficult tasks in machine
vision is to segment a complex scene into‘relevant’ parts. Generally one seeks areas that
contain discrete objects, such as the coffee
mug shown in Figure 10. In Figure 10(a) weshow a training object identified by a user.
This low-skilled method of teaching thesystem is the only kind of input the trainer
gives. Following this, the system has to finddiscrete objects in the image to be recognised
as of the same type as the training items.

Principle 1. Low retinal configurations will
aggregate to form higher level constructs
Principle 2. The constructs will depend on
spatial relations
Principle 3. The retinal configurations should
not be constrained by design, but should beallowed to emerge from the images and scenes
in its environment
Principle 4. The spatial relations in the systemshould be implicit in its topology so that
Cartesian geometry need not be used

Figure 10(b) illustrates the many problems
involved in segmenting images. The mug has
no well defined contour, since neither it nor
the background are homogeneous in greyscale.
In some places the mug merges into the dark
background, while in other places it is arelatively light grey due to the reflected light.

Principle 5. Higher level spatial configurations
should not be constrained by design, butshould be allowed to emerge from the images
and scenes in its environment
Principles 1 and 2 are the fundamental
theoretical underpinning of our approach. They
are supported by algebraic mathematics thatcan be implemented as data structures in realcomputers.
Principle 3 is based on the need for the system
to adapt to new things. Any system with predesigned primitives is constrained by what thedesigner puts in. This spans the space ofpossibilities. Any object not in that spacecannot be recognised. This is one reason why(a) user defined object (b) how to segment?
Figure 10. The segmentation problem
The white ellipse is a strong signal to humans
that this is a cylindrical object, but the machine©ISAROB 2005

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conventional machine vision collapses outside
its design domain.

system in a way that overcomes combinatorial
explosion. The human brain has some ten
billion neurons with five to ten thousand
connections per neuron. This apparently hugeamount of processing power and information
distribution ability appears more modest whencompared to the numbers of ways that retinal
configurations can be defined and connected ina multilevel architecture. Furthermore, we
expect to implement our architecture on
standard computers with orders of magnitudeless memory and orders of magnitude lesscomputational ability than biological visionsystems.

If the low level configurations are not to be
designed in, where can they come from? We
have experimented with forming low level
constructs by random configurations of pixels.
We have found that generating random masks
gives some discrimination between the circularand diamond shapes discussed earlier.
However, there remain many open questions,
including the optimum diameter for a retinal
configuration.
A similar argument suggests there can be no
fixed multilevel architecture, and this too must
incorporate random processes. Thus the‘relevant’ configurations of configurations,and the resulting ‘construct’ have to bediscovered by the machine.

Currently our idea is that spatial structure isdetermined by the initial connections to theretinal configurations, where the image isgrounded, and subsequent connections arethrough the multilayer system. There is amajor challenge in establishing a theoreticalarchitecture that can be implemented inpractice, followed by the major practicalchallenge of inducing the system to selforganise as it adapts to new visualenvironments.

Thus there are two parts to our architecture.
The simplest involves the machine learning
particular objects and scenes within a given
hardware topology. In other words, in thesimplest case the machine is fixed, andrecognition takes place by values andparameters changing within that structure.

We are aware of the difficulty of the research
we propose. We are optimistic that success ispossible because the combinatorial
mathematics underlying our research contains
some of the essential structures necessary toachieve our objectives.

The more demanding part of the architecture
involves evolutionary principles to generateand select ‘appropriate’ retinal primitives, andto generate and select appropriate topologies tosupport relational structure throughout themultilayer aggregation. This lies at the heart ofour research programme to achieveautonomous evolutionary machine visionsystems.

References[1] Johnson, J.H., ‘Some structures andnotation of Q-analysis’, Environment andPlanning B, 8, 73-86, 1981.

9. Discussion and conclusions[2] Gregory, R. L., Eye and brain: thepsychology of seeing, Oxford University Press,
1998.

In this paper we have illustrated some of thebasic ideas of our research programme, andreported briefly some preliminary experiments
on evolving retinal configurations. Thoseexperiments combined with the experiments
reported on designed retinal configurations
suggest that this part of the research will be
relatively straight forward. In other words, theresearch on the evolution of retinal
configurations has already begun and we are
beginning to understand this part of thechallenge relatively well.

[3] Johnson, J.H., Picton, P.D., Mechatronics:
Concepts of Artificial Intelligence,
Butterworths (London), 1985
[4] Johnson, J.H., Simon, J-C, ‘Fundamental
Structures for the Design of Machine Vision
Systems', Mathematical Geology, Vol. 33,
No.3, 2001.
[5] Marr, D., Vision, W Freeman andCompany, (New York), 1982.
[6] Rose, V., Results of experiments in shaperecognition through random selection of sets ofpairs of pixels within 75x75 pixel binary
images of various simple shapes. Mimeo Open
University, 2004.

By far the greatest challenge is in
implementing the multilayer architecture to
support the hierarchical assembly of
information towards object and scene
recognition. One major unresolved challenge
in this is to design spatial structure into the©ISAROB 2005

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Real-time adaptive maintenance for performance improvement
on daily-use computer
Shinichi Hirose

Shinichiro Yoshii

Graduate School of Information Science and Technology,
Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido,
060-0814, Japan
E-mail: s.hirose@complex.eng.hokudai.ac.jp

Graduate School of Information Science and Technology,
Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido,
060-0814, Japan
E-mail: yoshii@complex.eng.hokudai.ac.jp

Abstract

algorithm in them is called simple genetic algorithm [3].
Hereinafter, we explain the simple genetic algorithm .
A certain individual is formed based on the genetic
code described in its chromosome. The chromosome isconstituted as a gene arrangement with which two or
more genes were located in a line, and it is decoded when
an individual is born. The character of the individual
decoded and generated is called phenotype. On the other
hand, the pattern of the gene of the origin of it is called
genotype. In genetic algorithms, it is usually assumed thatthe individual has only one chromosome. The gene
information will be recorded by the following
arrangement S in the individual with n genes.

When it continues using a personal computer, it hasoften come to operate slowly rather than the time ofhaving purchased it. In this paper, the authors argue about
the easy tuning method of personal computers. The
general users without much deep special knowledge will
be able to use it easily. By using the simple genetic
algorithm, we search for the optimized set of theparameters of the operating system. the effect is verified
in an experiment. The authors propose the adaptive
method for the improvement in performance for personal
computers.

S = s1 , s 2 ,L, s n ,1. Introduction
It has been long time since the personal computersturned to be daily commodities. As being used for a longtime, the personal computer comes to lose its quickness.
For example, it comes to take a long time to boot up. So
the personal computer users have often come to feelinconvenience for use. The most of general users do nothave deep knowledge as much as IT professionals.
Therefore, general users cannot help making a uniform
improvement in performance by installing the packaged
software for tuning, or returning their personal computers
to the initial state by installing the operating system again.
However, since various factors for the prolongation of thebooting up time and the other inconvenient phenomena
can be considered, these methods do not necessarily
become better solution.
Several system integration companies have proposed
the self-control function in the information systems called
Autonomic Computing in enterprise field [1]. But it isexpected that the maintenance method of personal
computers as daily commodities is more adaptive than the
packaged software for tuning and simpler thanAutonomic Computing. The authors focused on tuning
parameters of the operating system as the maintenancemethod of personal computers.

2. Genetic algorithm
Genetic algorithms bases on the research of J. H.
Holland [2] in University of Michigan in the 1960s. He
made the model in engineering paying attention to thegenetical mechanism in a nature. The algorithms basedon such a view have been studied as various and concrete
techniques. The most fundamental kind of genetic©ISAROB 2005

si ∈ {0,1}

The position of each gene on a chromosome is called
locus, and the gene in the position specifies a certain
characteristic character. For example, s2 is recording theinformation on the color of the eye of an individual etc.
The candidate of the gene that can be taken on a locus iscalled allele. As mentioned above, an individual will beborn to environment by decoding and expressing a
genetic code from a chromosome. And a set of anindividual is called population. The individual with thehigh degree of adaptation to the environment survives byhigh probability. The evaluation value is called fitness.
Generally the genetic algorithm is advanced by thefollowing procedures. When solving an optimization
problem, it is necessary to express a problem to optimize
with a gene and to set up end conditions.
(1) initialization : Building the individuals by generating
their chromosomes randomly(2) evaluation : Calculating the fitness for eachindividual according to the evaluation function(3) selection : Selecting the individuals by their fitness(4) crossover : Generating the chromosomes of the newindividuals by crossover two parents’ chromosomes(5) mutation : Generating the chromosomes of the newindividuals by the probability of mutation(6) reproduction : Replacing with all or some of new
generations(7) repetition of (2) to (6) until fulfilling end conditions

3. Experiment
Generally, that users feel some inconvenience tocomputer systems becomes the opportunity that tunes

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them up. In many cases, after the tuning target is set up, amethod required in order to attain the target is examined.
The tuning is performed according to the plan. The
phenomenon which user feels inconvenient is various.
There may be two or more tuning methods for the same
phenomenon.

This emulation software had the function that rollbacks
the operation to the virtual operating system, and we used
this one when acquiring an individual’s evaluation.
Because the internal environment of the operating system
is changed by the repeating configuration of the registries,
it is impossible to acquire the exact starting time of thepersonal computer.

3.1 Tuning method
3.3 Results
In order to improve the prolonged startup time of thepersonal computer, we search for the optimized set ofparameters of the operating system by the simple genetic
algorithm. In this experiment, Microsoft Windows XP,
which is the most popular operating system among
general users, was taken up. The parameters of operating
system of Microsoft Windows, which are called registry,
can be set with the numerical values other than 0 or 1, or
character strings, or binary data, etc. The authors selected
the 14 target parameters, which can be set with 0 or 1,
taking account of their roles with expertise acquired
through professional practice of system engineering. We
coded to genotypes the set of parameters chosen in thisway. Instead of using the defined evaluation function, weused the starting time actually obtained by measurementin the set of parameters as each individual's evaluation. In
order to measure the starting time of a personal computer,
we referred the document [4] that provided guidelines for
system manufacturers to improve boot and the tool
introduced there.
We summarized some main genetic operators andparameters of the simple genetic algorithm for our
experiment in Table 1.
GA operators
(1)
(2)
(3)
(4)

selection(elitist preserving selection)
crossover(uniform crossover)

Boot Times
100
100
300
300

Case A
Case A’
Case B
Case B’

Startup time [s]
50.64
45.79
68.81
57.44

effect－△ 9.58 %－△ 16.52 %Table 2. The experiment of the results

4. Conclusion

GA parameters
N (population of individuals)= 30

initialization

mutation

We repeat the reboot of the virtual operating system
supposing the situation that the personal computer wasused daily. In the case A and B, after rebooting the virtual
operating systems 100 times and 300 times respectively,
we measured the start time without tuning parameters.
Similarly, in the case A’ and B’, after rebooting thevirtual operating systems 100 times and 300 times
respectively, we measured the start time with tuning
parameters. Table 2 shows the results. When a case A and
A' were compared, 9.58% of improvement was found,
and when a test case B and B' were compared, 16.52% of
improvement was found.

We consider that a certain amount of improvement was
found.
In an experiment of this paper, it is hardly taking into
consideration that the state of the hardware and softwarecomes to change with using the personal computer. The
remaining issues will be to build the model that realizes
the method of tuning up the value of the parameters withtaking into account the change state of hardware
environment, and to check it in the experiment.

G (generation gap) = 0.8
pc (probability of crossover)= 0.8
mc (probability of mutation)= 0.01

Table 1. The genetic operators for the experiment

3.2 Experiment System

References

Figure 1 shows the experiment environment. We
experimented on the virtual operating system that was
realized by this emulation software.

[1] IBM Corporation, Autonomic Computing,
http://www-3.ibm.com/ autonomic/index.shtml, 2004.
[2] J. H. Holland, Adaptation in Natural and Artificial
Systems, MIT Press 1992.
[3] M. Sakawa and M. Tanaka, Genetic Algorithm,
Asakura-Syoten, 1995.
[4] Microsoft Corporation, Fast System Startup for PCs
Running Windows XP, http://www.microsoft.com/whdc/
system/sysperf/fastboot/fastboot-winxp.mspx, 2002.

Experiment System (Windows 2000)
Emulation Software

Operations･Change parameters

Virtual OS･Reboot(Windows XP)･Measure the startup time

Figure 1. The experiment environment©ISAROB 2005

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Swarm Search for Fast Face Detection with Neural Networks
Xinjian Fan, Masanori Sugisaka
Department of Electrical and Electronic Engineering, Oita University, Japan
Email: {msugi, fxinjian}@cc.oita-u.ac.jp
method to speed up neural network (NN) based facedetection systems. The method is based on the idea thatthe face search (FS) problem can be formulated into an
integer nonlinear optimization problem (INLP). To finda face, we only need to find a local maximum filter
response which is above a threshold. The integer
variables are parameters that represent a subwindow in
the image. The objective function is based on the outputof the face filter.
PSO is a novel evolutionary computation (EC)technique [8], which has been improved and applied tovarious problems. Although the original algorithm wasbasically developed for continuous optimization problem,
it can be expanded to handle discrete variables easily.
Furthermore, PSO has only a few parameters that makeit easy-adjusted to get better performance. Therefore,
PSO is expected to be suitable for FS formulated as an
INLP.
Based on a NN-based face filter, this paper presents a
PSO for the FS problem formulated as an INLP. The
feasibility of the proposed method is demonstrated andcompared with the exhaustive search method on a set of
42 test images with promising results. In this paper, we
assume that there is only one face contained in the test
image. The extension of the method to detect multiple
faces will be done in our future work.

Abstract: This paper presents the application ofparticle swarm optimization (PSO) to improve the search
speed of neural network based face detection problems.
The method is based on the idea that the task of finding awell-matched subwindow (face) can be formulated as aninteger nonlinear optimization problem (INLP). To finda face, we only need to find a local maximum filter
response which value is above a given threshold. The
proposed method has been tested and examined with aset of 42 images to demonstrate its effectiveness. The
results confirm the potential of the proposed approachand show its superiority over the classical technique.
1. Introduction
Face detection is a very important task that serves as
the first step of a large area of applications: automatedface recognition, secure access control, advanced
human-computer interfaces, etc. Its accuracy andefficiency have a direct impact on the usability of thewhole system.
Face detection has been a well researched problem
and there are many approaches on it [1]. Up to now one
kind of the most successful methods is known as neuralnetwork-based methods [2-7]. In these methods, a facemodel (filter) is first learned from a large number ofexamples (face images and non-face images). And then asliding window is used to scan all possible subwindows
across multiple-scale images.
However,thesemethods
aregenerally
computationally expensive because: (a) the searchwindow is a high-dimensional vector that has to beclassified in a very non-linear space; (b) there are
hundreds of thousands of windows to search. Althoughmany efforts have been done to reduce the runtime ofneural network based methods, most of them focused on
reducing the computational complexity of classifiers[4],[5]. Only a few attentions were given to improving
the search efficiency. In Ref. [6], the search window
moves every q pixels (q=3~5) instead of every pixel.
Thus the number of searched windows is only about 1/q2of the exhaustive search, but with the disadvantage oflowering the system’s performance. Many methods use
skin color information to limit the search area [6],[7].
But color information is not always able to be used and it
is very difficult to build a skin color model robust toillumination changes.
In this paper, to reduce computational cost while
retaining high detection accuracy, we propose a new

2. Neural network based face filter
The purpose of the face filter is to classify a windowof size 20×20 pixels extracted from an image, as a faceor as a non-face.
We use a retinally connected neural network [3] toserve as the face filter. The network takes a 20×20 pixel
window as input. Each hidden unit receives inputs only
from part of the input layer (called a receptive field).
There are 3 kinds of receptive fields: four 10×10 pixel
regions, sixteen 5×5 pixel regions, and six 20×5 pixel
overlapping horizontal stripes. Each of these receptive
fields has full connection to two hidden neurons. It has asingle output. The output is a real value from -1.0 to 1.0,
giving the likelihood as to what extent the input window
looks like a face.

3. Formulation of FS as an INLP
Figure 1 shows a 3D plot of the neural network output
with the image on the left as input. The highest peak
represents the face location. It can be seen that the face
filter is very selective: it responds strongly within a©ISAROB 2005
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The features of the algorithm can be summarized asfollows:(a) PSO searches the solution space using a group of
searching points like genetic algorithm (GA) and thesearching points gradually get close to the optimal point
using their pbests and the gbest.
(b) As explained in Ref. [9], the first term of the rightside of Equ. (2) is corresponding to the exploration of
the search space. The second and third terms of that are
corresponding to the exploitation of the best solutions
found so far. Namely, the method has a flexible andwell-balanced mechanism to utilize exploration andexploitation in the search procedure.
(c) The original PSO was originally developed for
nonlinear optimization problems with continuous
variables. However, the method can be expanded todiscrete problems easily [10].
(d) Because the update process of PSO is based on
simple equations, the algorithm is easy to implement andcomputing economically. In addition, only a few input
parameters need to be adjusted in PSO which makes it
easy-adjusted to get better performance.
Due to the above features, PSO is expected to besuitable for the FS problem formulated as an INLP.

several pixel radius of the face while its output on the
background is low. Moreover, around the face, theoutput of the neural filter is a monotonous and growing
function. The properties lead to the following heuristic:
The face search (FS) problem can be formulated as aninteger nonlinear optimization problem (INLP). To finda face, we only need to find a local maximum filter
response which value is above a threshold. Let T
represent an input image, SW represent a subwindow
and dv be its detection value (the corresponding outputof the neural network). With these notations the FS
problem can be stated as:arg max SW dv (SW )∀SW ∈ T
(1)
If dv(SW) is higher than a given threshold, thecorresponding portion of SW is declared as a face.

4. Particle swarm optimization
Particle swarm optimization (PSO) is a novel
evolutionary computation method, modeled after thesocial behavior of flocks of birds [8]. PSO is a
population based search process where individuals,
referred to as particles, are grouped into a swarm. Eachparticle in the swarm represents a candidate solution tothe optimization problem at hand. The performance ofparticles is measured using a predefined fitness function
which encapsulates the characteristics of the
optimization problem.
Each particle i maintains the following information:
Xi, the current position of the particle; Vi, the current
velocity of the particle; pbesti, the personal best position
discovered by the particle so far, and also the best
position found by the entire swarm so far, denoted by
gbest. All particles start with randomly initialized
velocities and positions. At iteration step t, the current
velocity and position (searching point in the solution
space) are updated by:
Vi (t + 1) = ω Vi (t ) + c1r1 (t )( pbesti − Xi (t ))5. Face search using PSO
The main steps of the proposed method are shown in
Figure 2. In the following, we will describe the approachin detail.

5.1 Encoding and rescaling
In our problem, each particle represents a subwindow
in the input image. We use its center (Cx, Cy) and length
S to encode a subwindow. To evaluate subwindows ofdifferent sizes using the neural network, we should
rescale them to the size of 20×20 (the input size of theneural network). However, if this computation is done on
every size of subwindows, it will be very timeconsuming. To avoid it, we first build an image
pyramid†:+ c2 r2 (t )( gbest − Xi (t ))(2)
Xi (t + 1) = Xi (t ) + Vi (t + 1)
(3)
where w is the inertia weight, c1 and c2 are theacceleration constants, r1(t) and r2(t) ~ U(0, 1). The
velocity of a particle will be set to a predeterminedmaximum velocity (Vmax) if it exceeds Vmax.

Output the foundwindow and stop

Input image
PSO

YN
Encoding subwindows(particles)

Fig. 1 left: an input image; right: 3D view of the neuralnetwork output, obtained by superposing the outputs ofsubwindows at all scales.

Stopping
criterion?

Rescaling

Particle flying

Preprocessing

Fitness evaluation(Trained face filter)

Fig. 2 Main steps of the proposed method©ISAROB 2005
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The proposed RRM has proven more efficient for our
problem than the traditional penalty approach [11].

W H
W H
W H
× , ", k × k , ", L × L
(4)
q q
q q
q q
where W and H are the width and height of the input
image respectively, and q is the scale factor. The top
level (level L) should have a size more than 20×20:
min(W , H )≥ 20 , givesqL
W × H,

5.4 Particle flying
Based on their fitness, particles in the swarm are
guided by Equ. (2) and (3) to fly to possible face regionsin the image. New (Cx, Cy, k) generated by Equ. (2) and(3) are real values. When corresponding to a subwindow
in the input image, they are transformed into integers byusing the floor function. During flying, if a variable
extends the defined search boundary, it will be set to theclosest limit, i.e.
⎧⎪ x j min if x j < x j min
xj = ⎨(9)⎪⎩ x j max if x j > x j max
where xjmin and xjmax are respectively the lower and uppersearch limit of variable xj, x j ∈ X .

⎢ ln(min(W , H )) − ln 20 ⎥
L=⎢
(5)
⎥
ln q
⎣
⎦
Then we let S to be chosen among the following
geometric sequence†:
20, 20q, " , 20q k " , 20q L(6)
For a subwindow SW = (Cx , C y , ⎢⎣ 20q k ⎥⎦ )T , we findits mapped 20×20 window SW ′ = (Cx' , C y' , 20)T in level
k of the pyramid by:⎢ Cy ⎥⎢C ⎥Cx' = ⎢ kx ⎥ , C y' = ⎢ k ⎥
⎣q ⎦
⎣q ⎦

5.5 Stop criterion(7)

The algorithm is stopped when 1) a “face” is found –the detection value of the best particle is above the given
threshold or 2) the maximum iteration number is reached.

So each particle X is constructed as X = (Cx , C y , k )T .
Cx, Cy and k are defined in [10, W-10], [10, H-10] and [0,L] respectively.

6. Experiments

5.2 Preprocessing

A number of experiments were performed to evaluate
the proposed method. The experiments were performed
on 42 images with complex backgrounds. Some of theimages were chosen from CMU Test Set [12] and other
Internet resources; the others were taken by us in anindoor environment using a CCD camera. Each image
contains only one face and all the faces can be detectedby the neural filter. All the images have the same size of
320×240 and the face size ranges from 34×34 to
178×178. The threshold of the neural network output
was set to 0.1.
According to pre-simulation, the parameters of PSO
were set as:
c1, c2: 0.2,
w: 1.2,
Vmax: 0.2×(Xmax-Xmin),
Swarm size P: 60,
Maximum iteration number MaxIt is set to 70. But
one restart is allowed, i.e., if the algorithm fails to find aface within MaxIt it will be re-initialized and perform a
new search.
For each image in the test set, we ran our algorithm
100 times. The total detection results are listed in Table
1. Some examples are shown in Figure 3. The time
consuming was reported on an AMD Athlon 750 MHz
PC with Windows 2000 as its OS.
As shown in Table 1, the proposed search method
yielded a high success rate (93.6%) on average (the bestis 100% and the worst is 72%). Moreover, about 39% of
the failures are because PSO fell into a false detection,
the other failures are due to non-convergence. A further
reduction of false detections can be achieved byarbitrating among multiple networks [3]. From the

Before a 20×20 window is passed to the trainedneural network, it is preprocessed with lighting
correction (by subtracting a best fit linear function) andhistogram equalization as in Ref. [2],[3]. The former
reduces the effect of different lighting conditions and thelatter improves contrast across the window.

5.3 Fitness evaluation
To evaluate each particle (subwindow), we directly
use its detection value (the corresponding output of theneural filter): the larger its detection value (dv), the more
the subwindow resembles a face. The fitness functionf (SW ) is given asf (SW ) = dv(SW ) SW ∈ T(8)where T is the input image and SW is a subwindow,
dv(SW ) ∈ [−1, 1] .
The corresponding subwindow of a particle may go
beyond the image’s boundary even if all its variables lie
in the search boundary. To guarantee feasibility ofsolutions, a random repair method (RRM) is investigatedin this paper. If a particle is checked to be infeasible, itwill be forced to “fly” to a new position, which is
randomly generated but feasible. The method works asfollows:
If SW ∉ T , thenStep 1: Randomly generate a new position SW ′ .
Step 2: If SW ′ ∈ T , replace SW with SW ′ ;otherwise, go to step 1.
† Each term in Equ. (4) and (6) is transformed from a real value to an
integer value by using the floor function.

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to solve it. The feasibility of the proposed method isdemonstrated on a set of 42 images with promising
results. With fine-adjusted parameters, PSO only
requires less than 2000 evaluations of subwindows forfinding the face in an image. The result is much more
effective and superior over the classical exhaustive
search method. Many object detection problems can beformulated as an INLP and the results indicate thepossibility of PSO as a practical tool for various INLPs
of object detection.
However, we have found that the method doesn’t
work well on some images, especially when the face size
is very small. How to improve the robustness is the
future work.

Table 1: Experimental results
Success False Non-convergence ANSEs APT (ms)
93.6%

2.52%

3.88%

1965

250

False: false detection rate; ANSEs: Average Number ofSubwindow Evaluations; APT: Average Processing
Time.

References[1] M.H. Yang, D. Kriegman and N. Ahuja, “Detecting faces
in images: a survey”, IEEE Trans. Pattern Anal. & Mach.
Intell., vol.24, no.1, pp.34-58, 2002.
[2] K.K. Sung and T. Poggio, “Example-based learning forview-based human face detection”, IEEE Trans. PatternAnal. & Mach. Intell., vol.20, pp.39-50, 1998.
[3] H.A. Rowley, “Neural Network-Based Face Detection”,
Thesis submitted for the degree of Doctor of Philosophy,
School of Computer Science, Carnegie Mellon University,
1999.
[4] L. Huang, A. Shimizu, Y. Hagihara and H. Kobatake,“Face detection from cluttered images using a polynomial
neural network”, Proc. Conf. on Image Processing,
pp.669-672, Thessaloniki, Greece, 2001.
[5] B. Fasel, “Fast multi-scale face detection”, Technical
Report COM-98-04, IDIAP, 1998.
[6] R. Feraud, O. J. Bernier, J. Viallet, and M. Collobert, “Afast and accurate face detector based on neural networks”,
IEEE Trans. Pattern Anal. & Mach. Intell., vol.23, no.1,
pp.42-53, Jan. 2001.
[7] S. Karungaru, M. Fukumi and N. Akamatsu, “Human facedetection in visual scenes using neural networks”, IEEJ
Trans., vol.122-C, no.6, pp.995-1000, 2002.
[8] J. Kennedy and R. Eberhart, "Particle swarm
optimization", Proc. IEEE Int’l Conf. on Neural Networks,
vol.4, pp.1942-1948, Perth, Australia, 1995.
[9] Y. Shi and R. Eberhart, "A modified particle swarm
optimizer", Proc. IEEE Int’l Conf. Evolutionary
Computation, pp.69-73, Anchorage, 1998.
[10] H. Yoshida, Y. Fukuyama, K. Kawata, S. Takayama, and
Y. Nakanishi, “A particle swarm optimization for reactive
power and voltage control considering voltage security
assessment”, IEEE Trans. Power Systems, vol.15, no.4,
pp.1232-1239, 2001.
[11] Z. Michalewicz, “A survey of constraint handlingtechniques in evolutionary computation methods”, Proc.
4th Annual Conf. on Evolutionary Programming, MIT
Press, Cambridge, MA, pp.135-155, 1995.
[12] CMU Test Set:
http://vasc.ri.cmu.edu/idb/images/face/frontal_images/ima
ges.tar[13] P. Viola and M. Jones, “Robust real-time object
detection”, Proc. 2nd Int’l Workshop on Statistical andComputational Theories of Vision, Vancouver, Canada,
2001.

Fig. 3 Examples from the test set

examples shown in Figure 3, we can see that the
proposed method maintains robustness in images whichcontain faces under a very wide range of conditions
including scale, pose, position, complex backgrounds,
illumination conditions, etc.
Table 2 gives the comparison of the proposed searchmethod (called swarm search) with the exhaustive
search method. It’s clear that the time consuming and thenumber of subwindow evaluations of the proposed
method are much less than those of the exhaustive search.
Although with a little loss of detection rate (due to nonconvergence), a great speedup has been achieved byusing the swarm search compared to using theexhaustive search.
The method proposed by Viola and Jones [13] isabout 2.7 times faster than ours even performing an
exhaustive search. The reason is that they use acomputationally extremely efficient face filter, which ismade of a boosted cascade of classifiers built with the
AdaBoost algorithm. However, it is possible to combine
our swarm search method with their face filter to make amore powerful face detection system.

7. Conclusion
This paper presents a new search method for NNbased face detection. The proposed method formulates
the problem of face search into an integer nonlinear
optimization problem (INLP) and expands the basic PSO
Table 2: Swarm search vs. exhaustive search
Swarm search Exhaustive search*
Ratio
ANSEs

1965

193737/2

1 : 49APT (ms)

250

20169/2

1 : 40* Because we only consider the single-face detection
problem in this paper, for fair comparison, we supposethat it takes only half of an exhaustive search to find aface.
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Development of the Fuzzy Control for a GPS-located Airship
Masanori Sugisaka 1, Fengzhi Dai 2, Yutaka Fujihara 2, Takumi Kamioka3
12

Oita University, Oita, Japan. msugi@cc.oita-u.ac.jp
Matsue National College of Technology, Japan. daifz (fujihara)@matsue-ct.jp
3
Nara Institute of Science and Technology, Nara, Japan

Abstract
The purpose of the work is to develop the fuzzy control for the experimental GPS-located airship,
which has the overall length of 5.8m and the maximum diameter is 2.2m. The basic technology
including the hardware and the program to control the airship navigating along the shortest flight pathto the target point is developed. Fuzzy control is used because the kinematics and dynamics featuresof an airship are non-linear and it is difficult to describe the motion equations. At last, to compare theefficiency, PD control is also applied. The result of comparison shows the validity of the fuzzycontrol.
Keywords：Autonomous airship, GPS, Side thruster, Fuzzy control

1. Introduction
For many situations, autonomous green flight systems are needed and important. They have beendeveloped for uses such as mine detecting, crop dusting and military surveillance. Aerosonde [1],which is developed by Aerosonde Robotic Aircraft Pty Ltd., PREDETOR [2] by Inc. of General
Atomics Aeronautical Systems, and RQ-4A Global Hawk [3] by Northrop Grumman Corporation are
examples of these kinds of flight systems. Also the development of helicopter system was reported by
Sugeno et al [4, 5] and an airship by Ouchi et al [6]. Suzuki et al have studied the control of theairship by reinforcement learning [7, 8].
In this study, the experimental airship is proposed, which can remain stationary in the sky and since
it uses helium gas, it has low fuel consumption. In the paper, the basic technology including the
hardware and the control method by which the airship navigates along the shortest path to the target
point is discussed.

2. Structure of the system
Figure 1 shows the structure of the airship. The balloon is elliptical and has an overall length of
5.8m, a maximum diameter of 2.2m and a volume of 17.5m3. Helium gas is used for buoyancy. The
gondola at the bottom contains a notebook computer, controllers, motors, batteries, a geomagnetic
sensor and ballast. For navigation, GPS (Global Positioning System) is mounted and an antenna forreceiving GPS signals is fixed to the head of the balloon.
The power sources consist of a side thruster and two main fan ducts. The side thruster is used torotate the airship around the vertical axis (yawing rotation shown in Figure 2). Two main fan ducts areused to control the airship going up, down, forward and backward.
In Figure 2, the ox axis is coincident with the axis of symmetry of the envelope and the oxz plane

Figure 1 Structure of airship©ISAROB 2005

Figure 2 kinematics of airship
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coincides with the longitudinal plane of symmetry of the airship. Since the volume of the gondola isnegligible compared with that of the envelope, it is reasonable to assume that the cv (center ofvolume) lies on the axis of symmetry of the envelope.
The airship is able to move in translation and rotation in three dimensional spaces. Rotation aroundthe x axis is called rolling, around the y axis is pitching and around the z axis is yawing. In this study
the rotation of the airship is mainly yawing.

3. Control method
3.1 General algorithm
The flow chart of the automatic control algorithm is shown in Figure 3. When it starts, the airshipflies up first. Next, the notebook computer receives data from each sensor. Subsequently, the positionof the airship compares with the target point. When it reaches the target, the program terminates andthe airship hovers. Otherwise the computer calculates the direction and distance to the target point
shown in Figure 4. Values of control parameters are outputted to each motor. Eventually by repeatingthese processes, the airship arrives to the target point.

Figure 3 Flow chart

Figure 4

Coordinate of the airship to target

3.2 Control of side thruster
3.2.1 PD control
The side thruster is first controlled by PD adjustment as comparison. The thrust St is determined by
the angular differenceθd = φ-θshown in Figure 4, the angular velocity ω (= dθd/dt) and theangular accelerationα (= d2θd/dt2). Thus the thrust of the side thruster is expressed as follows:
St = Kθ(0－θd) + Kω(0－ω) + Kα(0－α)(1)

where Kθ, Kωand Kα are constants and the target point is set to (0, 0, 0). However, it is difficult todetermine these parameters because the airship is slow to respond and the characteristic of the airshipis non-linear. Therefore, these constants are adjusted and determined by repeating the experiment.

3.2.2 Fuzzy control
As the other control method for the side thruster, fuzzy control is applied. Figure 5 shows themembership functions being used [9]. Inputs are the angular difference θd and the angular velocity
ω. The membership functions have triangular forms and seven vertices. Each set is labeled NL, NM,
NS, ZR, PS, PM and PL, meaning negative large, negative medium, negative small, zero, positive
small, positive medium and positive large for each symbol.

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Figure 5

Membership functions

The fuzzy rules are as follows
If θd is A11 and ω is A12, then St is B1
If θd is A21 and ω is A22, then St is B2
Where Aij and Bi are labels of each fuzzy set. The fuzzy output set Bi is discrete. These rules are
expressed as the rule table shown in Table 1.
Table 1

Rule table for St

4. Experiment
4.1 Procedure of experiment
The purpose of the experiment is to confirm that the airship navigates to the target point and tocompare the two control methods for controlling the side thruster. Since the power of the motors is notvery large, it is desirable that the experiment is conducted indoors, where there is little effect to disturb
the flight. The space for experiment needs an area of over 1000m2 and a height over 20m due to thesize of the airship. And since GPS signals cannot be received in a reinforced concrete building, theexperiment is done in wooden building Izumo dome.

4.2 Result and Discussion
The result is shown in Figure 6. It gives two trajectories of the flight path, plotting longitude along
the ordinate and latitude along the abscissa. By PD control, the airship flew changing the direction
right and left before arriving at the target point. The main cause is that gains of Kθ, Kω, Kα inequation (1) were not able to be adjusted properly. The adjustment is very difficult without analyzing
the dynamic characteristics of the airship. By fuzzy control, the airship could autonomously navigate
by the almost shortest flight path. The flight time is 1 minute and 27 seconds by PD control, and 20
seconds by fuzzy control.
The results of this experiment show that the fuzzy control method is more effective than the PD
control method from viewpoint of tuning parameters, and the PD control method is not suitable for the©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

autonomous airship system in the present situation.

Figure 6

Results of flight controlled by Fuzzy and PD

5. Conclusions
In this paper, we proposed the control system and the algorithm for the airship to navigate
autonomously to the target point. To control the side thruster, the PD and fuzzy control are applied ascomparison. As the result, it is clear that the fuzzy control is better for the airship to finish the task offlying to the target point.
Further work of image processing and recognition by the CCD camera and the technology of thetelecommunication are being considered. Telecommunication needs to communicate mutually the data
between the computer mounted on the airship and the computer at the base station. The monitoring
program using wireless LAN is developed in the project. It enables us to monitor the condition of theairship from a remote location. In the near future, we will develop integrated monitoring and operating
software.

Reference[1] Web site of Aerosonde Robotic Aircraft Pty Ltd: http://www.aerosonde.com[2] Web site of General Atomics Aeronautical systems, Inc: http://www.uav.com/home/index.html[3] Web site of Northrop Grumman Corporation:
http://www.is.northropgrumman.com/products/usaf_products/global_hawk/global_hawk.html[4] S. Nakamura, K. Kataoka and M. Sugeno (2000), “A study on autonomous landing of an
unmanned helicopter using active vision and GPS”. Journal of the Robotics Society of Japan,
Vol.18, No.2, pp252-260, 2000.
[5] S. Nakamura, A. Sato, H. Shibata and M. Sugeno (2000), “A study on autonomous searching andtracking by an unmanned helicopter based on GPS and image information”. Journal of the
Robotics Society of Japan, Vol.18, No.6, pp862-870, 2000.
[6] Web site of Tomakomai. C. T:
http://www.tomakomai-ct.ac.jp/mech/thermal/sotuken2001/airplane_cont.files/frame.htm[7] K. Suzuki, Y.Akisato and A Ohuchi (1999), “Micro airship control based on reinforcement
learning”. Proceedings of 99th robotics and mechatronics conference on JSME, No.99-9, 1999.
[8] K. Motoyama, M. Yamamoto, K. Suzuki, A. Ohuchi (1999), “Evolutionary state space
configuration for RL applied to airship control”. Proceedings of the 9th intelligent system
symposium on JSME, No.99-18, 1999.
[9] Yutaka Fujihara, Takumi Kamioka, et al (2004), “Development of the autonomous airship by fuzzycontrol”. Proceedings of the Seventh International Conference on Industrial Management(ICIM’2004), Okayama, Japan, Nov. 15-17, 2004, pp. 183-190.

©ISAROB 2005

439

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fuzzy Information Retrieval Based on Weighted Power-Mean
Averaging Operators
Won-Sin Hong*, Shyi-Ming Chen*, and Shi-Jay Chen***Department of Computer Science and Information Engineering, National Taiwan
University of Science and Technology, Taipei, Taiwan, R. O. C.
**Department of Information Management, Ching-Yun University, Jung-Li, Taiwan
R. O. C.

In this paper, we present new averaging operators,
called weighted power-mean averaging (WPMA)
operators, based on the concept of the weighted
power-mean [8] to deal with fuzzy information retrieval.
We also prove that the proposed WPMA operators are“positively compensatory” operators. In [3] and [6], Kim
et al. pointed out that operators which have the “positively
compensatory” property could provide high retrieval
effectiveness. The proposed WPMA operators are more
flexible and more intelligent than the average operatorspresented in [7], [9], [10] and [11] for dealing with fuzzy
information retrieval.

Abstract
In recent years, some researchers used averaging
operators (i.e., Infinite-One operators, Waller-Kraft
operators, P-Norm operators and GMA operators) to deal
with AND and OR operations of users’ queries for fuzzy
information retrieval, but they still have some drawbacks,e.g., sometimes query results do not coincide with theintuition of the human being. In this paper, we present
new averaging operators, called weighted power-mean
averaging (WPMA) operators, based on the weighted
power-mean for dealing with fuzzy information retrieval
to overcome the drawbacks of the existing methods. The
proposed WPMA operators are more flexible and more
intelligent than the existing averaging operators to deal
with users’ fuzzy queries for fuzzy information retrieval.

12

In [8], the definition of the weighted power means
is defined as follows:

Introduction

Definition 2.1: Assume that a and w are two positiven-tuples and r ∈ R, then the rth power mean
M n[r ] (a, w) of a with weight w is define as follows:

In [1], [2], [3], [4] and [5], the T-operators (i.e.,
T-norms and T-cornorms) are used to deal with fuzzy
information retrieval. Although T-operators support the
ranking facility, they still have some drawbacks, e.g.,sometimes query results do not coincide with theintuition of the human being. In [6], Lee et al. pointed
out that three averaging operators (i.e. Waller-Kraft
operators [7], P-Norm operators [8] and Infinite-One
operators [9]) have been proposed to achieve highretrieval effectiveness for fuzzy information retrieval,
where these three averaging operators can avoid thedrawbacks of T-operators. However, in [10], Chen et al.
pointed out that these three averaging operators still havesome drawbacks, i.e., it is subjective and hard todetermine appropriate values for the parameters of these
averaging operators, respectively. Thus, in [10], Chen etal. presented new averaging operators based on thegeometric mean, called the geometric-mean averaging(GMA) operators, to overcome the drawbacks of theWaller-Kraft operators, the P-Norm operators and theInfinite-One operators. However, the GMA operators
still have some drawbacks, i.e., in some specificsituations, the retrieval results do not coincide with theintuition of the human being. Thus, it is necessary todevelop new averaging operators to overcome thedrawbacks of the existing averaging operators for dealing
with fuzzy information retrieval.

©ISAROB 2005

Preliminaries

1

⎞r
⎛ 1 n
r
M n (a , w ) = ⎜⎜a i w i ⎟⎟ ,∑⎠⎝ W n i =1
where Wn = w1 + w2 + ⋅ ⋅ ⋅ + wn .
[r ]Since M n

[1]

= An and M n
[0 ]

[−1]

(1)

= H n , in [8], Bullen

et al. defined M n = Gn and proved that the definitionof the weighted power mean is reasonable. Thus, theweighted power means form a natural extension ofelementary means. Furthermore, when r → ∞,(a, w) = max a;
M n (a, w) = min a.
Mn

[r ]

when

r → −∞,[r ]In [3], Kim et al. pointed out that an information
retrieval system based on the conventional fuzzy set
model is defined by a quadruple <T, Q, D, F>, where{}(1) T is a set of index terms, T= t1 , t 2 , ⋅ ⋅⋅, t m , wherethese index terms are used for representing queries
and documents.
(2) Q is a set of queries, where query q ∈ Q is a
Boolean expression composed of index terms t j ,1 ≤ j ≤ m, and the logical operators “AND”, “OR”

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

(3)and “NOT”.
D is a set of documents, D = d 1 , d 2 , ⋅ ⋅⋅, d n , where{di ∈ Disrepresented

by t1 , ei1 , t 2 , ei 2 , ⋅ ⋅⋅, t m , eim ,eij denotes

each

document

((

) (

)

(

q1 and q2 , shown as follows：
d1 = {(t1 , 0), (t 2 , 0)},d 2 = {(t1 , 0), (t 2 , 1)},d 3 = {(t1 , 1), (t 2 , 0)},d 4 = {(t1 , 1), (t 2 , 1)},}))the degree of strength of term t j in document d i ,(4)eij ∈ [0, 1], 1 ≤ i ≤ n, and 1 ≤ j ≤ m.

q1 = t1 AND t 2 ,F is an evaluation function,

q2 = t1 OR t 2 .

F: D × Q → [0, 1],(2)which assigns a real value in the closed interval [0, 1]
to each pair (d, q). It is a similarity measure between
document d and query q.

Case 1: If the parameter r = 0.0001, then the degree ofsatisfaction F ( d 3 , q1 ) of the document d 3 withrespect to the query q1 and the degree of satisfaction
F ( d 2 , q 2 ) of the document d 2 with respect to the

From [12], we can see that the weight eij of term tj inquery q2 can be evaluated, shown as follows:

document d i is determined either subjectively by domain
experts or objectively by some algorithmic procedures, where1 ≤ i ≤ n and 1 ≤ j ≤ m. One way for determining the degreeof strength eij of term t j in document d i objectively is

(

(

(

)

(

F d i , q OR = F d i , t 1 OR t 2 OR ⋅ ⋅ ⋅ OR t m

3

where r ∈ {0.0001, 0.5},

2

4

2

Table 1. Query Result of the Proposed WPMA Operators( when r = 0.0001))

Documents
(3)

)`
1
r

⎤
⎡ 1
＝ 1 − ⎢ 2 ∑ (2k − 1) (1 − e * ik ) r ⎥ ,⎦⎣ m k =1
m

10000

Table 1. From Table 1, we can see that whenr = 0.0001, the proposed WPMA operators become thetraditional Boolean operators. The operator graph [10] of
the proposed WPMA operators is shown in Fig. 3.

1
r

⎡ 1 m
r⎤
＝ ⎢ 2 ∑ (2m − 2k + 1) e*ik ⎥ ,⎦⎣ m k =1

)

(
)
(
)
(
)
(
)
F (d , q ) and F (d , q ), respectively, as shown in

In this section, we present new averaging operators,
called the Weighted Power-Mean Averaging (WPMA)
operators, for fuzzy information retrieval, shown as
follows:

)

= 0,

(

3 Fuzzy Information Retrieval Based on the
Weighted
Power-Mean
AveragingOperators

(

10000

⎡1
0.00010.0001 ⎤= 1.
F (d 2 , q 2 ) = 1 − ⎢ 1 × (1 − 0 )+ 3 × (1 − 1)⎥⎦⎣4
In the same way, we can calculate the values of
F d1 , q1 ,F d 2 , q1 ,F d 4 , q1 ,F d1 , q 2 ,to consider the frequency of occurrence of index term tj indocument di.

F d i , q AND = F d i , t 1 AND t 2 AND ⋅ ⋅ ⋅ AND t m)⎡1⎤
F (d 3 , q1 ) = ⎢ 3 × 0 0.0001 + 1 × 10.0001 ⎥
⎣4
⎦

(4)

Terms
t1
t2

Queriesq1 = t1 AND t 2 q 2 = t1 OR t 2

d1

0

0

0

0

d2

0

1

0

1

d3

1

0

0

1

d4

1

1

1

1

eij denotes the degree ofstrength of term t j in document d i , e*ik denotes thekth smallest value of eij ; the weight of the term in
document d i which is associated with the kth
smallest value of eij in the AND query qAND is
2m − 2k + 1; the weight of the term in document d iwhich is associated with the kth smallest value of eij

Fig. 3. The operator graphs of the proposed WPMA
operators (when r = 0.0001 ).

in the OR query qOR is 2k − 1, 1 ≤ i ≤ n, 1 ≤ j ≤ m,
1 ≤ k ≤ m, F ( d i , q AND ) ∈ [0, 1] and F ( d i , qOR ) ∈ [0, 1] .

Case 2: If the parameter r = 0.5, then the degree ofsatisfaction F (d 3 , q1 ) of the document d 3 withIn the following, we use two cases to discuss how
the proposed WPMA operators are controlled by aparameter r. Assume that there are four documents
d1 , d 2 , d 3 and d 4 , and assume that there are two queries

respect to the query q1 and the degree of satisfaction
F (d 2 , q 2 ) of the document d 2 with respect to the©ISAROB 2005

query q2 can be evaluated, shown as follows:

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005()

Based on formula (4), we can see that

2

⎡1⎤
F (d 3 , q1 ) = ⎢ 3 × 0 0.5 + 1 × 10.5 ⎥ = 0.063,
4
⎣
⎦

(

1

)

⎡1
⎤r
F (d , t1 OR t2 ) = 1 − ⎢ (1 × (1 − x) r + 3 × (1 − x) r )⎥
⎣4
⎦

2

⎡1
0 .5
0 .5 ⎤F (d 2 , q 2 ) = 1 − ⎢ 1 × (1 − 0 ) + 3 × (1 − 1) ⎥ = 0.938.
4⎣⎦
In the same way, we can calculate the values of
F d1 , q1 ,F d 2 , q1 ,F d 4 , q1 ,F d1 , q 2 ,

[

2

4

= x.

Thus, the proposed WPMA operators are idempotent.

2

Table 2. From Table 2, we can see that when r = 0.5,
the proposed WPMA operators are compatible with theextended Boolean operators [11]. The operator graph ofthe proposed WPMA operators is shown in Fig. 4.

Q.E.D.
Property 3.2: Assume that there is a documentd = {(t1 , x ), (t 2 , y )}, where t1 and t 2 are terms, and0 ≤ x ≤ 1 and 0 ≤ y ≤ 1 . Then,

Table 2. Query result of the Proposed WPMA Operators(when r = 0.5 )Terms
Documents
t1
t2

Min( x, y ) ＜ F (d , t1 AND t 2 ) ＜ F (d , t1 OR t 2 ) ＜Max( x, y ) , where x ≠ y .
Proof:(i) If x ＞ y , then we can see that Min( x, y ) = y andMax( x, y ) = x . Furthermore, based on formula (3), we
can see that

Queriesq1 = t1 AND t 2 q 2 = t1 OR t 2

d1

0

0

0

0

d2

0

1

0.063

0.938

d3

1

0

0.063

0.938

d4

1

1

1

1

1

= 1 − (1 − x)

(
)
(
)
(
)
(
)
F (d , q ) and F (d , q ), respectively, as shown in3]= 1 − (1 − x) r r

1

⎤r
⎡1
F (d , t1 AND t2 ) = ⎢ (3 × y r + 1 × x r )⎥
4
⎦
⎣
1

1

⎡1
⎡1
r
r ⎤r
r
r ⎤r
⎢ 4 (3 × y + 1 × y ) ⎥ ＜ ⎢ 4 (3 × y + 1 × x ) ⎥ ＜
⎦
⎣
⎦
⎣
1

⎡1
r
r ⎤r
⎢ 4 (3 × x + 1 × x ) ⎥⎣⎦F (d , t2 AND t2 ) ＜ F (d , t1 AND t2 ) ＜ F (d , t1 AND t1 )Fig. 4. The operator graph of the proposed WPMA
operators ( r = 0.5 ).

Min( x, y ) ＜ F (d , t1 AND t2 ) ＜ Max( x, y ).

Based on formula on (4), we can see thatIn [6], Lee pointed out that an operator which has
the ”positively compensatory” property could provide
higher retrieval effectiveness. The ”positively
compensatory” operators are functions of the formp : [0, 1] × [0, 1] → [0, 1] . They must satisfy the following
two properties:

1

⎡1
⎤r
F (d , t1 OR t2 ) = 1 − ⎢ (1 × (1 − y ) r + 3 × (1 − x) r )⎥
⎣4
⎦
1

⎤r
⎡1
1 − ⎢ (1 × (1 − y ) r + 3 × (1 − y ) r )⎥ ＜⎦⎣4(1) p( x, x) = x; i.e., p is an idempotent function.
(2) Min( x, y ) ＜ p( x, y ) ＜ Max( x, y ), where x ≠ y.
In the following, we assume that 0 ≤ x ≤ 1,
0 ≤ y ≤ 1 , and prove that the proposed WPMA operators
satisfy the following two properties.

1

⎤r
⎡1
1 − ⎢ (1 × (1 − y ) r + 3 × (1 − x) r )⎥ ＜
⎦
⎣4
1

⎤r
⎡1
1 − ⎢ (1 × (1 − x) r + 3 × (1 − x) r )⎥⎦⎣4

F (d , t2 OR t2 ) ＜ F (d , t1 OR t2 ) ＜ F (d , t1 OR t1 ),Property 3.1: F (d , t1 AND t 2 ) and F (d , t1 OR t 2 )are idempotent, where document d = {(t1 , x ), (t 2 , x )}, t1
and t 2 are terms, and 0 ≤ x ≤ 1.
Proof: Based on formula (3), we can see that(by formula (4))(by Property 3.1)

y ＜ F (d , t1 OR t2 ) ＜ x,Min( x, y ) ＜ F (d , t1 OR t2 ) ＜ Max( x, y ).

1

Then, we prove “ F (d , t1 AND t 2 ) ＜ F (d , t1 OR t2 ) ”
based on formulas (3) and (4), shown as follows:⎡1⎤r
F (d , t1 AND t 2 ) = ⎢ (3 × x r + 1 × x r )⎥
4
⎣
⎦

1

[ ]

1
xr r

⎛ yr + yr + yr + xr ⎞ r⎟ ＜F (d , t1 AND t2 ) = ⎜⎜
⎟
4
⎝
⎠

=
= x.
©ISAROB 2005

(by formula (3))(by Property 3.1)

y ＜ F (d , t1 AND t 2 ) ＜x

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

⎛ y+ x+ x+ x⎞⎛ y+ y+ y+ x⎞
⎟=
⎜
⎟ ＜⎜
4
4
⎝
⎠
⎠
⎝

Acknowledgements⎛ (1 − y ) + (1 − x) + (1 − x) + (1 − x) ⎞
1− ⎜
⎟＜
4
⎝
⎠

This work was supported in part by the National
Science Council, Republic of China, under Grant NSC
93-2213-E-011-018.

1⎛ (1 − y ) r + (1 − x) r + (1 − x) r + (1 − x) r ⎞ r⎟
1 − ⎜⎜⎟
4⎝⎠= F ( d , t1 OR t2 ).

References

From the above discussions, we can see that Min( x, y )＜ F (d , t1 AND t 2 ) ＜ F (d , t1 OR t 2 ) ＜ Max( x, y ) .
(ii) In the same way, if x ＜ y , we can get
F (d , t1 AND t1 ) ＜ F (d , t1 AND t2 ) ＜ F (d , t2 AND t2 ),(by formula (3))
x ＜ F (d , t1 AND t2 ) ＜ y,(by Property 3.1)
F (d , t1 OR t1 ) ＜ F (d , t1 OR t2 ) ＜ F (d , t2 OR t2 ),(by formula (4))
x ＜ F (d , t1 OR t2 ) ＜ y,(by Property 3.1)
and F (d , t1 AND t2 ) ＜ F (d , t1 OR t2 ).
(by formulas (3) and (4))
Thus, Min( x, y ) ＜ F (d , t1 AND t 2 ) ＜ F (d , t1 OR t 2 ) ＜Max( x, y ) , where x ≠ y .

Conclusions

In this paper, we have presented the weighted
power-mean averaging (WPMA) operators for fuzzy
information retrieval. The proposed WPMA operators are
more flexible and more intelligent than the averaging
operators presented in [7], [9], [10] and [11] to deal withusers’ fuzzy queries for fuzzy information retrieval dueto the fact that the proposed WPMA operators have thefollowing advantages:(1) The proposed WPMA operators can overcome thedrawbacks of the existing averaging operators for
fuzzy information retrieval.
(2) The retrieval results of the proposed WPMA
operators are much closer to the intuition of thehuman being than the existing averaging operators.
(3) We can easily determine appropriate values for theparameters of the proposed WPMA averaging
operators. If we use the proposed WPMA operators
to deal with traditional Boolean query processing for
fuzzy information retrieval, then we can set theparameter r = 0.0001; if we use the proposed
WPMA operators to deal with extended Boolean
query processing for fuzzy information retrieval, thenwe can set the parameter r = 0.5.

©ISAROB 2005

D. A. Buell, “A problem in information retrieval
with fuzzy set,” Journal of the American Society
for Information Science, Vol. 36, pp. 398-401,
1985.

[2]

D. H. Kraft and D. A. Buell, “Fuzzy set andgeneralized
Booleanretrievalsystems,”
International Journal of Man-Machine Studies,Vol. 19, pp. 45-56, 1983.

[3]

M. H. Kim, J. H. Lee, and J. Lee, “Analysis of
fuzzy operators for high quality information
retrieval,＂Information Processing Letters, Vol. 46,
pp. 251-256, 1993.

[4]

D. Lucaralla and R. Morara, “FIRST: Fuzzy
information retrieval system,” Journal of
Information Science, Vol. 17, pp. 81-91, 1991.

[5]

S. Miyamoto, Fuzzy Sets in Information Retrievaland Cluster Analysis, Kluwer, Dordrecht, 1990.

[6]

J. H. Lee, “Properties of extended Boolean modelin information retrieval,” Proceedings of theSeventeenth Annual ACM Conference on Researchand Development in Information Retrieval, Dublin,
pp. 182-190, 1994.

[7]

W. G. Waller and D. H. Kraft, “A mathematicalmodel of a weighted Boolean retrieval system,”
Information Processing and Management, Vol. 15,
pp. 235-245, 1979.

[8]

P. S. Bullen, D. S. Mitrinovic, and P. M. Vasic,
Means and Their Inequalities, D. Reidel
Publishing Company, Dordrecht, 1988.

[9]

M E. Smith, Aspects of the P-Norm Model ofInformation Retrieval: Syntactic Query Generation,
Efficiency and Theoretical Properties, Ph.D.
Dissertation, Cornell University, New York, 1990.

Q.E.D.

According to the above properties, we can see thatthe proposed WPMA operators have the “positively
compensatory” property. Moreover, they have neither the“single operand dependent” property nor the “negatively
compensatory” property. Therefore, they can overcome
the drawback of the T-operators.

4[1][10] S. J. Chen and S. M. Chen, “A new method for
fuzzy
information
retrieval
basedongeometric-mean
averaging
operators,”
Proceedings of the 2002 International Computer
Symposium: Workshop on Artificial Intelligence,
Hualien, Taiwan, Republic of China, 2002.
[11] G. Salton, E. A. Fox, and H. Wu, “Extended
Boolean information retrieval,” Communicationsof the ACM, Vol. 26, pp. 1022-1036, 1983.
[12] G. J. Klir and B.Yuan, Fuzzy Sets and Fuzzy Logic:
Theory and Applications, Prentice Hall, New
Jersey, 1995.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Temperature Prediction Based on Genetic Simulated Annealing Techniques andHigh-Order Fuzzy Time Series
Li-Wei Lee*, Li-Hui Wang**, and Shyi-Ming Chen**Department of Computer Science and Information Engineering, National Taiwan Universityof Science and Technology, Taipei, Taiwan, R. O. C.
**Department of Finance, Chihlee Institute of Technology, Panchiao, Taipei, Taiwan, R. O. C.
F(t-n), …, F(t-2), F(t-1) → F(t),(1)
where F(t-n), …, F(t-2), F(t-1) and F(t) are fuzzy sets,“F(t-n), …, F(t-2), F(t-1)” is called the current state of
the nth-order fuzzy logical relationship, and F(t) is called
the next state of the nth-order fuzzy logical relationship.

Abstract
In this paper, we present a new approach fortemperature prediction based on genetic simulated
annealing techniques and high-order fuzzy time series,
where the simulated annealing techniques are used todeal with the mutation operations of genetic algorithms.
The proposed method uses genetic simulated annealing
techniques to adjust the length of each interval in theuniverse of discourse to increase the forecasting
accuracy rate. It can get a higher forecasting accuracy
rate than the existing methods.

1

3 Basic Concepts of Simulated Annealing
Algorithms and Genetic Algorithms
In [16], Kirkpatrick et al. proposed the simulated
annealing algorithm. A simulated annealing algorithm
takes into account not only downhill move, but also
permits uphill moves with an assigned probability
depending on the “state temperature”. The basic conceptof a simulated annealing algorithm is derived byobserving the change of energy in a process in whichmaterials solidify from the liquid state to the solid state.
When the system’s temperature decreases gradually inthe annealing schedule, if the energy of the material in a
new state is lower than the energy of the material in thecurrent state, then the system will replace the current
state by the new state. Otherwise, whether the new state
can be accepted or not depends on the probability p
shown as follows:

Introduction

It is obvious that people are always interested inoncoming events, where an event may be affected bymany factors. If we can consider these many factors for
dealing with forecasting problems, then we can get ahigher forecasting accuracy rate. In recent years, somemethods have been presented for dealing with
forecasting problems [1]-[15].
In this paper, we present a new method fortemperature prediction based on genetic simulated
annealing techniques and high-order fuzzy time series,
where the simulated annealing techniques [16] are used
to deal with mutation operations of genetic algorithms.
The proposed method uses genetic simulated annealing
techniques to adjust the length of each interval in theuniverse of discourse for increasing the forecasting
accuracy rate. It can get a higher forecasting accuracy
rate than the existing methods.

2

− ∆E
kT

p = e
,
(2)
where p denotes the probability that the system accepts
the new state, T denotes the current system temperature,k denotes the Boltzmann’s constant, and ∆E denotes thedifference between the energy of the new state and theenergy of the current state.
The concept of genetic algorithms was proposedby Holland [18], where a population consists ofchromosomes and a chromosome consists of genes. The
number of chromosomes in a population is called the“population size”. The reproduction operation, crossover
operation and mutation operation of genetic algorithms
can refer to [18], [19] and [20].

Fuzzy Time Series

In [10]-[12], Song and Chissom presented theconcepts of fuzzy time series based on the fuzzy set
theory [17]. In the following, we briefly review thedefinitions of fuzzy time series from [3], [11] and [13].
Definition 2.1: Let Y(t) (t = …, 0, 1, 2, …) be theuniverse of discourse and be a subset of R. Assume thatfi(t) (i = 1, 2, …) is defined in the universe of discourse
Y(t), and assume that F(t) is a collection of fi(t) (i = 1,2, …), then F(t) is called a fuzzy time series of Y(t) (t= …, 0, 1, 2, …).
Definition 2.2: Let F(t) be a fuzzy time series. If F(t) iscaused by F(t-1), F(t-2), … , and F(t-n), then thenth-order fuzzy logical relationship is represented by©ISAROB 2005

4

A New Method for Temperature
Prediction Based on Genetic Simulated
Annealing Techniques and High-Order
Fuzzy Time Series

In this section, we present a new method fortemperature prediction based on genetic simulated
annealing techniques and high-order fuzzy time series.

444

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005ym-1], Vmin denotes the minimum value in the universe of
discourse V, Vmax denotes the maximum value in theuniverse of discourse V, and y1 ≥ y2 ≥ … ≥ ym-1.
Define each chromosome consisting of n-1 “X genes”
and m-1 “Y genes”, where the contents of eachchromosome are represented by an array <x1, x2,…, xn-1,
y1, y2,…, ym-1>, x1 ≤ x2 ≤ … ≤ xn-1, and y1 ≥ y2
≥ … ≥ ym-1. In this paper, we assume that a
population consists of 30 chromosomes and assume thatthe system randomly generates 30 chromosomes as theinitial population.
Step 2: Define linguistic terms of the main-factor
represented by fuzzy sets A1, A2, …, An, shown as
follows:

The method is essentially a modification of the method
presented in [7]. Table 1 shows the historical data of thedaily average temperature from June 1996 to September
1996 in Taipei, Taiwan [21]. Table 2 shows the historical
data of the daily cloud density from June 1996 to
September 1996 in Taipei, Taiwan [21]. In [2], the daily
average temperature is called the “Main-Factor” of the
fuzzy time series and the daily cloud density is called the“Second-Factor” of the fuzzy time series. First, based on
Table 1, Table 2 and [7], we define the universe of
discourse of the daily average temperature U = [23, 32]
and define the universe of discourse of the daily clouddensity V = [0, 100].
Table 1. Historical Data of the Daily Average Temperaturefrom June 1996 to September 1996 in Taipei, Taiwan(Unit: °C) [21]
June

July

AugustSeptember

26.1
27.6
29.0
30.5
30.0
29.5
29.7
29.4
28.8
29.4
29.3
28.5
28.7
27.5
29.5
28.8
29.0
30.3
30.2
30.9
30.8
28.7
27.8
27.4
27.7
27.1
28.4
27.8
29.0
30.2

29.9
28.4
29.2
29.4
29.9
29.6
30.1
29.3
28.1
28.9
28.4
29.6
27.8
29.1
27.7
28.1
28.7
29.9
30.8
31.6
31.4
31.3
31.3
31.3
28.9
28.0
28.6
28.0
29.3
27.9
26.9

27.1
28.9
28.9
29.3
28.8
28.7
29.0
28.2
27.0
28.3
28.9
28.1
29.9
27.6
26.8
27.6
27.9
29.0
29.2
29.8
29.6
29.3
28.0
28.3
28.6
28.7
29.0
27.7
26.2
26.0
27.7

27.5
26.8
26.4
27.5
26.6
28.2
29.2
29.0
30.3
29.9
29.9
30.5
30.2
30.3
29.5
28.3
28.6
28.1
28.4
28.3
26.4
25.7
25.0
27.0
25.8
26.4
25.6
24.2
23.3
23.5

…

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

An = 0/u1 + 0/u2 + 0/u3 + 0/u4 + 0/u5 + … + 0/un-2 + 0.5/un-1 + 1/un.

Define linguistic terms of the second-factor represented
by fuzzy sets B1, B2, …, Bm, shown as follows:
B1 = 1/v1 + 0.5/v2 + 0/v3 + … + 0/vm-2 + 0/vm-1 + 0/vm,
B2 = 0.5/v1 + 1/v2 + 0.5/v3 + … + 0/vm-2 + 0/vm-1 + 0/vm,
B3 = 0/v1 + 0.5/v2 + 1/v3 + … + 0/vm-2 + 0/vm-1 + 0/vm,
…

Month
Day

A1 = 1/u1 + 0.5/u2 + 0/u3 + 0/u4 + 0/u5 + … + 0/un-2 + 0/un-1 + 0/un,
A2 = 0.5/u1 + 1/u2 + 0.5/u3 + 0/u4 + 0/u5 + … + 0/un-2 + 0/un-1 +0/un,
A3 = 0/u1 + 0.5/u2 + 1/u3 + 0.5/u4 + 0/u5 + … + 0/un-2 + 0/un-1 +0/un,

Bm = 0/v1 + 0/v2 + 0/v3 + … + 0/vm-2 + 0.5/vm-1 + 1/vm.

Based on [22], fuzzify the historical data of themain-factor and the second-factor, respectively, based oneach chromosome of the population. For example, if thevalue of the main-factor of day i belongs to interval uj,
and fuzzy set Aj whose maximum membership value
occurs at interval uj, then the value of the main-factor of
day i is fuzzified into Aj, where 1 ≤ j ≤ n; if the value ofthe second-factor of day i belongs to interval vs, andfuzzy set Bs whose maximum membership value occurs
at vs, then the value of the second-factor of day i isfuzzified into Bs, where 1 ≤ s ≤ m.
Step 3: Construct two-factors kth-order fuzzy time series
relationship groups, where k ≥ 2.
Step 4: Forecast the values based on the principles
presented in [7].
Step 5: Perform the reproduction operations based on
the roulette wheel selection method [19]. In this paper,
the average forecasting error rate (AFER) is used as thefitness value of each chromosome in the genetic
algorithm for temperature prediction, where.

Table 2. Historical Data of the Daily Cloud Density from June
1996 to September 1996 in Taipei, Taiwan (Unit: %)
[21]
Month
Day
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

June

July

AugustSeptember

36
23
23
10
13
30
45
35
26
21
43
40
30
29
30
46
55
19
15
56
60
96
63
28
14
25
29
55
29
19

15
31
26
34
24
28
50
34
15
8
36
13
26
44
25
24
26
25
21
35
29
48
53
44
100
100
91
84
38
46
95

100
78
68
44
56
89
71
28
70
44
48
76
50
84
69
78
39
20
24
25
19
46
41
34
29
31
41
14
28
33
26

29
53
66
50
53
63
36
76
55
31
31
25
14
45
38
24
19
39
14
3
38
70
71
70
40
30
34
59
83
38

∑ in=1 (Forecasted Value of Day i - Actual Value of Day i)/Actual Value of Day i(3)
The smaller the fitness value (Note: The fitness value isthe average forecasting error rate) of a chromosome, thehigher the chance of the chromosome to be chosen forput into the mating pool. In this paper, the systemchooses chromosomes from the current population into
the mating pool according to their reciprocal fitness
values. For example, let fi denote the fitness value of theith chromosome and let ri be the reciprocal fi, i.e., ri = 1/fi.
The selected probability pi of the ith chromosome isdenoted by p i = ri ∑ r j . The larger the selected
AFER =The proposed method is now presented as follows:
Step 1: Partition the universe of discourse U = [Umin,
Umax] into n intervals u1, u2, …, un, where u1 = [Umin, x1],
u2 = [x1, x2], …, un = [xn-1, Umax], Umin denotes theminimum value in the universe of discourse U, Umax
denotes the maximum value in the universe of discourse
U, and x1 ≤ x2 ≤ … ≤ xn-1. Partition the universeof discourse V = [Vmin, Vmax] into m intervals v1, v2, …,
vm, where v1 = [y1, Vmax], v2 = [y2, y1], …, vm = [Vmin,©ISAROB 2005

n

j

445

× 100%.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

chromosome C’ is always allowed to replace the current
chromosome C. Otherwise, the system is allowed toaccept the newly generated chromosome C’ dependingon the probability e(-∆f/T). In this situation, if the random
number generated by the system is smaller than theprobability e(-∆f/T), then the newly generated chromosome
C’ replaces the current chromosome C. Repeatedly
perform the above process, until the current systemtemperature T is smaller than the frozen temperature
Tfrozen. In this case, the simulated annealing mutation
process finishes.

probability of a chromosome, the higher the chance ofthe chromosome to be chosen for put into the mating
pool. Repeatedly perform the reproduction operations,
until the number of chromosomes in the mating pool is
the same as the number of chromosomes in the current
population. Then, let the mating pool become the current
population.
Step 6: Randomly select two chromosomes from the
population to perform the crossover operations, until all
chromosomes in the population have been selected. If
the system randomly generates a real value between zeroand one that is smaller than or equal to the crossover rate,then the system randomly selects a crossover point of a
X gene and a crossover point of a Y gene from the two
selected chromosomes of the current population to
exchange genes after the crossover point. Otherwise, theselected chromosomes will not perform the crossover
operation. In this paper, the crossover rate is set to 0.8.
When performing the crossover operation, the system
randomly selects one crossover point of X genes and one
crossover point of Y genes, where the crossover point of
X genes is an integer between 1 and n-1, n is the numberof X genes, the crossover point of Y genes is an integer
between 1 and m-1, and m is the number of Y genes.
Furthermore, if the derived values of the chromosomes
are not sorted by the values of genes in an ascending
sequence, the system will sort the values of genes in thechromosomes in an ascending sequence.
Step 7: Use the simulated annealing mutation (SAM)
algorithm shown in Fig. 1 to perform the mutation
operations. For each chromosome in the population, thesystem generates a real value between zero and one todetermine whether the system performs the simulated
annealing mutation or not. If the real value generated by
the system for a chromosome is smaller than or equal tothe mutation rate (Note: In this paper, the mutation rate
is 0.05), then the system applies the simulated annealing
mutation algorithm to perform the mutation operation on
this chromosome. Otherwise, the system will not
perform the mutation operation on this chromosome. In
Fig. 1, Tinitial denotes the initial temperature; T denotes
the current system temperature; Tfrozen denotes the frozen
temperature; C and C’ denote the current chromosome C
and the newly generated chromosome C’, respectively;
we use the average forecasting rate (AFER) shown informula (3) as the fitness value of each chromosome in
the genetic algorithm for temperature prediction; ∆fdenotes the difference between the fitness value
fitness(C’) of the newly generated chromosome C’ andthe fitness value fitness(C) of the current chromosome C;
α denotes the annealing constant. First, the system
randomly chooses the ith X gene and the jth Y gene fromcurrent chromosome C, and then replaces the value xi ofthe ith X gene and the value yj of the jth Y gene ofcurrent chromosome C by the random numbers xi* andyj*, generated by system, respectively, to form the newgenerated chromosome C’. Then, the system calculates
the fitness value of chromosome C and chromosome C’,respectively, and then calculates their difference ∆f. If ∆fis smaller than or equal to zero, then the newly generated©ISAROB 2005

Procedure SAM (Tinitial, Tfrozen, α)begin
T ← Tinitial;
while (T > Tfrozen) do
Randomly choose the ith X gene and jth Y geneof chromosome C, where 1 ≤ i ≤ n, 1 ≤ j ≤ m, n
is the number of X genes of chromosome C,
and m is the number of Y gene fromchromosome C, and replace the value xi of the
ith X gene and the value yj of the jth Y gene ofchromosome C by the random numbers xi* andyj* generated by the system, respectively, toderive newly generated chromosome C’(i.e., C’← < x1, x2, …, xi*, …, xn, y1, y2, …, yj*, …,
ym >);
Let ∆f ← fitness(C’) – fitness(C);r ← random number between 0 and 1 generated
by the system;if ∆f ≤ 0 or r < e (-∆f / T) then C ← C’;
T ← T × α;
end;
return C
end.

Fig. 1. Simulated annealing mutation algorithm.
Step 8: Based on formula (3), calculate the average
forecasting error rate (AFER) of each chromosome in the
population. If the system has evolved a predefined
number of generations, then the chromosome that has thesmallest average forecasting error rate is the optimal
solution to be used to deal with the forecasting problem;
Stop. Otherwise, go to Step 5.
We have implemented the proposed method using
Visual Basic version 6.0 on a Pentium 4 PC. We use theaverage forecasting rate (AFER) shown in formula (3) as
the fitness value of each chromosome in the genetic
algorithm for temperature prediction. We use different
annealing constants in the simulated annealing mutation
algorithm to forecast the daily average temperature from
June 1996 to September 1996 in Taipei, Taiwan, where
we partition the universe of discourse U of themain-factor (i.e., the daily average temperature) into 9intervals and partition the universe of discourse V of thesecond-factor (i.e., the daily cloud density) into 7
intervals. In other words, each chromosome in a
population consists of 8 X genes and 6 Y genes. A
comparison of average forecasting error rates of the
proposed method with the existing methods is shown in
Table 3, where the average forecasting error rates arecalculated by executing the proposed method three times,
and the number of generations, the population size, thecrossover rate, the mutation rate, the initial temperature

446

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and the frozen temperature are 1000, 30, 0.8, 0.05, 100
and 0.0001, respectively. From Table 3, we can see thatthe proposed method gets smaller forecasting error rates
than the methods presented in [2] and [7]. That is, theproposed method gets higher forecasting accuracy rates
than the methods presented in [2] and [7] for dealing
with temperature prediction.

[3]

[4]

Table 3. A Comparison of the Average Forecasting Error
Rates of the Proposed Method with the Existing
Methods
Month
Chen’s
Method[2]

Lee etal’s
Method
[7]

June
July
AugustSeptember
Month
June
July
AugustSeptember
Annealing
Constant α

Month
June
July
August0.25

September

The
Proposed
MethodJune
July
August0.5

September

June
July
August0.9

September

5

w=2
2.88%
3.04%
2.75%
3.29%

w=3
3.16%
3.76%
2.77%
3.10%

First
Order
1.44%
1.33%
1.16%
1.28%

Second
Order
0.47%
0.46%
0.48%
0.98%

Third
Order
0.50%
0.50%
0.48%
1.02%

First
Order
0.79%
0.66%
0.64%
0.69%
0.84%
0.66%
0.69%
0.66%
0.79%
0.62%
0.66%
0.62%

Second
Order
0.44%
0.45%
0.43%
0.58%
0.50%
0.50%
0.40%
0.62%
0.46%
0.46%
0.40%
0.59%

Third
Order
0.42%
0.42%
0.47%
0.59%
0.45%
0.47%
0.38%
0.59%
0.42%
0.45%
0.40%
0.61%

Window Basis
w=5
W=6
w=7
3.33%
3.39%
3.53%
4.17%
4.35%
4.38%
3.40%
3.18%
3.15%
3.22%
3.39%
3.38%
Order
FourthFifth
Sixth Seventh
Order
Order
Order
Order
0.49% 0.49% 0.50% 0.49%
1.50% 0.50% 0.49% 0.50%
0.49% 0.50% 0.49% 0.50%
1.12% 1.02% 0.74% 0.86%
Order
FourthFifth
Sixth Seventh
Order
Order
Order
Order
0.42% 0.42% 0.44% 0.40%
0.41% 0.41% 0.40% 0.41%
0.40% 0.41% 0.38% 0.40%
0.57% 0.56% 0.57% 0.58%
0.42% 0.38% 0.43% 0.39%
0.44% 0.40% 0.38% 0.44%
0.37% 0.37% 0.39% 0.42%
0.59% 0.56% 0.54% 0.56%
0.44% 0.42% 0.41% 0.46%
0.44% 0.44% 0.41% 0.40%
0.40% 0.36% 0.41% 0.39%
0.57% 0.54% 0.59% 0.57%

w=4
3.24%
4.08%
3.30%
3.19%

[5]
[6]

w=8
3.67%
4.56%
3.19%
3.29%

[7]

Eighth
Order
0.46%
0.50%
0.49%
0.50%

[8]

Eighth
Order
0.40%
0.40%
0.45%
0.47%
0.46%
0.42%
0.45%
0.53%
0.39%
0.40%
0.44%
0.50%

[9]
[10]
[11]

Conclusions[12]

In this paper, we have presented a new method fortemperature prediction based on genetic simulated
annealing techniques and high-order fuzzy time series,
where the simulated annealing techniques are used todeal with the mutation operations of the genetic
algorithms. The proposed method uses genetic simulated
annealing techniques to adjust the length of each intervalin the universe of discourse for temperature prediction toincrease the forecasting accuracy rate. From Table 3, we
can see that the proposed method gets smaller average
forecasting error rates than the methods presented in [2]and [7]. That is, the proposed method gets higher
forecasting accuracy rates than the methods presented in[2] and [7].

[13]

[14]

[15]
[16]
[17]

Acknowledgements[18]This work was supported in part by the National
Science Council, Republic of China, under Grant NSC
93-2213-E-011-018.

[19]
[20]

References
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[2]

[21]

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pp. 311-319, 1996.
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prediction using fuzzy time series,” IEEE
Transactions
on
Systems,Man,
and©ISAROB 2005

[22]

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Design of a Fuzzy Expert System for Electric Vehicle Speed Control
Masanori SUGISAKA & MBAÏTIGA Zacharie
Oita University, Department of Electrical & Electronic
Engineering ,msugi@cc.oita-u.ac.jp ,zack145@oita-cc.cc.oita-u.ac.jp

Abstract
Fuzzy logic systems have demonstrated throughnumerous application areas, to be an effective procedurefor hardware control problems. The basic concept offuzzy controller is to formulate the control protocol of ahuman operator in a way that is tractable formicrocomputer-based controller. The present paper
describes the fuzzy expert system applied to control the
speed of the autonomous electric vehicle (EV). The
parameters of the speed to be followed by the vehicle
during the driving operation at each moment are definedby a set of fuzzy rules and have two inputs and one
output. The following two points are the main points ofthis control. The accelerator pedal position that governs
the vehicle speed and the speed categories to be used.
The experimental result obtained proves the merit of thecontrol method as the controller accomplishes asatisfactory speed control of three categories.

was based on the current model. This control was very successfully inthe open loop control of fuzzy control system. But unfortunately thestarting procedure from standstill was very difficult under the
proposed method, due to the use of both voltage and current for
estimation of the rotor position by sensor drive. Therefore no
information was available before starting. The other reason is thatsome incremental control inputs to the motor have been notdetermined from the fuzzy logic subset. Normally, to well control the
speed of any system with a complex hardware such as for example,
robot, EV as in our case etc., the choice of the parameters for decision
making is the most important part. In this paper, we develop a fuzzy
expert control system algorithm for electric vehicle speed, whichallows the EV to adapt to three speed levels, based on the knowledge
acquired from fuzzy rules base. This method provides, in addition to
the well-known efficiency of the fuzzy system in control a continuous
running performance of the vehicle.

2. Drive system of the electric vehicle
Key Words: Electric vehicle, fuzzy expert system
24 V Lead
Acid
Battery

1. Introduction
As an alternative to conventional control technique, fuzzycontrol is gaining increased interests, both in the academic world aswell as in the industrial field. For those systems whose accurate
mathematical models are not available or are difficult to formulate,fuzzy control can often provide a good solution by incorporating
linguistic information from human expert and which is considered
as an efficiency tool for hardware control. Due to its efficiency,
many researchers have used it in the domain of robot speed control
and vehicles speed control in the open literature [1, 2, 3]. In the
proposed method of [4], the fuzzy approximator and sliding mode
control scheme are considered. The fuzzy logic theory is applied todesign the sliding mode controller then a simple adaptive law isused to approximate the unknown function f that defines the motor
parameters via fuzzy logic system. However, its only drawback isthat the controller does not provide a continuous performance ofthe motor, due to the considerable number of overshoot. In [5] thepaper presents the speed and position control of a permanent
magnet (PM) motor, with a sinusoidal flux distribution using fuzzy
Logic. Two approaches were proposed and compared with eachother; one was based on the voltage model of the motor and other©ISAROB 2005

Pulse
generator

DrivecommanPWM
3-phInverter

f
V/f

3-ph
Stepping
Motor

Gear
box

Controller

Accelerator
pedal signal
Brake signal

Fig.1 Block diagram of the drive system
The stepper motor use in the electric vehicle is a variable speed;
multi-task three phase permanent magnet with 16 stator teeth per
stack.. Its maximum output is 5.2 KW at 72V, 50 Hz. The rated slip
is only 2 percent to minimize the motor copper loss and to behave
stiffer characteristic. The rated iron loss is only 15 W; the reductionof iron is an important factor because of the high frequency

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

harmonics induction by the PWM. The motor frame is made ofaluminum alloy to minimize the weight and the frame size is 130.
The drive system of the electric vehicle is shown in fig.1.ThePWM inverter converts the battery pack dc voltage into a variable
voltage, variable frequency ac in-sinusoidal voltage for supply of
the three-phase stepping motor. The motor then drives the wheels
through a variable ratio gearbox assembly. The power path isreversed in the case of regeneration i.e., the motor becomes an
induction generator and the inverter rectifies the ac power into dc
and recharges the battery. In the battery system, a range prediction
device (RPD) has been connected to the battery. The device
provides a real-time estimation of the remaining battery capability
as well as residual range of the EV by means of compensatedampere-hour measurement method. The device takes care of therecharging case; it renews the value of remaining ampere-hours byadding the value of recharged ampere-hours, which are obtained byintegrating the recharging current. The device also was proven tobe essential for providing reliable information to the EV about thestate of the charge of battery and the EV residual. The drivecommand, gear box, etc., are slaved under the controller. The
controller gathers the information of voltage, current, brake signal,
accelerator pedal position, motor speed and other signals; then thecontroller gives a proper control to the motor according to thedesigned control strategy.

inference rules which have the general form:
If x is A and B is y then z is C
Where, x is the input variable, and y is the output variable. The
values A, B, C are expressed linguistically rather than numericalforms. Example of linguistics values is very low, low, high, veryhigh. Using the concept of fuzzy set proposed by L.A Zadeh in
1965, these linguistics values can be translated into numericalvalues to perform calculations.

4. Control strategy using fuzzy expert system
In this section, our fuzzy expert system is described in detail. We
first present the construction of its knowledge base, before
describing the three main steps. The membership functions of the
input variables, the membership functions of the output variables,
and the creation of the rule base have been done

5. Construction of Knowledge Base
As mentioned before, the knowledge base rule in a fuzzy expert
system consists of a data base and a rule base. The data baseincludes the membership functions of inputs and output, and therule base contains the inference rules. Our fuzzy control system has
two inputs and one output. The input variables are the speed level
and the accelerator pedal position (current pedal position- desired
pedal position ) and throughout the rest of this paper we will note it
AccPP. The accelerator pedal as for any road vehicle governs the
motor speed and its position reflect the vehicle speed. The output
variable is a vehicle speed that depends on the accelerator pedal
position and we have used some error tolerance modulator to adjust
the error speed. The input variable here is used to describe the levelof the desired AccPP rate and to evaluate the perceived vehicle
speed result, while the output variable is used to determine therequired vehicle action. Next we need to create one set ofmembership functions for each input and output variable. The
membership functions defined on the input variables are applied totheir actual values, to determine the degree of truth for each rulepremise. If a rule’s premise has a nonzero degree of truth, then therule is said to fire. Then we compute the truth value for the premiseof each rule and applied to the conclusion part of each rule. This
result is one fuzzy subset to be assigned to the output variable of
the rule.

3. Fuzzy Expert System Control
A fuzzy expert system as you know or you may read somewhereis a collection of membership functions and rules that uses toreason about data. Unlike conventional control methods, whose
strongly relies on the accuracy of the analytic control model, which
are mainly symbolic reasoning engines, fuzzy expert systems areoriented toward numerical processing. Like other control
mechanisms, fuzzy control is also a feedback control system aspresented in Fig. 2

e

W

C

U

S

y

6. Membership functions of input variables

Encoder

To define the membership function of the AccPP, we divided therange of the AccPP into five linguistic sets, Negative-Big (NB),
Negative Small (NS), Zero (ZO), Positive Small (PS), and Positive
Big (PB), and the membership functions are shown in figure 3. In
the figure, the horizontal axis indicates the desired accelerator
pedal position to be used, which is the difference between the thecuurent and expected position. The vertical axis denotes themembership value of a given AccPP. This value is used to indicate
the degree to which a difference belongs to a linguistic set. For
example, from the figure we can see that the degree of AccPP of
0.8 to PS is 0.6, and the degree of AccPP of 0.8 to ZO is 0.4. In thisFig.2 A feedback control system
The object to be controlled is called the system, denoted as S,
which is the vehicle speed in our case. The controller denoted as C,
is to generate a desired response of the output y, i.e., keeping theoutput y close to the reference point W (keeping e small). The
output U of the controller C, is the control action in our application.
In essence, the fuzzy control relies on a set of IF…..THEN©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

example a precise AccPP value is mapped to two linguistic sets.
The objective of this research is to control the electric vehicle using
on-board computer of which its operation will be done via mouse,
therefore the choice of the speed to be used during each task is very
important. So the speed level is divided into threecategories, ”High(H)”, ”Medium (M)”, and ”Low (L)”, and the membershipfunctions are shown in figure 4. The value of 500 is the value ofacceleration and deceleration time when accelerate or decelerate
the motor and is set in millisecond.

To determine the singleton values for the linguistic sets, we need toperform the profiling runs. The singleton is an impulse function,and is defined by simply assigning a single numeric value to eachsub-domain of the output. For example the “crisp “output of ourcontrol must be in the range between 0 and 2500 rpm and that allow
us to define the speed as follows: Low speed = 300 rpm, medium
speed = 900 rpm and high speed =1500 rpm. These values of theoutput singleton sets are used to get the exact desired speed in the
linguistic sets for the three speed categories (Fig. 7, 8,9). If thevalues of the output singleton are not used it will be very difficult
for the operator to know the speed to be selected for the vehicle
navigation. Table 1 shows the parameters of the control. Column 1
shows that the speed level is categorized into three levels (H, M, L)..
To obtain the singleton values for the output linguistic sets HNS,
MNS, and LNS,we averaged the error tolerance modulation values
for each of the three speed level categories (High, Medium, andLow). Other singleton values can be obtained in the same way.

Membership value
0.１ ZO

NS

NB

PS

PB

0.6
0.4

-50 0 50-200 -100 -80

80
0.8

100

200

AccPP

8. Creation of rule baseIn this step, we construct the fuzzy reasoning rules that governs
the relations between the input and output variables. As our present
system has two inputs and one output, the form of each rule is: “ IFspeed level is A and the accelerator pedal position is B, then thevehicle speed is C”, where A is chosen from “L”, “M”, “H”, B ischosen from “NB”, “NS”, “ZO”, “PS”, “PB” and C is a singleton
output. A sample rule can be written as follows: If the speed levelis L, and the accelerator pedal position is PB, then the vehicle
speed is LPB. The following is the overall of the control base.

Fig.3 Membership function for input: Accelerator pedal position
Membership value
0.1

Medium

Low

High

0.87
500
0.29
0

800

300

Vehicle Speed (rpm)
2500

900

Table 1 Fuzzy reasoning rule base

5.3

NB

NS

ZO

PS

PB

H

HNB

HNS

HZO

HPS

HPB

7. Membership functions of output variables

M

MNB

MNS

MZO

MPS

MPB

To define the membership function of the output variable, thevalue of the vehicle speed is categorized into fifteen linguistic sets,
which are: LNB, LNS, LZO, LPS, LPB, MNB, MNS, MZO, MPS,
MPB, HNB, HNS, HZO, HPS and HPB. We have used fifteen output linguistic sets because there are three levels of vehicle speed, L,
M, H, and five levels of accelerator pedal position as follows, NB,
NS, ZO, PS, PB. That results in fifteen different combination andwe have used a singleton membership function to represent each of
the linguistic set, as it is easy to perform defuzzification. Figure 5
shows five output singleton membership functions.

L

LNB

LNS

LZO

LPS

LPB

Fig.4 Membership function for input: Speed level

9. Experimental Results
The proposed algorithm was carried out using a three phase
stepping motor MFA05KEV connected to an oriental rotary
encoder E6C2-CWZ62 for recording data during the test operation
whose specifications is given in Table 3. The output of the encoder
is directly input into IN04, IN05 and IN06 of the CPU unit for
using these three points as built-in high-speed counter.

Membership value
LNB

LNS LZOLPS

LPB

Table 2 Motor parameter specifications-200

Rate output
Max. Output
Max. Revolution

200
0
80
Fig.5 output singleton membership functions©ISAROB 2005

-80

450

0.59 kw (1H)
5.2 kw (3min)
6000 rpm

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Power supply
Rate torque72 DC V
98 kgfTorque constant

0.532 kg fcm

approaching to its target position, so the fuzzy reasoning again
create the rule base that start to decrease the speed to a low speed
before falling to zero. During the acceleration, the equilibrium
position is maintained between 12.5 and 15.5 steps ahead of rotor
position, and during the deceleration the equilibrium position is
also maintained between 12.5 and 15.5 steps behind of rotor
position, and the angle between the stator and rotor flux was kept
close to 90° . The test practice has been done on the road in front of
the department of electrical and electronic engineering till theventure business laboratory (VBL) that is built at the end of thisroad. The distance between the two buildings is approximately 150m.

The single-phase response speed is 5 KHz, and the two-phase
response speed is 2.5 KHz. The counter value is within a range
between 65,535 in incremental mode, which uses only phase A and
32,767 in decrement mode. The electrical ratings of the encoder are
as follows: current consumption 80ms, maximum response
frequency 100 KHz, insulation increases 100M Ω min and output
phases A, B and Z (reversible). We tested the effectiveness of our
fuzzy expert control system under two different scenarios. Firstly
the brake signal that determines the desire braking torque is sent tothe controller allowing the operator to manipulate the gear i.e.,changing it from Neutral position to Drive or Reverse. The brake
control part is not reported in this paper but it will be reported in
the coming month. We have mentioned here in order to well
explain how the algorithm has been tested. Secondly theaccelerator pedal signal that determines the required vehicle speed
is sent to the motor driver through the controller to drive thevehicle. The controller is required to control the motor speed very
accurately to achieve and maintain speed from 0 to 2500 rpm.

Speed level: Low
350

Motor speed (rpm)

300
250
200
150
100
50

90

0
1

80

2

3

4

70
60
50
40
30

Motion (m)/Force (N)Desired motion

5

6

7

8

9

10

T ime(sec)Fig.7 Low speed

Force
Speed level : Medium
1000
Motor speed (rpm)

20
10
0
2.5

3

Time (second)Fig.6 Motion and force trajectories
But during the test of our algorithm, we have limited the highspeed to 1500 rpm in order to well control the vehicle. In the Fig.6
that shows the motion trajectory and force trajectory, the desire
motion obtained consists of three segments. From the initial
position of 0 m to approximately 75 m the vehicle moves
progressively to the goal position in 3 minutes. The first segmentfrom 0 m to about 67 m is the approaching phase having the fastest
motion. In this phase the fuzzy reasoning created a rule base that
selected the AccPP for medium speed in order to avoid producing alarge impact force, which might results in loss of control. During
this phase, the desired interaction force is increased from 0 to avalue of 75 N at 1.65 second and decreased from 75 N to a value of
33 N at 2.3 s. The decrease of the force indicates that the vehicle is©ISAROB 2005

400
200
1

0
11

2

9

1.75

7

1.5

5

1

600

3

0

800

T ime (sec)Fig.8 Medium speed
Fig.7, Fig.8 and Fig.9 show the three speed levels variation under
the fuzzy expert controller and that agree with the membershipfunction for input (speed level) shown in Fig.4 during the software
design. Fig.7 and Fig.8 are the low and medium speed of which the
motor increase gradually then stabilize at the corresponding limited
speed level at 7 and 9 second respectively. At this stage, the vehicle
keep moving smoothly without changing the speed rate till we

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

released the acceleration pedal in order to bring the vehicle to acomplete stop. When we released the acceleration pedal, the fuzzycontroller through the inference rules sends a signal to the pedal.
This signal is passed particularly through a sensor, which release
the force applied previously to the accelerator pedal. Hence pedal
release.

References
1)

C.C. Lee, “fuzzy logic in control system: fuzzy logic
controller-part I, II.” IEEE Transaction on system, Man, andcybernics. SMC-20 No.2, pp. 404-435, 1990

2)

Xinyue Li and Han-Wein Shen, “ Adaptive volume rendering
using fuzzy logic,” The Ohio state university Columbus,
Department of science and information science magazine, vol.

Speed level: High45, pp. 56-59, 2004

Motor speed (rpm)

1600

3)

Ch, S. Kim, W. H. Seo,, S. H. Han, S. Ch. Fan, S. H.

1400

Lee,”Fuzzy logic control of a robot manipulator based on

1200

visual servoing,” Proceedings of the ISIM 2000, Oct, 4-7,
kyongnam, Korea, pp 390-395.

1000

4)

800
600

5)

400
200
0
1 2 3 4 5 6 7 8 9 10 11 12 13Time (sec)Fig.9 High speed level
In Fig.9, when the acceleration pedal signal is sent to the motor
driver by the controller, again the motor starts and very quickly
reaches 1000 rpm and gains its maximum peak speed ofapproximately 1500 rpm at around 13 Seconds, which is followedby the increase of the interaction force. The advantage of ourtechnique is that, during the running test, if the hardware has sometechnical problems the controller notified the operator about the
problem and stop the motor 2 second after the notification.

7. Conclusion
We have presented the EV motor speed control using fuzzy expert
controller. Our algorithm allows the operator to control the vehicle
without the need to have extensive knowledge about the data. It
provides a continuous running performance of the vehicle, and can
automatically select the desired speed level, when the speed isabout to reach a certain level that can produce a large impact force,and that might results in loss of control. Since we have started totest the running perfornace of the vehice under the fuzzy expert, so
far there was no overshoot and noise. In this paper the constructionof the knowledge base of the fuzzy expert controlller and thefollowing three main steps, fuzzyfication, fuzzy reasoning anddefuzzification have been described.

©ISAROB 2005

Byung. Yoo, Sacheul, Jeoung, Woochl. Han, “Adaptive
slinding mode control using fuzzy logic system”, Proceedings452

of the ISIM- 2000, Oct, 4-7, kyongnam, Korea, pp. 26-29
C. C Chan, “ speed and position control strategy for electricvehicles,” IEEE Transaction on industrial electronics vol.
IE-34, No.4, pp.447-456, 1987

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A Harness Line Color Recognition Method
Based on Fuzzy Similarity Measure
Youn Tae Kim, Hyeon Bae, Young In Kim, 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: dream0561@pusan.ac.kr, baehyeon@pusan.ac.kr, kyi98@pusan.ac.kr, sskim@pusan.ac.kr
Abstract

problems. Several intelligent approaches for color
recognition are:y Fuzzy colors
y Neural networks
y Fuzzy similarity measure
But, intelligence methods generally require too manyarithmetic operations to handle complicated algorithm.
In this paper, we propose a simple algorithm fornatural color recognition with low computational burden.
First, the HLS color coordination system that converted
from RGB information is introduced. The HLS color
coordinate system is similar to the human’s color
recognition system. Second, fuzzy memberships based
upon the RGB and HLS information is represented.
Third, fuzzy similarity measure of each color arecomputed and compared for the color recognition.
Finally, the proposed algorithm is applied to the harness
line color recognition system and the performance isdiscussed.

Conventional methods for color separation in computerbased machine vision has weak performance because of
the environmental dependency like light source, camera
sensitivity, etc. In this paper, we propose an improved
color separation method using RGB, HLS, color
coordination space and fuzzy similarity measure. RGB
that consists of red, green, blue is light's three primarycolors. HLS that includes hue, light, saturation ishuman's color recognition elements. A fuzzy similarity
measure is employed for evaluating the similarity among
fuzzy colors with six features in RGB and HLS. Color
recognition system for the harness line is designed andimplemented as a testbed to evaluate the physicalperformance. The proposed color separation algorithm istested with different kind of harness lines.

Keywords: color recognition, fuzzy entropy, fuzzymeasure1. Introduction
By the development of high efficiency computer
recently, image processing algorithm and improvementof image devices helped test systems to improve theperformance. What is called, it is given a name fieldthat is artificial vision. Artificial vision is not concept
of conventional contact-based sensor but concept of
information-based sensor that comes from videotex inshort or long distance using camera. The status oftarget is recognized through suitable digital image
processing algorithm or numerical solution.
Many parts of several functions in the artificial
vision are implemented and used for industrial
equipments. But, the part of color recognition has still
many problems to be solved. One of the underlying
problems is the subject measurements that come fromseveral values according to strength, direction, andcolor temperature of light and quality of the material of
product. We normally try to recognize and represent an
object as one color but the object consists of manycolor elements which is called natural color.
If a red color electric line in harness line is captured
from camera, we recognize the line as a red color but
the information of the pixels includes variable colors
such as brown, black, etc. Because of these problems,
the conventional color recognition methods in natural
color image has many restricts. Many intelligent
methods have been developed to overcome these©ISAROB 2005

2. RGB and HLS Color Coordination
2.1 RGBRGB coordinate of reflex gives us information about
three dimensional spaces that consist of red (R), green
color (G) and blue color (B). Three dimensional spaces
are represented as a color cube. Here, the origin of the
cube displays and means pure black. Color density is
increased as the value in the each axis of coordinates isreceded from origin. Image device that follows NTSC
rules basically shows the RGB information. Image
processing based on RGB information has an advantage
in time aspect because the pretreatment process is notnecessary. For these reasons, RGB color coordinate
system is more popular than other color coordinate
systems in image processing until present.

Fig. 1 RGB color cube.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005⎡
⎢ 0⎢
− I min⎢ I* L'
S = ⎢ max
I max + I min⎢
I max − I min⎢* L'⎢⎣ 2 − ( I max + I min )2.2 HLS
The coordination of human's color recognition foran object is based upon the HLS color coordination(hue, light and saturation) [2]. Alternative method toexpress color information consists of hue, saturationand light. The hue marks color frequency that isrepresented as chromaticity graph as shown in Fig. 2.
The saturation mixing with white and density
expresses luminance of object or realized brightness.
Structure of this coordinate is cone style. Distance isdensity from cone top and position of archetype. Cross
section of cone is hue and distance from origin tooutside is saturation. The hue is put according to orderof spectrum (on right from observer red and left blue,
green). HLS is color space that marks brightness to
specified frequency fastest.
Proved that more correct recognition is possible than
RGB in color recognition by H. Palus, D. Bereska. For
these reason, HLS information through HLS
conversion of RGB is utilized by means of color
awareness along with RGB information.

if I max = I min
if L < 0.5(4)otherwise

3. Fuzzy Similarity Measure
In this section, we introduce some preliminary resultsof fuzzy measures. Measure of fuzziness is an interesting
topic in the fields of pattern recognition or decision
theory. Measure of crisp set can be determined byclassical mathematical study, whereas the concepts offuzzy measures and fuzzy integrals had been proposedby Sugeno[8]. Recently, Liu suggested three axiomatic
definitions of fuzzy entropy, distance measure andsimilarity measure as Definitions [9]. Among thesedefinitions, we used fuzzy similarity’s concept for color
recognition.
Definition 1 [9] A real function s : F 2 → R + is called asimilarity measure on F (X ) if d satisfied the following
properties:(S1) s ( A, B) = s ( B, A), ∀A, B ∈ F ( X )(S2) s ( A, A c ) = 0 ∀A ∈ F ( X )(S3) s ( D, D) = max A, B∈F s ( A, B), ∀D ∈ P ( X ),∀A, B ∈ F ( X )(S4) ∀A, B, C ∈ F ( X ), if A ⊂ B ⊂ C , then s ( A, B)≤ s ( A, C ) and s ( B, C ) < s ( A, C ) .

Fig 2 HLS Graph.
2.3 HLS Conversion Using RGB Information
HLS can be easily converted from RGB informationby the following arithmetic expressions.
R
G
B
, g=
, B=
255
255
255
I max = max(r , g , b), I min = min(r , g , b)r=⎡
⎛ g −b ⎞⎟⎟if r = I max⎢ 60 * ⎜⎜⎝ I max − I min ⎠⎢⎢⎛g −b ⎞⎟⎟
60 * ⎜⎜ 2 +if g = I max
H =⎢Imax − I min ⎠⎝⎢⎢⎛g −b ⎞⎟
if b = I max⎢ 60 * ⎜⎜ 4 +I max − I min ⎟⎠⎝⎢if I max = I min⎢⎣ 0

Above definitions are the axiomatic, Liu also pointed
out that there is an one-to-one relation between all
distance measure and all similarity measures, d + s = 1 .
We used a mathematical expression in equation (5) asa similarity measure that satisfies definition 1.

(1)n∑ min(µ (i), µ (i))

s ( M 1 , M 2 ) = in=1

I max − I min
2L− 0 .5
L' = 1 −0 .5©ISAROB 2005

if H < 0

M2∑ max(µ (i), µ (i))
i =1

(2)

M1

(5)

M2

4. Experimental Results
4.1 Color Recognition Using Fuzzy Similarity
MeasureWe verify, in this section, the proposed color
recognition algorithm and demonstrate experiment resultthat uses fuzzy similarity measure. First we make color
fuzzy membership functions for RGB and HLS. 100
samples are acquired from color information for eachcolor in image to make color distribution. We make
distribution of acquired RGB and HLS information thatconverted from RGB information.
The prepared data use to generate membership
information through the following calculation:

H = H + 360 if H < 0

L=

M1

(3)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

µ M (i ) =nN

i = 0,1, L 255 , if R, G, B, L, S (6)Where µ M are membership values, n is number ofmember data and N is the number of whole data. But a
hue is different from the other data, discontinuous data.
Because of this reason we don’t make hue membership
information.
Pink 1

Pink 2

Pixel

R:XXX
G:XXX
B:XXX

Fig. 4.3 Fuzzification of color distribution (red line).
Color of Harness line that used by this research has
pink, blue, white, black, violet, blue, green, yellow,
orange, red, brown and gray of 12 colors.
After data of a set that consist of 12 colors makes
membership by prior method, compare template fuzzy
membership to unknown fuzzy membership in eachcolor.
In table 1, you can see distribution special quality ofeach color Hue values. Various Hue values appear in
achromatic color, but chromatic color has only one hue
value. It can gain advantage that may not do comparison
calculation between unnecessary each membership
except achromatic color using this nature.

Pink nBlu

Pink

PurpleFig 3 Gathering color information.
In this research, our sample of color informationacquired from harness image of twelve different
colored lines. The following among line of other colors,
red line’s color information will R, G, B, L and S
distributions of each line.

Table 1 Color distribution by hue value
Hue value
0 value (Red like)

ColorsBROUN, RED, ORANGE, PINK

120 value (Green like) GREEN
240 value (Blue like)BLUE, SKY-BLUE

Uniformly distributed

BLACK, YELLOW, PURPLE, GRAY, WHITE

After consider this Hue condition, we can recognize
the color using fuzzy similarity measure method and wehave displayed red and gray color recognition result ofalgorithm.
Table 2 Results of color recognition (red line)
Input data
Comparison (Red like)Red line, Hue values are all 0.
R.

G.

B.

S.

L.

Fig. 4.2 Distribution graph of R, G, B, L, S (red line).
Figure 4.2 is non-convex form. It doesn't treat to fuzzy set.
Also, connect out-line of this distribution chart apply concept
to compensate information that was lost by digitizing andthen changed in form of fuzzy membership.

Brown

0.2084 0Red

0.2344 0.4468 0.4624 0.5773 0.3813

Orange0.0044 0.0152 0.2181 0.5549 0.0223

Pink

0.0630 0
Max value
Result©ISAROB 2005

455

0.0219 0.0329 0.0635

000.0016

0.2344 0.4468 0.4624 0.5773 0.3813
Red

Red

Red

Red

Red

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 3 Results of color recognition (blue line)
Input data
Comparison (Blue like)Blue line, Hue values are all 240.
R.

G.

B.

S.

L.

Blue

0.4461 0.2980 0.6661 0.5961 0.4786

Sky Blue

0.4439 0.1788 0.0975 0.3427 0.1972

Max value
Result5. Conclusion
Color recognition which use color information of a
pixel or mean value of neighbor pixels that is methodthat is accomplishing main current in existent research
had limit. In this paper, we did so that can analyze color
distribution of objects that describe person's index
process to a these alternative method and color
recognition is more exactly.

0.4461 0.2980 0.6661 0.5961 0.4786
Blue

Blue

Blue

Blue

Table 5 Results of harness’s line order recognition

Blue

Tested harness’s line order:
BK BR RD GY SB GR BL YL OR PK PP WH

Table 4 Results of color recognition (gray line)
Input data
Comparison (Red like)

Gray line, Hue values are uniformly distribute
R.

G.

B.

S.

L.

No.

Test Result (Order of Harness’s line color)1

BK BR RD GY SB GR BL YL OR PK PP WH

2

BK BR RD GY SB GR BL YL OR PK PP WH

Black

0.0082 0.0055 0.0081 0.0092 0.1188

3

BK BR RD GY SB GR BL YL OR PK PP WH

Yellow0.0361 0.0105

04

BK BR RD GY SB GR BL YL OR PK PP WH

Gray

0.2609 0.2921 0.3252 0.5952 0.3063

5

BK BR RD GY SB GR BL YL OR PK PP WH

Purple0.1338 0.0995 0.6525 0.3144 0.0855

6

BK BR RD GY SB GR BL YL OR PK PP WH

White

0.0038 0.0056 0.2633 0.0460 0.3243

7

BK BR RD GY SB GR BL YL OR PK PP WH

0.2609 0.2921 0.6525 0.5952 0.3243

8

BK BR RD GY SB GR BL YL OR PK PP WH

Gray

9

BK BR RD GY SB GR BL YL OR PK PP WH

10

BK BR RD GY SB GR BL YL OR PK PP WH

Max value
ResultGray

0 0.3073

PurpleGray

Gray

4.2 Color Recognition Using Fuzzy Similarity
We manufacture the harness line color recognition
system on the basis of algorithm that introduced insession 4.1 and verified performance. Fig 4.2 shows
this system and tested harness. System of Fig 4.2 canrecognize harness's line color and distinguishes
whether order of line is right.

References[1] Rafael C. Gonzalez and Richard E. Woods, “Digital
Image Processing,” Prentice Hall, 2002.
[2] Been-Chian Chien and Ming-Cheng Cheng, “A
Color Image Segmentation Approach Based on
Fuzzy Similarity Measure,” IEEE, 2002.
[3] Cox, E. The Fuzzy Systems Handbook, Academic
Press Professional, pp.21-105, 1994.
[4] Klir, G. J. and Folger, T. A., Fuzzy Sets, Uncertainty,
and Information, Pretice-Hall, Englewood Cliffs,
1988.
[5] Takagi, H. and Hayashi, I., “NN-Driven Fuzzy
Reasoning,” International Journal of Approximate
Reasoning, Vol. 5, pp.191-212, 1991.
[6] S. Wesolkowski, R. D. Dony, and M. E. Jernigan,“Global color image segmentation strategies:
Euclidean distance vs. vector angle,” Nueral
Networks for Signal Processing 4. Proceedings ofthe 1999 IEEE Signal Processing Society Workshop,
pp 419-428, 1999.
[7] D. Yining, B. S Manjunath, and H. Shin, “Color
image segmentation,” Computer Vision and Pattern
Recognition, IEEE Computer Society Conference,
Volume 2, 1999.
[8] Z. Wang and G. T. Klir, Fuzzy measure theory, NewYork : Plenum Press, 1992.
[9] B. Kosko, Neural Networks and Fuzzy Systems,
Prentice-Hall, Englewood Cliffs, NJ, 1992.

Fig 4.2 Test system and picture of harness.
We confirmed the recognition rate of actuality
Harness's line color using this system. Harness thatused in experiment has 12 colors (order of black,brown, red, ash color, blue, green color, blue color,yellow, orange, pink, violet and white). Result of anexperiment appeared to Table 5. An experiment has
10th trial and show the 100% recognition rate.

©ISAROB 2005

456

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

An Electric Power Steering Control by Fuzzy logic in HILS system
Mun Ki Lee, Sung Ki Ha
Department of Interdisciplinary
Program in Mechatronics
Pusan National University
Busan, 609-735, KOREA

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

Camuffo, et al [6] tuned an EPS to have thesteering feel of an HPS. Generally EPS systems arecontrolled by comparing the measured steering
torque with the reference steering torque. Using asteer-by-wire EPS, Tong Jin Park, et al [7] utilized
the steering wheel motor to alter the steering feelaccording to vehicle speed and controlled the front
wheel motor by PID Control to minimize the error
between steering angle and wheel angle. Withsteering wheel angle and torque sensors attached toa steering column, Anthony W. Burton [1]calculated assistance torque by summing the highgain related to steering torque and the low gain
related to steering position. To improve returnability,
M. Kurishige, et al [8] developed a control strategy
based on an estimation of alignment torquegenerated by tires and road surfaces without sensors.
Since steering torque assistance and returnability are
not active at the same time, Kim and Song [9]separated the two control algorithms where thereference steering torque was determined by thetorque map based on vehicle speed and steering
wheel position.
In this paper, the 2 d.o.f. bicycle model will be
used to calculate the tire road interaction torque.
Although the steering of a vehicle affects its rolling
motion, a description of the rolling system isbeyond the scope of this dissertation since an EPS
does not control the active driving angle but theassisting torque. In previous EPS systems [5],assisting torque for the measured driving torque wasmaking a chart as a boost curve like the HPS of
Adams's research [2]. In the research of Kim andSong [9], the reference steering torque depended on
vehicle speed and steering wheel angle by encoder.
The evaluated assisting torque map by fuzzy logic
will be simulated in HILS system [10]. The fuzzy
logic is useful for nonlinear system and decision
making for controllers.
The organization of the paper is as follows:
Chapter 2 describes a vehicle system and ahydraulic servo system and verifies the mathematicalmodels for the vehicle system. Chapter 3 presents
HILS system for EPS. In chapter 4, the assisting
torque map is proposed by fuzzy logic. The control
results in HILS are discussed in chapter 5. Finally,
the main conclusions are given in chapter 6.

Abstract
This paper discusses a DC motor equipped
electric
power
steering(EPS)
systemanddemonstrates its advantages over a typical hydraulicpower steering (HPS) system. The tire-road
interaction torque at the steering tires is calculatedusing the 2 d.o.f. bicycle model, which is verified
with the J-turn test of a real vehicle. By using
hardware-in-the-loop simulation(HILS), the control
responses of the vehicle are obtained. In previous
EPS systems, the assisting torque for the measured
driving torque is developed as a boost curve similar
to that of the HPS system. To improve steering
stiffness and returnability of the steering system,
assisting torque map is determined by fuzzy logic.

1 Introduction
Recently the automotive industry has focused on
improvingvehicleperformance,
safetyandconvenience for drivers. Steering assist systems play
an important role in each area. The conventional
HPS system, which is made up of an engine-driven
hydraulic pump and a hydraulic actuator, decreases
engine efficiency but requires complex hydrauliccomponents. To cope with the deficiencies of HPS,an EPS system has been vigorously researched.
Since the EPS system uses an engine-independent
motor without complex hydraulic units, the weightand volume of steering systems can be reduced.
Thus, EPS systems achieve better fuel and spaceeconomy and maintains the feel of the steering evenduring quick changes in driving conditions throughsoftware. Moreover no harm is done theenvironment because no hydraulic fluid is used [1].
To improve steering stiffness and returnability ofthe steering system, steering feel should be set up.
Adams, F. J. [2] researched the feel of power
steering and Norman, K. D. [3] introduced on
center handling performance. Gary P. Bertollini andRobert M. Hogan [4] drew up a preference curve
as a function of vehicle speed based on the effort
needed for steering by various drivers using VTI
driving simulators. Rakan C. C. and Le Yi Wang[5] used boost curves of assist torque for a given
vehicle speed. Based on these objective indices,©ISAROB 2005

Man Hyung Lee
School of MechanicalEngineering,
Pusan National University,
Busan, 609-735, KOREA

457

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 20052the distance from the vehicle C.G. to front axle.
Since tires can be modeled as linear within ≦0.3 g, the lateral forces at the front and the reartires are obtained asTire-road Interaction Torque  ⋅ 













To find the tire-road interaction torque,  , thekingpin torque [11] is obtained as       

(1)

where  and  are the vertical torque and thelateral torque, respectively. And the third term,  ,is aligning torque. Because of the kingpin offset
angle,  , and a lateral inclination angle,  , thevertical force,  , on the tire produces the verticaltorque,  . When the kingpin offset angle and thelateral inclination angle are small, the vertical torquegenerated by the vertical force can be approximatedby      ⋅ (2)where  is the steering angle. Since the lateralforce,  , acting at the tire center produces a torquethrough the longitudinal offset resulting from thecaster angle, the lateral torque generated by thelateral force is given by     (3)3

where  is a tire radius and  is a caster angle.
The lateral force,  , is developed by a tire at apoint behind the tire center. So the aligning torqueis written as                  

(4)



(5)

where   is the length of the tie-rod and  is thetie-rod angle.
The ground reactions on the tire are described by  ⋅ (6)where  is the radius of the pinion. Additionallythe friction torque,  , is defined as follows:  ⋅  (7)

where  is the friction gain. Although thesteering system has more complex frictions, these
are ignored here. The classical single-track model[12] is obtained by lumping the two front wheels
into one wheel in the centerline of the vehicle, thesame is done with the two rear wheels as shown infigure 1. For the lateral direction and the yaw axis,
the vehicle kinetics at the center of gravity (C.G.)
are describes as(8)                 
where  and  are the lateral acceleration and theyaw rate,  and  are the vehicle total mass andthe yaw moment of inertia,  and  are thelateral forces at the front and the rear tire, and  is

©ISAROB 2005

(9)

HILS System for an Electric Power
Steering

For the design, implementation and testing ofcontrol systems, some actuators are real, and theprocess and the sensors are simulated. The reason isthat actuators and the control hardware very often
form one integrated subsystem, also actuators are
difficult to model precisely and to simulate in realtime. By using this HILS, the effects of faults andfailures of actual sensors and computers on theoverall system can be tested in spite of extreme anddangerous operating conditions. The expenditure ofdeveloping cost and time can be cut down since
experiments
arereproducible
andfrequently
repeatable [13]. Especially, HILS systems are usefulfor the active or semi-active vehicles. By using
HILS, the control responses of the vehicle for thevarious conditions of speed, steering angles, anddisturbances are obtained precisely, safely, cheaply,
and rapidly. The tire-road force related to thevehicle dynamics is calculated by software and isexerted on rack-bar by hydraulic actuator, wheresteering torque is measured by torque sensor
attached on the steering column. Steering systemorganized by steering wheel, steering column, andrack-bar is embodied in hardware. In figure 2, ECU
makes the EPS motor generate a proper torquewhich is dependent on vehicle speed,  , andmeasured driving torque,  . A hydraulic actuator
controlled by a servo-valve realizes lateral force,
which is calculated steering wheel angle bypotentiometer and vehicle speed by dial. By Visual

where  is the pneumatic trail distance. Considering
the length from kingpin to rack-bar, the rack-bar
force is written by     ⋅ 

where  and  are the front and the rearcornering stiffnesses. Usually to verify the steering
performance, a J-turn test is fulfilled. For theverification, the steering wheel angle,  , is stepped
up to 34° within 0.2 sec when the longitudinalvelocity,  , is 22 m/s. Figure 2 shows the J-turnresults for the single-track model and the actual
vehicle. The lateral acceleration,  , and the yaw
rate,  , settled to around 3.4 ㎨ and 8.3 deg/s. The
integrals of time muliplied by the absolutemagnitude of the error (ITAE) for the lateral
acceleration and the yaw rate are 0.29 ㎨ and 0.42
deg/s, where the nonlinearity of the tire may be themain factor for the errors. As a result, thesingle-track model has characteristics almost similar
to the actual vehicle's. A hydraulic servo system isused for realization of the lateral force in HILS
system. In this paper, there are several assumption.
A velocity difference between going and returning isregarded as disturbance, servo valve is symmetric,
supplied-pressure and falling-pressure at the valve
orifice are constant, returned-pressure is zero, andthere is no loss of friction at the pipe.

458

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The output range of frequency is tuned by 104 ㎌
condenser, where the maximum frequence is 200 Hz
for the maximum speed of vehicle,   83 ㎧.
The servo-valve is a proportional valve, direct
operated, which provides both directional andnon-compensated flow control according to theelectronic reference signals. This operates inassociation with electronic drivers, which supplies
the proportional valves with correct current signal toalign valve regulation to the reference signal
supplied to the electronic driver. This valve has a
4-way spool, sliding into a 5-chambers and directly
operated by solenoids. In order to make theaccurate lateral force, axile force on rack bar shouldbe measured and controlled. Sensors and actuatorsin the HILS system need the sources of electricity.
Because they are operated by DC, transformer withbridge circuit converts AC into DC. To stabilize thesupplied voltage, linear voltage regulators andcondensers are used, where the suppling states arechecked by LED. The sensor signals are acquiredby PIC-MIO-16X-4 of National Instrument Co.

C++ under MS window 98, the 2 d.o.f. vehicle
dynamics yields lateral force, yaw rate, and lateral
acceleration. To control the hydraulic actuator
precisely, LMI based H∞ controller is used and theforce is measured by loadcell. And also the output
of sensors are displayed by indicators and an
emergency button is setup for the malfunction. The
EPS (C-EPS of KOYO SEIKO Co.) mounted on acolumn axis provides an assist force to a column
shaft via a worm gear. The optimal value of acurrent required for a motor is calculated by ECU(Electronic Control Unit), based on a electric signal
from a torque sensor and a signal from a vehicle
speed sensor. The torque sensor sends the signal
depending on a torsional angle of a torsion bar
mounted in the inner part of a column axis, which
is proportional to a steering force. This EPS system
adopts a brushless DC motor which has a lot ofadvantages versus a brushed motor. By non-contact
electronic switching of brushless motor, the life
cycle is lengthened, and the absence of brushed andcommutator enables the motor to be downsized andreduces the noise of it. In addition, lower inertia ofthe brushless motor gives good steering feel because
there is no permanent magnets as the rotor [16].
Commonly, it is not necessary to measure a steering
wheel angle, since an EPS system is related with asteering torque. However, lateral force in the HILS
system is determined by the vehicle speed and thesteering wheel angle, which is measured by apotentiometer(Model
534,
VisheyCo.).
The
resolution of measured angle is doubled by 2:1 gear
between the potentiometer and the steering column.
Vehicle speed has influence on the lateral force attire and the EPS system. By dial-gauge equipped
potentiometer, vehicle speed is set up in the HILS
system. The realized speed as analog voltage isadopted by DAQ and used for the calculation of
the lateral force. However, ECU (Electronic Control
Unit) in vehicles recognizes the speed byaccumulating the pulse of speed. So the speed asvoltage type should be converted to the pulse type.
©ISAROB 2005

4

Steering Feel and Assisting Torque Map

Vehicle dynamics and steering systems behave
strongly nonlinear which causes difficulties indeveloping a classical controller system. Fuzzy logic
however facilitates such system designs andimproves tuning abilities [10]. A Fuzzy logic
controller is used to give a desired assisting torque.
The longitudinal speed of a vehicle,  , and themeasured torque,  , are applied to a Fuzzy
controller. The membership functions are composedof the triangular and the trapezoidal functions. Table
1 shows the rule base for the Fuzzy controller. By
the Mamdani's fuzzy implication and the max-min
composition, the control surface is made up as
shown in figure 3.

1
0.8
0.6

Assisting torque

0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
1
0.8
0.6
0.4
0.2
0
Vehicle speed

-1

-0.8


NB NM NSZERO NX NB NM
LOW NB NM NS
MID NM NS NSHIGH NS NS NS

459

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Measured torque

ZE

PS

PM

PB

ZE
ZE
ZE
ZE

PM
PS
PS
PS

PB
PM
PS
PS

PX
PB
PM
PS

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

map was drawn by fuzzy logic. This proposed
torque map waw simulated in HILS system with areal steering system. By using this map, the EPS
system was improved sufficiently.

non
ecu2

180
Torque120

Acknowledgements

60
0
-800

-600

-400

-200

0

200

400

600

This research was financially supported by Korea
ScienceandEngineering
Foundation(KOSEF)
through the Engineering Research Center for Net
Shape and Die Manufacturing(ERC/NSDM) at Pusan
National University and by Pusan National
University Research Grant.

800

-60
Angle(deg)
-120
-180



References
non
ecu2

[1] Anthony W. Burton, “Innovation Drivers for
Eletric Power-Assisted Steering,” IEEE Control
Systems Magazine, pp. 30-39, 2003.
[2] Adams F. J., 1983, “Power Steering Road Feel,”
SAE Paper 830998, 1983.
[3] Norman K. D, “Objective evaluation of
On-center Handling Performance,” SAE Paper
840069, 1984.
[4] Gary P. Bertollini, Robert M. Hogan, “Applying
Driving Simulation to Quantify Steering Effort
Preference as a Function of Vehicle Speed,”
SAE Paper 1999-01-0394,1999.
[5] R. C. Chabaan, L. Y. Wang, “Control ofElectrical Power Assist Systems : H∞ design,
torque estimation and structural stability,” JSAE
Review 22, pp. 435-444, 2001.
[6] I. Camuffo, G. Caviasso, L. Pascali, M. Pesce,
E. Alviano, “Simulation Tools and Evaluation
Criteria for Steering Wheel Feel Improvementof an Electric Power Steering System,” SAE
Paper 2002-01-1593, 2002.
[7] Tong Jin Park, et al, “The Design of aController for the Steer-by-Wire System Using
the Hardware-In-the-Loop-Simulation System,”
SAE Paper 2002-01-1596, 2002.
[8] M. Kurishige, et al, “A New EPS Control
Strategy
toImprove
Steering
Wheel
Returnability,” SAE Paper 2000-01-0815, 2000.
[9] J. H. Kim, J. B. Song, “Control Logic for an
Electric Power Steering System Using Assist
Motor,” Mechatronics 12, pp. 447-459, 2002.
[10] F. Tahami, R. Kazemi, and S. Farhanghi, “A
Novel Driver Assist Stability System for
All-Wheel-Drive
Electric
Vehicles,”IEEE
Transactions on vehicular technology, Vol. 52,
No. 3, pp. 683-692, 2003.
[11] Thomas D. Gillespie, Fundamentals of Vehicle
Dynamics, SAE Inc., pp. 284-291, 1992.
[12] J. Y. Choi, et al, “Lateral Control ofAutonomous Vehicle by Yaw Rate Feedback,”
KSME International Journal, Vol. 16, No. 3,
pp. 338-343, 2002.
[13] R. Isermann, J. Schaffnit, and S. Sinsel,“Hardware-in-the-loop simulation for the design
and testing of engine-control systems,” Control
Engineering Practice 7, pp. 643-653, 1999.

80
Torque
60
40
20
0
-120

-80

-40

0

40

80

120

-20
Angle(sec)
-40
-60
-80


non
ecu2

120
Torque
80
40
0
-160

-120

-80

-40

0

40

80

120

160

-40
Angle(sec)
-80
-120



5

Experimental Results

Figures 4-6 show the plots of steering wheel
angles versus steering torques. When a vehicle isstopped as shown in figure 1, the EPS system helps
a driver to park. As the speed of vehicle isincreased, the assisting torque by the EPS system isdecreased.

6 Conclusions
In this paper, the vehicle model utilized 2 d.o.f.
bicycle model and was verified by the J-turn test ofa real vehicle. We introduced a cubic curve as atorque map. To improve steering stiffness andreturnability of the steering system, assisting torque©ISAROB 2005

460

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The Analysis for the Movement Characteristics of the Flying Object
with Genetic Algorithms
Ryuji Goto

Yuji Sato

Graduate School of IT Professional Course,
Hosei University:1914-7, Kosuzume-cho,Totsukaku,Yokohama-shi 244-0004, Japan
E-mail: rgoto@paw.hi-ho.ne.jp

Faculty of Computer and Information Science,
Hosei University:3-7-2, Kajino-cho, Koganei-shi
Tokyo 184-8584, Japan
E-mail: yuji@k.hosei.ac.jp
based on the passive observed data must containthe distance components. If the flying object isenemy aircraft for the observer, the observer
should not radiate the EMW for concealing theexistence of observer itself, because the enemy
aircraft may starts counter attack operation tothe observer. Therefore, the analysis for the
movement characteristics of the flying object
based on the passive observed data has thetactical meanings. The movement characteristics
are the present distance, the velocity and thethree dimensional proceeding course of the flying
object. The other hand, to analyze the movement
characteristics of the flying object rightly fromonly the passive observed data, it is necessarythat the removal observer changes the velocity
or course of itself at least one times during theanalysis, because there are infinite solutions incase of no changing of the velocity and course ofobserver. This can be took the place by the plural
foxed position observers arranged different places.
Even though, the movement of the flying object
has constant velocity and course in short period,
this analysis has the problems such as describedin the beginning of abstract.

Abstract
Issues such as multiobjective optimization, time -series
prediction, the analysis from noisy observation data, andthe solution of implicit functions are all crucial in theconsideration of real world problems, and research into
the applicability of evolutionary computer techniques tothese problems has already begun [1–9,13]. However,there are only a few examples of studies where
evolutionary computer techniques have been applied toproblems that involve all of these issues at the same time.
One such examples are previous studies in which wereported on the effectiveness of genetic algorithms (GA)
as a tool for tracking objects as they move towards adestination while making evasive maneuvers in order toavoid pursuit or attack. In another study, we reported on
the effectiveness of GA as a tool for tracking objects inthe earth orbit. All our previous reports are based on theactive observed data. In this paper, we verify theapplicability of GA to the problem of analyzing the
movement characteristics of flying objects based on only
passive observed data. Key words : Tracking, Analysis
based on observed passive data, Analysis from noisy data.

1

Introduction

In previous reports, we considered the twodimensional movement of an object whose evasive
motion was assumed to consist of constantvelocity straight-line, simple sinusoidal andsawtooth motion [10, 11 ]. In another previous
report, we considered the three dimensional pathelements of the moving objects in a earth orbit
whose motion consists of circles, ellipses [12].
These previous analysis are based on active
observed data gathered by radar, laser or active
sonar. In this paper, we report the three
dimensional movements analysis for the flying
object like a fighter in the air, from passive
observed data without the active observed data.
The passive observed data means that they can begathered by the observation equipment in its
surveillance mode without radiation of electricmagnetic wave(EMW), only by observation of the
EMW radiated by the flying object. The passive
observed data consist of the bearing and elevation
angles from observer. Active observed data meansthat they can be gathered by the observationequipment in its radiation mode of the EMW.
Active observed data contain distance component.
The movement characteristics to be analyzed©ISAROB 2005

2

Tracking of a Flying Object in the Air

2.1

Earth Surface coordinates and Movement
Characteristics of the Flying Object

The relationship between the earth surface coordinates
xyz and the movement characteristics is illustrated in
Figure 1. This shows a flying object is proceeding to thedestination. Its velocity and course are assumed as
constant in the period of analysis. The velocity is V. EastWest(x) components of velocity V is Vx. North-South(y)components is Vy. Radius direction of the earth
component(y) is Vz. Proceeding horizontal course
measured clockwise from the north direction (y) of theflying object is Cmh, vertical course measured upwardfrom surface is Cvm. Initial position of the observer is
Xo(t0),Yo(t0),Zo(t 0). This point is the origin of the earth
surface coordinates. Exact values of them are 0, 0, 0. The
position of the observer at time tn is Xo(tn), Yo(tn), Zo(tn).
Initial position of the flying object is Xm(t0), Ym(t0),Zm(t 0) and position of time tn is Xm(tn), Ym(tn), Zm(tn).
The observer observes the bearings and elevations offlying object intermittently as removing on the ground.
This removal observer must change its velocity or course
at least one times during the analysis because there areinfinite solutions in case of no changing. This can be took
461

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005z

Xo(tn):observer X position of time tn{Xm(tn),Ym(tn),Zm(tn)}Yo(tn):observer Y position of time tn

TRadius of earth direction

Zo(tn):observer Z position of time tn
Xm(tn):mover X position of time tn.

Xm(tn):mover Y position of time tn

N
Cmv

V

Xm(tn):mover Z position of time tn

Vz

Vx

y
Vy

{Xm(t0),Ym(t0),Zm(t0)}Cmh

North(N)E

B(tn):bearing of time tn
E(tn):elevation of time tn
D(tn):distance of time tn
V:velocity of mover
Vx:East(x) component of V

D(t0)Vy:North(y) component of V

D(tn)Vz:Radius of earth(z) component of VN

Cmh:mover horizontal courseE(tn)Cmv:mover vertical courseB(tn)Vo(t): observer velocity of time t

E(t0)
B(t0)
{Xo(t0),Yo(t0),Zo(t0)}Co(t): observer horizontal course of time t{Xo(tn),Yo(tn),Zo(tn)}Eo(t): observer vertical course of time t

Vo(t),Co(t),Eo(t)East(E) x

Figure 1: The movement of the flying object and the observer at time from t0 to tn in the coordinates xyz.

the place by the plural fixed position observers arranged
different places. Initial bearing is B(t0) and bearing attime tn is B(tn). Initial elevation is E(t0) and elevation attime tn is E(tn). Intial dis tance from observer to the flying
object is D(t0) and distance at time tn is D(tn).

Xm(t 0) = D(t 0) cos E (t 0) sin B (t 0) 
Ym (t 0) = D( t 0) cos E (t 0) cos B( t 0)  ………….(2)Zm( t 0) = D(t 0) sin E (t 0)

2.2

Position of the flying object at time tn is expressed byequation (3) as the function of x,y,z component of flying
object’s velocity.

Formulation of Analysis for the Movement
Characteristics of the flying object

In the following, we show the relationship between theinferred values of the movement characteristics —initial
distance D(t0), East-West components of velocity Vx.
North-South components of velocity Vy. Radius directionof the earth component velocity Vz —and flying object’s
bearing B(tn) and elevation E(tn) at time tn.
Position of the observer at time tn is expressed byequation (1).

Xm(tn) = Xm( t 0) + Vx * ( tn − t 0) 
Ym (tn) = Ym( t 0) + Vy * (tn − t 0)  .……..…….(3)Zm( tn) = Zm(t 0) + Vz * ( tn − t 0) 
Distance x,y,z components of the flying object from theobserver at time tn is expressed by equation (4).


Xo(tn) = ∫ Vo(t ) cos Eo( t ) cos Co( t ) dt 
t0

tn

Yo (tn) = ∫ Vo(t ) cos Eo(t ) sin Co(t ) dt  ……(1)
t0

tn


Zo(tn) = ∫ Vo(t ) sin Eo( t ) dt

t0
tn

Dx (tn) = Xm(tn) − Xo(tn)

Dy (tn) = Ym(tn) − Yo (tn)  …………….………(4)Dz ( tn) = Zm(tn) − Zo (tn) 
Distance at time tn is expressed by equation (5).
D(tn) =

(Xm(tn) − Xo(tn))2 + (Ym(tn) − Yo(tn))2 + (Zm(tn) − Zo(tn))2

Position of the flying object at time t0 is expressed byequation (2) as the function of initial distance D(t0).
©ISAROB 2005…….………………..(5)462The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Bearing at time tn is expressed by equation (6).

B(tn) = tan−1

Xm(tn) − Xo(tn)………….……….….(6)Ym(tn) −Yo(tn)Elevation at time tn is expressed by equation (7).

E(tn) = tan−1

Zm(tn) − Zo(tn)22( Xm(tn) − Xo(tn)) + (Ym(tn) −Yo(tn))....(7)

Velocity of the flying object is expressed by equation (8).

V =Vx 2 + Vy 2 + Vz 2………………(8)Horizontal course of the flying object is expressed byequation (9).

Cmh = tan −1

Vx
Vy

3.3

…………….………….(9)Vz
Vx 2 + Vy 2………..……….(10)

Accordingly, the problem addressed in this paper —i.e., that of analyzing the three dimensional movement ofa flying object — can be formulated as an inverse
problem involving complex implicit functions where it isnecessary to find the four characteristics—initial distanceD(t0), velocity x component Vx, y component Vy, z
component Vz—of a flying object by working backwards
from noisy time-series observations of its bearing andelevation obtained from the observer. The present
distance D(tn), velocity V, horizontal course Cmh,vertical course Cmv of the flying object are calculated
from D(t0), Vx, Vy, Vz by equation (5), (8), (9), (10).

3Method to Apply Genetic Algorithms

3.1

The Movement Characteristics Determinedby Genetic Algorithms

f = k (n + 1) /{Where∑ dB(ti) + dE(ti) } ……………….(11)
2

2

i=0

Here, esBti, esEti are the bearings and elevations
estimated by GA at time ti, oBti and oEti are the bearingsand elevations observed at time ti, n +1 is the number ofobservations, and k is a suitable constant. For example,
case of k =1, it is set so that f=1.0 when the average angle
difference is 1.0 degree.

3.4

Genetic Operation

The method for selecting the group of individuals
carried forward to the next generation from the current
generation is shown in Figure 2. All individual (the total
number of chromosomes)P sent from the previous
generation is evaluated by calculation of fitness andsorted in descending order. Fixed proportion E from thehighest fitness individual is retained as elite, and thenumber of discarded individuals are supplemented byroulette selection to preserve the original population P. M(=P-E) is the number of discarded individuals. Making upof the deficit for M is done as follows. One pair ofindividual is chosen as parent by roulette selection fromall individual P of the current generation. The sub
chromosomes Dga, Vx, Vy, Vz of this one pair ofindividual is then subjected to single point crossover
between each of the same kind of sub chromosomes
independently (i.e. crossover between Dga and Dga, Vx
and Vx, Vy and Vy, Vz and Vz,of both individual) to
produce one pair of child individual.
After crossover operation, all sub chromosome of
produced one pair child individual is subjected tospontaneous mutation independently. Through thecrossover and spontaneous mutation processes, one pairof new individual is produced and carried forward to the

Chromosome Coding Method

We defined chromosomes respectively corresponding to
the characteristics—initial distance Dag and flying
object velocity x component Vx, y component Vy, z
component Vz. And we expressed a single individual as aset of these characteristics as sub chromosomes. But the
sub-chromosome of the initial distance Dga and thevelocity Vx,Vy,Vz consist of different length bits
according to range of its value and necessary resolution.
The initial distance Dga consists of integer 18 bits,
velocity of Vx,Vy,Vz consists of integer 17 bits.
©ISAROB 2005

i=ndB(ti) = esB(ti) − oB(ti), dE(ti) = esE(ti) − oE(ti)Four movement characteristics — initial distance D(t0),flying object velocity x component Vx, y component Vy, z
component Vz — constitute a complex implicit function,so we will try to use genetic algorithms to determine theirvalues. Initial distance D(t0) can be biased by offset value
because observer can detect the existence of the flying
object before it approach to certain minimum area of
distance to the observer. If bias value is Dbias, GA
operation value for initial distance Dga is D(t0)-Dbias.

3.2

Fitness Function

We determine the angles error between the observed
angles(bearing and elevation) data of the moving object
and the angles data inferred by GA, and we defined thefitness of an individual based on the reciprocal of thesquare root of the sum o f these errors. The fitness
function is shown in Equation (11).

Vertical course of the flying object is expressed byequation (10).

Cmv = tan −1

The physical range over which each sub chromosome
are expressed are set considering the possible range of
distance and velocity of flying object, analysis period oftime and the observation precision of passive
observations. Assumed range of initial distance is
50,000m~200,000m, assumed range of velocity is± 500m/sec and assumed bearing and elevation error isless than 0.1degree. Analysis period of time to get
effective accuracy should be less than 100sec.
The minimum units of these sub chromosome are set as
follows by considering above conditions: initial distanceD(t0) is 1m, flying object velocity component Vx, Vy, Vz
is 1/100m/sec. In exact physical calculation of thissimulation, initial distance D(t0) is biased by constantvalue Dbias=50,000m (Dga= D(t0)-Dbias).

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

current generation
evaluation & sort
high
elite
from
E

next generation
crossoverprevious
geneneration

mutationparent individual -1

child individual -1

Dga ,Vx,Vy,Vz

Dag ,Vx, Vy,Vz

Dag crossover

P

M
=P-E

low

roulette
selection

Vx crossover:

Dag ,Vx, Vy,Vz

elite
E

Dga mutation

supplemented

Vx mutationM:

Vz crossoverVz mutation

Dag ,Vx ,Vy, Vz

Dag ,Vx, Vy,Vz

parent individual -2

child individual -2

Dag ,Vx, Vy,Vz

Above selection, crossover, mutation processes are repeated M/2 times

Figure 2: Flowchart of genetic operation. 1 pair of chromosome is selected by rouletteselection, and crossover and spontaneous mutation are done for each sub-chromosome
respectively.

next generation. Above processes are repeated until the
making up of the deficit for M is completed (M/2 times).
In preliminary trials we found two phenomena.
The first of those phenomena is that the solution starts toconverge around the 20th generation, so for subsequent
generations we reduced the spontaneous mutation rate forall sub chromosomes. We did those to avoid destroying
the sub chromosome that had already approachedconvergence. In the exact simulation, the spontaneous
mutation rate is changed from the 20th generation
onwards as described in Section 4.2 Simulation Data. The
results of the reducing the spontaneous mutation rate is
described in Section 4.3 Results of Simulation.
The second phenomenon is as follows. From the 10th
generation onwards, new two type individuals(chromosome) are effective for the earlier convergence.
One of them is calculated as much 2% uniform random
numbers of the sub chromosomes contained in the fittest
individual of the current generation. The other iscalculated as much 2% uniform random numbers of the
sub chromosomes contained in the roulette selectedindividuals from the current generation. The number ofthese new two type individual are 10% for total numberof individual. The results of the supplement the modified
sub chromosomes is described in Section 4.3 Results ofSimulation.

4

Evaluation Tests

4.1

Evaluation Method

We made a software system shown in Figure 3 for theevaluation of this research. This system consists of threemodules of software, Observed data Generator, Estimatedvalue Generator and Estimated value Evaluator. They docooperative works for evaluation.
Observed data Generator
Based on the theoretical movement characteristics of aflying object provided by the operator, the observed data
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generator calculates the theoretical values of the object’s,
bearing (Bti) and elevation (Eti) at time ti for 1-second
intervals from the time t 0 at which the observation starts.
It then adds normal random number errors εbti and εeti tothe calculated theoretical values to simulate the errors
produced by an observation system, such as electric
magnetic wave beam fluctuations, instrumentation errors,
and conversion errors, to produce the observed bearing(oBti) and observed elevation (oEti).
As the observation time ti is increased, the observed
data generated for experimental use are stored along withthe observation time ti in the database for observed data.
This observation data gathering cycle is continued until
simulation ends.
Estimate value Generator
The Estimated value Generator generates the estimatedbearing (esBti) and estimated elevation (esEti) based on
estimated sub chromosomes in the chromosomes. The
initial values of the sub chromosomes are set randomly tovalues in the defined ranges by using uniform random
numbers. and uses genetic algorithms to renovate theestimated values of sub chromosomes. The values of
esBti and esEti (t 0 ≤ ti ≤ tn) are calculated by Equations(6), (7) based on the renovated values of sub
chromosomes. These renovated esBti and esEti are sent to
the Estimated value Evaluator.
Next, we will describe how the GA is used to renovate
the estimated values of the sub chromosome. In the
Estimated value Generator, the values of esBti and esEti
corresponding to each individual are sent to the Estimatedvalue Evaluator.
Ne xt, based on the received fitness values, the method
described in section 3.4 is used to select the fittest
individuals and perform crossovers and spontaneous
mutations, thereby updating the generation i.e.,renovating the estimated values of the sub chromosomes.
Here, the renovating of the estimated values of the sub
chromosomes using a genetic algorithm is started at the
point when a certain set of observed data is stored into theThe Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Observed

data

Generator B (ti), E (ti)value

Initial setting(randomly)DB

Add error

True Dga,Vx ,Vy ,Vz
EstimatedoB(ti),oE(ti)Generator

Estimated

Estimated esDag , esVx , esVy , esVz

value

esB(ti),esE(ti)Evaluator
Calculation of
Fitness

Renovation of chromosomes

Fitness(selection, crossover, mutation)

Figure3: The flowchart of GA evaluation system. The GA evaluation system consists of Observed
data Generator, Estimated value Generator and Estimated value Evaluator.

database for observed data. Spontaneous mutation rate arechanged at around convergence generation of the analysis
in order to broaden the search space before convergenceand to avoid destroying the solution after convergence.

were set as follows:(1)maximum genetic manipulation generation:50 (2)observed data set prior to GA start: 60 (3) number of total
individuals: 6000 (4)number of elite:60 (5)crossover
ratio: 0.8 (6)spontaneous mutation rate: 0.00001 before
the 20th generations, and 0.000005 from the 20th
generation onwards.
The physical parameters are as follows:(1) Initial distance : 150000m (2) Initial bearing : 5.0deg(3) Initial elevation : 5.0deg (4) Velocity of flying object :
340m/s (5): Horizontal-course of flying object:110.0deg(6) Vertical-course of flying object : 5.0deg (7)observing:by two observers arranged 5km apart. (8) observation
error (maximum) : 0.004 deg, 0.020deg, 0.100deg

Estimated value Evaluator
For every observation time, the Estimated value
Evaluator calculates the fitness of each individual
according to Equation (11) based on the values of theobserved bearing (oBti) and elevation (oEti) input fromthe database for observed data and the estimated bearing(esBti) and estimated elevation (e sEti) input from theEstimated value Generator. These calculated fitness
values are sent to the Estimated value Generator, where
they are used for genetic manipulation. The above
processes of generating observed values, generating
estimated values and performing evaluation are repeateduntil a stopping criterion is met. The stopping criterion
was taken to be the fulfillment of either of two
conditions: that an individual appears whose fitness
exceeds a preset standard fitness, or that the number ofgenerations of genetic manipulation becomes greater thana certain value.

4.2

4.3

Figure 4 shows how the best fitness for three kinds oferror of the observation vary with the number ofgenerations in cases where the movement of the fly ing
object is assumed to be linear movement. To obtain amaximum fitness value of 1.0, the fitness fg on thevertical axis in this figures is the normalized value
obtained from the relationship fg = 1 − 1/f, where f is thefitness defined in Equation (11). The observations are
made at 1-second intervals by the observer having an
bearing and elevation observation errors. At the time of

Simulation Data

In this simulation, the parameters of genetic operation
1

ＢｅｓｔFitness(Fg)Table1: The accuracy of analyzed initial distance D(t0),velocity component Vx, Vy, Vz, velocity V, horizontal course
Cmh and vertical course Cmv.

err=0.004deg

0.995

Results of Simulation

err=0.02deg

0.99

sub-chromo

0.985
err=0.1deg

0.98
0.975
0.97
0

5

10

15

20

25

30

35

40

45

50

Generations

Figure4: The maximum and averagefitness for generations.
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theoryerr=0.004o
err=0.020o
err=0.100o

D(t0)(m)
15000037155522

out put
theoryerr=0.004o
err=0.020o
err=0.100o

V(m/s)
340.0
0.1
0.5
0.6

Vx(m/s)Vy(m/s)Vz(m/s)

333.6
0.1
0.6
0.8

-58.8
0.1
1.2
4.0

29.6
0.1
0.2
0.3

Cmh(deg)
100.0
0.0
0.2
0.7

Cmv(deg)
5.0
0.0
0.1
0.2

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the 60th observation (i.e., 60 seconds after theobservations are started), the first generation of GA starts.
Figure 4 shows the best fitness (Fg) of three kinds ofobservation error grow up sharply and they converge
around at 7~15th generation. The fitness of theobservation error 0.004deg grows up at the earliest
generation. The fitness of observation error 0.1deg grows
up last. Table 1 shows the accuracy of the analyzed
movement characteristics (sub chromosomes) for thethree kinds observation error at the 50th generation. The
accuracy of the observation error 0.004deg is the best andthe accuracy of observation error 0.1deg is the worst
among three kinds of error. Reducing the spontaneous
mutation described in Section 3.4 Genetic Operation iseffective for the early convergence by several %. New
two type chromosomes described in Section 3.4 effects tomake the convergence generation earlier by more than
10%. These data are average values of 20 trials.

4.4

References[1] Oakley, H.: Two Scientific Applications of Genetic
Programming: Stack Filters and Non-Linear Equation
Fitting to Chaotic Data. in K.E. Kinnear, Jr (ed.),Advances in Genetic Programming, pp. 369-389, The
MIT Press (1994).
[2] Reynolds, C.W.: Evolution of Obstacle Avoidance
Behavior: Using Noise to Promote Robust Solutions.
In K.E. Kinnear, Jr (ed.), Advances in Genetic
Programming, pp. 221-241, The MIT Press (1994).
[3] Sato, Y., and Nagaya, S.: Evolutionary Algorithms
that Generate Recurrent Neural Networks for
Learning Chaos Dynamics. Proc. The 3rd. IEEE Intl.
Conf. on Evolutionary Computation, pp. 144-149(1996).
[4] Tamaki, H., Kita, H., and Kobayashi, S.: MultiObjective Optimization by Genetic Algorithms: A
Review. Proc. The 3rd. IEEE Intl. Conf. on
Evolutionary Computation, pp. 517-522 (1996).
[5] Beyer, H-G., and Arnold, D.V. : Fitness Noise andLocalization Errors of the Optimum in General
Quadratic Fitness Models . Proc. The 1999 Genetic
and Evolutionary Computation Conference, pp.
817-824 (1999).
[6] Parmee, I.C., and Watson, A. H.: Preliminary Airframe
Design Using Co-Evolutionary Multiobjective
Genetic Algorithms. Proc. The 1999 Genetic andEvolutionary Computation Conference, pp. 16571665 (1999).
[7] Blumel, A.L., Hughes, E.J., and White, B.A.: Fuzzy
Autopilot
DesignUsing
A
Multiobjective
Evolutionary Algorithm. Proc. The 2000 Congress on
Evolutionary Computation, pp. 54-61 (2000).
[8] Yoshihara, I., Aoyama, T., and Yasunaga, M.: GPBased Modeling Method for Time Series Prediction
with Parameter Optimization and Node Alternation.
Proc. The 2000 Congress on Evolutionary
Computation, pp. 1475-1481 (2000).
[9] Tominaga, D., Koga, N., and Okamoto, M.: Efficient
Numerical Optimization Algorithm Based on Genetic
Algorithm for Inverse Problem. Proc. The 2000
Genetic and Evolutionary Computation Conference,
pp. 251-258 (2000).
[10] Goto R., and Sato Y.: Motion Analysis of Moving
Objects with Genetic Algorithms. Proc. The 2001
Genetic and Evolutionary Computation Conference,
pp.1276-1283 (2001).
[11] Goto R., and Sato Y.: Applicability of Genetic
Algorithms to Motion Analysis of a Moving Object.
Proc. The 2002 Congress on Evolutionary
Computation, pp. 765-770 (2002).
[12] Goto R., and Sato Y.: Analysis for the Path of an
Object in Earth Orbit with Genetic algorithms . Proc.
The 2004 Artificial Life and Robotics, pp. 253-258(2004).
[13] Goto R., and Sato Y.: The Analysis for theCharacteristics of Time Series Signals with Genetic
Algorithms. Proc. The 2004 International Conference
on Computing, Communications and Control
Technologies, Volume I, pp. 267-272 (2004).

Discussion

From the above experimental results, it can be judgedthat by applying GA to the analysis for the movement
characteristics of a flying object whose fourcharacteristics are all unknown, it is possible to analyze
these values only from time-series values of the observed
bearing and elevation of the flying object obtained fromobservation equipment. Since the analysis results are allbased on observation data, it can be judged that thenecessary time for the completion of analysis is effectedby the errors contained in observed bearing and elevation.
The smaller the errors contained in observed data are, thefaster and the more accurately the analysis completes.
However, even though, the errors contained in the bearing
and elevation extend to around 0.1 degrees, the accuraciesof the results and the completion time of the analysis arestill in effective range.
To shorten the analysis time, we must do furthermore
investigation for the effect of the parameters of GA.
These parameters are number of stored data prior to
analysis start, number of chromosome, number of elite,
crossover rate and mutation rate. Also, we must deepen
the quantitative analysis of the effectiveness of reducing
spontaneous mutation and new two type chromosome
described in Section 4.3 Results of simulation.

5

Conclusion

As a problem including various issues such as
multiobjective optimization, time-series prediction, theanalysis from noisy observation data, and the solution of
implicit functions, we have investigated the applicabilityof genetic algorithms to the analysis of the three
dimensional and dynamic linear movement of a flying
object in the air based on the passive observed data. In the
future ,we aim to demonstrate that genetic algorithms are
suitable for solving three dimensional and non linear
movement analysis problems for objects moving in the airand seawater by expanding this technique in a practicalamount of time and practical level of precision. Also, we
aim to demonstrate that genetic algorithms are suitable
for the more wide and complex analysis for the facts inthe nois y data.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Proposal of Serially and Dynamically Separating Genetic Algorithm
and Its Application to Optimization of Robot Control Systems
Koichi NAKAYAMA1),2) Hirokazu MATSUI3) Katsunori SHIMOHARA2) Osamu KATAI1)
1) Graduate School of Informatics, Kyoto Univ.
2) ATR Network Informatics Lab. 3) Faculty of Engeenering, 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

In the environment of an actual robot, there is atime lag between the decision of the agent’s action
and the action of the robot. Whether or not the action is appropriate is determined after a certain time’s
passing. In order to optimize the control system of an
actual robot, because of the above environment, an algorithm that can obtain not short-term but long-term
optimality is required.
DS-GA has the ability to increase system-level optimality by the autonomous learning of agents basedon local information by using the dynamic separationof the agent’s interaction. In other words, system-level
information emerges from collective agent-level information by ”Swarm-Sensing,” which is a characteristicof DS-GA. We expect that extended DS-GA will havethe ability to increase long-term optimality by the autonomous learning of agents based on short-term information.
The conventional DS-GA uses the dynamically separating mechanism of its agents. However, in the casewhere an agent controls a single hardware unit suchas an intelligent robot, the DS-GA is inapplicable asa learning algorithm for a robot. In order to optimize
a single hardware unit that is designed by an agentoriented control system such as an intelligent robot,
we extend DS-GA to form a Serially and Dynamically
Separating Genetic Algorithm (sDS-GA) that includes
the time-separating mechanism of the agents.
In DS-GA, many agents act simultaneously, and theinteractions of many agents are restricted in a colony.
In sDS-GA, a control-agent is chosen serially and theinﬂuence of the agent’s action is decreased with time.
In order to reduce an interaction with an agent thatbelongs to other colonies, the agents who belong to
the same colony are chosen continuously.
In order to verify the validity of the proposed sDSGA, we applied the sDS-GA to an object tracingtask for a Movable Intelligent Evolutional Computer(MieC) as an actual robot.

In this paper, we propose a Serially and Dynamically Separating Genetic Algorithm (sDS-GA), andapply it to optimize an agent-oriented control system
for an intelligent robot. The conventional DS-GA isinapplicable as a learning algorithm for a single hardware unit such as an intelligent robot. By the extension of a dynamically separating mechanism, the proposed sDS-GA becomes applicable as an optimization
algorithm for a single hardware unit. We conducted
experiments with sDS-GA that optimize the parameters of a control system for an intelligent robot called
MieC. The sDS-GA obtained not short-term but longterm optimality. We also found that sDS-GA is eﬃcient for the optimization of an actual intelligent robot
under an unknown and dynamic environment.

1

Introduction

There have been many studies of control systems
for robots[1, 2]. In particular, many intelligent robots
operate by agent-oriented programming. However, inagent-oriented programming, it is diﬃcult to design
the entire agent optimally beforehand. In this paper, we focus on a Dynamically Separating Genetic
Algorithm (DS-GA)[3, 4] as an optimization algorithm
for agents in an intelligent robot that is controlled byagent-oriented programming.
An intelligent robot must be designed taking thefollowing into account. It has to do various processing
at the same time, such as target determination, image
processing, data transferring, and arm and wheel control. The priority of the processing changes in responseto the inﬂuences of external factors and internal factors, such as interaction with humans, and the residual
quantity of the battery. The optimal parameters forprocessing are dynamically changed by these factors.
As such, an adaptation algorithm is required.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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2

Serially and Dynamically Separating
Genetic Algorithm (sDS-GA)

or extinction of agents according to their private performance, e.g., accumulated proﬁt. Consequently, anagent’s autonomy is not spoiled and agents can stilllearn by means of evolution. The learning algorithm
used by the DS-GA is as follows.

In this section, we propose the Serially and Dynamically Separating Genetic Algorithm (sDS-GA) as
an applicable DS-GA for an actual robot. The conventional DS-GA uses dynamic separation as follows.
Agents that are separated into colonies act simultaneously. The interactions of agents are restricted in acolony, and an agent cannot contact any agent that belongs to another colony. The colonies change dynamically according to the number of agents they contain.
When the number of agents in a colony increases, thecolony is divided into two halves. A colony is extinguished when the number of agents it contains becomes 0.
We propose sDS-GA for use as a control system foran actual robot. The basic idea of sDS-GA is as follows. In sDS-GA, control-agents are separated intocolonies. Agents in a colony control a robot in a period serially. In a certain period, control-agents who
belong to a certain colony are chosen randomly, andthey control the robot serially. In the next period,
control-agents who belong to the next colony are chosen randomly, and they control the robot serially.
As a result, the interval of control by an agent thatbelongs to the same colony is much shorter than theinterval of control by an agent that belongs to othercolonies. In other words, the inﬂuence by a certain
agent’s action is strong for the agent that belongs to
the same colony, and weak for the agent that belongs
to other colonies. Serial separation is realized by sucha mechanism. We show the main routine of the algorithm using sDS-GA in Fig. 1.

(1) Initialization: NA (t) agents at t = 0 are created and separated into colonies. The numberof agents in a colony is NLim /2. 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.
(2) Colony Loop: Every colony takes charge of thecontrol in order.
(3) Agent Choosing: An agent is chosen from thecolony for robot control randomly.
(4) Action (Robot control of 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.
(5) 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)). The two agents inherit half of theoriginal agent’s evaluation value. An action geneis mutated according to the mutation probability
Pmut . An agent is extinguished from the colony
when its evaluation value becomes less than orequal to zero.
(6) Dynamic Separation of Colonies: When the number of agents in a colony exceeds the limit NLim ,the agents are separated into two half-colonies.
The diﬀerence in the number of agents between
the two colonies is either 1 or 0.

Initialization (1)Colony loop (2)(7) Random Elimination: When the total number of
agents that can exist in the robot control system
becomes greater than the initial number of agents(NLim ), a colony is eliminated at random.

Agent loop
Agent Chooseing (3)Action (4)In the experiments, the number of initial agents was
set to NA (0) = 100, the number of maximum agentsin a colony was set to NLim = 20, the mutation probability Pmut = 0.1, and the initial accumulated proﬁt
EA (a, 0) = 100 for all agents.

Split and Extinction (5)Dynamic Separation (6)Random Elimination (7)Figure 1: Main routine of the sDS-GA shown by NS
chart.

3
Speciﬁcally, in the experiments described in this paper, the evolution of a population is based on the split©ISAROB 2005

Experiment

Each agent has an evaluation value for its own task
achievement, and has no information about the other468

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

task achievements. We think that the total of theirevaluation values is maximized, so that the system becomes optimal. But, it is not always maximized as anentire system, even if each agent acts in order to maximize its task achievement, i.e., the system may have adilemma on a task achievement such as the following.
An agent may be unable to use computer resources to
do a task, if another is doing a task by using computerresources. An agent may be unable to maximize a task
achievement in a longer term, if it maximizes a task
achievement in a shorter term.
In this section, we verify experimentally that sDSGA is eﬃcient for actual intelligent robots. Here, we
use an auto guided tracked vehicle with a camera,
called (MieC) as an actual intelligent robot.
Concretely, applying sDS-GA, the control parameters of MieC are optimized when MieC traces a ballthat moves on the same plane.

The current position of the target 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
commands, respectively. (6) Return to (1).

3.1

Figure 3: The block diagram for the target tracing of
the traced vehicle

+ + -

(2)

Movable Intelligent Evolutional Computer (MieC)

Motor(L)

(4)

(5)

GM

Motor(R)

θL(t)
θR(t)

Tracked
Vehicle(1)
Pose(t)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 sDS-GA.

3.3

sDS-GA Coding

We give each agent gene GI or GM . GI takes oneof 10 quantized values 0.0, 0.1, 0.2, · · ·, 0.9. GM takes
one of 10 quantized values 0.0, 0.3, 0.6, · · ·, 2.7. 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. Each agent is evaluated by how much andhow fast MieC can trace the target during the time inits duty.

Figure 2: Movable Intelligent Evolutional Computer(MieC)

3.4

Experiments and Discussion

We gained the following results. The histories ofthe population ratios of GI (0.0, 0.1, 0.2, · · ·, 0.9) and
GM (0.0, 0.3, 0.6, · · ·, 2.7) are shown in Fig. 4, respectively. The horizontal axes express the number

Tracing Control

The ﬂow of target tracing control is shown in Fig. 3.
This control ﬂow is one of the simplest in this case. (1)©ISAROB 2005

+ -

GM

(3)

Here, we use the Movable Intelligent Evolutional
Computer (MieC) shown in Fig. 2as an actual intelligent robot. MieC has two motors as movable actuators, two encoders for the motors as internal sensors,
and a camera as an external sensor.

3.2

GI

InverseKinematic

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005of times the colony loop was performed. The verticalaxes express the ratios of GI and GM with each of 10
quantized values, respectively.

1

1

0.8

0.8

0.6

0.6

0.4

Gene 0.2

0.2

0

0

0
50
100
150
(a) The history of ratio G I

Gene 2.7

0.4

0.2

and the action of the robot, and even if the agent
learns based on short-term information, sDS-GA canobtain not short-term but long-term optimal parameters of the control system. These characteristics ofsDS-GA are similar to the characteristics of the conventional DS-GA, which obtains system-level optimality by agents’ learning based on local information by
the use of ”Swarm-Sensing”. These results suggestthat sDS-GA is eﬃcient for the optimization of an actual robot control system under an unknown environment.

Gene 0.4

Gene 1.2

0
50
100
150
(b) The history of ratio G M

Acknowledgements
This research was conducted as part of ’Research on
Human Communication’ with funding from the National Institute of Information and Communications
Technology.

Figure 4: The history of the population ratio

GI converged on the optimal value 0.2. GM did
not converge, but the average of GM in each colony
converged near the optimal value 1.8. It is believedthat the action time of each agent is so short in thiscase that an agent’s action aﬀects not itself but otheragents in the same colony, evenly. In other words,
agents in the same colony evenly aﬀect each other. As
the results, the average in each colony converged, but
the agents did not converge. Therefore, a single agent
with GM cannot aquire the optimal value, but aquires
the optimal value as the average. On the other hand,a single agent with GI aquired the optimal value. It
is believed that the average was equal to the value ofthe agent, since only a few agents with GI are in thesame colony.
From the above discussion, it is believed that both
GI and GM are optimized by applying sDS-GA. The
sDS-GA is eﬃcient for the optimization of a control
system for MieC as an actual intelligent robot underan unknown environment.

4

References[1] A. Mori, F. Naya, N. Osato, T. Kawaoka,“Multiagent-Based Distributed Manipulator Control,” Proceedings of the 1996 IEEE/SICE/RSJ
International Conference on Multisensor Fusion
and Integration for Intelligent Systems, pp. 289296, 1996.
[2] J.D. Morrow, “Sensorimotor Primitives for Programming Robotic Assembly Skills,” Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, 1997.
[3] K. Nakayama, H. Matsui, Y. Nomura, “Dynamically Separating GA (DS-GA),” IPSJ Transactions
on Mathematical Modeling and Its Applications (InJapanese) , Vol. 43, No. SIG 10 (TOM 7), pp. 95109, 2002.
[4] K. Nakayama, K. Shimohara, O. Katai, “Dynamically Separating GA: A New Method of Achieving
the System-level Optimality in MAS,” The journal of three dimensional images, Vol.16, No.4, pp.
177-183, 2002.

Conclusion

In this paper, we proposed the Serially and Dynamically Separating Genetic Algorithm (sDS-GA) as an
optimization algorithm for an actual intelligent robot
control system.
In order to verify the validity of sDS-GA for the optimization of intelligent robot control, we applied sDSGA to a target tracing task for a Movable Intelligent
Evolutional Computer (MieC) as an actual intelligent
robot.
Experimental results show that even if there is atime lag between the decision of the agent’s action©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Pr oposal of Genetic Oper ations Reducing the Evaluator Wor kload to the Voice
Quality Conver sion Using Inter active GA
Takenori Nishizono

Satoshi Noami

Yuji Sato

Graduate School of Computer and Information Sciences, Hosei University
3-7-2 Kajino-cho, Koganei-shi, Tokyo, Japani04t0014@k.hosei.ac.jp

Abstract

2. Voice
modificationinteractive GA

We have already proposed the voice quality
conversion system using interactive evolution. Users canconvert our voice to various voice qualities because it
does not require a codebook. However the practicalapplication of the system requires reducing the evaluator
workload. This paper proposes two kinds of genetic
operations, that is, “individual complement” and“adjustment after genetic operations” that aim to reduce
the evaluator workload by improving the efficiency of
tournament selection. The experimental result using the
expediential function shows the proposed operations
have effect of reduction of the workload.
Keywords:
Interactive
Evolution;
Computation; Voice Quality Conversion;

using

Fig. 1 shows the voice modification system using
interactive evolution. The system is trisected by a voice
conversion part which converts input voice withconversion coefficients, a learning part which search theconversion coefficients with genetic algorithms (GA),and a GUI part. This section explains the system with thefocus on the voice conversion part and the learning part.
The voice conversion part treats three prosodic
elements – voice frequency, voice amplitude, and speech
tempo – as control information. It creates new voice
quality by compressing or expanding the above elements
according to conversion coefficients that are real number.
This paper describes the conversion coefficient of
frequency as PIT, of amplitude as POW, and of speech
tempo as TMP. The learning part searches PIT, POW, and
TMP which realize the target voice quality.
The learning part repeats the search processexplained below until the system detects the target. Thissection explains it by assuming the population size is 2N
if it does not apply the operation proposed in this paper.
The individuals in population consist of three kinds ofchromosome. The each chromosome keeps PIT, POW,
and TMP as bit string. First the process performs
tournament selection that a user chooses one candidate
among three. The selection is repeated N times.
Therefore, it has N individuals after finishing theselection. Then it creates new N individuals byperforming one-point crossover and mutation to theselected N individuals. At last it creates the population in
net generation by adding the N individuals before andEvolutionary

1. Introduction
The growth of communication technology increases
variety of the online communication methods. In thesedays, as typified by online meeting services, we cancommunicate by our real voice on line. The developmentof voice processing technology is important for thegrowth of application area of these.
The usual voice modification system realizes a
specified voice quality by modifying another specified
voice according to a codebook. It requires getting thevoice data of conversion object and creating thecodebook corresponding to the conversion. Therefore, it
is only used to improve synthetic voice quality and toconvert between the specified speakers. On the other
hand, we have already proposed the voice modification
system using interactive evolution [1]. Users can convert
a voice selected freely to various voice qualities with it
because it does not require the codebook. However asystem using interactive evolution requires reducing theevaluator workload to put into practical use [2].
We applied tournament selection for evaluator
workload reduction to the system. For further
improvement, this paper proposes two kinds of genetic
manipulation that aims to improve the efficiency of
tournament selection, and reports the evaluation result.

©ISAROB 2005

systemFig. 1: The system configuration outline of voice
quality conversion system.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

after performing the crossover and mutation.
The selection presents individual in population atleast one time, and forbids selecting a candidate twice.
Although the selection reduces the evaluator workload
required in one generation, it may increase the generation
number until convergence. For further reduction, the next
section proposes two kinds of genetic manipulation to
reduce the search time.

chromosome. Next it investigates the maximum and theminimum of real-valued PIT kept in the group. Then it
calculates an absolute value of difference between the
maximum and the minimum of PIT. If the absolute value
is over a threshold which is set beforehand, it creates the
PIT chromosome by using the upper creation method in
Fig. 2. It choices one chromosome from the group, andmakes it the PIT chromosome of individual forcomplement. If the absolute value is under the threshold,it uses the lower method in Fig. 2. It investigates thepercentage of bit value in each digit of PIT chromosome
in the group of PIT, and creates a probability table of bit
value as shown in Fig. 2. Then it creates bit string
according to the table, and makes the created bit string
the PIT chromosome of the individual for complement.

3. Proposal of genetic operations reducing
the evaluator workload
This section proposes two kinds of genetic operations
in order to reduce the evaluator workload. First thesection 3.1 proposes “individual complement” forreduction of evaluation times required in one generation.
The section 3.2 proposes “adjustment after genetic
operations” for diversity maintenance of population. At
the last of this section, we show the search process which
applying the proposed operation.

3.2.

Adjustment after genetic oper ations

The reduction of evaluator workload is realized bycurtailment of evaluation times in one generation.
However it has adverse effect to search ability because it
cut down the population size. The operation “individualcomplement” realizes the curtailment by complementing
the reduction of individuals with individuals createdbased on the individuals selected in same generation. In
addition, this operation aims at improvement of searchability by complementing with individuals created basedon bit information of the selected individuals. This
operation creates the individuals a reduction number of
evaluation times, and uses these to complement.
This section below explains how to create theindividual to complement the individual reduction. The
individual consists of PIT, POW, and TMP chromosomesthat are created by a process explained below. Thissection explains the process if it creates a PIT
chromosome. A similar process creates the others. First
the process takes PIT chromosome form the individuals
selected in same generation, and creates a group of PIT

Next this section proposes the operation “adjustment
after genetic operations” in order to maintain diversity of
population. The “individual complement” proposed inprevious section promotes early detection of the target
individual. However it may make the population drop
into premature convergence because it reduces diversity.
The operation proposed in this section maintains
diversity, and tries to improve the effect of the individualcomplement.
This operation is illustrated by Fig. 3. First it
investigates whether the population has two same
individuals. If the population has, it performs mutation tochromosome in the one individual with fixed probability.
This mutation makes a chromosome mutate within about
between -0.256 and 0.256 from the original value if thechromosome has 3 decimal places. In Fig. 3, the lower
individual in the squared two individuals in mutated. The
convergence speed is easy to become blunt by the target
individual. Therefore, this mutation does not take very
big mutation amount.
The purpose of this operation is near to the purposeof heterogeneous recombination in CHC (crossgenerational
elitistselection,
heterogeneousrecombination, and cataclysmic mutation) [3]. However
this operation differs from the heterogeneousrecombination because this operation maintains theFig. 2: The creation method of chromosome
based on chromosome group

Fig. 3: The image of adjustment after genetic
operations

3.1.

Individual complement©ISAROB 2005

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number of individuals. This operation increases diversity
with keeping the number of individuals, and maintains
the number of candidate’s kinds. According to the above,
this operation aims at the early detection of target
individual.

crossover rate is 1.0. The mutation rate was 0.3. The
threshold used in the individual complement was set
0.0025 to PIT or TMP, and set 0.025 to POW. The
mutation rate in the adjustment after genetic operations
was set 0.4. A coefficient value of the initial individuals
was created randomly within the range from 0.4 to 2.5 if
it is PIT, within the range from 0.4 to 2.5 if it is POW,
and within the range from 0.5 to 2.5 if it is TMP.

At last, we explains the search process if the systemapplies the proposed operations. We assume that the
population size is 2N to contrast with the search processexplained in section 2. First the process performs
tournament selection with threefold choice. The selection
is performed 2N/3 times in one generation. The numberof evaluation times is decided by the lowest times
required to bring up all individuals with the threefold
choice evaluation. As the result of this selection, 2N/3
individuals are selected. Next it increases the number ofthe individuals to N by performing the “individualcomplement”. After that, it creates new N individuals byperforming one-point crossover and mutation to the N
individuals. It creates 2N individuals by adding the N
individuals before and after performing crossover andmutation. Finally, it performs the “adjustment aftergenetic operations” to the 2N individuals, and makes theindividuals the population in next generation. By
performing the proposed operations as described above,
the operations aims at improvement of efficiency of
tournament selection.

4.2.

This section reports the evaluation results. We
performed experiment to the 4 kinds of the system; thesystem that does not apply the proposed operations, thatapplies one of the proposed operations, and that applies
both proposed operations. Each experiment is performed
5000 times, and graph-ized the result. The target
conversion coefficients of these experiments were set 1.5
to PIT, set 4.0 to POW and set 0.8 to TMP.
First we show the result applying the individualcomplement by Fig. 4. The horizontal axis of the graphshows the generation number when the system detected
the target. The vertical axis shows the number of times
which the system could detect on the generation numberof the horizontal axis. The result shows the operation
decreased the average number of generation required
until detection from 50.5426 to 43.314, the shortest
number form 19 to 16, and the maximum number from
128 to 110. On the other hand, the application ofadjustment after genetic operations decreased the shortest
number of generation required until the target detection
from 19 to 14, and the maximum number from 128 to 98.
However, it had little effect to the average number.
Next, this section shows the result when the systemapplied both proposed operations. Fig. 5 shows theexperimental result. This experiment took the best resultin this paper. The average number of generations was
38.3840, the shortest number was 12, and the maximum
number was 84. In addition, as shown in Fig. 6, we
compare this result with the result in the case the systemapplied half of the proposed operations. Although theadjustment after genetic operations had little effect to theaverage number in the previous result, it had effect when
it is applied with the individual complement. By applying
it to the system that applies the individual complement, it
decreased the average number of generations from43.3140 to 38.3840, the shortest number from 110 to 84,
and the maximum number from 16 to 14.

4. Evaluation exper iment4.1. Evaluation method
Since the voice modification system adopts
interactive GA, repeating the experiment of this systemtasks the evaluator. Therefore, as a matter of convenience,
this paper experiments with an evaluation function
described below.
The evaluation function calculates a point of eachcandidate, and choices a candidate which has the lowest
point. The point of a candidate is decided by a total ofthree values. These are absolute value of difference
between each conversion coefficient of the target and thecandidate. The experimental system searches the target
with it, and records the generation number when thesystem detects the target.
This experiment was performed with the parameters
below. The population size was 30. Therefore, it
performs the selection 10 times in one generation if thesystem applies the “individual complement”, andperforms 15 times if it does not apply. PIT and TMP have3 decimal places, and POW has 2. The bit length of PITor TMP chromosome is 13, and that of POW is 11. The©ISAROB 2005

Exper imental r esults

4.3. Discussion
Fig. 4 shows the individual complement is effective
to improve the efficiency of tournament selection. It

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

decreases evaluation times required in one generation by
2/3 in addition to the reduction effect of the generation
number. The adjustment after genetic operations did nothave effect to the average number of generations required
until target detection. However, Fig. 7 shows it reduces
the average number when applied system applied theindividual complement. We consider that this resultshows the adjustment after genetic operations
complements diversity which is decreased by theindividual complement.
The section below estimates how much theevaluation times were reduced by the proposed
operations according to the experimental results. If thesystem did not apply the proposed operations, it
performed evaluation 15 times in one generation andrequired 50.5426 generations until target detection on anaverage. Therefore, it gave users 758.139 times
evaluation on an average. On the other hand, if thesystem applied, it evaluated 10 times and required
38.3840 generations. Therefore, it required 383.84 times
evaluation on an average. Altogether, the proposed
operations decreased the evaluation times by about 49%.
It means that the proposed operations are effective to theefficiency improvement of tournament selection.

The detection times on thenumber of vertical axis

apply the individual complement
250
200
150
100
50
0
1

11

21

31

41

51

61

71

81

91

101

The generation numbers when the system detects
targetFig. 4: The result applying the individual complement

The detection times on thenumber of vertical axis

apply the proposed two operations
not apply the proposed two operations

5. Conclusion
This paper proposed the “individual complement”
and the “adjustment after genetic operations” to reduce
the evaluator workload in the voice modification systemusing interactive GA which applied tournament selection.
As a result of the experiment using a function which
evaluates voices replaced by users, the proposed
operations reduced the evaluation times required until
target detection about 49%. In future, the system requires
further improvement of a system side and interface side
to the system easy to use.

250
200
150
100
50
0
1

11

21

31

41

51

61

71

81

91

101

The generation numbers when the system
detects target

Fig. 5: The result applying the proposed operations

The detection times on thenumber of vertical axis

apply both the operations
apply only the individual complement
apply only the adjustment after genetic operations

References[1] Sato Y.:”Voice Conversion Using Interactive Evolution of
Prosodic Control”, Proc. Of the 2002 Genetic and Evolutionary
Computation Conference (GECCO-2002), Morgan Kaufmann
Publishers, pp.1204-1211 (2002).
[2] H. Takagi: Interactive Evolutionary Computation: Fusion of
the Capabilities of EC Optimization and Human Evaluation. In:“Tutorial Book of the 2001 Congress on Evolutionnary
Compuation”, IEEE Press, 2001.

250
200
150
100
50
0
1

11

21

31

41

51

61

71

81

91

101

The generation numbers when the system
detects target

Fig. 6: The comparison between the result applying
the proposed operations and the result applying the
one-half of operations[3] D.E. Goldberg, “Genetic Algorithms in Search,
Optimization, and Machine Learning”, Addison-Wesley, 1989.

©ISAROB 2005

not apply

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Fitness Modification in Genetic Algorithms for Function Optimization Problems
Takanori Yoshida, Tomoharu Nakashima, and Hisao Ishibuchi
College of Engineering, Osaka Prefecture University
Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan｛dayoshi, nakashi, hisaoi｝@ie.osakafu-u.ac.jp
Abstract

where the shape of the objective function changes overgeneration.

In this paper, we propose a fitness modification
method to improve the performance of genetic algorithms. In the proposed method, the distance fromeach individual to the best one in the current population is used to modify its fitness value. First we examine the behavior of a genetic algorithm for various
function optimization problems from the viewpoint ofdiversity of the population. Next we demonstrate thatthe performance of the genetic algorithm is improvedby the proposed method for those problems that are
difficult for the genetic algorithm to efficiently find optimal solutions. Finally, we examine the performanceof our fitness modification method on a dynamicallychanging function optimization problem.

1

2

Performance of GAs

This section examines the performance of genetic
algorithms on some function optimization problems.
The performance is measured from the viewpoint ofdiversity as well as the search ability.

2.1

Function Optimization Problems

In the computer simulations in this paper, we use
the following functions for function optimization. Allof them are to be minimized.

Introduction

F1 : f1 (xi |1 ≤ i ≤ n) =

n
X

x2i ,

i=1

The balance between exploration and exploitationis undoubtedly a key issue in the research of genetic
algorithms. In genetic algorithms, this issue can be
discussed in terms of the diversity and the selection
pressure. That is, the diversity in the population corresponds to the exploration ability and the selection
pressure corresponds to the exploitation ability of genetic algorithms. It is, however, difficult to find thebest balance between the diversity and the selection
pressure. For example, if one tries to keep a large diversity in the population, the search speed may slow
down. On the other hand, if the selection pressure
is high, the population is likely to converge to a local optimal solution. This issue has been discussed invarious literature (for example, see [1]).
In this paper, we examine the effect of fitness modification on the performance of genetic algorithms fromthe viewpoint of diversity of the population. The aimof our fitness modification is to have high search ability
with a large diversity in an entire population. We also
examine the performance of the fitness modification
method in a dynamic function optimization problem©ISAROB 2005

xi ∈ [−5.12, 5.11],(1)

F2 : f2 (xi |1 ≤ i ≤ n)
" n
#
X
2
= 10 × n +xi − 10 cos(2πxi ) ,i=1

xi ∈ [−5.12, 5.11],(2)

F3 : f3 (xi |1 ≤ i ≤ n)
=

n−1
X·

¸
¡
¡
¢
¢2
2 2
100 xi+1 − xi + 1 − xi ,i=1

xi ∈ [−2.048, 2.047],(3)

F4 : f4 (xi |1 ≤ i ≤ n)
=

n−1
Xµ

¶
¡¡
¢
¡ 2 ¢¢2 ¡¢2
100 i + 1 yi+1 − iyi+ 1 − iyi,

i=1

yi = xi /i,475

xi ∈ [−2.048, 2.047].

(4)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005individuals. Parameter specifications of the genetic
algorithm in our experiments are shown in Table 1.
Table 2 shows the average function value of the obtained solutions over ten runs and the number of successful runs (R) where the optimal solution was found.
We can see that the genetic algorithm could not findthe optimal solution of F3 nor F4. In Figure 2 andFigure 3, we show the entropy measure of 20 neighboring individuals for the best individual and the entropy measure of the entire population, respectively.
From these figures, we can see that during the execution for F3 and F4 the genetic algorithm keeps higher
diversity maintained in 20 neighboring individuals andin the entire population than for F1 and F2. This is
why the genetic algorithm could not find the optimal
solution of F3 nor F4. We also show in Figure 4 the
distribution of individuals in the final generation for
F3. From this figure, we can see that the population
does not converge around the optimal solution.

Function F1 is the first function of the DeJong’s
test suite, Function F2 is the Rastrigin function, Function F3 is the Rosenbrock function and Function F4 isthe ill-scaled Rosenbrock function. The optimization
problems of the above functions are static (that is,they do not change their shapes during the executionof optimization algorithms).

2.2

EntropyIn this paper, we use entropy as a measure of thediversity of the population [2]. The entropy measureof a population P is calculated as follows:
E(P ) = −LX
{pl log pl + (1 − pl ) log(1 − pl )},(5)lwhere pl is the proportion of the value 1 in the l-th bit
in the population P, and L is the length of a bit string(i.e., an individual). We examine the entropy measureof the following subpopulations: 20 × m neighboring
individuals for best one in genotype space, where m =1, 2, 3, …. Figure 1 shows the subpopulations of which
we examine the entropy. A low entropy measure meansthat a large number of individuals are the same bit
string.

Table 1: Parameter settings
Population size
Crossover probability
Mutation probability
Stopping criterion (no. of generations)

400
1.0
0.05
500

Genotype space
Elite individual

Table 2: Simulation results for each problem
Function value R
F1
0.0
10
F2
0.0
10
F3
0.0002190F40.000155020 individuals (m=1)
40 individuals (m=2)
60 individuals (m=3)

380 individuals (m=4)

In this section, we propose a fitness modification
method and demonstrate that the performance of thegenetic algorithm is improved by our fitness modification method.

Figure 1: Examined subpopulations (m=1,2,3,4)

2.3

Fitness Modification

Experiments
3.1

We applied a genetic algorithm to each function optimization problem ten times. During the executionof the algorithm, we monitored the diversity of the
subpopulation as well as the function values of elite©ISAROB 2005

Fitness Modification Method

In our fitness modification method, the best individual with the lowest function value in the populationis selected from the current population. The selected

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

0.75

Optimal solution

2.047

Entropy

0.65
F1

0.55

F2

0.45

F3

0.35

x2

F4

0.25
0

20

40

60

80

100

Generation

Figure 2: Entropy measure of 20 neighboring individuals for the best individual-2.048-2.0480.90

Entropy

2.047

Figure 4: Distribution of individuals in the final generation for F3

0.85
F1

0.80

F2

0.75

Table 3 shows the average function value of obtained solutions over ten runs and the number of runs
where the optimal solution was found. In this table,
the genetic algorithm (GA) with our fitness modification method is denoted as GA+. From this table, we
can see that the performance of genetic algorithms was
improved by our fitness modification method. Figure
5 shows the entropy of various subpopulations in the
final generation. From this figure, we can see thatthe entropy was decreased by our fitness modification
method.

F3

0.70

F4

0.65
0

20

40

60

80

100

Generation

Figure 3: Entropy measure of the entire populationindividual is called a reference individual. The fitness
value is calculated for each individual in the population for a function minimization problem as follows:(µf itnessi = Fi ×

10di
D

¶2

Table 3: Simulation results for F3 and F4

)
+1 ,

(6)
F3

where Fi is the function value of the i-th individual,
di is the phenotype distance from the i-th individual
to the reference individual, and D is the length of the
diagonal line in the phenotype search space.
The proposed fitness modification method in (6)means that the fitness value is increased if di is large(i.e., if the i-th individual is far from the reference individual.). On the other hands, if di is not large, thefitness value of an individual is almost the same as its
objective function value.

3.2

x1

F4

4

R
0
10
0
10

Adaptability for Dynamic Environment

Dynamic environments form a difficult class of
optimization problems for evolutionary algorithms.
Branke [3] suggests that his memory-based genetic algorithm is able to efficiently adapt to changing environments. In this section, we examine the performanceof our fitness modification method for a dynamicallychanging function.

Experiments

In this section, we use F3 and F4 in computer simulations. We applied the genetic algorithm with ourfitness modification method to each function optimization problems ten times.

©ISAROB 2005

GA
GA+
GA
GA+

Function value
0.0002190.0
0.000155
0.0

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

GA(F1)

GA(F2)GA(F3)

GA+(F3)GA(F4)

GA+(F4)0.9

GA+(TS=2)

GA(TS=3)

GA(TS=5)

0.1

0.8

0.08
Function value

0.7
Entropy

GA(TS=2)

0.6
0.5
0.4
0.3
0.2
20

60

0.06
0.04
0.02
0

100 140 180 220 260 300 340 380

0100

Number of neighboring individuals

Function Optimization Problems

500

Summary

In this paper, first we examined the behavior of
the genetic algorithm for several function optimization
problems from the viewpoint of the diversity of the
population. Next, we examined the effect of our fitness
modification method for difficult problems where thegenetic algorithm can not efficiently find their optimal
solutions. In our computer simulations, we showedthat the performance of the genetic algorithm was improved by our fitness modification method. Finally, we
examined the performance of our fitness modification
method on a dynamically changing function optimization problem. It was shown that the fitness modification method improved the adaptability of the genetic
algorithm.

In this section, We examine the performance of our
method on the following function:
F5 : f5 (xi |1 ≤ i ≤ n)
¸
n−1
X·
¡
¢2 ¡
¢2
=
100 yi+1 − yi2 + 1 − yi ,i=1t− 1), xi ∈ [−2.048, 2.047](7)500
where t is the index of the generation, which serves as atime index during the execution of genetic algorithms.
The characteristic feature of the above function is thatthe optimal point gradually moves over generations
while the optimal function value is always the same(i.e., 0.0).
yi = xi − (References

Experiments[1] D. Whitley, “The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of
Reproductive Trials is Best”, Proc. of the third
International Conference on Genetic Algorithms,
pp. 116–121, 1989.

We examine the performance of the genetic algorithm and the genetic algorithm with our fitness modification method on the dynamically changing function
optimization problem in (7). The genetic algorithms
were executed ten times in the same manner as in theprevious section. In the genetic algorithm, we examined three specifications of the tournament size (T S):
T S=2, 3, 5. Figure 6 shows the best function value for
F5 at each generation of each algorithm. From Figure
6, we can see that the modified version of the genetic
algorithm (GA+) is more adaptable to the changing
environment than the original version without the fitness modification method.

©ISAROB 2005

400

Figure 6: Best function value for F5

5

4.2

300

Generation

Figure 5: Entropy measure of subpopulations in the
final generation

4.1

200

[2] E. K. Burke, S. Gustafson, G. Kendall, “Diversity in Genetic Programming: An Analysis of Measures and Correlation with Fitness”, IEEE Trans.
on Evolutionary Computation, Vol. 8, No. 1, pp.
47-62, 2004.
[3] J. Branke, “Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems”,
Proc. of Congress on Evolutionary Computation,
pp. 1875-1882, 1999.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Data Mining using Genetic Network Programming
Takeshi FUKUDA* Kaoru SHIMADA* Kotaro HIRASAWA* Takayuki FURUZUKI**Graduate School of Information, Production and Systems, Waseda University
2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japane-mail: kurt2@moegi.waseda.jp

Abstract

statistical techniques such as decision tree, correlation
rule, and cluster analysis have been proposed in orderto carry out data mining efficiently.[1][5]Finding the correlation rules[1][5], that is, discovering the relevance or pattern existing in the set ofitems of the records, is one of the methods which canbe easily understood.
But, the cost of extracting the correlation rules using the traditional statistical techniques is fairly high.
In this paper, we propose a novel data mining technique using Genetic Network Programming (GNP)[2]
whose gene has directed graph structures in order toovercome the disadvantages of the conventional methods.
A complex mathematical algorithm could not solve
the problems caused by the large size of database but
an evolutional algorithm could do it because GNP hassuch features as 1) reusing the nodes and 2) having thedirected graph structure. In other words, GNP can
extract correlation rules efficiently using the directed
graph structure and keeping the size of the individuals
fixed due to the reused nodes.
The fitness of GNP having a number of correlation
rules is calculated and GNP is evolved by the calculated fitness.
Data mining and GNP are briefly discussed in section 2 and 3, respectively. The basic structure of the
proposed method is described in section 4, followed bysimulation results in section 5, and the conclusions in
section 6.

Recently, Data Management System (DMS) is usedin various fields, and the importance of data mining
extracting important rules from a large-scale databasehas been recognized widely. Although the conventional data mining using statistical techniques has
been developed, a novel algorithm of data mining using Genetic Network Programming (GNP)is proposedin this paper. The proposed system using GNP withdirected graph structures can extract automaticallythe correlation rules showing the importance of data.
GNP has been proposed and studied as a new methodof evolutionary computations. GNP is constructed by
the network structure whose gene consists of the directed graph, so it is possible to search solutions effectively by the implicit memory function of the network
structure. Until now, the applicability and availabilityof GNP to the real-world applications have not beenstudied, whereas it has been applied to virtual-world
examples such as Tile-world and its effectiveness has
been proved through the comparison with GP. We describe a method to extract correlation rules automatically using support and confidence, and experimental
results are described to show the effectiveness of the
proposal method.
Keywords: Data Mining, Correlation Rule, Genetic Network Programming, Evolutional Algorithm.

1

Introduction
2

Information is spreading in the informationoriented society almost every day. In this circumstance, Data Management System (DMS) has been
attracted recently. People interested in DMS have not
only managed the data but tried to extract knowledge
from the data, and this has become important themein these days. In this sense, data mining capable of extracting hidden knowledge from an enormous databasehas been noticed recently. Database techniques using©ISAROB 2005

Data Mining (Market Basket Analysis)

In this section, the market basket analysis is reviewed briefly, which is used in the simulation of thispaper as the benchmarking problem.
When we deal with the shopping cart filled withgoods in a supermarket, we can talk about the market
basket analysis.[1][5] The shopping cart tells what a

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

customer buys. Each customer buys a quality of different items because the characteristics of customers
are different from each other. Therefore, we can get a
lot of information through them.
We can analyze the purchase lists of buyers in
the market basket analysis to understand the generaltrend of the purchase of customers. The market basket
analysis can give us the insight about the information
on buying goods and can suggest us the new design ofthe shopping site because it shows us what combination of the goods is sold well. Furthermore it can show
us where better sites for special goods are.
Merits of the market basket analysis are the availability and clearness of the results expressed by correlation rules. The correlation rules have intuitional
properties because they express what kinds of relations
the goods on the shelves have each other. Namely, themerits of finding the correlation rules by the conventional methods are:Figure 1: The basic structure fo GNP individual

nodes which are roughly classified into two kinds ofnodes: JUDGEMENT NODE and PROCESSING NODE.
JUDGEMENT NODEs correspond nearly to elementaryfunctions of GP and PROCESSING NODEs correspond almost to terminal symbols of GP. JUDGEMENT NODEs are
the set of J1 , J2 , · · · , Jm , which work as some kinds of
judging functions. On the other hand, PROCESSING
NODEs are denoted by the set of P1 , P2 , · · · , Pn , which
work as some kinds of action/processing functions.
The practical roles of these nodes are predefined andstored in the library by supervisors.[3]GP’s elementary functions and terminal symbols
are repeatedly used in a tree structure. In the sameway, there are some J1s , J2s , P1s , P2s and so on in
GNP as shown in Fig.1. These JUDGEMENT NODEs and
PROCESSING NODEs are the essential elements of GNP.
The number of these nodes may be determined as aresult of evolution like GP. Actually, GNP can use thisstrategy, in other words, GNP can adopt evolving the
genotypes with variable number of nodes, but in thispaper GNP evolves only the networks with the predefined number of nodes. It would be better to say that
GNP here evolves the genotypes with fixed number ofnodes. We set the number of each node in GNP equal
to each other, e.g., J1 ×3, J2 ×3, · · · , P1 ×3, P2 ×3, · · · ,and so on.
Additional specific nodes, start node S, is involvedin GNP. Start node indicates the start point of GNP,
which corresponds to GP’s root node.
Once GNP is booted up, the execution starts fromthe start node, then the next node to be executed is determined according to the connection from the current
activated node. If the activated node is JUDGEMENT

1. It is easy to understand the correlation results
clearly.
2. It is powerful to analyze the complicated data.
3. It is suitable to the data with variable size.
4. It is simpler than other algorithms for analyzing
the data.
However they have several shortcomings:
1. The cost of calculating correlation rules increases
exponentially with the size of problems.
2. It is difficult to deal with rare items.
We will show that the proposal method can overcome the above shortcomings by analyzing the market
basket analysis using GNP in this paper.

3

Genetic Network Programming

In this section, Genetic Network Programming(GNP) is explained in detail. Basically, GNP is anextension of GP in terms of gene structures. The original idea is based on the more general representation
ability of graphs than that of trees.[2]3.1

Basic structure of GNP

The basic structure of GNP is shown in Fig.1. As
shown in Fig.1, the directed graph structure is used
to represent individuals. GNP is composed of plural©ISAROB 2005

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Figure 2: The genotype expression of GNP node

NODE, the next node is determined by the judgement
at the activated JUDGEMENT NODE. When PROCESSINGNODE is executed, the next node is uniquely determinedby the single connection from PROCESSING NODEs.
The genotype expression of GNP node[4] is shownin Fig.2. This describes the gene of node i, then theset of these genes represents the genotype of GNP individuals. All variables in these genes are described byinteger. N Ti describes the node type, N Ti = 0 when
the node i is JUDGEMENT NODE, N Ti = 1 when the node
i is PROCESSING NODE. IDi is an identification number, e.g., N Ti = 0 and IDi = 1 mean node i is J1 .
Ci1 , Ci2 , · · · , denote the nodes which are connectedfrom node i firstly, secondly, · · · , and so on depending on the arguments of node i. The total number ofconnection genes depends on the arity of the node’sfunction. di and dij are the delay time. They are thetime required to execute the processing of node i anddelay time from node i to node Cij , respectively. GNP
can become materialized more realistically by settingthese delays.

3.2

Figure 3: Mutation of GNP

Figure 4: Crossover in GNP

Genetic operator of GNP

functions of the nodes, but evolves the connections
between nodes.

The following genetic operators[4] shown in Fig.3
and 4 are used in GNP. Mutation operator affects
one individual. All the connections of each node arechanged randomly by mutation rate of Pm (shadowedin Fig.3). Crossover operator affects two parent individuals All the connections of the uniformly selected
corresponding nodes in two parents are swapped eachother by crossover rate of Pc between the two parents(shadowed in Fig.4). GNP evolves the fixed numberof nodes, as I mentioned before, crossover is applied
to the corresponding nodes selected uniformly in twoparent genotypes as shown in Fig.4.[2][3][4]Note that these genetic operators will not change
any node functions, they only change the connection
among the nodes. Therefore GNP doesn’t evolve the©ISAROB 2005

4

Data Mining Structure using GNP

In this section, the proposed method for extracting
correlation rules is described. First, we describe thefundamental concept dealing with the application of
GNP to data mining.
1. Set the structural condition of GNP to extract
correlation rules effectively.
2. Express a correlation rule by transition from thegroup of judgment nodes to the group of processing nodes in GNP.

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3. Determine if the extracted rules are good enoughor not using the support and the confidence.

4.1

Table 1: Database example
Database
ITEM
TID
A B C D E
1 0 1 1 01
0 1 1 0 12
1 1 1 0 13
0 1 0 0 1
4

Expression of Correlation Rules with
GNP

Here, how to express the correlation rules using
GNP is stated. The correlation rule shows the following relation between attributes in database,if X then Y (X ⊂ I, Y ⊂ I, I : item set)(1)

rules having more than minimum Support andConf idence values in database, GNP has a featurethat it can extract correlation rules having high fitness considering the structural condition of GNP.

JUDGEMENT NODE corresponds to each element of set
X, while PROCESSING NODE deal with each element of
set Y , when a correlation rule is expressed by GNP.
We define a correlation rule as the transition from thestarting node S to the PROCESSING NODE following a
JUDGEMENT NODE J. For example, a correlation rule ofif A, B then C, D is expressed by the node connection
like Fig.5.

4.3

Fitness

Individuals of GNP are evolved by an evolutionary
algorithm using Support and Conf idence as the index
of fitness. The fitness of the proposed algorithm uses
the following:
1
F itness =
|L|

Ã
X

!
Support(l) × Conf idence(l)

l∈L

(3)

Figure 5: The expression of the correlation rule using
nodes in GNP

4.2

Support and Confidence

4.4

When the correlation rule is expressed by (X ⇒ Y ),where X is called antecedent and Y is called consequent, we define Support as the ratio of the records
satisfying both X and Y , and Conf idence as the ratioof records satisfying Y among the records containing
X.[1][5] The following Support and Conf idence areused as the index to evaluate the importance of thecorrelation rule.
Support(X ⇒ Y )Conf idence(X ⇒ Y ):Support(l)Confidence(l)::set of the number of rulesin GNP
Support of rule l in GNP
Confidence of rule l in GNPExtracting correlation rules by GNP

We set up several constraints to extract the correlation rules effectively in designing GNP. First, GNP
reuses the nodes. If the node already used is reused at
JUDGEMENT NODEs, a loop is created among JUDGEMENTNODEs. To overcome the loop problem, we set up several JUDGEMENT NODEs in GNP which express the sameitem.
In addition, we set several starting nodes connecting to JUDGEMENT NODEs, which means that all therules are created by searching all the tuples in the
database from the starting nodes.
The method to generate correlation rules using
GNP is as follows:(2)

For example, Support of (X(A, C) ⇒ Y (D)) is 0.25,
and the Conf idence is 0.5 in Table.1. In the case of(X(B, C) ⇒ Y (E)), Support is 0.5 and Conf idenceof it is 1.0.
Comparing with the conventional data mining
method (apriori algorithm[1]) that extracts all the©ISAROB 2005

L

1. Generate if part of the rule by searching the tuples
in the database.
2. Generate then part of the rule by transfering the
PROCESSING NODEs in GNP.

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crossover probability were set at 0.01 and 0.1
respectively[2][3][4]. We also used the elite strategy
to move the elite individual to the next generation.[2]5.2

Simulation Results

Database is made up of 10 attributes (from A to J)
and 100 records. Each attribute is a binary code. Assuming the market basket analysis as an benchmarking
problem, 0 denotes that the customer doesn’t buy theitem and 1 indicates that he buys the item. Moreover,the database used in the simulation is made artificially.
For example, we set five artificial rules in the databaselike the records from 1 to 6 are the ones for extractTable 2: Support and Conf idence of the given 5 kindsof rulesCorrelation Rule Support Confidence
0.500000
0.050000
A⇒J
10.461538
0.060000B⇒E20.636364
0.070000C⇒D
30.666667
0.060000F ⇒G
40.428571
0.060000
H⇒I
5Figure 6: The flow chart of the proposed algorithm

We use a general evolutionary algorithm and selectthe better individuals for the next generation.
The flow chart of the proposed algorithm is as follow(See Fig.6).
1. Initialize GNP randomly.

Table 3: The results wiht evolutionary algorithm
Correlation Rule Support Confidence
0.500000
0.050000
A⇒J
10.461538
0.060000B⇒E20.636364
0.070000C⇒D
30.666667
0.060000F ⇒G
40.428571
0.060000
H⇒I
50.100000
0.010000
A⇒C
6
0.166667
0.010000
BE ⇒ D
7
0.076923
0.010000B⇒H
80.214286
0.030000E⇒D
90.181818
0.020000
C⇒J
100.400000
0.060000
I⇒H
110.250000
0.030000D⇒E
120.416667
0.050000
J ⇒A
131.000000
0.010000
DJ ⇒ C140.200000
0.030000I⇒G
150.181818
0.020000
C⇒G
16
1.000000
0.010000
DF ⇒ E170.166667
0.020000
J ⇒C
18
0.060000
0.375000
G⇒F
19
20
B⇒G
0.000000
0.000000
21I⇒A
0.010000
0.066667
22
D⇒C
0.070000
0.583333
23E⇒B
0.060000
0.428571

2. Apply genetic operations to GNP.
3. Generate correlation rules using GNP.
4. Evaluate the generated rules by Support andConf idence of the rules.
5. Calculate the fitness of each individual.
6. Select the better individuals to the next generation.
7. Move to 2, until a terminal condition meets.

5

Simulation

The effectiveness of the proposed algorithm isstudied by extracting the correlation rules from thedatabase having 10 attributes.

5.1

Simulation Parameters

200 individuals of GNP are used consideringthat many rules should be extracted in a shortrunning time. The mutation probability and the©ISAROB 2005

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0.1
Elite
Average
Minimum

Table 4: Comparing the elite individual at each generation
The total number ofFitness
Generation
extracted rules
0.076441
89300
0.07815192500
0.080956
89
1000

0.08

0.06

0.04

0.02

ing (A ⇒ J), the records (7 ∼ 12) are for (B ⇒ E),
the records (13 ∼ 18) are for (C ⇒ D), the records(19 ∼ 24) are for (F ⇒ G), and the records (25 ∼ 30)
are for (H ⇒ I). The records from 31 to 100 are maderandomly.
Table.2 shows Support and Conf idence of thegiven 5 kinds of rules and Table.3 shows the resultswith the proposed algorithm. Comparing Table.2 andTable.3, we can see that the rules from 1 to 5 in Table.2
and.3 are the same. The results from 6 to 23 in Table.3
denote the rules extracted from the records (31 to 100).
These results show that it is possible to extract rules
using the proposed algorithm.
Table.4 shows the total number of rules and thefitness from the elite individual at each generation.
According to Table.4, we can see the elite individual extracted 92 rules at 500th generation, which are
more than the ones of 1000th generation. However, we
also see the fitness at 1000th generation is higher than
500th generation. We can see the elite individual at
1000th generation extracted better rules than the one
at 500th generation according to the proposed algorithm because the fitness of 1000th generation is higher
than that of 500th generation, although the elite individual at 500th generation extracted more rules thanthat at 1000th generation.
The simulation results show that Support andConf idence of the extracted rules increase as generation goes on. This indicates that the proposed algorithm is useful to extract rules and can extract manyeffective rules through generations.
Fig.7 denotes the fitness of the elite individual, theaverage fitness values over all individuals, and theworst fitness at each generation. We extracted therules of Table.3 from the elite individual.

6

0
0

400

600

800

1000

Figure 7: Simulation result

proposed method in terms of extracting rules. Concretely, the fitness of each individual is calculated using Support and Conf idence of the database, the better individuals having the higher fitness are selected,
and finally the effective rules from the elite individual
are extracted.
We have studied the effectiveness of the data mining
using the proposed algorithm so far. The comparisonof the proposed method with other methods is goingunderway.

References[1] T. Fukuda, Y. Morimoto, and T. Tokuyama, “DataMining - in japanese -”, Kyoritsu Press, 2002.
[2] K. Hironobu, K. Hirasawa, J. Hu, J. Murata,
M. Kosaka, ”Network Structure Oriented Evolutionary Model : Genetic Network Programming”,
Trans. of the Society of Instrument and Control
Engineers, Vol.38, No.5, pp.485-494, 2002.
[3] K. Hirasawa, M. Okubo, J. Hu, J. Murata, Y.
Matsuya, ”Co-evolution of Hetero Multiagent Systems using Genetic Network Programming”, IEEJ
Trans. EIS, Vol.123, No.3, pp.554-551, 2003.
[4] K. Hironobu, K. Hirasawa, J. Hu, J. Murata,”Variable Size Genetic Network Programming”,
IEEJ Trans. EIS, Vol.123, No.1, pp.57-66, 2003.

Conclusions[5] David J. Hand, Heikki Mannila, Padhraic Smyth”Principles of Data Mining”, MIT Press, 2001.

We proposed the data mining technique using GNP
in this paper. And we studied the effectiveness of the©ISAROB 2005

200

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Performance Comparison between Fuzzy Rules and Interval Rules in
Rule-Based Classification Systems
Satoshi Namba, Yusuke Nojima, and Hisao Ishibuchi
Department of Industrial Engineering
Osaka Prefecture University
Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan{snamba, nojima, hisaoi}@ie.osakafu-u.ac.jp
Abstract

systems are homogeneously triangular. Homogeneous
fuzzy discretization works well because there are
significant overlaps between adjacent fuzzy sets. Decisionboundaries are adjustable over those overlapping regions
using the certainty grade of each fuzzy rule. On the other
hand, antecedent intervals in interval rule-basedclassification systems are generated from given numerical
data using an entropy measure in some approaches to thedesign of decision trees [3]. We combine some advantagesof fuzzy rule-based systems and interval ones into fuzzified
interval rule-based classification systems. That is, theantecedent fuzzy sets in fuzzified interval rule-basedclassification systems are derived from intervals in theinterval rule-based systems by fuzzification.
In this paper, we compare the performance of fuzzy
rule-based classification systems with that of interval rulebased classification systems. All the rule-basedclassification systems are constructed by using our EMO
approach [2]. Furthermore, we perform the same
computational experiments with fuzzified interval rulebased classification systems. Experimental results showthat interval rule-based classification systems can obtainhigh accuracy on training data, while high generalization
ability on test data can be obtained by fuzzy rule-basedclassification systems. It is also shown that the generalization ability of the interval rule-based classification
systems can be improved by their fuzzification.

In this paper, we compare the performance of fuzzy
rule-based classification systems with that of interval rulebased classification systems on real-world data sets. We
generate multiple rule-based classification systems using
an evolutionary multiobjective optimization (EMO)
algorithm. In our EMO approach, the classification
accuracy of rule sets is maximized, while their complexity
is minimized. A number of non-dominated rule sets withdifferent accuracy and different complexity are obtained byits single run. Through computational experiments on somedata sets, we compare the generalization ability of fuzzy
rule-based systems with that of interval rule-based systems
using the ten-fold cross-validation technique. Furthermore,
we perform the same computational experiments withfuzzified interval rule-based classification systems. Fromexperimental results, we clearly demonstrate characteristics
of each classification system: fuzzy rule-based classification systems have high generalization ability on test data,interval classification systems can achieve high accuracy
on training data, and fuzzified interval classification
systems have these advantages of two other classification
systems.

1

Introduction

Fuzzy rule-based systems have been applied to various
problems such as control, function approximation andpattern classification. The tradeoff between the accuracy
and the complexity of fuzzy rule-based systems was often
discussed in recent studies [1, 2]. When we design fuzzy
rule-based classification systems, it should be noted thatthe maximization of the accuracy on training data often
leads to the overfitting, which degrades the actual
performance of fuzzy rule-based classification systems on
test data.
Fuzzy rule-based systems are interpretable for humanusers. In our former study on fuzzy rule extraction [2],antecedent fuzzy sets in fuzzy rule-based classification©ISAROB 2005

2

Rule-Based Classification Systems

We use the following type of rules for an n-dimensional
pattern classification problem:
Rule Rq : If x1 is Aq1 and ... and x n is Aqn
then Class C q with CFq ,(1)

where x = ( x1 , ..., x n ) is an n-dimensional pattern vector,
A q = ( Aq1 , ..., Aqn ) is an antecedent part (i.e., fuzzy sets
in fuzzy rule-based systems, and intervals in interval ones),
C q is a consequent class, and CFq is a rule weight (i.e.,

485µ A q (x p ) = µ Aq1 ( x p1 ) ⋅ µ Aq 2 ( x p 2 ) ⋅ ... ⋅ µ Aqn ( x pn ) , (2)where µ Aqi ( ⋅ ) is the membership function of anantecedent set Aqi . We first calculate the confidence of theclassification rule “ A q ⇒ Class h ” in the field of data
mining:c( A q ⇒ Class h) =∑ µ A q (x p )x p ∈Class hmax

h =1, 2,..., M

1

1

1

0

1

Attribute value

1

0

1

m

∑ µ A q (x p ) . (3)K | D j | M ⎛ | D jh || D jh | ⎞

h =1⎝

j

H ( A1 , ..., AK ) = − ∑

p =1

j =1

∑ ⎜⎜ | D | ⋅ log 2 | D | ⎟⎟ , (7)
|D|
j

⎠

where ( A1 , ..., AK ) are K intervals generated by thediscretization of an attribute, D j is the set of training
patterns in the interval A j , and D jh is the set of training
patterns from Class h in D j . We can efficiently find the
optimal ( K − 1) cutting points that minimize the classentropy measure in (7). In computational experiments, we
use five partitions with K intervals where K = 1,2,3,4,5 as
in Fig. 2 (see [4]).

{c ( A q ⇒ Class h) } . (4)M∑ c( A q ⇒ Class h) . (5)h =1h ≠ Cq

K=5
K=4
K=3
K=2
K=1

If the rule weight CFq is negative, we do not generate therule “ A q ⇒ Class h ”.
Let S be a set of classification rules. A new patternx p is classified by a single winner rule Rw , which is
chosen from the rule set S as

0
1
Attribute value
Figure 2: Five partitions with different granularities.

µ A w ( x p ) ⋅ CFw = max{µ A q (x p ) ⋅ CFq | Rq ∈ S} . (6)2.3 Fuzzified Interval Rules

The winner rule Rw has the maximum product of thecompatibility grade and the rule weight in S.

We also examine the performance of fuzzy rule-based
systems of the antecedent fuzzy sets in Fig. 3. The
antecedent fuzzy sets of those rules are generated byfuzzifying intervals in Subsection 2.2. For more details on
our fuzzification, see [5].

2.1 Fuzzy Rules

MembershipAll attribute values are normalized into real numbers in
the unit interval [0, 1]. As antecedent fuzzy sets, we use“don’t care” and 14 homogeneous triangular fuzzy sets in
Fig. 1 (see [2]).

2.2 Interval Rules
The whole domain interval is divided into K intervals.
To specify ( K − 1) cutting points for each attribute, we usean optimal splitting method [3] based on the class entropy
measure:©ISAROB 2005

Attribute value

1

Attribute value
Attribute value
Figure 1: Four fuzzy partitions used in our computer
simulations.

Using the confidence measure, we specify the rule weight
CFq asCFq = c( A q ⇒ Class C q ) −00

The consequent class C q of the rule Rq is specified by
identifying the class with the maximum confidence asc( A q ⇒ Class C q ) =1

Membership

Membership

Membershipcertainty grade). First we explain how the consequent classC q of the classification rule Rq in (1) is specified fromnumerical data. Let us assume that we have m labeled
patterns x p = ( x p1 ,..., x pn ) , p = 1,2,..., m from M classes.
We define the compatibility grade µ A q of each training
pattern x p with the antecedent part A q of the rule R q
using the product operator as

Membership

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1

0

0
1
Attribute value
Figure 3: Fuzzy sets derived from intervals.

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3

Heuristic Rule Extraction and Genetic
Rule Selection

from 0 to 1. This is for efficiently decreasing the number ofrules. The other is the removal of unnecessary rules. Since
we use the single winner-based method for classifying eachpattern, some rules in S may be chosen as winner rules forno patterns. We can remove those rules without degrading
the first objective with respect to the classification
accuracy. At the same time, the second and third objectives
with respect to the complexity are improved by removing
unnecessary rules. Thus, we remove all rules that are notselected as winner rules for any training patterns from therule set S. The removal of unnecessary rules is performed
after the first objective is calculated for each rule set andbefore the second and third objectives are calculated.

As in our former studies on the design of fuzzy rulebased classifiers [2], we handle knowledge extraction as
the following three-objective rule selection problem:
Maximize f1 ( S ) and minimize f 2 ( S ) and f 3 ( S ) ,(8)

where S is a subset of candidate rules, f1 ( S ) is the numberof correctly classified training patterns by S, f 2 ( S ) is thenumber of fuzzy rules in S, and f 3 ( S ) is the total rule
length in S. The number of antecedent conditions of eachfuzzy rule is referred to as the rule length in this paper. To
solve (8), we apply a two-step method (i.e., heuristic rule
extraction and genetic rule selection).
First, in heuristic rule extraction, a pre-specified numberof candidate rules with the largest values of the following
criterion are found for each class.

f SLAVE ( R q ) = s ( A q ⇒ Class C q ) −4

4.1 Settings of computational experimentsM∑ s( A q ⇒ Class h) ,We use six data sets in Table 1. These data sets areavailable from the UC Irvine machine learning repository.
We do not use incomplete patterns with missing values inour computational experiments. All attributes are handled
as continuous attributes in this paper.
We evaluate the performance of our EMO approach by
ten independent executions (with different data partitions)of the whole ten-fold cross-validation (10CV) procedure(i.e., 10 × 10CV ) in [3]. We extract 300 candidate rules foreach class in the heuristic greedy manner using the SLAVE
criterion (i.e., 300M candidate rules in total where M is thenumber of classes).
The NSGA-II [7] is applied to the extracted 300M
candidate rules to find non-dominated rule sets withrespect to the three objectives. Each of the obtained rulesets is evaluated by test data. We use the following
parameter values in the NSGA-II:
Population size: 200 strings,
Crossover probability: 0.8 (uniform crossover),
Biased mutation probabilities:p m (0 → 1) = 1 / 300M and p m (1 → 0) = 0.1,
Stopping condition: 5000 generations.
During ten executions of the whole 10CV procedure,
the NSGA-II is employed 100 times. We examine theaverage error rates only for combinations of the number ofrules and the average rule length obtained from more than
30 (out of 100) runs. We also calculate the average error
rates on training data and test data using the majority votewhere all the obtained rule sets in each run except for too
small ones are used for the classification of each pattern.
Due to their poor performance, we do not use small rulesets with less rules than the number of classes.

h =1
h ≠ Cq(9)where f SLAVE ( ⋅ ) is a modified version of a rule
evaluation criterion in an iterative genetic learningalgorithm called SLAVE [6], and s ( ⋅ ) is the support
defined as follows:s ( A q ⇒ Class h) =1
∑ µ A q (x p ) .
m x p ∈Class h(10)

For designing rule-based systems with high comprehensibility, only short rules are examined as candidate rules.
This restriction on the rule length is consistent with thethird objective (i.e., to minimize the total rule length) of
our three-objective formulation in (8).
Second, in genetic rule selection, let us assume that N
rules have been extracted as candidate rules using the
SLAVE criterion (i.e., N / M rules for each class). A
subset S of the N candidate rules is represented by a binary
string of the length N as
S = s1 s 2 ⋅ ⋅ ⋅ s N ,(11)where s j = 1 and s j = 0 mean that the j-th candidate rule
is included in S and excluded from S, respectively. Since
rule sets are represented by binary strings, almost all
multiobjective genetic algorithms are applicable. In thispaper, we use the NSGA-II [7] because its search ability ishigh and its implementation is relatively easy. We
incorporate two problem-specific heuristic tricks into the
NSGA-II. One is biased mutation where a larger
probability is assigned to the mutation from 1 to 0 than that

©ISAROB 2005

Computational Experiments

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Table 1: Data sets used in our computational experiments.
Data set Attributes
Patterns
Classes
Breast W
9683*
2
Diabetes
8
768
2
Glass
9
214
6
Heart C
13
297*
5
Sonar
60
208
2
Wine
13
178
3
* Incomplete patterns with missing values are not included.

Table 2 shows error rates on test data by majority voting
for each data set. The results show that the generalization
ability of the interval rule-based classification systems canbe improved by their fuzzification for almost all data sets.

5

We generated multiple rule-based classification systems
using an EMO algorithm with respect to the classification
accuracy and the complexity, and compared theperformance of fuzzy rule-based classification systems
with that of interval rule-based classification systems onsome benchmark data sets. Experimental results showedthat interval rule-based classification systems can obtainhigh accuracy on training data, while high generalization
ability on test data can be obtained by fuzzy rule-basedclassification systems. It was also shown that thegeneralization ability of the interval rule-based classification systems can be improved by their fuzzification.

4.2 Experimental Results
We show the result on the Cleveland heart disease dataset (i.e., Heart C in Table 1) in Fig. 4. A number of nondominated rule sets with different accuracy and different
complexity were obtained for each classification system. In
the comparison between fuzzy rules and interval rules, we
can see that interval rule-based classification systems canobtain high accuracy on training data, while highgeneralization ability on test data can be obtained by fuzzy
rule-based classification systems from this figure. We canalso see that fuzzified interval rule-based classification
systems are comparable to interval rule-based classification
systems on training data, while their generalization ability
outperforms two other classification systems.
Interval

Fuzzified int.

Interval

Fuzzy

Majority voting (fuzzy)
Majority voting (interval)

Majority voting (fuzzified int.)

Majority voting (fuzzified int.)

45
40
35
30
25
20
15
5

10

15

20

25

Number of rules(a) Error rates on training patterns.

References[1] H. Roubos and M. Setnes, “Compact and transparent
fuzzy models and classifiers through iterative complexity
reduction,” IEEE Trans. on Fuzzy Systems, vol. 9, no. 4,
pp. 516-524, August 2001.

Fuzzified int.

Majority voting (fuzzy)
Majority voting (interval)

Error rate on test patterns (%)

Error rate on training patterns (%)

Fuzzy[2] H. Ishibuchi and T. Yamamoto, “Evolutionary
multiobjective optimization for generating an ensemble offuzzy rule-based classifiers,” Lecture Notes in Computer
Science 2723: Genetic and Evolutionary Computation
Conference - GECCO 2003, pp. 1077-1088, Springer,
Berlin, July 2003.

52
51
50
49

[3] T. Elomaa and J. Rousu, “General and efficient
multisplitting of numerical attributes,” Machine Learning,
vol. 36, no. 3, pp. 201-244, September 1999.

48
47
46

[4] H. Ishibuchi and S. Namba, “Evolutionary multiobjective
knowledge extraction for high-dimensional patternclassification problems,” Lecture Notes in Computer
Science 3242: Parallel Problem Solving from Nature PPSN VIII, pp. 1123-1132, Springer, Berlin, October 2004.

45
44
5

10

15

20

25

Number of rules(b) Error rates on test patterns.

Figure 4: Error rates for the Cleveland heart disease data set.

[5] H. Ishibuchi and T. Yamamoto, “Performance evaluationof fuzzy partitions with different fuzzification grades,”Proc. of IEEE International Conference on Fuzzy Systems,
pp. 1198-1203, Hawaii, USA, May 2002.

Table 2: Error rates on test data by majority voting. The best
result for each data set is shown by boldface.
Data set
Fuzzy
Interval
Fuzzified int.
Breast W
3.75
4.06
3.31
Diabetes
25.73
25.27
24.29
Glass
39.31
30.60
32.67
45.74
Heart C
46.77
47.58
Sonar
22.16
25.58
23.59
4.15
5.00
4.78
Wine

©ISAROB 2005

Summary[6] A. Gonzalez and R. Perez, “SLAVE: A genetic learning
system based on an iterative approach,” IEEE Trans. on
Fuzzy Systems, vol. 7, no. 2, pp. 176-191, April 1999.
[7] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fastand elitist multiobjective genetic algorithm: NSGA-II,”IEEE Trans. on Evolutionary Computation, vol. 6, no. 2,
pp. 182-197, April 2002.

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©ISAROB 2005

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©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Evolution and Niche Construction in NKES Fitness LandscapeReiji Suzuki and Takaya Arita
Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan{reiji, arita}@nagoya-u.jp
Abstract

The effects of niche construction on evolution havebeen mainly investigated in population genetics. For instance, Laland et al. constructed two-locus models, inwhich one locus affects the niche-constructing behavior
which produces the resources in the environments andthe fitness of the other locus is affected by the amountof accumulated resources [2]. They have shown thatniche construction and ecological inheritance yield unexpected results such as the maintenance of polymorphisms, evolutionary momentum and so on. However,previous studies were based on simplified cases as described above despite the fact that real biological systems are more complex in the sense that existing species
have mutually affected their courses of evolutions byniche-constructing their shared environment, although
only a few individual-based models which focused on
the perturbational effects of niche constructions were
investigated recently [3].
Our purpose is to clarify the complex relationships
between evolution and niche construction by focusing
on non-linear interactions between genetic and environmental factors shared by interacting species. For thispurpose, we have constructed a new fitness landscapemodel termed NKES model by introducing the environmental factors and their interactions with the genetic factors into Kauffman’s NKCS model [4]. Then,
we conducted the evolutionary experiments based on
the hill-climbing and the niche-constructing processesof species on this landscape, in which each species canincrease its own fitness by changing not only its genetic
factors but also the environmental factors. Based on
experiments using various settings of the ruggedness offitness landscape and the strength of the effect of niche
construction on the fitness of genetic factors, we clarify how niche-constructing behaviors can facilitate theadaptive evolution of interacting species via the sharedenvironment.

Niche construction is known as the process whereby
organisms, through their metabolism, their activities,
and their choices, modify their own and / or eachother’s niches. Our purpose is to clarify the interactions between evolution and niche construction by focusing on non-linear interactions between genetic andenvironmental factors shared by interacting species. We
have constructed a new fitness landscape model termed
NKES model by introducing the environmental factors and their interactions with the genetic factors into
Kauffman’s NKCS model. Then, we conducted the evolutionary experiments based on the hill-climbing andthe niche-constructing processes of species on this landscape. Experimental results have shown that the average fitness among species strongly depends on theruggedness of the fitness landscape (K) and the degreeof the effect of niche construction on the fitness of genes(E). Especially, we observed two different roles of niche
construction which brought about the high average fitness. One of the roles prevents the species from getting
stuck in the local optimums when K is large and E
is small. The other role yields the completely stablestate which maintains the high average fitness when K
is small and E is large.
Keywords: NKES fitness landscape, niche construction, ecological inheritance, epistasis, artificial life.

1

Introduction

Organisms can modify their own and / or each other’sniches (sources of selection) through their metabolism,
their activities, and their choices. This process is calld“niche construction”, and there are many evidences thatit has strong effects on the evolution of organisms in various taxonomic groups although it had been neglected
for a long time in evolutionary biology [1]. A typicalexample of niche-constructing organism is earthworms
that change the structure and chemistry of soils throughtheir burrowing behaviors. These changes are accumulated over generations, and then bring about different
environmental conditions which expose successive population to different selection pressure. This effect is also
called “ecological inheritance” which makes the generation inherit both genes and a legacy of modified selection
pressures from ancestral organisms.
©ISAROB 2005

2
2.1

Model
NKES fitness landscape

We constructed NKES model by introducing environmental factors and their interactions with the genetic factors into Kauffman’s NKCS model. There are S
species who share the same environment of which prop493

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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erties are described as N -length binary values ei (i=0,· · ·, N -1). We define ei as environmental factors which
represent abstract conditions of the shared environment
such as the chemistry of soil, the temperature, the humidity, the existence of burrow or nest and so on. Eachspecies si (i=0, · · ·, S-1) has N genetic factors which
are represented as binary values gi,j (j=0, · · ·, N -1).
The fitness of each genetic factor gi,j has epistaticinteractions not only with other K genetic factors
gi,j+kmodN (k=1, · · · , K) in its own species but also
has non-linear interactions with E environmental factors ei (i=0, · · · , E-1). The fitness contribution ofeach genetic factor caused by interactions among genetic and environmental factors is defined in similarmanner to the NKCS model. For each gi,j , we prepare a lookup table which defines its fitness corresponding to all possible (2K+E+1 ) combinations of interacting
genetic and environmental factors. The value of each
fitness in the lookup table is randomly set within therange of [0.0, 1.0]. Thus, the parameter K represents
the ruggedness of the fitness landscape of each species
and E represents the strength of the effect of niche construction on the fitness of genetic factors in this model.
Figure 1 shows an example image of this model when
N =5, K=1, E=2 and S=3. Each table represents thevalue of genetic or environmental factors, and thin arrows that issue from these values represent the existenceof non-linear interactions with values of other genetic or
environmental factors.

2.2

K=1
g0 0

0

0

1

E=2

0

1

e 1

g1 1

1

0

0

niche construction

1

0

1

1

1

g2 0

1

1

0

0

Figure 1: An example of NKES model when N =5,
K=1, E=2 and S=3.

the direct interactions among them like NKCS model.

3

Experimental results

3.1

General analyses

We have conducted experiments using various settings of K and E (N =80 and S=3) for 100000 generations. The initial values of genetic and environmental
factors were randomly decided. Firstly, we focus on
the effects of K and E on the average fitness among
all species. This index does not only represents how thespecies could evolve on the current environment but also
shows how the environment was modified and become
better for all species through niche constructions.
Figure 2 shows the average fitness among all species
during the last 1000 generations in various cases of K
and E. The x and y axes correspond to the conditionsof K and E, and the z axis represents the average fitness
on corresponding conditions. Each value is the averages
over 20 trials. The first thing we notice is that the average fitness is large (exceeds 0.75) when either K or E
is relatively small. In particular, there are two different
conditions which brought about the peaks of the average fitness: the cases when K=4 and E=1 (0.78), andwhen K=1 and E=4 (0.77). Figure 3 also shows the
proportion of trials in which the population completely
converged to the stable state (the condition that thefitness of any species can not be improved by neither
evolution nor niche construction). There is a peak of
the proportion of convergence (0.95) in the latter condition, while it is 0.0 in the former condition. It implies
that different dynamics of evolution and niche construction brought about the high average fitness under bothconditions.

Evolution and niche construction

In each generation, each species independently
chooses the process which yields the best increase in
its own fitness from “evolution”, “niche construction”
or “doing nothing” by using the following procedures:First, we calculate the fitness of the species when randomly selected one genetic factor is flipped. At the sametime, we also calculate its fitness when randomly selected one environmental factor is flipped. The former
value corresponds to the expected result caused by theevolutionary process and the latter corresponds to thatby the niche-constructing process. Then, the species
adopts the process which brings about the best fitness
by comparing these two fitness and its current fitness.
If the current fitness is the best, it does nothing in thisgeneration. After all species have chosen the processes,
they actually conduct the adopted processes at the sametime. Note that if some species decide to flip the sameenvironmental factor, it is flipped only once in each generation.
The outlined arrows in Figure 1 represent examplesof evolutionary process and niche-constructing process.
If one species flips the environmental factor by niche
construction, this can change the fitness contributionsof the other species’ genetic factors, and then can bringabout different evolutionary or niche-constructing dynamics of the other species. There are indirect interactions among species via niche constructions instead of©ISAROB 2005

1

evolution

N=5

3.2

Evolutionary dynamics when K=4 and
E=1Here, we investigate the two conditions whichbrought about the high fitness respectively in detail.
First, we focus on the case when K=4 and E=1. In
this case, it should be noticed that the average fitness494

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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average fitness
0.8
0.75

averagefitness
0.75
0.7
0.65
0

0.7
6

0.65

5

0.6

4
3

1

2

K

2

3

4

E

0.55

1
5

0.5

6 0

Figure 2: The average fitness in various cases of K and
E.

0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

6
5
4
1

3
2

3
E

2
4

K

1
5

6 0

Figure 3: The proportion of the convergence to stablestate in various cases of K and E.

10000

15000
20000
generation

25000

30000

NC-evolvability

HC-evolvability

5000

10000

15000
generation

20000

25000

30000

Figure 5: The transitions of the HC-evolvability (thinline) and NC-evolvability (thick line) when K=4 andE=1.

was higher than the corresponding condition without
niche construction (K=4 and E=0). When E=0, theevolution of each species rapidly gets stuck in the local
optimum because each species is able to climb the fitness landscape to increase its fitness only by changing
its genetic factors (not shown). Actually, Figure 3 showsthat the population always converged to the stable state
in all cases of E=0.
However, when E=1, each species can change its fitness landscape by the niche-constructing process. Figure 4 shows a sample transition of the average fitness
among species during the first 30000 generations. Notethat the transition of the fitness of each species wasapproximately similar to that of the average fitness, although it tended to fluctuate around the average fitness.
We can see that the species gradually and smoothly increased their fitness and fluctuated around 0.78, butthey never converged to the stable state.
In this model, the niche construction does not only
simply increase the fitness of performer of the niche construction, but also can decrease the other species’ fitness
by changing their fitness landscapes. The difference inthe average fitness between with and without niche construction is mainly caused by the latter effect of niche
construction. Figure 5 shows the transition of the evolvability provided by hill climbing (HC-evolvability) andthe evolvability provided by niche construction (NCevolvability) in the same experiment as Figure 4. The
HC-evolvability (or NC-evolvability) represents the av©ISAROB 2005

5000

Figure 4: The transition of the average fitness when
K=4 and E=1.

convergenceto the stable state
1
0.8
0.6
0.4
0.2
0
0

0

erage proportion of genetic (or environmental) factors
for each species which can increase its own fitness by
flipping them. These indices measure how often eachspecies can apply the evolutionary or niche-constructing
process in order to increase its fitness. Figure 5 showsthat the NC-evolvability kept a relatively large value,
while the HC-evolvability approached to almost 0.0 after
the drastic decrease in both indices until a few hundreds
generation. This means that the species were almost
getting to local optimums, but the continuous niche
constructions through generations prevented them fromgetting stuck in the local optimums by slightly changing their landscapes and enabled them to obtain higher
fitness regardless of their high ruggedness. Thus, theniche construction worked as a moderate perturbationon the other species’ hill-climbing processes in this case.

3.3

Evolutionary dynamics when K=1 and
E=4

The other condition which yielded the high averagefitness is the case of K=1 and E=4. The importantdifference compared with the previous condition is thatthe population converged to the stable state in almost
all trials as shown in Figure 3. Figure 6 and 7 show thesample transitions of indices respectively. We observe
the average fitness completely converged to 0.78 around22000th generation after its temporal increase and sub495

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is large, the transition of HC-evolvability tends to besynchronized with the NC-evolvability as shown in Figure 7, and the fluctuation in HC and NC-evolvability
becomes larger as K increases (not shown). Thus, theconvergent state occurs the most frequently only when
K is small and E is large. In addition, the temporal decrease in the average fitness in this case is supposed tobe caused by the strong perturbational effects of niche
construction as described above.

average fitness
0.8
0.75
0.7
0.65
0.6
0.55
0.5

0

5000

10000

15000
20000
generation

25000

30000

Figure 6: The transition of the average fitness when
K=1 and E=4.

0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0

4

We have discussed the universal nature of interactions between evolution and niche construction by using
the NKES fitness landscape model. We found that the
average fitness among species strongly depends on theruggedness of fitness landscape (K) and the strengthof the effect of niche construction on the genetic factors (E). It should be emphasized that the two qualitatively different roles of niche construction brought
about the high average fitness in different conditions.
When K is large and E is small, the niche construction by one species works as moderate perturbation on
the other species’ hill-climbing processes on the highly
rugged landscapes, which prevents them from getting
stuck in the local optimums. On the other hand, when
K is small and E is large, the strong effect of niche
constructions on the fitness of genetic factors yields theconvergence to the completely stable state which maintain the high average fitness. Future work includes investigations into the effects of the other parameters on
the roles of niche construction and the introduction oflearning into the model.

NC-evolvability

5000
10000
HC-evolvability

15000
generation

20000

25000

30000

Figure 7: The transitions of the HC-evolvability (thinline) and NC-evolvability (thick line) when K=1 andE=4.

sequent decrease from the initial population. Such atemporal decrease is interesting because all species arealways trying to increase their own fitness in our model.
Also, the transitions of two indices in Figure 7 werequite similar and the HC-evolvability was just slightly
smaller than the NC-evolvability.
These phenomena are supposed to occur due to thereason as follows: As shown in Figure 3, the high average fitness was caused by the convergence to the stablestate in this case. It means that the HC-evolvability
and NC-evolvability became 0.0 at the same time asshown in Figure 7. When K=1, the NC-evolvability
tends to approach to the smaller value as E increases(not shown). It is because that the strong effects ofniche construction on the fitness of genetic factors makethe species difficult to improve its fitness by niche construction likewise the species more easily gets stuck in
the local optimum on the standard NK fitness landscapeas K increases. Simultaneously, the increase in E also
brings about the large fluctuation around the relatively
large value in NC-evolvability (not shown). It is becausethat as E becomes large, the change in the environmental factor by niche construction of one species more
drastically changes the other species’ fitness landscapesand draws them back into the bottom of their landscapes. Thus, NC-evolvability frequently approaches to
0.0 when these effects are well-balanced. Also, when E©ISAROB 2005

Conclusion

Acknowledgements
This work was supported in part by a Grant-in-Aid
for the 21st Century COE “Frontiers of Computational
Science”.

References[1] F. J. Odling-Smee, K. N. Laland and M. W. Feldman, Niche Construction -The Neglected Process in
Evolution-, Princeton University Press, 2003.
[2] K. N. Laland, F. J. Odling-Smee and M. W. Feldman, “Evolutionary Consequences of Niche Construction: A Theoretical Investigation Using Twolocus Theory”, Journal of Evolutionary Biology, 9:
293–316, 1996.
[3] T. Taylor, “Niche Construction and the Evolution ofComplexity”, Proceedings of Artificial Life IX, pp.
375–380, 2004.
[4] S. Kauffman, The Origins of Order:
SelfOrganization and Selection in Evolution, Oxford
University Press, 1993.
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derivation of equations of motion, which is the central
part of analytical dynamics.

Solving Constrained Motion Problems Using the GI
Method1

As the classical field of analytical dynamics stillserves as the theoretical foundation for many problems inmodern science and technology, the complexity andlarge scale of most challenging and exciting new
problems usually demand interdisciplinary collaborationand the aid of modern computers. The advanced
mathematical concepts in classical Lagrangianmechanics are often difficult to understand outside ofclassical mechanics. In addition, most classical methods
were developed before the computer era.
Theirformulation and derivation do not allow us to make thebest use of the modern computing environment. Lastly,
much of the classical analysis is based on the principle ofvirtual work, an assumption that Lagrange made to avoid
thermodynamics concerns.
Thus, constraints thatinvolve forces that do work, such as friction, have been
ruled out of classical methods. This limitation makes
modeling difficult in situations where friction is not
insignificant. For instance, in studies of the motion ofsports-injured and the elderly, friction is often the driving
force that causes great pain and limits the motion andcannot therefore be neglected. Therefore, new methods,
together with extension and generalization of classicalmethods, are required for theoretical and practicalreasons.

Y. Y. Fan
Department of Civil and Environmental Engineering
University of California
Davis, CA 95616, United States
Email address: yyfan@ucdavis.edu

Abstract Derivation of equations of motion is the central
part of analytical dynamics, which is important in thedesign of machines and prosthetic devices and in the
motion control of spacecraft, robotic devices, and humanbodies.
This paper summarizes some recent
developments of a new method for deriving equations of
motion that was originally invented by Kalaba and
Udwadia. Through simple examples, we demonstrate thesimplicity of this method, its easy numerical
implementation in modern computing environment, andits advantage of handling modification of constraints.

Key
Words
Analytical
dynamics,
nonideal
nonholonomic constraints, generalized inverses ofmatrices, equation of motion

Recent development [1,2] on equations of
motion for constrained mechanical systems opens
possibilities for addressing the above mentioned
limitations of classical methods. This new method
exploits the advantages of the modern computing
environment. It begins the analysis directly from theconstraints of motion imposed on the systems, andarrives at an explicit set of equations of motion by using
the chain rule of differentiation and the concept ofgeneralized inverses (GI) of matrices. No generalized
coordinate systems or any physical assumptions are
required in the derivation. This new method handles
nonholonomic2 constraints with the same ease asholonomic constraints. More importantly, it takes into
account the nonideal3 constraints in a systematic andconvenient manner. For brevity, the new method forderiving equations of motion will henceforth be referred
to as the “GI method”.

Introduction
Multibody dynamic mechanical systems occur
in many classical and modern fields in science andengineering, such as in the design of vehicles, machines,
and prosthetic devices, in the motion control ofspacecrafts, robots and human bodies, and in the
dynamic
machinery
control that can be
integrated into active
structuralcontrolagainstearthquakerisk.
For example,
studies of humanmotion often begin
with a simplified mechanical system of point masses andrigid bodies with constraints on motion. Flexible
building structures (for protection against earthquakes)
can be represented by a lumped system of point masses
and springs. All efforts in modeling, simulating andcontrolling such dynamic systems start with the

Recent Development of the GI Method
The original GI method [1] is equivalent to theclassical methods such as Lagrange equation, GibbsAppel Equation, Hamilton Equation, and Gauss’s

12This special presentation is dedicated to Prof. Robert
Kalaba (September 1926 - September 2004) by his last
student Dr. Yueyue Fan. The work is based on theirmost recent collaboration on analytical dynamics.
©ISAROB 2005

Nonholonomic constraints depend on time,
displacement, and velocity, and are nonintegrable.
3
Nonideal constraints involve forces that do work on thesystem in a virtual displacement.
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principle of least constraint. However, the GI method
has the advantages of providing an explicit equation of
motion, easily handling nonholonomic constraints, andrequiring no extra effort in treating dependent but
consistent constraint equations. Later, the GI formula
was further extended to systems including non-ideal
constraints [3]. Further studies on its relation to otherclassical principles and its potential contribution totheory toward general dynamic and underdetermined
systems are still ongoing [4].

notation recalls the names of Newton, Lagrange, andCoulomb. It has been shown in reference [3] that FN is
the newtonian impressed force vector, that FL is aconstraint force that does no work on the system in a
virtual displacement v, and that FC is a constraint force
that does work on the system in a virtual displacement v.
The type of force represented by FC is called non-ideal
constraint force, which includes sliding friction.
Eq. (3) is the most general possible equation of
motion that is compatible with the constraint condition
A&x& = b , assuming, of course, that the matrix M is
nonsingular. Only two essential mathematical ideas are
essential in the derivation of Eq. (3): the chain rule ofdifferentiation and generalized inverses of matrices.
Modern computing environments, such as Matlab, havebuilt-in commands for calculating the generalized
inverse of a matrix, so it makes the approach highly
suitable for numerical studies. On the physical side thenotions of mass, distance and time occur. There is no
mention of kinetic energy, potential energy, moments,
etc.. In the applications to specific systems, of course,
the customary centripetal and Coriolis forces, moments,and so on do appear. These notions emerge naturally
from the terms in the right side of Eq. (3), but no priorexposure to them is needed.

Suppose that a mechanical system that contains
p point masses is subjected to m holonomic ornonholonomic equality constraints of the formf i ( x, x& , t ) = 0 , i = 1, 2, …, m,(0)where x is the displacement vector of the system ofdimension 3p = n. We also introduce the mass matrix M,
which is of dimension 3p by 3p, is a diagonal matrix, is
positive definite, and has the masses m1, m2, …, mp down
the main diagonal in groups of three, with zeros
elsewhere. As usual, x& is the time derivative of x. Useof the chain rule of differentiation leads to a set of mequations that are linear in &x& , of the form

A&x& = b ,(1)where A is an m by n = 3p matrix function of x, x& , and t,
and b is an m by 1 column vector that may depend uponx, x& , and t. Given the initial conditions on x and x& , Eq.
(1) is equivalent to Eq. (0).

In classical analytical mechanics, it is assumed
that the constraint force does no work in a virtual
displacement.
This means that the fundamental
assumption of classical analytical mechanics is thatF C = 0 , so that the equations of motion, Eq (3) reduce
to Eq. (2). More generally, though, as in situations inwhich sliding friction is significant, we shall have
F C ≠ 0 , in which case the more general equation of
motion, Eq. (3), will apply.

If only ideal constraint forces are considered, it
has been shown that the actual system acceleration
vector is given by the explicit formula&x& = a + M −1 / 2 ( AM −1 / 2 ) + (b − Aa ) ,(2)

where ( AM −1 / 2 ) + denotes the usual pseudoinverse of

A Simple Example
A simple example is used to illustrate how the
GI formula works. Consider a double pendulum system
subjected to ideal constraints.
The rectangularcoordinates (x1 y1), (x2 y2) are as shown in Figure 1. The
two constraints on the system are(7)
x12 + y12 = l12 ,and(8)( x 2 − x1 ) 2 + ( y 2 − y1 ) 2 = l 22 .
Eqs. (7) and (8) on two differentiations give(9)
A&x& = b ,wherethe matrix AM −1 / 2 . Vector a is the free motion
acceleration if there were no constraint. Refer toreference [1] for the details.
Later, the GI formula was further extended tosystems including non-ideal constraints [3]. The generalequation of motion is

M&x& = F N + F L + F C ,

(3)

where

F N = Ma ,(4)

F L = M 1 / 2 ( AM −1 / 2 ) + (b − Aa ) ,(5)

F C = M 1 / 2 [ I − ( AM −1 / 2 ) + AM −1 / 2 ]M −1 / 2 c ,

(6)

⎡ x
A=⎢ 1
⎣ x1 − x 2andand a is the free motion acceleration vector, and c is anarbitrary vector, both being of dimension 3n by 1. The©ISAROB 2005

498

y1

0

y1 − y 2

x 2 − x1⎤,y 2 − y1 ⎥⎦0(10)The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005⎤⎡
− ( x&12 + y&12 )
.
b=⎢
2
2 ⎥
⎣− [( x&2 − x&1 ) + ( y& 2 − y&1 ) ]⎦Q1δθ1 + Q2δθ 2 = −[m1 gL1 sin θ1δθ1(11)+ m2 g ( L1 sin θ1δθ1 + L2 sin θ 2δθ 2 )]= −(m1 + m2 ) gL1 sin θ1δθ1 − m2 gL2 sin θ 2δθ 2 .
(16)
Therefore, the generalized forces are
Q1 = −(m1 + m2 ) gL1 sin θ1δθ1 ,andQ2 = −m2 gL2 sin θ 2δθ 2 .
The kinetic energy can be written as

T=

(17)
(18)

1
( m1 + m2 ) L12θ&12 + m2 L1 L2θ&1θ&2 cos(θ1 − θ 2 )2

1+ m2 L22θ&22 .
2(19)

The first Lagrange equationd ⎛ ∂T ⎞ ∂T
⎜
⎟−
= Q1
dt ⎜⎝ ∂θ&1 ⎟⎠ ∂θ1

Figure 1: A Double Pendulum
Under ideal constraints, Eq. (2) is the equationof motion for this double pendulum system, with A and b
defined by Eqs. (10) and (11). The mass matrix in thistwo-dimensional problem isyieldsd[(m1 + m2 ) L12θ&1 + m2 L1 L2θ&2 cos(θ1 − θ 2 )] +dt
m2 L1L2θ&1θ&2 sin(θ1 − θ 2 )= −(m1 + m2 ) gL1 sin θ1 ,⎡m1 0 0
⎢0 m
0
1
M =⎢
⎢ 0 0 m2⎢
⎣0 0 00⎤
0 ⎥⎥ .
0⎥⎥m2 ⎦Let the initial position be x1 = l1 , y1 = 0 , x 2 = l1 + l 2 ,and y 2 = 0 . Let the initial velocity of both particles be(21)

and the second equationd ⎛ ∂T ⎞ ∂T
⎟−
⎜
= Q2
dt ⎜⎝ ∂θ&2 ⎟⎠ ∂θ 2

(22)

becomesd[m2 L22θ&2 + m2 L1 L2θ&1 cos(θ1 − θ 2 )] −dt
m2 L1 L2θ&1θ&2 sin(θ1 − θ 2 ) = − m2 gL2 sin θ 2 .

zero. The position and the velocity of the two particles
at any time t can be obtained by integrating Eq. (2) withthe given initial conditions.

(23)
Differentiating the left hand members of Eqs. (21, 23)with respect to time, we obtain the equations of motionof the system,

Next, we will show how this double pendulum
problem would have been solved in the classical
Lagrangian mechanics. Lagrange considered mechanicalsystems as being characterized by potential energy,
kinetic energy and the constraint function, with anemphasis on the use of generalized coordinates to
describe the current configuration.

⎡ (m1 + m2 ) L12
m2 L1 L2 cos(θ1 − θ 2 )⎤⎛ θ&&1 ⎞⎢⎥⎜⎜ && ⎟⎟m2 L22⎣m2 L1 L2 cos(θ1 − θ 2 )⎦⎝ θ 2 ⎠ .
2⎛ − m2 L1 L2θ&2 sin(θ1 − θ 2 ) − (m1 + m2 ) gL1 sin θ1 ⎞⎟= ⎜⎜⎟m2 L1 L2θ&12 sin(θ1 − θ 2 ) − m2 gL2 sin θ 2⎝⎠

Let us use the two generalized coordinates θ1and θ2 as shown in Figure 1. The virtual work done by
the force of gravity is m1 gδy1 + m2 gδy 2 . But(12)
y1 = L1 cosθ1 ,so that(13)
δy1 = L1 sin θ1δθ1 ,and(14)
y 2 = L1 cos θ1 + L2 cos θ 2 ,so that(15)
δy 2 = −( L1 sin θ1δθ1 + L2 sin θ 2δθ 2 ) .
Using these expressions in the expression for virtual
work, we get©ISAROB 2005

(20)

(24)
By integrating Eq. (24) with the initialconditions θ1(t0) = 0 and θ2(t0) = 0, we obtain the resultsof the position of the two particles in the generalized
coordinate system. These results, after being converted
to the rectangular coordinate system, are compatible withthe results from the GI formula.
In an ideal situation, the GI formula and theclassical methods are equivalent.
However, thederivation of the equations of motion using the classical499

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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These are the only changes needed for solving themodified problem. The rest of the procedure remains thesame. However, this one-degree-of-freedom problem
cannot be easily handled if we were to use the classicalmethods. To express the kinetic energy and potential
energy of the system in terms of single variable can bequite messy.

method is quite complicated even for this simple
example.
Handling Modification of Constraints
Most engineering design of mechanical systems
involves modification of constraints on the base model.
Handling such modification using the classical Lagrange
mechanics requires change of the generalized coordinate
system, and thus requires change of the generalized
forces and the kinetic energy. Each time a constraint is
modified, we will have to solve a completely new
problem starting from the beginning.
However,modification of constraints can be easily handled by the
GI formula. Changing constraints of the system onlychanges matrix A and vector b in the equation of motion,
Eq. (4). The rest of the procedure and the data required
all remain the same. We will demonstrate this easy
implementation using two examples below.

Conclusions and Discussion
In this paper, we have shown that the GI
formula is suitable for the modern computing
environment, and has potential to facilitate the analysis
and control of large and complex mechanical systems.
The only inputs required are the equations of theconstraints and the initial conditions on the system. The
rest of the procedure, such as differentiating the givenfunctions, computing the generalized inverses ofmatrices, and integrating systems of differential
equations, can be automated for execution by acomputer. In order to fully utilize the advantage of the
GI formula, a necessary step for future research is toautomate the entire analysis of constrained mechanicalsystems. Full development of the new theory will
require long-term cross-disciplinary collaboration frommany scholars in mechanics, computational mathematics,
and system optimization and control. We hope thispaper will serve as an introduction of this GI method tothe community of biomechanics and robotics design andwill inspire further interest in applying and extending
this new method.

First, let us remove the first constraint in the
double pendulum problem. Because only the second
constraint is imposed on the system, as given in Eq. (8),only the second row of matrix A and vector b remains.
Thus, we have

A = [x1 − x 2

y1 − y 2

x 2 − x1y 2 − y1 ] ,

(25a)

and

b = −[( x& 2 − x&1 ) 2 + ( y& 2 − y&1 ) 2 ] .

(25b)Given initial position and velocity of the two particles,
the problem can be solved by integrating the equation of
motion with the new A and b.

References
1. Udwadia FE and Kalaba RE (1992), A New
Perspective on Constrained Motion, Proceedings of theRoyal Society of London, Series A, v. 439, pp. 407410.
2. Udwadia FE and Kalaba RE (1996), Analytical
Dynamics, Cambridge University Press, New York.
3. Kalaba RE and Udwadia FE (2001), Analytical
Dynamics with Constraint Forces that Do Work in
Virtual Displacements, Applied Mathematics andComputation, v. 121, pp. 211-217.
4. Fan YY, Kalaba RE, Natsuyama HH, and Udwadia
FE, Reflections on the Gauss’s Principle of Least
Constraint, to appear in Journal of Optimization
Theory and Applications.

Next, let us add an extra constraint to theoriginal double pendulum problem

y 2 = −d(26)

to keep the second particle moving along a horizontal
line. Differentiate both sides of Eq. (26) twice, we have&y&2 = 0 ,(27)which adds a third row to the original constraints
A&x& = b . The new A and b become⎡ x1
A = ⎢⎢ x1 − x 2
⎢⎣ 0

y1

0

y1 − y 2

x 2 − x1

00⎤y 2 − y1 ⎥⎥ ,
1 ⎥⎦
0

(28a)

and

⎤
⎡
− ( x&12 + y&12 )⎢22 ⎥.
b = ⎢− [( x&2 − x&1 ) + ( y& 2 − y&1 ) ]⎥
⎥
⎢
0
⎦
⎣

©ISAROB 2005

(28b)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Moving Robot Path Search including Obstacles by GA using Quadrant Idea
Hidehiko Yamamoto* and Etsuo Marui
Faculty of Engineering,
Gifu University
1-1, Yanagido, Gifu-shi, 501-1193, Japan* yam-h@cc.gifu-u.ac.jp
3. QUIZ and SCAM

Abstract
This paper describes the problem to search therobot path. We solve this problem by using GA system.
To operate GA system for searching the path,
Quadrant Usage to Improve Zone (QUIZ), to reduce
the search space by using a quadrant idea is proposed.
QUIZ also includes new method, Split Coordinatesand Avoid Method (SCAM), to avoid obstacles in themaze by splitting zones surrounding the obstacles andto find the path. The Quiz including SCAM is applied
to some path search examples. As a result, it isascertained that QUIZ is useful.

To operate GA system for searching the path, thepaper proposes the new method, Quadrant Usage to
Improve Zone (QUIZ), to reduce the search space byusing a quadrant idea. QUIZ also includes new
method, Split Coordinates and Avoid Method (SCAM),
to avoid obstacles in the maze by splitting zones
surrounding the obstacles and to find the path. The
path search including QUIZ considers a moving pathas finding the path by arranging the x-y coordinates in
the moving space where a moving robot moves andindicating the coordinate points.
Fig. 2 shows the outline of GA system including
QUIZ. Though the conventional GA system carry out
to generate initial individuals, calculate fitness foreach individual generated and to give crossover andmutation, our GA system starts QUIZ before
generating initial individuals. The fitness used in our
GA system is adopted as the distance between the
robot current point and the next point. The smaller the
distance is, the better the fitness is.
The characteristics of QUIZ are to reduce thesearch space in order to create the excellent
individuals beforehand and to create initial individuals
corresponding to the ones that will not collide withobstacles or will not become lethal individuals by
SCAM system.

Key words: Path search, GA, Obstacles, Initialindividuals
1. Introduction
This research describes the problem to search therobot path. The search is done in the space including
obstacles and the robot visits some places in the space.
The path search research has been done [1]. Our
research finds the path by using Genetic Algorithm(GA). We have developed the path search by using
GA [2][3]. This paper proposes the new idea to use thequadrant of an x-y axis to decrease the search space increating initial individuals of GA. By using the idea,
we develop the system to search the path and apply it
to some examples.

3-1. QUIZ
2. Environments
Quiz is the method to limit the search space by
excluding useless points beforehand in order that amoving robot does not visit these points. The strategy
of QUIZ is to consider the 4 quadrants (1st quadrant,
2nd quadrant, 3rd quadrant and 4th quadrant) that
divides the robot moving space with the orthogonal
axis (x-y axis) whose origin of the coordinates is therobot current point and consider the search space as
one of the 4 quadrants. Because of the strategy, thesearch space can be reduced to a quarter.
The algorithm of QUIZ is as follows.

The problem of the paper is to search the moving
robot path in the space including obstacles. As shownin Fig.1, the environments of the research have themaze including many square obstacles. We will search
the path that a moving robot moves via some points
and solve it by using GA system.
The research for the path problem solved byusing GA system has been done. The conventional
research environments are simple and it did notinclude complicated obstacles.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Step1: Consider the start point and the end point as
Ai(xi,yi) and Az(xz,yz) for each.
Step2: Calculate the value k of the equation (1) and,by using the value, C1 of the equation (2) and C2 ofthe equation (3) are acquired. The next rules arecarried out and the quadrant that Ax exists is found.

element and express it as An.
Step 4: Carry out SCAM system (judgments to avoid
obstacles) between the point Ai and the point An. If
there is an obstacle, return to Step 3 and if not,consider An as the next Ai and go to Step 5.
Step 5: Search a new An from among the Set(Aq) and
if An =Az , go to Step 6, if not, return to Step 4.
Step 6: Consider Ai and the An value sequenceacquired in Step 3~5 as an initial individual and finish
the algorithm. □In this way, in the process of Step 2, QUIZ
searches the quadrant where the current point and theend point exist. In other words, QUIZ calculates thevalues of sinα and cosα in Fig. 3 and searches thequadrant depending on the positive and negative
numbers of the values. Because of this, whenever eachgene of an initial individual is decided, QUIZ searches
the quadrant corresponding to the search space andcan reduce the search space as shown in Fig. 4.

Fig. 1 Search space including obstacles

Seco nd qu adr ant

First quad rant

Sinα ≧0

Sinα ≧0Cosα＜ 0Cosα≧ 0k
α

GA S ystem
・Create Initial individuals
・Start to calculate fitness
・Evaluate fitness
・Crossover and mutation
・Generate new individuals

Az(x z,yz)

Ai(xi ,yi)

QUIZ
SCAM
SCAM

Third qu adr ant

Fourth qu adrant

Sinα ＜0Sinα ＜0Cosα＜ 0Cosα≧ 0y z − yikxz − xi
cosα =ksin α =
Fig. 2 Outline of GA system

Fig. 3 Quadrant of QUIZ

k = ( x z − xi ) 2 − ( y z − yi ) 2・ ・ ・ （１）y z − yi・ ・ ・(2)kx −xc2 = z i・ ・ ・(3)kif: c1 ≥ 0, c2 ≥ 0, then: Az exits in the 1st quadrant
if: c1 ≥ 0, c2 < 0, then: Az exits in the 2nd quadrant
if: c1 < 0, c2 < 0, then: Az exits in the 3rd quadrant
if: c1 < 0, c2 ≥ 0, then: Az exits in the 4th quadrant
c1 =3-2. SCAMSCAM is the system to judge whether there is an
obstacle between two points a moving robot visits ornot. The obstacles of the paper are square. When thestraight line that links the current point and the cornersof an obstacle is found as shown in Fig. 5, SCAM
judges the existence of an obstacle whether the next
point is included in the shaded portion or not. In otherwords, if the next point exists in the shaded portion, it
is judged that there is an obstacle between the two

Step 3: Express all points set in the quadrant where Az
exists as Set(Aq), from among the set, select one©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4. Application Examples

points.

The developed QUIZ including SCAM wasapplied to some examples. The examples are thatmoving robots visit some machine (M1~M6) andfinally get the end point in the space including
obstacles. Fig. 6 and Fig. 7 are the search results.
The examples’ moving robots start M1, visit M2,
M3, M4, M5 and M6 in turn and finally return M1. In
the figures, red marks indicate the bays of eachmachine and moving robots visit there. Fig. 8 shows
the change curves for the maximum fitness of theresult used QUIZ and the one not used QUIZ. The
curve of Quiz converged earlier and it acquired higher
fitness. Judging from the results, it is ascertained thatthe research to use QUIZ and SCAM is useful.
5. CONCLUSIONS
The research described the path search problem
that a moving robot gets the final destination via somepoints in the space including obstacles by using GA
system. Specifically, the idea of the quadrant in thex-y coordinate space was adopted in order to findbetter individuals in generating initial individuals in
GA system. The idea can limit the solution space andconverge early to find the solution. The idea is called
as QUIZ and in the process of QUIZ, SCAM system
not to generate lethal genes corresponding to the paththat collides with an obstacle was adopted.
After applying the developed system to some
path search examples, it is ascertained that thedeveloped system can get better paths and earlier
converge.

Aiobstacle

Ai+1

Ai
Fig. 4 Example to deduce search space©ISAROB 2005

obstacle

Ai+1

Fig. 5 Obstacles and moving points

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fig. 6

Acquired path result 1

Fig. 7

Acquired path result 2[2] Yamamoto, H. Robot Path Search Including
Obstacles by GA, Proc. of the 4th International
Symposium on Artificial Life and Robotics, Vol.1,
pp.270-273, 1999.
[3] Yamamoto, H. Robot Path Planning for Visiting FA
Working Points by Obstacle Avoiding Using GA, Proc.
of the 6th International Symposium on Artificial Lifeand Robotics, Vol.2, pp.524-527, 2001.

References[1] Barraquand, J and Latombe, JC, A Montecaro
Algorithm for Path Planning with Many Degrees ofFreedom, IEEE International Conference on Robotics
and Automation, pp.1712-1717, 1990.

60

Maximum fitness

55

50

Conventional method
45

QUIZ

40

35

30
0

4

Fig. 8©ISAROB 2005

8

12

16

20 24 28
Generations

Maximum fitness curves

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36

40

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Design of Novel Adaptive Routing by Mimicking Enzymic Feedback Control
Mechanism in the Cell
Takashi Kawauchi*, Masahiro Okamoto**
Dept. of Bioinformatics,
Graduate School of Systems Life Sciences,
Kyushu University,
Fukuoka, 812-8581 Japan* kawauchi@brs.kyushu-u.ac.jp** okahon@brs.kyushu-u.ac.jp

Miwako Hirakawa***
Dept. of Digital Media,
Fac. of International Communications,
Fukuoka International University,
Dazaifu, Fukuoka, 818-0193 Japan*** miwako@fukuoka-int-u.ac.jp

ABSTRACT

equilibrium of the biochemical reaction. In metabolic

The routing algorithm of SPF (Shortest Path First) [1] ispathways, the product of a late (or the last) step frequently

widely distributed in large scale network such as internet.

acts as an inhibitor of the first committed step in thisSince this routing algorithm is designed in order topathway (“negative feedback control”). This way, the end

improve throughput of each packet which is sequentially

product of a pathway controls its own synthesis andgenerated at the nodes, it is not suitable for averaging load

prevents useless accumulation of intermediates. Enzymic

balance in the network. The enzymic feedback in the cell

feedback control can be considered to be a bandwidth

is the typical and basic control mechanism which cancontrol; rate velocity of consumption of substrate can berealize homeostasis of the value of every reactant in the

represented by the function of substrate (A) and feedback

metabolic pathway.

The purpose of this study is toinhibitor (B). There are so many function mechanisms

design an adaptive routing in which the packets generatedof feedback control in the cell, however, for example,

at the nodes can be sent to the final destinations withwhen the B is assumed to control the rate velocity of A

avoiding the partial and time-variant traffics in thewith a manner of competitive inhibition [2] (one of the

network, and the load balance in the network can be

feedback functions), the rate velocity of A (d[A]/dt, taveraged.

represents time) can be mathematically written as follows:

We have proposed here a new adaptive

routing algorithm by introducing an enzymatic feedback

d[A] / dt =control mechanism in the cell.

−

V max[ A]Km(1 + [ B] / Ki ) + [ A]

(1)

Keywords: Adaptive routing, Biomimetics, Enzymaticwhere Vmax represents maximum velocity (reaction rate)

feedback, Homeostasis and Fault-tolerant networkof enzyme activity, Km is the value of substrate giving

topology

0.5Vmax, Ki is the feedback coefficient.

Anyway,d[A]/dt is the function of the substrate A and the feedback
inhibitor B.

1. INTRODUCTION AND BACKGROUND

In the case of accumulation of B, theThe metabolic pathway in the cell is so-called “a stream ofabsolute value of the term in the right-side of eq.(1)water” and is composed of a lot of enzymic reaction steps

become to be small. Since the B is the end product of thein which reactant (substrate) is converted to the product by

pathway, we can easily considered that the accumulation

unique “enzyme” (catalyzed protein), and the producedof B corresponds to be “traffic” of the pathway; theproduct is converted to the product by enzyme at theabsolute value of the term in the right-side of eq.(1)subsequent reaction step and so on.

Enzymes are

represents new metric of “traffic” from the view point ofproteins which catalyze the turnover of substrates without

network routing. Thus, in this study, we define thebeing consumed themselves and without changing thefollowing metric of routing by mimicking enzymic©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

starting node 0 and sent to the destination node 2, and thefeedback control in the cell:k = p /V max
V max[a]p=Km(1 + [b] / Ki ) + [a ](2)last one is those generated at the starting node 0 and sent
to the destination node 5. Each packet has 3072B size

(3)

and is generated at every 150 µsec. The maximum

where [a] represents the reserved sending packet size

sending rate between binding nodes is fixed at 100Mbps.

(Byte) to the nearest node, [b] is the total accumulated

The control packet (64B) is sending to the nearest node atpacket size (Byte) at the nearest node, Vmax is maximum

every 5000 µsec. This control packet involves thesending rate (Mbps), and Km and Ki are the arbitrary

information of the value of [b] (total accumulated packet

coefficients. The value of k in eq.(2) decreases with thesize) in eq.(3). The time between the generating andincrease in “traffic”. Furthermore, we define the following

arriving at the final destination (passage) of every packet

weighted multi-objective f:and transient sending route of every packet weref = (1 − α )k + α (1 / h )

0<α<1

(4)

examined. The default route was supposed to be 0 Æ 1 Æ

where α represents arbitrary coefficient; if α=1, the2 Æ 3 for the packets sending to the node 3 and to be 0

network routing will be performed according to the SPF

Æ1Æ 2 for the packets sending to the node 2 and to be(Shortest Path First) algorithm.

0 Æ 4 Æ 5 for the packets sending to the node 5 ; theAt the branching of node-pathways, the value of f at eachroute 0 Æ 1 Æ 2 is overlapped which will lead to thebranching pathway is calculated and it determines the

traffic of the packets at this route.

node to be sent with having the larger value of f. This is

For comparison, the passage profile with packets to be

the outline of the proposed dynamic adaptive routing

sent was examined in the case of SPF algorithm; every

algorithm where most of the packets will be sent to thepacket is sent to the final destination according to thefinal destination with escaping from the traffic nodes; thedefault route. The results can be summarized as follows:

QOS (quality of service) of the proposed algorithm is

As shown in Fig. 2, the passage increases with the packet

expected to be “averaging the load within the network”.

ID that means the traffic is occurred at the route between
the nodes 0 and 2.

2. CASE STUDY AND VALIDATION
The following node-network was used in order to

SPF

evaluate our algorithm, where the numeral (0 to 5)

7000

represents the node-number, and the bold line is the6000connection pathway between nodes:

5000

1

passage

A

2

4000
3000
2000

3

0

B

1000
0
0

50

100

150
PN

4

5

200

250

300
PassageFig. 2 Passage profile with packet by using SPF algorithm. The
abscissa and the ordinate represent data packet ID and passage (µsec),

Fig. 1

6 nodes-network

respectively. A, packet sending to the nodes 2 and 3; B, packet

Supposing the three kinds of sequential packets to be sent

sending to the node 5.

randomly in the network (total number of packets is 300);
one is the packets generated at the starting node 0 and sent

Fixed the value of α in eq.(4) at 0.5, the passage profile

to the destination node 3, second is those generated at thewith packets to be sent was examined by introducing our©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

proposed algorithm. The results are shown in Fig. 3.
dest=2

Also the passage profiles for the packets sending to thenode 3 and for those sending to the node 2 and for thosepassagesending to the node 5 are shown in Figs. 4, 5 and 6,
respectively. The average, minimum and maximum
passage of Figs. 2 and 3 are summarized in Tables 1 and 2,
respectively. Those of Figs. 4, 5 and 6 are also
summarized in Tables 3, 4 and 5, respectively.

1600
1400
1200
1000
800
600
400
200
0
0

50

100

150
PN

200

250

300

passage

AR

2500

Fig. 5 Passage profile with packet sending to the node 2 by using

passage2000

the proposed routing algorithm. The abscissa and the ordinate

1500

represent data packet ID and passage (µsec), respectively.

1000

dest=5
500

1000
0
50

100

150
PN

200

250

800

300
Passa gepassage0

Fig. 3 Passage profile with packet by using the proposed routing

600
400

algorithm. The abscissa and the ordinate represent data packet ID and200

passage (µsec), respectively. The arrow shows the time-point when

0
0

50

100

the control packet was sent to the nearest node.

150
PN

200

250

300

passagepassage

dest=3Fig. 6 Passage profile with packet sending to the node 5 by using2500

the proposed routing algorithm. The abscissa and the ordinate

2000

represent data packet ID and passage (µsec), respectively.

1500
1000

Table 1 Summary of passage profile shown in Fig. 2.

500
0
0

50

100

150
PN

200

250

300

passageFig. 4 Passage profile with packet sending to the node 3 by using

Total number of data packets

300

Average of passage (µsec)

2129Standard deviation of passage

1592

Minimum passage (µsec)

490

Maximum passage (µsec)

6090

the proposed routing algorithm. The abscissa and the ordinate
represent data packet ID and passage (µsec), respectively.

©ISAROB 2005

Table 2 Summary of passage profile shown in Fig. 3.

507

Total number of data packets

300

Average of passage (µsec)

702Standard deviation of passage

206

Minimum passage (µsec)

490

Maximum passage (µsec)

2205

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 20050Æ1Æ4Æ5Æ3 are alternative ones with escaping from

Table 3 Summary of passage profile shown in Fig. 4.

Total number of data packets

89

the traffic nodes.

Since the maximum sending rate

Average of passage (µsec)

831

between all binding nodes is fixed at 100Mbps and theStandard deviation of passage

217

each data packet size is 3072B, the minimum required

Minimum passage (µsec)

735

time sending to the nearest node is 245µsec (minimum

Maximum passage (µsec)

2205

required time sending from the node to the node 3 is 245 x
3 = 735µsec). The passages for PID=290, 297 and 299 are
1280, 1225 and 980µsec, respectively, which were near to

Table 4 Summary of passage profile shown in Fig. 5.

Total number of data packets

114

245 multiplied by the number of hops; 1280 is

Average of passage (µsec)

688

approximately equal to 245 x 5, and 1225 and 980 areStandard deviation of passage

201

quite equal to 245 x 5, 245 x 4, respectively. These

Minimum passage (µsec)490

results show the proposed algorithm can find alternative

Maximum passage (µsec)

1455

non-traffic routes with considering the smaller number ofhops to the destination.
Even if each packet arrives at the final destination with

Table 5 Summary of passage profile shown in Fig. 6.

Total number of data packets

98

Average of passage (µsec)

601Standard deviation of passage

123

Minimum passage (µsec)

490

Maximum passage (µsec)

930

escaping from the traffic nodes, it is key issue that all
packets, which constitute one data-file, arrive at the finaldestination in the order of sending from the starting node.
For example, when the packet 3 arrives at the finaldestination prior to the packets 1 and 2, sorting process
can not be performed until both of these packets arrive. In
the case where this waiting time is larger than a given

As shown in Figs. 2 and 3, and Tables 1 to 5, most of thethreshold value, routing system should request to thepassages of data packets were averaged, which means that

sending node for re-sending of packets. In the 6-node

our proposed algorithm is effective for dynamic adaptive

network shown in Fig. 1, we examined the waiting time atrouting. According to Figs. 2 and 3, part of the transient

the final destinations (node 2, 3 and 5). The simulationpassage profiles of packets and route sending to the final

conditions are as follows: (1)three kinds of sequential

destination by using SPF algorithm and by using thepackets to be sent randomly from node 0. (2)total number

proposed algorithm are summarized in Tables 6 and 7,of packets is 300, and the number of packets to the nodes

respectively.

2, 3 and 5 is 100, respectively. (3)each packet has 3072B

In Table 7, the shadowed columns represent the packets

size and is generated at every 125 µsec. Figure 7 shows

which were sent by using the non-default routes (default

the histogram of waiting time at the node 3 when theroutes are 0Æ1Æ2Æ3 for the packets sending to the node

weighting coefficient α in eq. (4) is fixed at 0.5. The

3, 0Æ1Æ2 for those sending to the node 2, and 0Æ4Æ5ordinate represents the relative frequency at each class.

for those sending to the node 5). In Table 7, most of the

The abscissa shows class of histogram, for example, 1000

packets sending to the node 2 or 5 were sent by using theand 2000 means 0-1000µsec and 1001-2000µsec,

default route, however, focused on the packets between

respectively. Figure 7 shows the average of 10 trails. Next

242 and 247 their routes are flexible such as 0Æ4Æ5,

we change in α value according to the following

0Æ1Æ4Æ5 and 0Æ1Æ2Æ5 with escaping from thealgorithm and examined waiting time with the same

traffic nodes. The routes for the packets sending to theconditions above:node 3 are most flexible; the route 0Æ4Æ5Æ3 is anothershort-cut route and the routes 0Æ1Æ4Æ1Æ2Æ3 and©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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Table 7 Transient passage profiles of packets and the route sent toif ( sHop > Cnt )α = 0.5
if ( sHop ≤ Cnt < 2sHop)0.2
α = 0.5 +× (Cnt − sHop)
sHop
if (Cnt > 2 sHop)
α = 0.7

the final destination by using the proposed algorithm
Packet ID generatearrivepassage
route
240
36151
36966
815
0>1>2>3
241
36301
37036
735
0>4>1>2
242
36451
37036
585
0>4>5
243
36601
37526
925
0>1>4>5
244
36751
37281
530
0>4>5
245
36901
37771
870
0>4>5
246
37051
37786
735
0>1>2>5
247
37201
38016
815
0>4>5
248
37351
38086
735
0>1>2>3
249
37501
38086
585
0>1>2
250
37651
38576
925
0>4>1>2
251
37801
38331
530
0>1>2
252
37951
38686
735
0>4>5>3(5)

where, sHop and Cnt represent the number of the shortest
hops from stating node to the final destination node, andthe accumulated number of nodes for the packet to pass

275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299

through, respectively.
Table 6

Transient passage profiles of packets by using SPF

algorithm.
Packet ID generate arrive
passage
route
240
36150
40500
4350
0>1>2>3
241
36300
40500
4200
0>1>2
242
36450
36940
490
0>4>5
243
36600
37185
585
0>4>5
244
36750
37430
680
0>4>5
245
36900
37675
775
0>4>5
246
37050
37920
870
0>4>5
247
37200
38165
965
0>4>5
248
37350
40990
3640
0>1>2>3
249
37500
40990
3490
0>1>2
250
37650
41235
3585
0>1>2
251
37800
41480
3680
0>1>2
252
37950
41970
4020
0>1>2>3
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299

41400
41550
41700
41850
42000
42150
42300
42450
42600
42750
42900
43050
43200
43350
43500
43650
43800
43950
44100
44250
44400
44550
44700
44850
45000

46625
46870
46870
42340
47360
42640
47605
47850
48095
48095
48340
43540
48585
48830
49320
49565
44290
49565
49810
50055
50300
45040
50790
45340
51035

5225
5320
5170
490
5360
490
5305
5400
5495
5345
5440
490
5385
5480
5820
5915
490
5615
5710
5805
5900
490
6090
490
6035

41401
41551
41701
41851
42001
42151
42301
42451
42601
42751
42901
43051
43201
43351
43501
43651
43801
43951
44101
44251
44401
44551
44701
44851
45001

42136
42326
42191
42341
42736
42641
43036
43186
43336
43336
43636
43636
43881
44126
44236
44931
44291
44441
44931
45421
45176
45041
45926
45341
45981

735
775
490
490
735
490
735
735
735
585
735
585
680
775
735
1280
490
490
830
1170
775
490
1225
490
980

0>1>2>3
0>4>5>30>1>2
0>4>5
0>1>2>30>4>50>1>2>3
0>4>5>3
0>1>2>30>1>2
0>4>1>2
0>4>5
0>1>2
0>1>2
0>4>5>3
0>1>4>1>2>3
0>4>5
0>1>2
0>1>2
0>4>1>20>1>20>4>5
0>1>4>1>2>3
0>4>5
0>1>4>5>3

For example, in the case of packets generating at the0>1>2>3
0>1>2>30>1>2
0>4>5
0>1>2>30>4>50>1>2>3
0>1>2>3
0>1>2>3
0>1>2
0>1>2
0>4>5
0>1>2
0>1>2
0>1>2>3
0>1>2>3
0>4>5
0>1>2
0>1>2
0>1>2
0>1>2
0>4>5
0>1>2>30>4>50>1>2>3

node 0 and sending to the node 3, sHop is 3 and when Cnt
is over 3, α value increases linearly to 0.7 with the Cnt; αvalue is 0.7 at Cnt = 2sHop (6 in this case) and fixed at 0.7
when Cnt is over 2sHop. Figure 8 shows the histogram of
waiting time where α value is variable according theabove schedule.
Compared Fig. 8 with Fig. 7, changing in α value isefficient method for reducing the waiting time; in Fig. 7,
the sum-up of relative frequency at the classes over 4000
is 16.1%, whereas in Fig. 8, the corresponding frequency
is 0%.
3. DISCUSSIONThe OSPF (Open Shortest Pass First) [3] is the routing
protocol by using various cost parameters as metrics; the©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Relative Ratio

1
0.8
0.6
0.4
0.2

10
00
0
11
00
0
12
00
0
13
00
0
14
00
0
15
00
0
16
00
0

90
00

80
00

70
00

60
00

50
00

40
00

30
00

20
00

10
00

0

Class of Waiting̲time[μsec]

Fig. 7 Histogram of Waiting_time at the node 3 under fixing the α value in eq.(4) at 0.5.

Relative Ratio

1
0.8
0.6
0.4
0.2

10
00
0
11
00
0
12
00
0
13
00
0
14
00
0
15
00
0
16
00
0

90
00

80
00

70
00

60
00

50
00

40
00

30
00

20
00

10
00

0

Class of Waiting̲time[μsec]

Fig. 8 Histogram of Waiting_time at the node 3 under the condition where theα value changes with schedule (5).
followings are considered to be cost parameter: reliability,

the cell we can observe various kinds of feedback function

delay, bandwidth, load, maximum transfer unit,

mechanisms except for eq. (1) or (2) [2].

communication cost. In this study, we proposed here eqs.

functions including eq. (3) have high possibility to be(2), (3) and (4) by mimicking the mechanism of

acceptable as new metrics in OSPF.

These

enzymatic feedback function in the cell. As shown in eq.
(3), the p is the integrated parameter considering both the

REFERENCES

current traffic status between the self-node and the nearest[1 ]Dikjstra,E.W.: A note on two problem in connection

bonding node (the [a] in eq. (3) numerically reflects thiswithgraphs,information) and the most recent traffic status between the

pp269-271(1959)

Numerische

Mathematik,

1,

nearest bonding node and the subsequent nodes (the [b] in[2]Okamoto, M., Takeda, Y., Aso, Y., Hayashi, K.:

eq. (3) numerically reflects this information). The Km

Steady-state approximation of enzyme activation andrepresents the [a] value giving the half speed of maximum

inhibition,

sending rate (Vmax); the smaller Km value gives the

pp1453-1463 (1983)

steeper decrease of p-value. The Ki determines steepness

andBioengineer.,25,[3]Thomas M. Thomas II: OSPF Network Design
Solutions 2nd. ed., Cisco Press (2003)of the p-value vs. [a]-value; the smaller Ki value represents
the stronger feedback control. In metabolic pathways in©ISAROB 2005

Biotechnol.

510

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Agent based plant allocation and transfer routing of products in case of emergency
Al-Sehaim Sulaiman
Graduate School
Okayama University

Masami Konishi
Faculty of Engineering
Okayama University

other is the interruption of transfer to somereason such as the destruction of a propertransfer rout to consumer cities.

Abstract
In this paper, two problems, plant allocation problem and that of transfer routing from plants to customers, are considered simultaneously. Especially,
adaptation scheme for emergency cases are checked.
To solve these problems, decentralized agent based
optimization procedures are used. In our study, oil
production and products transfer in Saudi Arabia aretreated. Through numerical experiments, practicability of the proposed method is verified.

3. Mathematical description
In our treatment, the time required for movement
between nodes is made one unit period and all tracks
are assumed to have a uniform velocity. The mathematical models for track operations are described in
the following. Decision variables are [0,1] variables
representing moving from one node to the adjacentnode whose attributes are track number, corresponding to nodes, time and plant number. In the following,these definitions will be described mathematically
together with restrictive conditions and objectivefunctions for optimization.

Keywords: decentralized agents, optimization, oil
production, ant system, allocation problem, conveyance planning
1. Introduction
In recent years, the system optimization technologies are studied briskly and applied widely to production and distribution problems. These advancements
are supported both by the progress of computer technologies and that of the optimization technologies forlarge scale system.[1] This research deals with an optimization problem for oil production planning and its
transfer control in case of emergency. When an emergency occurs, it becomes necessary to determine
once again the appropriate allocation of plants andtransfer routes from plants to customers. To the purposes, a decentralized agent method for optimizing
both the allocation of plural oil plants and the transfer
route of oil products in a wide area are studied. Optimization of oil production and products distribution
system are taken up as the aimed problem of real
scale. In order to optimize the arrangement of oil production plants, delivery routes of a product are taken
into consideration simultaneously.

3.1 Notation for variables
Variables, parameters and functions used in our paper are summarized as follows.
C : city number (C = 1,⋅ ⋅ ⋅⋅,48) , S C (t ) : Volume
of inventories , DC (t ) : amount of demands , RC (t ) : amount of consumption , p : Plant
number, Ω p :Plants Quantity of production, X α :search track (main agent) of plant arrangement , X β : transportation track of an oil product(sub agent), a β : freight per track, bβ : loading of a
track (if b = 1), l p •c : distance form plant tocity , fα (li• j ) : arrangement cost of a plant,f β (li• j ) : transfer cost between a city and aplant , I α :evaluation function for optimum
plants arrangement, I β : evaluation function for
optimization of sub agent's transportation (conveyance route length), ∆Wα : amount of pheromone of amain agent, ∆Wβ : amount of pheromone of a sub
agent, ρα : evaporating ratio of pheromone of a main
agent, ρ β : evaporating ratio of a sub agent, Q : penalty cost reflecting the number of consumption nodes
which is not delivered,

2. Problem Description
To treat the problem, 48 nodes representing consumer places are arranged in Saudi Arabia and 3 production plants are set up on the corresponding nodes.
The delivery route of a product shall be constitutedby the adjoining node sequence. The tracks madewith a uniform velocity are belonged to each plant.

3.2 Restrictive Conditions
Conditions for continuation are as follows.

[Emergency cases]∑x

In this research, there are two kinds of emergency
cases. One is plant damage or building a new plant tocover consumer’s demands for plants in work. The©ISAROB 2005

Kazuo Nose
Faculty of Engineering
Osaka Sangyo University

j∉N i

k ,m
i , j ,t

=0，

∑x
j∈N i

k ,m
i , j ,t=1

(1)Where Ni denotes a set of adjacent nodes of node

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005b

# i . As shown here, decision variable is x that ex-

As for the parameters in equation (6), f β ,i . j is thedelivery cost between nodes i, j . Where, b denotes
parameter showing approach or return route of a
track. The value of b is one when a track is on an
approach route and is zero for return.

press the movement of a track between adjacentk ,mnodes in unit time. Variable xβ , i , j , t takes 1 when a
track # k belonging to the plant # m moves
from node # i to node # j at time period t .
Condition for shipment from a plant is as follows.

H m = g (Ω m − a∑ xβ

k , j∈N n ( m )k , m ,1n ( m ), j ,t)

4. Algorithm for optimization
In our method the decentralized agent method isadopted for the optimization. [2]-[4] As shown in
Figure 1, agents corresponding to plant accompaniedby tracks autonomously search their locations anddistribution area including transfer routes.

(2)

Where, Ω m , N (m) and a are the quantity of production of a plant# m , a set of nodes corresponding
to plant # m and the capacity of a truck.
Condition for inventory volume at consumer place isgiven by

S CMin < S C < S CMax
(3)
Min
Max
Where, SC , SC and RC , t are minimum volumeof stock, maximum volume of stock and current stock
at a consumer node.
3.3 Objective functions for plants arrangement
Delivery cost is used for the objective function inplants arrangement problem. Namely, the objectivefunction I α is defined by equation (4).

I α = ∑ f α ,i. j xαk ,,mi , j ,t + γ 1Q → Min(4)

Fig.1 planning plant location and transfer routes
by decentralized agentsi , j , m ,tIn equation (4), penalty cost Q depending on thenumber of consumption nodes which is not deliveredis added to the transfer cost.

4.1 Search algorithm for optimal plant allocation
Step1. input number of plants.
Step2. generate the node of a plant location.
The Plant node number is not allowed tooverlap the node number of other plants.
Step3. search delivery route for a track randomly.
These serve as a primary solution candidate.
Step4. sprinkle pheromone ∆Wα on delivery
nodes employing the following relation.

3.4 Objective function for transfer routing
Objective function for transfer routing is defined by
W = H e + H o + γ 2 H υ + µH m
(5)
e

o

Where, H , H , Hνand H m show the transfer
costs of approaching, returning, penalty value for inventory volume at consumers and penalty for excessive loading of a truck. Except H m ,these are defined
as follows.

H =
e

∑

f β (li , j ) x β i , j ,t∑f βo (li , j ) x β i , j ,ti , j , k ,m ,t ,b =1

Ho =i , j , m ,t ,b = 0

H υ = f (∆S )ek ,m ,bk , m ,bα(9)Phk : amount of accumulated pheromone.
Step5. calculate evaluation of primary solution.
Step6. search again the delivery course of a track randomly, This serves as a secondary solution
candidate.
Step7. compare pheromone information for primaryand secondary solution

(6)
(7)

, ∆S = f ∑ ( S Ci − S iMin )Phkα , 2 ≥ ∑ Phkα′ ,1

i

(10)

k ′≠ k(8)eoWhere, f and f show the cost per unit distance formovement in approach and return respectively.
f ( y ) is a nonlinear function whose value takes
positive large one if the sign of y becomes negative.

©ISAROB 2005

Phkα = ∆Wα

If the amount of pheromone for a secondary
solution is larger, then go to Step 9, else go to
Step8.
Step8. calculate probability of adoption of a secon-

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

solution is large then go to Step 7, else go to
Step6.

dary solution candidate, Ra ( k )⎡ ⎡ r (k ) ⎤ ⎤Rα (k ) = max ⎢exp ⎢ α⎥, Ro ⎥ (11) 1⎢⎣ ⎢⎣ TRα ⎥⎦ ⎥⎦rα (k ) = Phkα , 2 − ∑ Phkα′ ,1(11) 2

Step6. Probability it calculates.

⎡ ⎡ r (k ) ⎤ ⎤Rβ (k ) = max ⎢exp ⎢ β⎥, Ro ⎥ (15) 1⎢⎣ ⎢⎣ TRβ ⎥⎦ ⎥⎦rβ (k ) = Phkβ , 2 − ∑ Phkβ",1(15) 2k ′≠ k

If it becomes rα ( k ) < 0 , return to Step6. withprobability Rα ( k ) . Otherwise, go to Step9.

k '≠ krβ (k ) < 0 If it becomes, it will return Step6.
By probability Rβ ( k ) . Otherwise, it pro-

Step9. pheromone is sprinkled on nodes of a secondary solution.
Step10. compare cost of the primary solution and thatof the secondary. If the secondary solution isbetter than the primary solution, go to Step12.
If not good, go to step 11.
Step11. calculate probability of adoption of the secondary solution even if its cost is not good.

⎡ I 1 (k ) − I α2 (k ) ⎤Pα (k ) = exp ⎢ α
⎥
TP
⎣
⎦

gresses to Step9.

Step7. a pheromone is sprinkled on a secondary solution node.
Step8. Compare the Cost of a primary solution andSecondary solution. If Secondary solution Costis better than the primary solution, it will progress to Step6. If bad, it will progress to next.
Step9.

(12)⎡ I β1 (k ) − I β2 (k ) ⎤Pβ (k ) = exp ⎢
⎥
TP
⎣⎢
⎦⎥

When not improved, go to step 12 with probability Pα ( k ) . Otherwise, return to step 6.
Step12. Secondary solution is exchange with primarysolution.
Step13. Convergence condition is investigated to finish the iterations. When convergence is not attained, returns to Step6.

When not improved, it returns to Step6 by
probability Pβ (k ) Otherwise, it progresses to
Step10.
Step10. replace a Secondary solution and primarysolution .

4.2 Algorithm for transfer routing
In transfer routing after plant allocation Algorithm
is over plan node number and plant transfer area
nodes, or in emergency case the arc cant be used are
passing to transfer routing Algorithm.

Step11. Convergence situation of a solution is investigated. When convergence is not enough, it
returns to Step6. It will end, if it is converging.
5. Experimental conditions
Numerical experiment was conducted in order toverify the effect of the algorithm stated above. The
problem of plants arrangements and distribution fromplants to consumers was solved simultaneously. In
the experiment, the number of plants was set as 3.
The production data of oil plants are given together
with data of customers in Table 1.

Step1. Input plants node number, delivery node numbers.
Step2. Create the track primary solution in random,and calculate Evaluation.
Step3. Sprinkle pheromone ∆Wβ on delivery
nodes.
β

Phkβ = ∆Wβ(13)

Phk the amount of accumulation pheromones.
Step4. Create the track Secondary solution, calculate
Evaluation.
Step5. Compare pheromone information for primaryand secondary solution
Phkβ , 2 ≥ ∑ Phkβ′ ,1Table 1. Plants production and customers number
Plant
1
2
3

(14)

producing
11
21
16

No of track1
1
1

customers
48 nodes76 arcs

Parameters for decentralized agents stated above are
given as shown in Table 2.

k ′≠ k

If the amount of pheromones of a secondary©ISAROB 2005

(16)

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Table 2. Constant in algorithm

ρ

∆W

TP

TR

0.3

4.0

12.0

18.0

Thus, the solution corresponding to the plant capability and the demand in a consumer place is obtained.

6 Experimental results
Using the algorithm and conditions described in thepreceding sections, numerical experiments were carried out.

6.1 Plant allocation design
Plants allocation search is made by decentralized
agent algorithm explained in the above sections. The
transition of evaluation function, cost, is shown in
Figure 2. In the figure, three examples of calculated
customer cities by plant #1 is shown with excess ofiterations. According to the change in cost, numbersof customers converge to its final solution. The delivery zone from each plant in the target area which isthe converged solution is shown in Fig. 3.

Fig.3 Simulation result
To check the validity of the proposed algorithm,
pheromone consumption on the selected transfer
route is checked. Fig.4 shows the accumulated
amount of pheromone corresponding to plant #1 at
nodes #7 and #19. As is shown in figure 3, node #7
is served by plant #1 and node #19 is not served.

1

900
800
700

Cost

600
500

2

400

Node 7

300
200

3

100

Iteration

0
1

26

51

76

101

126

151

176

201

226

251

276

301

326

1
2
5

2

4

7

6

13

14

19

20

24

15

12

26

31

32

38

37
44

22

33
39

40

45

46

12
18

23
28

27

29

34

41

36

14
20

42

43

26
32
38

44

48

17

16
22

21

31

37

11

10
15

25

30

47

9

8

13
19
24

35

4

7

6

11
17

16
21

25

30

3

5

9

8

10

18

3

376

1

3

12

351

Pheromones

1

27

23
28

29

34

35

33
39

40

45

46

41

42

36
43

47

48

2

5

8

9

18

19

15

14

26

38
44

17
22

27

32

31

30
37

16
21

20
25

24

34

33
39

40

45

46

25

49

73

97 121 145 169 193 217 241 265 289 313 337 361 385
Iteration

23
28

41

29
35
42

36

Fig.4 Change of pheromone accumulation（Plant #1）
As shown in figure 4, accumulated amount of pheromone increased with iteration. Contrary to this, thatof #19 decreased with iterations.

43

47

48

Fig.2 Transition of Cost during search procedure
The result of the plant arrangement node and costwhich used and searched for this algorithm is shownin Table 3. In Table 3, calculated numbers of customers for 3 plants are shown together with their costs.
As shown in the table, calculated number of customers (nodes) of a plant coincides with production rateof the corresponding plant. Costs in the table meansummation of arcs on each transfer route.

5.2 Results for emergency cases
As for emergency, case of route destruction isconsidered. As shown in Figure 5, arc between
node #8 and #14 is destructed. As the results, theother transfer route except this arc should be determined. Using the proposed method, surrogatetransfer route was determined as such shown in
Figure 6.

Table 3 Numerical experiment result(cost and the optimal solution)
Production
optimal solution
Node
Cost
of plant
Plant 111
11
14
Plant 221
21
26
Plant 316
16
18

©ISAROB 2005

4

11

10
13

6

1

4

7

6

8

0

3

12

10

2

Tot al iterat ion =58

12

Node 19

14

As shown in figure 6, new arc is created between
nodes #7 and #8. Addition to this, transfer direction ischanged to fulfill the minimization of transfer distance. As the result, total traveling distance of thesurrogate route is the same before the destruction oftransfer route.

514

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

6. Conclusion
This research examined the subject about the arrangement of oil production plants, where to locate
and to what consumer places the oil products are to
be delivered, and how to employ the distribution
routes. In the proposed method, a delivery tracks was
set up as a distributed agents, and it tried for theseagents to search for an appropriate solution autonomously.
Using the developed program for planning, the arrangement of three plants and the delivery problem to48 cities were set up supposing cities in Saudi Arabia,
and the solution was calculated. Consequently, thevery suitable solution could be found and the validityof the proposed method has been checked. The proposed method is expected to extend for more advanced problems about optimization of both production base planning and logistics in a wide area.
Based on the result, further research will be made tosolve the problem of seven plants of a real scale, and48 cities under the various conditions.

Fig.5 Emergency case of route destruction

Acknowledgments
The authors gratefully represent acknowledgments
for the financial support by the Japan society for thepromotion of scienceFig.6 Finding of surrogate transfer route

References

Next, as the second case of emergency, #1 plantis forced to shut down due to emergency. In thiscase, two plants must cover the whole consumers in the area. Figure 7 shows the result of twoplants case. As shown in figure 7, two plants #2
and #3 could successfully cover the whole consumer cities.

[1] Osaki, Akagi, Fujiwara, and Kikuchi: production
system technique, Kyotitsu syuppan (Tokyo) pp.162184[2] Al-Sehaim Sulaiman, Masami Konishi, Kazuo
Nose, Ryuji Otani: Scheduling Method by Decentralized Agents for Soft Drinks Production Line the Japan society for production management, Vol.10, No2( 2003), PP35-43[3] Al-Sehaim Sulaiman, Masami Konishi, Kazuo
Nose: Simulation Model for Production and Logistics of Petroleum, Preprint of the Japan society for
production management, (Sept. 2003), PP 245-248[4] Al-Sehaim Sulaiman, Masami Konishi, Kazuo
Nose: Optimization of Production Control and Logistics for Oil Products- Decentralized Optimization ofConveyance Planning for Oil Products , Preprint of
the Japan society for production management,(March 2004), PP107-110

Fig. 7©ISAROB 2005

Solution for #1 plant shutdown

515

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

AN AUTOMATIC DECOUPLING CONTROL SYSTEMFOR SHIP HARBOR MANEUVERS AND ITS ROBUSTNESS EVALUATION
Minh - Duc LE,

Anh – Tuan DANG,

Vietnam Shipbuilding Industry Corporation (VINASHIN),
Hanoi, Vietnam
le_minh_duc@yahoo.com

Shipbuilding Industrial Software Joint Stock Company(SHIPSOFT JSC.)
Hanoi, Vietnam

Abstract

system, a robust control methodology must be employed.
Over the last three decades, the problems of achieving
decoupling, or non-interaction, in MIMO control
systems has been widely studied and Decoupling Control
Method (DCM) has been motivated by the needs of awide range of applications. Because of highly couplednature of ship dynamics, high performance requirements,
and possibility to divide ships’ maneuvering motions inharbors into elemental motions for practical purposes [5],the DCM can be seen as the best solution for this study.
Recently several studies concerning automatic
control systems for ships’ harbor maneuvers have been
carried out [6] - [10], however, in most of those studies,
bow and stern thrusters were used as the means toprovide controlling forces and moment. But in practicalhandling of ships, control of large ships in harbor areas,
especially in berthing and de-berthing maneuvers,
usually involves the use of tugboats. This study applies
the DCM to construct an automatic control system forlarge ships in harbor maneuvers through the use oftugboats. Excellent effectiveness of the automatic
control system is illustrated by simulation results of the
VLCC in a typical pattern of approaching and berthing
maneuvers. Moreover, not only the accuracy of theposition tracking is emphasized, but the robustness of thecontrol system is also considered carefully.

Presented and discussed in this paper aremathematical model used for expressing ship motions,application of Decoupling Control Methodology toconstruct the automatic control system andcorresponding designing issues. Computer simulation
results for a Very Large Crude Carriage (VLCC) in a
typical harbor maneuver are given to verify thedesigning of the control method. Excellent effects of theautomatic control system are showed by very good
simulation results of ship motions during several 180 deg.
turning maneuvers under various strong wind conditions.
Robustness of the control system against parameters'
uncertainty, strong environment disturbances such as
strong wind and currents is also studies and presented inthis paper.
Keywords: Nonlinear control, Decoupling control, Ship
dynamic, Robustness

1 Introduction
Controlling ship motions in harbor areas is always
one of the most sophisticated actions carried out byhuman operators. When a ship moving at a low speed
approaches or leaves a berth, the ship is often in the mostcomplicated and dangerous operation. Therefore, to keep
ships’ safety, it is a very important task to construct anautomatic control system for ships’ harbor maneuvers.
To develop such an automatic control system forlarge ships, several problems must be solved. Amongthem the most difficult is the how to lead the ships follow
a desired trajectory precisely. Then a suitable
mathematical model of ship maneuvering motions inharbors and a proper control method are necessary. Since
ship dynamics in harbor maneuvers are fundamentally
non-linear in nature, a multi-term mathematical model ofship motions should be adopted to describe a wide rangeof ship maneuvering motions in harbors. The model usedhere was based on a well-known and widely applied one,
known as the MMG model that expresses surge, sway
and yaw motions of ship by open-water characteristics ofhull(s), propeller(s), rudder(s) individually andinteraction terms among them [1]. The model was
originally presented by K. Kose et al. [2] and has further
been developed by Le and Kose [3], [4] recently. All theparameters (in the model) for a Very Large Crude
Carriage (VLCC) have also been estimated with highaccuracy, and used in this study for simulation purpose.
Besides, to automatically control such a non-linear©ISAROB 2005

2 The mathematical model and a typical
pattern of approaching and berthing forlarge ships
The non-linear, multi-term mathematical model
The MMG model [1] shown in formula (1)(non-dimensional form) consists of the open-water
characteristics of hull(s), propeller(s) and rudder(s)individually and interaction terms among them:m * (u& * − v * r * − xG* r *2 )= X H* + X P* + X R* + X E*

m (v& * + u * r * + xG* r& * )= YH* + YP* + YR* + YE**(1)

I r& * + m * xG* (v& * + u * r * ) = N H* + N P* + N R* + N E**
zz

Here, u * , v * , r * are the ship’s surge, sway and yaw
velocities, respectively and u& * , v&* , r& * are theircorresponding derivatives with respect to time; m* , I zz*
are ship mass and moment of inertia; xG* is distance frommid-ship to the ship’s center of gravity; X , Y , N termswith subscripts H , P , R , E respectively are forces inlongitudinal and lateral directions and moment inducedby ship hull(s), propeller(s), rudder(s) and external

520

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A typical pattern of harbor maneuvers for a large
tanker [5] is shown in Fig. 2. The ship firstly enters theapproaching maneuver, stops at some point located in
front of a berth (this position is called as a “false goal”).
There is enough safety distance between the false goal
and the real berth (about 2-3 B, where B is the shipbreadth molded [5]). The ship then turns around the false
goal, her heading is adjusted parallel to the real berth, herlongitudinal position is also adjusted to the just in frontof the berth. Lastly, the ship enters the berthing
maneuver by shifting laterally to the berth.

effects, respectively. With the aim of controlling large
ships in harbors, only the forces and moment producedby hull(s) and tugboats are considered in this study.
The forces and moment induced by ship hull(s) in low
speed motions are described by a multi-terms
mathematical model [2], its form is given in formula (2).
22

X H* = − m*xu&*+ X uu* u* + X vv* v* + ( X vr* + m*y )v*r* +2*
X|*v| vr | v* | v*r* / U * + X vvu
v* u* /U * + X|*v|u | v* | u*

= − m v& + Y v + Y | v | v* + Yr*r* +

*
H

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y

Y

* *
v

*
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NH* = − J zz* r&*+ (Yv*v* + Y|v*|v | v* | v* )(L*2 + L*3 β ) +3 Application of the decoupling control
methodology

*
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*
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are added mass and moment of inertia in the forward,transverse, and yaw directions, respectively.
Figure 2 shows the coordinate systems used in thisstudy. All the terms in the above mathematical model are
expressed in the ship-fixed coordinate system XYZwith the origin at the center of symmetry of the hull, andthe Earth-fixed coordinate system is X0Y0Z0 .

X

0CURRENTψc

Vc

Decoupling control methodology applied to thenon-linear model of ship in harbor maneuvers
System of equations (1) and (2) can be rewritten in
the following form of non-linear equation system:(3)
M ν& + N (ν , η ) = T
η& = J (η )ν
(4), where η = [ x y ψ ]T and ν = [u v r ]T are the vectors that
express ship position (and Euler angle) and velocity inthe horizontal plane (surge, sway, yaw), respectively.
Both η andν are usually assumed to be measured. M ,N , T are matrices expressing influence of inertia,
damping part, and control forces and moment as well as
environment effects, respectively; J is transformation
matrix that expresses the relationship between the X0Y0Z0
and XYZ coordinate systems (see Fig. 1).
cosψ − sin ψ 0 
J = sin ψcosψ 0 
(5)
 0
0
1

V WIND
U
θ
A

A

- ?A

N, r

X,u

Z
Y ,v

ψ

Equation (3) suggests a non-linear solution aν (in thebody-fixed reference frame) that satisfies:(6)
T = Maν + N(ν ,η)Taking differentiation of both sides of the equation(4) with respect to time yields:
η&&= J (η )ν& + J& (η )ν
or ν& = J −1 (η )[η&& − J& (η )ν ](7)
Denoting:
Mη =J −T (η) M J −1(η) and aη = J& (η )ν + J (η ) aν
(8)

Z

0

Y

0

Figure 1. The coordinate system
g
hin
ac
o
r
p
Ap

Stopping

2•` 3B, and using equations (3) and (6) with notation (8), thefollowing result is derived:
M η [η&& −aη ] = 0(9)Turning

This equation suggests that η&& −aη should have theform of a 2nd order differential expression:
η&& −aη = η&~& + K d η~& + K pη~(10)~, where η = η - η and η denotes the desired vector

BerthingdBert hFigure 2. A typical pattern of approaching and berthing
for large ships.
Typical patterns for harbor maneuvers of large ships©ISAROB 2005

dof state variables, K d and K p are two positive definite
matrices. In order to keep the error dynamics of thecontrol system stable, the real part of solutions of thecharacteristic equation S 2 + K d S + K p = 0 for (10) shouldbe negative.
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The commanded acceleration should be chosen as:&& + K η~& + K η~ ) = η&& − K η~& − K η~ (11)aη = η&& − (η~
d
d
p
d
p

4 Compute simulation resultsrobustness of the control system(13)m = M e / M and n = N e / N, then the relations between the values of m , n and thecorresponding values of Dmin show the influence of thecoefficients’ mismatch on the performance of theautomatic control system.
Simulation results of these relations are shown in Fig.
4, for 5 values of m and n : 0.25, 0.5, 1.0, 2.0, and 4.0.
m = 1.0 means that there is no coefficients’ mismatch on
added mass and moment. Similar thing does for damping
coefficients. For the cases of added mass and moment
coefficients’ mismatch, it is clear that the coefficients’mismatch has almost no influence on the control results.
For damping coefficients’ case, although the value
Dmin = 1.07 when n = 4.0 is little bit larger compared toother values of Dmin (about 1.01), the influence ofcoefficients mismatch is not significant. In other words,
the controller can well compensate influence of theuncertainty of model’s coefficients.

andSimulation results of a typical harbor maneuver
Applying the above described method, a position andattitude tracking controller was designed for the VLCC
[4], [11]. To illustrate the application, let examine ship
motions in a simple harbor trajectory similar to thetypical pattern of approaching and berthing maneuvers
described Fig. 2, with position and heading (x, y, Psy) of
marked points in the ship trajectory given as:- Starting position: (1000m, 900m, -145deg.),- False goal: (0m, 150m, -180deg.),- Real berth: (0m, 0m, -180deg.).
2

0.6
Error_X(m)Error_Y(m)Error_Psy(deg.) 0.4

0
0.5

1.1
1.08
1.06

-0.2
0
0
-0.5

500

1000

1500

2000

Time (sec.)

2500

3000

3500

n

1.04

-0.4
4000

1.02

-0.6

1
0

Figure 3. Tracking errors of the controller during atypical pattern of approaching and berthing

0.5

1

1.5

2
2.5
Relative values of m, n

3

3.5

4

4.5

Figure 4. Influence of the coefficients’ mismatch on
the control results during 180 deg. turning

Figure 3 shows tracking errors (deviations from thedesigned trajectory) of the controller. Except for somesmall periods when the ship entered new manoeuvres,the tracking errors are considerably small and the final
errors were limited to the allowable values for harbour
manoeuvres (of the order of decimetre level).

Robustness of the automatic control system again
environmental disturbances
To study the ability of the controller in dealing with
influence of environmental disturbances, several
simulations of the VLCC’s motions in the 180 deg.
turning maneuver under various wind conditions werecarried out. Simulations were carried out with the winddirection varied each 30deg. in the range from – 180deg.
to 180deg., while wind velocities varied with 5 values ofm and n : 0.25, 0.5, 1.0, 2.0, and 4.0. Fig. 5 gives overall
results of influences of the 15 m/s wind and coefficients’mismatch on the 180 deg. turning. In the case of added
mass and moment coefficients’ mismatch, although thevalue of Dmin varies with the change of the wind

Robustness of the control system again parameters’
uncertainty
Since in de-berthing process ships often have to turn180deg. in a very limited space, it is important to study
the turning ability of the ship in this manoeuvre.
Denoting the largest distance from initial mid-ship
position to any point in the ship during ship manoeuvringby R max , the minimum required diameter(non-dimensional) of the basin’s space for that
manoeuvre is given by:(12)
D min =2 R max / L, where L is the ship length. The smaller the value of
Dmin is achieved, the better the controller is.
Suppose that M and N respectively are the true
values of added mass and moment, and damping
coefficients in the formula (3) while M e and N e are thecorresponding estimated values of M and N .
Defining the relative values:©ISAROB 2005

m

Dmin

Error x, y (m)

0.2
1

1.12

Error Psy (deg.)

1.5

direction, value of Dmin is only a little different from thecorresponding value where no mismatch has occurred( m = 1 and n = 1). In the case of damping coefficients’mismatch, results are quite different. If N e ≤ N (orn ≤ 1), value of Dmin is as small as the in the situation of
no mismatch, no environmental disturbances. But if
N e > N (or n > 1), values of Dmin are a bit larger thanthe corresponding value of Dmin when there is no522

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mismatch occurred. However, even in this case valuesof Dmin are smaller than 1.2 and that shows excellent
effect of the controller on cancelling the influence of thewind since the value 1.3 is considered as desired value
for advanced controllers.

Acknowledgements

1.01

The deepest gratitude is expressed to Prof. Kuniji
Kose of Hiroshima University for his guidance in thisstudy. Sincere acknowledgements is extended tomembers of “Low Speed Model” team (Lab. of Ship
Maneuvering and Motion Control, Hiroshima Univ.) for
their cooperation in carrying out the experiments, which
resulted in the ship mathematical model used in thisstudy. Hearty thanks are expressed to Mrs. Houmyou
Saitou and Mr. Shodo Seta of Houryuji (Hiroshima,
Japan), for their encouragement and financial support
during the time this study was carried out.

1.005

References

Influence of added mass and moment's mismatch
1.02

4*M
2*M
1*M
0.5*M

1.015
Dmin

0.25*M[1] Kose K., et al (1989), “Practicalization of ship
Manoeuvring Mathematical Model at Low Speed”(in Japanese), Transactions of Japan Society ofNaval Architects, Vol. 721, pp. 403-411.
[2] Le M. D., and Kose K. (2000), “Estimation of Ship
Hydrodynamic Coefficients at Low Speed Range
and Application to Control Ships”, The Journal ofJapan Institute of Navigation, Vol. 103, pp. 33-39.
[3] Le M. D., and Nguyen D. H. (2000), “Estimation ofship hydrodynamic coefficients in harbour
manoeuvres and its applications”, Preprints of IFAC
Workshop, Control Applications of Optimization,
CAO2000, pp. 227-232, Saint Petersburg.
[4] Kose K., (1987) “Study on A Computer Aided
Manoeuvring System in Harbors”, Proceedings of
the 8th Ship Control Systems Symposium, Vol. 2,
pp. 1-13, The Hague.
[5] Iwamoto S. (1999), “An Automatic Control System
Design of Berthing Manoeuvring with Decoupling
Control” (in Japanese), The Journal of Japan
Institute of Navigation, Vol. 101, pp. 75-82.
[6] Ogawara Y., and Iwamoto, S. (1998), “Studies on
the Control System Design of Ship Manoeuvring
Motion with Decoupling Control”, Proceedings of
IFAC Conference, Control Applications on Marine
Systems, CAMS’98, pp. 147-153, Fukuoka.
[7] Berge S. P., et al (1998), “Non-linear Control ofShips Minimizing the Position Tracking Errors”,
Proceedings of IFAC Conference, Control
Applications on Marine Systems, CAMS’98, pp.
141-146, Fukuoka.
[8] Ohtsu K., et al (1991), “A Fully Automatic Berthing
Test Using the Training Ship Shioji Maru”, The
Journal of Navigation, Vol. 41, pp. 213-223.
[9] Fossen T., Guidance and Control of Ocean Vehicles,Great Britain, John Wiley & Sons, 1994, Chapter 4,
pp. 93-165.
[10] Kose K. (1982), “On a New Mathematical Model of
Manoeuvring Motions of A Ship and Its
Applications”, International Shipbuilding Progress,
Vol. 336, pp. 201-219.

1
-200

-150

-100

-50
0
50
Wind direction (deg.)

100

150

200

Influence of damping coefficients' mismatch
1.24

4*N
2*N
1*N
0.5*N
0.25*N

1.2

Dmin

1.16
1.12
1.08
1.04

-210 -180 -150 -120 -90

-60

1
-30
0
30
60
Wind direction (deg.)

90

120

150

180

210

Figure 5. Influences of coefficients’ mismatchand 15m/s strong wind on the control results
during the 180 deg. turning manoeuvre.

5 Conclusions and future works
The Decoupling Control Methodology has been
applied to design an automatic control system using a
non-linear model of ship harbor maneuvers. The control
method helps to reduce the complicity of the ship control
system. Excellent simulation results of a typical patternof approaching and berthing maneuvers using the control
system show that the automatic control system can very
well deal with the non-linear dynamics of ship motionsin harbor maneuvers. The Decoupling Controller also
produces extremely robustness in canceling influencesof the parameter uncertainty and the environmental
disturbances such as strong wind.
Some future works can be pointed out as follows:
more effective methods to deal with the influence ofstrong current and shallow water conditions in harbour
are necessary. Another possible future work is to study
the use of tugboats in practice, including an optimal
method for allocation of required control forces andmoment to the tugboats.

©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

CPU Resource Double Auction System with an Anonymous Protocol
Takashi Matsumoto

Hidenori Kawamura

Azuma Ohuchi

Graduate School of Information Science and Technology, Hokkaido University
Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan{matumo, kawamura, ohuchi}@complex.eng.hokudai.ac.jp

Abstract

auction can be sold at a price determined by interactions in the auction. Moreover, the Internet is themost suitable for carrying out a real time auction sinceparticipants can bid anytime regardless of a particular place. Therefore, at present an electronic auction
spread. However, an auction has problem of leakingof personal information from bid history or a corrupt
auctioneer.
Especially in case trading among companies, it isnecessary to conceal identity of a bid for protecting
an intellectual property. Since a company must notknow worth of CPU resource needlessly. Moreover, itis necessary to conceal trading partner too. Since if
it turns out that a trading partner is a rival company,
there is possibility of adverse effect to the auction. On
the other hand, buying and selling prices are published
to all participants since trading are made easy to do.
Kikuchi et al. [2], propose anonymous protocols
based on a multiparty secret computation protocol [3].
This auction protocol is based on that identity of seller
is known by the auctioneer, and bid price distribution
is concealed to the auctioneer and all participants. In
double auction, both seller’s identity and buyer’s identity should be concealed, and bid price distribution is
published to all participants. Therefore, this protocol
is not able to use double auction system.
In this paper, we describe a set of protocols of CPU
resource electronic double auction system among companies for performing protected identity of bid. An
identity means the thing of what can be specified aparticipant, such as IP address, account which was assigned by the auctioneer and so on, in paper.
In our system, there are four features.
First, the system conceals an identity of bid from theauctioneer and all participants of the auction. Second,
the system publishes selling and buying price distribution to all participants. Third, the system makes
known an identity of participant whose trading was decided to only the auctioneer. Fourth, the system conceals trading partner from a participant whose trading
was decided.
For realizing these terms, the following things are
required.
· A bid data, which is sent when a participant bid,

CPU resource of a computer has a case of using
all the power and a slight amount of the power, according to time and circumstances. A company wants
to buy CPU resource when large-scale processing orprocessing of emergency is performed, and wants tosell CPU resource when it uses a slight amount of thepower. The way of marketing an electronic auction is
performed actively. In a general way of auction, it has
problem that the auctioneer finds out identity of bids.
In case an auction is performed among companies, it
can not make use of the auction with a sense of security unless such problem is solved. Since, if a company
bids, a trend of the company leaks out as a company
secret from the bid price whether it goes through ornot. Moreover, if a company finds out that the trading partner is rival company, there is possibility of adverse effect to the auction. In this paper, we describe
a set of protocols of CPU resource electronic double
auction system among companies for protect identityof bid from other companies and the auctioneer by using Secret Sharing Scheme, public key encryption, andparticular method for sending data.
keywords:resource, auction, Secret Sharing Scheme

1

Introduction

CPU resource of a computer has a case of using all
the power and a slight amount of the power, according
to time and circumstances. Thus research on utilization of idle CPU resource is increasing in recent years,
suchlike grid computing [1].
As a general trend, company has large-scale CPU
resource and there are often two distinguishing situations. One is a case of using all the power whenlarge-scale processing or processing of emergency is
performed, in this instance the company wants to buy
CPU resource from other companies at a low price tospeed up processing. The other is a case of using aslight amount of the power, in this instance the company wants to sell idle CPU resource for profit.
In the way of marketing, an auction is the best
mechanism for price setting since a product of the©ISAROB 2005

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has the feature that the bid-price is found out and theidentity can not be found out. Moreover when trading
goes, only the auctioneer finds out the identity.
· It is necessary to change a method of sending bid
data, since by using a usual auction method an identityof the bidder founds out from IP address.
· It is necessary to work out the way of processing
using the bought CPU resource.
These tasks are realized by using Secret Sharing
Scheme (SSS), public key encryption, and particularmethod for sending data. At the following section, we
describe details of the auction-protocol.

If a bidder own self i-datai , there is fear, such asa participant who is not bidding the selected bid insists on his bid, and a participant who bided the selected bid erase the i-datai for misbehave. Therefore,
an i-datai is kept by all participants. But in this way,
they can see i-datai of other participant. Though abidder encrypts a i-datai with a public key of the auctioneer, a participant can see i-datai of others by atmost one participant cooperates with the auctioneer.
Therefore, a bidder encodes the aci into multiple distributed codes by SSS.

2.3

2
2.1

(k, n)threshold SSS make the number of n distributed data, denoted by wj (1 ≤ j ≤ n), from secretdata [4]. It has a feature that if the number of k or
more of wj are collected the secret-data can restore,
but in case less than the number of k it can not restore
even partial information of the secret-data.
A bidder makes the number of n i-datai s, denotedby Wij , including one of wij which is distributed fromaci , and one of them own himself and remainder sends
to other participants.
Figure 1A shows destination of p-datai and Wij .

Proposed Protocol
Auction Style

We consider an electronic double auction formedby the auctioneer and participants registered by theauctioneer.
A participant can bid a selling and buying priceand cancel a previous bid any time one likes. A participant sends these orders to the auctioneer as a bid.
The auctioneer publishes selling and buying price distribution obtained from these bids to all participants,
and selects pair of bid to trade.
In case a trading goes through, only the auctioneer
can know identity of the pair of bids, and a participant
can know his bid were decided except identity of his
trading partner.
In order to conceal a trading partner, the following
procedure is done. A participant which bought CPU
resource from other participant hands over a process
to the auctioneer, and a seller participant receive theprocess from the auctioneer and return the result tothe buyer participant through the auctioneer.
In this auction, volume of CPU resource is definedas Bi which is possible number of processing which was
decided beforehand in a unit of time. Bi is assigned
interger value.

2.2

Figure 1: A:Destination of p-datai and Wij . B:Method
for sending data

Bid data

It is impossible that restore variable aci of otherparticipant unless the number of k or more participants cooperate and misbehave by using this method.
Figure 2 shows image of this method.
This algorithm is shown below. A bidder obtains
the j-th distributed data (wij ), by evaluating a k − 1polynomials of the form

A selling or buying bid data is sent to the auctioneer. If the bid data includes identity of the bidder, theauctioneer finds out it. Therefore, it is necessary to divide the bid data into a price-data, denoted by p-datai ,including selling or buying price from an identity-data,
denoted by i-datai including account, denoted by aci ,which was assigned by the auctioneer. A p-datai issent to the auctioneer (i ∈ N :N is a set of participants). The auctioneer selects trading partners fromp-datai . But an i-datai can not afford to be sent tothe auctioneer, since auctioneer finds out identity ofbid. Therefore, an i-datai is kept by participant.

©ISAROB 2005

(k, n) threshold SSS

f (x) = a + r1 x + r2 x + ... + rk−1 xk−1 (mod p)
at x = j :wij = f (j)where p is a large prime number greater than any of thecoefficients and is made available to all participants

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

sign
aci
Bi
pricei
M 1i
M 2i
p-datai
wij
Wij

type
integer value of optional digit
integer value
integer value
stringstring{M 1i , Bi , pricei }
integer value of{M 2i , wij }explanation
identity of a participant which was assigned by the auctioneer
possible number of processing which was decided beforehandselling or buying bid priceresult from SHA algorithm of a bid time and aci
result from SHA algorithm of M 1i
be sent to the auctioneer
destributed data of aci by using SSS
be sent to other participants

Table 1: Contents of data

On the other hand a participant cancels a bid, amark which associates a cancel order with bid is necessary too.
If these marks are equal, a participant can cancel abid of other participant since participant has a mark ofothers Wij . Therefore, these two makes have to different things. Moreover, it is necessary to be connectedwith two marks.
These two marks must not overlap with it of otherparticipants. Since, if same marks exist in databaseof the auctioneer, the marks can not be recognized.
Therefore, these two marks are assigned a value of
one-way function.
This algorithm is shown below. First, a bidder calculates the value, denoted by M 1i , from SHA algorithm, which is one-way function, of a present timeand his account. Next, bidder calculates the value, denoted by M 2i , from SHA algorithm of the M 1i . M 1i
and M 2i are assigned by string. M 1i is attached to
p-datai and M 2i is attached to Wij . In case cancel
a bid, it is necessary to M 1i . A participant can notcancel bid of other participant by using this method,since it is very difficult to calculate M 2i from M 1i .
When a trading goes through, the auctioneer collects
Wij based on a value (M 2i ) from SHA algorithm of
the M 1i which is attached p-datai .
Table 1 shows compilation of these data.

Figure 2: image of using SSS
and the auctioneer, and the coefficient a is account of
the bidder while other coefficients r1 , r2 , ..., rk−1 are
all randomly chosen. Wij includes one of these wij .
When a trading goes through and the auctioneer
collects the number of k or more of Wij s, it can reconstruct the original polynomial by solving a set of linear equations over a finite field GF (p). Assume thatthe auctioneer collects the number of k wij s, which
is wi1 , wi2 , , wik , from the number of k Wij s. The
original polynomial f (x) can be restored by Lagrange
interpolation.
f (x) =

k
X
(wi ·
i=1

k
Y
j=1,j6=i

2.5

When a participant sends p-datai to the auctioneer, the data is sent to the auctioneer not directly
but through several participants selected at random.
Since, if the data is sent to the auctioneer directly like
Figure 1A, the auctioneer finds out the identity of thesender.
If a number of roam participants is decided, theoriginal sender is found out from the number. Therefore, the number is decided from probable. Moreover
time limit of roam participants is set, since it has possibility that the p-datai is hard to be sent to the auctioneer.
The algorithm is shown below. First, a bidderx−j) (mod p)
i−j

The variable aci can be restored by calculating f (0).

2.4

Marks

When the auctioneer collects Wij , a mark whichassociates a p-datai with Wij is necessary. The auctioneer selects trading partners from p-datai and whenpair of bids are selected, and collect the Wij based on
this mark.

©ISAROB 2005

Method for sending data

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3.3

chooses integer number, denoted by N U Mi , from 1 to10, and set a limit time. Next, a bidder sends p-datai
to a participant selected at random. Participants who
receive the p-datai check the limit time and N U Mi .
In case run past the limit time or N U Mi equal to 0,
the participant sends the p-datai to the auctioneer; theother case, the participant subtracts 1 or adds 1 fromN U Mi , leaves it, or is set to 0 by restrictive probability, and sends the price-data to other participant
selected at random. This operation is repeated until
the p-datai is sent to the auctioneer.
But using this method, a participant can see p-dataiof others. Therefore, bidder encrypts a p-datai with apublic key of the auctioneer.
Figure 1B shows an example of p-datai ’s path.
In case of sending Wij , it is in the same way that
sending p-datai , since it has same problem. Therefore, Wij includes a destination of sending. Moreover,a bidder encrypts a Wij with a public key of a participant of destination of sending, since a participant who
receives k or more of Wij can restore the account.

3
3.1

Role of the auctioneer in auction is publishes pricedistribution which be sent as a p-datai , selects trading
partners, collects pair of Wij and restores a variable
aci of participant whom dealings determined, and relays process between participants. Here is a procedureof collect pair of Wij and restore a variable aci .
Step 1: The auctioneer selects trading partners
from p-datai , and calculates M 2i from SHA algorithmof M 1i of the p-datai .
Step 2: The auctioneer collects Wij of the M 2i fromall participants, and order them to delete the Wij .
Step 3: The auctioneer restores a variable aci fromthe Wij , to use method of described at preceding section.

4

Procedure
Selling or Buying Bid

References[1] William E. Johnston et al.“Grids as Production
Computing Environments :The Engineering Aspects of NASA’s Information Power Grid”, Proceedings of 8th IEEE Symposium on High Performance Distributed Computing, IEEE Press, 1999.
[2] H. Kikuchi et al. “Multi-round Anonymous Auction Protocols”, Proceedings of the First IEEE
Workshop on Dependable and Real-Time ECommerce Systems, June, pp. 62-69, 1998.

Cancel a previous Bid[3] D. Chaum et al.“Multi-party unconditionally secure protocols”, Proceedings of ACM STOC ’88,
pp. 11-19, 1988.

When a participant bids cancel bid, the procedure
is equal to Step 1 to Step 4 of selling or buying bid. Incase a participant cancels bid, he uses M 1i and M 2i
which restored at bid the selling or buying bid and thevalue of Bi and pricei is set to 0.

©ISAROB 2005

Conclusion

We presented protocol of CPU resource electronic
double auction system among companies. Using this
proposed protocol, identity of bid will not become
clear to the auctioneer and all participants unless k,that is threshold, or more participants cooperate andmisbehave. Moreover, only selling and buying pricedistribution is published to all participants and theauctioneer.
If the auctioneer collects Wij unjustly, a bidder notices this misbehave since the bidder owns one of his
Wij . Therefore, the auctioneer who has the authority
to collect Wij can not misbehave either.
It should be noted that in a general protocol of electronic double auction, auctioneer can find out identityof all bids. A construction of a system using the proposed protocol will be studied in future work.

When a participant bids selling or buying bid, thesame procedure is performed. Here is a procedure of
the algorithm.
Step 1: A participant makes M 1i and M 2i , to use
method of described at preceding section.
Step 2: A participant makes p-datai include M 1i ,Bi , and pricei .
Step 3: A participant encrypts the p-datai with apublic key of the auctioneer, and attach N U Mi andtime limit.
Step 4: A participant sends the encrypted data to
the auctioneer, to use method of described at preceding section.
Step 5: A participant make wij from aci , to use
method of described at preceding section.
Step 6: A participant makes Wij include M 2i andone of wij .
Step 7: A participant encrypts Wij with a public
key of a participant of destination of sending, and attach N U Mi and time limit.
Step 8: A participant sends the encrypted data to aparticipant of destination, to use method of describedat preceding section.

3.2

Auctioneer[4] A. Shamir. “How to share a secret”, Communication of the ACM, Vol. 22, No.11, pp. 612-613, 1979.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Issues and Application on Robot Control Using Internet
Masanori Sugisaka1 Desa Hazry2,
Department of Electrical and Electronic Engineering
2
Graduate School of Engineering, Department of Electrical and Electronics Engineering
The University of Oita, 700 Oaza Dannoharu, Oita, 970-1192, Japan
Tel: +81-97-554-7831, Fax: +81-97-554-7841,
e-mail: msugi@cc.oita-u.ac.jp hazry@cc.oita-u.ac.jp
1

Abstract:

2.1 One to one
An interesting internet controlled pet robot witharithmetical inclination has been developed within thisarchitecture [3]. The robot is programmed to recognize
the image signals of the arithmetic operation using thebinary method. If the equation is right it will nod and if theequation is wrong it will shake its head

Robot control using internet basically uses theinternet as command transmission medium and obtain
feedback signals. This paper introduces the fundamentals
of such robot control relationships. Four variations ofrobot control relationship have been identified and they
are one to one, one to many, many to one and many to
many. Samples for each of the relationship are given. The
development of internet are further considered in thevarious applications areas. The applications made byvarious projects are referred. Some research issues andproblems brought up by the projects are also reflected.
The issues directed are time delay, communication,
dynamic environment and dispersing the whole robot
system on the internet.

2.2 One to many
The ARMAGRA Project [4] also depicts the samerobot control architecture. It involves a few robots with an
assistance system for disable persons. Both robot projects
illustrated uses a decentralize system which enables eachpart of hardware to be develop individually. The
flexibility of decentralize system is also economical as the
web server workstation can be shared through LAN or a
WAN. Our robot TAROU allows one remote user tocontrol a group of autonomous robots. The operator sends
commands using interface in HTML to the internet host
onto the internet system. The command is received by
RDIS/LT which is attached to Tarous body. Upon
execution of command, reports are generated by Tarousand sent to the operator.

Keywords: Internet robot, robot control

1 Introduction
Robot control using internet basically uses theinternet as command transmission medium and obtain
feedback signals. Our laboratory is developing atechnique to remote control a group of autonomous
mobile robots (Tarou) by mobile phone or via the internet[1]. The remote control equipment used in Tarou istransmitter/receiver RDIS/LT-08 which has 8 input ports,
8 output ports, 2 analog input ports and RS232 port.

2.3 Many to one
A system that allows multiple users to control andindustrial arm robot has been developed [2]. Each users
monitors different sensors and submits control inputs
based on different control information. The inputs are
combined to a single control signal for the robot. However,time delay and transmission latency remain as two
prominent areas of concerns.

2 Robot Control ArchitectureInternet operated robot generally uses hardware
which includes the robot, the robot server workstation,
web server workstation and other user computers [2]. The
user access the worldwide web and give commands. The
web server receives these commands and transmits themto the robot server. The robot server decides the robot
motion and behavior. The tasks are transmitted to therobot which is supposed to carry out the commands. The
relationship of the user and the control system can beclassified into four control architectures which are; one toone, one to many, many to one and many to many.
©ISAROB 2005

2.4 Many to many
Marin et all [5] has described an experiment on
multirobot internet based architecture. In the experiment,
they tested on several telerobotic configurations thatenable access from multi-users. They highlighted thathaving many robots connected to the servers manager
would cause a bottleneck and proposed a configuration
for the synchronization of the robots operation and
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specification for reliable multirobot tasks.

examinations is it alleviates the problems of humanphysical conditions among the sonographers [8]. This
robot assisted system for medical diagnostic ultrasoundhelps to reduce the problem of having to performawkward body positions in doing the test. The systemcomprises of a master hand controller, a slave manipulator
and a computer control system. It enables the operator to
remotely position the ultrasound transducer onto thetargeted patients body parts. The teleoperated quality
enable the operator to position the ultrasound at ease withadditional assistance of machinery force and image
controllers enable the robot to be remotely position andused in telemedicine.
Another medical robot system is developed by the
Kanagawa Institute of Technology in the area of Face
Robot [9]. The robot system is within a personal computer(PC). The purpose of the robot is to remind the patients
when and which medication is to be taken using facial
expression, a voice communication system and a display
on the PC.

3 Application
The literature reviews have revealed many aspect ofinternet robot. Many of it has focused on the technicalaspect of the robot system development. This part of thepaper discusses the research of internet robot which has
been developed within an application area. The recent
developments in the research and development forinternet based robot have been focused on eight areas ofapplication. The areas identified are; industrial robot,
medical robot, entertainment robot, autonomous robot,
service robot, hazardous environment robot, educational
robot and other types of robot.
3.1 Industrial Robot
Industrial robots are used in various areas inmanufacturing and production environment. The robot
are used to assemble parts, paint, weld and do other tasks.
An internet based robotic assembly planning system
namely the WebROBOT has been developed using a
modular architecture [6]. User can model desired
assembly sequence using a variety of parts in theassembly area. The objects are classified onto two
categories which are the stationary objects and themovable objects. Users plan an assembly sequence using
the movable objects onto the stationary objects. A
common assembly done by the robot involves placing fivecylindrical parts into an array of holes of a block in polar
fashion. This way the user can specify the assembly
operation while the robot converts the orders into detailed
robot path by computing robot joint positions using
inverse kinematics. The ability to automatically generate
the robot path enable the WebROBOT to carry out robot
programming at the task level compared to the commonlyused strategies which is at joint level or teach-in typerobot programming.
An added feature of industrial robot has beendeveloped using the Virtual Reality Modeling Language
VRML model [7]. The VRML model is developed using
various software components and it has the capability tocontrol and monitor the robot via the internet. The system
is designed as such it allows users to control the robot
without the need to know the details of the programming
language used. In factory environment, it allows theoperators or managers to visualize the robot system in thefactory online at remote locations.

3.3 Entertainment RobotEntertainment robots are developed as a variation ofamusement means. However its potentials can beextended for other arising needs. The interesting internet
controlled pet robot with arithmetical inclination has been
explained earlier is a good extension of entertainment
robot. Apart from the basic movements of walking,seating and standing up it can also do fundamental
mathematics calculations. The pet robot can determine theequation of addition, subtraction; multiplication anddivision are correct or otherwise. If the equation right thepet robot will nod and if it is wrong, the pet robot will
shake its head. Another entertainment robot is being
developed by the LunaCorp Inc. and Carnegie Mellon
University [10]. They aimed to operate a pair of
teleoperated robotic vehicles on the surface of the moonwith a television network as customer.
3.4 Service Robot
The service robot can provide many services in homeor office. Sawasaki et all [11] describe the application of
Humaniod robots to building and home management
service. The system enables users to remote control
humanoid robot in home environment. Another service
robot WorkPartner has been developed to perform tasks
like garden work, transferring of light weight obstacles
and environment mapping. The centaur like service robot
is a hybrid, lightweight outdoor robot [12]. Its hybrid
system combines both legged and wheeled locomotion,

3.2 Medical Robot
The main advantage of medical robots in ultrasound©ISAROB 2005

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4 Research Issues

providing good terrain negotiation and large velocity
range.

Most recent researches highlighted the issues
concerning the robot work environment, communication
delay and the tools needed for robot performance.

3.5 Autonomous Robot
Unlike the conventional conveyance robot system
which uses the line trace system, the autonomous robot
can make judgment and equipped with dynamic sensors.
Ohchi et all [13] described its robot which can follow theinfrared rays emitted by a transmitter on a guide and moveto the destination. An interesting autonomous robot withbook browsing system has been developed [14]. The
development has been made with consideration on theapplication and usability in book browsing environment.
The aspects of movement towards the bookshelf,
extraction and return of book and book perusal have beendeveloped.

4.1 Usage in Dynamic Environment
Using such robot in dynamic environment is aconcern due to a few reasons. One of the concern is on theinformation gap about the robot remote workplace.
Virtual Reality (VR) is one of the common countermeasure [16]. 3D graphic simulator is usually used as thehuman interface for robot in remote place operation. It isalso used to visualize the work place environment inoffline simulation to preview the robot movements in
virtual workcell before operating the real robot. The
KAERI project uses real time monitoring by gathering thetask based operation data from the sensor to the graphic
workstation which is then used to simulate the operationof the real device [16]. They proposed dedicatedcommunication protocols and dividing the simulation
programs into a number of small modules to execute each
event massage from the control computer.

3.6 Hazardous Environment Robot
Environment such as managing spent nuclear fuels is
obviously hazardous due to its high radioactivity nature.
Cragg and Hu [15] proposed an integrated architecture
which combines the strengths of available distributed
computing, autonomous multiple robot and internet robot
architectures for the use in nuclear decommissioning
environment. Korea Atomic Energy Research Institute(KAERI) has developed a 3D graphic simulator tomonitor the operation of multiple devices operated in such
hostile environment [16]. Most of the devices are operated
within a hot cell involving various sensors. They havesuccessfully transmit the operational information from theactual system to the graphic workstation in realtime andvisualization of the operating devices was simulated
successfully in the virtual workcell.

4.2 Communication, Time Delay and Other
Unresolved Issues
The underlying problems of communication and timedelay are rooted in the data transmission of the robot andthe operator. The whole system also can be upset due toirregular time delay. Accumulation of these problems
then leads to time varying system which requires various
control methods. Kikuchi et all [19] proposed a system
which consist of three subsystems; a bilateral
teleoperation subsystem, a visual information subsystem
and an environment predictive display subsystem. The
first subsystem is stabilized using the virtual time delay
method and the second subsystem transfers the visual
information. Finally the third subsystem predicts the
behavior of the environment for the operator. Other
theories proposed on maintaining the system stability andsynchronization of operator and robot terminal. This
would involve predictive methodology including
traditional predictive control, internal model control andKalman filtering [20]. Other issues involve having thewhole robot system being disperse on the internet. The
sensor, the operator and controller are located in different
nodes and coordinated via internet connection [21]. A
perfect example would be a homeland security robot
which have sensors at various locations, operator and therobot at remote places, all connected through the internet
for full execution.

3.7 Educational Robot
Safaric [17] has made an application of internet robot for
education and training in using expensive equipment. The
trainees uses offline virtual environment for task planningwhich then, exported to remote physical hardware
through the internet for robot execution. This method has
increases the training possibility and it is low cost. The
downtime of the critical equipment is minimized while
the gaining of valuable experience is minimized.
3.8 Other Internet Based Robotic
A space robot experiment ROTEX has been carriedout [18]. One of its control modes operated from theground using predictive computer graphics.

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Conclusion“Application of Humanoid Robots to Building andHome Management Services,” Proc. IEEE Int.
Conf. Robotics and Automation, Taipei Taiwan,
Vol. 3, Sept.14-19, 2003, pp.2992-2997[12] S. J. Ylonen and A. J. Halme,“WorkPartner-Centaur like Service Robot,” Proc.
IEEE/RSJ Int. Conf. Intelligent Robots and Systems,
Lausanne Switzerland, Vol. 1, Sept.30-Oct.5, 2002,
pp.727-732[13] M. Ohchi, T. Furukawa, M. Takayama and S.
Yanagi, “Development of Autonomously Mobile
Robot with Control System Connected by TCP/IP
Network,” The 27th Ann. Conf. IEEE Industrial
Electronics Society (IECON’01), 2001, pp.311-316[14] T. Tomizawa, A. Ohya, S. Yuta, “Book Browsing
System using an Autonomous Mobile Robot
Teleoperated via the Internet,” Proc. of the 2002
IEEE/RSJ Inl. Conf. Intelligent Robots and Systems,
Lausanne Switzerland, Oct. 2000, pp.1284-1289[15] L. Cragg and H. Hu, “Application of Mobile Agentsto Robust Teleoperation of Internet Robots inNuclear Decommissioning,” Int. Conf. IEEE
Industrial Technology (ICIT 2003), Maribor
Slovenia, Vol.2, Dec.10-12, pp.1214-1219[16] S. H. Hong, J. W. Jeon, J. S. Yoon, “A Robot
Graphic Simulator for Tele-operation,” Proc. of theIEEE Industrial Electronic (ISIE 2001), Pusan
Korea, Vol. 1, June 12-16, pp.195-200[17] R. Safaric, M. Debevc, R. M. Parkin and S. Uran,“Telerobotics Experiments via Internet,” IEEE
Trans. Industrial Electronics, Vol. 48, No.2, April
2001, pp.424-431[18] G. Hirzinger, B. Brunner, J. Dietrich and J. Heindl,“Sensor-based space robotics-ROTEX and its
telerobotics features,” IEEE Trans. Industrial
Electronics, Vol. 9, Oct. 1993, pp.649-663[19] J. Kikuchi, K. Takeo, and K. Kosuge,“Teleoperation System via Computer Network for
Dynamic Environment,” Proc. IEEE Int. Conf.
Robotics and Automation, Leuven Belgium, May
1998, pp.3534-3539[20] X. Huo and J. Su, “New Approaches to Internet
Based Intelligent Robotic System,” Proc. IEEE Int.
Conf. Robotics and Automation, New Orleans LA,
April 2004, pp.3363-3368[21] X. Huo and J. Su, “A Distributed Architecture forInternet Robot,” Proc. IEEE Int. Conf. Robotics andAutomation, New Orleans LA, April 2004,
pp.3357-3362

The direction of research for robot control using
internet is patterned on having the operator and the robot
in remote areas. The capitalization on true quality of theinternet should be reap for closer correlation.

References[1] M. Sugisaka and M. R. M Johari, “Remote Controlof Autonomous Robot via Internet,” ICCAS 2004,
Bangkok Thailand, August 2004, pp.25-27[2] Ren C. L, Kuo L. S, Shen H. S and Kuo H. T,“Networked Intelligent Robots Through theInternet: Issues and Opportunities,” Proc. of theIEEE, Vol. 91, No.3, March 2003, pp.371-382[3] Ren C. L, Kuo L. S, and S. H. Phang, “Multisensor
Based Control of Pet Robot Through the Internet,”
The 27th Annual Conference of IEEE Industrial
Electronics Society (IECON’01), 2001, pp.416-421[4] G. Pradel and O. Comfaits, “A Framework for a
Web-based Supervisory Control of Mobile
Robots,” IEEE Int. Workshop on Robot andHuman Interactive Communication, 2001,
pp.249-255[5] R. Marin, P. J. Sanz, P. Nebot and R. Esteller,“Multirobot Internet-Based Architecture forTelemanipulation: Experimental Validation,”
Systems, Man and Cybernetics IEEE Int. Conf.,
Vol. 4, Oct. 5-8, 2003, pp.3565-3570[6] V. B. Sunil and S. S. Pande, “WebROBOT: Internet
based robotics assembly planning system,” Science
Direct Computers in Industry 54, 2004, pp.191-207[7] H. Vermeulen, T. I. Niekerk, J. Huang, D. Hattingh,“VRML to Monitor and Control an Industries
Robot via the Internet,” AFRICON, IEEE Int. Conf.,
Vol.1, Sept. 28-Oct. 1, 1999, pp.561-564[8] P. Abolmnesumi, S. E. Salcudean, W. H Zhu, M. R
Sirouspour and S. P. DiMaio, “Image-Guided of arobot for Medical Ultrasound,” IEEE Transactions
on Robotics and Automation, Vol. 18, No.1, Feb.
2002, pp.11-23[9] Y. Takahashi, H. Goto and T. Saito, “Health Care
System using Face Robot and Tele-operation via
Internet,” 7th Int. Conf. on Control, Automation,
Robotics And Vision (ICARCV’02), Dec 2002,
Singapore, pp.1025-1030[10] K. Taylor and B. Dalton, “Internet Robots: A New
Robotics Niche,” IEEE Robotics and Automation
Magazine, March 2000, pp.27-34[11] N. Sawasaki, T. Nakajima, A. Shiraishi, S.
Nakamura, K. Wakabushi and Y. Sugawara,©ISAROB 2005

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Hard / soft switching particle ﬁlters for eﬃcient real-time visualtracking
Takashi Bando1,4 Tomohiro Shibata1,2,4 Kenji Doya2,3,4,1 Shin Ishii1,4
1

Nara Institute of Science and Technology
ATR Computational Neuroscience Laboratories
3
Initial Research Project, Okinawa Institute of Science and Technology
4
CREST, Japan Science and Technology Agency
takash-b@is.naist.jp
2

Abstract

algorithms have not, however, been fully evaluated under circumstances speciﬁc to real-time vision systems,
i.e., there are unknown occluders and distracters. It is
important for real-time visual tracking systems to realize high accuracy and robustness, coping with thesediﬃculties.

Particle ﬁltering is an approach to Bayesian estimation of intractable posterior distributions from timeseries signals with non-Gaussian noises. Several particle ﬁlters employing diﬀerent sampling methods havebeen proposed for approximating Bayesian computation with ﬁnite particles. Our previous work ﬁrst studied the diﬀerence between two ﬁlters, Condensation
and Auxiliary Particle Filter. They can be considered compensatory in terms of accuracy and robustness
under severe circumstances having unknown occluders
and/or distracters. We then proposed a new particle ﬁltering scheme, called switching scheme, which allows robust and accurate visual tracking under severe
circumstances. This scheme utilizes the two complementary sampling algorithms above by switching thembased on the conﬁdence of ﬁltered state of the visualtarget. This article presents an alternative switching
method called soft switching while we call the previously proposed method hard switching. The soft
switching method softly changes the population ratioof each sampling method for assigning particles. We
examine their properties in terms of accuracy and robustness via real visual tracking experiments as well as
computer simulations.

Our previous work studied the diﬀerence between
two ﬁlters, Condensation [1] and Auxiliary Particle Filter [2] (APF). Condensation employs the Sampling Importance Resampling (SIR) method, and can run ona cheap standard PC. Condensation, however, has aproblem called an outlier problem, i.e., large diﬀerence between prior and true distribution causes crude
approximation of the posterior distribution. Unfortunately the outlier problem often occurs in a real-timevisual tracking task due to unknown occlusions, distracters and target dynamics. APF was proposed tosolve the outlier problem using the information of current observation. These two ﬁlters can be considered
compensatory in terms of accuracy and robustness under the circumstances speciﬁc to real-time vision systems. To exploit their advantages, we proposed a new
ﬁltering scheme that switches these ﬁlters according toa simple criterion [3].
This article presents an alternative switching
method called soft switching while we call the previously proposed method hard switching. The soft
switching method softly changes the population ratioof each sampling method for assigning particles. We,
then, examine their properties of accuracy and robustness via computer simulations that model realistic circumstances include occlusion and distracters. Next,
the eﬀectiveness of our methods are demonstrated byreal visual tracking experiments. In our tasks, thetracking target is a red ball, and the ball moves sinusoidally behind a board as an occluder. Because there
are in addition many objects similar to the tracking target in the background as distracters, they are realistic
visual tracking tasks.

keywords – particle ﬁlter, sampling, real time, visualtracking, switching1

Introduction

Particle ﬁltering is an approach to Bayesian estimation of intractable posterior distributions from timeseries signals with non-Gaussian noise. This approachhas been attracting attention in various research areas, including real-time visual processing which deals
with images contaminated by non-Gaussian noises due
to not only signal noises but also the existence of obstacles and/or distracters. Several particle ﬁlters havebeen proposed for approximating Bayesian computation with ﬁnite particles. The performances of such

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2

Particle Filters

visual target [3]. Our proposed method fully switched
the two sampling algorithms according to a simple criterion, the variance of current estimated target states
as conﬁdence in the estimation. We call it hard switching method. The variance of the current estimated target states is deﬁned as

Particle ﬁlters are based upon point-mass representation of probability densities. A continuous state vector of a target object at time step t is denoted byxt ∈ RNx , and a measurement vector is zt ∈ RNz .
(n)(n)
Using a sample set {(xt , πt ), n = 1, ..., N } at time
step t, the posterior density is approximated asp(xt |zt ) =kt p(zt |xt )p(xt |zt−1 ),2
2, ..., σest,N)T(3)
Vest (t) =(σest,1
x
 (n)
(n)
(n) T
π (x̂t − xt )(x̂t − xt )
,
(4)
= n t
N −1
 (n) (n)where, x̂t = n πt xt . Then, using threshold vectorγ, the hard switching method is described as(1)

where kt is the normalization term. Then, prediction
density p(xt |zt−1 ) as prior is approximated as
 (n)
(n)
p(xt |zt−1 ) ≈πt−1 p(xt |xt−1 ).
(2)

if σest,i > γi (∃i), then use APF,
otherwise, use Condensation.

n
(n)

(n)

(n)

The weights πt are determined by πt = p(zt |xt ).
If we can prepare suﬃcient large number of particles,
Eq.2 becomes accurate. However, we cannot preparesuch a large number of particles for real-time processing.
In Condensation, p(xt |xt−1 ) used as the proposal
density is independent of observation zt at each time
step t, and the state space is explored regardless of zt .
This property suﬀers from the outlier problem [2], i.e.,model-implausible observations may occur when there
are unexpected occluders, distracters, and changes inthe target motion.
Auxiliary Particle Filter [2] proposed by Pitt andShepherd employs elegant resampling method thatsolves the outlier problem. In their approach, likelihoods are calculated as weights at any likely point that(n)characterizes p(xt |xt−1 ), e.g., mean or mode. Then, by
resampling with the weights that include informationof current observation p(zt |xt ), the estimation aroundthe likely points tends to be more accurate in APF thanin Condensation. The diversity of particles, however,is lower in APF than in Condensation. For particle
ﬁlters, we need to prepare an observation model, or
likelihood function, of the target to be tracked. In thisarticle, we employ the same observation model as thatin [3].

3

The vector γ is a thresholding parameter that discriminates successful tracking from unsuccessful tracking
based on the conﬁdence of the current estimated target states. We set γ at the variance of estimated target states during successful tracking. For example, in
our experiments described later, γ was set as follows.
We assume that the ball size on the image plane is invariant and xi (i = 1, 2) are independent of each otherfor simplicity. Under such assumption, the covariancecomponents of the current estimated target state are
expected to be 0, and all we have to do is to compare
dethe threshold vector γi (i = 1, 2) to the standard

viation of the current estimated target state Vest (t).
If we cannot assume the component-wise independenceof state vector, we need to extend the variance Vest (t)to the variance-covariance matrix. In our experiments
we employ the target ball radius as threshold γ because we found the standard deviation of the current
estimated target states during successful tracking were
almost equal to the ball radius.
In this article, we propose an alternative switching method called soft switching. Although the soft
switching method is the same as the hard switching
method except that it controls the population ratio of
each sampling method for assigning particles, based on
conﬁdence level of estimation. The number of particles
sampled by each sampling method, Ncond and Napf , aredetermined as

Hard / Soft Switching Particle Filters

As described in the last section, Condensation emphasizes prediction of target transition (prior) morethan APF, while APF emphasizes observation morethan Condensation for estimating current target state.
Emphasis on observation makes tracking accurate,
while emphasis on prior would make tracking robust
especially under the severe circumstances having unknown occluders and distracters. To exploit their advantages, we previously presented switching sampling
scheme based on the conﬁdence of ﬁltered state of the©ISAROB 2005

Ncond = N − Napf , Napf = max Napf,i ,i⎧(σest,i < γmin,i )⎨ Napf,i = 0
N (σest,i −γmin,i )(γ
Napf,i = γmax,i
min,i ≤ σest,i ≤ γmax,i ) ,−γmin,i⎩(σest,i > γmax,i )Napf,i = N
meaning that the more conﬁdent of tracking the tracker
has, the larger number of particles are sampled by Condensation, while the less conﬁdent of tracking it has,the larger number of the particles are sampled by APF.

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occlusion
widthocclusion width

true target state
distracter

distracter
tracking
targetsuccess area
occluderdistracter

2

4

6

8 10 12 14 16 18 20
distracter size

2

4

6

8 10 12 14 16 18 20
distracter size

time step

Figure 1: The setting of the computer simulations (left)
and the success area deﬁned encompassing the true target state (right).

maintain its accuracy of tracking under the broad conditions better than the soft switching method.
In order to use switching methods, switching threshold γ must be set beforehand. Although we foundthat the target ball radius was enough in our experiments, the switching methods depends on this parameter, which is more salient for the hard switching
method. An inappropriate parameter causes failure
tracking because the tracker misses timing of switching and then the balance of divergence and convergence of particles is broken. As for the soft switching
method, setting γmin at smaller than the ball radius,and γmax at larger than the ball radius adding a margin to the timing of switching leads to robust tracking. The soft switching method becomes equivalent
to the hard switching method in terms of the number of particles sampled by each sampling method if
γmin = γmax . When suﬃcient resource cannot be allocated as in real-time visual tracking, the hard switching method is more eﬃcient than the soft switching
method if the precise switching threshold can be predetermined.

To investigate the performance under realistic circumstances, we prepared simulated image sequencesin which occlusion width, distracter size and positions
varied (cf. Figure 1 (left)). The radius of the target was
5 pixels, and the target moved sinusoidally with amplitude 200 pixels and frequency 1 Hz. The occlusion
width varied from 5 to 100 pixels with step size 5 pixels.
For each image sequence, three distracters were randomly chosen from six candidate positions, and theirradius were varied from 1 to 20 pixels. The length ofeach image sequence was set to 60 frames.
First, the hard switching method (γ = 5) and thesoft switching method (γmin = 4, γmax = 20) were
tested for the simulation image sequences. We examined robustness of the two methods in terms of the
tracking success rate, where “success” means the number of failure frames in which estimated target state
deviated from the success area (cf. Figure 1 (right))
was smaller than 15 frames. Their accuracies were also
examined based on the mean tracking error in successful trials.
To examine diﬀerence in the success rate, we usedthe χ2 test. Figure 2 (left) shows results of the χ2 test
(p < 0.01), indicating the hard switching method ismore robust against unknown occluder and distracters
except for too severe conditions, i.e., large occlusionand distracters, or very simple conditions, i.e., small
occlusion and distracters.
To examine diﬀerence in the tracking error, we applied the Wilcoxon test because the tracking errors
and the number of successful trials were too small forthe t-test under the severe condition with low successrate. Figure 2 (right) shows results of the Wilcoxon
test (p < 0.05), indicating the hard switching method©ISAROB 2005

Wlcoxon test (soft - hard switching)
10
20
30
40
50
60
70
80
90
100

Figure 2: Result of the χ2 test (left) and the Wilcoxon
test (right). White, black, gray, dark gray colors meanthat the hard switching method was better signiﬁcantly, the soft switching method was better signiﬁcantly, there was no signiﬁcant diﬀerence between theirperformances, and neither the χ2 test nor the Wilcoxon
test was not applied because of the too small numberof successful trials, respectively.

Simulation Results
state

4

10
20
30
40
50
60
70
80
90
100

χ2test (soft - hard switching)occlusion width1%

The threshold vectors in the soft switching method
are also determined using the target ball radius. We set
γmin at smaller than the ball radius, and γmax at larger
than the ball radius. We will examine the performanceof the soft switching method with various combinationsof γmin and γmax in the next section.

5

Real Experiments

In this section, we compare the performance of thehard and soft switching methods with Condensation,
APF and Unscented Particle Filter (UPF) [4]. Similar
to APF, UPF proposed by Merwe et al. emphasizes theinformation of current observation. Its proposal distribution is estimated by Unscented Kalman Filter for
maintaining accurate estimation of target states. Because we cannot calculate xt from zt , we use weighted
mean by redness at each pixel as an observation in
UPF. We evaluate the eﬀectiveness by comparing thenumber of lost-target frames and mean error in successful trials.

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5.1

Experimental Setup

circumstance with occlusion and distracters, however,
UPF was not able to keep accurate tracking.

Figure 4: Lost frames (left) and mean absolute position
errors (right).

5.2

6Tracking with an Occluder and distracters18condensation
APF
UPF
soft switching
hard switching

16
14
12
10
8
6
4
2

50

100 150 200 250
number of lost frames

300

0

cond

apf

hard

soft

upf

Summary

We have examined new particle ﬁltering methods,
hard and soft switching particle ﬁlters, devised for duality with real-time vision systems. These ﬁlters employ two particle ﬁlters, Condensation and Auxiliary
Particle Filter (APF), which incorporate diﬀerent resampling algorithms. To exploit their advantages, ourproposed ﬁlters switch them dynamically according toa simple criterion: the conﬁdence level of the current
estimated target state. The two methods, hard switching and soft switching, were examined in their properties in terms of accuracy and robustness via computer simulations that model realistic circumstances include occlusion and distracters. The simulation results
showed that the soft switching method is more tolerant
to the switching threshold, although the hard switching
method is more eﬃcient if precise switching thresholdis pre-determined, especially when suﬃcient resourcecannot be allocated as in real-time visual tracking. We
also demonstrated that the switching methods outperform other well-known particle ﬁlters through real visual tracking experiments.
Automatic determination of the threshold parameter can be regarded as learning of a hyper-parameter,
which will be our future work.

To statistically compare the performance of diﬀerent
particle ﬁlters under usual circumstances for vision systems, we prepared an environment in which we placeda board as an occluder between a red ball and a camera, cf. Figure 3. There were also some distracters, i.e.,
red objects around the trajectory and the board.

red ballbord

Figure 3: A sample image in the our task.
Figure 4 (left) shows histograms of “lost” frames(length of video sequence is 423 frames for 14 sec).
“Lost” means that the estimated target state deviatedfrom the success area deﬁned around the true target
state (cf. Figure 1 (right)). Figure 4 (right) shows themean absolute position error of each tracker in successful trials. A successful trial means that the number oflost frames was less than 100.
These ﬁgures show that our switching scheme outperforms the other ﬁlters including UPF. Because APF
takes particular note of the current observation, it
sometimes failed in tracking the target due to distracters, especially as it crossed behind the board.
Condensation was more robust for crossing the board,but subsequent convergence to the target was very slow
and not promising. The UPF was more robust andaccurate than those two ﬁlters, but its accuracy wasmuch worse than that of the switching methods. Because the current target state cannot be observed directly from the image (only the redness at each pixel isobserved), we choose the weighted mean of pixels using
their redness as the observed state in UPF. In spite of
inaccurate observation, UPF worked well. In a severe©ISAROB 2005

50
45
40
35
30
25
20
15
10
5
0
0

error

frequency

The task was to track a red ball moved sinusoidally
either by hand or by a robot manipulator. Real video
images were taken by a digital video camera and processed by a Pentium 4 (2 GHz) Linux PC. These images were downloaded to the PC by Video for Linux at
a resolution of 640 × 480 pixels, then reduced to 320
× 240. We also need to determine the number of particles. From simulation results using artiﬁcial images,
we set the number of particles to 800 to maintain theaccuracy and real-time quality. Our system processed
one frame in about 8 msec.

References[1] M. Isard and A. Blake, “CONDENSATIONconditional density propagation for visual tracking,” IJCV,Vol. 29, pp. 5-28, 1998.
[2] M. k. Pitt and N. Shephard, “Filtering via Simulation: Auxiliary Particle Filter,” J. of American
Statistical Association, Vol. 99, pp. 590-599, 1999.
[3] T. Bando, T. Shibata, K. Doya, and S. Ishii,“Switching Particle Filters for Eﬃcient Real-time
Visual Tracking,” ICPR, Vol. 2, pp.720-723, 2004.
[4] R. v d Merwe, A. Doucet, N. de Freitas, E, Wan,
“The Unscented Particle Filter,” NIPS 13, 2001.

535

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Robotics and the Q-analysis of Behaviour
Pejman Iravani

Jeffrey Johnson

Lucia Rapanotti

p.iravani@open.ac.uk
Computing Department

j.h.johnson@open.ac.uk
Design and Innovationl.rapanotti@open.ac.uk
Computing Department∗

The Open University, Milton Keynes, MK7 6AA, UK

Abstract

1. Which features “best” describe a scene?

A new method is presented for the analysis of complex
multi-agent robotic behaviours, based on observing behaviour and abstracting models on which to base control. This involves (i) building an appropriate representation of the scene using the ‘best’ features, (ii) classifying
each scene by its features, and (iii) learning the correlation between scene classes and the actions performed ineach. A novel aspect is the use of Q-analysis to investigate
relational structure between many possible observable features, and configurations in the scene. Q-analysis helps inthe selection of appropriate features and is the basis of our
combinatorial classification. The methods and algorithms
presented are experimentally validated using data from the
RoboCup simulation league. We conclude that Q-analysis
could be a powerful new approach in robot control.

1

2. How can the features be used to classify scenes?
3. How can relationships be learned between configuration classes and the agent control actions?
The first issue, known as feature extraction in pattern recognition, relates to which features should andshould not be part of the representation. In general,
adding irrelevant features “obscures” the effects of relevant features. Thus it is desirable to find a method toinclude only ‘relevant’ features in the representation.
The second issue is a general problem in classification,
which relates to finding an appropriate criteria of ‘similarity’ between instances. The third issue is related
to machine learning, in finding a method for learningfrom classified observed behaviours.
This paper develops a framework for behaviour
analysis based on the Methodology of Q-analysis [3, 4].
As explained in the next section, Q-analysis is a multidimensional generalisation of network theory, able
to model general n-ary relations between features andconfigurations. Through its notion of ‘q-connection’ it
provides a graded method of classification according
to shared features.
This is in marked contrast to methods of classification that map objects into multidimensional data
spaces, and cluster them into components based on
similarity metrics. The essential difference is that Qanalysis is very sensitive to the selection of features,and this can be exploited to detect and remove features that add little or no information.
Before starting the discussion, we establish somebasic notation. A scene is an instantaneous observation of the system including the robots and environment. Scenes have sub-scenes associated with subsets
of players that we will call configurations. In this paper
we are concerned with selecting features to describe
those configurations, and classifying the configurations
for control purposes.

Introduction

We refer to behaviour analysis as the task of constructing a model of the behaviour exhibited by anagent or multiagent system (MAS) using observations.
In this paper the agents are autonomous robots working together as a team to play soccer.
Behaviour analysis starts by observing scenes as
the interaction between agents and their environment.
Scenes have sub-scenes, here called configurations. In
the first step, the configurations are described by aset of observed features, for example, ‘opponent toleft’, ‘ball moving fast’, ‘team-mate to right’, ‘close
to goal’, or any other ‘relevant’ characteristic. In thesecond step, the configurations are classified, based on
combinations of their features. Finally, the configuration classes are correlated to the observed actions
performed by the agents. This is then used as the basis of control for the agents, effectively giving a learned
behaviour [1, 2]. In order to analyse behaviours in thismanner, the following issues need to be addressed:©ISAROB 2005

536

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2

The Methodology of Q-analysis

2.1

their connections. In practice a network with a million inputs will never converge.
Thus, the classification of multidimensional data
addressed by many clustering techniques often begs
the essential question: what are the relevant dimensions for the particular application?
As we will show, Q-analysis is an approach thatstays very close to the data. At times its sensitivity
to the dimensions used can be frustrating, as can theeffects of objects that dominate the structure. But
often, this just reflects the nature of the system underinvestigation.

Classifying Multidimensional Data

Of the large literature on classification, we canabstract two complementary approaches. To illustrate this, consider a set of objects to be classified,A = {a1 , a2 , ..., am }, and a set of classificatory features, B = {b1 , b2 , ..., bn }. An observation of an object
ai consists of making two decisions for each bj :1. is object ai related to feature bj ?
2. what is the strength of that relationship, weighted
as a number?

2.2

In classification, geometric models are often used toevaluate the similarity between instances. Geometricmodels represent instances as points in a multidimensional coordinate space and define similarity between
instances as the Euclidean distance separating them.
This means that the information stored in a set of dimensions is subsumed into a distance value, and thus,
it is critically important that this distance reflects accurately the relevant information in the data [5]. Figure 1 illustrates an example of geometric similarity; on
the left, three robots (R1,R2,R3) are positioned withrespect to a goal, on the right, two different coordinate
spaces are used to represent each situation. Following
the top coordinate space, robots R1 and R3 are in
more similar situations than R2. On the bottom coordinate space, the angle feature (α) has been scaled
differently (e.g. changed representation units), as aneffect R1 and R2 are now more similar. This exemplifies the importance of finding a distance value that
accurately represents similarity among instances.

Behind the first of these questions is a subtlety often lost by unquestioning use of standard classificatory
techniques. Whether or not an object is related to afeature is a binary yes/no decision. For example, consider whether or not a battery-powered robot has apower source. If the battery is removed it does not(relationship). Suppose the robot has a battery fitted,
but the battery is flat. Then it does have a powersource (relationship), currently capable of deliveringzero power (number).
This distinction makes a big difference in the application of classification techniques. When ‘non-present’characteristics are included in the classification withweight zero, this impacts on the classification. In
particular, it established ‘similarity’ between objects
weakly related to a feature, and objects that are logically unrelated to that feature. In the case that thefeature represents a dimension with little or no information, this creates spurious similarities.
This is one of the reasons why it is not possible tosolve all problems using fully connected neural networks as classifiers. Neural networks such as the multilayer perceptron effectively map an m-dimensional
input space to an n-dimensional output space. The
distinction being made above corresponds to there being no connection between input xi and neuron yj (notrelated), and there being a connection for which all
the input values are zero, or the weight, wi j, being
held at zero. In fact this last possibility cannot be
guaranteed within the standard multilayer perceptron
architecture, and the only way to achieve the required
effect is to cut the link between xi and yj .
The practical implications of this are profound. The
idea that one can throw any combination of ‘data’ at a
network, or any other classifier, founders on combinatorial complexity. In principle the network will filter
out the ‘irrelevant’ data by assigning low weights to©ISAROB 2005

Similarity

R1

d1

Goal1
"1

"

R3
R2
R1

R2

d
d2
"2

R3

Goal2
"

d3

R3
R2

Goal3

R1
d

"3

Figure 1: Mobile robots in different situations with respect to a goal and their similarity based on Euclidean
distance.

537

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.3

x4

x4

Representing relations by simplices

x5

Given the limitations of geometric models based ongeometric similarity metrics, the Methodology of Qanalysis offers new insights to the concept of similarity. This analysis is especially suited for discovering
relational structure in multidimensional data. In Qanalysis, similarity is no longer defined as a distance,
but is based on structural ideas of connectivity between particular instances.
Q-analysis provides a set-theoretic approach to thestudy of relationships. In general a relation R between
a set of elements, {x0 , x1 , ..., xp }, can be considered
to determine a new object called a simplex, denoted
hx0 , x1 , ..., xp ; R i.
Simplices can be represented by polyhedra in multidimensional spaces. Let the individual xi be calledvertices, denoted as hxi i. Then a simplex with one vertex is a point, a simplex with two vertices is a line, asimplex with three vertices is triangle, a simplex withfour vertices is a tetrahedron, a simplex with five vertices is a 5-hedron, and so on. This is illustrated in
Figure 2, where (a) and (b) are tetrahedra, (c) is atriangle, and (d) is line. As can be seen, a simplex
with p + 1 vertices is a p-dimensional object. Thus werefer to a simplex with p + 1 vertices as a p-simplex.
We illustrate this using an example from robotics.
Let {x0 , x1 , ..., xp } be a set of features describing a
sub-scene, or configuration, in a robot soccer game.
Let a configuration be denoted by cj , so that each xi
is R-related to cj . Then we can write the simplex
associated with cj as σ(cj ) = hx0 , x1 , ..., xp ; R i.
To illustrate this consider a matrix M representing
five configurations, {c1 , c2 , c3 , c4 , c5 }, related to subsets of six binary features {x1 , x2 , ..., x6 }:
c1
c
M= 2
c3
c4
c5

x1
1
0
0
0
1

x2
1
1
1
0
1

x3
0
1
0
0
0

x4
1
1
1
0
1

x5
1
0
0
1
1

x2

(a)

x4

x6

x5

x6

x2

x2

(b)

(d)

(c)

Figure 2: Some examples of different simplices.
An important idea in Q-analysis is that high dimensional simplices can be decomposed into their lower
order simplices called their faces. For example, thesimplex representing, σ(c1 ), (Figure 2a) can be decomposed into the following face simplices of dimension q:
q
3
2
1

2.4

#
1
4
6

simplicesσ(c1 ) = hx1 , x2 , x4 , x5 i
hx1 , x2 , x4 ihx1 , x2 , x5 ihx1 , x4 , x5 ihx2 , x4 , x5 i
hx1 , x2 ihx1 , x4 ihx1 , x5 ihx2 , x4 ihx2 , x5 ihx4 , x5 i

q-nearness and structural similarity

Let the intersection of two simplices be defined tobe their shared face. For example, σ(c1 ) ∩ σ(c2 ) =hx1 , x2 , x4 , x5 i ∩ hx2 , x3 , x4 , x6 i = hx2 , x4 i (Figure 3).
x4
x1

x3

x5

x6
x2

Figure 3: Q-connectivity of two simplices.

More generally, two simplices σ and σ 0 are said tobe q-connected if there is a chain of pairwise p-near
simplices between them, p ≥ q.
When the relation is represented by a binary matrix, M the q-nearness can be calculated as, M M T −1,
where, M T , is the transpose of M and 1 is a matrix
with all elements equal to 1.

x6
0
1
1
1
0

Table 1: a configuration-feature incidence matrix.
c1
c2
c3
c4
c5

Each row, i, of matrix, M , can be represented by asimplex, σ(ci ). Thus σ(c1 ) and σ(c5 ) are the 3-simplex
or tetrahedron hx1 , x2 , x4 , x5 i (Figure 2(a)). σ(c2 ) is
also a 3-simplex, hx2 , x3 , x4 , x6 i, (Figure 2(b)). σ(c3 )is a 2-simplex or triangle, hx2 , x4 , x6 i, (Figure 2(c)),
and σ(c4 ) is a 1-simplex or line, hx5 , x6 i (Figure 2(d)).

©ISAROB 2005

x6

x3

x1

c1
3
1
1
0
3

c2
1
3
2
0
1

c3
1
2
2
0
1

c4
0
0
0
1
0

c5
3
1
1
0
3

Table 2: A shared face matrix.
For example, the symmetric matrix above represents the q-nearness of the simplices in M given in the538

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

previous section. The diagonal of this matrix represents the dimension of each simplex, this value is also
known as q-top.
This shared face matrix represents the direct connectivity of the simplices based on their shared vertices. Every p-simplex is p-near to itself. In this case,
c1 is 3-near to c5 because they are related to the same
set of vertices, and are identical with respect to thisvertex set. On the other hand, c1 and c2 are both
3-simplices (tetrahedra), but they are only 1-near because they share only a 1-dimensional face.
The conjugate matrix, calculated using M T M − 1,gives the number of configurations shared by pairs offeatures. It represents the connectivity between features, and we call it the feature shared face matrix.

3

a2
p
a3

b4

b2
a1
b1

b5

a4
(a)

(b)

Figure 4: a) Passing scene. b) Player configuration
For a configuration, many features could be abstracted. For example, let player p have possession of
the ball. Then each other player has an associated triangular area of ‘controlled space’ defined by p and thepositions of other neighbouring players (Figure 4(a)).
Then each triangle has an angle, αi , and a distance,
di between the player and p.
Both, αi and di are continuous variables that aresegmented into four intervals, ‘very-small’, ‘small’,‘big’, and ‘very-big’, denoted as vs, s, b, vb. Two relations indicating whether or not the right and left
neighbouring players are of the passer’s team are also
represented. We also use the feature of whether or not
an opponent player is closer to the ball than a teammate. Many other features could be incorporated, but
we arbitrarily select these 11 binary variables:

Q-analysis in Behaviour Analysis

4 distances: dvs , ds , db , dvb
4 angles: αvs , αs , αb , αvb
2 neigbours: Rneigh own team , Lneig own team
1 opponent closer: oppcloser
Thus, each configuration (Figure 4(b)) can be described by simplex with vertices subsets of these elevenfeatures. As will be explained, these vertices themselves were the result of a selection process according
to relevance.

3.2

Representing Configurations

Feature Selection using Q-analysis

Given an arbitrary set of features, which ones are
the most ‘relevant’ ? The usual approach to this question has been to leave it to the designers intuition todecide which are the most important features, whether
new ones need to be added or if any need to be discarded. In this context, a method to evaluate featuresas ‘relevant’ or otherwise, could be helpful to humanrobot designers, and in the longer term, could lead
into automating the feature selection process. Here
we present a method of feature selection based on Qanalysis.
Let X be a configuration represented by n binary

The number of possible features that could be used
to describe a configurations, or position, in a robot
soccer game is enormous. Also, there is no obvious apriori ‘best-set’ of features. We constructed our set offeatures as follows.
Figure 4a illustrates a ‘passing scene’, simplified to
5 players per team rather than the 11 used in thisexperiment. Player, p, is the one holding the ball,while the players ai are on the same team as p, andplayers bj are the opposition team.

©ISAROB 2005

p
b1

d4
"4

This section investigates the suitability of the Qanalysis framework for behaviour analysis, defined in
the Introduction.
The underlying hypothesis is that classes of ‘similar’ configurations can form more abstract concepts.
These can be associated with particular control actions
that have been experienced previously by the robot as
successful. Thus the classification is the means of generalising from particular experiences.
The study is conducted by analysing the example
of robot football passing behaviour. The data for thiscome from observing ‘passing configuration’ from the
Log-Files of the RoboCup 2003 Competition, and constructing a model of a passing behaviour. As seen earlier, to construct such model the following issues need
to be addressed: (i) selecting ‘relevant’ feature, (ii)using these ‘relevant’ features to best classify ‘similar’
configurations, (iii) using these classes as the basis forlearning behaviour.

3.1

b2
a1

b3

539

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

features, Xi = {x1 , x2 , ..., xn }. For illustration it will
be assumed that each configuration is classified by experience into ‘passing configuration’ and ‘non-passing
configuration’. Thus the configurations will be denoted cj,passing and ck,non−passing . Some features add
no discriminative information for a classification, whileothers give complete information.
For the simplest cases, we will define a feature tobe ‘distracting’ when observing it adds little information in its context. For example, a feature related
to all configurations adds no information, and a feature related to no configurations adds no information.
The first case would apply when a thermometer always recorded xtemperature above a safety thresholdin all cases, associated with the action “sound alarm”,
while second case would apply when a thermometer always recorded xtemperature as below the safety threshold associated with action “do nothing”.
A feature xi is a perfect classifier if all configurations of one class are are related to that feature, and no
configuration from other classes is related to that feature. This last occurs when, for example, xtemperature
below a safety threshold is related to a class “do nothing”, or otherwise “sound alarm”.
In this paper we are concerned with discovering features that are neither distracting or perfect classifiers,
but become relevant in combination with other features. In order words seek features that form the vertices of classifying simplices. These simplices can beconsidered to be ‘concepts’ at a higher level or representation [1, 2, 6].
In the next section we will illustrate a number ofheuristics for selecting ‘relevant’ features for the case ofpassing and non-passing configurations. These heuristics amount to seeking feature simplices that are facesof many configuration simplices. In other words, weseek combinations of feature vertices that give themost powerful discrimination, and we seek features
vertices that belong to many such combinations.

3.3

Each of the ten ‘team-mate’ areas can be describedby the eleven features defined above. Some of theseare mutually exclusive, and the maximum dimensionof the simplices representing the triangular configurations is q = 4. The pass can only be made to oneof these areas, so the remaining nine become ‘nonpasses’.
We now apply Q-analysis on this data to study
whether any structural difference emerges between thepass and non-pass simplices, both enabling us to isolate powerful classificatory simplices, and thereby relatively the powerful features that make up their vertices.
3.3.1

x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8 x_9 x_10 x_11
x_1 12
0
0
0
7
1
4
0
5
2
11
x_2
0
45
0
0
13
9
13
10
10
18
30
x_3
0
0
44
0
11
10
16
7
20
15
33
x_4
0
0
0
17
8
4
2
3
8
4
10
x_5
7
13
11
8
39
0
0
0
12
16
35
x_6
1
9
10
4
0
24
0
0
8
8
19
x_7
4
13
16
2
0
0
35
0
21
14
23
x_8
0
10
7
3
0
0
0
20
2
1
7
x_9
5
10
20
8
12
8
21
2
43
11
34
4
16
8
14
1
11
39
33
x_10 2 18 15
7
34
33
84
x_11 11 30 33 10 35 19 23(a)x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8 x_9 x_10 x_11
x_1 33
0
0
0
10
13
6
4
17
7
24
x_2 0 108
0
0
29
38
29
12
46
54
76
x_3 0
0 187
0
68
58
41
20
82
73 116
x_4 0
00 734 465 211
48
10 300 315 344
x_5 10 29 68 465 572
000 243 233 3140 320
00 136 150 172
x_6 13 38 58 211
0
0 124
0
57
54
60
x_7 6 29 41 48
0
0
0
46
9
12
14
x_8 4 12 20 109 445 164 308
x_9 17 46 82 300 243 136 57
x_10 7 54 73 315 233 150 54 12 164 449 307
x_11 24 76 116 344 314 172 60 14 308 307 560(b)Figure 5: a) Pass shared face matrix, M . (b) Non-passshared face matrix, M̃

Let M be the incidence matrix of the relation between the set of triangular configurations associatedwith passing. Let M̃ be the incidence matrix of the relation between the set of triangular configurations not
associated with passing. Then we form the two sharedface matrices M T M and M̃ T M̃ , for the passing andnon-passing configurations. Figure 5 illustrates thesematrices.
The diagonal entries of the two shared face matrix
give the number of passes or non-passes related to the
diagonal feature. For example, out of 118 passes, x1
is related to 12 of them while x11 is related to 84 ofthem. There are many more non-passes (1062) thanQ-analysis of a GamesTo investigate the use of Q-analysis as a classification method, we took the Final game of RoboCup
2003 and studied the passing behaviour observed in it.
Let, S, be the set of successful passing configurations(passer and receiver players belong to the same team)observed in that game.
For every pass that is made, the pitch can be divided into 21 triangular configurations, one for eachof the 21 players not possessing the ball. Of these we
focus on the areas of the 10 team-mates of the passing
player.

©ISAROB 2005

The Shared Face Connectivity Matrices

540

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

passes, and x1 is related to 33 of them while x11 isrelated to 560 of them.

hub simplex
hx4 , x5 , x9 , x10 , x11 i
hx4 , x6 , x9 , x10 , x11 i...
hx3 , x7 , x9 , x10 , x11 i
hx3 , x8 , x9 , x10 , x11 i...
hx3 , x9 , x10 , x11 i
hx3 , x7 , x9 , x11 i...
hx4 , x5 , x10 , x11 i
hx5 , x9 , x10 , x11 i...
hx4 , x5 , x11 i
hx4 , x6 , x9 i
hx5 , x10 , x11 i...
hx7 , x9 , x11 i
hx3 , x7 , x9 i
hx2 , x5 , x11 i...
hx9 , x11 , i
hx4 , x11 i
hx4 , x5 i...
hx2 , x11 , i
hx7 , x11 i
hx7 , x9 i
...
hx4 i
hx5 i
hx11 i
...
hx2 i
hx3 i
hx8 i

Already it is clear that the vertices are responding differently to pass and non-pass configurations.
For example, a comparison of the diagonals of
the matrices using the ‘normalised ratio’ formula(M T Mii /((M̃ T M̃ii )/9) gives the following:

x1
0.3

x2
0.2

x3
0.5

x4
4.8

x5
1.6

x6
1.5

x7
0.4

x8
0.2

x9
1.1

x10
1.3

x11
0.7

This means, for example, that x1 is related to relatively less non-pass positions than it is pass positions(1 : 0.3), as is x11 ( 1 : 0.7). However, x4 has a relativelarge ratio of ( 1 : 4.8 )in favour of non-pass positions.
This is easy to explain, since x4 means “large distanceto team mate”. In fact x4 approaches being a perfectclassifier, as far as non-passes are concerned.
The further the ratios are from unity in the table
above, the more discriminating are the features, xi .
As we have noted, some numbers are less that unity(better response to pass configurations) and some areabove unity (better response to non-pass configurations). In combination these features may exploit
these differences to give robust classification.
For example, recall that there are 118 passes and1062 non-passes in the data. hx7 i is related to 35
passes (30%), hx9 i is related to 43 passes (36%), while
hx7 , x9 i is related to 21 passes (18%). In contrast, hx7 i
is related to 124 non-passes (11%), hx9 i is related to
445 passes (42%), while hx7 , x9 i is related to 57 passes(5%). Thus the q-nearness analysis suggests that x7
and x9 , in combination, have more subtle discriminating power. In other words we should classify bysimplices rather than vertices.

3.3.2

%
1%
0%
...
3%
1%
...
6%
7%
...
2%
2%
...
5%
2%
12%
...
13%
10%
10%
...
29%
8%
7%
...
25%
19%
18%
...
14%
33%
71%
...
38%
57%
17%

# non-passes
57
35
...
7
1
...
36
13
...
120
74
...
232
86
157
...
42
16
23
...
308
344
465
...
76
60
57
...
734
572
560
...
108
187
46

%
5%
3%
...
1%
0%
...
3%
1%
...
11%
7%
...
22%
8%
15%
...
4%
1%
2%
...
29%
32%
44%
...
7%
6%
5%
...
69%
54%
53%
...
10%
18%
4%

Table 3: A selection of hubs and their frequencies.
Any discussion of Table 3 must be qualified by theobservation that the frequencies are small, especiallyfor passes. Nonetheless, distinct patterns emerge.
For example, even at the relatively high dimension ofq = 4, the hx4 , x5 , x9 , x10 , x11 i is associated with 5%of non-passes and almost no passes. At q = 3 there isa marked difference for hx4 , x5 , x10 , x11 i between 2%
passes and 11% non-passes. At q = 2, for example,
hx3 , x7 , x9 i accounts for 10% of the passes and just
1% of the non-passes. At q = 2 hx2 , x11 i accounts
for 25% of the passes and 7% of the non-passes. At
q = 0 it can be seen that the vertices alone have considerable discriminating power. For example hx4 i is astrong precursor for a non-pass (14% passes to 69%
non-passes, while hx2 i is a strong precursor for a pass
( 38% passes to 10% non-passes).

Star-Hub Analysis

Given any set of simplices, σ1 , σ2 , ..., σn , their hub, isthe largest face of them all. Thus hub(σ1 , σ2 , ..., σn )=∩ni=1 σi . In the light of the comment at the last section, we should be seeking those simplices that have arelatively large hub for the ‘passing’ class, and a relatively small hub for the ‘non-passing’ class. For our
set of eleven features its is possible to examine all of
its 128 possible combinations (4 × 4 × 2 × 2 = 128).
Table 4 shows a summary of this star-hub analysis.

©ISAROB 2005

# pass
1
0
...
4
1
...
7
8
...
2
2
...
6
2
14
...
15
12
12
...
34
10
8
...
30
23
21
...
17
39
84
...
45
44
20

541

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4

Discussion

Question 2: can the observed frequencies for one
game be added to those of another game in a meaningful way for this kind of analysis?

In this paper we have identified passing configurations in robot soccer. When a player makes a pass,
they have a choice of ten other members of their team
to pass to. Each team member has a structure, relative to the player possessing the ball, determined as
combinations of the eleven features (simplices) to characterise the game. In simple systems any one suchfeature or simplex would be sufficient to classify theconfigurations as ‘pass’ or ‘non-pass’. In more complicated cases it is not so clear cut. We have shownthat particular vertices and simplices may have strong
predispositions toward one class or another.
The ‘better’ the vertices, the stronger will be thediscriminating power. It is suggested that ‘weak’ vertices can be identified by belonging to few ‘powerful’simplices, while ‘strong’ vertices can be identified bybelonging to many simplices with strong discrimination between classes. Elsewhere we develop this idea
towards a methodology providing heuristics for the inclusion and exclusion of features in the classificationof objects determined by relational data [8].

4.1

Question 3: can the observed frequencies of one team
be added to those of another team in a meaningful way
for this kind of analysis?
Question 4: what is the impact of adding features on
generalisation - does combinatorial explosion reduce
the frequencies to statistically insignificant levels?
These issues are studied by Iravani [8]. There hereports that the patterns observed here persist for thesame team in different games, and between different
teams. This suggests that data from many games canbe combined to give much larger sample sizes as a morerobust basis for machine learning.
Iravani also addresses the issue of machines learning over time, and even changing their tactics basedon experience. For example, a particular configuration may give a good outcome initially, but then given
a bad outcome when another team invokes a new strategy. In such circumstances the frequencies associatedwith the particular simplex(es) will ‘drift’ from pass
to non-pass, or vice-versa, and the robot’s behaviour
will change as it learns and adapts.

The dynamic context

In the current context, the classification of the position is intended to underlie the decision as to which
team member to pass to. In practice this is only part ofthe story, since the passing decision may also dependon the current (static) system state being part of a(dynamic) trajectory of states [7]. However, in thiscontext a player may decide that the demands of the
trajectory to pass the ball to particular player shouldbe over-ridden by the strong possibility that this could
lead to loss of possession given the structural position.
In this circumstance the player may abort the previous
plan and initiate a new trajectory.

4.2

References[1] P. Iravani, ‘Behaviour-based architecture for abstract
control and learning’, Proc. TAROS, 2004
[2] P. Iravani, Johnson, J.H., Rapanotti, L., ‘Applicationsof concept grounding techniques to reduce the dimensionality in sensorory-motor space’, STAIRS, 2004
[3] Atkin, R.H., Multidimensional Man, Penguin (Harmondsworth), 1981.
[4] Johnson, J.H., ‘Some structures and notation of Qanalysis’, Environment and Planning B, 8, 73-86, 1981.

Learning from observed populations[5] Gordon, A. D., Classification, Chapman & Hall, 1999.
[6] Johnson, J., H., ‘Stars, Maximal Rectangles, and Lattice: a new persepctive on Q-analysis’, International
Journal of Man-Machine Studies, 24, 293-299, 1986.

The examples given here have come from a single
game, the 2003 RoboCup Simulation League Final,
which 118 pass configurations and 1062 non-pass configurations. As the tables show the frequencies of someconfigurations are relatively small - probably too small
for any reliable generalisation on which to base learning. Although they are outside the scope of this paper,
the research prompts the following questions:[7] Johnson, J. H., ‘Visual communication in swarms ofintelligent robot agents’, Artifical Life and Robotics, 5,
1-9, 2001.
[8] P. Iravani, ‘An architecture for multilevel learning androbotic control base on concept generation’, Ph.D. Thesis, The Open University, 2004.

Question 1: for any given simplex used to discriminate two classes such as pass/non-pass, what is theminimum number of observations required to make thediscrimination ’significant?

©ISAROB 2005

542

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Cooperative Behavior Acquisition for Multiple Autonomous Mobile Robots
Koji Nakano*

Masanao Obayashi**

Kunikazu Kobayashi**Takashi Kuremoto*** Graduate School of Science and Engineering
Yamaguchi University
2-16-1 Tokiwadai Ube, Yamaguchi 755-8611 Japan** Faculty of Engineering
Yamaguchi University
2-16-1 Tokiwadai Ube, Yamaguchi 755-8611 Japan

Abstract

converges to an optimum solution for Markov
Decision Process (MDP). However, in a multiagent
environment, as plural agents’ behavior may effectstate transition, the environment is considered as non
Markov Decision Process (non-MDP), and we must
face critical problems whether it is possible to solve.
In such situation, Arai et al.[1] evaluated bothQ-learning and PS for the pursuit problem which isone of the multiagent tasks, and suggested that PS issuited to a multiagent environment more thanQ-learning.
In this paper, we propose a learning method for a
multiagent environment based on PS. And the featureof this method is using two kinds of PS-tables. The
one is to learn cooperative behaviors using
information of other agent’s position and present state,
the other is to learn how to control own basic
behavior like movements.
We apply the proposed method to garbage
collection problem which is one of the multiagent
tasks, and demonstrate effectiveness of the proposed
method through computer simulation and realexperiment.

This paper proposes a method for multiple
autonomous mobile robots to acquire cooperative
behaviors through a garbage-collection problem. In
the proposed method, robots select the most available
target garbage for cooperative behaviors by visual
information in unknown environments, and move tothe target avoiding obstacles. The learning system in
the robot uses Profit sharing (PS), which is one of thereinforcement learning, and the feature of this method
is using two kinds of PS-tables. The one is to learn
cooperative behaviors using information of otherrobot’s positions, the other is to learn how to control
movements. This paper demonstrates effectiveness ofthe proposed method through simulation and realexperiments.

1. Introduction

Recently, many researches on solving problems
cooperatively with plural agents have been studied
enthusiastically. Specially, a research field that agents
get cooperative behavior through reinforcement
learning in a dynamic environment has gotten a lot of
attention.
Reinforcement learning is a method that agentswill acquire the optimum behavior by trial and error
by being given rewards in an environment as acompensation for its behaviors. Most of studies on
reinforcement learning have been done for a single
agent learning in a static environment. Q-learningwhich is a typical learning method is proved that it©ISAROB 2005

2. Reinforcement Learning

Reinforcement learning is to learn what to do(how to map situations to actions) so as to maximize
a numerical reward signal [2].

543The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

ActionEnvironment

evaluated using different criteria.

Action Controller

Input

3.2 Action controller

Cooperative

The agent controls its action based on w(s,a). It
selects an action using following Boltzmann
distribution which is one of the probability action
selections (equation (2)).

Behavior

Evaluator

PS Table

Behavior

Basic
Behavior

Basic

WeightTable

Behavior

Cooperative
Behavior

Basic
Behavior

Refer to Tables

Cooperative

Update Tables

Reward, Punishment

Learning Controllerp(a|s) : probability of a on s
T : temperature parameterA : set of all actions

Figure 1. System architecture.

3.3 Learning controller

Profit sharing (PS) is one of the learning methods.
PS defines a pair of state s and action a as a rule.
Rules are stored in PS table each step, and when alearner achieves a goal PS reinforces weight w(s,a)
based on PS table. W(s,a) is updated with equation(1).

w(s t , a t )f (t , r )The agent learns using PS (as shown in Section
2.1). The parameter w(s,a) is updated by equation (3)and the reinforcement function uses geometricdecreasing function.

w( s t , a t )(1)w( st , a t ) rt(3): rate of attenuationw(s,a) : weight of rule (s,a)
f : reinforcement functiont : time , r : reward

3.4 Evaluator

The behavior of the agent is evaluated using next
state s’. The evaluation of behavior is similarly
established by two criteria.

3. Proposed System
3.1 System architecture

Figure 1 shows proposed system architecture.
The system is composed of three parts; action
controller, learning controller and evaluator. The
feature of the system is to divide behavior of agent
into cooperative and basic behavior to learn
separately. The learning of cooperative behavior isusing information of the other agent’s position andpresent state. The learning of basic behavior is tolearn how to control own basic behavior likemovements. In a general learning method, when an
agent acquires a reward it can hardly estimates own
action whether it can cooperate or not. To solve thisproblem, the proposed system divides the learning
into two kinds of behavior, and each behavior is©ISAROB 2005

(2)

ai A

Inside of Agentw( s t , a t )e w( s , a ) / Te w( s ,a i ) / Tp( a | s )4. Experiment

We apply the proposed method to garbage
collection problem which is one of the multiagent
tasks. There are plural agents, garbage and one trash
can in the environment, and agents collect garbage
and take it to the trash can. Agents learn cooperative
behavior and basic behavior by themselves.

4.1 Computer Simulation

Figure 2 illustrates the simulation environment
which field size is 21x21 and there are 10 garbage, 2agents and a trash can on the field. One trial isdefined as until all garbage are collected, and 100
trials are considered as 1 episode. We calculate the544

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005(a)Agent 1 arrives at
the trash can.

①,②：agent△：garbage■：trash canFigure 2. Initial positions of agents, garbage,and trash can.

Figure 4. Cooperative behavior acquired by theexperiment with observing the other agent.

Table 1. The average number of steps to the goal one agent

experiment. In the case that one agent can observe theother, the agent using the proposed method learns
faster than the agent using conventional method.
However, when the agent is compared with the agent
which is using the conventional method and do not
observe the other agent, the performance of the
proposed agent is similar to that of the conventional
agent.
Figure 4 illustrates a cooperative behavior
observed in the experiment in which agent observes
the other one. After agent 1 took garbage to the trashcan (Figure 4 (a)), it do not select the garbage nearagent 2 as the object, but another one opposite toagent 2 (Figure 4 (b)). Such behavior often occurred
after learning with observing the other agents.

can observe the other or not.
agent can observe

agent cannot observe

conventional method

118.7

111.1

proposed method

113.3

123.8

280

Conventional Method (considering agent)
Conventional Method (not considering agent)
Proposed Method (considering agent)
Proposed Method (not considering agent)

260

Number of Step

240
220
200
180
160
140
120
100

0

10

20

30

40
50
60
Number of Trial

70

80

90

4.2 Real Experiment100

Figure 5 illustrates the field of the experiment
which field size is 1x1(m) large and has 5 garbage, 2
robots and a trash can. We try following three kindsof the experiments.
Exp 1: Using weights which are learned in thecomputer simulations repeating for 10 trials
in Figure 5(a)Exp 2: Using weights which are learned in the realexperiments repeating for 10 trials in
Figure 5(a)Figure 3. Change of the number of steps.

number of average steps after repeating 100 episodes.
At this time, w(s,a) are initialized for each episodes.
To verify the effectiveness of the proposed method,
we compare the proposed method with theconventional method which also learns using a
PStable.
Figure 3 and Table 1 show the result of the©ISAROB 2005(b)Agent 1 goes to garbage
opposite to agent 2△:garbage①、② :agent■:trash can

545

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

environment. On the other hand, the number of
average steps in Exp 3 is not increased compared with
Exp 2. Thus the weights learned in real environment
are applicable to different environments, and thisshows that the proposed system is robust.

5. Conclusion

In this paper, we proposed the method which
separates the learning of the cooperative behavior andthe learning of the basic behavior for a multiagent
environment. We demonstrated availability of themethod through computer simulation and realexperiment, and confirmed that the agents learned
quickly and behaved cooperatively.
However, there are two problems in the proposed
method. The first is that most of cooperative
behaviors are emerged by agents acting basic
behaviors each other, so it is difficult to separate thelearning of the cooperative behavior and the learningof the basic behavior in other multiagent tasks.
Therefore, to apply the proposed method to many
multiagent tasks, it should be generalized more. The
second is that separating two ways of learning may
restrict emergences of cooperative behavior. The
cooperative
behavior
shouldbeemergedfundamentally. Therefore we should establish theframework of the learning of the cooperative behavior
not restricting emergences of the cooperative
behavior.

(a) Initial Position 1
Agent 1

Agent 2(b) Initial Position 2△：robot○：garbage□：trash canFigure 5. Initial positions of robots,
garbage, and trash can.
Table 2. The number of average steps to garbage
collection in the experiment 1-3.

References[1] S. Arai, K. Miyazaki, S. Kobayashi: Methodology
in Multi-Agent Reinforcement Learning -Approaches
by Q-Learning and Profit Sharing-, Artificial
intelligence, Vo.13, No.4, pp.609-618, 1998 (inJapanese)[2] Richard S. Sutton, Andrew G. Barto:
Reinforcement Learning An Introduction, MIT Press,
1998

Number of average steps
Exp 1

201.9

Exp 2

178.2

Exp 3

161.1

Exp 3: Using weights which are the same as Exp 2
repeating for 10 trials in Figure 5(b)Table 2 illustrates the result of the experiment.
Table 2 illustrates that the number of average steps of
Exp 2 is decreased compared with Exp 1. Thus theweights learned in computer simulation are available
for the real environment, and furthermore, theproposed method can learn flexibly in real

©ISAROB 2005

546

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Multi-agent Learning Mechanism Based on Diversity of Rules :from the Viewpoint of LCS ∗Hiroyasu Inoue∗† Yutaka Leon Suematsu∗† Keiki Takadama†‡
Katsunori Shimohara∗† Osamu Katai∗∗ Kyoto University Graduate School of Informatics, Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501 Japan† ATR Network Informatics Laboratories 2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288 Japan‡ Tokyo Institute of Technology 4259 Nagatsuta-cho, Midori-ku, Kanagawa 226-8502 Japan
E-mail: hir inoue@atr.jp

Abstractand exporting their knowledge. Though such agentscan share knowledge, thoughtless knowlege sharing
can disturb the learning process, since knowledge sharing homogenizes agents’ knowledge. Inoue [2] showedthat the eﬀect of knowledge diversity has a relationship with position information on other agents. This
paper focuses on this problem.
In this paper, we investigate the relationship between the eﬀect of knowledge sharing and informationon other agents’ positions.
This paper is organized as follows. Section 2 starts
with a description of the rule-sharing mechanism. Section 3 explains the experimental settings, and Section
4 provides the results. Section 5 we discuss the results,
and conclude in Section 6.

Multi-agent learning requires eﬀective means because of its vast learning space. One means is thatagents share their knowledge. However, thoughtlessknowlege sharing can disturb its learning process. In
this paper, we investigate the relatioship between theeﬀect of knowledge sharing and information on otheragents’ positions.
We proposed a novel rule-sharing mechnism employing LCS. Through experiments using simpliﬁed
soccer, we demonstrated that if agents can ﬁnd otheragents, sharing rules is eﬀective; if not, then sharing
rules is ineﬀective.
Keywords: multi-agent, learning, rule sharing,learning classiﬁer system

1

Introduction

2

Arai [1] and others have shown that multi-agent
learning includes many diﬃculties. Those problems include the simultaneous learning problem, the rewardassignment problem, and so on. Another problem isthat even if a single agent has a small learning space,
the whole learning space is enormous because of thenumber of possible combinations. Thus, a more eﬀective method is required for multi-agent learning. One
means of this is that agents share their knowledge.
Generally, it seems that two conditions should be
fulﬁlled to achieve knowledge sharing. The ﬁrst is that
representation of knowledge is sharable, while the second condition is that imported knowledge from otheragents can be interpreted. One approach that fulﬁlls
these conditions is that agents have the same mechanical functions so as to interprete knowledge from eachother, and to employ a rule-based system for importing

There are various possible mechanisms for sharing
rules. For the purpose of this paper, we should consider a general mechanism. An inevitable aspect of thegeneral mechanism is sharing priorities of rules. In accordance with this, in this section we propose a novel
mechanism based on the Learning Classiﬁer System(LCS: proposed by Holland [4]).
LCS is a general learning system in which a setof condition-action rules called classiﬁers compete tocontrol a target system, gaining reward from the environment. We employ ZCS (Zeroth level Classiﬁer System) proposed by Wilson [5], which has no message
list, and propose the Rule-Sharing Learning Classiﬁer
System (RS-LCS), extending ZCS to share rules between agents.
RS-LCS has the following diﬀerences to ZCS: (1)RS-LCS does not use GA because the eﬀectiveness of
GA is ambiguous and still under discussion. Also, RSLCS does not use #. # is used to condition parts ofrules and matches any condition. # does not work

∗ This research was conducted as part of ’Research on Human
Communication’ with funding from the National Institute ofInformation and Communications Technology.

©ISAROB 2005

Rule-Sharing Mechanism

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

eﬀectively without GA. (2) The covering process in
RS-LCS creates rules so that all kinds of actions canbe chosen. (3) RS-LCS does not use the roulletteselection process. Twenty percent of all actions are
random, while the rest are chosen from the rule thatmatches conditions and has the highest priority. (4)RS-LCS does not use a bucket brigade process; instead
it uses a proﬁt-sharing process proposed by Grefenstette [6] because the proﬁt-sharing process ﬁts multiagent learning. We employ Miyazaki’s proﬁt-sharing
process [7], and its decreasing ratio is 0.9.
LCS, including ZCS, is basically a mechanism forsingle agents. Hence, RS-LCS should include an extension for rule sharing. This paper has the assumption that agents simultaneously acquire a reward fromthe environment. Based on this, agents share theirrules every ten reward events. In the rule-sharing process, all of the agents’ rules are merged, an if any of
those rules have the same condition parts and action
parts, their strengths are averaged. The merged rules
are then returned to each agent.

3

Experimental Setting

3.1

Simplified Soccerof the ﬁeld. The left agents are set to the left half of
the ﬁeld randomly, and the right agents are also set in
the same way.

Figure 1: Simpliﬁed Soccer
Each right agent has a greedy mechanism, which
always makes an agent chase the ball. Through all experiments, the right agents’ mechanism is the greedy
mechanism. On the other hand, left agents have RSLCS implemented. By ﬁxing the right agents, performance comparisons with the left agents are possible.
The greedy mechanism does not need any additional description. To use RS-LCS, however, sensor
description is necessary, which is provided in the following section.

We employ simpliﬁed soccer as an example. The
following is a discription.
Figure 1 shows the landscape of simpliﬁed soccer.
(The meaning of dotted lines is described in the following section.) The ﬁeld has 8×30 squares, and there
are two teams, a left team and a right team, and a ball.
Each team has three agents. The vertical line on theright side is the left team’s goal, and vice versa.
At every step, all agents move simultaneously, tothe left, right, above, or below squares. Agents canalso stay in the same squares, and the agents and theball can freely move into the same square. If at least
one agent moves into the same square as the ball, theball is moved (kicked). If more than two agents moveinto the same square, one agent is selected randomly,
and the ball is moved. If the left team’s agent moves
the ball, the ball moves 12 squares to the right, andalso moves vertically and horizontally by less than two
squares. This is also applied to the right team, but thedirection of movement is left. If the ball goes over theleft goal, the left team gets a goal, and vice versa. If
the ball goes over the horizontal lines, it goes back towithin the range of the horizontal lines.
At the beginning of experiments, or after a goal,
positions of the agents and the ball are reset. The ballis set randomly from a choice four squares in the center©ISAROB 2005

3.2

Left Agent Sensor

In Section 2, the substance of the sensor was not
mentioned. In fact, the sensor includes six bits in total.
The ﬁrst four bits are assigned to information on
positions of other agents in the left team 1 . The dotted lines in Fig. 1 are two imaginary lines for an agent.
Based on those two lines, the ﬁeld is divided into fourareas. The squares on the border are included in theright area, or left area, and the square in which theagent sits is included in the right area. If at least one
other of the left agents is found in an area, the bit forthat area is set to 1, if not, it is set to 0.
Next, three bits are assigned to directions of theball. If the ball is in the right area, 000 is set. (Thedistance is not considered.) In the same way, left,
above, below are set to 001, 010, 011, respectively.
The remaining areas, upper-right, upper-left, lowerright, and lower-left, are set to 100, 101, 110 ,111.
The last one bit is assigned to the distance of theball. The horizontal diﬀerence of position between theagent and the ball is calculated and its absolute value
is taken. If the sum of the horizontal value and the
1 Position information of right team’s agents can be included.
But because of large learning space experiments are diﬃcult to
conduct. Hence this information is omitted here.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

vertical one is larger than 10, the bit is set to 0. If
not, it is set to 1.

3.3

Reward Assignment

As we mentioned in Section 1, the rewardassignment problem for multi-agent is still being discussed. Hence we describe a speciﬁc method of reward
assignment for the simpliﬁed soccer problem, because
the validity of reward assignment cannot be discussed.
To assign a reward, an episode of moving the ball isrecorded. This episode records a left agent moving theball every step. If no left agent moves the ball, that is
also recorded. When the left team scores a goal, left
agents obtain a reward. The last agent that moved theball gets a 1.0 reward. After that the episode is traced
back, and at every one step the reward is multiplied by
0.9. If a record of an agent is found, the reward is given
at that step. However if the agent has already received
a reward, the reward is not given. After tracing backthe episode, the agents which did not receive a reward
get a 0.0 reward, and the episode is cleared.
If the right team scores a goal, all left agents get
0.0 reward. The episode is then cleared.

3.4

Figure 2: Result (Agents cannot ﬁnd other teammates)
than that of no share agents’ goals, but the number ofﬁnite goals is similar between two agents.

Experimental Combinations

The purpose of this paper is to verify whether or
not sharing rules is always eﬀective. Therefore we conduct the following experiments. (1) Left agents cannot
ﬁnd other teammates, and cannot share their rules.
(2) Left agents cannot ﬁnd other teammates, and canshare their rules. (3) Left agents can ﬁnd other teammates, and cannot share their rules. (4) Left agentscan ﬁnd other teammates, and can share their rules.

Figure 3: Result (Agents can ﬁnd other teammates)

5
5.1

4

Discussion
Construction on Results

Results
If left agents take the same greedy strategies as rightagents, the performance should be identical between
them. The performance, however, is not identical: theleft agents are superior to the right agents. (Becauseof space restrictions, the right agents’ results can not
be included.) This is because each left agent performs
diﬀerent roles, which enables them to pass the ball toeach other so as to outwit right agents. The important
point is the method of performing those roles. The
diﬀerence between Fig. 2 and 3 can be analyzed inlight of this point.
In these experiments, role assignments are similar
to positioning. That is, left agents try to take theirappropriate posiions. If left agents cannot ﬁnd other
teammates (Fig. 2), each agent has to take a speciﬁc

Figure 2 shows the two experimental results. The
vertical axis represents the number of goals every 2,000
steps, and the horizontal axis representss the steps.
Both results are the average of ten trials. In both experiments, agents cannot ﬁnd other teammates. In one
experiment, agents can not share their rules, whereas
in the other, they can. At the start, the number of noshare agents’ goals is smaller than that of share agents’goals. At the end, however, no share agents overtake
share agents.
Figure 3 shows the two other experimental results.
The axes of Figure 3 are identical to those in Fig. 2. In
both experiments, agents could ﬁnd other teammates.
At the start, the number of share agents’ goals is larger©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

position; otherwise they may conﬂict in their positions.
This specialty can only be realized by the diﬀerence ofrules. Hence, if agents share their rules, that specialization collapses, and the performance deteriorates.
If left agents can ﬁnd other teammates (Fig. 3),
none of the agents have to take a speciﬁc position because they can arrange their positions dynamically.
Thus even if agents share their rules, the performancedoes not deteriorate.
Sharing rules makes agents homogeneous, and viceversa. As we mentioned in Section 1, there are concerns with the results. This is because heterogeneity
between agents is necessary when agents cannot ﬁndother agents, as Inoue [2] demonstrated.
From the above discussions, we could show the relationship between the eﬀect of sharing rules and information of other agents’ positions. Especially, if agentscan ﬁnd other agents, rule sharing is eﬀective. If not,rule sharing is not eﬀective.

proposed by Inoue [9]. In that mechanism, agents retain other agents’ rules as diﬀerent rule sets and exploit them according to the situation the agent faces.

5.2[1] Arai S (2001), Multiagent reinforcement learning
frameworks: Steps toward practical use (in Japanese).
Journal of Artificial Intelligence 16(4):476-481

6Multi-agent learning requires eﬀective means because of its vast learning space. One means is thatagents share their knowledge. We proposed a novel
rule-sharing mechnism employing LCS. Through experiments using simpliﬁed soccer, we demonstrated
the relationship between the eﬀect of sharing rules andinformation of other agents’ positions. In particular, if
agents can ﬁnd other agents, sharing rules is eﬀective.
If not, it is not eﬀective.

References

Open Problems

This research has only just started. Therefore,
many open problems still remain, as the described bellow.
5.2.1[2] Inoue H, Takadama K, Shimohara K, et al. (2003),
Exploring Conditions of Heterogeneous Knowledge forEﬀective Multi-agent Systems (in Japanese). The Second Joint Agent Workshops and Symposium, 208-215

Constraints of Problems[3] Takadama K, Hajiri K, Nomura T, et al. (1998),
Organizational Learning Model for Adaptive Collective Behaviors in Multiple Robots. Advanced Robotics(The International Journal of the Robotics Society ofJapan), 12(3):243-269

In this paper, agents do not communicate with eachother. It should be investigated as to whether the ﬁnding of this paper is still valid, if agents do communicate
with each other.
Rule-sharing has a cost, which is not discussed inthis paper. Not only the ability to ﬁnd other agents,
but also the cost should be taken into account.
5.2.2[4] Holland H (1985), Properties of the Bucket Brigade
Algorithm. The 1st International Conference on Genetic Algorithms 1-7[5] Wilson S (1994), ZCS: A Zeroth Level Classiﬁer System. Evolutionaly Computation 2(1):1-18

Rule-Sharing Mechanism[6] Grefenstette J.J. (1988), Credit assignment in rule
discovery systems based on genetic algorithms. Machine Learning, 3:225-245

A rule-sharing mechanism has many parameters. For
example, the number of rules for sharing, the timingof rule sharing, and so on. The sensitivities of theseparameters need to be investigated.
This paper’s rule-sharing mechanism has all agents
merge their rules. However, there are various kinds ofmerger. For example, speciﬁc two agents always merge
their rules.
Another important open problem is the contradiction of rules between agents. There are some solutions
to this problem proposed by Inoue [8], but these are
not insuﬃcient.
Although this paper’s rule-sharing mechanism hasagents merge their rules, agents do not necessarily
merge their knowledge. One such mechanism has been

©ISAROB 2005

Conclusion[7] Miyazaki K, Arai S, Kobayashi S (1999), A theoryof proﬁt sharing in multi-agent reinforcement learning (in Japanese). Journal of Artificial Intelliegence,
14(6):1156-1164[8] Inoue H, Takadama K, Shimohara K, et al. (2002),
Learning Classiﬁer System Enabling Adaptation to
Mixed Known Environments (in Japanese). The Journal of Three Dimensional Images, 208-213[9] Inoue H, Takadama K, Shimohara K, et al. (2000),
Agent Architecture Based on Self-Reﬂection Learning
Classiﬁer System. The 5th International Symposium
on Artificial Life and Robotics, 454-457

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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Nanoparticles as biosensors components - a brief review
Tomasz Kubik††

Masanori Sugisaka∗

Institute of Cybernetics Engineering, WrocÃlaw University of Technology,
Janiszewskiego 11/17, WrocÃlaw 50-372, Poland
E-mail: tkubik@ict.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: nanoparticles, biosensors.

Abstract

The physical element translate information from thebiological element into a chemical or physical output
signal with a defined sensitivity.
There are several kinds of nanoparticles that can beused as biosensors components. Most of them work asprobes recognizing and differentiating an analyte of interest for diagnostic and screening purposes. In suchapplications biological molecular species are attachedto the nanoparticles through a proprietary modification procedure. The probes are used then to bind andsignal the presence of a target in a sample by theircolor, mass, or other physical properties. The otherbiosensors employ nanoparticles in a different way.
They work as sieves through which charged molecules
are transported in an electrical field.
In the next section a short description of nanoparticles used in biosensors as nanoprobes and nanosieves
is provided.

Biosensors are chemical sensors, in which recognition processes rely on biochemical mechanisms utilization. There are several kinds of nanoparticles that canbe used as biosensors components. Most of them work
as probes recognizing and differentiating an analyte ofinterest for diagnostic and screening purposes.
The probes are used to bind and signal the presence of a target in a sample by their color, mass,or other physical properties. This paper provides abrief description of the following nanoprobes: quantum dots, nanobarcodes, metallic nanobeads, silica
nanoparticles, magnetic beads, carbon nanotubes, andnanopores.

1

Introduction

The most common definition of nanotechnology isthat of manipulation, observation and measurement at
a scale of less than 100 nanometers (one nanometer is
one millionth of a millimetre). In biology and chemistry nanoscale operations are involved in the synthesis of inorganic, organic and hybrid nanomaterials foruse in nanodevices, the development of novel nanoanalytical techniques, and the manipulation of biologicalmolecules such as DNA and the evolution of molecular machines. This review is focused on the potentialof nanotechnology in the developing and constructing
artificial nanodevices, such as biosensors.
Biosensors consist of a biological element (responsible for sampling), and a physical element (transmitting sampling results for further processing) [1, 2].
The biological element of a biosensor contains a biosensitive layer, which can either contain bioreceptors
or be made of bioreceptors covalently attached to thephysical element. The type of biological element defines biological specificity conferring mechanism used.

©ISAROB 2005

2

Nanoparticles as parts of biosensors

Most of nanoparticles used in biosensors work asprobes translating information from the biological element into measurable signal. Nanoparticles made ofsolid-state materials currently available (see Figure 1)
are presented in the foregoing paragraphs.
Nanocrystals, quantum dots – these particles
are inorganic crystals of cadmium selenide, 200–10000
atoms wide, coated with zinc sulphide. They emit fluorescent light when irradiated with low-energy light.
The size of the dots (< 10 nm) determines the frequency of light emitted. The dots usually have a polymer coating with multivalent bio-conjugate attached,or are embedded into microbeads. Collection of dotsof different size embedded to a given microbead emits
distinct spectrum of colors - spectral bar code specificfor this bead. Detection technique with the use of 10

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intensity levels and 6 colors could theoretically provide
106 distinct codes. Quantum dots, for example CdSeZnS nanocrystals, do no emit in the near infrared, sothey cannot be used for analysis in blood [3].
Area of use: multicolor optical coding for biologicalassays [4]; labelling of the breast cancer marker HeR2
on the surface of fixed and live cancer cells; stain actin
and microtubule fibbers in the cytoplasm; detection of
nuclear antigens in side nucleus [5]; immunohistochemical analysis of paraffin-embedded tissue sections [6];in vivo targeting [7].

ZnS
CdSe
polymer

1-50 mmbio-conjugate

quantum dot

nanobarcode
15-60 nmAu

silica
silan

S
S
S

Nanobarcodes are cylindrical nanoparticles withspecific patterns of submicron stripes of noble metal
ions, produced by alternating electrochemical reduction of the appropriate metals. They are between 12
nm and 15 µm in width and 1-50 µm in length. The
striping patterns make them distinctive under light, or
fluorescent microscopy, or mass spectrometry. Nanobarcodes are easy to make in a nearly unlimited number of uniquely identified flavors [8, 9].
Area of use: coding in multiplexed assays for proteomics, population diagnostics and in point-of-carehand-held devices; proteins detection by either mass
spectrometry or fluorescence measure.

S~60 nmmetalic nanobead

silica nanoparticle

polymericlayermagnetic
core

50-200 nm

magnetic nanoparticles
30-150 nm~25 mm

Metallic nanobeads are made of noble metals withdiameters between 15 to 60 nm [10, 11]. They can bedetected by the transmissive and reflective light measure, plasmon resonance, quartz crystal microbalance,and differential pulse voltametry.
Area of use: cancer diagnosis [11]; DNA detection assay [10]; DNA diagnostics [12].

1.8-3 nm5-10 nmnanoporecarbon nanotubes

Figure 1: Schemes of different nanoparticles used for
diagnostic and screening purposes.

Silica nanoparticles are synthesized using standard water-in-oil microemulsion method (60nm in diameter.) They are silanised, and coated by oligonucleotide before use (DNA immobilization.) They
are observable by fluorescence measurements methods[13].
Area of use: efficient nucleic acid hybridisation; detection of nanomolar range target DNA probes; ultrasmall nano-biosensors for trace analysis [13].

Area of use: imaging specific molecular targets using
nanoparticles as magnetic resonance contrast agents[15]; very sensitive cells or other tissue samples capture
and/or separation.
Carbon nanotubes are carbon cylinders rising in
the process of folding graphitic layers. The cylinders
have remarkable strength and unique electrical properties making them insulting, semiconducting or conducting depending on their structure. They may becomposed of a single shell single-walled nanotubes(SWNTs), or of several shells multi-walled nanotubes(MWNTs) [16, 17]. They are produced by microwave
plasma enhanced chemical vapor deposition. Carbon
nanotubes may be grown on a wide variety of substrates (including quartz and glass slides, platinum
substrates) in the presence of catalyst (like nickel.)

Ferrofluid magnetic nanoparticles are particles(25–100 nm in radius) consisting of a magnetic coresurrounded by a polymeric layer (biological substrate)
coated with affinity molecules, such as antibodies.
Macro magnetic beads are bigger in size. They might
be coated with streptavidin to bind to biotin with asingle-stranded DNA probes specific for a bioagent or
sample DNA attached. They are detectable troughamperometry or resistance measure. They can be also
manipulated in a magnetic field [14].

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12-15 nm10-15 nm

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Average length of these nanoparticles is of 25 mm order. Mean diameter may vary from 30 nm up to 150
nm. But with the use of super-lattice nanowire patterntransfer individual semiconductor nanowires can becreated that are as little as 8 nm in diameter with thesame distance between each wire [18]. In most cases
electrical properties of carbon nanotubes are utilized
for measurement purposes. When used in biosensing,carbon nanotubes have specific biomolecules attached.
Area of use: promotion of electron transfer reactions
when used to fabricate electrodes for the oxidationof biomolecules including dopamine, protein and bnicotinamide adenine dinucleotide [19, 20]; extracellular analysis[21]; in vivo diagnostics.

cess dominates. The depth of nanopore fabricated ina membrane 510 nm thick is smaller than the molecule
persistence length (50 nm for dsDNA), and a pore diameter 3-nm is slightly larger than the cross-sectional
size of the molecule (∼2 nm.) In another attemptnanopores are created as an array of cylindrical gold
nanotubes with as small as 1.6 nm inner diameter [29].
Area of use: manipulation and electronically registering of single DNA molecules in aqueous solution [30];discrimination and characteristic of unlabelled DNA
molecules at low copy number [31].

3

Optical fibres are made from optical fibres pulled
down to tips having distal end sizes of approximately
3060 nm. Fabrication procedure involves pulling froma larger silica optical fibre using a special fibre-pullingdevice what yields fibre with submicron diameters.
One end of such fibre is polished from 600-mm silica/silica to a 0.3 mm finish. The other end is pulledthen to a submicron length using a fibre puller. Thus
the distal end of the fibre reaches 60 nm size. To prevent light leakage of the excitation light on the taperedside of the fibre, the side wall of the tapered end canbe coated with a thin layer of silver, aluminium or gold
(100-300 nm) leaving the distal end of the fibre free.
These nanoparticles are not subject to electromagnetic
interferences from static electricity, strong magnetic
fields, or surface potentials. Fibreoptic nanoprobes
can be covalently bound either with bioreceptors, suchas antibodies, or with other, synthetic receptors, suchas cyclodextrins [22, 23].
Area of use: in situ measurements of benzopyrene tetrol in single cells with the antibody-based
nanoprobe [24, 1]; specific detection of target DNA
at zeptomole levels, in the presence of non-cDNA [25];analysis of mRNA isoforms in human cancer cell lines
in conjunction with a new enzymatic detection method
termed RNA-based annealing, selection and ligation(RASL) [26].

This paper highlighted few examples of nanoparticles with their applications. Nowadays technology
provides tools which allow researchers to produce several kinds of nanoparticles. Over the next couple ofyears it is widely anticipated that nanotechnology willcontinue to evolve and expand in many areas of lifeand science, and the achievements of nanotechnology
will be applied in medical sciences, including diagnostics, drug delivery systems and patient treatment.

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P systems with dynamic channels transporting membrane vesicles
Rudolf FREUND
Marion OSWALD
Faculty of Informatics
Vienna University of Technology
Vienna, Austria
rudi@emcc.at
marion@emcc.at
Abstractcles, multisets of elementary objects are transported
in a membrane vesicle, which is the most importantnew feature of the model introduced in this paper.
Attributes assigned to the cell membranes, for example, can be their electrical charges (polarizations) as
this was formally already considered in P systems withactive membranes. Moreover, the transport of a membrane vesicle through a nanotube between two cells
may depend on the contents of the membrane vesicle itself as well as on the objects contained in thetwo cells connected by this nanotube and on the speci…c attributes assigned to the cell membranes and themembrane of the vesicle to be transported through thenanotube.
As it is often observed in the P systems area,
processes may happen in parallel according to a universal clock; yet although in nature many processes
are carried out in parallel, they are not synchronized.
Therefore, to model these biological systems as described by A. Rustom et al., we consider P systems
with dynamic channels transporting membrane vesicles also working in the asynchronous mode (an arbitrary number of rules that do not interfere with eachother can be carried out in parallel in one derivationstep) or in the sequential mode (exactly one rule iscarried out in one derivation step); for an overview on
P systems working in the asynchronous or in the sequential mode see [1]. On the other hand, processes
in computers as for simulating mobile software agentscan be simulated by P systems with dynamic channels transporting membrane vesicles working in the
maximally parallel mode (which means that as many
processes as possible are carried out in parallel).

Based on the biological model of cell-to-cell communication proposed by A. Rustom et al. in [5] we consider
cells able to dynamically form connections (channels)
between them according to speci…c constraints possibly relying on some attribute assigned to the membranes of the cells as well as of their contents. From atheoretical point of view, such P systems with dynamic
channels transporting membrane vesicles are computationally complete even when using only some of thecharacteristic features of the general model. Also thee¢ ciency of modelling speci…c processes in various application areas depends on the speci…c features of thechosen model of P systems with dynamic channels
transporting membrane vesicles.

1

Introduction

Membrane systems were introduced in 1998 by Gh.
P¼
aun in [2] as a parallel distributed model of computation abstracted from cell functioning. In a membrane
structure (that can be represented as a tree in P systems or as an arbitrary graph in the more general caseof tissue P systems), multisets of objects can evolve
according to given evolution rules. Many variants ofmembrane systems (P systems) have been considered
so far, see [3] for a comprehensive overview and [4] forthe actual status of research.
Like in the biological model of cell-to-cell communication proposed by A. Rustom et al. in [5], in this paper we consider cells (like in tissue P systems) able todynamically form connections (channels or nanotubes
in the sense of [5]) between them according to speci…c constraints possibly relying on some attributes assigned to the membranes of the cells as well as of theircontents. In contrast to the P systems considered sofar, through these nanotubes (channels) of P systems
with dynamic channels transporting membrane vesi©ISAROB 2005

In the second section, we now will introduce thegeneral model of P systems with dynamic channels
transporting membrane vesicles. From a theoreticalpoint of view, P systems with dynamic channels transporting membrane vesicles are computationally complete, i.e., in the sense of Turing we can simulate the559

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The set of rules R itself consists of several sets ofdi¤erent types of rules:

actions of Turing machines; depending on the working
mode (sequential, asynchronous or maximally parallel), di¤erent variants of rules and corresponding membrane attributes are needed to obtain this computational universality (we will prove universality for one
speci…c variant working in the sequential mode in thethird section). Also the e¢ ciency of modelling speci…cprocesses in various application areas depends on thespeci…c features of the chosen model of P systems withdynamic channels transporting membrane vesicles as
we will discuss in the fourth section.

2

Re;c is a set of evolution rules for objects in a cell;
Re;v is a set of evolution rules for objects in a
vesicle;Rc;e is a set of communication rules for exchanging (multisets of) objects between a cell and theenvironment;
Rc;v is a set of communication rules for exchanging (multisets of) objects between a vesicle in acell and the surrounding cell;

The General Model

Rc;c;i is a set of rules initializing a channel (nanotube) between two cells (or a cell and the environment);

In this section we describe the general model of P systems we are going to investigate in this paper. Intuitively, we consider cells enclosed by a membrane
which allows for communication with the environment
as well as for dynamically forming connections (channels or nanotubes in the sense of A. Rustom et al. in[5]) between cells that transport (multisets of) objects
enclosed in a membrane (vesicle).

Rc;c;t is a set of rules transporting a vesicle
through a channel (nanotube) between two cells(or a cell and the environment);
Rc;c;d is a set of rules deleting a channel (nanotube) between two cells (or a cell and the environment; a vesicle sent out from a cell into theenvironment gets the status of a cell).

A P system with dynamic channels transporting
membrane vesicles (in the following we shall use thenotion P system only) is a construct

In some restricted variants, the sets Rc;c;l ; l 2fi; t; dg ; may be combined in only one set Rc;c of rules
which establish a channel between two cells for moving a vesicle from one cell to another one (or between
a cell and the environment for expelling a vesicle into
the environment) and immediately after having moved
this vesicle deletes the channel again.

(O; OT ; O1 ; C; Q; F; I; R)
where
O is the set of objects;
OT

O is the set of terminal objects;

O1 O is the set of objects occurring in…nitely
often in the environment of the cells;

Rd is a set of rules eliminating a vesicle in a cell(in a biological sense, this deletion of a vesiclere‡ects a kind of phagocytosis);C
O is the set of catalysts; a catalyst is neverchanged or moved by a rule;

Rg is a set of rules generating a new vesicle in acell.

Q is the set of states for the channels between
cells;

R is a set of rules eliminating the membrane ofa vesicle and expelling its contents into the surrounding cell;

F is the set of attributes assigned to a cell or avesicle;R is a set of rules generating a new vesicle containing the whole contents of a cell.

I speci…es the initial contents of the environment(only those objects not occurring in an in…nite
number of copies, which were already speci…ed byO1 ) as well as the cells and the vesicles and theirinitial contents the system starts with (observethat we assume no channels to exist at the beginning);

All the rules described above may depend on thefeatures currently assigned to the involved cell(s)and/or the involved vesicle and possibly also changethese features. In static variants of the general model,
we omit the sets of rules Rd ; Rg ; R ; and R and do
not allow vesicles to be expelled into the environment.

R is a set of rules.
©ISAROB 2005

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rule r; LinLab (GC ) is the set of initial labels,
and Lf in
Lab (GC ) is the set of …nal labels. For
r = (l(r) : p (l (r)) ; (l (r)) ; ' (l (r))) and v; u being
two multisets over O we say that (u; k) is directly
derivable from (v; l (r)) if and only if

Moreover we should like to point out that all thesets in the general model described above need not be…nite, e.g., the set of objects may be the set of strings
over a given alphabet, and the set of attributes may
be in…nite, too. If the sets of rules are in…nite, we
assume that given an instance of the P system, weare able to e¤ectively list the rules applicable to thisinstance and to decide whether a set of rules chosen
so far is maximal or not.
The P system
may work in di¤erent derivation
modes: In the maximally parallel mode, for each
derivation step we choose such a subset of the rulesin R that cannot be extended any more; in the asynchronous mode, an arbitrary number of rules is appliedin parallel; in the sequential mode, exactly one rule isapplied.

3either p (l (r)) is applicable to v, v is the result of
the application of p (l (r)) to u, and k 2 (l (r)),
or p (l (r)) is not applicable to v, u = v, andk 2 ' (l (r)).
The language generated by GC is the set of all multisets u such that (u; k) can be derived in an arbitrary
number of steps from (w; l) for some k 2 Lf in andl 2 Lin :We now de…ne a restricted variant of the generalmodel of P systems with dynamic channels transporting membrane vesicles that allows for the simulationof graph-controlled grammars: Let

Theoretical Results

The general model of P systems with dynamic channels
transporting membrane vesicles introduced in the previous section is very powerful from a theoretical pointof view, i.e., not all possible features are needed to obtain computational completeness. Moreover, we may
use these P systems for di¤erent tasks, e.g., as generating devices or as accepting devices (P automata),
but also for modelling di¤erent processes carried out
by other devices, yet needing only speci…c features of
the general model for obtaining e¢ cient simulation results. We here restrict ourselves to a model whichworks in the sequential mode and only uses communication rules (antiport rules) as well as, of course, rules
for transporting vesicles from one cell to another one,and we show how in this restricted model we can simulate graph-controlled grammars (which are devices
well known to be computationally complete (e.g., see[6]).

(O; OT ; w; R; Lin ; Lf in )be a graph-controlled grammar (without loss of generality, we may assume Lin and even Lf in to contain
only one label and, moreover, l (r) 2= (l (r)) for all r).
Then we construct the P system with dynamic channels transporting membrane vesicles working in thesequential mode
(O; OT ; O1 ; C; Q; F; I; R )as follows:
We do not use catalysts, and we do not need states
for the channels to be created dynamically; therefore,
we omit C and Q; as well as O1 ; because we assume
all objects from O to be available in the environment
in an unbounded number, hence,is speci…ed as aquintuple (O; OT ; F; I; R ) only.
As set of attributes F we take Lab (GC )[f ; 0; +g ;where we assume Lab (GC ) \ f ; 0; +g = ;: The initialcon…guration consists of card (Lab (GC )) cells having
the labels from Lab (GC ) as attributes; moreover, thecell with attribute l (r) contains the multiset u (r) ; and…nally, the initial cell (having the initial label from Lin
as attribute) also contains a vesicle with attribute 0
and the contents w (i.e., the initial multiset from GC ).
The rules in R simulate the rules in R as follows:

Now let us consider a …nite set of elementary objects constituting O and a subset OT to be the setof terminal objects. A graph-controlled grammar GC
then is a construct(O; OT ; w; R; Lin ; Lf in )where w is the initial multiset over O; R is a …nite set ofrules r of the form (l (r) : p (l (r)) ; (l (r)) ; ' (l (r))),with l (r) 2 Lab (GC ), Lab (GC ) being a set of labels
associated (in a one-to-one manner) with the rules r in
R, p (l (r)) is a rewriting rule of the form a (r) ! u (r)with a (r) 2 O and u (r) being a multiset over O;(l (r))Lab (GC ) is the success …eld of the ruler, and ' (l (r))
Lab (GC ) is the failure …eld of the©ISAROB 2005

(l (r) ; 0; a (r) =u (r) ; +) 2 Rc;v : In the cell withattribute l (r) ; a (r) in a vesicle (with attribute 0
which by the application of this rule is changedto +) contained in this cell is replaced by u (r)which is taken from the cell and regained by
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cells themselves are organisms that may interact inan unguided manner via the environment or else, ina direct way, by establishing channels for exchanging
complete sub-organisms (vesicles). By using suitableevolution rules, the organisms may develop also basedon the development (evolution) of their components
mostly represented by the vesicles contained in them,which usually happens in a parallel but unsynchronized way (and which corresponds with the asynchronous working mode of the P systems).
On the other hand, the vesicles can also be seen
as packages of information transported from one computer (cell) to another one or as autonomous agents
performing their tasks on di¤erent servers. In thatcase, these agents work in parallel, either in an unsynchronized way (which corresponds with the asynchronous mode of the P system) or even in a synchronizedway (which corresponds with the maximally parallelmode of the P system); using su¢ ciently powerful operations (evolution rules) in the vesicles, we are able
to describe the processes taking place in distributed
systems.

(l (r) ; a (r) =u (r)) 2 Rc;e ; in that way, one application of the rule r is simulated.
(l (r) ; 0; :a (r) ; = ; ) 2 Rc;v : Provided thatthe symbol a (r) is not present in a vesicle contained in the cell with attribute l (r) ; the attribute of the vesicle is changed from 0 to (without communicating symbols between the vesicle
and the cell, which is indicated by the notion = ;where denotes the empty word).
Rc;c consists of all rules of the form (i; k; j; 0) suchthat
– k = + and j 2–k =(i) orand j 2 ' (i) :The application of (i; k; j; 0) means establishing achannel between the cells with attributes i and j
for transporting a vesicle with attribute k fromcell i to cell j thereby changing the attribute of
the vesicle from k to 0:
As results of a computation in we take the contents of any vesicle which appears in the …nal cell (i.e.,the cell carrying the attribute from Lf in ) and contains
only terminal symbols. In that way
generates thesame set of terminal multisets as GC :As is well-known (see the results cited in [1]), P systems with antiport rules are computationally complete
in only one membrane when working in the maximallyparallel mode, whereas in the sequential mode universality cannot be reached even with an arbitrary number of membranes. The proof sketched above takes
advantage of the inherently new feature of P systems
with dynamic channels transporting membrane vesicles allowing for transporting the complete multiset ofsymbols in a vesicle from one cell to another one.

4

Acknowledgements
The work of Marion Oswald was supported by FWFproject T225-N04.

References[1] R. Freund, Asynchronous P systems, Proceedings
WMC5 (2004), 12-28[2] Gh. P¼
aun, Computing with membranes. Journal ofComputer and System Sciences 61 (1), 108-143[3] Gh. P¼
aun, Membrane computing - an introduction,
Springer-Verlag, Berlin, 2002

Variants for Applications[4] The P systems webpage:
http://psystems.disco.unimib.it

The biologically motivated model of P systems withdynamic channels transporting membrane vesicles introduced in this paper can be used for modelling various mechanisms in di¤erent application areas. Again
the main feature we have to take advantage of is thepossibility to establish connections between cells andto transport whole multisets of objects from one cellto another one in a single step.
As the model of P systems with dynamic channels
transporting membrane vesicles incorporates several
other features well motivated from biology, it can beused for modelling various aspects of arti…cial life. The©ISAROB 2005

[5] A. Rustom, R. Sa¤rich, I. Markovic, P. Walther,
HH. Gerdes, Nanotubular highways for intercellular organelle transport, Science 303 (2004), 10071010[6] A. Salomaa, G. Rozenberg (eds.), Handbook ofFormal Languages, Springer-Verlag, Berlin, 1997

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Design of Soccer-ball-shape DNA Molecules and Preliminary Experiments in Vitro
Yutaka Kita∗∗

Atsushi Kameda∗∗

Masahito Yamamoto∗,∗∗Azuma Ohuchi∗,∗∗

Graduate School of Information Science and Technology, Hokkaido University
Nishi 9, Kita 14, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan
ki-yu, 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

1.2

DNA molecules are functional materials to apply
to nanotechnology. DNA polyhedra are one of DNA
nanostructures and are expected to be implemented
to nanomachines and other fields. There are manytechnical difficulties to construct DNA polyhedra. We
employ some techniques developed in DNA computing
research to construct DNA polyhedra. In this paper,
we design Soccer-ball-shape (truncated icosahedron)
DNA Molecules by means of gradual self-assembly using circular single-stranded DNA molecules (ssDNA).
Then we report some preliminary experimental results
toward the construction of the whole soccer-ball-shapestructure.
key words: DNA nanotechnology, DNA polyhedra,
constructive method

1
1.1

DNA tiles and DNA polyhedra belong to DNA
nanostructures. DNA tiles are formed structuresthat some double-stranded DNA molecules (dsDNA)
crossover their ssDNA at some point. DNA tiles are
utilized as a two-dimensional periodic structure and ascaffold for the production of a nanowire [2]. DNA
polyhedra are three-dimensional structures. Since
DNA polyhedra have a characteristic that can trap
some materials within them, DNA polyhedra are expected to be applied to a drug delivery and crystal
structure analysis etc. However, a construction of
DNA polyhedra have many technical difficulties in itself, DNA polyhedra have not reached to be utilized
actually yet. Hence we employ some techniques developed in DNA computing research to construct DNA
polyhedra. In DNA computing, reactions of DNA
molecules must be controlled with more remarkableaccuracy than traditional molecular biology. Therefore we can use experimental methods and protocols researched in DNA computing to perform a selfassembly of DNA molecules. Moreover, we can use
the sophisticated sequence design methods developedin DNA computing to construct a certain structureof DNA molecules because a lot of constraints usedin DNA nanotechnology are common to that of DNA
computing.
In this paper, we design Soccer-ball-shape DNA
Molecules by means of gradual self-assembly using circular ssDNA as the basic structures. Soccer-ball-shape
DNA Molecules have 90 edges, 60 vertexes, and 32
faces. Each edge of Soccer-ball-shape DNA Molecules
consists of 22-mer oligonucleotides. The diameter ofSoccer-ball-shape DNA Molecules is approximately 40
nm. The more faces DNA polyhedra have, the moredifficult the material trapped in them escape out-

Introduction
DNA nanotechnology

DNA nanotechnology is a research field that aims
at a construction of nanostructures, nanodevices,and nanomachines using DNA molecules [1]. DNA
molecules have some advantages as follows. DNA
molecules are stable materials. DNA molecules haveability to associate with and to recognize other DNA
molecules by means of Watson-Crick complementarity. Watson-Crick complementarity is that four nucleotides A (adenine), T (thymine), C (cytosine), andG (guanine) form two complementary base pairs (A,T), (G, C). Therefore the states of DNA molecules
can be controlled by using the hybridization of ssDNA.
Moreover, by using properly designed DNA sequences,
we can control a self-assembly of DNA molecules in order to construct a target structure.

©ISAROB 2005

DNA nanosturctures

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side. Therefore our proposed Soccer-ball-shape DNA
Molecules are more effective in terms of feature as the
cages that trap some materials than typical DNA polyhedra such as DNA cube [3] and DNA octahedron [4].

22.1

have either same sequence or unique. Secondly weconstruct the intermediate unit that is made from circular ssDNA concatenated and intertwined by multiple arm molecules. Arm molecules are 44-mer ssDNA molecules that associate with circular ssDNA
and formed hexagons. In consideration of the reaction
efficiency, we design and utilize two types of the arm
molecules: the type 1 arm molecules are concatenatedby circular ssDNA help; the type 2 by hybridizationof sticky-end is formed by two arm molecules. Theintermediate unit consists of six pentagons and fivehexagons. Finally Soccer-ball-shape DNA Molecules
is completed concatenating two intermediate units by
the arm molecules. The details of Soccer-ball-shape
DNA Molecules in Fig. 2. Each edge of Soccer-ballshape DNA Molecules forms dsDNA (20 base pairs).
Bulged T2 loops are placed at each of the junctions togive flexibility at vertexes of Soccer-ball-shape DNA
Molecules [6].

Design of Soccer-ball-shape DNA
Molecules
Structure design

To construct DNA polyhedra, several constructive
methods are proposed as follows.
The first method is self-assembly of one DNA threearm junction that same DNA three-arm junctions areformed from three ssDNA by hybridization reactions
and self-assemble into DNA cages [5]. This constructive method is simple and required sequence number
is only three. However, to construct desired structures are unfavorable. The second method is stepwise
self-assembly that substructures are formed from multiple ssDNA and gradually self-assembly into desired
structures. This method can construct desired structures confirming construction of substructures gradually. However, this method requires more sequencethan the first method. DNA cube [3] is constructed
this method. The third method is self-assembly utilizing DNA foldings that one long ssDNA folds into
polyhedra by several short ssDNA help. The advantage of this method is that long ssDNA can amplify
by the polymerase chain reaction. However, one long
ssDNA sequence must be designed in one DNA polyhedra. DNA octahedron [4] is constructed this method.
In view of constructing Soccer-ball-shape DNA
molecules, the products may contain many undesirable
polyhedra in the first method. Designing long ssDNA
folding into soccer-ball-shape is difficult in the third
method because truncated icosahedron has more faces
than octahedron. Therefore we design Soccer-ballshape DNA Molecules based on the second method.
When required sequence set is designed in accordancewith the edge length, the number of required sequenceis 90 in total edge number of Soccer-ball-shape DNA
Molecules. However, there is a trade-off between thesize of sequence set and the constraint of sequence.
Therefore we propose reutilization of edge sequencesubset that utilized at previous reaction and is expected not to affect next. Thus we succeed in reducing
required number of designed sequences to 14.
The construction step is shown in Fig. 1. Firstly
we synthesize circular ssDNA corresponding to thepentagon of Soccer-ball-shape DNA Molecules. Sequences of circular ssDNA are designed that all edge

©ISAROB 2005

Figure 1: Construction step of Soccer-ball-shape DNA
Molecules.

2.2

Sequence design

In using DNA molecule, experimental accuracy andefficiency depend on DNA sequences as well as experimental methods. To construct DNA polyhedra, DNA
sequences must avoid mis-hybridization and folding
undesirable secondly structures. With the reductionof sequence number by structure design, we can design
DNA sequences satisfy strict constraints and decrease
the undesirable reactions.
The sequence design process is proceeded as follows. Firstly we make a sequence set that consists

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Figure 2: The ditails of structure.

of sequence satisfy constraints such as Energetic constraints, Hamming Distance, and Consecutive Match
by Two-Step search algorithm [7]. Secondly we use
Mfold [8] as a sieve to eliminate undesirable sequencesthat are expected to fold secondly structure from sequence set.

3

Figure 3: Result of synthesizeing circular ssDNA. Lane
1:after ligation; lane 2:after digestion; lane 3:after purification; lane 4, 5, 6:110 mer, 56-mer, and 54-mer ssDNA; lane 7:complementary ssDNA. Since a new band
excepts the material ssDNA are appeared in lane 1 andremain lane2, we obtain circular ssDNA.

Gradual self-assembly by chemical
experiments−0.01◦ C/s, 4◦ C for over 1 hour (annealing). Next, wemix the mixture and other circular ssDNA (c2 or c3).
All edge of c2 and c3 are unique sequence. Next, wemix the arm molecules that concatenate between c2 or
c3 at 4◦ C and perform ligation reactions.
The construction with the type 2 is shown in Fig.
5. First, we mix each circular ssDNA and corresponding the arm molecules that associate with circular ssDNA and form sticky-ends. We use equal condition of
hybridization reactions in type 1 arm molecules. Welabel after hybridization products as c1− h, c2− h, andc3− h that c1, c2, and c3 circular ssDNA hybridize withthe corresponding five arm molucules. Next, we mix
c1− h with c2− h and c3− h at 4◦ C and perform ligation
reactions.

We practically performed experiments in vitro. 5’
end of ssDNA are phosphorylated for ligation reactions that concatenate ssDNA. We generally perform
ligation reactions at 16◦ C for over 1 hour. We use
constant denaturing polyacrylamide gel electrophoresis (CDPAGE) to detect the desired structures.

3.1

Synthesis of circular ssDNA

The synthesis of circular ssDNA is shown in Fig. 3.
First, we mix 56-mer, 54-mer, and complementary ssDNA (non phosphorylated) to form circular structure
by hybridization reactions at room temperature. Next,
we concatenate ssDNA by ligation reactions. Next,
we confirmed these products circularly concatenatedor not by restriction enzyme digestion reactions with
ExonucleaseIII. Finally, we purify circular ssDNA withthe column purification.

3.2

4

We proposed design of Soccer-ball-shape DNA
Molecules and significantly reduced required numberof designed sequences by our methods. Reutilization of
DNA sequences will be indispensable for meeting more
complex needs in DNA nanotechnology. Moreover, we
reported some experimental results of the synthesisof circular ssDNA and the construction of the intermediate units toward the construction of the wholesoccer-ball-shape structure. We confirmed construction of the ring structure that several circular ssDNA

Construction of intermediate unit

The construction of two intermediate units with thetype 1 arm molecules is shown in Fig. 4. First, we
mix circular ssDNA (c1) and the arm molecules. Alledge of c1 are same sequence. The conditions of hybridization reactions are: room temperature, heated
at +5.0◦ C/s, 94◦ C for 10 s (denaturation), cooled at©ISAROB 2005

Conclusion

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intertwined. Since our detection method of CDPAGE
is unfavorable to detect complex structure, we must
additionally use other detection methods such as ascanning probe microscope using in nanothecnology
field.
As the future work, we will improve experimental
protocols and methods to construct Soccer-ball-shape
DNA Molecules.

References[1] N. C. Seeman, “DNA in a material world”, nature,
Vol. 421, pp. 427-431, 2003.
[2] H. Yan, S. H. Park, G. Finkelstein, J. H.
Reif and T. H. LaBean, “DNA-Templated SelfAssembly of Protein Arrays and Highly Conductive Nanowires”, science, Vol. 301, pp. 1882-1884,
2003.

Figure 4: Construction of the intermediate units withthe type 1 arm molocule. Lane 1, 2:after and before
ligation with c2; lane 3, 4:after and before ligation with
c3; lane 5, 6:circular ssDNA and the arm molecules.
Compare with after and before ligation reactions, newbands appeared in lane 1, 3 are partial products;lower
bands are 88-mer and upper are 132-mer ssDNA that
two and three arm molucules are concatenated[3] J. Chen and N. C. Seeman, “The synthesis fromDNA of a molecule with the connectivity of acube”, nature, Vol. 350, pp. 631-633, 1991.
[4] W. M. Shih, J. D. Quispe and G. F. Joyce, “A
1.7-kilobase single-stranded DNA that folds into a
nanoscale octahedron”, nature, Vol. 427, pp. 618621, 2004.
[5] K. Matsuura, T. Yamashita, Y. Igami andN. Kimizuka, “’Nucleo-nanogages’:
designed
ternary oigodeozyribonucleotides spontaneously
form nanosized DNA cages”, Chem Comm pp. 376377, 2003.
[6] P. Sa-Ardyen, N. Jonoska, and N. C. Seeman, “Self-assembling DNA Graphs”, Eighth International Meeting on DNA-Based Computers(DNA8), in Lecture Notes in Computer Science,
pp. 1-9, 2003.

Figure 5: Construction of the intermediate units withthe type 2. Lane 1,2:after and before ligation withc1− h and c2− h; lane 3, 4:after and before ligation withc1− h and c3− h; lane 5, 6:circular ssDNA and the arm
molecules. New bands of 88-mer and 132-mer ssDNA
are apppeared in lane 1, 3 as well as with the type
1. Moreover, new bands are appeared upper 132-mer
ssDNA. Since density of circular ssDNA bands in lane
1, 3 are thinner than that of in lane 2, 4, circular
ssDNA and arm molecules form the ring structuresthat several circular ssDNA are intertwined. However,
we cannot detect the construction of the intermediate
unit yet.

©ISAROB 2005

[7] S. Kashiwamura, A. Kameda, M. Yamamoto, A.
Ohuchi, “Two-Step Search for DNA Sequence Design”, IEICE (The Institute of Electronics, Information and Communication Engineers) TRANSACTION. FUNDAMENTALS, Vol. E87-A, No.6,
pp. 1446-1453, 2004.
[8] M. Zuker, “Mfold web server for nucleic acid folding and hybridization prediction” , Nucleic Acids
Res, Vol. 31, pp. 1-10, 2003.

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Amoebic ability to arrive at signal sources in an obstacle-rich space
Shin I. Nishimura
Graduate School of Engineering
Nagoya University
Nagoya 464-8061, Japan
shin@sasai.human.nagoya-u.ac.jp

Sasai Masaki
Graduate School of Information Science
Nagoya University
Nagoya 464-8061, Japansasai@info.human.nagoya-u.ac.jp

Abstract

dead-ends, narrow holes, and other unexpected difficulties. In order for amoebic cells such as immune cells
to arrive at appropriate tissues, they need to avoid
these difficulties. The aim of this paper is to elucidate amoebic abilities of migration in an obstacle-richspace.

Amoebic cells are able to trace signal molecules andarrive at sources of those molecules. An interesting
problem is on how amoebic cells can avoid obstacles
and find the correct way to reach the signal source.
To answer this problem, we develop a discrete modelof amoeba. We put a wall in the model space witha hole through which the amoeba can migrate. The
wall also has a dummy hole or “permeable membrane”
through which the signal permeates but the amoeba
can not pass. If we place the cell near by the permeable
membrane, the cell initially tries to pass it but finallyfinds the true hole and succeeds in passing it.

1

2

Our model has discrete two-dimensional grids onwhich some concentrations of molecules are defined.
A cell is defined on the grids as a domain. We adopt
hexagonal grids for convenience. A grid is either external or in the cellular domain. When the grid is in
the cellular domain, three real numbers are definedon the grid, which indicate densities of activator, inhibitor and actin filaments. We give four rules in orderto move the cell: Kinetics, Diffusion, Cellular domain
extension and Keeping the cell. The following paragraphs explain those rules.
(1) Kinetics: Both activator and inhibitor are produced by the stimulation of the external signal[8]. The
activator enhances polymerization of actins, whereas
the inhibitor suppresses the polymerization. First, thisrule selects a grid in the cellular domain randomly. If
densities of activator, inhibitor and actin filaments atthe selected grid j are expressed as Aj , Ij and Fj ,respectively, those variables are changed obeying thefollowing equations:Introduction

Amoebic cells are widely seen in many eukaryotic
species. Cellular slime mold at the unicellular period,for example, moves and searches foods with the amoebic locomotion [1]. Human neutrophils which attack
the external microbes are also well-known examples ofamoebic cells [2].
One of important features of amoebae is “chemotaxis”. Immune cells migrate from vessels to the inflamed tissue in order to destroy the external microbes.
Cells in the inflamed tissue are known to produce signal molecules (usually called “chemokine”). Immune
cells detect the gradient of the signal and move along
the gradient to get to the tissue.
The mechanism that amoebae can detect the verysmall difference in signal density between their head
and tail, which is often as small as the signal fluctuation, has been a challenge to researches, and many
secrete mechanisms of chemotaxis have been brought
to light through intensive studies [3, 4, 5, 6, 7].
However, many problems remain elusive. One of interesting problems is the mechanism that amoebic cells
can migrate among tissue cells which are constructed
as if a maze in animal bodies. There suppose to be©ISAROB 2005

Model

A0j
Ij0
Fj0

= Aj + fiSj ¡ kα Aj= Ij + flSj ¡ kβ Ij‰° ¡ kf Fj ( AI > h)
= Fj +,¡kf Fj(otherwise)

(1)
(2)
(3)

where fi, fl, °, kα , kβ , kf , ° and h are constants. Sj
indicates the concentration of chemoattractants or thestrength of the external signal at the jth grid. Grids
at the border of the cellular domain are regarded as

567

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the cellular membrane. We call the grid in the cellulardomain the membrane if at least one of its six nearest
grids is external. Sj is set to zero if the jth grid isin the cellular domain but not in the membrane. The
functional form of Sj represents the chemical gradient. A schematic picture of the kinetics is depicted in
Figure 1(2) Diffusion: Only the inhibitor diffuses into thewhole cytoplasm[8]. This rule selects a grid from thewhole cellular domain. At the selected jth grid and its
nearest cellular lth grid, Ij and Il obey the following
equations:
Ij0
Il0= Ij ¡ DIj
DIj= Il +,
nlength of the membrane becomes as small as possible.
Note that if removing is chosen, the values of A, I
and F in the removed grid are added into the nearest
cellular grid.
We also give the “master” rule that randomly selects one of the above rules. Each rule has the probability of selection. The probabilities of selection forrules from (1) to (4) are written as P1 , P2 , P3 and P4 .
P1 + P2 + P3 + P4 should equal to 1. When the master rule selects one of the four rules, the selected ruleis executed. We iterate this process several millions
times.
External signal is defined in external grids. The
signal diffuses over external grids but not into cellular
grids. When Sk is the signal density in the external
grid k, Sk is updated to Sk0 by the following equation:

(4)
(5)

where D is the diffusion constant. n is the number ofthe nearest cellular grids. D should be smaller than 1
by definition.
(3) Cellular domain extension: The rule randomly selects a grid from the membrane. When Fj
at the selected jth grid in the membrane exceeds thethreshold Fth , an external grid in the six nearest gridsof the jth grid is turned into a cellular grid. When
there are two or more than two external grids aroundthe jth grid, a grid is randomly selected. If this gridis referred to as l, Fl = Fj /2 and other variables are
set to zero. Fj0 equals to Fj /2 by definition, where theprime indicates the value at the next time step.
(4) Keeping the cell: We also give a rule toprevent cell from breaking into pieces. The cellularvolume is kept and the cellular surface length is constrained to be as small as possible. This rule randomly
selects a grid from the membrane. Then the rule decides either to remove the grid or to add a new cellular grid around the grid. This rule checks the cellular“tension” by calculating energy of tension as:
E = (V ¡ V0 )2 + cL2 ,Sk0 = Sk ¡ Ds Sk + Ds

Sl /nl ,(8)lwhere l indicates the lth nearest external grid, nl is
the number of nearest external grids around the lth
grid, and Ds is constant. Although this equation looks
similar to Equations 4 and 5, it should be noted thatsignal densities are synchronously updated at all external grids. After the master rule randomly selects
the rules nc times, it executes Equation 8 once, andthis cycle is repeated. The reason why we adopt thesynchronous updating rule for external signal densities
is that the external signal diffuses much more rapidly
than intracellular molecules. External signal does not
diffuse into cellular grids. We define rectangle boundary grids in which the external signal sinks: The signal
flows into the boundary grids but not from the grids.
When a signal source is defined at the kth grid, Sk is
kept constant instead of Equation 8 as Sk = S0 at thesource grid.
We calibrate parameters in the model by examining
how our cell moves in a simple linear gradient. The
initial diameter of the cell is set to be 30 grids. The cellgoes up the linear gradient as observed experimentally[9] when the following set of parameters are chosen:
fi = 1.0, fl = 0.1, kα = 0.9, kβ = 0.02, kf , D =0.45, h = 1.0 P1 = 0.0419, P2 = 0.03, P3 = 0.03,
P4 = 0.898, V0 = 900, c = 105 , kT = 100, Ds = 0.3,
S0 = 0.5 and nc = 10.
Although we have not yet exhaustively tried different parameter sets, we expect that the cell behaviors are robust against the parameter change. We use
95 £ 95 grids in which both external and cellular grids
are defined. Simulation is designed to terminate before
the cell reaches the boundary of the grid space.

(6)

where V and L are the cellular volume and length ofthe membrane and V0 and c are constants. When E 0
denotes the energy after either removing or adding acellular grid, we define the probability P as follows:µ¶E0 ¡ EP = exp ¡,(7)
kT
where kT is a constant. We generate a random number
between 0 and 1 and then compare the number with
P . If the number is smaller than P , we “undo” theevent of removing/adding. From the definitions of P
and E, the volume of the cell tends to be V0 and the©ISAROB 2005

X

568

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Boundary

Signal Source

(2)

(1)
diﬀuseS
A
activateIFsignal source

inactivate

diﬀusecytoplasma

F

cell
push

(3)

Figure 1: A schematic picture of a cell in the spacewith boundary through which signal drops out of thespace. Both activator A and inhibitor Iare producedby the stimulation S of the external signal. The activator enhances polymerization of actins F , whereas
the inhibitor suppresses the polymerization. Only the
inhibitor diffuses into the whole cytoplasm. Externalsignal diffuses from signal sources.

3

Figure 2: Snapshots of a cell moving to a signal source
as if to eat a “food”. A large, dark gray area is thecell. The signal density is indicated with gray scale.
A sinal source is a small dark gray area. Around thesource, contour lines of signal density are shown. In
Subfigure (4), a dark small area has vanished because
the source has been touched by the cell and removed.

Results

First we put a source near by the cell. When the celltouches the source, the source is eliminated from thegrid space. This operation naively represents “phagocytosis” . Does the cell arrive at the source as reaching “food”? Figure 2 shows that the cell succeeds inreaching the food.
If there are multiple foods, how does the cell behave? Interestingly, the cell moves to one of foods and“eats” it. It then goes to the next one and eats all the
foods in the end. (Figure 3).
Figure 4 shows that there is a wall with a hole anda “permeable membrane” through which the signal
can permeate but the cell cannot pass. When the cellstarts near by the permeable membrane, the cell stays
at the membrane for a while then moves to the truehole and reaches the source.

4

(4)

(1)

(2)

(3)

(4)

Discussion
Figure 3: Snapshots of a cell “eating” three foods.
The cell moves to the right bottom, then to the left
bottom, and finally to the top.

In actual animal bodies amoebic cells seem to selectsuitable paths by avoiding obstacles or local maxima
of signal density. Results in the last section showedthat the model amoebic cell can also choose a suitable path as natural amoebic cells do: When multiple
foods are placed in the space, the simulated cell did©ISAROB 2005

569

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

(1)

(2)

[4] T. D. Pollard. The cytoskeletion, Cellular Motility
and the Reductionist Agenda. Nature, 422:741–
745, 2003.

signal sourcewall[5] T. D. Pollard and G. G. Borisy. Cellular Motility Driven by Assembly and Disassembly of Actin
Filaments. Cell, 112:453–465, 2003.

cell

(3)

(4)

[6] M. Iijima, Y. E. Huang, and P. Devreotes. Temporal and Spatial Regulation of Chemotaxis. Dev.
Cell, 3:469–478, 2002.
[7] A. B. Verkhovsky, T. M. Svitkina, and G. G.
Borisy. Self-polarization and Directional Motilityof Cytoplasm. Curr. Biol., 9:11–20, 1999.

Figure 4: Snapshots of a cell moving in the space separated by a wall. The wall has a membrane indicatedby a dashed line at the left part, through which thesignal permeates. The wall also has a hole at the rightpart. A vertical part of the wall is a stand in cell’sway.

[8] A. Levchenko and P. A. Iglesias. Models of Eukaryotic Gradient Sensing: Application to Chemotaxisof Ameobae and Neutrophils. Biophys. J., 82:50–
63, 2002.
[9] S. I. Nishimura and M. Sasai. Inertia of amoebic
cell locomotion as an emergent collective propertyof the cellular dynamics. Phys. Rev. E. in press.

not freeze but chooses one food to another to gain asmuch food as possible. When the obstacle is placed
between the food and the cell, the cell can find out
the proper way to get round the obstacle to reachthe food. Simplicity of our model leads to the ideathat such seemingly complex motion is based on theessentially simple mechanism. Although the real biological process is supported by enormous numbers ofdifferent types of proteins, the key mechanism underlying the behavior might be the nonlinear and historydependent response of cell to the external stimuli as
demonstrated in the model.
Our next task is to quantify the efficiency of amoebic strategy by using our model. Such works will bedone in future publications.

References[1] J. Ishikawa, J. Okano, K. Ohki, A. Amagai,
Y. Maeda, and H. Miyata. Phagocytosis of Dictyostelium discoideum studied by the particletracking method. Experimental Cell Research,
288(2):286–276, 2003.
[2] I. Roitt, J. Brostoff, and D. Male. Immunology.
Mosby International Ltd., 1998.
[3] F. I. Comer and C. A. Parent. PI 3-Kinases
and PTEN: How Opposites Chemoattract. Cell,
109:541–544, 2002.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 200530

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Automatic control of an Electric Vehicle using visual information
Koji TOKUDA, Masanori SUGISAKA, MBAITIGA Zacharie
Dept. of Electrical and Electronic Engineering, Oita University
700 Dannoharu Oita 870-1192 JapanE ma i l : m s u g i @ c c . o i t a - u . a c . j p
DENKEN. The stepping motor(CSK543AP-TG20, CSK564AP-TG20) are used for an accelerator and a brake. AC servo
motor (SGM-01B314) is used
for the Steering. The CCD
camera is attached to on the top
F i g . 1 Electric Vehicle
the EV. In order to control theEV, there are two computers located in the EV body particularly in the trunk. The computer for image processing isequipped with the capture card (FDM-PCIⅢ) and a video
frame received form the camera capture card has size of
640×480 pixels which 640 pixels represent the height of
the image and 480 pixels represent the width of the image
respectively are in RGB format. The computer for driving
is equipped with the motor controller board and each
motor operation using the data from computer for image
processing.

Abstract
The aim of this research is to realize automatic control
of an Electric Vehicle (EV), which the line ahead of the EV
is detected by CCD camera and EV is made to follow theline. In this paper, image processing for line detection andcalculation of a steering angle is described. For the methodof line detection, edge detection forms the grayscale image.
Then, both edges of the line are extracted using Houghtransformation.
1. Introduction
In recent years, serious traffic problems, such as traffic
accident and traffic congestion, have been encountered by
the automotive society. Then, research of ITS (Intelligent
Transport Systems) is made to improve and solve the traffic problems. Because of that, the research on automatic
operation of a car manage attracts attention. Driver operation support and automatic operation can prevent the accident by artificial control.
Automatic control of a small recognition vehicle has
been studied in this laboratory [1][2]. Research of the nextstage of automatic operation of a car, the aim of this research is to realize automatic control of an Electric Vehicle(EV), which the line ahead of the EV is detected by CCD
camera and EV is made to follow the line.
In this research, image processing for line detection andthe compensation for deriving a steering angle is stated.
In this paper, by using EV system composition, image
processing for line detection, the EV experiment is described.

3. Image processing for line recognition
In order for EV to carry out an autonomous travel, theposition of EV on the road is recognized and it is required
to carry out steering operation using the information. Then,the purpose of the computer for image processing is thetarget line which it is marked on the road is recognized.
Next, the steering is operated based on the recognized line.
3.1 The recognition method
Line recognition was performed using hue and saturation of a HLS table color system. However, the method
was not stabilized when disturbance occurred such as light,
and has not being able to recognize the target line. In order
to be stabilized and to perform line recognition, processing
using the edge produced of both edges of the line is performed.

2. Overview of the EV
The composition of the EV used in this research is explained. The EV use for our experiment “MINI-SWAY”
and is produced by DAIHATSU Company. The“MINI-SWAY” shown in figure 1 is 1,010[mm] of overall
width, 2,395[mm] of overall length, and 1,490[mm] ofoverall height. In order to control from the computer, theEV was improved with accelerator brake and handle by©ISAROB 2005

3.2 Reduction of the image and Grayscale image
The input image from the camera is 640×480 pixels.
Therefore, an input image is reduced in order to shorten

575

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

processing time. The input image is scanned at intervals of
2 pixels, and it reduces to 320×240 pixels. Next, in orderto perform edge processing, reduced image is converted toa grayscale image as equation (1).
Grayscale _ image = ( 222 ⋅ r + 707 ⋅ g + 71 ⋅ b) / 1000

3.5 Code area
When extracting the center of gravity coordinates of aline, code area is limited and processed.
The code area in width is made within of 60~89 pixels
and 120~149 pixels. The code area in depth depends on
center of gravity coordinates in the previous one. For example, when the center of gravity coordinates (x,y) are
acquired, next code area in width is immovable and next
code area in depth is taken as the range of x-50 and x+50
pixels. This is because the target line is continuity and it
does not have a sudden curve. The center of gravity coordinates of a line can be searched by using this method.
Moreover, even if strong edge other than a line exists on
the road, only the edge of both edges of the line can beextracted. In each code areas, a target line can be regarded
as a straight line, when Hough transformation is performed,
the straight line of both edges can be obtained correctly.
The same method is used even when there is a target
line. The code area is shown in figure 4.

(1)

3.3 Edge detection
The white line on a roadproduces the edges whichclarify the edges of the line.
The Sobel filter of the transverse direction of primarydifferentiation is used for edge
detection. Using this method,Fig.2 Edge detection result
both edges of line can beemphasized and the edge produced in the road is reduced.
The detected edge using the Sobel filter is shown in figure
2.

320[pixel]

3.4 Straight line detection
Not only the edge
produced to the both edges ofthe line but the other edge,
such as edge produced on theroad surface, is remained in
the image which had edge
detected. In order to extract Fig.3 The straight line
only the edge produced to extraction result using
both edges of the line, straight Hough transformationline is extracted using Houghtransformation. Two straight lines are extracted. Conversion in polar coordinates can be obtained using equation(2). Reverse conversion in rectangular coordinates can beobtained using equation (3).

ρ = xi cos θ + yi sin θ
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ρ
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60[pixel]
89[pixel]

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Code area
Fig.4 Code area
3.6 Experimental result for the center of gravity coordinates
The extraction result for center of gravity coordinatesin case of one target line and two target lines is shown infigure 5 and 6. The center of gravity coordinates can also
be acquired by one target line, or two target lines.

(2)
(3)

When the target line is straight, the both edges of thestraight line can be extracted using Hough transformation.
However, when the target line has curved, two obtained
straight lines using Hough transformation may miss thetarget. Such a problem is solved by regional division. The
straight line extraction result using Hough transformationis shown in figure 3.

©ISAROB 2005

-50[pixel] +50[pixel]

Fig.5 The extraction
result for one target line

576

Fig.6 The extraction
result for two target line

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

turbance. When the next code area is set up from the centerof gravity coordinates, the true center of gravity coordinates are no longer acquired. Then, if the acquired center ofgravity coordinates exceed the width of evaluation, thenext code area is decided from the predictive value. And
the code area is set up with ±65 pixels from the prediction
value. The algorithm of the return to the code area is shownin figure 7.

4. Robust design
Image data tends to be influenced by disturbance. In
the steering operation using image data, wrong image
processing will cause big accidents. Moreover, since thecode area of the inputted image is divided and the target
line is extracted. If it fails, it cannot return to the reference
code area. Then, the image processing is made robust to
disturbance.

320[pixel]
4.1 Evaluation algorithm
The center of gravity coordinates for a code area isconsidered. The Center of gravity coordinates change because the EV moves. Therefore, the next center of gravity
coordinates can be expected from the old center of gravity
coordinates. In order to calculate a quadratic approximation
curve, least-squares method is used. It is based on the oldcenter of gravity coordinates which has being acquired
correctly. And, the following center of gravity coordinates
are evaluated. The evaluation method configures a certain
fixed deviation from the center of gravity coordinates expected.
The x-coordinate (xi) of the center of gravity coordinates acquired from image data is called actual measurement. And, (xi_Pre) from the x-coordinate of the old thecenter of gravity coordinates is called predictive value.
When the actual measurement (xi,ti) in Time t is obtained,
the predictive value (xi_pre,ti_Pre) is calculated on the basis ofactual measurement (xi-1,ti-1)~(xi-5,ti-5), and the actual
measurement (xi,ti) is evaluated. If the acquired coordinates
are successful, a quadratic approximation curve is updated,and next the center of gravity coordinates acquired are
evaluated. When the actual measurement (xi+1,ti+1) in Timet+1 is obtained, the predictive value (xi_pre,ti_Pre) is calculated on the basis of actual measurement (xi,ti)~(xi-4,ti-4),
and the actual measurement (xi+1,ti+1) is evaluated. If theacquired coordinates are successful, a quadratic approximation curve is updated. The width of an evaluation value
is set as −25 pixels~25 pixels and center of gravity coordinates are evaluated. If the acquired coordinates are s fails,
a steering angle is calculated from the predictive value.
Thus, the evaluation is repeated.

-50[pixel] +50[pixel]
60[pixel]
89[pixel]-65[pixel]

120[pixel]+65[pixel]
5[pixel]149[pixel]

Measurement value
Code area

Predictive value

Fig.7 Code area of return algorithm
5. Steering control
5.1 Steering angle
In order to determine a steering angle from image data,
the principle of “Method of the move target point following” [4] is used. Coordinates (x1,y1) are made into the target point of the EV among the center of gravity coordinates(x1,y1) and(x2,y2) acquired from the image. At this time, thesteering angle δ of the EV is calculated on the basis of theposition (x1,y1) and the target direction θ of the EV in theposition from the move coordinate system.

δ = arctan 2l (3 × y1 − x1 × tan θ ) / x12(4)

It changes into a move coordinate system from the acquired coordinates.
X _ real _ coordinate [mm]− 10.32 × y1 + 5733=
× x _ pixel _ length640
Y _ real _ coordinate [mm]= 16.5 × y _ pixel _ length
4.2 Return algorithm
A code area is returned using the evaluation method
which described the point. If the acquired center of gravity
coordinates are over the width of evaluation, center ofgravity coordinates may be recognized incorrectly by dis-

©ISAROB 2005

240[pixel]

577

(5)

(6)

x _ pixel _ length [ pixel] = x1 − 319(7)

y _ pixel _ length [ pixel ] = 479 − y1
l = 1500 [ mm ]： Wheel base

(8)
(9)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A steering angle is calculated as the equation (4), and apulse required for a steering angle is also calculated. The
equation (10) changed into a pulse is from experiment data.

6.2 Experimental result
As for an initial position, the target line is located at
750 [mm] from the center of the EV. The EV is driven in a
zigzag from the start to about 45[s]. The result shows thedeviation from the center of the EV is about 50~100[mm].
The line width was 100 [mm], EV is able to run on the line.
Even if a target line is in the both ends of a road, the same
result can be obtained.

Pulse = 3360 .3 × steering _ angle (δ ) [deg] (10)640[pixel](0,0)(x2,y2)(319,239)(x1,y1)480[pixel]Deviation[mm]θx
X_pixel_lengthyY_pixel_length
(0,0)

1000
800
600
400
200
0
-200 0
-400

10

20

30

40

50

60

70

80

90

Time[s]Fig.10 The deviation from the center of the EV
Fig.8 Relation between the EV and the target point
7. Conclusion
The stable the target point can found by replacing theline recognition using color information with the line edge.
In cruise Control, a target line is arranged in the centerof a road. Although the deviation from the center of the EV
was not 0[mm], it was able to run on the line. But then, theEV is driven in a zigzag until it converges, the cruise control of the EV has been improved.

5.2 Steering operation
AC servomotor used for the handle is operated for continuation. And, the rotation direction is determined fromthe target pulse which is being received from the image
data. For example, when the servomotor is carrying out theright rotation, If the target pulse transmitted from the PC
for image processing is larger than the present pulse, therotation direction of the servomotor is maintained, if thetarget pulse transmitted from the PC for image processing
is smaller than the present pulse, the rotation direction of
the servomotor is reversed.

References[1]Daisuke Matsumoto, “Run control of a move recognition vehicle”, Oita University Graduate School Master Paper, 1997 (In Japanese)

6. Run experiment of EV
6.1 Experiment method
The
experimentuse
recognition
andsteering
operation of a target line. In an
experiment, A target line isarranged to position forward
right of vehicles, the EV iscontrolled automatically to Fig.9 EV and a target line
follow the target line. However, the experiment performed
the operation of the accelerator and the brake, and the
speed is fixed at low speed. The EV and a target line areshown in figure 7.

©ISAROB 2005

[2]Osamu TAKADA, “Development of the steering control
system of the move recognition vehicle”, Oita University
Graduate School Master Paper, 1998 (In Japanese)[3]Kouichi SAKAI, “A guide to digital image processing”,
CQ publication, 2002 (In Japanese)[4]Hiroaki MORI, Shin KATO, Sadayuki TSUGAWA, “A
Lateral Control Algorithm for a Vision-Based Vehicle witha Moving Target in the Field of View”, JOURNAL OF
MECHANICAL ENGINEERRINGLABORATORY,
Vol.53, July 1999, No.4 (In Japanese)[5]MBAITIGA Zacharie, “IMAGE PROCESSING AND
NETWORK COMMUNICATION FOR ELECTRIC VEHICLE”,
Oita University Graduate School Master Paper, 2002

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Mobile sensor device in Intelligent Space
H. Isu, T. Sasaki, H.Hashomoto
Institute of Industrial Science, University of Tokyo
Tokyo, 153-8505 (Email: isu@hlab.iis.u-tokyo.ac.jp)
Abstract

formation of the objects, it is necessary to obtain thesensor’s own position and posture information. In thisresearch, in order to construct simple system, we estimate the status of the device by solving Perspective nPoints (PnP) problem of camera on the device. Thisis an appreciate approach is for employing the advantage of Intelligent Space that the information of thespace is given as world coordination. In this reserach,
we try the methods whose perspective points is 3 and4. But in this paper, we will describe the metod using P4P problem solution only. Then, we will describe
position measurement method of the objects. This is
the method in the past researches.

In this research, we propose mobile sensor device
in Intelligent Space. Intelligent Space, that is our recent research topic, is the system consistituted of distributed sensor devices and robots agents. We focuse
on the capabiility of sensor devices which are mainly
CCD cameras for acquisition of position informationof the object in Intelligent Space.
In this paper, we tried to the estimation of positionand posture of mobile distributed sensor device in Intelligent Space. This mobile sensors are for extensionof technologies for Intelligent Space in scalability andso on. The estimation method is solving Perspective nPoints problem on camera device on the device. Then,
we describe the measuring experiments of the objects
in Intelligent Space briefly.

2

KeywordMobile Sensor

Intelligent Space, position estimation, camera calibration, Perspective n Points Problem

1

As we mentioned above, disributed sensor device inIntelligent Space acts for localization. Since our system uses CCD camera, to provide accurate position
information of the object, camera position is necesary.
We have researched on evaluation of camera arrangement [2], but we have treated simple environment. To
intellectualize more complicated environment, we needto consider more dynamic system. Then, we propose
mobile sensor device in Intelligent Space.

Introduction

This research is about mobile sensors in Intelligent
Space[1][2]. Intelligent Space is a kind of platform thatserves users through the robots connected the space
based on the information from distributed sensor devices. In the recent research, these sensor devices havebeen realized with general CCD cameras and computers as vision processing system. This is mainly because
we assume the services that Intelligent Space will offer
are based on the users’ and the robots’ (received the
directions from the space) position information and vision sensors system is very useful and reasonable forthis purpose. We note this point, and so purpose mobile sensor device as a technique for acquiring moredetailed position information of the objects in Intelligent Space.
To achieve mobile sensor device, first, we will purpose the method of estimation of the position and posture of mobile sensor device. To provide position in-©ISAROB 2005

The proposal will bring other some merits. For example, since we consider that tracking or trace of theobject is basic action of the robots in Intelligent Space,mobility of the sensor will make those task more easily.
and, mobile sensor device may make the task whichneeds more accuracy, such as face or gesture recognition, more easier.
To realize mobile sensor device, we consider thecamera self-localization. This is neccesary to cooperate with other distributed sensor device that is
basis of Intelligent Space.

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3

Self-position estimation of the mobile sensor using its camera system

3.1

where R is 3 × 3 rotation matrix
m11 m12 m13
R =  m21 m22 m23 
m31 m32 m33

Estimation method

In this research, in order to construct the simplest
system, we estimate the status of the device by solving Perspective n Points (PnP) problem of camera on
the device by numerical method. PnP problem is the
problem to solve the position and posture of camera
using the pixel value of n points whose position inworld coordinate system.
There are some reasons for using PnP Problem following;

and T is translation vector£
T = m14 m24

m34

¤T

1 ]T , w̃ = [ xw

yw

(3)

and c̃, w̃
c̃ = [ xc

yc

zc

zw

1 ]T

Using orthogonal property of R, the following equation;

1. The amount and time of computation is stable
2. Estimation error is predictable

3X

3. Easy to realize

k=1
3
X

1. is fulfilled by taking numerical method and usinga small number of landmarks as perspective points.
The number of landmarks is related to 3., . The least
number for solving PnP Problem is three, but in caseusing P3P problem the estimation error is too large.
Then, in this paper, we abridge the approach using
P3P Problem solution and discribe the approach of
Perspective 4 Points Problem (P4P Problem). 2. isthe one of the merits of this approach.

3.2

(2)

m2km

=

1

(4)

mkm mkn

=

0

(5)

k=1

P4P Problem of on-board camera

In this paragraph, we describe the simplified P4P
problem solution as the sensor device position estimation. To simplify the solution, we assume that;Yw• Distortion of CCD camera can be ignored.
• Internal parameters of the system (focus length,
center of vision, etc) is known.

Figure 1: Coordinate system• Position and posture of sensor device are determined unique if the exteranal camaera paremeters
are determined.

Transformation from camera coordinate system tovision coordinate system is as followed[3];
¸
·
¸
·
f
xc
xp
(6)
=
yp
zc y cThe transfer equation from world coordinate system[xc yc zc ]T to camera coordinate syetem [xw yw zw ]Tis as followed[3](Fig:1);c̃ = M w̃
where M is

m11
 m21
M =
 m31
0

©ISAROB 2005

m12
m22
m32
0

m13
m23
m33
0


m14
·
m24 
=
m34 
1

(1)

R
0 0 0

T
1

¸

580

Since we have assumued that the internal parametersof the camera is known, we could easily obtain thetransformation from vision coordinate to pixel value
in vision. This transformation is depend on the valueof internal and external parameters of camera, thenlandmarks whose position is known in world coordinates system and vision coordinates system will determine the unknown parameters.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Four landmarks used for self position estimation
shall arranged on the same plane. At this time, conversion that is made the plane into zw = 0 exitsts,
then generality is not lost as zw = 0.
Since equation(1) and equation(6),xi p = f

m11 xi w + m12 yi w + m14
m31 xi w + m32 yi w + m34

Table 1: Landmarks Pattern2
1
landmark no.
(0.30 -1.0 0.0) (-1.0 0.60 0.0)
position
43
landmark no.
(0.0 0.0 0.0)
(0.70 0.40 0.0)
position(7)

normalized with m34 , the previous equation is equalto;The result of the experiment is shown Fig:2. Mean
estimation error at eacch step was 10.2 cm.

xp = f m011 xw +f m012 yw +f m014 −m031 xw xp −m032 yw xp
and through the same procrdure about yp ,yp = f m021 xw + f m022 yw + f m024 − m031 xw yp − m032 yw yp
Then, that two equations for four landmarks[xiw yiw 0]T (i = 1, 2, 3, 4) are shown as;

Estimation
Reference
Landmark1
Landmark2
Landmark3
Landmark4

4.5
4

32
3
−x1p y1w
f m0110
6
7−x2p y2w 7
7 6f m12
7
6f m014 7−x3p y3w 7
76
7
0
6
7
−x4p y4w 7
7 6f m21
70
6f m22 7−y1p y1w 7
76
7
0 7
6
−y2p y2w 7
7 6f m0 24 7
−y3p y3w 5 4 m31 5−y4p y4w
m032
˜
y3 p y4 p(8)−x1p x1w−x2p x2w−x3p x3w−x4p x4w−y1p x1w−y2p x2w−y3p x3w−y4p x4w
y2 p

m0km m0kn = 0

3
2.5
2
1.5
1
0.5
0
2
1

2
1

0
0

-1
Y[m]

Since this equation and focus length f , m011 , m012 ,
0m14 , m021 , m022 , m024 , m031 , m032 can be calulated.
On the other hand, since equation(5),
3
X

3.5

Z[m]

2
x1w y1w 1
0
0
0
6x2w y2w 1
0
0
0
66x3w y3w 1
0
0
0
6
6x4w y4w 1
0
0
0
6
6 0
0
0
x
y
1
1w
1w
6
6 0
0
0
x
y
1
2w
2w
6
4 0
0
0 x3w y3w 1
0
0
0 x4w y4w 1
ˆ= x1p x2p x3p x4p y1 p

-1

-2

-2

X[m]

Figure 2: Self position estimation

(9)

k=1

then, m013 , m023 , m033 are determined. Next, Since
equation(4), m34 can be calculated. Then, the parameter of R and T is determined.

3.3

4

Position mesurement of the object
using the mobile sensor

Computer simulation

We experimented the validity of the approachshown in the previous clause by computer simulation.
The procedure of the experimetnt is following. We
operated virtual mobile sensor device so that a spiral was drawn in the world coordinate system, andthen, we obtained virtual picture from camera model.
Next, based on the picture information, we apply the
self-position estimation of the camera inthe precedingclauses.
Table1 is landmarks pattern used in this experience.

©ISAROB 2005

The self-position estimation we described in theprevious paragraph is for position measurement ofsome object (mobile robots or users) in intelligent
space. In this paragraph, we show the position measurement experience of the robot whose height isknown. Fig:3 is the result of computer simulation
based on the self-positionestimation of the sensor in
the previous paragraph.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.5
Measurement
Estimation

2

1.5

1

Y[m]

0.5

0

-0.5

-1

-1.5

-2

-2.5
-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

X[m]

Figure 3: Measurement of the object position
In this case, mean measurement error at each stepwas 2.44 cm. The figure show this results is sufficientaccurate for almost application we consider.

5

Summary

In this paper, we proposed the mobile sensor device
in Intelligent Space, and described about self-position
estimation of the mobile sensor required for the sensor to measure the position of the object. And we
introduced the meaurement experiment of the object
in Intelligent Space.

References[1] J.H Lee, H.Hashimoto, “Intelligent Space - concept
and contents,” Advanced Robotics, Vol.16, No.3,
pp.265-280, 2002.
[2] T.Akiyama, J.H.Lee, H.Hashimoto, “Evaluation of
CCD Camera Arrangement for Positioning System in Intelligent Space,” Proc. of The seventhInt. Symp. on Artificial Life and Robotics (AROB
7th’02), pp.310-315, 2002.
[3] K.Deguchi, Fundamental of
CORONA PUBLISHING, 2002.

©ISAROB 2005

Robot

Vision,582

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Real-time Path Planning For Senor-based Mobile
Robot Based On Probabilistic Roadmap Method
Zhiye Li
Xiong Chen
Department of Electronic Engineering,
Fudan University, Shanghai, PRC
celletor@hotmail.com, chenxiong@fudan.edu.cn
each approach has its own flaws, therefore new and fastermethods are still needed in this case. In [4], Claudio raised amethod about using improved PRM to resolve the on line
navigation problem, which brought a new way to develop PRM
in the path planning field. Although the paths that generated by
Claudio’s algorithm is not too effective when the environment
gets complex, it could help the robot to succeed in many cases.
The new method that presented in this paper is also based on
PRM to solve this kind of problem, but the difference between it
and the Claudio’s is that our new proposal still uses the
traditional PRM to generate a path in the already known maps,
but not the Lazy-DRM or other improved PRM. And the mostimportant discrimination is we use PRM more than once in thewhole process, that is to say, the randomly distributed nodes thatgenerated by PRM are not uniform during the task.

Abstract
This paper presents a new real-time path planning approach,which is based on the algorithm of probabilistic roadmap, forsenor-based mobile robot in unknown environments. The novelidea is that the algorithm changes the on-line navigation
problem into off-line one, which makes the work simple. Oncethe path planning problem becomes finding a way in alreadyknown environment, we can use traditional PRM to solve it.
Thus, our idea is to guide the robot to make real-time global
route planning each step, during the whole planning process, sothat the knowledge of the environment is updated gradually.
Therefore, the problems of path planning in unknown
environments is turned into the problem of that in alreadyknown environments in each step. The purpose of the new
method is to build a path that not only is relatively the shortest,
but also avoids the local minima and other problems effectively.
Some experimental results are presented in this paper tostrengthen the new proposal.

The rest of this paper is organized as follows. In section II
theories relevant with the new approach are provided. Section III
contains a detailed and complete description of our method. In
section IV several simulations are provided with which the new
approach is evaluated. In the final section we draw theconclusions and the future work are also presented.

Key words Probabilistic roadmap, path planning
I, Introduction
There are two main branches dealing with the path planningproblem for mobile robot. The first one is about looking for apath in a previously known environment map, in which we canuse some fast and effective methods such as the Rapidly
Exploring Random Trees (RRT), the Probabilistic Roadmap(PRM)[1,2,3]and the Artificial Potential Field (APF), etc. The
second type is for sensor mounted mobile robot to generate apath in an unknown environment, and in this case, methods like
Hierarchical Generalized Voronoi Graph (HGVG), BUG and
APF have already been used. The problem we are discussing inthis work belongs to the latter, that is, a robot equipped withsensors is given a task to move from a set beginning point to agoal, in a completely unknown environment.

II, Probabilistic path planning
In this section we briefly introduce the main theory of theProbabilistic Roadmap method. The framework of PRM
planning algorithm consists of two phases: roadmap
construction (learning) and query. In the learning phase, thealgorithm constructs a probabilistic roadmap by generating
random free configurations of the robot and connecting themusing a simple, but very fast motion planer, also known as alocal planner. And then the connected roadmap is stored as agraph whose nodes are the configurations and whose edges are
the paths computed by the local planner. In the query phase, apath will be found from the start and goal configurations to two
nodes of the roadmap. Then the planner searches the graph tofind a sequence of edges connecting those nodes in the roadmap,
and finally a feasible path for the robot is generated byconcatenating the successive segments. If each segment
between its two ending nodes is the shortest one among thesegments connecting the same couple nodes, and if between
any two nodes in the path, no more effective connection could
be found, the path we got is the relatively shortest way from thestart to the goal. The following figure gives a simulation result

To direct the robot to generate an effective path, Lumelsky havedone great job on BUG algorithm, which is simple but practicalin this problem, so is HGVG, raised by Choset and Burdick,however, both of these two may generate redundant paths.
The APF algorithm is also effective in real time avoiding
obstacles and planning paths, but it has to face the local minima
problem, which may cause the oscillation of the path. Because©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

again. In the algorithm we call this map as built_map. The
built_map is a bitmap which is described by a 2 dimension
matrix in the database of the robot and it has the same size withthe physical environment map. During the task, built_map isgenerated and updated gradually, which represents the robot’s
increasing knowledge about the environment. Each time after
the renewal of the built_map, the algorithm replans a new pathwith PRM for the robot to move along. When the robot sees
new obstacles at point A in the environment, the algorithm firstly
calculates the coordinates of A with the knowledge of this path,and then computes the positions of the collision detected.

of probabilistic planning in which a feasible path is foundquickly by PRM.

The real-time PRM can be divided into two steps that aredescribed as follows:
1, The robot arrives at a new point in the environment map;
update the built_map with the feedback knowledge of thesensors; go to step 2;
2, In the built_map, find the shortest path from the current pointof the robot to the goal with the method of PRM; the robot
moves along this path until each of the following things
happens:I, some new obstacles are detected by the sensors, go tostep 1.
II, the robot reaches the target, stop.

Figure 1 An example of planning result with the methodof PRM. In the figure there is a path generated from start
to the goal.
The new theory presented in this work is mainly based on theconventional PRM method, and uses it in the case of on-line
path planning. In the next section, we will describe the new
method in detailed.

III, The Proposed Method
A, The robot
The robot we are using in this paper is car-like mobile robot withsonar sensors all around. All the sensors are regarded as thenearly ideal sensors which can detect well and truly, so that it
can know everything near it within the detection region. The
robot’s task is to go to a set target from a given beginning point
in an unknown environment. In order to simplify the planningproblem, we treat the mobile robot as a represented point in therest of this paper. We also suppose the robot has self-localizationequipment in order that it always knows its own position in thecoordinate space of the environment during its motion. In fact
self-localization may bring some errors on the positions, which
affects the effectiveness of the approach. In this paper we do notconsider the error brought by the self-localization, and it is
supposed that the robot can get its real time position exactly.

To generate the shortest path from the current point of the robot
to the target with PRM, we need to use the following process:
Generate a set number of randomly
distributed points in the built map.

Delete the points that are covered by
the obstacles.

Connect every two points within thesensor’s detection region, and eliminate thesegments that cross the obstacles.

B, The algorithm
Our new proposal is a real-time path planning approach which isbased on the method of probabilistic roadmap, and it helps therobot to generate a new and accessible path in the unknown
environments. To study it more easily, we call the new method
as Real-time PRM. The novel idea of real-time PRM is its
combining the problems of route planning both in previously
known environment and unknown situation. In each step of therobot’s moving, the sensors will collect the new informationfrom the environment, and then an updated map is generated inwhich there are the circumstances already detected by the robot
so far. According to this map, a most effective path from thecurrent configuration to the target could be found with PRM
method. Subsequently, the robot goes along the newly built way
until it meets new situation, and if so, it means this loop ends andanother one just begins, and the renewable map will be updated©ISAROB 2005

If several separated parted are generated, thenbuild more nodes between these parts.

From the figure that composed by the edges andthe nodes, choose the shortest path from the startpoint to the goal.
Figure 2
The pseudo-code of the algorithm is given in table 1

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Figure 5 shows 12 slips of the whole process, each of which is abuilt_map in the current step. In each slip, the random nodes areregenerated, and the shadow represents the obstacles that aredetected by the sensors. More shadow means more informationof the environment is got by the robot and the built_map has
been updated. The path in each slip is different from each otherbecause it is only built by PRM in that situation. The nodes in
the paths that are represented by ”o” means the real track passedby the robot in the previous steps, whereas the nodes still
described by “*” are the nodes of the new generated route. The
path in the last slip is the final path from the start to the goal,
which can be seen clearly in figure 4.

Table 1
1, M ←φ2, P0 ← the start point
3, Pi ← the current point
4, Pg ← the goal
5, i ←1
6, Loop7, If Pi = Pg
8, The robot arrives at the goal, and the path is generated.
9,

Return10,Else11, Obst(i) ← the obstacles information got by the robot at Pi
12, N ← M ∪Obst(i)13, if M!=N
14,

M←N

15,

Path(i) ← the shortest path built by PRM in M, from current point to Target

16,

Pi+1 ← the next point after the current in Path(i)17, else Pi+1 ← the next point after the current in Path(i-1)
18, End19, The robot goes to Pi+1, i ←i +1

The number of the random nodes that the algorithm generated is
directly relevant to the results, but it the number is too big, thenthe consuming time will increase dramatically. Therefore thenumber should be chosen carefully.

Figure 4 The final path.

V, Conclusion and Future work
This paper presents a new motion planning method whichdevelops the trditional PRM on the path planning problems in
unknown environments. The main contribution of this work lies
in it simplifies the on-line planning to many static computing
processes. From the simulation results we can find that the pathbuilt by the real time PRM is effective and in built_map a part ofthe environmental information is stored, which also benifits tomap-building work. However, to further evaluate the robutnessof the new algorithm, more comparison of the distance and thecomputing time and other charaters with other methods arenecessary, which is work we will do in the future. And the errorof the sensors will be considered too in our future work.

IV, Experimental results
In this section the results of some simulations are presented,
which show the robustness of the new algorithm. To make ourpoint clearly, we present a complete detailed process of theplanning. In these simulations, the environment maps we use
are all squares with the size of 50mX50m, and the sensor’s
maximal detection distance is set as 3m. The number of theprobabilistic nodes that generated in each of the robot is set as
200 in experiments. The following experiments are completed
on a PC with Pentium IV 2.4G CPU and the algorithm was
executed in Matlab. The following figure is the environment
map used in the simulation.

Reference[1] L. E. Kavraki, M. N. Kolountzakis, J-C Latombe, “Analysis of
Probabilistic Roadmaps for Path Planning,” IEEE TRANSACTIONS
on ROBOTICS AND AUTOMATION, 1998.
[2] L. E. Kavraki，P. Svestka, J-C Latombe, et al, “Probabilistic
Roadmaps for Path Planning in High-Dimensional Configuration
Spaces,” IEEE TRANSACTIONS on ROBOTICS AND
AUTOMATION, 1996.
[3] N. M. Amato, O. B. Bayazit, L. K. Dale, C. Jones, D. Vallejo,“Choosing Good Distance Metrics and Local Planners for Probabilistic
Roadmap Methods,” IEEE Conf. on Robotics & Automation,1998.
[4] C. Lanzoni, A. Sanchez, R. Zapata, “Sensor-based motion planningfor car-like mobile robots in unknown environments,” IEEE Conf. on
Robotics & Automation,2000.

Figure 3 The environment map for the simulation. In the
figure, the dark areas represent the obstacles.

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b

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Gesture Clustering and Imitative Behavior Generation for Partner Robots
Yusuke Nojima
Naoyuki Kubota
Dept. of Industrial Engineering
Dept. of Mechanical Engineering
Osaka Prefecture University
Tokyo Metropolitan University, PREST
Gakuen-cho 1-1, Sakai, Osaka 599-8531 Minami-Osawa 1-1, Hachioji, Tokyo 192-0397
Fumio Kojima
Dept. of Mechanical & Systems Engineering
Kobe University
Rokkodai 1-1, Nada-ku, Kobe, Hyogo 657-8501
Abstract

the robot, we should consider the entire structure ofintelligence for processing information flow over the
hardware and software of the robot, not a single intelligent capability. Accordingly, we have proposed aconcept of structured learning which emphasizes theimportance of interactive learning of several learning
modules through the interaction with its environment.
We apply this concept to an imitative behavior learning for generating and accumulating behaviors of thepartner robot. For human being, imitation is a powerful tool for gestural interaction with children andfor teaching behaviors to children by adults. Partner
robots should also obtain various behaviors by using
an interactive learning based on the imitation. As thehuman imitation, there is an advantage that the imitative behavior learning performs without exact human
instructions.

This paper proposes a method for generating behaviors based on imitation of a partner robot interacting with a human. This method is constructedby a two-step procedure. At first, a gesture clustering is performed to classify a human hand motion.
An evolutionary computation is applied for a patternmatching, and then a spiking neural network and aself-organizing map classify the motion as a gesture.
Second, the robot take an imitative behavior which
follows human hand motion generated by an evolutionary computation using the previous trajectory information identified by the clustered gesture. The goalof this study, by using these learning modules structurally, the robot and its user share various gestures
for user friendly communication. Several experimental
results show the effect of the proposed method.

1

In this paper, we propose a method that the robot
generates behaviors by imitating the motions of thehuman hand. Figure 1 shows the total architectureof the proposed method. First of all, the robot detects a series of human hand positions by processing
images from CCD camera on the robot. We employ asteady-state genetic algorithm (SSGA) to this image
processing in order to cope with environmental noise.
We call this SSGA SSGA-1 in this paper. Next, themotion is recognized as a gesture by using a spiking
neural network (SNN), since SNN can learn the spatial
and temporal pattern from a series of the human hand
positions. Thereafter, a self-organizing map (SOM)
is applied for clustering the motion to reuse previous behaviors as the initial trajectories of a SSGA for
generating an imitative behavior. We call this SSGA
SSGA-2 in this paper. After generating the behavior, the robot accumulates the behavior by associating

Introduction

Recently, various partner robots have been developed by many companies and academic groups. Mostof these robots can execute a number of given motions to communicate with its user. However theirmotions are designed based on the expert knowledgeof the designer who is not an actual user. Thus, totake suitable actions for the user, the robot should
acquire own behaviors through the interaction withthe user. Moreover, the robot should accumulate obtained behaviors to reuse them at same situation. To
realize the above functions, the robot needs a numberof learning modules. In general, since the mechanismof the robot becomes complicated and large increasingly as intelligent capabilities are added gradually to©ISAROB 2005

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p(t)

G(t)
Images

SSGA-1SNN(0, Y)

θ init.

SOM

SSGA-2(Database)

θ*

Figure 1: Total architecture for generating and accumulating behaviors.

position of neuron(0, 0)(0, Y)(X, Y)(X, 0)

Figure 3: Spiking neurons arranged on the image.

Image(X, Y)

is relatively changed according to the fitness values ofthe population. A fitness value is calculated by thefollowing equation,(0, 0)(X, 0)

fi = CT arget − ηCOther

where η is a coefficient for penalty; CT arget and COther
indicate the number of pixels of the color corresponding to a target and other colors, respectively. This
problem results in the maximization problem. The
robot extracts motion of the human hand from images by using SSGA-1 where the maximum number ofimages is T . The sequence of the hand positions is represented by G(t) = (Gx (t), Gy (t)) where t = 1, 2, ..., T .

Figure 2: A coordinate system for image processing ofa robot.
with the output of SOM. In this way, each learning
module (i.e., two SSGAs, SNN, and SOM) relates with
one another to achieve the generation and accumulation of imitative behaviors. Experimental results showthat the robot can generate imitative behaviors fasterby accumulating obtained behaviors.

2
2.1

2.2

Gesture Recognition and Clustering

We apply a SNN ([1]) for memorizing several motion patterns of a human hand. SNN is often calleda pulse neural network and considered as one of theartificial NNs based on the dynamics introduced theignition phenomenon of a cell, and the propagation
mechanism of the pulse between cells.
In this paper, spiking neurons are arranged on aplanar grid (Fig.3) and N = 25. By using the value ofa human hand position, the input to the ith neuron is
calculated by the radial basis. The weight parameters
are trained based on the Hebbian learning algorithm.
Because the adjacent neurons along the trajectory of
the human hand position are easily fired by the Hebbian learning, the SNN can memorize the temporally
firing patterns of various gestures.
The temporally firing pattern of SNN is used as an
input for the clustering by SOM in order to detect aspatial pattern of a human gesture. SOMs is often applied for extracting a relationship among inputs data,
since SOMs can learn the hidden topological structurefrom the learning data. The inputs to our SOM is thesum of pulse outputs from SNN. The output node is selected by comparing the Euclidean distance with eachreference vector. Moreover, the reference vectors are
updated for classifying several gestures. That is, theImitation for Partner Robots
Human Hand Detection

The robot takes an image from the CCD camera,and extracts a human hand (Fig.2). The human wears
a blue glove for performing a gesture. The taken image
from CCD camera is transformed into the HSV color
space, and the color corresponding to the blue globe
is extracted. Next, the blue globe is detected by using
SSGA-1 based on template matching. We employ flexible templates to the candidate solutions in SSGA-1,
since the robot must identify several hand shapes.
In SSGA-1, only a few existing solutions are replaced by new candidate solutions generated by genetic operators in each generation. The worst candidate solution is eliminated (delete least fitness selection) and replaced with an offspring solution generated by the crossover and mutation. We use elitist crossover and adaptive mutation. Elitist crossoverrandomly selects one solution and generates an individual by incorporating genetic information from theselected solution and best solution. In the adaptive
mutation, the variance of the normal random number©ISAROB 2005

(1)

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Table 1: Parameters used in SSGA-1 and SSGA-2.
θi,n,1IC2 θi,1,2 θi,2,2...

θi,n,2...

θi,n,k...

θi,n,mICk θi,1,k θi,2,k...

θi,n,k

...

IC1
ICk

IC2

Phenotype

Parameter
Chromosome length
Population size (gn)
Number of evaluations
Crossover rate
Mutation rate

...

...
...

IC1 θi,1,1 θi,2,1...

θi,2,k...

θi,1,k

ICT θi,1,m θi,2,m

Genotype

Figure 4: The representation of the ith trajectory candidate composed of m intermediate configurations.

fi = fp + µfd

A trajectory planning problem for a behavior can
result in a path planning problem from an initial human hand position to a final human hand position.
Here a configuration q is expressed by a set of joint
angles, because all joints are revolute,(2)

3

where n denotes the degree of freedom (DOF) of arobot arm. Because a trajectory can be represented
by a series of T intermediate configurations (i.e., ICk
in Fig. 4), the trajectory planning problem is to generate a trajectory combining several intermediate configurations corresponding to G(t) = (Gx (t), Gy (t)),
t = 1, 2, ..., T . SSGA-2 is applied to generate a trajectory for an imitative behavior corresponding to ahuman hand motion.
A trajectory candidate is composed of all joint variables of intermediate configurations (Fig.4). Initialization generates an initial population based on the previous best trajectory stored in the knowledge databaselinked with SOM. The jth joint angle of the kth intermediate configuration in the ith trajectory candidate
qi,j,k , which is represented as a real number, is generated as follows (i = 1, 2, ..., gn),∗θi,j,k ← θj,k
+ βjI · N (0, 1)

Experiments

This section shows an experimental result using ahumanoid-type robot. The size (X, Y ) of an image is(160, 120). Here a trial is defined as one cycle fromhuman hand detection by SSGA-1, spatial and temporal pattern generation by SNN, gesture clusteringby SOM, and behavior generation by SSGA-2 (Fig.
1). Table 1 show the parameters used in SSGA-1 and
SSGA-2. T is the maximum number of human hand
positions detected by SSGA-1. According to T , thenumber of intermediate configurations is changed inthis experiment.
Figure 5 shows the history of the selected node in
SOM and the history of the gesture pattern displayed
to the robot. First of all, the human tried to show various patterns to the robot, because the human wanted
to know the reaction of the robot. At the time, various nodes in SOM were selected. After 11 trials, thehuman tried to teach the hand motions like clockwise
and counterclockwise circles. The robot classifies the
motions by using the 12th and 18th nodes mainly. And
then, the human tried to show various motion again,
in order to search for better motions that the robot
can follow them well. Based on this searching process, the human tried to teach the hand motions like a(3)∗where θj,kis the previous best trajectory referred fromthe knowledge database; βjI is a coefficient for the jthjoint angle; N (0, 1) is a Gaussian random variable withmean 0 and standard deviation 1. A fitness value isassigned to each trajectory candidate. The objective
is to generate a trajectory realizing the possibly short©ISAROB 2005

(4)

where µ is a weight coefficient. The first term, fp ,denotes the distance between the human hand position
and the position of robot end-effector. The second
term, fd , denotes the sum of squares of the difference
between each joint angle between two configurationsof t and t − 1. Therefore, this trajectory planningproblem can result in a minimization problem. After
SSGA-2, the best trajectory obtained is stored in theknowledge database.

Trajectory Generation based on Imitationq = (θ1 , θ2 , · · ·, θn ) ∈ Rn

SSGA-2
4T
200
1000T
0.2
0.2

distance from the initial configuration to the final configuration while realizing good evaluation. To achieve
the objectives, we use a following fitness function,

selected output unit means the nearest gesture among
the previously learned gestures.

2.3

SSGA-1
10
120
300
0.2
1.0

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Selected node No.
20
18
16
14
12
10
8
6
4
2

20

Gesture patternCircleRandom:30

Trials

50

40

SquareRandom::Random
:

(a)

(d)

(c)

(b)

Figure 7: The snapshots at the 51st trial.

square from 44th to 51st trial. The robot classifies the
motions by using 2nd, 14th, and 18th nodes mainly.
In this way, SOM classifies the human hand motions
as specific gestures.
Figure 6 shows the result of image processing at the
51st trial. Figures 6 (a), (b), and (c) show the human
hand motion, the detected result of the human hand
positions, and the outputs of the SNN, respectively.
The light and shade of the boxels indicate the time
sequence where the lighter boxel indicates the later
hand position. Although the detected hand positions
were separate, SNN interpolated the detected hand
positions and made a sequence as a result of temporal firing. Figure 7 shows the Robot could generate amotion following the human hand motion. That is, itshows that the robot recognized the human hand motion by SNN and SOM, and reproduced the behavior
based on imitative behavior generation.
Figure 8 shows the change in fitness value on 2nd,
14th, and 18th nodes. In each node, the fitness in
SSGA-2 decreased trial by trial. This shows the effectiveness of the SOM clustering and the database.

2nd node's fitness

14th node's fitness

18th node's fitness

10
8
6
4
2
0

44th trial 46th trial 48th trial 50th trial

45th trial

47th trial

49th trial

51st trial

Figure 8: Change in fitness value on 2nd, 14th, and18th nodes. A scale interval of the horizontal axis is1000 iterations.

References[1] W.Maass, C.M.Bishop, Pulsed Neural Networks,
MIT Press, 1999.
[2] J.S.R.Jang, C.T.Sun, and E.Mizutani, NeuroFuzzy and Soft Computing, Prentice-Hall, Inc.,
1997.
[3] G.Syswerda, ”A Study of Reproduction in Generational and Steady-State Genetic Algorithms,”
Foundations of Genetic Algorithms, San Mateo:
Morgan Kaufmann Publishers, Inc., 1991.

Summary[4] T.Kohonen, Self-Organization and Associative
Memory, Springer, 1984.

This paper proposed a method for imitative behavior generation of a partner robot. We applied SNN for
extracting spatial and temporal patterns of gestures,
SOM for clustering gestures, and SSGA for generating trajectory to perform a behavior following to thehuman hand motion. Experimental results show thatthe robot learns various motion patterns by imitating
human hand motions. Furthermore the robot could
reuse obtained trajectories for generating better imitative behaviors at the same situations.

©ISAROB 2005(c) SNN Output

Figure 6: The results of image processing at the 51st
trial.

60

Figure 5: Change of selected nodes in SOM and humanmotions.

4(b) Detected human
hand position(a) Human motion

10

0

2

1

[5] A.Billard, ”Imitation,” Handbook of Brain Theory and Neural Networks, MIT Press, pp.566-569,
2002.
[6] N.Kubota, ”Intelligent Structured Learning for a
Robot Based on Perceiving-Acting Cycle,” Proc.
of The Twelfth Yale Workshop on Adaptive andLearning Systems, pp.199-206, 2003.

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How to make a mobile robot move more reliably?
Jiwu Wang Masanori Sugisaka
Department of Electrical and Electronics Engineering, Oita University, Oita 870-1192, Japan
Email: wangjiwu@cc.oita-u.ac.jp, msugi@cc.oita-u.ac.jp
navigation with the errors of sensors and slip at high speed.
And the unstructured environment is the structured
environment that is changed by some unexpected
requirements. Such environment is similar to some
practical environments.
In order to solve above problems efficiently, the visionsubsystem, voice subsystem, touch screen control
subsystem and tele-control subsystem by Internet weredeveloped. Those subsystems shared necessary
information from sensors by TCP/IP protocol. For mostcases, only one of the subsystems is used. But it will berobust when it works with all the subsystems in somecomplex environments. All subsystem mainly use the data
extracted from the captured images by the CCD video
camera to control the driven subsystem. Although the
CCD video camera may provide more information thanother sensors, we may not develop any corresponding
algorithms for any specified applications. That means therobot can not automatically make full use of theinformation from the CCD video camera. On the other
hand, the supervisor may not always follow the robot even
if the robot is working. Thus we developed the voicesubsystem when the supervisor is not far from the robot,
the tele-control subsystem by Internet when the supervisor
is far from the robot and the touch screen subsystem when
the supervisor is near to the robot. Especially in the telecontrol subsystem, the images taken by that CCD video
camera will also send to the supervisor’s computer at thesame time. The supervisor can send the corresponding
commands directly to that robot based on some practicalrequirements. When that robot is confused in a strange
environment, with the help of the supervisor by the telecontrol through Internet, that robot will complete specified
tasks.
On the other hand, making use of as many sensors as
possible is another solution to increase the robustness andflexibility of a robot. But it also brings new problems: 1)
the cost of sensors; 2) develop algorithms to make gooduse of the data received from sensors; 3) how to efficiently
compound those data received from different sensors etc.
At the same time, we have to admit the robot will not havethe similar intelligence like a human being till now. The
robot can not make good use of all data from all sensors. It
is not difficult for us to control a robot to work in a
structured environment. But we can not make a robot work

Abstract When we explore the navigation of a mobile
robot, we have to consider how to get necessary data fromsensors and how to make good use of those data. Based onpresent technology, sometimes we can not automaticallyextract data used for reliable navigation in any
environments from those sensors with present algorithms.
Even in the structured environments, because of the errorsof sensors, the extracted data are not always reliable. Inview of above problems, we developed the tele-control
subsystem through Internet besides the general control
strategy. With the tele-control, the data gotten fromsensors, will be sent to the supervisor’s computer directlyin addition to the robot. And the supervisor can also send
the control commands to the robot directly based onhuman’s experience. By this way, the robot may becontrolled even in a strange environment.
Key Words Mobile robot, Navigation, Tele-control

1. Introduction
In this paper, we mainly consider how to make amobile robot move more freely and reliably in structured
and unstructured environments. Here the structured
environment is the ideal environment with fixed settings.
Although the states of some objects in that environment
can be changed, but those change do not influence theworking state of that robot. On the contrary, it is the
unstructured environments. That is, the unstructured
environments are more complex. And the practicalenvironment is one of the unstructured environments.
Because the change of the environment should beconsidered when the robot is moving, the control of thatrobot should be more flexible and reliable.
Navigating freely in the specified environment is thebasic and necessary function for a mobile robot. Then the
robot can carry out more specified tasks. Here at least two
main problems will be considered for the solutions. One isthe environment may not always be ideal structured
environment. Another is the self-location in the navigation.
The ability to solve above problems will determine whatkind of jobs the robot can do. Although they seem to be
separate problems, they are the necessary components of
the navigation subsystem being developed. Here the selflocation is mainly explored for the long distance©ISAROB 2005

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in any environments automatically and freely. That is themain purpose that we developed the tele-control throughthe Internet.
The robot developed in our laboratory is one with theapproximate height of 1.7m. All control information isbased on the data extract from the CCD video camera. The
head is designed with one degree of freedom, which canrotate 360o. The camera has two degree of freedoms,
which can tilt up and down, left and right. Thus the robot
can searching specified objects. Just like the common
mobile robots, it can move forward and backward forspecified distance, and it can rotate for specified angles.
Some landmarks, such as triangle and circle, can berecognized. It can also move at a high speed along the
double guideline. Moreover, it can perform simple
obstacle avoidance. However, with such functions, it can
only move freely at structured laboratory. If moving in the
more complex environments, the robot will be helped by
the voice subsystem or tele-control subsystem base onpractical requirements. The corresponding experiments
have been done in our laboratory.

receive Japanese and English commands and translatethem to the corresponding signals used for the robot. At
the same time, any explanation text will be spoken by thissystem. The behavior system is responsible for all basic
actions of the robot. It can receive specified signals fromother subsystems to perform required actions. If necessary,
the signals can also be sent to the supervisor’s computer
by Internet for the tele-control. Based on such system, therobot can satisfy most requirements.

3. Experiments
The experiments on the voice subsystem and image
processing subsystem can be found in the reference[1,2].
Here we mainly explain the problems about the telecontrol through the Internet. In order to make the telecontrol reliable and robust, the communication subsystem
must be reliable, and the necessary data should be sent tothe supervisor in time. Because the images taken by the
CCD video camera will be sent to the supervisor, thevolume of the image data and the frequency of thetransformation should be controlled in order to make thetele-control system work for all time. On the other hand,the commands sent through Internet should be as simple
and short as possible. Most of important, the connection
priority between different subsystems should be parallel,
and in each system the connection is point to point. Thus
the problem produced by one subsystem, will not
influence the work of other subsystems. An simple
introduction of our system can be seen in figure 4.

2. The realization of the navigation system
The Alife robot prototype, as shown in Figure 1, is acustom built mobile robot which has four wheels: two
independently driven wheels located along the central axis,
and two auxiliary castor wheels in its front and back.
There are six ultra sonic sensors-switches for obstacle
detection within a preset range. The robot has two color
Charge Coupled Device (CCD) cameras mounted on its
head. Each camera is capable of independent pan and tilt
movement. The head can be rotated left-right using a
stepping motor. Speaker and a microphone are mounted in
the body and on the head respectively. A mobile phone
may be used instead of the speaker/microphone.
To control the robot, two Personal Computers (PCs)have been built into the body. The PCs are connected tothe hardware via controller cards and the serial ports. The
PCs also have Ethernet cards, so that network message
may be used to carry information (about items seen orheard), from one PC (which has access to the frame
grabber/sound card) to another PC (responsible forcontrolling hardware). If necessary, those information may
also be sent to the supervisor’s computer.
The integration of the system is shown in figure 2. The
figure 3 is the illustration of the implementation process.
All of the subsystems were developed separately. It isconvenient to later increase any specified based onrequirements. In the vision subsystem, the image taken by
the CCD video camera, will be processed to extract
necessary data for recognition and some signals fornavigation. Till now, the sign landmarks, such as circle
and triangle etc, and the continuous landmark, the double
guideline, have been developed. The voice subsystem can©ISAROB 2005

4. Conclusions
In order to realize reliable navigation, the robust system,
the reliable data extracted from the signals of sensors andthe reliable feedback information are necessary. In oursystem, we make good use of the volume of signals fromthe CCD video camera. Because we can not deal with allof that information with present algorithms, the telecontrol subsystem was developed to realize reliablenavigation in the strange environment.

Reference[1][2]

J. Wang, M. Sugisaka, Study on a color based target
detection technique”, Proceedings of 7-th international
symposium in artificial life and robotics (AROB 7-th’02),
B-Con Plaza, Beppu, Oita, Japan, Jan. 16-18 : 532-536
J. Wang, M. Sugisaka, Study on visual-based indoor
navigation for an alife mobile robot，ICSEng2002, Las
Vegas, USA, 2002, August 6-8 : 390-396.

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Figure 1. The structure illustration of our mobile robot

Figure 2 Illustration of the processing of input and output data

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Figure 3 Illustration of the implementation of all subsystems

Figure 4 Illustration of the tele-control through Internet©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Automated Moving Objects Detection
with an On-Board Camera for Avoidance of Car Accident
Shohei Sunahara, Yusei Tsuboi, and Osamu Ono
Control Systems Laboratory,
Department of Electric and Electronic Engineering,
Meiji University
1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa, JAPAN(ce33031, tsuboi, ono)@isc.meiji.ac.jp
changes,

Abstractandarolling

accident

withatwo-wheeled vehicle at left turn. and driver is
In this paper, we propose a system thatwarned. In this paper, we propose a system that

detects moving object detection with a CCD

detects moving object detection with a CCD

camera which captures visual information aroundcamera which captures visual information arounda car. The system warns a driver when othera car. A CCD camera is attached on the door

vehicles come near to his car, which helps him

mirror of the car. The system detects moving

with avoidance of the car accidents. HSV color

objects (car and two wheeled vehicle thatspace method is employed for the optical flow inapproaches the car ) from the camera, and warns a

the system. In addition, it is possible to finddriver when they come close to his car. It helps

direction of moving objects because the systemhim with avoidance of the car accidents. For

will be installed on a side door, toward the backconstructing the system, we improve the opticalof the car. The simulation result indicated someflow method by using HSV color space technique.

remarks; forty percent of a whole image was cut
off by using the HSV color space method; the2

Methodology

search time of optical flow was able to be
shortened by 50% on the average. As a result, thisIn the proposed system, we use an image

system could shorten the entire processing time

obtained from the camera on a door mirror. The

by 60% compared with the case when only therange of the image obtained from the camera on

optical flow technique is used.

the door mirror that can be recognized is wider
than the door mirror. So, some moving objects

1

within the range that was not able to be

Introduction

recognized only by seeing the door mirror can be
Human error occupies a lot of ratios of therecognized. If a wide angle camera is used, morecause of traffic accident. This research is support

wide-ranging recognition for the moving objectsof safe driving, which is one of nine development

can be done. Moreover, it has the feature thatfields of Intelligent Transport Systems (ITS).

safety is given to the driver only the camera

Especially, it aims at the avoidance of the contact

image to be displayed in the monitor.
In this research, it was simulated on PC. The

accident with a vehicle in the next lane at course©ISAROB 2005

599

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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image obtained from the camera on the door

shortening the processing time is examined in thismirror is taken into PC by the AVI form, it had

research.

divided into the BMP image of full-color, and the

2.2 HSV color space method

image was processed on PC. The size of the
To solve the problem of the optical flow

image is 320×240, and the 10 frame at a second.

method, we employ the HSV color space method.
To decrease the computational complexity that isthe problem of the optical flow method, theprocessing area in the image has been reduced. As
the method, the image with RGB color space first
input is converted into the image with the HSVcolor space. And, the saturation(S) of each pixel ismeasured, and the pixel below the threshold has
been reduced. It succeeded in the deletion of thearea where the road and the sky so on saturation
were low: it succeeded in the deletion of an
unnecessary area from 40 to 60% by thisprocessing.

Fig. 1. Image from the camera (upper) and
Reflection in a door mirror (below)

2.1 Problem of Optical flow method
When the camera is moving, the backgrounddifference method used well for the moving
object detection cannot detect the moving body,
becausethegeostationary

thingsofthebackground are recognized the moving body.
Therefore, we use optical flow method that
detects the movement of the objects and makes
the vector. However, long processing time isFig. 2. Original Image (upper) and Conversion

generally necessary for the detection of the rate

Image by HSV color space method (below)

vector of the optical flow method. Then,©ISAROB 2005

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The following equation is a conversion

place where the template moved in the matchedequation from RGB color space to HSV color

place. And, the vector is made for the place thatspace.

moved from former place of the template. It is

H = tan[r=

R
R+G+ B(1)

understood beforehand that the background flowsg=

G
R+G+ B(2)

moves forward. Then, only the area for lowerb=

B
R+G+ B(3)

a noteworthy point, a useless calculation is(4)

be shortened. The search time of optical flow was−1[(g − b ) / (2r − g − b )]to the back of the screen, and the moving objects
right one matches the correspondence point fromshortened, and the omission processing time can]able to be shortened by 50% on the average.

S = (b − r ) + (r − g ) + ( g − b ) / 3 (5)222

V = (r + g + b )(6)

Fig. 3. Illustration of Pattern matching

Fig. 4. Original Image (upper) and
Output Image (below)

2.3 Improvement of optical flow method

3

Simulation result

The pattern matching method is used as amethod of requesting optical flow. The template is

The technique that has been employed ismade in the first frame, and it searches for theapplied, and the effectiveness is examined. The

place matched from the 2nd frame. It makes thetargeted image is an image that a two-wheeled©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

vehicle approached the car from rear side. The

time of detection of moving objects. However, thethree moving images recorded under eachsimulation on a normal computer did not realize

individual place were tested. These moving

real-time process. We believe it is able to improveimages have 10 frames per a second. Method of

the proposed system in term of computational

detecting moving objects is that the point with atime. As for that, in future, the computational

lot of vectors in the same direction was made aalgorithm of the proposed system will bemoving object. As a result, this system could

implemented on DSP.

shorten the entire processing time by 60%
compared with the case when only the optical

References

flow technique is used. However, it is not possible
to process in real time. Moreover, a lot of noises
occur, and it causes the miss-detection. It is[1] Cucchiara R, et al (2001), Improving shadow

necessary to improve further algorithm to solve

suppression in moving object detection with HSV

this problem. Moreover, we hope further speed up

color information. IEEE Intelligent Transportation

by installing hardware such as DSP.

System Conference Proceedings: 334-339[2] Prati A, et al (2000), Shadow detection
algorithms for traffic flow traffic flow analysis: aComparative

Study.

2001

IEEE

Intelligent

Transportation Systems Conference Proceedings:
340-345[4] Honda T, Development of Techniques for
Vehicle Maneuvers Recognition with Sequential
Images from High Altitude Platforms[3] Ootani S (2003), Long-range Recognition
Method for Moving Objects by Means of Variable
Threshold Values on Wide-angle Image Domain.
Proceedings

of

the

22

Japan

Simulation

Conference: 295-298[4] Kouzai A (2001), A Detection of Moving
Objects by using Image Processing. The Instituteof Electrical Engineers of Japan, Electronics,
Information and Systems Society

Fig.5. Comparison of Processing Time

[5] Agui T, Nagano T (2000), Introduction to
Image Processing using programming language C,

4

Shoko-Do

SummaryIn

thispaper,

wehavediscussedcharacteristics of the system with the HSV
method and the improved optical flow. As a result
the system considerably reduced computational©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Improving Odometry Accuracy for a Car Using Tire Radii Measurements
Hee Chang Lee

Chang Sup Kim

Keum-Shik Hong, Man Hyung Lee

Department of Interdisciplinary
Program in Mechatronics
Pusan National University
Busan, 609-735, KOREA

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

School of MechanicalEngineering,
Pusan National University,
Busan, 609-735, KOREA

errors accumulated with the traveled distance, andgrow without bound. Many cases of relative
positioning methods use inertial navigation withaccelerometers and gyros. Accelerometer data must
be integrated twice to yield position thereby making
these sensors exceedingly sensitive to drift. Gyros
provide information only on the rate of rotation ofvehicle so their data must be integrated once toprovide the heading. Besides the deterministic errors
contained in accelerometers and gyros measurements,
they have also stochastic errors which call for theuse of estimation and optimal filtering to correct
them. It is common to combine relative positioning
with other absolute positioning methods[2][3][4].
Odometry errors are caused by two error sourcesin vehicles: Systematic errors and nonsystematic
errors[1].
Systematic
errors(uncertaintyofwheelbase, unequal wheel diameters, etc.) stayalmost constant over prolonged periods of time andcan be calibrated. In most case of mobile robot,
unequal wheel diameters are systematic errors. But,
they are not systematic errors in case of flexible
tires like passenger car. Radii of flexible tires arealways varied by road conditions, rolling of vehicle
and etc. So, it is important to measure thevariations of tire radii for accurate positioning inodometry navigation of car-like vehicles.
Although odometry has several disadvantages, it isimportant positioning method. Improved odometry
can reduce the cost for installations of vehicle
systems because it simplifies the fundamental
problem of position determination, and theimprovement in accuracy of odometry could makehigh positioning accuracy and robustness by fusing
other absolute positioning methods. In modern cars,
breaking system is assisted with ABS systems thatutilize angular encoders attached to the wheels. In
this case, the sensors basically measure the wheel
speeds and this measure can be use to estimate
travel distances. So, extra encoders to measurewheel rotations are not needed.
This paper reduces such odometry problems withcalibration of systematic errors and tire radiimeasurement. Experiments show the efficiency of
consideration of tire radii variation.

Abstract
Odometry is the most widely used method for
determining the momentary position of a mobile
robot. In most practical applications, odometry
provides easily accessible real time positioning
information in-between periodic absolute position
measurements. Odometry errors are caused by two
dominant error sources in vehicles: Systematic errors
and nonsystematic errors. Systematic errors stayalmost constant over prolonged periods of time andcan be calibrated. In most case of mobile robot,
unequal wheel diameters are systematic errors. But,
they are not systematic errors in case of flexible
tires like passenger car case. Radii of flexible tires
are always varied by road conditions, rolling ofvehicle and etc. So, it is important to measure thevariations of tire radii for accurate positioning inodometry navigation of car-like vehicles. The
method for measurement of tire radii is describedand experimental results are presented in this paper.

1 Introduction
In most positioning system, relative and absolute
positioning methods are employed together. Absolute
positioning methods usually rely on satellite-basednavigation signals (GPS), landmarks or beacons, andmap matching. GPS can be used only outdoors andit has poor resolution in a local range (Its errors
are about 10m[2]). With a radio station as acompensative reference, differential GPS (DGPS)
method has been developed to reduce the errors.
GPS suffers from satellite mask occurring in urban
environments, under bridges, tunnels or in forests.
Moreover, radio frequency-based systems are very
expensive. Landmarks or beacons usually require
costly installations and maintenance. Map matching
methods provide the position and pose of thevehicle. If there were several areas with similarfeature, the method would obtain mistake result. In
general, absolute positioning methods have the errorsthat do not accumulate with the movement of thevehicle. Dead reckoning is the representative ofrelative positioning methods. It has the advantage ofcheapness, simplicity, good performance in shortterm and working in real-time. But its positioning©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

wheelbase, the covered distances of the right andleft rear tires respectively, and we assume that,between two samples, the wheels do not slip andthat the distance T is known and constant. Then

2 Odometry Model for a Car
Consider a car-like vehicle. The mobile frame ischosen with its origin P attached to the center ofthe rear axle. The x -axis is aligned with thelongitudinal axis of the car. At time t k , the vehicle
position is represented by the ( x k , y k ) Cartesian
coordinates of P in a world frame. The heading
angle is denoted θ k .

∆PRL = ( R − T / 2)∆θ∆P = R ⋅ ∆θ

x k +1I∆P = (∆PRR + ∆PRL ) / 2∆θ = (∆PRR − ∆PRL ) / T∆θ / 2

∆θ

xk

R

θk
Pk

∆P
∆x

O

traveled distance of rear tires. Let RRR and RRL beright and left radius of rear tire of a car
respectively, ∆PRR and ∆PRL can be expressed as:x0

Fig.1 Elementary displacement between two samples
Pk +1 be two successive positions.
Let Pk andSupposing the road is perfectly planar andhorizontal, as the motion is locally circular. (Fig.1)∆P = R ⋅ ∆θ(1)3

∆x =|Pk Pk +1 |⋅ cos(θ k + ∆θ k / 2)

(6)

C = 2π / N E(7)Tire Radius Measurement Sensor

Tires are attached to axle and they haverotational mechanism, so it is more difficult problem
to measure their radius. We suppose that distance
between center of tire rotation and the groundwhich is contacted with tire is approximately samewith actual tire radius. From this assumption, theSharp GP2D12 is used in this paper. It is a shortrange infrared (IR) proximity sensor. Its output
voltage is proportional to the distance between it
and an object directly in front of it. It works wellin a variety of lighting conditions.

(2)

In general, the sampling rate of state is verysmall compared to their rate of change, so we canapproximate ∆P ≈|Pk Pk +1 |. The integration process isthen:

x k +1 = x k + ∆P ⋅ cos(θ k + ∆θ / 2)
y k +1 = y k + ∆P ⋅ sin(θ k + ∆θ / 2)

θ k +1 = θ k + ∆θ

∆PRR / RL = C ⋅ RRR / RL ⋅ N RR / RL

where C RR / RL are conversion factor that translate
encoder pulses into linear displacementof right
and left rear tires. N E , N RR and N RL denote encoder
resolution, right and left incremental pulses of reartires respectively.

where ∆P is the length of the circular arc
followed by P,θ , R (the radius of curvature), I (theinstantaneous center of rotation)
Supposing the car is moving forward, thevariation on the position is expressed as:∆y =|Pk Pk +1 |⋅ sin(θ k + ∆θ k / 2)

(5)

Equation (5) shows that computation of odometry
use left and right traveled distance of tires andwheelbase. That is, ∆P is the average of the rightand left traveled distance of rear tires and ∆θ isproportional to the difference of the right and left

Pk +1

∆y

3.1 GP2D12(3)
GP2D12 use triangulation and a small linear CCD
array to compute the distance and/or presence ofobjects in the field of view. The angles vary basedon the distance to the object. This method of
ranging is almost immune to interference fromambient light and offers amazing indifference to thecolor of object being detected. Detecting a black
wall in full sunlight is possible. Characteristics of

In Fig.1, the distance traveled, ∆P , and theangle changed, ∆θ , resulting from the movement
Pk +1 form Pk can be calculated in terms of theincremental changes of the odometric measurementsof the right and left wheel motions.
∆PRL denote theLet us T, ∆PRR , and©ISAROB 2005

(4)

Thus, we haveyky0

∆PRR = ( R + T / 2)∆θ

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the GP2D12 are listed below:- Output Type: Analog value (0V to ~3V) based on distancemeasured- Range: 10cm - 80cm
- Enable Method : Continuous readings ~38ms per reading

Analog output voltage Vo [volt]

3.0
2.8
2.4

Draft

Reflectivity

White

90%

Gray

18%

2.0
1.6

Fig.3 Implementation of wheel encoder and infrared
range finder sensor

1.2

Gray
0.8

4 Calibration of Systematic Error

0.4

White

Equation (5) and (6) show that computation ofodometry use wheelbase, tire radii and counted
pulse of each wheels. In this section, installation
uncertainties of infrared range finders and effective
wheelbase are considered to calibrate systematic
errors.
In case of linear translation, odometry use only
information of tires. Therefore, linear translation of
specified distance can provide offset of infrared
range finder from wheel center.

0
0

10

20

30

40

50

60

70

80

Distance to reflective object I [cm]Fig.2

Analog output voltage vs.
reflective object (GP2D12)

distance

3.2

Non-linear Outputs and data fittingtoBecause of some basic trigonometry within thetriangle from the emitter to reflection spot toreceiver, the output of these detectors is non-linearwith respect to the distance being measured. The
Fig.2 shows typical output from these detectors. The
output of the detectors within the stated range (10cm - 80 cm) is not linear but rather somewhat
logarithmic.
This curve will vary slightly fromdetector to detector so it is a good idea to fit thesensor outputs. In this way, we calibrate eachdetector and end up with polynomial data that is
consistent from detector to detector. Moreover,amplifiers used because of small variation of sensor
outputs near nominal tire radius (about 32 cm).

D = ∑ ∆PRR / RL = ∑ C ⋅ RRR / RL ⋅ N RR / RL

where D is specified distance of linear translation
for a test car .
If we suppose slow translation,
constants.

RRR / RL are

D = ( RRR.OFF / RL.OFF + average( RRR.S / RL.S )) ⋅ C ∑ N RR / RL

RRR.OFF / RL.OFF =3.3 Sensor implementationD− average( RRR.S / RL.S )C ∑ N RR / RL
(9)where RRR.OFF / RL.OFF and RRR.S / RL.S are offset ofInfrared range finder from wheel center andmeasurements of tire radii respectively.
We drive the test car along a 10m straight lanefor 5 times. From equation (9), the average of
RRR.OFF / RL.OFF are calculated. If offsets of range

Getting the best results of tire radius measurementwith the GP2D12 will require some adjustment. It
works best to mount the sensor vertically about 4cm
offset from the plane of the tire. Although thesensor has a very narrow beam-width, if the sensor
is mounted too close to tire surface it may detecttire itself. Fig.3 shows an implementation of wheel
encoder and infrared range finder sensor (GP2D12).
Wheel encoder is attached to the center of each tire
rotation and a rod is used to prevent case forencoder from rotation. Vertical movement of the rod
is supported for suspension system of a car.
Infrared range finder is attached to encoder caseand it detects the distance between rotation centerof a wheel and the ground near a point of the tire
contact.

©ISAROB 2005

(8)

finder sensors are calculated, effective wheelbase T
can be calculated form equation (5). The test car isdriven along circular path (CW direction and CCW
direction) and accumulated heading angle θ iscompared with data of electrical compass.

T = (∑ (∆RRR − ∆RRL )) / θ COMPASS(10)

where θ COMPASS denotes heading angle of electricalcompass. Initial heading angle of electrical compass
is treated as zero degree.
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

city (especially in a tunnel) or in the forest, manyof GPS system can not be available.

Average of T (for five CW/CCW direction test) is
selected as an effective wheelbase.

120

5

100

Experimental Results
Y Position(m)80
Variable tire radius
Fixed tire radiusStart Point
End Point

60
40
20
0
-20
-40

Fig.6

0

50

100
X Position(m)

150

200

An experimental result (Case 2)

Fig.4 A test Area

6 Conclusions
Fig. 4 shows a test area. The tested paths areshape of "8" which can test CW direction and
CCW direction together (Fig.5). The car is run
twice time for an each test. And other tested paths
are also considered for a long distance (Fig.6).
Tested road is asphaltic, but it is not flat.
Infrared range finder offset and the effective
wheelbase are calculated from the results of section
4. Two rear wheels of the test car are used only.
Two cases of results are compared. One case is thatthe effective wheelbase and initial tire radii (fixedtire radii) are used for odometry computation (dottedline of experimental results). Another case is thatthe effective wheelbase and unfixed tire radii areused for computation (solid line of experimental
results).
120

100

This paper presents improving accuracy ofodometry for car-like vehicles. Systematic odometryerrors are calibrated by straight and circular pathnavigation. Tire radii are measured by infrared
range finders and they are used to reduce
nonsystematic odometry errors. Experimental results
show that calibration of systematic errors is not
sufficient for vehicles which use flexible tires. In
case of odometry navigation using information oftire radii, results show good performance without
any other sensor fusion.

Acknowledgment
This work was supported by the Ministry of
Science and Technology of Korea under a programof the National Research Laboratory, grant number
NRL M1-0302-00-0039- 03-J00-00-023-10.

Y Position(m)80References
60[1]

JohannBorenstein
andLiqiangFeng,"Measurement and Coreection of Systemamtic
Odometry Errors in Mobile Robots," IEEE
Transactions on Robotics and Automation,
vol.12, no.6, December, 1996, pp.869-880.
[2] R. Thrapp, C. Westbrook, D. Subramaian,"Robustlocalization
algorithms
foranautonomous campus tour guide," in Proceedingsof the IEEE Conferenceon Robotics andAutomation, 2001, pp.2065-2071.
[3] Ph. Bonnifait, G. Garcia, "Design andexperimental validation of an odometric andgoniometric localization system for outdoor
robot vehicles," IEEE Transactions on Robotics
and Automation, vol.14, no.4, 1998, pp.541-548.
[4] P. Goel, S. I. Rounmeliotis, and G. S.
Sukhatme, "Robust localization using relative
and absolute position estimates," in Proceedingsof the IEEE/RSI Conference on Intelligent
Robots and Systems, 1999.

40

20

0

-20
-40

Fig.5

Variable tire radius
Fixed tire radiusStart Point
End Point

-30

-20

-10

0
10
X Position(m)

20

30

40

50

An experimental result (Case 1)

Form Fig.5 and Fig.6, we can see that odometry
computation using fixed tire radii has a weak point
for roll motion of the car or uneven road surface.
Experimental results using information of tire radii
show more robust to those situations. The results
are meaningful because the positions of the carusing odometry are closed for closed reference path.
Accumulative error can not be eliminated forodometry computation, but improving odometry
accuracy is important for a long navigation which is
not available for absolute positioning methods. In a

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Improving the Tuning Capability of the Adjusting Neural Network
Yoichi Sugita
Hitachi Research Labolatory
Hitachi Ltd., Japan6, Kanda-surugadai 4-choume
Chiyoda-ku, Tokyo
101-8010, Japan

Kotaro Hirasawa
Graduate School ofInformation, Production and Systems
Waseda University
Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi
Fukuoka-ken, 808-0135, Japansubtracts the output of the ECNN for all region of dθ,accuracy of the output of the AJNN at the region far from dθ=0 may decrease.
In this paper, we discuss the method to increase the AJNN
capability where dθ is large using the non linear function
which restrict the output of the ECNN we have proposed
before[5]~[7] and describe the optimization of this function.

Abstract

We have proposed the adjusting neural network (AJNN),
which is the extended multi-layered network for control model
tuning in the process plant. The AJNN consists of 2 networks(conventional neural network (CNN) and error calculationneural network (ECNN)) and can calculate the accurate tuning
value using the ECNN which output the error of the CNN.
However, the output of the ECNN does not correspond to theerror of the CNN where the input value is large, and decrease
the accuracy at this region. In this paper, methodology to
improve the accuracy of the AJNN by introducing theprocedure to control the ECNN output properly is describedand its effectiveness is shown by simulation results.

1

2

Reheating furnace plant is one of the typical plant of which
the model based control is effecive in the control. The
pourpose of this system is to raise the slab temperature toapproximetry 1200 centigrate for the rolling process. For this,appropriate furnace temperatures to obtain the target slab
temperature are calculated using the control model, which
estimate the plant behaivior precicely using the phisicalequiations. The accuracy of the model directly effects thecontrol quality, therefore, the model has to be modified
corresponding to the plant behaivior change. Fig.1 shows the
structure of the model tuning system for the reheating furnace
plant. When the slab is extraced, a model tuner modifies one
parameter (Φcg) included in the control model and correct themodel behaivior using the difference between the detected
temperature value and the target one (dθout), and several statevariables such as the furnace temperature. Since therelationship between the deviation of theΦcg (ΔΦcg) and dθout is basicaly non-linear and the several variables affect thisIntroduction

In the control system for the plant such as the iron steel
plant or chemical plant, which has large dead time and has
restricted number of sensors, the model based control is
generally applied[1], which predicts appropriate control
commands to be inputted to the plant using the model which
identifies the plant behavior precisely. In that case, theaccuracy of model directly influents the control performance.
Therefore, in such control system, model tuning approach isapplied and the neural network, which has the high capability
to identify the non-linear relationship, is considered to be oneof the effective means[2][3].
The neural network establishes the relationship between
the error of controlled output (dθ) and the deviation of model
parameter to be modified. We have introduced the AJNN in
our previous papers [4]~[7], which is extended model ofconventional multi layered neural network(CNN) to increase
the tuning performance. The AJNN consists of 2 networks
which has the same architecture, and one of them calculates
the output error of the other network at dθ= 0 ( called ECNN;
Error Calculation Neural Network). The AJNN generates final
output by subtracting the output of ECNN from the others’.
The AJNN can calculate the accurate deviation of model
parameter in the neighborhood of dθ=0, by eliminating the
network error at dθ=0. However, since the ECNN cancalculate the network error only at dθ=0 and the AJNN©ISAROB 2005

Model Tuning for Reheating Furnace Plant

Reheating furnace control system
Furnace temperature reference

Furnace temp. reference
calculator

Reheating furnace
model

Model
Tuner
Fig.1.

607

Actual reheating furnace plant
Temp. error‑

Detected temp.
of slab+State variables

Control system for reheating furnace plant.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

relationship, the CNN is considered to be effective to identify
this relationship and realizes the model tuner.

3

4

The output of the ECNN directly corresponds to the output
error of the CNN at dθout =0, but not where dθout is not equal
zero. Therefore, when the dθout is large, the output of the
AJNN may possibly decrease its accuracy by subtracting theoutput of the ECNN which does not correspond to the error of
the CNN. The problem of the AJNN is the decreasing accuracy
where dθout is large cause the increase of tuning number
compared to the CNN.

The Adjusting Neural Network

Let the outputs of the model tuner by given by F(dθout,
x1，...，xn), where x1，...，xn are state variables. Since desired
output of the model tuner is obviously zero when dθout isequal to zero, the model tuner must always satisfy thefollowing equiation.
F (0, x1，...，xn) =0(1)

5 Non Linear Function

This is a constrain condition for the accurate parametertuning. However, if the CNN is utilized as the model tuner, it
is quite difficult to realize the Eq.1 permanently because of
infinite training data and the learning error. The output error atdθ out =0 cause the steady state error when the tuning isconverged.
Fig. 2 shows the structure of the AJNN. It has an
ECNN( Error Calculation Neural Network) added in parallel to
the CNN, which has the same structure of the CNN. The
ECNN receive zero instead of dθout and calculate the output
error of the CNN at dθout. By subtracting the output of the
ECNN from the one of the CNN, an output of the AJNN, FAJNN(dθout，x1，...，xn ) is given by

5.1 The AJNN with the Non Linear Function
To solve the problem discussed above, we have introduced
the AJNN with the non linear function (see Fig.4). The
proposed AJNN has the non linear function, which restrict theoutput of the ECNN corresponding to the volume of the dθout,
added at the output side of the ECNN and increase theaccuracy of output where the dθout is large. The output of
the AJNN with this function is given by
FAJNN (dθout，x1，...，xn ) = FNN(dθout, x1，...，xn) –α(dθout) ･FNN(0, x1，...，xn)(4)
Whereα(dθout) is the non linear function.

FAJNN (dθout，x1，...，xn ) = FNN(dθout, x1，...，xn) - FNN(0,x1，...，xn)(2)
Where, FNN(dθout, x1，...，xn) is the output of the CNN andFNN(0, x1，...，xn) is the one of the ECNN.

The non linear function should have the smooth shape,
however, we assume the piecewise function to facilitate thefurther discussion.
α(x) =0
1 ( x + A/2 + T )T

When the dθout is zero, Eq.2 indicates the following equation,
it is obvious the AJNN satisfies the Eq.1 permanently andcompletes the tuning without the steady state errors.
FAJNN (0，x1，...，xn ) =0

(3)

AJNN

dθout
…

…

+
-

( -A/2 – T ≦ x < -A/2 )1( -A/2 ≦ x ≦ A/2 )- 1 ( x - A/2 – T )T0( A/2 < x < A/2 + T )( A/2 + T < x )In the active region, the output of the ECNN is active and in
the transient region, the output is decreased gradually. Byutilizing this function, the AJNN can calculate the accurate
tuning value independently of the volume of dθout.

Δφcg

０
…

…

ECNN

5.2 Optimization of the Non Linear Function

Fig.2. Architecture of AJNN.

©ISAROB 2005

( x < - A/2 – T )(5)where A is the size of active region and T is the size oftransient region.

CNN

State
variable
X1〜xn

A Problem of the AJNN

To maximize the performance of the AJNN, the non linear
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

AJNN

Inputs for the AJNN in this simulation are the current Φcg
the control model has, dθout and six state variables (the four
furnace temperature, an initial slab temperature, thickness ofslab). Output of the AJNN in this simulation is the tuning
ratio P for the currentΦcg. The currentΦcg is modified into
(1 + P) Φcg when the slab is extracted from the reheating
furnace plant and the output temperature is detected. 672
training data to train the network are prepared by simulation.

CNN
dθout
…

…

+

Non linear
functionStatevariable
x2〜xn

Δφcg

-

０
…

…

α(dθout)

6.2

ECNN
Fig.3.

Inactive
regionFig.5 shows an example of simulation results. A linear
equation, which is a conventional tuning method and identify
the relation between dθout and P by the linear equation. The
linear equation, which can not identify the non linear relation ,can not output appropriate tuning value and took 5 times toconverge. The CNN can converge quickly but finished thetuning to remain the steady state errors because of the output
error at dθout =0. On the other hand, the AJNN convergedby 2 tuning numbers without any steady state error.
Fig.6 indicates the error behavior against the size of active
region of the non linear function. The mean error in verticalaxis is mean of |training data – output of the CNN|. The size oftransient region is fixed at 20[℃]. A size of active region
which minimizes the errors is 24[℃], and the error raises
gradually corresponding to the increase of the size of active
region. Effect of the ECNN is maximized at 24[℃] and after
that, the ECNN decrease the accuracy of the output of the
CNN gradually. As a result of this examination, 24[℃] is
considered to be the best size of active region for the nonlinear function. Besides, the error at 24[℃] is detected to belower than the one of the CNN and also the AJNN without thenon linear function.
The comparison of tuning performance between the
AJNNs which have different size of the non linear function are
described in Fig. 7. 30 different conditions which were notused as the training data were applied. A tuning number at
24[℃], which is decided as the size of active region for thenon linear function is smallest. Since the dθout was less than200[℃] in this simulation, a tuning number at 200[℃] isexactly equal to the result of the AJNN without the non linear
function. The tuning number of the proposed AJNN is
obviously lower than the one of the conventional AJNN, and it
tells the methodology discussed above can increase theperformance of the AJNN and can determine the appropriate
the non linear function.

AJNN with a Non-linear Function.

Transientregionα

Active region
TransientregionInactive
region1-A/2-T

-A/2

0

A/2

A/2+T

dθout

Fig.4. Shape of non-linear function

function has to be optimized, that is, methodology for
determination of the size of active region has to be discussed.
We propose the methodology to utilize the training data
effectively. The determination procedure is described asfollows.
Step1; Train the CNN using the training data with BP method.
Step2; Set the initial value( usually 1[℃]) for the size of active
region.
Step3;For all training data, evaluate the error between the
output of the AJNN with the non linear function and thetraining data.
Step4; Increment the size of active region by predetermined
step width, and find a active region which minimizes
the error described at Step 3.
This method is expected to produce the AJNN which
minimizes the errors of the training data for all region of dθout
and even decreases the errors compared to the CNN. That is,
the AJNN with the non linear function decided by thisprocedure can identify the relationship between dθout andΔ
Φcg described with the training data as much accurately as
possible.

7
6

Simulation Results

6.1

Simulation Conditions©ISAROB 2005

Results and Discussion

Conclusion

We discussed the improvement of the tuning capability of
the AJNN, which can modify the control model immediate andaccurately for the process control. A methodology to
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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

dθout （℃）

optimize the size of non linear function is introduced, which isa procedure to found the size to minimize the training data
error, and its effectiveness is evaluated and found to work
effectively to decrease the tuning numbers by simulation result
for the reheating furnace plant.

References[1] D.W.Clarke and P.S.Tuffs, “Properties of Generalized
Predictive Control”, Automatica, Vol.25, pp. 859-875,
1989.
[2] D. Rumelhart et al., “Parallel Distributed Processing”, MIT
Press, Cambridge, MA, 1986.
[3] W.T. Miller et al., “Neural Networks for Control”, MIT
Press, Cambridge, MA, 1990.
[3] Y. Sugita et al., “Architecture and Training Algorithm of
the Adjusting Neural Network for Accurate Model Tuning”,
T.IEE of Japan, Vol.115-D,pp461-469, 1995.
[4] Y. Sugita et al., “Extending architecture of the Adjusting
Neural Network for Improving Tuning Capability”, T.IEE
of Japan, Vol.116-C,pp859-866, 1995.
[5] M. Kayama et al, “Adjusting Neural Networks for
Accurate Control Model Tuning”, FUZZ-IEEE/IFES’95,
pp.1995-2000, 2001.
[6] Y. Sugita et al., “Improving Tuning Capability of the
Adjusting Neural Network”, IEEE IJCNN’97, pp.761-767,
1995.

20

0

1

3

5

-20

Tuning numbers(times)Linear equiation
CNN
AJNN

Fig.5. An example of simulation result.

AJNN without non linear function

0.036

Mean error

0.035

CNN

0.034
0.033
0.032
0.031
0.03
0

24

50

100

150

200

250

Active region [℃]

Fig.6.

Error behavior against the size of active region

Tuning numbers [times]

2.05

AJNN without non linear function

2
1.95
1.9
1.85
1.8
1.75
0

24

50

100

150

200

250

Size of active region [℃]

Fig.7.
©ISAROB 2005

Comparison of tuning performance
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Fault Diagnosis for Electro-Mechanical Control System by Neural Networks
Takashi Torigoe, Masami Konishi, Jun Imai, and Tatsushi Nishi
Department of Electrical and Electronic Engineering
Okayama University
3-1-1 Tsushimanaka, Okayama, 700-8530, Japankonishi@elec.okayama-u.ac.jp
Abstractof the system is diagnosed qualitatively from geometrical information of the classified node.
Addition to that, a quantitative diagnosis method
using HNN[2, 4] is described. The inputs to HNN are
measured data and geometrical information of classification by SOM. The HNN describes correlation
between input data and internal fault of the target
system. Through learning, the HNN model is updated so as to generate correct value of the internalfault state quantitatively.(Fig. 1)

In this paper, neuro based intelligent diagnosis
methods for electro-mechanical control system areproposed. A self organizing map neural network(SOM) is used to classify measured data of the target system as a qualitative diagnostic method.
Besides of the above procedure, it is expected
to attain more eﬃcient maintenance by a quantitative estimation of failure. For the purpose, newmethod is proposed using a hierarchical neural network (HNN). In the method, classified results by
SOM are processed for the quantitative diagnosis.
Hierarchical neural network can identify inner structure of the relations between failure causes and its
results that enables a quantitative diagnosis.
Keywords : neural network, Self organizing
map, fault diagnosis

Fig. 1 Diagram of estimation system

1

Introduction

First, the looper height control system[1, 5, 6] isselected as a target system of the proposed diagnostic method. After that, diagnosis of an induction motor and that of a robot arm control system
are made to verify the versatility of the proposed
method.

When some failure occurs in a mechanical system,
diagnosis of the system is to be performed. Then,
maintenance or repair action is carried out recovering the system performance to its normal state. As
it is impossible to acquire whole internal informationof the system, it is usual to analyze output data of
the system for the estimation of failure causes.
Traditionally, fault diagnosis is governed by human experts with plenty of experiences and knowhow. In these days, mechanical systems are becoming more complex and enlarged in its scale together
with its control system. So it becomes necessary
to develop new technologies for fault diagnosis not
only in a qualitative but also a quantitative manner coping with the needs for automatic diagnosis ofcomplex control systems.
In this paper, SOM[3] method is used for qualitative fault diagnosis in a electro-mechanical control
system. Measured data of a system is input to SOM,
and classified to particular nodes of SOM. The fault©ISAROB 2005

2
2.1

Diagnosis of looper height control
systemThe looper height control

The looper height control system consists oflooper height detection, PID controller and feedback
operation. The block diagram of a looper height control system is shown in Fig. 2.
Here, G(s) represents the transfer function oflooper dynamics. There is a time delay in measurement after the output of G(s). This time delay element is a representation of detection delay.

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The looper height waveforms when the KP ischanged when ωn is small are shown in Fig. 3(b),and Fig. 3(e). The oscillation of looper height waveform is enlarged by change of ωn or τ in comparson
with Fig. 3(d). However, By tuning the KP , oscillation of the waveform is reduced.
Fig. 2 Block diagram of a looper height control system

3

When human get data, it is compared with pastdata, and result of the comparison leads the conclusion. However, it is diﬃcult to execute those procedure automatically. In this section, the classification
method by using SOM neural network is proposed.

Based on detection of the looper height, VR1 is manipulated by PID control. There are 2nd order timedelay element to manipulate VR1 , because of force
transmission by torsion.

2.2

Classification of looper data

3.1

Waveform of looper angle

Self Organizing Maps(SOM) N. N.

SOM[3] is a multidimensional scaling method projecting input data space to lower dimentional output
space. Typically, output data space is made as 2dimension. Thus, input data space is visualized into
2-dimensional plane.
Here, the condition in the application of SOM forthe classification of looper height waveforms. The
looper height waveform is analyzed by wavelet transform, the feature of the waveform is emphasized towavelet coeﬃcient which is 2-dimentional matrix.
The classification method by SOM is applied to thematrix data.
An illustration of the classification is shown in
Fig. 4. SOM is trained by a lot of waveforms generated by looper control system in various cases.

Here, the movement of looper height is simulated
when the characteristics of looper height control system in the hot strip mills are changed.
In this paper, 3 factors (KP , τ, ωn ) are concerned
as the characteristics which will be changed.
The looper height is contolled by tuning the proportional gain KP . The other two elements are internal values of the looper control system. Time delay τ of looper height sensing becomes large withthe progression of deterioration in sensing euqipment. Natural angular frequency ωn becomes small
according to fatigue or deterioration in transmission
system. These states are abnormal necessary to befound. Where, normal state means that time delay of the element is short time, and abnormal statemeans that time delay of the element is long time

Fig. 4 Qualitative diagnosis by SOM
Where the input waveforms are analyzed by using wavelet transformation as a pre-processing. The
number of training times is set to 10000. The number of training data is 250. Where, sampling period
is 0.05[s] and sampling time is 5[s]. The size of SOM
N. N. in this case is 15 × 15.

Fig. 3 Waveform of looper angle
The looper height waveforms when the KP ischanged are shown in Fig. 3(a), and Fig. 3(d). Thereis an overshoot when the KP is large.

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3.2

Classified results (Qualitative diagnosis)

Also, there are many industrial applications. The hierarchical neural network (HNN) is one of artificial
neural networks.[2, 4]
In this paper, the input of HNN is data whichcontains looper height waveform and classified results described in previous section. And the outputof HNN is the parameters in the looper height control system, which provides the fault of the system.
Structure of the HNN with the classified results isshown in Fig. 9.

The classified ratio of each nodes in the SOM
is described. The failure is considered at small ωnvalue and at large τ value. In this case, for failure
conditions, ωn < 13 and τ > 0.25 are considered.
Fig. 5 shows classified results for normal conditions. Lines in Fig. 5 are contour lines corresponding to the ratios with which the data is recognized as
normal state. The results in failure state are shownin Figs. 6, 8 respectively.

Fig. 5 Classified resultsof normal stateFig. 7 Classified resultsof ωn failure

Fig. 6 Classified resultsof failure stateFig. 9 Quantitative diagnosis by HNN

The HNN model with the classified results (22-4010-3 4-layer NN) and HNN model without classified
results (20-40-10-3 4-layer NN) are trained and compared. These models are trained 5000 times using
500 looper height waveform data which is ramdomly
generated. After the training is finished, another1500 waveform data is applied to these models.
As described above, In this paper, 3 factors
are concerned as the characteristics which will bechanged. KP is a configurable parameters by human. Although, parameters to be estimated is τ ,ωn and KP concerning tuning mistake.

Fig. 8 Classified resultsof τ failure

These figures show that, data are classified in
SOM separately according to the fault.
By classifying the wave form, the data is projected to the 2-dimensional plane. From the position of the plane, it is possible that diagnosis of
waveform vibrations or estimation of failure causes
qualitatively.

4.2

Estimated results

Table 1 shows RMSE (Root Mean Square Error)of these estimated results. The results of the estima-

4

Quantitativerolling mills

diagnosis

forhotTable 1 Error comparison
τωnKP
without som 0.0089 0.50 0.020
with som
0.0044 0.36 0.0072

As described above section, the qualitative estimation is made by using SOM. In this section,
quantitative estimation method of failure state is described.

4.1

tion are shown in Figs. 11, 10. The horizontal axis
in these figures is actual parameters of looper heightcontrol system, And the vertical axis indicates theestimated parameters. If parameters are estimatedwith no error, dotted points in these figures are ona straight line.

Hierarchical neural network

Neural network is a simplified model of the human brain. It consists of one or more artificial neurons. Therefore, it has the ability to learn and adapt.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005of the system. Although, the rotor bar resistance is
too large to learn as output of neural network, so theneural network learns the resistance as log RBn . Table 2 shows RMSE of these estimated results. Fig. 14

Fig. 10 Estimation
result without SOM

Table 2 Error comparison
bar1
bar2
bar3
without som 0.0673 0.0769 0.0670
with som0.0608 0.0577 0.0597

Fig. 11 Estimation
result with SOM

is the result of RB2 estimation using normal neuralnetwork. Another estimation result using combined
system is shown in Fig. 15.

Figs. 11, 10 shows that result of estimation using SOM are more accurate. This means that theestimation error is reduced by using SOM.

5

Further applications

5.1

Induction motor

To verify versatility of the proposed diagnostic
methods, the estimation system is applied to induction motor. The physical model of induction motor
is shown in Fig. 12
The induction motor consists of rotor and stator. In this case, fault of rotor bar is considered todiagnose. This time, the motor has six rotor bars
(RB1 . . . RB6 ), and three of them have possibilityof fault. When the fault occurs, the electric resistance of the rotor bar is rise. So, it is intended that
detection of the rotor bar resistance from angularvelocity. Where, the resistance at normal state isRB1 = 33.5[Ω] in this time. Fig. 13 shows angularvelocity of the induction motor, when the resistanceRB1 is changed to 335[Ω], 3350[Ω].

Fig. 12 Physical modelof induction motor

Fig. 14 Resistance ofrotor bar #2

These results show that estimation error is reduced by neural network combined system, and realized that the neural network system is applicablewidely.

5.2

Robot arm

Next, the estimation system is applied to robot
arm control system. The robot arm control system
is shown in Fig. 16.

Fig. 13 Angular velocity

Fig. 16 Robot arm control system

The rotor bar resistance is estimated from the angular velocity waveform shown in Fig. 13 using proposed estimation system consists of neural networks.
The angular velocity is input to neural network system, and rotor bar resistance is learned as output©ISAROB 2005

Fig. 15 Resistance ofrotor bar #2 with SOM

The arm has two motors #1 and #2. The angle θn of motor #n are controlled to reference angle
θnr by PID controller. Although, there are sensing
error dn in the part of measuring the θn . So, themeasured θnd includes sensing error. Detection of614

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dn from θnd is intended. An example waveform ofθ1 , θ2 are shown in Fig. 17. Sensing error d1 , d2 areshown in Fig. 18.

Fig. 17 Angles of arm

Table 3 Error comparison
d1
d1
without som 0.029 0.024
with som0.019 0.017

neural network is proposed. It is realized that faultof the looper height control system is detected byclassifying the system data using SOM neural network model. Using the model, an accurate quantitative estimation of parameters in a looper controller
can be made. similarly, eﬀective diagnosis could be
carried out for induction motor. Thus, not only detection of failure occurrence but also quantitative
analysis of fault causes could be realized.

Fig. 18 Disturbances

Two estimation system is applied to θ1d and θ2d ,and output the estimated d1 and d1 individually.
Figs. 19, 21 shows estimated sensing error by using normal neural network. Figs. 20, 22 are results
by using proposed estimation system.

References[1] H.Asada et al, ”Adaptive and Robust Control
Method with Estimation of Rolling Characteristics for Looper Angle Control at Hot Strip Mill”,
ISIJ International, Vol.43, No.3(2003), pp358-365[2] I.M.Mujtaba et al, ”Application of Neural Networks and Other Learning Technologies in Process
Engineering”, Imperial College Press, 2001

Fig. 19 Estimated d1[3] Lakhmi C.Jain et al, ”INDUSTRIAL APPLICATIONS of NEURAL NETWORKS”, The CRC
Press, 1999

Fig. 20 Estimated d1
with SOM[4] Cihan H. Dagli, ”ARTIFICIAL NEURAL NETWORKS for Intelligent Manufacturing”, Chapman & Hall, 1994
[5] S.Imajo et al, ”Human Model and its Learning
for Setting of Looper Control Gain in Threadingof Hot Tandem Mills,” Proc. of the 46th Annual
Conf. of the Institute of Systems, Control and Information Engineers (2002)，pp.521-522

Fig. 21 Estimated d2[6] S.Imajo et al, ”Human Model for Gain Tuningof Looper Control in Hot Strip Rolling,” CAMPISIJ, Vol.15(2002), p931

Fig. 22 Estimated d2
with SOM

Table 3 shows RMSE of these estimated results.
It is realized thatThe estimation system using SOM
generates better results.

6

Conclusion

In this paper, a quantitative diagnosis method
for electro mechanical control system by using SOM©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Image processing for GIS applications supported by the use of
artificial neural networks∗.
Tomasz Kubik†
Adam Iwaniak‡†

Witold Paluszyński†PrzemysÃlaw Tymków‡

Institute of Cybernetics Engineering, WrocÃlaw University of Technology,
Janiszewskiego 11/17, WrocÃlaw 50-372, Poland
E-mail: {tkubik|Witold.Paluszynski}@ict.pwr.wroc.pl‡
Agricultural University of WrocÃlaw
Norwida 25/27, WrocÃlaw 50-372, Poland
E-mail: iwaniak@ar.wroc.pl, przemyslaw.tymkow@wp.pl

Keywords: image processing, neural networks, GIS

Abstract

60x60 cm. While this is still not as good as the finest
resolutions provided by aerial photographs, satellite
images can be acquired much faster.
An information system that is designed to workwith data referenced by spatial or geographic coordinates is GIS (Geographic Information System). GIS
should posses ability of automatic capturing, retrieval,analysis and displaying of spatial data. Because satellite images provide a wealth of information, there isa growing demand for automatic routines that can beapplied in GIS. Therefore image processing methods
and models targeted at satellite images are being developed.
For uniform areas reflectance measured is enoughto perform proper objects classification. However thisapproach fails for areas with a high heterogeneity ofspectral response, such as urban areas. Texture processing algorithms can be applied in this case. Thesealgorithms are usually divided into three major categories: structural, spectral and statistical. Structural
methods consider texture as a repetition of basic primitive patterns with a certain rule of placement. Spectral methods analyze the power spectrum. Statisticalmethods are based mainly on local statistical parameters (entropy, fractal dimension, local variance, variogram, etc. [1, 2, 3, 4, 5, 6, 7, 8, 9].) Other methods
make use of artificial neural networks [7, 10, 11, 12].

This paper presents techniques that can be used asa part of data processing in the remote sensing applications. The techniques proposed employ artificial
neural networks for automatic recognition and classification of individual land cover classes, such as forestsof some type, buildings, etc. Various quantitative measures have been applied to evaluate the quality of theresults. Apart from this a simple Bayesian classifier
has been proposed. Presented solutions can be applied
for processing of satellite images and aerial images as
well.

1

Introduction

A basic problem for any country or region in economic planning, environmental studies, or resource
management is availability of accurate, current information. In highly developed countries over 70% of
administrative decisions is made on the basis of data
related to the earth surface. One significant method
for providing current, reliable surface information isremote sensing.
Remote sensing relies on detecting and measuring ofelectromagnetic energy (usually various spectral fractions of light are measured) emanating from distantobjects made of various materials, so that they canidentified and categorized by class or type, substance,and spatial distribution. Nowadays this kind of data
is mainly acquired in form of satellite images.
Satellite images are an invaluable source of information. Initially providing a resolution of 1x1 km, nowcommercially available satellite images go down do

2

The images used in the experiments described inthis paper were received from Landsat satellite andproduced by the TM (Thematic Mapper) scanner.
The TM sensor records reflected electromagnetic energy from the visible to thermal infrared regions of the∗ This research was supported in part by the Committee for
Scientific Research through grant 5T12E02924©ISAROB 2005

Image processing

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

spectrum in 7 bands. The spatial resolution of TM
images is 30x30 m for all bands except the thermal,
which has 120x120 m resolution. In our case the problem of image processing has been transformed into the
problem of classification defined as follows:

The size of the mask was an odd number, the centerof the mask was a subject of classification. In thiscase neural networks were design in that way, that
input level of a particular network matched a maskof predefined size, but the output level was made of
one neuron. Different neural networks were createdfor different kinds of objects and different masks.
The results of experiments were evaluated by comparing images segmented by the neural networks withcorresponding reference images (see Fig. 1.) For thispurpose we involved the measures described in the section 5.

Given: 1) a reference image, consisting of points represented by the set of corresponding pixels intensities
in n spectral channels, together with a correct points
classification into K classes; 2) a test image, consisting of points represented by the set of corresponding
pixels intensities in n spectral channels.
Find: correct classification of points in a test image.
The reference images were produced with the aidof CORINE Program (Coordination of Informationon the Environment) database and an expert knowledge. The CORINE classification consists of twentytwo classes of land use. After conducting some initial experiments, it turned out that this material isinsufficient and does not represent correctly physicalfeatures, such as: leafy forests, and parks within acity. Therefore, an improved reference classification
map was hand-created by an interpreter. The resulting reference map contained only 12 different classesof land use.

3

reference

Figure 1: The reference map and the image obtainedwith a neural network trained to recognize coniferous
forests (3 input neurons, 24 hidden neurons, 1 output
neuron), κ = 0.945.

Neural network based classifier

The design of artificial neural network based classifier involved the following: 1) preparing data sets
and patterns from images, 2) building and training a
network, 3) processing and visualizing results, 4) estimating the classification error.
The patterns files were usually made up of all Landsat spectral images and one reference image. The
reference images was partitioned into two parts: thetraining set and the testing set, for result evaluation.
We used traditional feed-forward, multi-layer neural networks trained in a supervised mode. The architecture and size of a particular network was dependent
on classification problem currently defined.
For classification per pixel an input layer of a neuralnetwork consisted of as many neurons as the numberof spectral channels used. The output level consistedof the number of classes the neural network was expected to be sensitive for. Thus the training pattern
was build from the intensity values of the corresponding pixels in all spectral channels and the reference
image (classification results for different architecturesare shown in Fig. 2.)
For classification per parcel training patterns werecreated moving a square mask within a reference image(for which the results of classification were known).

©ISAROB 2005

neural network result

12 hidden neurons

36 hidden neurons

Figure 2: Classification results for different neural network architectures (3 input neurons, 12 output neurons.)

4

Naive Bayes classifier

Naive Bayes classifier combines probability modelwith a decision rule. Usually the rule is specified as
the choice of hypothesis which is the most probable.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

be very valuable, readily comparable to the humanprocessed CORINE map and in some areas more accurate. To quantify the quality of the results in an automatic manner several statistical measures have been
considered. For the confusion matrix A = [aij ] where
aij is the number of sample pixels from the jth classthat have been classified as belonging to the ith class,the following measures have been proposed[13, 14]:
Pii
, where ari = i ai.
– the users accuracy of class i: aari(sum of ith row entries);ii, where aci =–Pthe producers accuracy of class i: aaci
i ai. (sum of ith column entries); P– the overall accuracy of the method: i aii /at , whereat is the total number of pixels;– simple Kappa coefficient:P
Po − PeP =aii/atwhere o Pi
(6)κ̂ =Pe = i ari aci /a2t
1 − PeThis rule is called maximum a posteriori rule. Corresponding classifier is defined by the function B:
B(x̄) = arg maxck P (C = ck )nY

P (Xi = xi |C = ck ) (1)i=1where: x̄ = [x1 , . . . , xn ] – vector of features xi (inthe case of multispectral image these are pixels intensities for one point in n spectral channels); ck –classes labelled with k = 1, . . . , K; P (C = ck ) – probability, that observed case belongs to the class ck ;P (Xi = xi |C = ck ) – conditional probablitities (thatintensity of a pixel Xi equals given xi in a channel i,when it is known that corresponding point is of theclass ck ).
To build classifier B at first step the probability
P (C = ck ) should be approximated. This can be done
as follows:
Nk(2)
P (C = ck ) = Pn
k=1 Nk– weighted Kappa coefficient:
P P
aPow = i j wij aijt
Pow −Pew
P
P
where
κ̂w =
ai. a.j
Pew = i j wij a2
1−Pewt

Nk - amount of points of the reference image classified to the class k (sum of all Nk equals total numberof points of the reference image). Next conditional
probabilities should be approximated by the normal
distributions:
P (Xi = xi |C = ck ) ∼= f (xk,i ) =1√

σk,i 2πe(7)
The weights wij are constructed so that 0 <= wij < 1
for all i 6= j, wii = 1 for all i, and wij = wji .

−(xk,i −µk,i )2
2σk,i

6

(3)
where xk,i - pixel intensity level for class k and forspectral channel i.
Approximation of σk,i can be expressed by the standard deviation of case:
v
u
Nk
X
u 1
(xk,i,j − x̄k,i )2(4)
SNk −1,i = t
Nk − 1 j=1

Conclusions

Because of the limitations not all of the results obtained had chance to be presented here. Nevertheless the experiments performed proved that satellite
images can be processed automatically by supervised
learning with a neural network. The best results ofclassification per-pixel were obtained for areas such as
those in the Fig. 1. But sometimes the results for urban areas were only barely acceptable. In such cases
the improvements were done by applying per-parcel
classification. There information about neighborhoodof a pixel (pixels values within moving mask) helped inproper classification. Presented naive Bayesian classifier is an another option (see Fig. 3.) It can be used, as
the other techniques discussed, for satellite and aerial
image processing.

where: xk,i,j - intensity level of pixel j from channel i,for which corresponding point has been assign to classk (according to the provided reference image classification).
Approximation of µk,i is given by:N

x̄k,i =

5k
1 X
xk,i,jNk j=1(5)

References[1] J. R. Carr: Spectral and textural classification of
single and multiple band digital images. Computers
and Geosciences, 22(8):849–865, (1996).

Quality measures

A number of experiments have been conducted resulting in many classification maps generated. They
have been evaluated by human experts and found to©ISAROB 2005

[2] M. Tuceryan and K. Anil: The handbook of Pattern recognition and computer vision, chapter 2.1

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

training

outputexpectation

Figure 3: Classification results for naive Bayes classifier build on data coming from three spectral channels (notshown). training is a reference image used for classifier building, output shows classification results, expectation
is a reference image showing expected classification results.
Texture Analysis, pp. 207–248 World Scientific Publishing Co., 2nd edition, (1998).

cover characterization in remote sensing. Remote
Sensing of Environment, 84(4):550–560, (2003).

[3] 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).

[9] G. M. Foody: Estimation of sub-pixel land covercomposition in the presence of untrained classes.
Computers and Geosciences, 26(4):469–478, (2000).
[10] G. W. H. German and M. N. Gahegan: Neuralnetwork architectures for the classification of temporal image sequences. Computers and Geosciences,22(9):969–979, (1996).

[4] J. Keuchel, S. Naumann, M. Heiler, and A. Siegmund: Automatic land cover analysis for Tenerife
by supervised classification using remotely sensed
data. Remote Sensing of Environment, 86(4):530–
541, (2003).

[11] D. M. Miller, E. J. Kaminsky, and S. Rana: Neural network classification of remote-sensing data.
Computers and Geosciences, 21(3):377–386, (1995).

[5] Q. Cheng: Multifractality and spatial statistics.
Computers and Geosciences, 25(9):949–961, (1999).

[12] J. Ruiz-del Solar: TEXSOM: Texture segmentation using self-organizing maps. Neurocomputing,
21:7–18, (1998).

[6] M. Chica-Olmo and F. Abarca-Hernndez: Computing geostatistical image texture for remotelysensed data classification. Computers and Geosciences, 26(4):373–383, (2000).

[13] L. Hubert-Moy, A. Cotonnec, L. Le Du,
A. Chardin, and P. Perez:
A Comparison of
Parametric Classification Procedures of RemotelySensed Data Applied on Different Landscape Units.
Remote Sensing of Environment, 75(2):174–187,(2001).

[7] 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).

[14] G. M. Foody: A Coefficient of Agreement for
Nominal Scales. Educational and Psychological
Measurement, 20:37–46, (1960).

[8] J. Ju, E. D. Kolaczyk, and S. Gopal: Gaussian
mixture discriminant analysis and sub-pixel land

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A supervised learning rule adjusting input-output pulse timing
for pulsed neural network
Makoto Motoki

Seiichi Koakutsu

Hironori Hirata

motoki@graduate.chiba-u.jp

koakutsu@faculty.chiba-u.jp

hiro@faculty.chiba-u.jp

Graduate School of Science and Technology
Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba-shi, 263-8522, Japan
Abstract

delay and synaptic weight among neurons. We verify
the effectiveness of the proposed method by computer
simulation. Results of computational experiments indicate that the proposed method enables learning ofpulse timing in multi-layered feedforward PNN.

Some supervised learning rules have been alreadyproposed for pulsed neural network (PNN). Thoselearning rules use information expression with pulse
frequency, however, does not use that with pulse timing. Therefore, the conventional learning rules cannot learn pulse timing, although it is important toadjust input/output (I/O) pulse timing of network.
The purpose of this paper is to propose a supervised
learning rule adjusting I/O pulse timing for PNN. In
the proposed method, pulse timing error and pulse
frequency error are corrected by adjusting transmission delay and synaptic weight among neurons. Results of computational experiments indicate that the
proposed method enables learning of pulse timing in
multi-layered feedforward PNN.

1

2

Pulsed neural network

This study uses a leaky integrate-and-fire neuronmodel which is the same model used by Gerstner etal[1]. and Eurich et al[2]., so the behavior of this neuron model is explained briefly.
A presynaptic neuron j and a postsynaptic neuron
i is connected with a synaptic weight w0i,j by synapse.
A set of firing time T j of the neuron j and a set ofarrival time Tdi,j at which pulses of the neuron j reachthe neuron i are defined as follows:
Tj

Introduction

Tdi,jPulsed Neural Network (PNN) has been getting attention recently as one of neural network models which
is proper for temporal data processing. As for learning
for PNN, Hebbian learning has been proposed[1][2].
Hebbian learning is one of unsupervised learning rules,
and enables to optimize the internal state of network.
However, Hebbian learning cannot construct a network which can represent required input/output (I/O)
temporal data mapping. In order to construct a network which can represent required I/O mapping, supervised learning rules are necessary. Some supervised learning rules have been already proposed for
PNN[3][4]. Those learning rules use information expression with pulse frequency, however, does not usethat with pulse timing. Therefore, the conventional
learning rules cannot learn pulse timing, although it
is important to adjust I/O pulse timing of network.
The purpose of this paper is to propose a supervised
learning rule adjusting I/O pulse timing for PNN. In
the proposed method, pulse timing error and pulse frequency error are corrected by adjusting transmission©ISAROB 2005=

{tjν ; 1 ≤ ν ≤ nj } = {t | y j (t) = 1},

(1)

=

j
i,j
j
{ti,j
d,ν = tν + τd ; 1 ≤ ν ≤ n },(2)

where nj is the maximum number of pulses of theneuron j, y j (t) is the output of the neuron j at timet, and di,j is transmission delay between the neuron j
and i.
When the pulse of the neuron j arrives, the memi
brane potential Pm(t) of the neuron i changes as follows:in case of t > tiµ + ri ,
i
τm

i
X i,j
(t)
dPm
i
(t) +
= −Pm
w0
dt
j∈J

X
t



δ t−ti,j
d,ν ,

(3)

i,j
i,j
∈T
d,ν
d

and in case of t ≤ tiµ + ri ,i(t) = 0,Pm

(4)i
is depression
where ri is absolute refractory period, mtime constant, J is a set of presynaptic neurons of theneuron i, w0i,j is synaptic weight, and δ(t) is Dirac’sdelta function. In this paper, synaptic weight w0i,j is−1 ≤ w0i,j ≤ 1.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

b. Pulse frequency error between actual outputof network and corresponding training signal isback-propagated from output layer to input layer.
And pulse frequency error is corrected by adjusting synaptic weight between a presynaptic neuron
and a postsynaptic neuron.
c. The proposed method is able to apply to multilayered feedforward PNN.
Procedure of the proposed method is described below.
1. Initialize parameters of PNN.
2. Input all pattern of input-time-series data LI of
training set to PNN. And record firing time T j of
all neurons on each pattern. T j on each pattern
p is expressed by Tpj = {tjν,p ; 1 ≤ ν ≤ njp }.
3. Calculate Ep on each pattern p.
4. Adjust transmission delay d between each pair ofneurons.
5. Adjust synaptic weight w0 between each pair ofneurons.
6. Repeat from 2. to 5. until satisfying given termination condition.
Step 4. and 5. are explained in section 3.3 and 3.4
in detail.

i
When Pm(t) exceeds threshold θ i , the neuron i fires,and the neuron i outputs 1 (eq.(5)). In addition, a setof firing time T i of the neuron i is expressed as follows:

i
i
i

y (t)
T

i

=

H Pm (t) − θ

,

(5)

=

{tiµ ; 1 ≤ µ ≤ ni } = {t | y i (t) = 1},(6)where H(t) is Heaviside unit function.

3

Proposed method

In this section, definitions of training set and error
function are given, and features and procedures of the
proposed method are explained.

3.1

Training set and error function

Definitions of training set and error function are
given before explaining the proposed method. Inputtime-series data LI and output-time-series data LO oftraining set are defined as follows:LI=i; i ∈ I , 1 ≤ p ≤ P, 1 ≤ ζ ≤ Npi },
{lζ,p

(7)

LO

=

o
; o ∈ O, 1 ≤ p ≤ P, 1 ≤ ξ ≤ Npo },
{lξ,p

(8)

3.3

where I is a set of input layer’s neurons, P is thenumber of learning patterns, and Npi is the number ofpulses which input to a input layer’s neuron i for apattern p. And O is a set of output layer’s neurons,
Npo is the number of pulses which should output froma output layer’s neuron o for the pattern p.
The error function which is used in this paper isdefined as follows:
no
Npo
p
X
XZ Tp X


o
o
Ep =

H t−tν,p dt,

H t−lξ,p −o∈O 0Procedures of adjusting transmission delay whichis mentioned at step 4 in section 3.2 are explained
in detail. Neuron o, j and k denote a output layer’s
neuron, a hidden layer’s neuron and a hidden or input
layer’s neuron, respectively. Besides, the neuron k isa presynaptic neuron of the neuron j, and the neuron
j is a presynaptic neuron of the neuron o.
First of all, do,j which is transmission delay between
the hidden layer’s neuron j and the output layer’s neuron o is updated as follows:

(9)

ν=1

ξ=1

E=

P
X

Ep ,

Transmission delay adjustment

(10)

p=1

o,j
τo,j
d (c+1) = τd (c)+ατdwhere Tp is the length of pattern p, nop is no on patternp, toν,p is toν on pattern p, Ep is the errors on patternp, and E is the total of Ep on all patterns.

p=1

X

o,j j
(tν,p ) =
∆τd,p
lo

ξ,p

3.2

o (t
∈Fp

Ep

X

o,j j
(tν,p ), (11)
∆τd,p

j
j
tν,p∈Tpo− to,jWτjd (lξ,pd,ν,p ),

Main features of the proposed method are the following.
a. Pulse timing error between actual output of network and corresponding training signal is backpropagated from output layer to input layer. And
pulse timing error is corrected by adjusting transmission delay between a presynaptic neuron anda postsynaptic neuron.

(12)

o,j
)
d,ν,p

j
o
j
o
Fpo (to,jd,ν,p ) = { lξ,p | tν,p < lξ,p ≤ tν+1,p },2
2πWτνd (ϕ) = e−ϕ sin( ν ϕ),τr

Features and procedure©ISAROB 2005

P
X

(13)
(14)

where c is learning cycle, α d is learning rate regardingo,j jtransmission delay, and ∆ d,p(tν,p ) is a function of tjν,p .
o o,jIn addition, Fp (td,ν,p ) is a set of elements which areincluded in LO on pattern p between tjν,p and tjν+1,p ,and W jd (·) is a window function regarding transmission delay.

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o,j j
γpj (tjν,p) which is the total of ∆ d,p(tν,p ) of all postsynaptic neurons is calculated for each pulse of theneuron j as follows:
X o,j o,jj jγp (tν,p) =∆τd,p (tν,p ).

p=1

X

j,k k
∆τd,p
(tν,p) =jjtµ,p∈Fp (t

Ep

X

tk
ν,p ∈Tp

Wτkd tjν,p+βτd γpj (tjµ,p)−tj,kd,ν,p ,(17)

j,k
)
d,ν,p

(18)

where Fpj (tj,kd,ν,p) is a set of elements which are includedin Tpj on pattern p between tkν,p and tkν+1,p , and β d isreduction rate regarding transmission delay.
β d should be set appropriate value in accordancewith time step of the finite difference ∆t and transmission delay d .
In order to update transmission delay of presynaptic neurons of the neuron k, γpk (tkν,p) which is the totalj,k kof ∆ d,p(tν,p ) of all postsynaptic neurons is calculated
for each pulse of the neuron k as follows:
X j,k kk k
∆τd,p (tν,p ).

γp (tν,p) =ν=1

Ψp (t) = Sign Signo¯otoν,p ∈(Tp ∩Gp )−n Z t+∆t/2
X
j

(19)

Sign(ϕ) =(20)

X

ν=1 g o ∈H o(to,j
µ,p
p

o
o
o
−to,j
Wwj 0(gµ,pd,ν,p )Ψp (gµ,p ),

d,ν,p

(21)

ϕ < 0,
ϕ = 0,
ϕ > 0,(28)

Ep ∆w0,p ,

(29)

k

)

j,k
∆w0,p
=

e
,
µ
(1 + e−2ϕ/τr )2oo} = {t | Ψop (t) 6= 0},; 1 ≤ µ ≤ NG,pGpo = {gµ,p

n
X

X

j
j
j
−tj,k
Wwk0(gµ,pd,ν,p )Ψp (gµ,p ), (30)ν=1 g jj j,kµ,p∈Hp(td,ν,p )

(22)

j
j
k
j
k
j
Hpj (tj,kd,ν,p) = {gµ,p | tν,p < gµ,p ≤ tν+1,p , gµ,p ∈ Gp }. (31)

o
j
o
o
j
o
Hpo (to,jd,ν,p) = {gµ,p | tν,p < gµ,p ≤ tν+1,p , gµ,p ∈ Gp }, (23)

©ISAROB 2005

for
for
for

(27)

p=1

µ
−2ϕ/τrWwµ0 (ϕ) =−10
1w0 (c+1) = w0 (c)+αw0

j

n
X

(

(26)

t−∆t/2where βw0 is reduction rate for synaptic weight, andG¯po is a complementary set of Gpo . In other words,
G¯po is a set of elements which are not included in Gpo ,but are included in a set of time {0, ∆t, 2∆t, · · · , Tp −∆t, Tp }, where the number of elements of this set isTp /∆t + 1. Consequently, (Top ∩ G¯po ) denotes a set of
required firing-time of the neuron o on pattern p.
Secondly, w0j,k which is a synaptic weight between
the hidden layer’s neuron j and the other hidden
layer’s or input layer’s neuron k is updated as following:
P
X
j,k
j,k
j,k

p=1

o,j
∆w0,p
=

!

j
j
} = {t |Ψjp (t) 6= 0},; 1 ≤ µ ≤ NG,pGpj = {gµ,pProcedure of adjusting synaptic weight which is
mentioned at step 5 in section 3.2 is explained in detail. Neuron o, j and k is the same as the neurons
described in section 3.4.
First of all, w0o,j which is synaptic weight between
the hidden layer’s neuron j and the output layer’s neuron o is updated as follows:o,jEp ∆w0,p,t−∆t/2δ(u−tν,p )du ,

ν=1

o

δ(u−toν,p +τdo,j )dujSynaptic weight adjustment

PX

Z t+∆t/2

X+

Eq.(16)∼(19) is repeated until the presynaptic neuron k becomes the input layer’s neuron.

o,j
wo,j
0 (c+1) = w0 (c)+αw0

βw0 Ψp (t+τd )

w0

o∈O

j∈J

3.4

t−∆t/2where c is learning cycle, αw0 is learning rate regardingo,jsynaptic weight, and ∆w0,p
is update value of weight
between the neuron j and o on pattern p. In addition, Wwj 0 (·) is a window function regarding synaptic
weight, Hpo (to,jd,ν) is a set of elements which are includedoin Gp on pattern p between tjν,p and tjν+1,p , Gpo is a setof time when Ψop (t) is plus or minus value, and Ψop (t)is a function which denotes the difference between the
output of the neuron o and corresponding training signal.
Ψjp (t) which denotes the difference between the output of the neuron j and Ψop (t) of the postsynaptic neuron o is calculated by eq.(26). Gpj which denotes a setof time when Ψjp (t) is plus or minus value is definedby eq.(27).
X
n
j
o,j
o
o,j

j,k k
∆τd,p
(tν,p ), (16)kjkj
Fpj (tj,kd,ν,p) = { tµ,p | tν,p < tµ,p ≤ td,ν+1,p },δ(u − toν,p )du,δ(u − lξ,p )du −t−∆t/2

(25)

(15)

o∈O

P
X

n Z t+∆t/2
X

n Z t+∆t/2
Xo
ξ=1Secondly, dj,k which is transmission delay between
the hidden layer’s neuron j and the other hidden
layer’s or input layer’s neuron k is updated as following:j,kτj,kd (c+1) = τd (c)+ατdoξΨop (t) =

(24)

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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100

Convergence Rate [%]In order to update synaptic weight of presynaptic
neurons of the neuron k, Ψkp (t) which denotes the difference between the output of neuron k and Ψjp (t) of
the postsynaptic neuron j is calculated by eq.(32). Gpk
which denotes a set of time when Ψkp (t) is plus or minus value is defined by eq.(33).
Xnw0j,k βw0 Ψjp (t+τdj,k )Ψkp (t) = Sign Sign

+

X

¯j
j
j
tν,p ∈(Tp ∩Gp )oδ(u−tjν,p +τdj,k )

60
40
20
0

j∈J

Z t+∆t/2

80

WithoutTiming
WithTiming
2

6

4

8

10

12

14

16

Neurons per a Hidden Layer

duFigure 1: Convergence rate of learning.

t−∆t/2Eq.(29)-(33) is repeated until the presynaptic neuron k becomes the input layer’s neuron.

the number of hidden layer’s neurons increases. However, the rate saturates when the number of neurons
becomes 15. In addition, the convergence rate of synchronized I/O pulse timing case is lower than that ofnon-synchronized one. This result indicates that the
proposed method enables PNN to learn pulse timingwith only small reduction of the convergence rate.

4

5

nk Z t+∆t/2
X
k

−

ν=1

!

δ(u−tν,p )du ,(32)t−∆t/2kk} = {t |Ψkp (t) 6= 0}.
; 1 ≤ µ ≤ NG,pGpk = {gµ,p(33)Experiments

The purpose of this paper was to propose a supervised learning rule adjusting I/O pulse timing for
PNN. We verified the effectiveness of the proposed
method by computer simulation. Results of computational experiments indicated that the proposed
method enables learning of pulse timing in multilayered feedforward PNN. However, the searching ability of solutions of the proposed method is not enough,since the convergence rate decreases when the numberof hidden layer’s neurons becomes small. To improve
the searching ability of solutions is one of future works.

In this section, results of computational experiments are given in order to demonstrate the advantages of the proposed method.
The network structure of PNN used in the experiments is a multi-layered feedforward network which is
one input layer, one output layer, and two hidden layers. The number of the input layer’s neurons is two,that of the output layer’s neurons is one, and that of
the hidden layer’s neurons per layer is 3∼15.
The training set used in the experiments is the following. Input-time-series data and output-time-series
data are both periodic data.
The termination condition of learning used in theexperiments is the following. The learning is stopped
as success when error E becomes less than 5.0 × 10−3,
and as failure when the learning cycle c reaches 4,000
epochs without E being less than 5.0 × 10−3 .
In the experiments, convergence rate of learning isobserved when the number of hidden layer’s neurons
is changed from 3 to 15. In addition, difference of convergence rate between synchronized I/O pulse timingcase and non-synchronized I/O pulse timing case iscompared. The learning is executed 100 times fromdifferent initial snaptic weights and transmission delay.
The convergence rate which is the average of 100
trials is showed in Figure 1. In Figure 1, x-axis indicates the number of hidden layer’s neurons par layer
and y-axis indicates the convergence rate. Figure 1 indicates that the convergence rate becomes large when

©ISAROB 2005

Conclusion

References[1] Gerstner W, Kempter R, van Helmmen JL and Wagner H (1996), A neuronal learning rule for submillisecond temporal coding. Nature, vol.383, pp.7678[2] Eurich CW, Cowan JD and Milton JG (1997), Hebbian delay adaptation in a network of integrate-andfire neurons. Artificial neural networks - ICANN’97,
pp.157-162[3] Kuroyanagi S, Iwata A (1998), An Supervised Learning Rule of Puled Neuron Model (in Japanese). Technical report of IEICE, NC97-151, pp.95-102[4] Fatamata N, Kuroyanagi S and Iwata A (2002), An
Implementation Method of Pulsed Neuron Model for
FPGA Devices (in Japanese). Technical report of IEICE, NC2001-211, pp.121-128[5] Maass W and Bishop CM (2001), Pulsed Neural Networks,” The MIT Press （2001）

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Remarks on tracking method of neural network weight change forlearning 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. This paper proposes a
new tracking method of neural network weight change.
The proposed tracking method can provide a new
confirmation of the neural network learning performance.

because the performance of the cost function is affected bydynamics of the plant. This fact leads that more accurate
examination of the neural network controller learning
performance requires to track the neural network weightchange directly.
This paper proposes a new tracking method of theneural network weight change. A leaning type neuralnetwork direct controller[3] for a second order discrete time
plant is selected in order to examine the proposed tracking
method and its simulation results show the usefulness of the
proposed method.

1. Introduction
Many studies have been undertaken in order toapply both the flexibility and the learning capability ofneural networks to control systems[1][2]. A neural network
controller is usually designed so as to minimize the error
between a plant output (or neural network output) and adesired output (teaching signal). For this aim, neuralnetwork learning rules are designed to change neuralnetwork weights whose number reaches into thousands ortens of thousands in some applications. The reason to use
these huge number of weights is that a biological neuralnetwork has huge number of neurons and it is proved that
more neurons realize more accurate nonlinear mapping
capability of the neural network. As mentioned above, theessence 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 may not bereflected in the cost function. This problem is especiallyserious in neural network controller applications. This is©ISAROB 2005

624

2. Tracking method of neural network
weight changeThis section proposes the tracking method of theneural network weight change and its application to thelearning type neural network direct controller. For our
tracking method, first, one weight vector is derived from theneural network weights. Next, we calculate an innerproduct of this weight vector and a standard vector. Any
vector, which has same order as that of the weight vector,
can be selected as this standard vector, for example, theweight vectors derived from the initial neural network
weights, the final neural network weights and so on. We
can also calculate an angle between the weight vector andthe standard vector. The track of the neural network weightchange can be drawn on a 2D plane through the use of thesecalculated inner product and angle. This track does notshow whole neural network weight change, but it is notaffect by the plant dynamics and it can show an anothercharacteristic of the neural network learning performance.
We can realize the new examination of the neural network
learning through the use of the proposed tracking method or
its combination with the cost function.
In order to verify the usefulness of the tracking
method, we applied it to the learning type direct controller.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A reason of this selection is that the direct controller issimplest. The another reason is that the cost function of
the learning type is the sum of the squared error at eachsampling time and the plant dynamics less affect it incomparison with an adaptive type. This fact is useful to
examine the proposed tracking method effectiveness. We
also select a discrete time SISO (single input and single
output) plant as an object plant because it is simplest anduseful for a practical controller application. When we use
above selections, an output layer of the neural network has
one neuron, the weights between the output layer and ahidden layer can be expressed as a vector ω and the weights
between the hidden layer and an input layer can be expressed
as a matrix W. To simplify, the neuron number of the
input layer is equal to that of the hidden layer. That is, theweight matrix W is the square matrix.
We can derive a new weight vector Γ from theseneural network weight vector and matrix as follows:ΓT = [ω 1ω n W11

3. Simulation
To verify the usefulness of the proposed tracking
method, it is applied to the learning type neural network
direct controller for the second order discrete time plant.
The simulated plant is 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)

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 and the cost function
J(p) of the trial number p are defined as follows:ε(k)=Yd(k)-Y(k)W1n W21

W2n

Wn1

Wnn ]

(1)

(5)

ρ

J(p)= ∑ ε 2 (k)

(6)

k=1

where n is the neuron number both the input layer and thehidden layer. When we define the standard vector Γ0 , thetrack of the neural network weight change on the 2D plane
can be expressed as the following equations.
X = Γ cos θ , Y = Γ sin θθ = cos-1 (<Γ0 ⋅ Γ >
Γ0 ⋅ Γ

(2)and the vector Γ, and Γ is the norm of the vector Γ. A s
mentioned above, we can draw a new weight performance on
the 2D plane by use of X and Y in equations (2) and (3).
The plant dynamics does not affect this weight performance
directly.

z -1
z -1
Yd

z

We select the following sigmoid function f(x) as the input
output relation of the hidden layer.
X {1-exp(-4x/Xg )}f(x)= g2{1+exp(-4x/Xg )}(8)

Where Xg is the parameter which defines the sigmoid
function shape. The plant input U(k) equals the neuralnetwork output composed as follows:
U(k) = ω T(p)f{W(p)I(k)}

U
Plant

(9)

Y
The block diagram of the learning type neural network directcontroller is shown in fig.1. The learning rule of thisneural network controller is shown in the following
equations.

+Fig.1 Block diagram of learning type neural network
direct controller for second order discrete time plant.

©ISAROB 2005

(7)

(3)

Where <Γ0 ⋅ Γ > is the inner product between the vector Γ0

NeuralNetwork

where ρ is the sampling number within one trial period.
In this simulation, ρ=300 is selected.
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)]

)

(4)

625

ρ

n

k=1

j=1

Wij(p+1) = Wij(p)+η ∑ [ε(k)ω i(p)Ij(k-1)f,{∑ Wij(p)Ij(k-1)}](10)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

0.08

0.8
Plant output (p=1)
Desired value

0.06

Y

Plant output (p=1)

0.6

0.4

0.04

0.02

0.2

0
2.418

100
200
Sampling number

2.422

Fig.5 Track of neural network weight change(p=1~200).

0
0

2.42
X

300

0.0006

Fig.2 Plant output (p=1).

0.8

Plant output (p=200)

Y

Plant output (p=200)
Desired value

0.00030.6

0.4

02.42120

2.42125

2.42135

Fig.6 Track of neural network weight change(p=191~200).

0.2

n

ρ

ω i(p+1) = ω i(p)+ηf [∑ {Wij(p) ∑ (ε(k)Ij(k-1))}]

0
0

100
200
Sampling number

300

Fig.3 Plant output (p=200).
1.5

Cost function

2.42130
X

1

0.5

0

0

100
Trial number

200

Fig.4 Cost function.

©ISAROB 2005

626

(11)
Where η is the parameter to determine the neural network
convergence speed. We select the weight vector derived
from the final neural network weights as the standard vector
Γ0 of the equations (2) and (3)Figure 2 shows an example of the plant output (p=1)
using an initial neural network weight. The solid line andthe dotted line show the plant output and the desired value
respectively. Figure 3 shows the final plant output(p=200). As shown in these figures, the neural network
learning well performs and the plant output converges to thedesired value. Figure 4 shows the cost function withregard to the trial number. As shown here, it appears thatthe neural network weights do not change after several tensof trials and the neural network learning is completely
finished. Figure 5 shows the track of the neural network
weight change (p=1~200) through the use of the proposed
tracking method. As shown in this figure, the neuralnetwork weights change relatively large from the first trail
j=1

k=1

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

IECON’90 (16th Annual Conference of IEEE Industrial
Electronics Society), pp.1244-1249(1990)pp.295-302,
1994
1.5

Cost function

p=1 to six trials p=6. After p=6, they change continuously
and this change is not finished at p=200. To verify this,figure 6 shows the expansion of fig.5 which is the track of
the neural network weight change (p=191~200). As
shown here, we confirm that the neural network weights
continuously change at p=200. That is, the neural network
learning is not finished yet at p=200. This fact can not be
observed by use of the cost function shown in fig.4 and the
proposed tracking method is useful for the neural network
performance examination. Figure 7 shows an anotherexample of the cost function. In this example, it also
appears that the neural network learning is finished withintree trials. Figure 8 and 9 shows the track of the neuralnetwork weight change (p=1~200) and its expansion(p=191~200) respectively. The neural network weights
change continuously and the neural network learning is notfinished yet.
As mentioned above, the proposed tracking method isuseful to track the neural network weight change on 2D
plane. This track shows another feature of the neuralnetwork learning performance.

1

0.5

0

0

100
Trial number

200

Fig.7 Cost function.

0.1
0.08

4. Conclusion

Y

0.06
This paper proposed the new tracking method of theneural network weight change. It was applied to thelearning type neural network direct controller and simulated.
The simulation results showed the usefulness of the
proposed tracking method and it could be observed that theneural network weights were continuously changed in somecases although the neural network learning appeared to befinished. The combination of the cost function and the
proposed tracking method is useful to examine the neuralnetwork learning performance more accurately.

0.04
0.02
0
2.415

©ISAROB 2005

627

2.425

2.43

X
Fig.8 Track of neural network weight change(p=1~200).

Acknowledgment
T h e author wishes to express his thanks to
Mr.Chihiro Kaneko, graduated student, Ibaraki University,
for his programming and simulation.

0.002

Y

References[1]K.S.Narendra and K.Parthictsarathy, “Identification andControl of Dynamics System Using Neural Networks”,
IEEE Transaction on Neural Networks, Vol.1, No.1, pp.427, 1990
[2]D.Psaltis, A.Sideris and A.Yamamura, “Neural Network
Controller”, Proceedings of 1987 IEEE International
Conference on Neural Networks, Vol.IV, pp.551-558, San
Diego, 1987
[3]Takayuki Yamada and Tetsuro Yabuta, “Nonlinear Neural
Network Controller for Dynamic System”, Proceedings of

2.42

0.001

0
2.42712.4272X

2.4273

Fig.9 Track of neural network weight change(p=191~200).

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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631

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Predicting selection of artificial network by cluster coefficient
Shinya Yoshimura, Shinichiro Yoshii
Information Science and Technology
Hokkaido University
Hokkaido, JAPANpossess and elements in the systems are almost equal. In
such a case, the system cannot be maintained unless thecollective quantity is reduced. For example, too many
employees are not necessary because it is not effective.
Therefore, the employees are reduced in such a case.
Besides, some efficiency improvements can be obtainedfor production line by reducing employees. This is the
top-down emergence [4]. The self-organization criticality[5] resembles it well. In a problem in real issues, thenumber of the element tends to be reduced after it isexpanded.
However, in case that the number has been reduced in
the simplicity, individual properties of the element areabandoned and the property of the whole system hasconsequentially been destroyed. Then, it is required thatthe number of the element is reduced without changing
the property of the system. In the case of the artificial
network, the relation of the node for which thepersistence node is selected as an evolution is acquired. It
can be expressed with the order formation of minimizing
the system components as an emergence [6]. The
important issue here is, how much of the elements can be
reduced without destroying the property of the network.
In other words, a prediction method by the simulation is
being required. In the case of factory, it is a prediction todetermine how many of employees can be reduced
without changing their process. However, the selection
cannot be predicted simply by the simulation since thesimulation becomes even more complicated and difficult
as it closes to the realistic.
Therefore, the relationship between cluster coefficientand general behavior of the network in the selection of
the system has been investigated. Since selections are
predicted only by network behaviors, the time and effort
for prediction trial calculation can be reduced. It can beapplied to the prediction for the composition result ofserver consolidation of the IT infrastructure in anenterprise. It is a representative example of the artificial
network.

Abstract
In this paper, an artificial network such as A-life istargeted. A predicting method for the selection limit in aheterogeneous system by using a cluster coefficient as anindex, which shows small world structure, is proposed.
As evaluation, evolutions of IT infrastructures in anenterprise have been employed for the study as the mosttypical example. Although IT infrastructures change
rapidly and evolution has been continued, the number of aserver has been increased. In practical, increase of thetotal cost of ownership (TCO), including management
cost, caused by the increase of servers is the current
problem. The relation between the simulation for theevolution and selections in IT infrastructures, and cluster
coefficient were observed. The result shows that cluster
coefficient and selection predictions are related. In fact,
the maturity for a system can be obtained by observing acluster coefficient for IT infrastructures simply. The
number of heterogeneous elements shows the selection
limit. The selection limit rate at which the global behavior
is not destructed can be obtained from these two
measurement values, without performing simulations.

1

Introduction

In information processing based on A-life, information
processing which can be realized by employing a groupthat varies dynamically, maintaining redundancy anddiversity, has been argued. In an artificial network, global
behaviors always change being accompanied with local
interaction fluidly as well. The artificial network for thispaper is defined as the one in which the global behavior isdetermined artificially.
A grid computing [1] which requires common global
behavior even in the case where it is distributing locally,
Web service [2] which forms a processing system on acommon network, and the IT infrastructure [3] of thecompany which connote a large number of theheterogeneous element are the examples of an artificial
network.
In an artificial network, after global behavior, such as
evolution and selection, are decided artificially, a local
interaction occurs. The quantity of elements mainly
becomes a problem in such cases. In most of those
problems, the quantity of the capacity that systems©ISAROB 2005

2

Artificial network

The artificial network described in this paper shows
the relationship between an element as a center and otherelements. It is formed by the top which shows edge andan element which shows the interaction in the simplicity.
They are shown in Figure 1.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The cluster coefficient [7] which shows small world
structure as an index is introduced here. Since a cluster
coefficient is an index with which the element itself
carries out the self reference of the relation with otherelements, it is believed to be effective for investigating
the relation of an artificial network. In other words, incase where a cluster coefficient is compared to an
acquaintance relation, their acquaintances becomeacquainted and come out and a certain probability isexpressed.
The cluster coefficient is obtained from the equation
below under the condition that Γv is a neighborhood ofvertexedge

Figure 1. Artificial network

v , E is a set of Γv edges and k v is the node number ofthe neighborhood.

rv =Figure 2. IT infrastructure in the enterprises describedby artificial network

E (Γv )⎛ kv ⎞⎜⎜ ⎟⎟⎝2 ⎠Next, an actual artificial network is measured based on
the cluster coefficient.

Figure 2 shows an enterprise IT infrastructures. The top
as a center is client PC, and the relationship between theservers for the treatment is shown in the circumference.
Set of vertex and edges indicate the property of thesystem. The form shown Figure 2 indicates therelationship between the systems. Therefore, the propertyof the system is destroyed in case that the top is reduced
simply. It is necessary to acquire the relation of the nodein which the persistence node is selected in order tomaintain the property of the system. In other words, theedge re-connects with the peak which substitutes for theneighborhood and is maintained even in case that the
number of the peak reduces. It is shown in Fig. 3.

4 The simulation of the selection in the IT
infrastructure
The network shown in Figure 4 which imitates the IT
infrastructure as a representative example is created, andthe simulation of the selection is executed. This resembles
the structure of actual IT infrastructure shown in Figure 2,
and almost the same form can be expressed.

1

a14
a24

4

a01
a02
・・・・

2

a15
a25

5

a03

3

selection

a47

7

・・・・

・・・・

8
・・・・

6

9

0
・・・・

Figure 3. Behavior in the selection

Figure 4. Simulation of artificial network

In case where the artificial order in which the element of
the system is minimized is given, decrease of the top andreconnection of the edge are repeated as shown in Figure
3 while the element maintains the property of the system.
However, this behavior can be obtained only in casewhere element and substitution element selected are
homogeneous relation. It is not possible to obtain thisbehavior in case that the relation is heterogeneous.

A random network is generated for each top. The edge isexpressed by aij . The edges and the tops are determined3

randomly. It is expressed by the equations below.
∧∧

a is determined as 1 or 0 randomly.

Cluster coefficient©ISAROB 2005∧∧

a0i = a0i , aij = a0i ⋅ aij , a jk = a0i ⋅ aij ⋅ a jk

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In the equal type (homogeneous), each top possesses theparameter of type and cost and the selection shall be
repeated as long as the parameter of the cost permits it. In
the other type (heterogeneous), the selection is not
generated. This transition is shown in Figure 5.

selected even at the maximum, in the case that 40% of itis the hetero genius. It will be able to estimate the cullingrate of the degree in which the system does not collapse
by these two measured value without performing thesimulation.

5

N=360,T=3,C=0

N=150,T=3,C=0.27

In this paper, the relationship between the transitions
for the selection of the cluster coefficient in an artificial
network has been clarified. The relationship between
them is almost an inverse proportion. The selection ratio
can be estimated regardless the number of the element incase where the cluster coefficient can be measured using
the property. The local behavior with the order in which
the number of the element in a system had to beminimized has been clarified by the change of the cluster
coefficient.
However, the parameter of precision criterion and costs
and types are not mentioned this time. These will be oneof our targets in the future.

N=360,T=3,C=1.0

Figure 5. Change of network
N is the number of the vertexes and C is a cluster
coefficient. C, at this time, changing from 0 to 1 finally is
noticed. Focusing on the selection simulation, the 100,
200, 500, and 1000 peaks and the type parameter were
fixed to the cluster coefficient of 0.3, and the simulation
was performed 50 times per each. The result is shown in
Figure 6.

6

0.9
0.8
0.7

n/N

0.6
0.5
0.4
0.3
0.2
0.1
0
0.2

0.4

0.6

0.8

1

C

Figure 6. Selection ratio and cluster coefficient
A vertical axis is a selection ratio and a horizontal axis isa cluster coefficient. A cluster coefficient and a selection
ratio have the relation of an inverse proportion. Therefore,
in the artificial network of IT infrastructure, in case wherea cluster coefficient is investigated, a grade of theselectivity can be obtained. For example, a selection
ratio is 0.7 supposing Figure 6 to a cluster coefficient is0.2. Selection will already have been performed about 0.3
from the original element. That is, in IT infrastructure, incase where a cluster coefficient is investigated simply, thedegree of a system’s maturity is obtained. Then, theparameter of consequences and types shows the selection
limit, as the hetero genius element can not be selected.
For example, it is clear that 60%of a system can be©ISAROB 2005

Reference[1] Adrian Li Mow Ching (2002), "Super resourcemanagment
for
Grid
Computing", London
Communications Symposium.
[2] Haverty, P. M. (2004), "CARRIE web service:automated transcriptional regulatory network inference
and interactive analysis", Nucleic Acids Res 32, W213W216.
[3] Sherri Smith (2004), "Successful server consolidation:
it's all in the preparation",
http://h200001.www2.hp.com/bc/docs/support/SupportM
anual/c00257494/c00257494.pdf , 2004.
[4] kanji ueda (1996), "METHOD ON ARTIFICIAL
LIFE", Kogyo Chosakai Publishing[5] B. A. Carreras (2002), "Dynamics, Criticality and
Self-organization in a Model for Blackouts in Power
Transmission Systems",Proceedings of the 35th HawaiiInternational Conference on System Sciences.
[6] Albert-Laszlo Barabasi (1999), "Emergence of
Scaling in Random Networks", SCIENCE VOL 286, pp.
509-512.
[7] D.J. Watts S.H. Strogatz (1998), "Collective dynamics
of "small-world" networks.", Nature 393, pp440-442.

10Conclusion

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Evolution of Development and Heterochrony in Artificial Neural Networks
Artur Matos
Reiji Suzuki
Takaya Arita
Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 461-8601, Japan
amatos@create.human.nagoya-u.ac.jp, {reiji,arita}@nagoya-u.jp
Abstract

for foraging food in a simulated environment. By comparing the development processes between ancestor and descendant networks, he could observe changes in the timingof developmental events. However, this analysis was only
done for a small number of individuals, and it was not extended to a whole phylogenetic tree. Thus, studies focusing
on heterochrony in a large scale in artificial systems arestill lacking. Specifically, these questions are still largely
unanswered: 1) What kind of heterochrony processes are
more common in artificial systems? 2) How does the developmental model and the evolutionary parameters affectheterochrony? 3) How does heterochrony in artificial andbiological systems relate to each other? These points are
the main motivations behind this paper.
In attempting to answer these questions, we applied theframework defined by Alberch et al [2] for biological systems to the evolution and development of neural networks.
This framework defines a precise terminology for classifying heterochrony. For simulating neural network development, we used the cellular encoding model first described by Gruau [1]. This is one of the earliest models described in the literature, and representative for grammar based models of development. We used this encoding
coupled with genetic programming for solving the odd-3parity problem and then analyzed the resulting growing dynamics on important traits like the number of neurons, theiraverage degree and also how the fitness value itself changes
within ontogeny.

Recently, evolutionary algorithms coupled with simulated developmental processes have been used successfully
for generating designs ranging from neural networks to
artificial creatures. Although several of these models do
exist, their evolutionary dynamics, and more specifically
how the ontogenic models themselves interact with artificial evolution are still poorly understood. One of these specific interactions, and of particular importance in biological systems is heterochrony — the change in timing andrate of developmental events by evolution. In this paper,
we analyze heterochronic change in one artificial developmental model - the cellular encoding model first describedby Gruau [1]. For this purpose, we apply the frameworkand methods defined by Alberch et al [2] for biological systems to neural networks evolved for the odd-3-parity problem. Preliminary results show that: 1) All heterochronicchanges occur with significant frequency; 2) The combined
effects of predisplacement, hypermorphosis, and neoteny
was the most common heterochronic change; 3) Pure recapitulation (isomorphosis) is prevalent.
Keywords: Heterochrony, evo-devo, Cellular encoding,
Artificial embriogeny, Neural Networks.

1 Introduction
Evolutionary algorithms have been used successfully
for generating designs ranging from neural networks, tofull artificial creatures with both generated morphology
and behavior. One of the reasons for this recent successis due to the integration of simulated developmental processes with evolutionary algorithms. Although several ofthese models do exist, their evolutionary dynamics, andmore specifically how the ontogenic models themselves interact with artificial evolution are still poorly understood.
In biological systems, one of the key concepts in this interaction is known as heterochrony. Heterochrony, as it isusually defined in evolutionary biology, is the change in therate and timing of developmental events caused by evolution. Heterochrony is prevalent in the evolution of species:
some examples include the conservation of juvenile characters in salamanders and the loss of the tadpole stage intoads. Due to this, there is a wide array of studies and data
available on heterochrony in biological systems.
For artificial neural networks, previous studies haveshown that heterochrony does indeed occur in artificial systems: For instance, Cangelosi [3] evolved neural networks©ISAROB 2005

2 Alberch et al’s framework
The framework by Alberch et al for classifying heterochrony is widely used for biological systems. This
framework is based on the measurement and comparisonof quantitative traits, for instance, body length, width orheight. The traits are measured as development unfolds,
yielding growth curves. These growth curves can then becompared between related species for understanding theheterochronic change involved.
The basis for comparison lies on three metrics that canbe extracted from the growth curves: α — the time whengrowth starts, β — the time when growth ends, and K —the growth rate. Comparing these values between species
yields the outcomes summarized in figure 1. For instance,
considering only changes in the K parameter, two outcomes are possible: if the descendant would grow fasterthan the ancestor (K would be larger), the corresponding
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didn’t produce a good performance so we used the fitness
function used by Gruau in [1]. It is defined by:f (outeval ) = 1 −I(outright , outeval ),H(outright )(1)where outeval is the output vector of the evaluated networkand outright the expected correct output vector for the problem. I(X,Y ) is the mutual information between X and Y :1

1

I(X,Y ) = ∑ ∑ PXY (x, y) · log2
x=0 y=0
PXY (x, y),
PX (x) · PY (y)(2)

and H(X) is the information entropy of X:

Figure 1: The formalism of Alberch et al. A trait measureis plotted against developmental time in the X axis. The
solid line plotted from α to β represents the growth curvefor the ancestor, while the remaining ones possible heterochronic outcomes for the descendant.

1

H(X) = ∑ PX (i) · log2 (PX (i)),

(3)

i=0

with PX (x) as the probability of X = x, and PXY (x, y) as thejoint probability of X = x and Y = y. This fitness function is defined in the range [0, 1], with 0 as the best fitness.
Please note that due to using mutual information, either
correct networks or networks that output the inversed expected output will have the same best fitness.
We adopted a GP based system without crossover, andtournament selection was used. Each individual is mutated(addition, deletion, replacement of nodes) with a certain
probability, while the others are simply reproduced. The
individuals are therefore connected among generations byeither reproduction or mutation, forming lineages.

outcome is acceleration. The reverse process — the descendant growing slower than the ancestor — is labeledneoteny. Furthermore, heterochronic changes can be combined on the three parameters, as for instance postdisplacement and hypermorphosis would refer to an increase inboth α and K respectively.

3 The model
The phenotypes in this model are simple boolean neural networks: Nodes are simple threshold neurons, with athreshold of either 0 or 1. The connections between neurons can either be -1 or 1. Neurons are activated if the sumof the values on their incoming connections is above theirthreshold.
For the developmental model, we used the cellular encoding model by Gruau [1]. In this model, the neural network starts as a single neuron and undergoes several developmental events as specified in the genotype. The genotype is represented as a Genetic Programming (GP) treewith nodes as developmental commands such as neuron division, setting the weight and threshold and similar. Eachneuron has a pointer to the GP tree representing its current developmental stage. Development occurs in a parallel fashion: in each time step, each neuron executes thecommand pointed by its register in the tree and moves tothe following leaf. The arity of each GP node depends onthe command used: for instance, commands for neuron division have two children, representing separate programsfor each of the daughter neurons. Development for the network finishes when all the neurons have reached their final
leaf node in the tree.
Using this model, we evolved networks for the 3-oddparity problem, a standard problem for GP. The solution
is defined as a neural network with at least 3 inputs, that
outputs true whenever the number of true inputs is odd.
A fitness function based on the number of wrong outputs©ISAROB 2005



4 Basic results
Using the model and fitness function described in theabove section, we evolved networks for analyzing heterochronic change. Population size was 200, and the mutation rate was set at 80%. The fitness graph for a typical run
can be seen in figure 2 and the best network in figure 3. Allof the results reported on this article are from this run. We
can see that the average fitness gradually decreased while
the best fitness decreased discontinuously, and this model
was able to solve the problem in 314 generations.
1

Average fitness
Best fitness

0.9
0.8

Fitness

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0

50

100

200
150
Generation

250

300

Figure 2: Fitness graph for a typical run.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
1
O0 -1 T0
I -1 T0
1
I -1

1

1

T0 1
T0
1
1
1
T0 1 T1 1 T0
1
-1
1 O0
1
1 T0
1
T1
T0
I
1
1
1
1
T1

1

I : the input neuron
Ox : the ouput neuron
whose threshold is x
T0
Tx : the neuron whose-1
threshold is x
1 T0 11
1 T0 1 O0
T0 1
1
T0
T0

Table 1: Summary for the determination coefficient for different growth models, on the number of nodes trait. R2 , thedetermination coefficient, indicates how well the model fits
the data, with 1 being a perfect fit. The value shown is theaverage R2 value among all the individuals in the winninglineage (the lineage of the best individual in the last generation).
Model
Average R2

Figure 3: The best neural network for the run. Please notethat the cellular encoding model may produce more inputs
and outputs then necessary, as it can be seen in this case,although only one output is actually used. The output nodein the center is the used one.

Linear regression
Von Bertalanffy
Sigmoid

5 Measuring heterochrony

ing on the trait used. The number of nodes trait seems tofollow dynamics similar to biological systems, so we attempted to fit the data to growth models commonly described in the literature. We attempted the Von Bertalanffy’s growth function defined as:

0 20 40 60 80
Developmental time(b)

Intermediate fitness
0.0 0.4 0.8

Avg connectivity
0.0
1.5
3.0

Number of nodes
5
15

Next, we analyzed the networks for the following traits:
number of nodes, average connectivity (taking into accountboth incoming and outgoing connections) and how the fitness value changes within ontogeny. Sample growth curves
for the best individual on each trait can be seen in figure 4.
One significant problem in using the Alberch et al’s
framework described before is how to extract significant
α , β and K parameters from the growth data. This is also
an issue in biological systems although they tend to follow more regular patterns. One common approach is to use
non-linear regression to fit the data to a growth model andextract the parameters from the fitted model. This, for instance, was applied by Creighton and Strauss [4] to rodent
growth data.
This approach works well for biological systems because their growth dynamics tend to follow regular patterns
and there are several sensible mathematical models. Thesemodels fit well the data and are grounded on experimental
evidence. In contrast, our artificial developmental model
can generate rather irregular growth curves — with sudden
reverting ontogenic polarity as it can be seen in figure 4 (b)or even with sharp discontinuities as in figure 4 (c).
Another possible approach is simply to extract the values directly from the experimental data, for instance K
could be defined as the average growth increment during
the development period, or estimated by linear regression.
An example of this approach can be seen in Pigliucci [5].

0 20 40 60 80
Developmental time(a)y = Sa(1 − e−k(t−t0) ),(4)

and also the standard sigmoidal function defined as:
Sa.
(5)
1 + e−k(t−t0 )In both functions, Sa stands for the maximum value
achieved during growth and k for the speed of growth. We
used a Gauss-Newton algorithm for fitting the data, with Sa
fixed to the last value reached during development and theother remaining parameters (k,t0 ) were initialized to adequate starting values. A summary of the results for the fit
can be seen in table 1. The determination coefficient R2
was the highest on average for the sigmoidal function andtherefore we adopted this model . We defined K as thesame parameter k in the sigmoidal function, α and β as theperiod when the model reaches 10% and 90% of the total
growth Sa respectively. This approach is coherent with theabove mentioned study by Creighton and Strauss [4]. An
example of this fitting can be seen in figure 4 (a).
For the other remaining traits, we used simple linear
regression and defined K as the slope of the fitted line.
This approach was used because the other models assume
monotonous increase in the trait while the developmental
curves in these traits can decrease. α and β were definedas the period where growth can be observed to effectively
start and stop in the data. Examples of these fittings canbe seen in figures 4 (b) and 4 (c). This approach cannot be
considered completely adequate as the fitting is not sufficient (R2 is low due to the complexity of the developmental curves in these traits), but nevertheless it allows to haveconsistent values for the three parameters.
By comparing the parameters between related individuals in the lineage it is therefore possible to classify theheterochrony process involved as depicted in figure 1. Figure 5 shows the transitions of α , β and K for each trait
in the winning lineage. In the transitions for the numberof nodes trait, we can see the gradual decrease in α andthe increase in β with large fluctuations, and that K converged to a small value. We also see transitions similar toy=

0 20 40 60 80
Developmental time(c)Figure 4: Growth curves for the analyzed traits on the bestindividual. Both growth data and fitted curves are shown.
For the number of nodes, a sigmoidal curve was fitted,
while simple linear regression was used for the other traits.
In this paper we decided to use both approaches depend©ISAROB 2005

0.933
0.968
0.974

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the number of nodes in the average degree trait, except forthe steady evolution of α , and a quite different evolutionof all parameters can be seen for the fitness trait. Figure 6
shows the occurrences of the combined heterochrony processes for the number of nodes trait in the winning lineage.
Isomorphosis refers to no change at all in any of the parameters between ancestor and descendant.

I
PostD+N+HM
PostD+A+HM
PostD+A+ProG
PreD+N+HM
PreD+N+ProG
PreD+A+ProG
0

20

40
60
80
Occurrences

100

120

Figure 6: Occurrences of heterochrony for the number ofnodes trait on the winning lineage. I - isomorphism; PostD- postdisplacement; PreD - predisplacement; N - neoteny;A - acceleration; HM - hypermorphosis; ProgG - Progenesis.
This framework is particularly well suited for artificial systems because it is solely based on observable quantitative
traits and it can be applied regardless of the epigenetic process involved. This should be particularly important whencomparing heterochronic change between different developmental models, as for instance, cell chemistry and grammar based models. Future work will therefore continue
on extending this analysis for different models and fitness
functions.

Figure 5: Evolution of the α , β and K parameters for thewinning lineage on all traits.

Acknowledgments
The simulated evolutionary experiments were conducted with the ECJ (Evolutionary Computation in Java)package. I would like to thank both Sean Luke andGabriel Balan for their helpful assistance with some technical ECJ programming details. This research is funded witha Japanese MEXT scholarship (400000357010989).

As it can be seen in figure 6, all possible heterochronicchanges occur with significant frequencies. One interesting point is that pure recapitulation (isomorphism) seems
to be a common heterochronic change in these runs. Evenaccounting for errors in estimating the parameters, thisshould be significant considering the low reproduction rate(20%). The fitness function shows sharp discontinuities (ascan be seen from figure 2) and therefore it may be exerting
selection pressure for either neutral or invalid mutations.
The most frequent heterochronic change is the combination of predisplacement, hypermorphosis and neoteny. The
net effect of this combination is that developmental time increases on the descendant. This can also be observed on thegradual increase in the β parameter in figure 5. In the cellular encoding model, developmental time is roughly proportional to the GP tree size; Therefore developmental timeis expected to increase, as in GP systems the average tree
size gradually increase with evolutionary time (tree bloat).

References[1] Frederic Gruau. Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm. PhD thesis,
Laboratoire de la Informatique du Parallelisme, Ecole
Normale Superieure de Lyon, 1994.
[2] Pere Alberch, Stephen Jay Gould, George F. Oster andDavid B. Wake. Size and shape in ontogeny and phylogeny. Paleobiology, 5(3): 296–317, Summer 1979.
[3] Angelo Cangelosi. Heterochrony and adaptation in developing neural networks. In Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1241–1248, Morgan Kaufmann, 1999.

6 Conclusion[4] G. K. Creighton and Richard E. Strauss. Comparative patterns of growth and development in cricetine rodents and the evolution of ontogeny. Evolution, 40(1):94–106, January 1986.

As the use of simulated developmental processes increases, it becomes more important to understand their dynamics. In this paper, we have shown that the framework
by Alberch et al is a valid method for studying dynamics in artificial systems, by measuring heterochrony in theevolution of neural networks. We successfully observed
heterochrony in the number of neurons trait such as the frequent occurrences of the combination of predisplacement,
hypermorphosis and neoteny, and also pure recapitulation.
©ISAROB 2005

[5] Massimo Pigliucci. Ontogenic phenotypic plasticity
during the reproductive phase in arabidopsis thaliana(brassicaceae). American Journal of Botany, 84(7):887–895, July 1984.
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Multi-Procedure Ozone Concentration Prediction
using Fuzzy Clustering and DPNN
Seong-Pyo Cheon, Seng-Tai Lee, and Sungshin Kim
School of Electrical and Computer Engineering, Pusan National University, Busan, Korea
Pusan National University Changjeon-dong, Keumjeong-ku, Busan 609-735, Korea
E-mail: buzz74@pusan.ac.kr, youandi@pusan.ac.kr, sskim@pusan.ac.kr
Abstract
Ozone reaction is a complex mechanism because of its
intrinsic complexity and nonlinearity. It is not easy to predict its
concentration using traditional numerical and statisticalmethods. Especially, we found that conventional single model
could not get corrected ozone concentrations at high range. We
propose a multi-procedure model to overcome the complexity
and nonlinearity of ozone concentrations. At first, we suggest
fuzzy clustering method to divide high- and low- concentrationgroups for input ozone data in past two years. Then, we
determine proper input data group and its data are used to input
data for DPNN models. We divide two groups for input ozone
data because the results of the proposed model seriously
depend upon their inputs. Preprocessing and postprocessing
algorithms are applied to the input data as two steps in order to
get more accurate and reliable prediction results of ozone
concentrations.
Keywords: Fuzzy clustering, DPNN, multi-procedure model
1

Introduction

One of the emerging major issues about air pollution is theabnormal ozone concentration of the troposphere in summer.
High concentration ozone is strong oxidizing material which is
responsible for various adverse effects on both human being
and foliage. And it frequently appears metropolis in thedaytime from June to August in summer. In general, it revealed
that the features on ozone distribution and creation are closely
related to the photochemical reaction and meteorologicalfactors. So far, there is not enough information about detailed
cause and effect of the ozone. In the ozone reaction mechanism
around the troposphere, nitric dioxide and hydrocarbon which
are components of exhaust fumes act as precursors andultraviolet radiation, wind speed, and temperature play the roleof meteorological materials. Therefore, ozone is a second
pollution material. To reduce harms by high concentrationozone, it needs prediction system which forecasts maximum
ozone concentration every morning in summer.
There are some conventional methods to predict ozone
concentration. For instances, multiple regression model [1] bystatic methods, a multivariate analyses and artificial neuralnetworks [2] have been developed and applied to predict ozone
concentration. However, it does not show a good prediction
performance. We analyses major related reasons as follows:First, ozone is a kind of second pollution material. Due to both
first pollution ones and other related factors affected on its©ISAROB 2005

639

behavior and concentration, it is impossible to create complete
model which was considered all factors. Second, there are notsufficient data when high concentration ozone appeared. Third,
we couldn’t get ultraviolet ray data directly. So, we couldn’t
help using solar radiation data that we estimated ultraviolet ray
indirectly. Finally, we couldn’t be considered that precursors
inflow from long distance. As shown in Figure 1, we suggested
multi- procedure ozone concentration forecasting system. In
this paper, we introduce a multi-procedure model which
consists of preprocessing, dynamic polynomial neuralnetworks (DPNN) models, and postprocessing. Fuzzyclustering method as a preprocessing is employed to decide thefuzzy sets of the low and high ozone concentration. After
clustering the two fuzzy sets, the different two models are
determined by the DPNN. The decision making process as a
postprocessing is applied to forecast the ozone concentrationby the compensation of the weight factors to the output of thetwo models.
The proposed forecasting system is adaptively constructedby a successive basic structure of the DPNN. Also, important
input variables for the final structure of the forecasting system
are selected from the possible input variables by a selection
criterion. The historical data that consist of pollution materials
and meteorological information are divided into training data
and testing data to identify dynamic system and to prevent
overfitting. The structure of the final model is compact and thecomputational speed to produce an output is faster than othermodeling methods. The proposed method shows that theprediction of the ozone concentration based upon the DPNN
gives us a good performance with ability of superior dataapproximation and self-organization.
Preprocessing
O3
NO2
CO

Fuzzy
ClusteringM

Postprocessing

M

M

L

M

Inference

Ozone
Prediction

O3 MaxTair Max
Dynamical Polynomial
Neural Networks

Figure 1. The full structure of the ozone prediction system.
2

Fuzzy Clustering
The fuzzy c-mean algorithm (FCM) generalizes the hard

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Bi

Output space

y
Ai

x2

Input

Layer1

x1
x2

Node1

Layer2

y1
Node3

2

x3
x4

Input space

Node2

yˆ

y2

x1

Ai1

z -1

Figure 2. The mapping of input variables into output spacec-mean algorithm to allow a point to partially belong tomultiple clusters. Therefore, it produces a soft partition for agiven dataset[3]. FCM of Bezdek [4] is normally applied for the
fuzzy clustering. In this paper, fuzzy space classification using
FCM based on the similar feature of ozone concentration
output data is implemented to compute the classified degree of
input

Figure 3. The basic structure of DPNN.
Equation (3) and (4) show the objective function and thecoefficient, respectively. Parameters are solved by the leastsquare method and polynomial functions of current node are
structured at each of layer. This process is repeated until thecriterion is satisfied. Thereafter, we could finally get the best
function for the best performance.

variables. Figure 2 illustrates that the output space classified byfuzzy clustering is mapped to input spaces, and then the related
features between input variables and output data are determinedby fuzzy clusters.
3

Output3.2 Self-Organization
Another specific characteristic of DPNN is self-organization[6]. The DPNN based on the GMDH method separates data intotraining data and testing data for modeling [7]. The purposes ofthis stage are to identify the behavior of the dynamic systemand to prevent overfitting problem. The DPNN estimates theparameters of each node and composes the network structure of
dynamic system using two-separated data sets. Training data
set is used to solve the parameter of function of each node andtesting data set is used to evaluate the performance of DPNN.
The final network structure is constructed by the relationship oferror in training data and testing data. Therefore, the DPNN
selects the input of the next node under a performance
criterion(PC) that is the relationship between training error andtesting error at each node. The final network structure isdetermined as shown in Figure 4. The PC could be determinedby following Equation (5), where is existed in the range of 0∼1.
The model performances are also evaluated by Equation (5).
This performance criterion can be applied for the testing data

Dynamic Polynomial Neural Network

3.1 The Basic Structure of DPNN
DPNN uses GMDH (Group Method Data Handling) method[5] to compose an input/output model based on observed data
and variables. This method is widely used for modeling ofsystem, prediction, and artificial intelligent control. As shownin Figure 3, the simple DPNN structure has four inputs and one
output at each node. Following polynomial equations between
input and output are used at each node in the DPNN. Output y1
and y2 at each node are expressed as follows.

y1 = w01 + w11 x1 + w21 x2 + w31 x1 x2 + w41 x1 + w51 x222y2 = w02 + w12 x3 + w22 x4 + w32 x3 x4 + w42 x3 + w52 x4
2

(1)
2

The final output yˆis represented by a polynomial equation.
22yˆ= w03 + w13 y1 + w23 y2 + w33 y1 y2 + w43 y1 + w53 y2(2)

where, wij (i=0,1,2,…,n, j=0,1,2,…,k) is the coefficient. If input
variables of each node are more than three, other combination
terms of input variables are added to the above equation. The
least square method is employed to estimate the parameters ofeach node in the DPNN. And it searches the solution ofparameters to minimize the objective function formed by error
functions between node outputs and actual target values.

J=#of datay − wA ||∑ ( y(k ) − yˆ(k )) = ||
2

2

(3)

Figure 4. The variation of model performances corresponding
to increment of layer.

k =1

−1

w = ( A A) A y
T©ISAROB 2005

T

(4)

640

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and the training data. And also it can be used for the unprepared
new data.

5nA

Ozone, CO, NO2, SO2, TSR, wind speed, wind direction,
temperature, solar radiation, humidity, and rain fall are used forthe parameters of air pollution materials and meteorologicalmaterials in ozone prediction systems. Within the data, theamount of rainfall is normally 0mm at high-level ozone, so itcannot influence the high-level ozone prediction. And TSR andSO2 are skipped because these values are decreased by the
restriction of air pollution material and wind direction is also
excluded due to the difficulty of quantification. Therefore,ozone, CO, NO2, wind speed, temperature, humidity, and solar
radiation, maximum O3 of previous day and maximum
atmosphere temperature of previous day are chosen as thepossible input variables. Because the daily maximum of ozone
concentration is appeared at 2∼5 p.m., the prediction of theozone concentration for this time is the goal of this paper.
Within the data, ozone, CO, and NO2 are extracted frommorning data and the other data are selected at 2∼5 p.m. These
data structure could show as Table 1. In the first simulation, thenumber of clusters is applied from 2 to 4. Basically, high-levelozone used the highest value and low-level ozone consists of
the other set. Figures 6, 7 and 8 show the ozone prediction
results of several areas in Seoul, Korea from August 1 to 10,
1997. Total data are constructed by the data from May to
September in 1996 and from May to July in 1997. And thetraining data and testing data are selected from among the total
data. When the input variables are applied to predict ozone
concentration, those of data are classified by the predicted timeand measured time. The upper column point out the input
variables and the higher columns of inner cells indicate the time.
The left row is a number of data. In this simulation, a model ischosen based on the RMSE that is yielded from the selectedmodel. This model is determined after clustering the training
data. When the number of the cluster is 4, the lowest training
RMSE is 27.918 and the prediction RMSE is 20.183. The slope
and intercept values for R-square are displayed in the scattergraphs of ozone observation (x-axis) against the predicted

e12 = ∑ ( yiA − f A ( xiA )) 2 / nA ,i =1nB

e22 = ∑ ( yiB − f B ( xiB )) 2 / nB ,

(5)

i =1

PC = e12 + e22 + η (e12 − e22 ) 2
where, e1, e2, nA, nB, and yi indicate training errors, testingerrors, the number of training data, the number of testing data
and measured outputs, respectively. And f A ( xiA ) and f B ( xiB )are outputs of training data and testing data separately. Total
number of data is n=nA + nB. From the results, the optimized
model structure is constructed at the point of minimized PC.
4

Fuzzy Inference in the Postprocessing

High and low levels are classified based on ozone
concentration data in the postprocessing and then ozone
concentrations are predicted by DPNN. The predicted results
are shown in Figure 5. As shown in Figure 5, the predicted
results are represented as YL and YH. Yi and Yj are calculated by
the means and variations of two input variables Xi and Xj (i≠j),respectively. In this case, the two input variables have thelongest distance between high and low level concentrations. It
means that the Xi and Xj affect strongly to ozone concentrations.
The outputs of the Yi and Yj influenced by the Xi and Xj arecomputed by the fuzzy inference in the Equation (6).
Yi =(0.7 × YL ) + (0.3 × YH )0.7 + 0.3

(6)

(0.2 × YL ) + (0.8 × YH )Y =0.2 + 0.8j

The final prediction result based upon the decision support
system is decided by Equation (7). In this equation, the weights
WXi and WXj are the relative distances of the input membershipfunctions.
Yˆ=For XiTable 1. Prospective input variables and data structure(WX i × Yi ) + (Wx j × Y j )(7)

Wxi + Wx j
Center of area

0.7

WXi
Input Xi

9
8
0

YL

Yˆ
For XjCenter of area
Center of area
0.8
Yj

WXj

0.2
Input Xj

NO2

CO

Rh

Sr

Ws

O3 MaxTair Max

O3

2
4
5

6

6

6

14

14

14

PRE

PRE

14

2
4
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7

7

7

15

15

15

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PRE

15

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PRE

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* Tair : Atmosphere temperature, Rh : Relative humidity, Sr : Solar radiation,
Ws : Wind Speed, O3 Max : The Maximum O3 of previous day, Tair Max : The
Maximum atmosphere temperature of previous day.

Postprocessing

Figure 5. The fuzzy inference in the Postprocessing.

©ISAROB 2005

O3

Yi

0.3

YH

Performance Assessment

641

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Figure 6. The prediction results for BangHak-Dong.

Figure 8. The prediction result for Ssang-Mun-Dong.

values (y-axis) for each model. The two diagonal lines in theplots represent the best-fit regression and the perfectcorrespondence between observations and predictions [8]. In
the second simulation, the predicted area is Ssang-Mun-Dongin Seoul, which is high-level ozone area in the summer. The
predicted period is from May to July in 1999. The training data
and testing data are constituted by the data from May to
September in 1996, 1997 and 1998. In this ozone prediction,
missing data are interpolated by the spline interpolation method.
For the decision support system, a low concentration model anda high concentration model are constructed by the fuzzyclustering method based on the basic training data. In thissystem, mean values and standard deviations are firstly foundwith respect to input variables of each model and then required
membership functions are selected by the correlative distanceof each membership function. The number of clusters is applied
from 2 to 4. Basically, the high-level ozone uses the highest
value and the low-level ozone consists of the other set. Figure 7
shows the result of the ozone prediction the period from May
20 to July 20 in 1999. When the number of the cluster is 4, thelowest training RMSE is 15.634, and the prediction RMSE is
18.034. In the decision support system, prediction data arepassed through the preprocessor and then the low and highconcentration models fulfill the prediction processing.
Thereafter, the final outputs are obtained by the outputs,

which are by the two models at the postprocessing. At the
postprocessing, the outputs of the models are handled bymembership functions, which are formed by the fuzzyclustering.
6

In this paper, we propose the multi-procedure prediction
system using various approaching methods. i.e. Fuzzyclustering method, DPNN, and weighted combining
postprocessing. The model designed by DPNN, which includes
decision support system, is suitable for the high concentration ozone prediction. When the models are daily updated based on n
ew input variables, the prediction performances are getting bet
ter than the results by the fixed model. And it is confirmed thatthe selection of the cluster number in the fuzzy clustering is also very important for the high-level ozone. Finally, the combini
ng over two models, using various input selection and optimizi
ng the structure of models will fulfill better performances.
References
[1]

[2]
[3]
[4]
[5]
[6]
[7]
[8]

Figure 7. The prediction results for GooEui-Dong.

©ISAROB 2005

642

Conclusion

Sungi Lee and Chong-Bum Lee, “The development of the AR model toestimate photochemical pollution concentration in Seoul,”
Meteorological research paper, 1991, pp 71-85.
Duc Trung Pham and Liu Xing, Neural Networks for Identification,
Prediction and Control, Springer-Verlag Inc., 1995.
Sungshin Kim, “A Neuro-Fuzzy Approach to Integration and Control ofIndustrial Processes: Part I,” KFIS, 1998, pp 58-69.
James C Bezdek, Pattern Recognition with Fuzzy Objective Function
Algorithms, Plenum, 1981.
A G Ivakhnenko, “The Group Method of Data Handling in Prediction
Problem,” Soviet Automatic Control, vol 9, no 6, 1976, pp 21-30.
S Farlow, ed., Self-Organizing Method in Modeling: GMDH-Type
Algorithms, Marcel Deckker Inc., New York, 1984.
A G Ivahnenko, “Polynomial theory of complex system,” IEEE trans.
System. Man and Cybernetic, 1971, pp 364-378.
Greg Spellman, “An application of artificial neural networks to theprediction of surface ozone concentrations in the United Kingdom,”Applied Geography, 1999, pp 123-136.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A model of emergence of reward expectancy neurons
using reinforcement learning and neural network
Shinya Ishii and Katsunari Shibata, Oita university
Munetaka Shidara, National Institude of Advanced Industrial Science and Technology
Abstract

In an experiment of mult-trial task using a monkey in which some successful trials are required until itgets a reward, some neurons that relate to reward expectancy have been observed in the anterior-cingulate in its brain.
The reward expectancy neuron is activated in each trial except for the reward trial. Therefore, it is difficult to explain
the emergence of the neurons simply by reinforcement learning. In this paper, a model that consists of a recurrent neuralnetwork trained based on reinforcement learning is proposed. From the simulation of the model, it is suggested thatsuch neurons can emerge to realize an appropriate value function in the transition period from the single-trial task to themult-trial task.
Keywordreward expectancy, anterior cingulate, reinforcement learning, recurrent neural networkf           ONv        N

1 Introduction     It is thought that motivation and reward expectancy
must be related to our action learning. Recently, in anexperiment of multi-trial task using a monkey in whichsome successful trials are required until it gets a reward,
some neurons that relate to schedule fraction, motivation
and reward expectancy has been observed in the Anterior
Cinglate and the Ventral Striatum in its brain by Shidara
who is one of the authors[1]. These neurons belong to theloop circuit that is working when taking actions in response
to important stimulus for emotion or motivation. The Anterior Cinglate is located in the frontal cortex, and has
nerve fiber connections with various areas such as prefrontal
cortex, supplementary motor area and limbic system, andbears an important role for motivation. On the other, theVentral Striatum is located in the basal ganglia. It is reported that the basal ganglia relate to action learning basedon reward, and some models has been already proposed to
explain the reward-related motion generation based on reinforcement learning. It is easy to explain the motivationneurons whose activation becomes large as it approaches
the reward. However, it is difficult to explain the reward
expectancy neurons which activate in each trial except for
the reward trial.
In this paper, a model that consists of a recurrent neuralnetwork trained based on the actor-critic type reinforcement learning is proposed, and the reason of emergence ofthe reward expectancy neurons observed in the physiological experiment is investigated by the analysis of the model
during learning. Furthermore, we aim to reinforce the possibility that reinforcement learning is a main principle oflearning in the brain.

          
(juice)Fig.1 Visual color discrimination trial
itor. When the monkey touches a bar, the fixation point
presented at the center of the monitor turns red. After thevarying waiting period lasting between 400 and 1200ms, thetarget color becomes green, which instructs the monkey torelease the bar. If the bar is released within 1sec after the
onset of green target, the target turns blue to signal themonkey that the trial is correct, and the monkey can getjuice as a reward after 250∼350ms.
After training of this visual color discrimination trial(single-trial task), the task transits to multi-trial reward
schedule task (multi-trial task). In the multi-trial task, thereward is given to the monkey when it succeeds in successive 1∼4 trials. The necessary number of trials to get
reward is determined at random. For example, the flow of
the four-trial schedule is shown in Fig.2. Since the visualcue becomes bright as the monkey approaches the reward
trial, it can recognize the number of trials remaining until it
can get the reward. The schedule fraction, in other words,
the number of trials in schedule is shown by (the number of

2 The experiment using a monkey[1]2.1 Task settingThis chapter explains multi-trial reward schedule task using a monkey. In the first stage, the monkey trains visualcolor discrimination trial as shown in Fig.1. At first, themonitor is pitch-black, and after 500ms a white bar lightcalled visual cue is presented at the upper edge of the mon-©ISAROB 2005

First
(1/4)

Second
(2/4)

Third
(3/4)

Forth
(4/4)

Fig.2 Multi-trial reward schedule task643

    The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

trials)/(schedule). For example, 1/4 in Fig.2 indicates thefirst trial in the four-trial schedule. In the control experiment, the cue sequence is random not depending on theschedule so that the cue loses its meaning.
2.2

harmony in the whole brain. One of the authors has beenshown that many functions including recognition, memoryand so on are acquired in a system constructed seamlessly
using a neural network that is trained based on reinforcement learning. Also in each area other than basal ganglia
in the brain of living things, we have thought that reinforcement learning can be a main learning principle. In theexperiment of hand reaching task using a tool by a monkey,
some neurons representing whether the tool is recognized
as a part of the body or not are observed in the Interparietal Sulcus. The activation of the neuron representing suchhigh order information can be explained well by the combination[6].

Experimental result[1]The activation of the Anterior Cinglate neurons areshown in Fig.3. The neurons A and C in Fig.3(A)(C) generate phasic activations, while B and D in Fig.3(B)(D) keep a
tonic activations. As for A and B, the activation decreased
before the reward trial, and as for C and D the activation
decreased after reward. It is said that A and B express the
expectancy for the reward, because they did not activate in
the last trial in which the reward is obtained certainly. C
and D express the distance to the reward, since the activation becomes the maximum in the trial when the monkey
can obtain the reward. The neurons like C and D can be
explained easily as the state value(critic) by reinforcement
learning. In this paper, the reason of the emergence ofsuch neurons like A and B are investigated. Moreover, theneurons which activate only in the reward trial existing inthe Ventral Striatum in the basal ganglia are focused on
together with the reward expectancy neurons.

3.2

Proposed model

The architecture of the model proposed in this paper isshown in Fig.4. Actor-critic architecture is employed inthis paper. The part called critic generates a state value
from a state vector. It playes a role to evaluate the action
generated by actor. Temporal difference error (TDerror) r̂t
is expressed byr̂t = rt+1 + γV (xt+1 ) − V (xt )(1)where, r: reward, V（xt ): output of the critic, xt :observed
state, and γ: a discount factor. A neural network is usedin the proposed model. The neural network is trained by
the following training signals that are generated based on
reinforcement learning.
Vs,t = r̂t + V (xt ) = rt+1 + γV (xt+1 )(2)

as,t = a(xt ) + r̂t・rndt(3)

where, Vs,t : training signal for the critic, as,t : training signal for the actor, a(xt ): output of the actor, rndt : trial
and error factors added to a(xt ).
In addition, in order to deal with the past informationin the neural network, a recurrent structure is introduced.
Fig.3 The response of the anterior cingulate neurons(These figures are copied from ”Representation of
motivational process reward expectancy in the brain”,
Igakuno ayumi,Vol.202 No.3,p181-p186,2002)     generator

actionTD error calculator           

selection
criticUsefulness of the combination of reinforcement
learning and neural network

upper-hidden layer neuron 10

In the conventional brain research, the main purpose
seems to analyze the function of each area in the brain.
Also, in robotics research, each functional module such asrecognition, action planning, and control was developed,
and by integrating such functions, intelligent robots havebeen developed. In this trend, reinforcement learning has
been used as the learning for the function of motion planning, and has been used in the models of the basal ganglia.
However, each area in the brain is inseparably connectedwith each other, and it is thought that learning is done in©ISAROB 2005actor

            

3 Proposed model
3.1

     generator                

input layer neuron 8+20







Fig.4 The proposed model using recurrent neural network

644

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

The number of layers is four. The number of neurons ineach layer is 8 in the input layer, 20 in the lower-hidden
layer, 10 in the upper-hidden layer, and 4 in the output
layer. The R,G,B signal of the visual cue are inputted into
the first 3 neurons in the input layer. Since the visual cue
is gray scale from black to white, the values are always thesame among the three neurons. The value was 1.0 in the
single-trial task. In the multi-trial task, it became large
as it approached to the reward such as 0.1 → 0.4 → 0.7 →1.0. The R,G,B signals of the target color are inputted into
the 4th∼6th neurons in the input layer. The signal to the
input neuron 7 represents whether the monkey touched thebar or not. If the bar is touched, the signal is 1, otherwise
it is 0. The signal to the input neuron 8 is 1 when the
reward is given, otherwise it is 0. In addition, the directconnections from the input layer to the output layer wereadded. This idea is based on the knowledge that there exist
different paths to the basal ganglia. One is through thefrontal cortex, and the other is not through the area. It isbecause the latter is thought to realize an easy linear inputoutput relation, and the former is thought to complement
the latter. Moreover, by considering that the output of eachneuron is expressing pulse density, the output function ofeach neuron is the sigmoid function whose value ranges from0 to 1.
The output neuron 1 is used as critic output, and the output neurons 2,3 and 4 are used as the actor output vector.
One of the three actions, ”keep”, ”touch”, or ”release”, isassigned to each actor neuron. An action is selected statistically by comparing the values after adding a random
number rnd to each actor output. The random number isin the range of ± 0.3. BPTT (Back Propagation ThroughTime) is used as a learning algorithm for the recurrent neural network, and the time to be traced back was set to
80 step. Sampling time, i.e., one step, was set to 100ms.
Furthermore, when it transited from the single-trial task
to the multi-trial task, the discount factor was changed as
0.96 → 0.976 since the necessary time steps to the reward
becomes long. Each initial weight from the input layer tothe lower-hidden layer or each from the lower-hidden layer
to the upper-hidden layer was set to a small random value.
All the weights from the upper-hidden layer to the output layer were set to 0. For this reason, at first, an actionis always chosen randomly among the above three actions
because the three actor outputs are the same. As for the
feedback connections, self-feedback connection weights are
set to 4.0, and the others to 0.0. By this setting, the propagated errors by BPTT propagate efficiently without divergence when the learning is traced back in the past, andtwo stable equilibrium points can be learned easily. The
value 4.0 is calculated as the reciprocal of the maximum
derivative of the sigmoid function.

4.1

The activation of each neurons

1

1

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0.6

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outputoutput4.1.1 The result after total 20000 trials
First, the activation change of some neurons after 20000
trials, in other words, soon after switching to the multi-trial
task is shown in Fig.5. The results are shown for the casewhen the reward is given after 4 successful trials.
The activation of the critic is shown in Fig.5(a). If thelearning is performed ideally, the critic increases exponentially and smoothly toward the time when the reward isgiven. However, in this case, the upward trend towards the
reward can be seen only in the reward trial after 6 second.
Henceforth, the upper-hidden neurons 3,4 and 9, which areconsidered to contribute to the critic greatly by judging
from the weight value to the critic, are observed. The activation of them after 20000 trials are shown in Fig.5(b)∼(d),and the change of the weight from each of the neuron to
the critic are shown in Fig.6. Here, since the upper-hidden
neuron 4 shown in Fig.5(c) has a negative connection to thecritic, the output value is observed after turning the value
upside down. In this case, the critic is expressed mainlyby the upper-hidden neurons 4 and 9 in Fig.5(c),(d) in thelast trial. In each trial except for the last one, since thereward cannot be got, a large negative TDerror appears.
Therefore, it is thought that the activation was depressed
greatly in non-rewarded trials.

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Fig.5 The response of some neurons after 20000 trials

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upper hidden3
upper hidden4
upper hidden92weight1

4 Simulation rusult

0
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In this simulation, when the learning could be performed
almost completely in the single-trial task, it moved to themulti-trial task. Here, the number of trials in the singletrial task was 16500.
©ISAROB 2005

0.4

-4

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40000

60000

80000

100000

trial

Fig.6 The change of the connection weights from theupper-hidden neuron 3,4,9 to the critic
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4.1.2 The result after total 30000 trials
Next, the activation of each neuron after 30000 trials isshown. The activation of critic is shown in Fig.7(a). Comparing with the previous activation curve of the critic shownin Fig.5(a), the critic is increasing even before the last trial.
The activation of the upper-hidden neurons are shown in
Fig.7(b)∼(d). In this case, the neuron that did not activate in the last trial emerged as shown in Fig.7(b). Since
the weight from the upper-hidden neuron 3 to the critic islarge around 30000th trial as shown in Fig.6 as well as theupper-hidden neuron 4,9, these neurons are contributing to
the critic output. Then the upper-hidden neuron 3 is considered to be equivalent to the reward expectancy neuron in
the experiment using a monkey. Then, in order to consider
how this reward expectancy neuron is represented, the activation of the lower-hidden neurons contributing the upperhidden neuron3 are observed. Fig.7(e),(f) show the activations. The upper-hidden neuron3 shown in Fig.7(b) receives
a positive connection from the neurons whose activation isdepressed in the reward trial as shown in Fig.7(e),(f). Eachof the neuron has a negative connection to the neuron which
activates only in the reward trial. This means that they arecontributing to both the reward expectancy neuron and theneuron which activates only in the reward trial.
4.2(1) Just after the transition to the multi-trial task, the critic
is inhibited by a large negative TDerror appeared in eachtrial except for the reward trial. The neuron that activate
only in the last trial as shown Fig.5(d) emerged.
(2) When the learning progressed to some extent, the critic
output was required to increase according to the distanceto the goal, and as a result, the reward expectancy neuron
emerged to complement the neurons which activate only inthe reward trial.

5 Conclusion
In this paper, a model that consists of a recurrent neuralnetwork trained based on the actor-critic architecture forreinforcement learning is proposed. From the simulation of
the model, a neuron that relates to ”reward expectancy”
was observed in the hidden-layer. It is suggested that suchneurons can emerge to realize an appropriate value function in the transition period from the single-trial task to themult-trial task. The relation between the reason of emergence and the function of ”reward expectancy” should beconsidered in the future, but we think that the result supports the idea that reinforcement learning is a main principle of learning in the brain.

Emergence reason of reward expectancy neuron

Acknowledgement

From the analysis of the above results, the reason for the
emergence of the reward expectancy neuron is summarized
as follows.

This research was partially supported by the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research, 14350227 and 15300064.

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[1] Shidara,M. ：Representation of motivational process reward expectancy in the brain, Igakunoayumi,Vol.202 No.3,
p181-p186, 2002

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[3] Barto,A.G., Hauk,C.J., Davis,J.L. and Beiser,D.G.：
Adaptive Critics and the Basal ganglia, in Models of Information processing in the basal ganglia eds.MIT Press,
1955

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[4] Williams,R.J. and Zipser,D. ：Gradient-Based Learning Algorithm for Recurrent Connectioninst Networks,
Tech.Report NU-CCS-90-9, Northeastern University, 1990

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time [s](c) upper-hidden4[5] Shibata,K. :Reinforcement Learning and Rbot Intelligence -Can Intelligence be Realized by Carrot-and-stick?
- , The 16th Annual Conference of Japanese Society for
Artificial Intelligence, 2002 (In Japanese)(d) upper-hidden9

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1

0

3 4

[2] Doya,K. ：Complementary roles of basal ganglia andcerebelum in learning and motor control, Curr. Opin.
Neurobiol.Vol.10,No.6,pp.732-739, 2000

[6] Shibata,K. and Ito,K. ：Hidden Representation after Reinforcement Learning of Hand Reaching Movement with
Variable Link Length, Proc. of IJCNN, 2003, 1475-674.pdf,
pp.2619-2624, 2003

0.4
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time [s](e) lower-hidden15

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Fig.7 The response of some neurons after 30000 trials©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Shape-recognition using randomly selected pixel-pair neurons
Valerie Rose and Jeffrey Johnson
Department of Design and Innovation
The Open University
Milton Keynes, MK7 6AA, England
j.h.johnson@open.ac.uk and vrose@ukonline.co.uk
are sixty-four associated retina neurons. Oncethe central neuron is triggered, the neuron fires
if the six satellite pixels match the black-whitegreyscales of the image. Thus, every dark pixelin an image responds to one of these sixty-four
retinal neurons, according to the black-whitestates of its satellite pixels.

Abstract
Designing low level recogniser/filters forvision systems restricts their potential. We
show how retinal neurons based on pixel-pairsensors can recognise shapes. The next step isevolving optimum retinal neurons to support
the evolution of vision systems that canabstract their own higher level constructs.

1. Introduction
Our group is investigating a new visionarchitecture based on a multilayer
representation [1].This architecture is intended
to produce machine vision systems that:••••evolve appropriate retinal configurations
evolve connectivities to represent spatial
relationships
abstract their own higher level constructs
have levels which are integrated by new
relational mathematics

A key feature of the architecture is a multilevel
representation, with pixels at the lowest level,
and objects and scenes at higher levels.
At the lowest layer, configurations of pixels
respond to objects and are connected to an
hypothetical neuron that fires when activatedby appropriate inputs. We call these retinal
configurations. The outputs of lower levelneurons feeds forward through the system,
ultimately allowing objects and scenes to berecognised.
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Figure 3 shows the three shapes used in theexperiments reported in this paper. Each shape
responds differently to the retinal neurons. For
example, the diamond shape has more type-57
responses than the circle or square, because
this configuration of the satellite sensors
responds well to the sloping edges. For a given
shape, the numbers of responses for eachretinal neuron can be counted, giving a 64element response vector for each shape.

satellite sensors

3

Figure 1. Inputs to a low-level neuron
Elsewhere [1] we have experimented withretinal configurations based on fixed pixel
positions, as illustrated in Figure 1. Here there
is a central sensor, surrounded by six satellite
sensors. The idea is that if the central sensor
detects darkness, the neuron is triggered. There
are sixty four light-dark responses for the six
satellite pixels, ( Fig 2 We assume that there©ISAROB 2005

1

Figure 2. 64 retinal neuron patterns

central sensor

1

0

Figure 3. The simple shapes used for our
experiments
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

There are four possibilities for the pixel pair:background-background (0) backgroundforeground (1), 2 - foreground-background (2),and foreground-foreground (3).

It has been shown that these vectors containsufficient information to discriminate thesimple shapes, in the context of ourhierarchical architecture, which is described indetail in [1].

For our experiments, we generated sixty pixel
pair configurations. These random
configuration were fixed and used for training
and recognition. The retinal neurons areillustrated in Figure 5.

2. Random Neural Pair Generation
An essential feature of our architecture is thatit must be able to adapt to changes in objects
and scenes. We believe that this pre-empts
approaches to machine vision in which
programmers design a fixed retinal processing
architecture. In particular we believe that theapproach typified by our designing the sixty
four retinal neurons in Figure 2 inevitably
leads to vision systems that will be limited intheir recognition ability, and incapable ofadapting to radically new objects and scenes.

0

We conclude from this that we need togenerate the retinal neuron configurations at
random. There are many questions associatedwith this, including:
•

•
•

1

2

3

Figure 5. Pixel pair retinal neurons
Three ‘template’ shapes were generated using
one shape of each class shown in Figure 3 as
the training set. If the sensor black-white
inputs correspond to the fixed black-white
input configuration of the neuron, then thatneuron fires. For example, if the neuron is ofType 0 and both input pixels are white, theneuron fires.

compared to one plus six sensors usedin Figure 1, how many sensors should
be connected together to produce a
retinal neuron?
what should be the maximum or
minimum ‘diameter’ of the neuron?
how many neurons are required forsuccessful pattern recognition?

3. Recognising unseen shapes

To begin our experiments we used pairs of
pixel sensors connected to hypotheticalneurons, as shown in Figure 4. They can occur
at any distance from one another within theimage, as shown in Figure 4.

For a given set of pixel pairs and a given
shape, the neuron response is recorded as atemplate. These templates are matched against
unseen shapes and used to recognise them.
Thus each unseen shape gets a score of thenumber of neurons that were matched andfired. We apply a winner-takes all strategy, andthe shape with the highest number of matches
is recognised. If two or more shapes have thesame score, a non-classification is made.
Circle88/88

Diamond
87/88

Square88/88

Table 1. Recognition of the three shapes
For this experiment we used eighty eight
examples of each test shape. All the circles, all
the squares, and all but one of the diamonds
were correctly recognised ( Table 1). This
result strongly suggests that this approach ofmatching shapes against randomly generated
retinal neurons is viable.

Figure 4. The four pixel-pair configurations
0 - background-background1 - background-foreground2 - foreground-background3 - foreground-foreground©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Trial

CircleDiamondSquare

%

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10

88/88
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88/88
87/88 *
88/88

87/88 *
88/88
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88/88
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88/88

88/88
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84/88 *
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88/88
88/88

99%
100%
94%
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100%
100%
99%
100%

Trial

CircleDiamondSquare

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10

44
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37
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34
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42

24
19
19
17
19
12
20
14
16
21

56
54
53
53
56
55
55
53
55
54

Table 2. Ten recognition trials (60 neurons)

Table 4. Frequency of Configuration 3

To strengthen this conclusion the experiment
was repeated ten times. The results of theseexperiments are shown in Table 2.

Using just configuration 4 for matching gave
an improvement in all the scores over the first
set of experiments (Table 2), and no worseningof any score. The performance for diamonds is
notably better.

These results support the conclusion that the
randomly generated retinal neurons can beused for shape recognition, though they showthat errors can occur.

So these black-black pairs are being generated
in sufficient quantity to give reasonably goodrecognition.

In order to see if these results could be
improved, the experiment was repeated using
sets of one hundred randomly generated neuralpairs. The results in Table 3 suggest that
adding extra neurons can improve the patternrecognition performance.
Trial

CircleDiamondSquare

%

1
2
3
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5
6
7
8
9
10

88/88
88/88
88/88
88/88
88/88
88/88
88/88
88/88
88/88 +
88/88

88/88 +
88/88
84/88 +
87/88 +
88/88
88/88
88/88
88/88
88/88
88/88

88/88
88/88
88/88 +
88/88
88/88
88/88
88/88
88/88
88/88
88/88

100%
100%
100%
99%
100%
100%
100%
100%
100%
100%

Increasing the number of random pixel-pairs to
100 further improves performance
The next set of experiments restricted thepermitted configurations to types ‘1’ and ‘2’,white-black and black-white.
This gives some slight improvements withrecognition of diamonds in Trial sets 1and 3,
but some deterioration in recognition with trial
sets 3, 4, 5, 7, 9 and 10.
Another factor which is possibly affecting
performance is that the ‘not 1 and 2’
configurations, ‘0’ and ‘3’, are opposites –background/background versus
foreground/foreground, so that grouping them
together into one type may reduce
discriminatory ability. In other words, it seems
that information about ‘edges’ and ‘not edges’
without further categorization of the ‘notedges’ as ‘shape’ or ‘background’ isinsufficient for reliable recognition.

Table 3. Ten further trials with 100 neurons
Not surprisingly, the location as well as thetype of configuration of the random pairs
affects performance, as does the number ofpairs selected.

Again selecting 100 random pairs increases thequantity of type ‘1’ and ‘2’ configurations andperformance improves correspondingly.

4. Discrimination by configuration
The next set of experiments is concerned withthe relative ‘usefulness’ of the different
configurations.

Increasing to 130 pairs brings further
improvement – 100% recognition for all but
the diamonds, with one trial giving a marked
deterioration . So, in general, even when thenumber of pairs is increased producing more
type ‘1’ and ‘2’ pairs, recognition is not as
reliable as for all four configurations or types‘3’ and ‘not 3’.

Table 4 indicates that, as one would expect,considering the relatively large shapes we areusing, that the largest proportion of the 60
pairs are of type ‘3’, black-black, for each of
the three template shapes.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

type ‘3’ configuration 10 pixels apart and atype ‘2’ configuration pair 60 pixels apart?
The answer to this probably lies in having
feedback of some sort about the varying
strengths of neuronal responses to the different
input patterns as the system learns. Rolls andDeco [2] have recognized the contribution
information theory can make to understanding
communications among individual neurons
and networks of neurons within the brain.

5. Distance between paired pixels
So far there has been no restriction on theseparation of the pixel pairs. In this section we
investigate whether restricting the distanceaffects the results.
It appears that restricting the permitted
distance between the pixels in each pair
adversely affects performance for a given
number of random pairs.

7. Conclusions and the next step
The recognition experiments were repeatedwith the distance, in both horizontal andvertical directions being reduced to <=10 to<=5 and finally to <=3. For 60 pixel-pairs
recognition deteriorates correspondingly.

The conclusions from the experiments reported
here are that:•

Increasing to 150 pairs and setting the distanceto <=10 pixels gives 100% recognition for all
except the diamonds in one of ten trial sets.
However, with 150 pairs and any distance, allshapes are correctly classified across all ten
random sets, so restricting the distance appears
to give no advantage.

•••

One might expect that restricting the distancewould provide useful ‘local’ discriminatory
information. Possibly the reason this does notappear to do so is the relatively small numberof pixel-pairs, and the lack of redundancy.
When more random pairs are generated, there
is a better chance of gathering enough useful
information to compensate for this.

These conclusions have to be set in the context
of the small number of test shapes we haveused, and their simple nature.
The results support our belief that we will beable to use randomly generated neurons forrecognising shapes and features. In future
experiments we intend to use evolutionary
methods to select the most appropriate neurons
for particular purposes. It is our intention to‘breed’ new sets of retinal neurons from thosethat perform best for a given application.

6. Informal Discussion
These experiments have addressed a number ofquestions. They have been concerned withhow randomness might used as the basis ofrecognition and what gives useful informationin order to develop strategies that able it torepeat successful behaviours.

The result that restricting distance does notappear to improve performance is particularlyinteresting. Combined with the result that
increasing the number of neurons improves
performance, this suggest that evolutionary
principles can best select the most appropriate
distances for any particular application,
provided enough neurons are used.

In order to succeed at this, the system needs tobe able to quantify the amount of informationis getting from parameters such as distance
between pixels, their location, theconfigurations they form in combination, andso on. It may even be useful to base its
evaluations on information theory related tothe inputs and outputs on the various
connections in some form of hierarchicalneural network.

References[1] Johnson, J.H., Rose, V., ‘Autonomous
Evolutionary machine vision systems’, AROB10, Beppu, Japan, M. Sugisaka (ed).

However this raises issues such as how to put avalue on the relative quantities of information
being conveyed. For example, how would thesystem differentiate between the amount of
information obtained from a pair of pixels of©ISAROB 2005

randomly generated retinal neurons
using pairs of pixel sensors can beused to recognise shapes
increasing the number of neurons
from 60 to 150 improved performanceof the four types of neuron, Type-3
appears to give the most information,
but this rather inconclusive
restricting distances between sensor
pairs did not appear to improverecognition performance, although theperformance improved with thenumber of neurons.

[2] E. T. Rolls, G. Deco, 2002, Computational
Neuroscience of Vision, Oxford.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Development of an autonomous personal robot“The visual processing system for autonomous driving”
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

Abstract

additional image processing capabilities in this system

Currently, an autonomous self-driving robot is
expected to provide various services within the humanliving environment. Such robotic technology isalready seeing practical use in industry and being used
in industrial applications. But the robots for industry
only faithfully follow the given motion which human
make. We are therefore working to develop an
autonomous personal robot with the ability to perform
practical tasks in a human living environment by using
information derived from sensors and a knowledge
database.

1. Introduction
There are many problems to develop autonomous
self-driving personal robots which can work likehumans instead of us. These problems can be roughly
divided into three categories: (1) how to processcontextual information regarding the robot’s
environment; (2) how to form a robot's action plan
based on this information; and (3) how to correctly
control the robot using the robot's action plan. The
purpose of the present study was to develop a way toallow the robot to determine its own position andposture as related to the above-mentioned problems.
We have thus developed a visual processing systemthat enables a self-driven robot to advance in a straightline.
The robot’s visual processing system uses only a
CCD camera and processes the image information
displayed by that camera. We have used the Houghtransformation, which is knot to be able to extract
straight lines from an image, to extract the features ofan image. We define the intersection of vertical lines
and horizontal lines as a “characteristic point,” and we
calculate the errors of position that occur while therobot is moving along a linear path by using the flowof the plotted characteristic points over time. However,the system that we previously developed could not
obtain useful data because the rate at which thatsystem acquired the characteristic point was very low.
Therefore, in the present research, we have included©ISAROB 2005

Fig. 1

Robot design

Fig. 2

Robot appearance

and have developed an algorithm for extracting
straight lines and characteristic points.
We explain herein how the additional image
processing and the improvement of the algorithm
enhance the stability of the system. In addition, we
have experimentally evaluated the visual processing
system for autonomous driving.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2. Recognition system and Decision system
The environment for robot activity assumed inthis study is a finite space such as a family room, an
office, or a hospital room. Our robot has a map
showing robot’s sphere of activity, and we call thismap ‘the finite space map’. The finite space map has
information including the size of each parameter of themap, the initial position coordinates of the robot, thecoordinates of the movement needed to get to thedestination, the number of placed objects, and thepositions and sizes of those objects. The robot can doautonomous driving based on the finite space map.
The finite space map is shown in Fig.3.
Fig. 3 Map of a finite space
In addition, we are developing a recognition
system that will allow the robot to recognize theexternal environment. The recognition system consistsof a CCD camera, an optical sensor, and a supersonicwave sensor. The supersonic wave sensor primarily
recognizes unknown objects that don’t exist on thefinite space map. The optical sensor recognizes
measurement of an actual distance with an unknown
object. The recognition system supports the robot's
autonomous action by using information for eachdevice and the finite space map. The composition of
the system is shown in Fig.4.

Recognition System
Supersonic wave sensor

CCD Camera

Optical sensor

Image processing board

Signal processing board

Visual processing system

Decision System
Mapof the“Finite-space”

Processor for
Autonomic drive

3. The visual processing system
Drive control System

3.1 Outline of the system
When the robot moves, the positions in the map
need to be synchronized with its real position in order
to correctly reach the desired destination. Therefore, it
is useful to consider a case in which the robot can't
correctly drive because of a subtle unevenness in the
ground's surface and the resulting slight difference inthe rotation of the right and left driving wheels. In
addition, we must reduce the error of position
produced by this case as much as possible. The
purpose of the visual processing system is to enable aself-driven robot to advance in a straight line. The
visual processing system captures objects on an image
in order to detect errors in the robot’s position.
Incidentally, this system doesn't consider large amountof error that is produced by disturbances.

Motor，Actuator（ Body，Finger…）

Fig. 4 Processing system for the personal robot.

Ⅰ. Image Acquisition
The image obtained by the CCD camera is read
into PC in the robot. The visual processing system uses
only form extraction processing, and color
information is not needed. Therefore, we only use an
8-bit gray-scale image.
Ⅱ. Edge processing
The system uses Laplacian edge enhancement.
.
Ⅲ. Segmentation processing
To limit the extracted data that are required forform extraction, the system performs Segmentation
processing.

3.2 System flow of the system
The system receives an image whenever the robot
moves a certain distance. At the same time, the systemgenerates the flow of characteristic points byprocessing the data obtained from such images. In
addition, the system analyzes whether the robot could
advance in a straight line. The flow of this system isshown in the following sentences (Ⅰ-Ⅴ).

©ISAROB 2005

Ⅳ. Thinning processing
The edge extracted by the edge processing has line
width. In order to decrease the amount of calculationand to stabilize the hough transformation, the system
performs thinning processing.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

that have been processed to extract straight lines. In
comparing differences between these two images, it
can be seen that the one image is normally processed
to extract straight lines, while the other is restricted to
extract only a rectangular object.

Ⅴ. Hough transformation and Characteristic point
The Hough transformation can extract thestraight lines contained in an image. And we define theintersection of vertical lines and horizontal lines as thecharacteristic point.

3.3 Processing for extraction of straight lines
and characteristic points
In this system, the portions of straight lines on animage are extracted using the hough transformation.
However, an actual image consists of many straightline portions. By virtue of that, the system produces
getting unnecessary data. And in the case of extracting
characteristic points, if the system captures acharacteristic point that are different fromcharacteristic points of the object currently being
pursued, the system causes errors. These problems
would directly affect the accuracy and speed of thesystem. So, in order to avoid these problems, we have
improved processing for extraction of straight lines
and characteristic point in the visual processing
system.

Fig. 6 Processed images to extract straight lines■Processing for extraction of characteristic points
The data for characteristic points are actuallyused for detecting errors in the robot’s position. It is
important in this processing that the characteristic
points of the objects being moved by robot's drive becontinuously captured. We have therefore developed aprocessing method that gets the nearest point from theformer characteristic point on an image as a new
characteristic point.

■ Processing for extraction of straight lines
When the system extracts straight lines, each of
two processings is working in order to raise accuracy
and processing speed.
In case of using the hough transformation, it ispossible to select the threshold value about thereliability of straight lines on an image. The
processing chooses automatically the threshold value
until it only extracts the effective data of straight lines.
The histogram about selecting the threshold value isshown in Fig.5.

Fig. 5 Histogram for selecting the threshold value
Fig. 7 System flow
The system is restricted to processing rectangular
objects, as the robot environment generally has many
rectangular objects.
Figure 6 shows the image taken with the CCD
camera, and the image after edge processing. In
addition, the bottom part of fig.6 shows the images©ISAROB 2005

3.4 Evaluation of whether the robot isadvancing in a straight line
The flow of characteristic points is changed by
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

the state of the robot's driving. Moreover, it is possible
to calculate geometrically the flow of characteristic
points in the case in which the robot advances
normally in a straight line. Therefore, we can evaluate
whether the robot advances in a straight by comparing
the actual flow with the theoretically calculated flow.

・The case of straight driving

・The case of not straight driving

4. Experiment for evaluating the visual
processing system
In this experiment, a case in which the robot
normally advances in a straight line is compared with acase in which a gap is intentionally produced. The
robot has been driven in a flat room using DC motors.

Fig. 9 Flow of characteristic points

Fig.10 The actual flow and the theoretically calculatedflow

5. Conclusions
In this research, we added additional image
processing capability to a robot’s processing systemand developed an algorithm for extracting straightlines and characteristic points. As a result, thesystem’s ability to capture the characteristic points has
improved remarkably.
Our results indicate that the system was able tooutput the flow of the feature point well against thesingle object. In addition, the system allowed us toevaluate whether the robot is advancing in a straightline.
Our next subject of study for the robot
processing system involves calculating actual data
regarding errors in the robot’s position. In addition, wemust develop a system that allows for feedback controlof the robot.

Fig. 8 Situation of experiment

We made the robot go straight toward a
rectangular object and used the system to generate theflow of characteristic. The flow of characteristic
points in the case where the robot normally goesstraight is quite different from that in the case where
the robot doesn’t go straight (Fig. 9). We haveconfirmed that the flow of characteristic points drawn
by geometrical calculation and such flow drawn by thevisual processing system are similar (Fig. 10). As such,
we can conclude that the developed system canevaluate whether a robot is advancing in a straight line.

©ISAROB 2005

References[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.
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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Development of a system for self-driving by an autonomous robot
Eiji Hayashi and Keisuke Ikeda
Faculty of Computer Science and Systems Engineering,
Kyusyu Institute of Technology
680-4, Kawazu, Iizuka-City, Fukuoka Prefecture, Japan

Abstract

autonomous robot" which corrects the position error that
occurs when the robot moves. This system corrects aself-position using the image information acquired from a
CCD camera.

Expanded and advanced functions for a self-driven
robot equipped with environment recognition and theability to learn using a sensor and a camera are being
developed. The robot is active in the manufacturing field,
the medical field, the welfare field, the public field, andelsewhere, in each case contributing to a higher quality oflife. However, it is difficult for the robot to understand thehuman environment because the environment is always
changing.
In this research, we are developing an autonomous
robot that can execute simple directions from a human in aparticular environment (the home, the office, the sickroom,and so on). The autonomous robot will execute an action
while referring to "finite mapping" when moving around in
the room. Finite mapping is data about the space in the fieldof activities of the robot. The robot recognizes known
objects that are shown by this mapping and drives, being
autonomous.

2. Outline of the personal robot
A general view of the robot being developed at ourlaboratory is shown in Fig 1. The composition of theself-driving system is shown in Fig 2. This paper explains
the robot's drive mechanism.

Fig 1. General view of the robot

1. Introduction
In recent years, the rapid development ofsemiconductor technology has allowed the development ofrobots that can perform advanced processing in response tocomplex information input using a sensor, machine vision,etc. Various functions are demanded of the personal robot
using such technology in people’s various environments(home, office, a sickroom etc.). However, developing arobot that can fill these requests is difficult.
In our laboratory, we are developing an autonomous
personal robot that can apprehend the environment andmeet the demands of its users. This robot has the known
information called “finite mapping”. The map has a
movable range and the information about an objective
position, and the robot drives with reference to the map.
However, the position of the map and the robot's actual
position do not always correspond. Therefore, the robot has
to aim at synchronization between the position of the robot
on the map and the actual robot's position.
This paper describes "a system for self-driving by an©ISAROB 2005

Fig 2.Composition of the self-driving system

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.1 Composition of the drive mechanism

Action pattern determination system

The robot we are developing consists of a drive
mechanism of two front wheels and a back wheel, for a totalof three wheels. The front wheels are attached to the motor
and are independent on either side, and the back wheel is acastor. This method has the advantage of allowing a smallturn, compared with the steering system that steers thewheel of a passenger car.
DC servo motors are used for the robot's drive
mechanism, and position control and speed control are used
for the control system of the drive mechanism. It is possible
to determine the amount of movement when the robot
drives from one known position to another by using position
control. Using speed control, the robot can apply load for a
fixed time and can drive it continuously. That is, it ispossible to move a load from place to place at the speed
specified.
The robot moves based on the specified position andspeed.

When it has a target position specified, the robot has tocalculate a course and has to generate an action pattern. By
this system, the robot refers to the information on the finite
mapping, searches for the course to the target point, andcreates a set of data for driving. The robot drives based on
this data.
Moreover, in consideration of the position error
searched for by the error correction system that we havedeveloped, the robot includes performing feedback control
into the calculations. Using this control, the robot should be
able to drive in spite of position errors.

2.2 System for self-driving
By this self-driving system, in order to perform an
operation determined, action patterns are generated. And
the system transmits the data for the drive to the drive
mechanism. Based on past research, the robot was able toaim at the synchronization of the self-driving system andthe drive mechanism.
Now, we have developed an image-processing system
and an error correction system. Using the image–processing system, a robot acquires an image using a CCD
camera and performs image processing. The error
correction system has the job of detecting the position error
generated at the time of the robot drive, with reference tothe data obtained from the image-processing system.

The finite mapping

Fig 3. Sample the finite mapping

The environment assumed by this research is alimited space, such as a home, office, or sickroom, and thegiven space is the range within which the robot can move.
When objects (furniture, a desk, shelves, etc.) exist in
the space where a robot drives, they are described on themap of the limited space as known objects. The robot refers
to the map. Evasion course calculation is attained by feed
forward. Therefore, the robot’s efficiency of autonomous
drive processing is raised. The finite mapping has
parameters indicating the size of the map and the objects, arobot's initial position condition, target position coordinates,and object arrangement coordinates.
Sample the finite mapping is shown in Fig 3.

©ISAROB 2005

3. Consideration of position errors
When a robot moves, an error occurs in the mismatch
between the position of the robot in the finite mapping andthe robot’s actual position. When the robot goes from astopped state to a moving state, the fact that an unstablestate is in control of a motor is cited as a cause of this error
generation.
The robot at a stopped state needs excessive torquecompared with the state where it is moving. Since power is
not well transmitted at this time, an error arises in positionand speed control of each motor. Then, each motor isconverged on the position and speed of the inputted target
by feedback control. However, it takes time until each
motor converges on the target. Therefore, the robot moves
in an unstable state.
Because of this error, the robot performs unstable

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

pulse(2500pulse = 1m)

movement at the time of initial action, and the robot has
produced an error in the target value and the actual position.
The moving distance (pulse) of each drive motor andits relationship to timing are shown in Fig 4.

-4500
-4000
-3500
-3000
-2500
-2000
-1500
-1000
-500 1 4 7 10 13 16 19 22 25 28
0
1/10 second(a) Sample(b)Edge processing

motor A
motor B(d) Hough transformation(c)Segmentation processing

Fig 6. Image processing

Fig 4 Relation between move distance and time

5. Error correction system
5.1 Geometric calculation for the acquisition of
Position information

4. Image-processing system
In order to cope with the problem of error generation
described in the preceding paragraph, the robot has to detect
its actual position. Therefore, when a robot acquires image
data using a CCD camera, the system in which thesurrounding environment is recognized can be developed.
This system extracts the characteristic point of the target
object. And the extraction is repeated and the data flow of
the point is created. The characteristic point is defined as
the information on the corner of the object obtained fromimage processing.
The flow of an image-processing system is shown in
Fig 5, and the results of processing using theimage-processing system are shown in Fig 6. From theimage data of the Hough transformation, as shown in figure
6 (d), a linear intersection is calculated. This point is
extracted as a characteristic point. At the time of movement,a robot repeats processing of Fig 5 and plots the flow of thecharacteristic point. The robot recognizes the environment
based on the flow.

The error correction system calculates a robot's position
from the feature point and a robot's move distance. As
shown in Fig 7, let picture coordinates be o-xy coordinates,
and let camera coordinates be O-XYZ coordinates. As forcamera coordinates and picture coordinates, the following
relation is realized:
XY
x= f ,y = f
ZZ
The x-coordinate of the picture obtained from image
processing of an initial state is set to x1. If the x-coordinateof the picture obtained when a robot z advanced is madeinto x2, the following expression of relations will be
realized:XX
x1 =f , x2 =f
Z
Z − ∆Zx1 * x 2 * ∆ZX,Z =ff ( x 2 − x1)x1
Therefore, it turns out that the present robot's position(X, Z) can be predicted.
X =Edge processingm(x,y)

I

Segmentation processing
Thinning processingoHough transformation

x

y

F

X

O

f
Y

Z

M(X,Y,Z)

Sampling “characteristic point”

Image plane: I
Focus plane: F
Fig 7. The relationship between picture coordinatesand camera coordinates

Fig 5. Image-processing system©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A characteristic point flow computed from the relation
between a robot and an object is shown in Fig 8. This
figure is the flow by which the robot moved as the ideal.
By comparing with the flow of an ideal state the flow
obtained by image processing, the robot computes the
position error.

6. Conclusions
When the autonomous drive personal robot moves, aproblem arises because coordinates change with respect toobjects in the environment. We have proposed an error
correction system to solve this problem. This system uses a
information on the motion distance acquired from a image
data and the drive mechanism, and the system computes anerror. However, when a robot's move distance is short, itcannot fully respond. A system to detect position errors
immediately is needed.
We have studied the unstable action generated as acause of the error at the time of initial movement of therobot. In the future, we will analyze the drive parameter at
the time of the robot’s initial movement and then build asystem that can perform action stabilized by the robot.

Fig 8. characteristic point flow

5.2 Consideration of error correction

References

By introducing this system, a robot's position
information can be acquired and error compensation can beattained. However, this is during motion, when a robot isstabilized. As shown in Fig 4, gaining stability takes about 1
second. At this time, a robot's driving speed is 0.5
m/second. By the time it goes into a stable state, it has
moved at least half a meter.
In areas such as a corridor, this system will have little
difficulty. However, error detection is difficult if the robot
is moving in a confined space where distances arecomparatively short. Therefore, detected errors must be
discovered very quickly.

©ISAROB 2005

[1]Funakubo, Process and recognition of the vision paturn,
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

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Research about ZMP of biped walking robot
Kota Imamura, Masanori Sugisaka
Department of Electrical and Electronic Engineering
Oita University
700 Oaza Dannoharu, Oita, 870-1192, JAPAN
Tel: 097-554-7831
Fax: 097-554-7841
E-mail: s1132005@mail.cc.oita-u.ac.jp
Abstract

successfully done.

In this paper, ZMP (Zero Moment Point) of biped

However, a lot of servo motors were broken because

walking robot is explained. OpenHRP2 (AIST) is usedof overload during the experiment. In order to save thefor walking pattern simulation. The purpose of thiscoot of servo motors, the robot walking pattern was

research is to calculate the theoretical value and actual

verified using simulator.

value of ZMP. Firstly, the reference ZMP of the robot is

In this research, ZMP concept is applied for “Dynamic

calculated from CoM (Center of Mass) and CoM position.

walking”, so that “Static walking” will be successfully

Then, the actual ZMP of the robot is calculated fromdone by the actual robot. For this purpose, the theoretical

Force/Torque sensor value. Next, the reference ZMP isvalue and the actual value of ZMP is calculated. Then,compared to the actual ZMP. Finally, the robot walking

the robot walking pattern behavior is controlled bypattern behavior is controlled by restoring the actual

restoring actual ZMP value to theory ZMP value.

value of ZMP to the theoretical value of ZMP.

2. Simulation environment
1. Introduction

2.1 OpenHRP2Recently, biped walking robots are widely known and

OpenHRP2 (AIST) is used for walking patternconcerned by mass media such as TV or magazines.

simulation. OpenHRP2 is composed of “Dynamics

People are interested in robots such as “ASIMO”,

server”, “Controller server” and etc. These servers are“QRIO” and “PINO”. Impressively, these robots are

distributed object system that used CORBA (Common

expected to be used for transportation device at complex

Object Request Broker Architecture). Each server isenvironment such as steps. Therefore, biped walking

mounted as CORBA object.
Computer specification is Windows2000 for OS androbots are studied by many research institutions.

AMD Athlon™ Processor (1000.04-MHz) for CPU.

Two years ago, research of biped walking robot has
been done for the first time in our laboratory. Then, inorder to study biped walking robots, a robot composed of

2.2 Robot model

microcomputer and servo motors was developed. By

Robot model is created by 3D modeling language

assigning the command value of each joint or servo

called VRML (Virtual Reality Modeling Language).

motors, “Static walking” of the biped robot was

Robot model that has been used for walking pattern©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

simulation in this research is shown by Fig.1. One leg

3.2 ZMP robot model
Eq.4 is obtained by expanding Eq.3 to each link ofhas 6 DOFs (Degree of Freedom), and 4 steps forward

robot.

pattern is used for walking pattern of this robot model.
This simple robot model is used for walking patternn∑ m {x (&z& + g ) − z &x& }simulation, so calculating the ZMP value of the robot is

x ZMP = i =1

focused in this research.

i

i

i

i

i

n

∑ m (&z& + g )
i =1

i

(4)

i

height0.45[m]where n is total number of piece of robot link. By

weight13[kg]
leg 6×2, arm 7×2,
body 3 (total 29)
4[steps]

replacing x with y , Eq.3 can adapt to lateral plane. It

DOF
step

is shown as Fig.3 that the reference ZMP on sagittal
plane is calculated by running the walking pattern
simulation using Eq.4.

Fig.1 Robot model

3. Reference ZMP
3.1 ZMP simple model

F: force effecting mass pointfrom floor
g: gravity
a: mass point acceleration
effecting to direction of
resultant of F and g.

Fig.3 Reference ZMP on sagittal plane (used Eq.4)
It is shown as Fig.4 that the reference ZMP is widely

Fig.2 Inverted pendulum model on sagittal plane

oscillating in walking. It is cited as a factor that solutions
are underspecified, so there are two variables ( x, z ). It is

Inverted pendulum model on sagittal plane is shown as

thought that optimization problem is solved. But, in thisZMP simple model by Fig.2. Firstly, Eq.1 is obtained

paper, it is decided that this problem is replaced withfrom dynamic equation of horizontal and verticalpseudo ZMP for simplicity. The following, pseudo ZMP

direction about mass point.

is explained.

m&x& = f x = F cosθm&z& = f z − mg = F sin θ − mg

(1)

3.3 Pseudo ZMP
Eq.5 is given by representing gravity M

Secondly, Eq.2 is given by ZMP definition that total

whole robot in Eq.4.

momentum around ZMP is zero.

zf x −（x − ZMP ) f z = 0

x zmp =

(2)

Finally, Eq.3 is derived from Eq.1 and Eq.2.

ZMP =m{x( &z& + g )} − z&x&}m( &z& + g )M ( g + &z&) x − M&x&z
M ( g + &z&)

(5)

At this moment, attention should be paid to z direction ofgravity position of walking pattern. By assuming that z(3)

direction of gravity position stays constant, z and &z&

By changing x to y , Eq.3 can adapt to lateral plane.

©ISAROB 2005of the

in

660

Eq.5

can

be

simplytransformed

intoThe Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

measuring CoP of sole. And so, it is thought that CoP, orz = href (const ) and &z& = 0 in Eq.6.
x zmp =

gx − href &x&
g

ZMP, is measured from Force/Torque sensor value ofsole of the robot.

(6)

F: vertical component offloor reactive force atsensor positionτ : torque at sensor position
x: distance between sensor
and ZMPBy changing x to y , Eq.3 can adapt to lateral plane.
Simulation results using Eq.6 is shown by Fig.4.

Fig.5 Foot model of robot on sagittal plane
Foot model of robot on sagittal plane is shown by
Fig.4. Moment of force around ZMP is expressed by
Eq.7.

Fx − τ = 0(7)

Eq.7 is rearranged to Eq.8.

x=

τ
F

(8)

The actual ZMP is calculated from x in Eq.8 andsensor position information. By replacing x with y ,Fig.4 Pseudo ZMP using Eq.6

Eq.8 is able to be used for lateral plane.
At x direction and y direction of ZMP, the results are

Simulation result using Eq.8 is shown by Fig.6.

displayed by Fig.4. It is taken that x direction ZMP
moves to forward and y direction ZMP is shifting on
supporting leg while the robot is walking. We understandthat x and y ZMP are greatly amplified at about 4 and 8seconds. From here, it is obtained that walking pattern of
the robot does not go well and the robot movement on
the floor taking extra time about 4 and 8 seconds.
Therefore, &x& of Eq.6 amplified widely by moving on
the floor.
As described, theory value of ZMP of robot iscalculated from gravity position and acceleration. This
theory value of ZMP is called the reference ZMP.
Fig.6 The actual ZMP using Eq.8 and4. Actual ZMP

sensor position information

In this topic, the actual ZMP is introduced. By
A.Goswami, ZMP = CoP (Center of Pressure) is

The actual ZMP of x direction and y direction areverified in 1999. Therefore, ZMP is calculated byshown as Fig.6. It is taken that x direction of ZMP©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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moves to forward and y direction of ZMP is shifting onon the point of falling to back or front. At y direction of

supporting leg while the robot is walking. We understand

ZMP, it is also understood that the actual ZMP gets larger

that x and y ZMP greatly amplified when supporting leg

than reference ZMP at one leg supporting period. Also,

shifts on next leg. This is because F in Eq.8 becomes

this indication shows that the upper body of the robot

much lower to almost zero in changing supporting leg.

leans to left or right.

Therefore, x in Eq.8 is made larger became F near to

6. Conclusionzero.
As described, measuring value of ZMP of the robot is

In this paper, ZMP concept for “Dynamic walking” iscalculated from CoP and sensor position information.

observed. Firstly, the reference ZMP of the robot isThis measuring value of ZMP is called the actual ZMP.

calculated from CoM (Center of Mass) and CoM position.
Next, the actual ZMP of the robot is calculated from

5. Comparing the reference ZMP
with the actual ZMP

Force/Torque sensor value. Finally, the reference ZMP is

The reference ZMP calculated from Eq.6 is compared

We understood that the reference ZMP and the actualwith actual ZMP calculated from Eq.8 for walking

ZMP used for walking pattern in this research have apattern simulation. Result of the comparison is shown byslight difference in track. However, more stable walking

Fig.7.

pattern is obtained by bringing the actual ZMP close tocompared to the actual ZMP.

the reference ZMP.
As for the future reference, it is thought that the actual
ZMP is brought closer to the reference ZMP by using
feed back control. For example, after actual ZMP is
obtained around the reference ZMP by accelerating hip
position according to error between the actual ZMP and
the reference ZMP, walking pattern behavior of the robot
is controlled by handling the reference ZMP. Finally,
optimization problem should be overcome, too.

Reference[1]H.Takeutchi (2001) “Real Time Optimal Control for
Fig.7 Comparing the reference ZMP with the actual ZMP

LeggedRobot”

Transactionsof

JSCES,

Paper

No.20010001

x direction and y direction are shown by Fig.7. It is[2]A.Masuyama, K.Mitobe and Y.Nasu (1999) “Ankle

understood that by comparing two graphs, two ZMPs

joint control for walking robots” The 184th Researchfollow the similar track. Comparing two ZMPs in detail,convention SICE Tohoku 184-3

about x direction of ZMP, the actual ZMP when

[3]F.Yamasaki (2003) “Sole sensor and Control”

simulation starts and finishes, or in dumping andROBO-ONE Technical Conference

pumping hip, amplify in comparison with reference ZMP.

[4]S.Kajita (2002) “ZMP and Walking Control” JournalThis indication shows that the upper body of the robot isof the Robotics Society of Japan Vol20 No.3

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Verification of trajectory generation of bipedal walking robot
Naoki Masuda, Masanori Sugisaka
Dept. of Electrical and Electronic Engineering, Oita University
700 Dannoharu Oita 870-1192 Japan.
Email:msugi@cc.oita-u.ac.jp
vibration by the velocity which is one of the fall reason, target
velocity was set up and it is considered as controlling, advancing
or translation velocity to converge on the target velocity. This isoperating ZMP and controlling the center of gravity, and istransposing the problem which determines the orbit of the centerof gravity to the problem which determines the orbit of ZMP.
By doing so, it was considered that generating the walking
orbit which can respond to the walking movement on theirregular ground which is the purpose of this research have beenachieved. In this paper, using a simulation, the walk orbit inleveling is generated and it verifies whether it can walk with thelevel of differences created virtually.

Abstract
A bipedal walking robot and a humanoid robot are developedin various research organizations in recent years, and research
towards utilization is done. Under the environment which has
irregular ground or various road surface, also the bipedal
walking robot and human, working and living together, need to
be studied, so that the bipedal walking robot will be able torespond to multiple conditions. By doing so, it was consideredthat generating the walking orbit which can respond to thewalking movement on the irregular ground which is the purposeof this research have been achieved. In this paper, using asimulation, the walk orbit in leveling is generated and it verifies
whether it can walk with the level of differences createdvirtually.

2. Generation of orbit
2.1 The equation of a strict model [5]First, gravity and the anti-power of a floor act on a bipedal
robot. The anti-power and the moment of a floor can be
described using the position of ZMP (Zero Moment Point), theinertia power of the whole body, and the gravity which aredefined on floor. Balance of the circumference of the X-axis andY-axis on the basis of the center of gravity uses the next
equation.

1. Introduction
A bipedal walking robot and a humanoid robot are developedin various research organizations in recent years. Currently,
research organization is working toward the utilization of thistechnology and manage to attract public society. By studying
and learning from biological model, in this case human, is the
direction of the bipedal walking robot. You may say that the
dynamic bipedal walking in reproducible leveling was realized
at present although having to be regarded as an actual thing likethe directions of bipedal walking robot or humanoid. However,
the technical study of bipedal walking robot movement beenconquered completely [1][2].
On the other hand under the environment which has irregular
ground or various road surface, also the bipedal walking robot
and human, working and living together, need to be studied, sothat the bipedal walking robot will be able to respond to multiple
conditions. There are various methods, [3][4] such as using thelinear inverted pendulum mode which fixed ZMP, in orbital
generation of the irregular ground, using moment as N = 0 .
However, in order to perform an irregular standpoint line, theorbit control of the center of gravity is indispensable first. And
there are some which fix the height of the center of gravity andthe height of the ground [5], and are generating the orbit by the
research which is carrying out orbit control of the center ofgravity. However, I fix the position and the ground of the centerof gravity, and think that the walk in the irregular ground is notpossible. Then, by paying attention to the center of gravity©ISAROB 2005

N y = m( x cg − x ZMP )( &z&cg + g ) − m( z cg − z gnd ) &x&cg
N x = − m( y cg − y ZMP )( &z&cg + g ) + m( z cg − z gnd ) &y&cg(1)

Here, N := [ N x , N y , N z ]T is the moment which acts on thecircumference of the center of gravity. And L is the angle
quantity of motion of the circumference of the center of gravity.
And [xcg, ycg, zcg]T is a center of gravity position. And[xZMP, yZMP, zgnd ]T is in ZMP. m is the mass of the whole body.
g is the size of gravity acceleration.
2.2 Assumption
Approximation is taken as assumption conditions is one and itis the moment N of the circumference of the center of gravity~
produced by change of a posture. The anticipation value N (t )is calculated beforehand.
~
(2)
N≅N
Since a moment is not correctly obtained until it determines
actual movement, an error is included in an anticipation value.
However, if the influence which it has on ZMP of the error issettled in the grade which does not jump out of a support leg, it

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

decided beforehand. It asks for the time of the center of both theleg grounding term according to the schedule. And a landing
position is decided near the point in the time on a target orbit.

is thought that the problem by approximation will be produced
as a result.
The next equation will be obtained, if the above assumption isapplied to the equation (1) of the model.
~

Ny
&
&
)
 xcg = a( xcg − x ZMP −
mg


~
Nx
 &&
=
(
−
+
)
y
a
y
y
cg
cg
ZMP

mg


3.2 Modeling
By aiming at the orbital generation,
the simplified model is used. A front is
the X-axis, a horizontal axis is the
Y-axis and, as for this model, thevertical axis is the Z-axis. A hip joint is
3flexibility, a knee joint is 1flexibility
and, as for flexibility, the leg joint has
2flexibility. (Fig.1)(3)

The center of gravity generation is performed based on theequation (3) of an approximation model.
2.3 The formulization in question
The center of gravity orbit is a problem which attains thecenter of gravity position and center of gravity velocity of thespecification with the appointed time, and is expressed with thenext equation.
  xcg (t 0 + T )  xcg =   x& cg (t 0 + T )  x& cg  y cg (t 0 + T )  =  y cg  y& cg (t 0 + T )   y&   cg 

Fig.1 Bipedal walking
robot model

3.3 An environmental setup of the irregular groundAn environmental setup of theirregular ground creates the level
differences of height (0.1[m] and0.05[m]) virtually as the first phase.
Moreover, sidewall is made leveling.
Fig.2 The irregular ground

(4)

(5)

3.4 Walking conditions of the model
The conditions of the model of the bipedal walking robot are
shown below.
・Time in one step＝0.5[s]・The rate of both legs grounding term in one step＝0.2[s]・Maximum acceleration＝0.4[m/s2]
・Maximum angle acceleration＝3.0[rad/s2]
・Distance from the target orbit to the landing position of a leg＝0.02[m]Since a center of gravity position and ZMP have the relationof the equation(3), by ZMP operation,it shows that a center ofgravity position is controllable. By applying ZMP, center of thegravity orbit can be obtained. However, the range which canoperate ZMP with the behavior is limited inside a support leg.

4. Results
4.1 Walking pattern and Advancing translation velocity totarget velocity, Velocity of center of gravity
Fig.3 is the walking pattern at the time of leveling. A closed
example and Fig.5 show the displacement of center of gravity
velocity to Fig.4 for the advancing translation velocity to target
velocity.

Here, t = t 0 is the time specification of t = t 0 + T at thepresent time. For specified center of gravity position ( x cg , y cg )and the appointed center of gravity velocity by ( x& cg , y& cg )above equation will become as shown below.

©ISAROB 2005(a) Right leg grounding term(b) Left leg grounding term

Fig.3 Waking pattern (Height of ground = 0.0[m])Velocity[m/s]

3. Simulations
3.1 Orbit marking
In order to decide the position of a leg, the target orbit of theimagination body is considered. The position of a leg is arranged
along this target orbit. A target orbit makes the starting point a
position, velocity, a present angle, and present angular velocity.
And in order to make it converge on the target speed and target
angular velocity which were specified, it is made constantangular acceleration and constant acceleration. And an orbit
which becomes uniform angular velocity and uniform velocity
from the middle is made.
Moreover, the length of the grounding term one of the bothlegs and the length of the grounding term of both legs are

Velocity[m/s]  xcg  xcg (t 0 ) T ( t −τ ) A
hx (t 0 + τ )dτ   = e TA 
 + ∫0 ae  x& cg  x& cg (t 0 ) y cg  = e TA  y cg (t 0 ) + T ae (t −τ ) A h (t + τ )dτ y& (t ) ∫0y 0
 y&  cg 0  cg Time[s]Translation velocity
Target velocity

Fig.4 Advancing translation velocity totarget velocity

664

Time[ms]

Fig.5 Velocity of center of gravity

Velocity[m/s]

The walk pattern was able to obtain the walk stabilized mostly.
Although target velocity is not as expected, by referring to Fig.3,
it turns out that acquaintance and uniform velocity are mostly
maintained at target velocity. Although Fig.4 is a figure showing
the velocity of the center of gravity, it turns out that it is walking
maintaining a certain fixed center of gravity velocity. Therefore
the stabilized walking pattern is studied.
In case the height of the ground goes up, which the level
difference is 0.05[m] as shown in Fig.6. And Fig.8 show thedisplacement of center of gravity velocity to Fig.7 for theadvancing translation velocity to target velocity.

Velocity[m/s]

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4.2 Displacement of the center of gravity (Form by eachroad surface)
The displacement of the center of gravity position in eachroad surface form is shown in Fig.12.

Velocity[m/s]Fig.8 Velocity of center of gravity(a) from front view

Time[ms](b) Height of ground = 0.05[m]Time[ms](c) Height of ground = 0.1[m]Fig.12 Displacement of the center of gravity

The displacement of the center of gravity position in eachroad surface is compared. Since the displacement of an almost
fixed center of gravity position is shown at the time of leveling,
as walking pattern, it turns out that it is possible within the limits.
Moreover, it is considered that this vibration occurred since thecontact power of the sole and the floor in sidewall is vibrating.
Next, for 0.05[m] and 0.1[m], when the center of gravity
position rises at a level difference in both, the center of gravity
position is changed completely. If by comparing these two levels,
although change of a center of gravity position may be sharp at
the moment of rising level difference at 0.05[m], after that, the(b) from width viewFig.9 Form of a walk (Height of ground = 0.1[m])©ISAROB 2005

Time[ms](a) Height of ground = 0.0[m]Displacement[m]Although the walk pattern was shaky, it was maintaining thewalk orbit. Since the center of gravity movement was the range
which a support leg can bear, the walk orbit which overcomesthe level difference of 0.05[m] was generable. By referring to
Fig.7, it turns out that advancing translation velocity is over a
few to target velocity compared with the walk at the time ofleveling. Although center of gravity velocity was carrying out
the velocity rise at the moment of going up a level difference,
the width is the width of the inside which can be walked andseldom affected the walk pattern. (Fig.8)
Next, walk form in case the height of the ground goes up thelevel difference which is 0.1[m] is shown in Fig.9. And Fig.11
show the displacement of center of gravity velocity to Fig.10 for
the advancing translation velocity to target velocity.

Displacement[m]Fig.7 Advancing translation velocity totarget velocity

Time[ms]
Displacement[m]Velocity[m/s]Translation velocity
Target velocity

Fig.11 Velocity of center of gravity

For the walking pattern, the model collapse at the beginning,
because putting the left leg on the difference level and carrying
the right leg. Although it has balancing translation velocity totarget velocity, it has balancing translation velocity clearly
beyond target velocity. Moreover, center of gravity velocity has
generated a double velocity, when rising a level difference ascompared with the walk at the time of leveling. Therefore, I
think that the rise of this velocity caused a fall. It is observed that
this double velocity caused the model to fall.

Fig.6 Form of a walk (Height of ground = 0.05[m])Time[s]Time[ms]

Fig.10 Advancing translation velocity
to target velocity(b) from side view(a) from front view

Translation velocity
Target velocity

Time[s]665

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

maintain the walk pattern at an angle of the waist. It is one of thecauses of moment generating.

(a) Height of ground = 0.0[m](a) Height of ground = 0.0[m]Angle[degree](b) Height of ground = 0.05[m]Time[ms](c) Height of ground = 0.1[m]Fig.14

Displacement of Hip Angle

5. Conclusions
From this research, it is observed when the height of theground was 0 [m] and 0.05 [m], the orbit of the walk which
follows a target orbit was able to be generated. However, since
the moment of the circumference of the center of gravity
occurred, 0.1 [m] brought a reverse result. In addition, since thecontact power of a sole and a floor occurred, the burden of eachjoint supporting a robot became large, and a result which affects
the vibration and the walk orbit in a center of gravity position
was brought. From research observation, for the moment control
produced by velocity rise, velocity control is performed and themoment of the circumference of the center of gravity iscontrolled. Also, you have to consider generation of the orbit
which performs an interference check etc to the contact powerof a sole and a floor.

Time[ms]

Time[ms]

Time[ms]

Time[ms]

Moment[N･m]Moment[N･m]

4.3 Displacement of Moment
The displacement of moment in each road surface form isshown in Fig.13.
It turns out that the moment in the height 0[m] and 0.05[m] ofthe ground is maintaining fixed width mostly, and there is no
influence of center of gravity portion. However, it has generated
clearly, and by comparing the moment of the circumference ofthe center of gravity in 0.1 [m] with 0[m] and 0.05[m], 3 [Nm] it
has slight a difference. It is the moment produced since this had
advancing translation velocity clearly beyond target velocity,and since the influence which the error has on ZMP has jumped
out of the support leg, I think that it is the cause of a fall. You
have to control generating of this moment.

Angle[degree]

Angle[degree]change of a center of gravity position will not be much, and itturns out that there is none. However, while rising the level
difference of 0.1[m], it can grasp that it is intense in center ofgravity movement. Too, the rise of center of gravity velocity led
to change of a center of gravity position. And it turns out that a
result to reverse was brought.

Moment[N･m](b) Height of ground = 0.05[m]Time[ms](c) Height of ground = 0.1[m]Fig.13

Reference

Displacement of Moment[1]Inoue,H.,Tachi,et.al：“Overview of Humanoid Robotics Project of
METI”, Proc.Int.Symp.Robotics, pp.1478-1482, 2001.
[2]Nishiwaki,K.,Sugihara,T.,et.al ： ”Designand Development ofResearch Platform for Perception-Action Integration in Humanoid
Robot:H6”, Proc.Int.Conference on Intelligent Robots and Systems,
pp1559-1564, 2000.
[3] Kajita, S. and Tani, K.,：
“Control of Dynamic Biped Locomotion
Based on Realtime Sensing of the Ground Profile”, Journal of the
Robotics Society of Japan, vol.14, No.7, pp.1062-1069, 1996. (inJapanese)[4]Kajita,S.,：“Real-time Dynamic Walking Control of a Biped Robot
Using the Linear Inverted Pendulum Mode”,Report of MechanicalEngineering Laboratory No.171,1996. (in Japanese)[5]Shino,et.al：“Real-time orbital generation of a bipedal robot”, Tokyo
institude of Techology project newmanoid,2001.(in Japanese)

4.4 Displacement of Hip Angle
The displacement of the waist angle of Hip Angle in eachroad surface form is shown in Fig.14.
When the height of the ground is 0[m] as it understands, even
if it sees a Fig.14, the difference of the maximum angle is10[degrees], when it is 0.05[m], it is 20[degrees], and in the caseof 0.1[m], it is 40[degrees]. The angle at the time of 0[m] and0.05[m] is the displacement of the angle within the limits which
can maintain a walk pattern. However, at the time of 0.1[m],since the displacement of a center of gravity position had
become large as the displacement of the center of gravity
position of the foregoing paragraph also showed, displacementof the angle of the waist was enlarged for it as a method of
prevention. And it was going to do by force and is going to©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Run control of the mobile robot using visual infor mation
Masanori Sugisaka , Tetsuro Hashizume
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 msugi@cc.oita-u.ac.jp , s1132061@mail.cc.oita-u.ac.jp

Abstr act

motor. Equipped with the rotary encoder of resolution 80(Pulse Per Resolution) beside the driving wheel, andcounting the number of pulses, the right-and-leftindependence can be achieved and a wheel can becontrolled. The difference in the rotation speed of aright-and-left driving wheel performs a steering function.
The CCD camera (EVI-G20: Sony) is carried by the heightof about 130 [mm] at 55 degrees of perpendicular
directions at the head. The picture obtained from thecamera is taken in by the memory on an image-processing
board (FDM-PCI3: FOTORON).

This research aims at capturing a mobile robot theenvironment information of based on the picture obtained
from the CCD camera carried in the mobile robot, andcarrying out the stable autonomous run. This paper
considers the technique of image processing with the CCD
camera for an autonomous run, and the basic experiment ofa run.

1.Intor oduction
In the recent years, the introduction of robot was beanconsidered in various fields. Various functions were beanrequired and the robot use was expanded. One of thefunctions requires environmental recognition andavoidance of an obstacle. In order to develop such robot,
this laboratory, for some time, has mainly respond to thefield of welfare and nursing. A mobile recognition robot
has been researched and developed [1][2][3].
This research is aimed at capturing the surrounding eachenvironment information of mobile robot based on the
pictures obtained from the CCD camera carried in themobile robot, and carrying out the stable autonomous run.
This paper considers the technique of image processing
with the CCD camera for an autonomous run, and the basic
experiment of a run.

3. Model of mobile robot
The mobile robot figure used in this research is shown in
Fig.2.[2][4]。The mobile robot is a system, which drives adriving wheel on either side by another actuator, and is anindependent driving wheel type and PWS (Powered Wheel
Steering).

Y：castorV

VL：Drive Wheel

θ

y

VR

ω
2d
2r

2. Exper iment System
The robot which is being used inthis research was manufactured byincorporated company DENKEN in
2000. The figure is shown in Fig.1. It
consists of 2 drive 2 caster (2DC2W)
systems. The mobile robot isequipped on the right and left with thedriving wheel, auxiliary caster ringsat the front and back, a driving
wheel on either side rotates by DC©ISAROB 2005

x

X

Fig. 2 Model of mobile robot

If the thickness of a wheel is disregarded and sets the
distance from a body center to a wheel is d, and the radiusof a wheel r, and the grounding speed of a wheel VR ,VL ,and a rotation angle of a wheelφR ,φL , it can write.

Fig. 1 View of The
Mobile robot

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

φ R, L = 2 * PI * count R, L / Encoder _ Slots

luminosity picture like the point for an objective boundary
and an objective outline. First, the difference the operator
is using and it asked for the strength of the edge between
each pixel. Next, if smaller than the value L with thedifference of the strength of edge altogether and it was
white and except it, the figure was displayed as Edge
Image as black. It turns out that an objective boundary andan objective outline are called for to some extent.

(1)
(2)
(3)

⋅

VR = r φ R⋅

VL = r φ L

Moreover, if the actuator which drives a wheel regards it． ．
as the control input of this system as (V,ω) (φR,φL)that
by which speed control is carried out, it can express a
relation with generalization coordinates in the following
forms.
 x•    cos θ y•  =  sin θ• θ   0 0 cos θ V  r 
0   =sin θω 2 1


1

 d


cos θ  • 
φsin θ  •R  (4)1  φ L − d (a) Or iginal Image(b) Monochrome Image

Moreover, the equation below a equation (4) is obtained
and this means the Nonholonomic restraint that a wheel
does not sideslip [4].
••x sin θ − y cos θ = 0(5)(c) Edge Image

4. Image Processing

Fig.3 Image Processing

The picture is taken in from a CCD camera, it is a
picture expressed with a total of 24 bits of 8 bits each of
RGB. It is very hard to treat RGB for correlation to bestrong in each and acquire the feature from a picture. Then,in order to treat the feature from a picture independently as
much as possible, the following equation performs LHS
conversion of lightness, hue, and the saturation.

L = 0.299R + 0.587G + 0.144B(6)

 R − L
 0.701R − 0.587G − 0.144B 
H = tan −1 
 = tan  B − L
 − 0.299R − 0.587G + 0.856B (7)

S = ( R − L)2 + ( B − L) 2=

5. Dr iving Exper iment
In order to run at the rate of following a course
linearly and a target, the experiment run on a mobile
robot was conducted the following condition.
・ Target number of rotations：8（pulse/0.1s）In orderto carry out a wheel one revolution in 1 second
・ Run a mobile robot only by Proportional control.
This is because it is shown that number of rotations
reaches to a target value only in Proportional control
by research of the past mobile robot [3].

(8)The number of rotations of an actual wheel is shown in
Fig4.The equation of speed control of the wheel at thistime has become like (11) equation, 0 has become themaximum and 255 has become the minimum. Moreover,the value to which applied the deviation by (9) equation
and which applied the proportionality parameter to thedeviation by (10) equation is expressed as an amount ofoperations.

(0.701R − 0.587G − 0.144B) + (− 0.299R − 0.587G + 0.856B)
2

2

Also in this, L picture serves as a numerical value
mostly proportional to the strength of the brightness which
man's eyes feel, and used only this picture also for
improvement in the speed of processing.
Edge detection was performed in order to ask the©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

er = r _ set − rightcount(9)

el = l _ set − leftcountr _ ope = r _ ope + r _ K p × er

(10)l _ ope = l _ ope + l _ K p × elrightspeed = 235 − 12.3 × r _ ope(11)

leftspeed = 235 − 12.3 × l _ ope

Number of Pulse(Pulse/0.1)

Since the picture is using only the left-hand side of thecamera carried in the robot, it turns out that the center of apicture and a robot's center have shifted. When the size of apicture serves as width 160 [pixel] and length 120 [pixel], ahorizontal axis is set to i and a vertical axis is set to j bymaking the upper left into the starting point, a robot's
center will be located in the position which is i= 93 pixels.
The equation, which gave repulsive potential so that it
might become so large that a robot's center and edge
portion are near, is shown in (12). At this time repulsive
potential in i=93-160[pixel] is made into Uo_r andrepulsive potential in i=1-93[pixel] is made into Uo_l.

10

Uo _ r = Uo _ r + 1 / (93 − i )2 + 0.5 j 2

8
6

Uo _ l = Uo _ l + 1 / (93 − i) + 0.5 j
2

4

(12)

2

2
0
0

20

40

60

80

100

Fig. 4 Right Wheel Rotation Number

Since an actual distance in the portion of the bottom of apicture is 240 [cm] at this time, an actual distance in the
portions of 126 [cm] and the top is rectifying the horizontal
axis using (13) and (14) equation.

Although the value of target number of rotations was
mostly detectable, it has checked carrying out amplitude
slightly. Therefore, it is necessary to examine the optimal
parameter using PI, PID control, and still newer control.

calibration = 1 + 0.0075 * (120 − j )Time(s)(13)

if i > 80
new _ i = 80 + (i − 80 ) * calibratio n
if i < 80

6. Obstacle Avoidance Exper iment

new _ i = 80 − (80 − i ) * calibratio n

The obstacle avoidance experiment was actuallyconducted in the environment, which is said to the methodof the forward left as the 1st obstacle, and is said to themethod of the forward right as the 2nd obstacle. At thistime, target speed in case there is no obstacle was set to15.7 [cm/s].

(a)Or iginal Image

When Uo_r is larger than Uo_l and an obstacle exists inright-hand side, load is given to left_speed, and the left isrevolved. Conversely, when Uo_l is larger than Uo_r andan obstacle exists in left-hand side, load is given toright_speed, and the left is revolved. The equation at thistime is shown in (16).

(b)Edge Image

Fig.5 Obstacle Image
Giving imagination repulsive potential between the edge
portion of a picture, and a robot’s center performs the
obstacle avoidance.

©ISAROB 2005

(14)

if Uo _ r < Uo _ l Uo = Uo _ l − Uo _ r
if Uo _ l < Uo _ r Uo = Uo _ r − Uo _ l(15)rightspeed = 235 − 12.3 × r _ ope + kUo
leftspeed = 235 − 12.3 × l _ ope + kUo(16)

Thus, change of the number of counts of the encoderwhen carrying out speed control has become like Fig.6. In
about 8 - 13 seconds, it turns out that the left number of669

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

counts is larger than the right number of counts, it circled
on the right, and the 1st obstacle is avoided. Next, it turns
out that the right number of counts became larger than theleft number of counts in about 18 - 22 seconds, it circled on
the left, and the 2nd obstacle is avoided. Since an obstacle
does not exist in a screen, by 4 [pulse / 0.1s], it is stabilized
after 23 second and it is carrying out the going-straight run.

References[1]Syuiti Otsu, Masanori Sugisaka : Research on theenvironmental recognition in a mobile robot ， Oita
University Graduate School Master Paper(2003)[2]Syohei Kuriyama , Masanori Sugisaka : Development of
the mobile robot run system using dead reckoning andvision information ， Oita University Graduate School
Master Paper(2003）[3]Toshio Maeyama , Masanori Sugisaka : The obstacle
avoidance run in the mobile robot using the CCD camera，
Oita University Graduate School Master Paper(2002)[4]Tsutomu Mita：A guide to nonlinear control - Be lacking
driving robot's skill control theory ， Syokodo ,pp61-63(2000)[5]Yoshihisa Hiyama , Kenji Murao , Yoshio Yamamoto :Flexible collision avoidance maneuver of wheeled mobile
robots , Japan Society of Mechanical Engineers [No.03-4]
Robotics and Mechatronics Division lecture meetingcollected papers(2003)

l _ countr _ count7[pulse/0.1s]

6
5
4
3
2
1
0
1

4

7

10

13

16

19

22

25

28

time[s]Fig.6 Wheel Rotation Number

7. Conclusions
Imagination repulsive potential was given to the speedof a wheel using the edge information on the picture
obtained from the CCD camera, and carrying out speed
change performed the obstacle avoidance run on a smoothorbit.
As a problem, after avoiding an obstacle, it will go
straight on with an angle as it is. Imagination repulsive
potential will receive influence not only in distance and asize with an obstacle but in the pattern and.
I decide a target point from now on, and think that therun stabilized by taking in not only imagination
repulsive potential, but also imagination attraction canbe performed.

©ISAROB 2005

670

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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674

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Modeling of Pneumatic Artificial Muscle Actuator
Huailin Zhao*,**,***(zhao_huailin@yahoo.com)***

Masanori Sugisaka*

Datai Yu**

Department of Electrical and Electronic Engineering, Oita University

School of Information Engineering, University of Science and Technology Beijing***

Zhengzhou University, China

Abstract: This paper mainly developed the static modelof pneumatic artificial muscles in theoretically. From its
physical model, the geometrical model was turned up,
and the mathematical model was established. It discussed
the contraction ratio, formulated the force output as afunction of gas pressure and the structure parameters,

Fig. 1 Pneumatic artificial muscle

and analyzed the stiffness.
Key word: Pneumatic artificial muscles, static model,
contraction ratio, force output, stiffness
1.

Introduction
In

recentyears,

Fig. 2 Physical structure
a

new

kind

of

artificial

muscles-pneumatic artificial muscle (PAM) has been

2.

Geometric model

developed [1][2]. It consists of a rubber tube covered in

A piece of pneumatic artificial muscle can be

tough braided fibre mesh, which shortens in length whengeometrically modeled as a cylinder. Of course theinflated with compressed gas. Sometimes it is also calledcylinder can change its volume when gas pressure is

as air muscles, because usually the used gas is air. It canapplied. The non-cylindrical end effects are ignored, andgenerate enough force and maintain proper compliance atthe thickness of the braided mesh is assumed to be zero.

the same time-a little like real muscles [3][4]. So, it can

The dimensions of this cylinder are the length L,

be used as the actuator in robots, so that the actuator will

diameter D, and the interweave angle θ. Neither of these

inherently behave a little like animals. Sometimes it isdimensions remains constant, because all the three

just called as pneumatic artificial muscle actuator. Fig. 1

parameters will change when it contracts or extends.

is a photo of a pneumatic artificial muscle actuator madeAssuming inextensibility of the mesh material, theby Robot Shadow Company [4]. Fig. 2 is its enlarged

geometric constants of the system are the mesh threadpart view.

length b, and n-the number of turns for a single thread.

Based on the possible applications in robots, it is

The interweave angle θ is the angle between the threadnecessary to establish its models. This paper is going toand the long axis of the cylinder (look at Fig. 3). θ

set up its static mathematics model.

changes as the length of the actuator changes. The
relationships between these parameters are shown in Fig.
4.

©ISAROB 2005

675

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

important characteristics. Now we can calculate it:
Rc = (L0-L)/L0= (L0-bcosθmax)/L0(4)

Here, Lo is the relaxed length, or called it free length.
To two pieces of the same kind of pneumatic artificial
muscles in different lengths, their interweave angle
should be same. Therefore,

Fig. 3 Geometrical model

L10=b1cosθ
L20=b2cosθ
Rc1=(L10-b1cosθmax)/L10=1- L10/b1cosθmax=1- cosθmaxcosθ0
Rc2=(L20-b2cosθmax)/L20=1- L20/b2cosθmax=1- cosθmaxcosθ0
Where, θ0 is the interweave angle corresponding to therelaxed length. So,

Fig. 4 Structure model

Rc1 = Rc2 =constantThis means that their contraction ratios are same to two

Now let’s formulate the relationship between theactuators with the same structure in different lengths.

length L and the angle θ.
L=bcosθ(1)nπD=bsinθ(2)

3.

Because it is a kind of motion-generating device, we

The cylinder volume V=πD2L/4, combining (1) and (2),V=b3sin2θcosθ/(4πn2)Static model

should try to find the force expression as a function of(3)

the related factors. Probably the force F has relationshipThe minimum length (the maximum contractedwith gas pressure, because the actuator acts when gas

length) should arise when the actuator’s volume gets topressure is applied. Besides the relationships between theits greatest, because this results in the equilibrium of theforce and the length, interweave angle and so on will besystem. So, at the equilibrium point, there should be:

developed too.

dV/dθ=0

Here we use a simple energy analysis. It is assumed

Based on (3),that the actuator is a conservative system in which thedV/dθ = (2sinθcos2θ-sin3θ)b3/(4πn2)work in (Wi) is equal to the work out (Wo). The losses=0will be accounted for later. Work is input to the actuator

Then we getwhen gas pressure is applied to the inner bladder surface.

θmax ≈54.7°

dWi=∫Si (Pabs-Patm)dli*dsi

We can say that the cylinder gets to the maximum of=( Pabs-Patm)∫Sidli*dsi

its volume when the interweave angle changes to 54.7°,=PrdV

or we say that the actuator gets to the greatest contraction

Where,

by applied gas.

Pabs = Absolute internal gas pressure

By now the geometrical model has been established.

Patm = Atmosphere pressure (a little more than 1 bar)

Besides, the contraction ratio Rc is one of the most

©ISAROB 2005

(5)

Pr = relative gas pressure
676

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

si = Total inner surface

is opened and laid flat, the weave geometry is easily

dsi = Area vector

observed (Fig. 5). The shape of the weave quadrilateral isdli = Inner surface displacement

governed by the interweave angle θ and the quadrilateraldV = Volume changeside length l.

The output work arises when the actuator shortens dueto the change in volume.
dWo=-FdL(6)

Now the ideal system assumption can be applied. The
work input to the system should be equal to the work
done by the actuator.
dWi= dWo
Combining (5) and (6),
PrdV=-FdL

Fig. 5 The weave mesh structure

So,
F=-PrdV/dL

The cylinder length L and circumference C of the=-Pr (dV/dθ)/(dL/dθ)

actuator is easy to be formulated by the following=Prb2(2cos2θ-sin2θ)/(4πn2)=Prb2(3cos2θ-1)/(4πn2)formulas:(7)

When θ=54.7°, F= Prb2(3cos2θ-1)/(4πn2) =0. So please

C = 2Blsinθ

(9)

A = number of lengthwise quadrilaterals

force output of the actuator is zero. In another word, theB = number of circumferential quadrilaterals

force output will be zero at the greatest contraction.

Since the circumference can also be expressed as πD, so

Besides, under the condition of Pr is constant, when θ isthere is:equal to its minimum cosθ gets to its maximum, so F will

πD=2Blsinθ

get to its maximum. We denote the maximum of F as

D=2Blsinθ/πFmax, therefore,(10)

Recall equations (1) and (2),Fmax = Prb2(3cos2θ0-1)/(4πn2),

L=bcosθθ0 is the interweave angle when the actuator is relaxed.

D=bsinθ/nπBesides, in (7), based on Fig. 4, we can imagine theComparing (1) and (8), (2) and (10) results in the

diameter D will get to its maximum Dmax when θ=90°.

following:Then
b=nπDmax
Take this into (7), it will become:

b=2Al

(11)

n=A/B

(12)

Therefore, to practically characterize an actuator only(7’)

the quadrilateral size and count are necessary. Now let us
try to remove θ from the equations. It is difficult to sense

The geometric variables used above provide a

the interweave angle during operation of the actuator.

straightforward calculation, but to use the resulting

But it is much easier to measure the length. If theequations in practice, they need to be modified, because

equations can be rewritten in terms of force, pressure,the parameters b and θ are not easy or convenient to be

and length, then they will be more useful, because thesemeasured. Now let us discuss this. If the cylindrical mesh
©ISAROB 2005

(8)

Where:

note that at the maximum interweave angle 54.7° the

F=πDmax2Pr(3cos2θ-1)/4

L = 2Alcosθ

677

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

variables can be measured most easily. Recall thelength is small, and can be neglected. Besides, there istriangle from Fig. 4, and note that the length of sidesome space in the inlet hose, and it can help to cause theopposite to angle θ has been rewritten in terms of b andpressure change to remain minimal through theL.

actuators’ range of motion. In this case we can assume:
dPr/dL≈0(18)

So, actuator stiffness is now given by
K=3PrL/(2πn2)(19)Or solving (16) for Pr and substituting the result into
(19),
K=6F/(3L-b2/L)
4.

(20)

Conclusion
From the above theoretical analysis, we can conclude:zFig. 6 The triangle relationship

Pneumatic artificial muscle is a linear motion
engine, because the force output is proportional
to the stimulating gas pressure (see (7));

The relationships between θ, L, and b can bez

expressed as:

To a constant gas pressure, force output iscosθ=L/b(13)

related with the length. And it will become zerosinθ=(b2-L2)1/2/b(14)

when it gets to the shortest length (see (7));
z

Substituting these relationships into the volume (3) and

The stiffness is not constant. It changes withboth the gas pressure and the length;

force (7) equations,
V=L(b2-L2)/(4πn2)(15)F=Prb2(3L2/b2-1)/ (4πn2)

(16)

z

To determine actuator state, only two of theparameters are needed: gas pressure, length,
force, or stiffness. The other two can be

It is easy to imagine that pneumatic artificial muscles

calculated from the above formulas.

act somewhat like springs. We know that the spring has
an important parameter-the constant elasticity coefficient.
Does the actuator exhibit the same property – theReference

constant stiffness as the spring? According to the concept[1] John Iovine, Artificial pneumatic muscle, Poptronics,
Nov.2000, p49-65

of stiffness, it is simply a derivative of force with respect[2] Graham-Rowe, Duncan, Power dressing, New

to length.

Scientist, 5/25/2002, Vol. 174, Issue 2344

K=dF/dL[3] Bjorn Verrelst, Frank Daerden, Pleated Pneumatic

Differentiating (16) with respect to L,
K= Prb2(3L2/b2-1)/ (4πn2)dPr/dL + 3PrL/ (2πn2)

Artificial Muscles for Robotics Applications, 1st(17)

The first term (dPr/dL) is the most difficult to formulate.

Flanders Engineering PhD Symposium about

When the valves are closed, the pressure changes

Industry-Ready Innovative Research, Brussels, 11

proportionally with volume according to gas laws. As a

December 2003
[4] http://www.shadow.org.uk/products. 2004.7.1

result, the relationship dV/dL would need to be
developed. However, when the gas valves are opened,
this relationship (dPr/dL) is even more difficult to model.
But we think that the pressure change as a function of©ISAROB 2005

678

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
Provide various security functions,synchronization function,
Agent communication functions

Antenna Connector

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

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 

LAN
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  .    : ./-The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
Predicate Group 01
Predicate Group 02

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Predicate
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Predicate_03…

Predicate Group 03

Predicate_N……

(Cyclic Execution)

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Predicate Group N
Predicate Group N+1

Unification Task

Predicate

Recursive Call

Internal State

Ready
ReadyPredicate
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<       

            

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 

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
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682

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A Soccer Robot Control Design Based on the Immune System
Jungo Ito, Kazunori Sakurama and Kazushi Nakano
Dept. of Electronic Eng., The University of Electro-Communications
1-5-1 Chofu-ga-oka, Chofu, Tokyo 182-8585, Japan

Abstract

environments. Kondo, et al. proposed applying the IS
to action control of an autonomous mobile robot [2].
However, there are still a few examples of applicationof the system to continually changing environments inwhich robots are tasked with a job.
In this paper, we apply the IS to soccer robot control in the RoboCup small league section, and show apossibility of designing a robust robot control system
which can flexibly cope with dynamic environments.
First, a simulation is performed for obstacle avoidanceof a small mobile robot in which the IS is installed asa controller. Furthermore, it is demonstrated to improve the capability of obstacle avoidance via a learning mechanism of the IS. Secondly, the other simulation of 3 to 3 soccer plays is performed, in which the
IS is used for both of obstacle avoidance and action
selection. Then, we show to improve the performanceof soccer play via the learning mechanism of the IS.
Finally, we give the conclusions.

This paper proposes a control system design for soccer robots based on the immune system. The immune
system is a kind of engineering model imitating thehuman immunity action. It has some features suchas self-optimization by learning, and easiness in dynamic reconfiguration of robots themselves. Thus, theimmune system can be effectively applied to robot control in dynamic environments.
The immune system is applied to construct the soccer robot control system for RoboCup. First, applying
the immune system to the obstacle avoidance problemof the mobile robot, we demonstrate its validity in simulation. Next, we execute 3 to 3 soccer plays by thesoccer robots in simulation. These results show thatthe immune system is useful for robot control.
Keywords: immune system, dynamic environments,
RoboCup2
1

Immune System

Introduction
2.1

The problem in development of industrial robots
until now is focused mainly on the improvement of accuracy and speed in robot motion, due to the use ofrobots in static environments such as automated factories. However, for robots which will infiltrate ourdaily life from now on, it is necessary to cope withdynamic environments which are continually changing
around the robots. In addition, it is important to develop the capability of cooperative behavior between
robot-robot and between robot-human.
In order to promote research on these problems, wehave dealt with RoboCup [1] as an international robot
soccer game. And we now try to design a new soccer
robot control system by applying the theory of immune system (IS) which imitates the human immunity action. The IS has some features such as selfoptimization by learning, and easiness in dynamic reconfiguration of robots themselves. Thus, this system
can be effectively applied to robot control in dynamic©ISAROB 2005

Summry of Immune System

The IS is a kind of engineering model imitating
the human immunity action. The human being has
complex immume responses via multiple mechanisms.
Here, we pay attention to the immune network, i.e.
network between an antigen-antibody and between
antibody-antibodies which is based on the idiotypic
network hypothesis proposed by Jerne [3]. We will
construct the IS based on Jerne’s hypothesis.

2.2

Dynamics of Immune System

The model equation which represents the behaviorof the immune system is given by Kondo, et al [2]. We
modify this in the following form : Nri (t + 1) = ri (t)

+

j=1

N

·Ai (t)683

Tij Aj (t)+ m i − ki(1)The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

2.3

Learning Mechanism

An immune network can optimize its parameters by
a learning method as shown below. The parameter Tij
is updated to improve the adaptation capability of thesystem by learning from a result of the action selectedby the IS. In the simulation shown in Sections 3 and4, the update rules of Tij are assigned as follows:[Update Rules]
1. In the case that the robot reaches the target point:
For the selected antibody i, if its concentration
exceed the fixed value, Tij , which is related toother antibody j, increases based on the following:

Fig. 1: Application of IS to robot control

Ai (t + 1)=

1
1 + exp{β − ri (t + 1)}

Tij (k + 1) = 0.95 × Tij (k) + 0.05

2. In the case that the robot collides with the obstacle: For the selected antibody i, if its concentration exceed the fixed value, Tij , which is related
to other antibody j, decreases based on the following:where Ai is the concentration number of antibody i, ri
is the parameter to determine Ai , Tij is the immune
network parameter which represents the relationship ofstimulation and suppression between antibodies i andj, mi is the reaction between antibody i and antigen,ki is the number of natural death, N is the number ofantibodies, and 
β is the threshold value. After beingN
updated by mi , j=1 Tij Aj , and ki , the concentration
number of antibody i is normalized via the sigmoid
function in eq.(2).
In our robot control, the IS is used by consideringthat the antigen is an environmental information suchas a target point and an obstacle, and the antibody is
the movement direction to be selected by a robot. For
example, to antibody 1 (Ab1) we give the following
command, “when a target point exists in the forward
direction, move forward”. On the other hand, to antibody 2 (Ab2) we give the following command, “whenan obstacle exists just in front, move left”.
Let m1 be 1(0) in the case that the target point
exists (does not exist) in the forward direction. Similarly, let m2 be 1(0) if the obstacle exists (does notexist) in front of the robot. On this setup, the antibodies concentration is continuously calculated by eqs.(1)and (2) from initial values Ai (0) = 0, ri (0) = 0. This
calculation is finished when one of antibodies concentration exceeds the fixed value decided previously. The
antibody who has eventually the highest concentration
determines the movement direction of the robot.
Unfortunately, it is quite difficult to give suitablenetwork parameters Tij because of a huge number ofthe combination of Tij . Thus, we need an optimization method of Tij by self-learning, which is discussed
in the next subsection.

©ISAROB 2005

(3)

(2)

Tij (k + 1) = 0.95 × Tij (k) − 0.05

(4)

3. In the case that the robot cannot reach the target
point in a limit time: For each i and j, Tij changes
based on the equation
Tij (k + 1) = 0.95 × Tij (k) + d,(5)-

where d is uniform random number on [ 0.5,0.5].

3

Obstacle Avoidance Simulation

3.1

Method of Obstacle Avoidance

In this simulation, the robot can search the field divide into 16 areas (refer to the left figure of Fig.2),
and move to one of 16 directions (refer to the rightfigure of Fig.2).
When the number of obstacles is only one, there exist 16 types of the obstacle detection pattern. Then,
we can specify the movement direction of the robot
according to each pattern in advance. Unfortunately,
it becomes difficult to specify the movement directionof the robot in advance as the number of obstacles
increases, because the detection pattern increases exponentially as it does. Therefore, we introduce an immune system in the next subsection to achive an easy
situation where we need assign only some types of simple behavior of the robot, e.g., when an obstacle exists
in the search area 6, move to direction number 15.

684

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Section 2.3. Finally, the solid line shows the path after
Tij are updated 12 times until the robot reaches thetarget point. The iteration of the update of Tij enables
the robot to reach the target point with avoidance ofthe obstacles.

4

3 to 3 Soccer Play Simulation

4.1

Next, we perform a simulation of a soccer game,
which continues until one of three player robots shoota ball to an opponent goal, which is defended by three
opponent robots. In this simulation, the antigens
are given from the position information of the player
robots, the opponent robots and the ball. The antibodies are assigned as robot actions such as a dribble,
a pass and a shoot. Each player robot distinguishes
9 types of the field situation from 2 information, thatis the ball position and the ball owner. Each robot
receives a command according to the situation, e.g., if
there exists the ball near the opponent goal and one ofplayer robots has the ball, then this robot receives thecommand “move to the opponent goal”, another does
“move to near the opponent goal”, and the other does
“move to center”.
We perform simulations in 10 cases, where one of
opponent robots is a goalkeeper the others chase theball without obstacle avoidance action. The movement
velocities of the opponent robots and the player robots
are equal.

Fig. 2: Search area and movement direction

4.2

Fig. 3: Obstacle avoidance

3.2

Extended Learning Rules

In the simulation of the soccer game, the following
are attached to the update rules of Tij .

Simulation Result[Additional rules]

This subsection shows a result of a simulation where
the robot tries to move a target point with avoidanceof stationary obstacles. In this example, the target
point is in front of the robot while the obstacles exist
between the robot and the target point. Let antigens
be the position information of the target point and the
obstacles, and the antibodies be the movement directions shown in the right figure of Fig.2.
The broken line in Fig.3 shows the path of the robot
in the case that Tij = 0 for any i and j. The robot
collides an obstacle and cannot reach the target point.
On the other hand, the dotted line in Fig.3 depicts thepath of the robot whose parameters Tij are updated
once using the learning mechanism, which is shown in©ISAROB 2005

Soccer Robot Control

1. If the player robots never score in a limit time,these parameters are updated so that the antibody selected then becomes more difficult to be
selected.
2. When the player robots change the role, the following update is performed for each robot according to the action evaluation which is introduced
below. If the value of the evaluation is positive,
Tij is updated for the antibody selected then to
become easier to be selected. If the value is negative, Tij is updated for it to become more difficult
to be selected.

685

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

[Action Evaluation]
Let ELi (t) be the distance between the ball and theopponent robot i at the time t:(bx(t) − exi (t))2 + (by(t) − eyi (t))2 ,ELi (t) =(6)

where (bx(t), by(t)) and (exi (t), eyi (t)) represent theball position and the position of the opponent robot i,
respectively, and t is the time just after the change ofthe robots’ roles. Similarly, let GL(t) be the distance
between the ball and the opponent goal at t:GL(t) =

(bx(t) − gx)2 + (by(t) − gy)2 .

(7)

The following is the action evaluation which the additional rule 2. uses.
Fig. 4: Simulation result of soccer play before learning

The action evaluation is set positive if P (t) >P (t − 1), and is negative if P (t) < P (t − 1).
The parameter Tij is not updated if P (t) =P (t − 1).
P (t − 1)

=

E

i=1

P (t)

=

E

i=1

4.3

1
1
−
, (8)
ELi (t − 1)
0.5 × GL(t − 1)
11−.
ELi (t)0.5 × GL(t)(9)

Simulation Results

Fig.4 shows the simulation result with the nonupdated Tij . Each player robot cannot pass and dribble well, and an opponent robot take the ball from one
in a moment. On the other hand, after the update of
Tij using that learning mechanism, the player robots
can get a score with suitable actions as shown in Fig.5.
The player robots can score 7 goals in 10 simulations
after learning, while they cannot before learning. This
simulation result illustrates clearly the improvement of
the ability of the player robots by learning.

5

Fig. 5: Simulation result of soccer play after learning

References[1] RoboCup Official Site
URL : http://www.robocup.org/[2] T. Kondo, A. Ishiguro, Y. Uchida : “An Emergent Approach to Construct Behavior Arbitration Mechanism for Autonomous Mobile Robot,”
T.SICE, vol.35, no.2, pp.262-270, 1999.

Conclusions[3] N. K. Jerne : “Idiotypic Networks and Other Preconceived Ideas,” Immunological Reviews, no.79,
pp.5-24, 1984.

In this paper, we have constructed the multi-robot
control system for RoboCup. This system can flexibly
deal with dynamic environments by using the IS. The
simulation results show the effectiveness of the IS for
robot control.
There remain some problems in developing more efficient learning mechanisms and implementing this IS
with real robots.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Robot’s behavior driven by internal tensions
regulated by pulsed para-neural networks
Junquan Li

Andrzej Buller and Juan Liu

School of Information Engineering
University of Science and Technology Beijing
Haidian Zone, Beijing 100083 China
tq097rr@163.com

ATR International, Network Informatics Labs,
2-2-2 Hikaridai, Keihanna Science City
Kyoto 619-0288 Japan{ buller; juanliu }@atr.co.jp

Abstract We tested a psychodynamic controller called
PreBrain using a simulated mobile robot. According toour approach, an agent seeks for pleasure that comesfrom the discharge of selected psychic tensions. The
quality of control depends on the pleasure record.
Perception may regulate the dynamics of tensions. The
discussed version of PreBrain processes three pleasurerelated tensions: boredom, interest, and excitement. An
object-of-interest appearance discharges the first tension,
but results in an increase of the second one. Contact
between a toy and the robot’s frontal touch sensor
discharges tension related to excitement.
There are two kinds of building elements used for
PreBrains: tension-accumulation cells (TAC) and pulsed
para-neural networks (PPNN). TAC produces spiketrains
whose frequency depend on accumulated tension, where
increment/decrement of tension levels depend onreceived spiketrains and spontaneous accumulation ordischarge. PPNN is a graph consisting of processing
nodes and directed edges representing defined delays.
We tested three kinds of PreBrains. The first one
forced the robot to rotate to get an image of an object-ofinterest (toy) to the central zone of the visual field andthen to go forward full-speed. The second one slowed theappropriate wheel to facilitate a turn when the image ofthe toy deviated from the center of the visual field. The
third one employed a mechanism detecting certain casesof disappearance of the object from visual field andfacilitating appropriately fast turn.

PreBrain is a simple controller employing basic
psychodynamic mechanisms intended to evolve toward acomplex neural-like circuitry in which an emergence ofcommunication behaviors and the phenomenon of
thinking could take place [7][8]. PreBrain has a highly
modular structure. One kind of modules, called tensionaccumulation
cells
(TAC)
constituteTensionAccumulator. The second kind of modules, called pulsed
para-neural networks (PPNN) constitute Tension
Discharger. There is a bi-directional communication
between Tension Accumulator and Tension Discharger(Fig. 1). Since not only perception contributes to increaseof tension level, PreBrain’s “mental life” (resulting invarious actions) can take place outside the framework ofsensing-action loop.
TensionAccumulator

PreBrainTensionDischarger

θn

ψm

θ2

ψ2

θ1

ψ1

1 Introduction
Sensors

The psychodynamic approach to machine intelligenceassumes that an agent is doing something because heseeks for pleasure, where pleasure is nothing but apossibly fast discharge of a psychic tension [1]. Such
relationship between tension and pleasure was suggested
by Sigmund Freud [2][3]. Although not all Freudianconcepts are supported by convincing empirical evidence,some of them seems to be valuable tips for building
artificial minds. One of such concepts is continuous
battle between conflicting mental forces [4] that, when
implemented, resulted in robot’s life-like hesitation[5][6].

©ISAROB 2005

Agent’s
Body

Actuators

Environment
Fig.1. PreBrain structure and its relationship withagent’s body and environment. θ1, θ2, …, θn –tension-accumulating cells (TAC); ψ1, ψ1, …, ψm– pulsed para-neural networks.

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pleasure is related to the dynamics of tension discharge.
The greater tension discharged in a given time, thegreater pleasure recorded; the faster discharge of a given
tension, the greater pleasure recorded.
PPNN is a graph consisting of processing nodes anddirected edges, called axons. The nodes represent
functions operating on spiketrains. Axons represent puredelays [13]. To date we used PPNNs in which a given
node can be a mexor or paraneuron. Mexor returns aspike at clock t if it received one and only one pulse atclock t-1. Paraneuron returns a pulse and resets its
counter at clock t if the counter was greater than 1 atclock t-1, while counter at clock t-1 is counter at clock
t-2 plus the weighted sum of pulses the cell received atclock t-2 (a weight can equal 1 or –1). If axons are
strings of one-input mexors and every cell is a 1×1×1
cube whose coordinates are three integers, then such a
PPNN is called NeuroMaze. We have developed the
NeuroMaze editor/simulator for rapid prototyping ofdesired PPNNs [14]. It was shown that a
PPNN/NeuroMaze can be build for every Booleanfunction and practically for every manipulation on
spiketrains [15].

The PreBrain research contributing to both theArtificial Brain project conducted at the ATR, Kyoto andIntelligent Controller based on Artificial Brain (IC/AB)
conducted at the USTB, Beijing [9]. Currently
investigated PreBrains still fit to the behavior-based
paradigm summarized by Ronald Arkin as sharing anaversion to the use of representational knowledge,emphasis on a tight coupling between sensing and action,and decomposition into behavioral units [10, p. 173].
Rodney Brooks’s postulate to reject “cognitive box” [11,p. 36] is treated in PreBrain research only as a ban on
handcrafted algorithms manipulating handcraftedsymbolic knowledge. Intended development of
psychodynamic architecture assumes facilitating
emergence of various models of reality (perceived andimagined) [12], which will require a kind of knowledge
representation to emerge as a result of social interactions.
2 PreBrain cells
The primary cause for any PreBrain action (related toenvironment or to itself) is a psychic tension being
accumulated in one or more TACs. PPNN is acomplementary technique used for purposefulmanipulation on spiketrains, which is difficult in theframework of TAC-only networks. Although internal
states of TAC are real numbers, whereas internal statesof PPNN cells are integers, all of the cells send/receive
only spiketrains, i.e. series of pulses of unitary amplitude
occurring at discrete moments of time called clocks.
TAC state changes according to the formula:

3 How PreBrain works
Figure 2 shows PreBrain that was a subject to thepresented research. The attached mobile robot uses two
motors equipped with 2-input frequency-to-voltage
converters (FVC). A frequency provided to the left
FVC’s “positive input” (L+) or the FVC’s “negative
input” (L-) forces the left wheel to roll forward or
backward, respectively. The same applies to the right
FVC’s inputs (R+ and R-). Hence, equal-frequency

u At Dt ⎞
⎛
G(x)
+
−
⎜
T
TA TD ⎟⎟
S⎜
θ t +1 = G θ t +
1
⎜
⎟
f
⎜
⎟
⎝
⎠
1
0
x
where
t – time (in clocks),
θt – accumulated tension,u – parameter determining direction of uncontrolledchange of tension (1 – increase, -1 – decrease),
At = A0, t + A1, t + … + A15, t – tension-increasing signal,
Dt = D0, t + D1, t + … + D15, t – tension-discharging signal,
TS – uncontrolled increase/discharge time,
TA – duration of tension change from 0 to 1 wheninfluence of uncontrolled charge/discharge is negligible
and the control signal At is a spiketrain of 1s only,
TD – duration of tension change from 1 to 0 wheninfluence of uncontrolled charge/discharge is negligible
and the control signal At is a spiketrain of 1s only,f – frequency of clocking (clocks per second).

spiketrains provided to L+ and R+ results in the robot’s
moving forward, whereas unequal spiketrains provided toL+ and R- results in the robot’s rotation. Spiketrains to

FVCs are provided by PPNN labeled Or8x4 (four 8-input
OR-gates). The TAC labeled θ0 is related to boredom.
Its TS = 10s and u = +1. Its output is connected to thecentral input of the PPNN labeled PB1. Tension θ0grows by internal causes and PB1 facilitates discharging
via forcing the robot to wander in search for somethinginteresting (spiketrains provided to L+ and R+). Whenthe PreBrain is switched on, we observe the robot start, 1
accelerate for 10s, and then proceed forward at full-speed.
Note that this behavior is not caused by any perception.
Although, according to psychodynamic approach,perception does not cause behaviors, it can regulate them.
If right or left touch sensor is excited, PB1 (responsible
also for obstacle avoidance) redirects one of involved
spiketrains (L+ to L- or R+ to R-), which results inrobot’s rotation. A device labeled V-Green5 processes
images from camera and recognizes an object-of-interest(and its direction) and provides signal Dt to θ0 where TD= 0.1s. Hence, when an object-of-interest is seen, therobot immediately ceases moving.

TAC produces an output spiketrain whose frequency is
proportional to current tension level so ft ≈ k f θ t ,where k∈[0; 1] is a parameter regulating the tension
impact. According to psychodynamic assumptions,
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Θi
Θ11

Θ9a

Θ5a

TAC

PPNN

mexor

Tensiondischarging
inputs to TAC

paraneuron

Θ10QTensionaccumulating
input to TAC

L7-Chaser
Θ9
Θ8

Or8x4
Θ8
Θ5

P

Θ12
PB1

Θ7

Θ13

Θ4

Θ3

Θ2

Θ1

L+ L- R+ RΘ0

V_Green5

Pulsesfrom the frontal
touch sensor

Pulsesfrom the left
touch sensor

Pulsesfrom the righttouch sensor

Right-motor
FVC
Left-motor
FVC

Signals from camera

Fig.2. An example of PreBrain. TAC – Tension-Accumulating Cell; PPNN – pulsed para-neural network. V_Green5 – adevice processing signals from camera, recognizing object of interest and determining the direction it is seen at (one offive considered). FVC – Frequency-to-Voltage Converter. L+, L- – positive and negative input to left-motor FVC; R+, R– positive and negative input to left-motor FVC (equal-frequency spiketrains provided to L+ and R+ results in the robot’s
moving forward; spiketrains provided to L+ and R- results in the robot’s rotation). Or8x4 – PPNN substituting four 8input OR-functions. Θ0 – tension related to “boredom”; Θ1, Θ2 – tensions resulting from touching an obstacle (disabledin case of touching an object-of-interest). P – “pleasure” block: Θ3 – tension resulting from stimulation of the frontal
touch sensor; if an object-of- interest is being touched and seen in the middle of visual field (Θ3 & Θ7), the tension Θ12(representing “excitement”) gets discharged contributing substantially to the “pleasure record”. PB1 – a PPNN providing
signal from Θ5, Θ6 and Θ8...Θ11 to L+ and R+ (via Or8x4) and responsible for collision avoidance (blocking the
spiketrain directed to L+ or R+ and providing a spiketrain toL- or R-, respectively, which result in robot’s rotation); Θ4– tension related to action-driving “interest” in a perceived object; it makes the robot going forward (in the course ofproviding spiketrains to L+ and R+ via Chaser and Or8x4) as well as increase ofΘ12 at P. Θ5…Θ9 – tensions related to
five directions an obiect can be seen at. L7-Chaser – a device providing a spiketrain from Θ4 to both L+ and R+ (viaOr8x4) and allowing spiketrains from Θ5, Θ6 and Θ8...Θ11 to lower the frequency of the spiketrain provided to L+ or
R+, which results in appropriate distortion of the robot’s trajectory facilitating approaching the object-of-interest. Q – adevice improving efficiency of the Chaser; it detects disappearance an the object of interest seen for the last time in thefurthest left or furthest right zone of the visual field and in such case provides a dense spiketrain to appropriate inputs of
the Chaser, which results in complete blocking of one of the Chaser’s outputs, which results in providing a spiketrain to
only one wheel, which results in a quick turn of the robot giving the object a chance to get back to the visual field.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

If PreBrain consisted of θ0 and PB1 only, the robot
would stay until the object’s disappearance. But othertensions are also involved. θ4, representing “interest” (u= -1; TS = 0.06s; TA = 0.05s; TD = 0.1s) gets high when
the toy is detected and sends a spiketrain to the central
input of L7-Chaser—a PPNN in which the axon carrying
the spiketrain from θ4 forks into two branches delivering
the spiketrain to both L+ and L- via Or8x4. Spiketrains
leaving the Chaser will remain equal only when the toy
is seen in the central zone of the visual field. Depending
on how far the object deviates from the middle of thevisual field more or fewer spikes are cut off the
spiketrain passing through a given branch. In this way
perceptual signals provided to other inputs of the Chaser
regulate chasing behavior. If the object is out of current
direction, frequencies of spiketrains provided to L+ andR+ differ, so the robot’s trajectory becomes a circle ofbigger or smaller radius.
Tension θ4 (to be interpreted as interest) is discharged
when at the same time the object of interest is seen in the
middle of the visual field (θ7) and being touched by the
robot’s frontal touch sensor. Necessary Boolean function
is implemented as TAC θ13 “&”. Related parameters areset so that discharging of θ12 (“excitement”) contributethe most to PreBrain’s “pleasure record”. From the pointof view of the “pleasure record”, the best control strategy
is to chase the toy, but not hurry with touching it.

Acknowledgements: J. Li’s research was supported by NSFC,
China, Grant 60374032. A. Buller’s and J. Liu’s research was
supported by National Institute of Information andCommunications Technology of Japan (NICT), Grant 13-05.

References

4 Experiment
PreBrains were implemented using the ParallelBrain
v.1.1 – a special client-server platform dedicated to rapid
prototyping of very large modular brain-like structures.
PreBrain clients were built partially as C++ programs
and partially as pulsed para-neural networks (PPNN)
synthesized using the NeuroMaze v. 3.0 Pro.
We tested three kinds of PreBrains. The first one used
L6-Chaser that forced the robot to rotate to get aperceived image of the object-of-interest in the central
zone of the visual field and then go forward full-speed.
Such strategy of chasing appeared suitable forapproaching motionless object. PreBrain of the second
kind employed L7-Chaser, described in the previous
section, which slowed down appropriate wheel when theimage of the object deviated from the center of the visual
field. Such strategy worked in case of chasing objects
going with constant speed along straight lines. The thirdkind of PreBrain employed L7-Chaser together with astructure (labeled Q in Fig. 2) detecting disappearance ofthe object of interest seen for the last time in the furthest
left or furthest right zone of the visual field and thenproviding a dense spiketrain to the highest or lowest
input of L7-Chaser. This results in complete blocking of
one of the Chaser’s branches, which results in providing
a spiketrain to only one wheel, which results in a quick
turn giving the object a chance of getting again to thevisual field.

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[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] Freud S. (1940/1989) An Outline of Psycho-Analysis,
W.W.Norton & Co, p. 15.
[4] Freud S. (1923/1962) The Ego and the Id, W.W.Norton &Co, p. 40-42.
[5] Buller A. & Shimohara K. (2001) On the dynamics ofjudgment: does the butterfly effect take place in humanworking memory?Artificial Life and Robotics, 5 (2), pp. 8892.
[6] Liu J. & Buller A. (2004) Tensions and Conflicts: Towarda Pleasure-Seeking Artifact, The 5th IFAC Symposium onIntelligent Autonomous Vehicles (IAV’2004), July 5-7, Lisbon,
Portugal.
[7] Buller A. (2004) “Artificial Brain and Psychodynamic
Mechanisms”, In: Yin Yixin and Dong Jie (Eds.) Proceedingsof 2004 Sino-Japan Symposium on KANSEI & Artificial Life,
July 3-4, University of Science and Technology, Beijing,
China, pp. 73-78.
[8] Buller A., Hemmi H., Joachimczak M., Liu J., Shimohara
K. and Stefanski A. (2004) “ATR Artificial Brain Project: Key
Assumptions and Current State”, 9th International Symposium
on Artificial Life and Robotics (AROB 9th '04), January 28-30,
2004, Beppu, Japan, pp. 166-169.
[9] Li J. & Yin Y. (2004) Intelligent Controller based on
Artificial Brain vs. Psychodynamic Brain, 2004 Sino-Korean
Symposium on Intelligent Systems, Pusan, October, pp. P8387.
[10] Arkin R. (1998) Behavior-Based Robotics, MIT Press.
[11] Brooks R. (2002) Flesh and Machine, Pantheon Books.
[12] Buller A (2002) Volitron: On a Psychodynamic Robotand Its Four Realities, In: Prince CG, Demiris Y, Maron Y,
Kozima H, Balkenius C (Eds.) Proceedings of the 2nd
International Workshop on Epigenetic Robotics: August 10-11,
Edinburgh, Scotland, Lund University Cognitive Studies 94,
17-20.
[13] Buller A. (2002) CAM-Brain Machines and Pulsed ParaNeural Networks: Toward a hardware for future robotic onboard brains, The Eight International Symposium on Artificial
Life and Robotics (AROB 8th '03), January 24-26, Beppu,
Japan, 490-493.
[14] Buller A., Joachimczak M., Liu J. & Stefanski A. (2003)
NeuroMazes: 3-Dimensional Spike-train Processors, 2nd
WSEAS International Conference on Electronics, Control and
Signal Processing (ICECS '03), December 7-9, Singapore.

[15] 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.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Robotic-Control Blocks (RCB) for Research and
Education
Adam Stefanski and Andrzej Buller
Network Informatics Laboratories
Advanced Telecommunications Research Institute International (ATR)
Kyoto, Japan{ajstefan, buller}@atr.jp

Abstract This paper proposes a new hardware platform,
called Robotic-Control Blocks (RCB), for robotic researchand education. RCBs are being developed to address therequirements specific to Psychodynamic Architecturedeveloped in the framework of the ATR Artificial Brain
Project. The presented CPLD-based implementation is aninterim solution. RCB-set contains blocks that can beinterconnected to create a control system of custom
complexity. Some of them are designed to cooperate withcertain sensors and/or actuators. At its proof-of-concept
state, RCB process three kinds of tensions: “boredom”,
physical contact with an external object, and being stuck in
an endless loop. The basic functionality can be easily
scaled using more kinds of blocks (only simulated to date)
and additional copies of universal behavior-mixing blocks.
Implemented as dedicated chips and enclosed in aesthetic
cases, RCBs right now might be used in classroom as aninexpensive aid free of tedious programming.
Keywords- Discrete Control Systems, Modular Robotic
Hardware, Mobile Robots, Robotic Education Aids

1 Introduction
Physical blocks covering elementary control functions
give children the opportunity to learn principles of robotics
without tedious programming. Maybe the most devoted
promoter of this approach is Henrik H. Lund of the
University of Southern Denmark who, inspired by works ofdevelopmental psychologists, elaborated Intelligent
Building Blocks (I-Blocks) enclosed in popular LEGO
DUPLO® elements. Particular I-Blocks represent several
useful sensor/motor functions. Standard I-Blocks forprocessing and communication are equipped with a
PIC16F876 micro-controller. Special I-blocks containinternal sensors, micro-motors, etc. [1]. The solution we
propose, called Robotic-Control Blocks (RCB), being
developed at the Advanced Telecommunications ResearchInstitute International (ATR), Kyoto, Japan in theframework of the Artificial Brain Project, shares with IBlocks the program-by-building paradigm, however, someassumptions differ substantially. Our project (albeit still in
its infancy) is striving to attain the research frontiers inhuman-machine communication in quest for emergent
communication [2]. While I-Blocks are to serve, first of all,as amusing education aid, the primary issue in RCB ispractically unlimited scalability that is indispensable in©ISAROB 2005

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building intelligent robots, however, small RCB sets also
can be used in classrooms. As for other differences, unlike
I-Blocks (that contain advanced pre-programmed microcontrollers), each RCB has only as many gates as it is reallynecessary to accomplish its task. Unlike the I-Blockapproach (aimed to provide components of both “brains”
and movable “bodies”), RCB approach deals only with“brains” to be connected to given commercially available“bodies”.
This paper first briefly explains the underlying concepts
and theories motivating the development of the RoboticControl Blocks (RCB). A new hardware test bench for
RCBs is also presented and examples of RCB-based control
systems are given. This is followed by a detailed
explanation of the prototype sets of RCBs that have beendeveloped and tested. Finally, the future research of RCBs
is discussed and concluding remarks are also included.
2 Robotic-Control Blocks (RCB)
The idea of RCB emerged in face of various drawbacks,
such as expandability, speed, and financial considerations,that arose in 2002 during development of firstpsychodynamic mobile robot, called Neko-1. The robot
used a Parallax Basic Stamp II microprocessor to collect thedata from three infrared proximity sensors and an
ultrasound sensor and then sent out serial packets
containing the data via a radio frequency transmitter. Thisdata was processed by a C++ program running on a PC
client which then sent out serial packets back to the robot
with speed inputs to the two motors. Although the robot
functioned correctly and depicted behaviors of boredom,
excitement, and fright, all of the processing was still
software based and accomplished using a PC [4]. This robot
was not self-contained and mainly re-emphasized thecorrect functionally of the software simulation since rather
than sending the processed data to a graphic user interface,
it was now being sent to an actual robot.
RCB set includes various Function Control Blocks(FCB), identical Behavior Mixing Units (BMU), Drivers,
Voltage-to-Signal Converters (VTSC) and Signal-toVoltage Converters (STVC). In the current version of RCB
hardware each FCB covers both accumulation of a given
tension and a related behavior. BMUs can form a column
defining priority when two or more FCBs produce pulse
trains. Drivers decode behavior-defining pulse trains andproduce pulse trains defining actions of particular motors.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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VTSCs convert analog voltage values of taken fromattached sensors into standard pulse trains. STCVs convert
pulse trains into appropriate analog voltage values
controlling attached motors. A system overview of theblocks is shown in Fig. 1. The RCB-based system can be
expanded from the bottom-up by adding more various
FCBs and from-centre-to-sides by adding more different
sensors and actuators. In future versions the functions will
be separated, making the set much more flexible. Although
the presented BMU-based “working memory” covers only akind of behavior subsumption, the simulated version
includes a block in which tension compete for access torelated behavior blocks [3].
The input for each sensor is encoded using a VTSC,
processed, and later decoded using a STVC before being
connected to the actuator. In order to allow for analog
values, all the input signals are quantized and framed as avalue between 0 and 255. The VTSC samples the input of
the analog sensor and sends out the corresponding amountof pulses during a predetermined time frame. This is similar
to duty cycles using pulse width modulation, however, in
RCBs the pulses do not have to be sent consecutively andcan be sent anytime during the predetermined time frame.
The exact location of the sampling frame does not matter,
therefore, the individual FCBs do not have to besynchronized.

CPLDs provide a sound economic alternative fordeveloping a prototype in a short period of time. Other
options were field programmable gate arrays, which aresuited for register-intensive logic designs, and individual
digital circuits, which are very time consuming to designand can not be easily altered.
3 Development and testing
The development and testing of RCBs was divided into
three distinct tasks in order to prove the functional concept.
First, a simple unintelligent robot was created that goesstraight ahead at all times. This is the most basic
arrangement of RCBs and comprises of only a few blocks
and two motors. All other current block sets expand thecapabilities of this fundamental robot kit. Next, an obstacle
avoidance expansion set was developed. It utilized twotouch sensors to detect objects that impede the robots
forward motion. A simple emergent phenomenon occurs
when using the obstacle avoidance expansion set, which led
to the design of the third expansion set, a meta-sensing
expansion module [6]. The development of these three RCB
sets will be discussed in this section.
A. Prototype Robotic-Control Block
In the future, each RCB will be implemented on a single
dedicated IC. However, the current prototypes were
designed on 2”x3” through-hole prototype boards. All theblocks, excluding the Power Block and the Clock Block,have ground, VDD, and clock input signals. Furthermore, allblocks also output those same three input signals whichallows for easy interconnecting, or daisy-chaining, of the
RCBs. Each block also has necessary input and output
signals, depending on the function of the block. The Powerand the Clock Block are special blocks since they help
generate the ground, VDD, and clock signals. A functional
diagram of a single regular RCB is shown in Fig. 2.

Figure 1. Robotic-Control Blocks Overview

The predetermined time frame was selected to be one
millisecond, which corresponds to the approximate pulse of
a biological neuron [5]. There is not single ‘correct’
predetermined time frame since the biological neuron
conducting velocity is affected by many factors such as
myelin, age of the neuron, etc. Furthermore, an exact timeframe is not necessary for simulating PDA concepts in its
simplest form. Assuming a one millisecond time frame, theminimum clock frequency required is 256 kHz in order tocorrectly quantize analog values as discrete neuron pulses.
Complex programmable logic devices (CPLDs) wereused to implement each function block. The devices werechosen because they are well suited for combinatorialintensive logic designs and the devices can be
reprogrammed easily using hardware description languages.

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Figure 2. Prototype Robotic-Control Block

B. Basic Robotic-Control Blocks Set
In order to be compatible with the existing pulsed paraneural networks concepts, the fundamental set of RCBs isvery primitive since it is supposed to represent instinctivefunctions. The basic set only includes the blocks necessary
to power-up the robot and for the robot to move in a
forward direction. It has actuators, two Lego® motors, but
no sensors. Table 1 describes the blocks included in thebasic RCBs set, while Fig. 3 shows the functional diagramof the robot that can be constructed using the supplied
blocks.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

TABLE I.

BASIC ROBOTIC-CONTROL BLOCKS SETBlock Name

Qty.

Description

Power1Clock11
1Forward1

Behavior Mixing
Unit

1

Motor Driver1

Signal-to-Voltage
Converter2

Outputs a VDD and ground signal
Outputs a clock signal
A Function Control Block that produces
pulses so the robot moves forward
Selects which behaviors is sent to theMotor Driver
Decodes the behavior and processes thesignal for each motor
Produces a voltage corresponding to thefrequency of the pulse train

by meta-sensing, i.e. trying to detect the endless loop
behavior. The meta-sensing expansion block set utilizes
these concepts to avoid being stuck in an endless loop.
Table 3 describes the blocks included in the meta-sensing
RCBs set, while Fig. 5 shows the functional diagram of therobot that can be constructed. The role of the Meta-Sensing
Unit (MSU) is to monitor the activity of the robot,
specifically that of the OAU, to make sure it is notoperating in an endless loop. Once the MSU detects anendless loop, it takes control of the robot and attempts toforce it out of the loop by sending pulses on the correct
signal lines.

1. Power and Clock Blocks are not shown in Fig. 3 since their signals are inputted to all blocks

C. Obstacle Avoidance Robotic-Control Blocks Set
The first expansion set of RCBs allows the robot tomaneuver around objects that impede its constant forward
movement governed by the Forward Block. This expansion
set has the blocks necessary to receive the touch sensor
inputs and encode them using pulse trains. These pulse
trains are then processed by a FCB, the Obstacle Avoidance
Unit (OAU). A BMU is also included to allow for further
expansion of the system. Table 2 describes the blocks
included in the Obstacle Avoidance RCBs set, while Fig. 4
shows the functional diagram of the robot that can beconstructed using the supplied blocks.

E. Test Robot
All of the three developed block sets were not only
tested for individual functionality but each set was put
together and tested using a constructed robot with two
Lego® touch sensors and two Lego® 9V motors. The
RCBs Robot with all three sets interconnected is shown in
Fig. 6. When the robot was powered up with only the basic
set attached, it went straight forward as expected. Next, theObstacle Avoidance Set was attached. The robot managed
to rotate when it hit a wall or another obstacle, however, it
sometimes got stuck in the corners of the room, which was
the reason for implementing the Meta-Sensing Set. Once all
the developed RCBs were correctly interconnected, therobot was able to wander around a room with various
obstacles on the ground for extended periods of time
without any difficulties. It would hit the obstacles, rotate,
and continue to move forward.

Figure 3. Basic Robotic-Control Blocks Set Schematic

TABLE II.

OBSTACLE AVOIDANCE ROBOTIC-CONTROL BLOCKS SET
Figure 4. Obstacle Avoidance Robotic-Control Blocks Set Schematic

Block Name
Voltage-to-Signal
Converter

Qty.
2

Obstacle
Avoidance Unit

1

Behavior Mixing
Unit

1

Description
Produces a pulse train corresponding to
the analog voltage detected at its inputs
A Function Control Block that produces
pulses so the robot avoid objects
impeding forward movement
Selects which behaviour is sent to theMotor Driver

D. Meta-Sensing Robotic-Control Blocks Set
One of the problems with the OAU is that the robot would
get stuck in endless loops. It would be functioning correctly
and still be moving around, however, it would repeat thesame action endlessly. This unfavorable phenomenon
emerges from the robots basic rule set. It can be eliminated©ISAROB 2005

693

Figure 5. Meta-Sensing Robotic-Control Blocks Set Schematic

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

F. Future Blocks Sets
There are many different possible blocks sets that can be
developed and added the current RCBs. Since the agent is apleasure seeking creature, the robot needs to have methods
to discharge tensions of boredom. One idea for a future
block set is to make the robot be excited and chase green
objects and be scared and run away from red objects. The
escaping action will have a higher priority than the pursuit
action. The existing obstacle avoidance and meta-sensing
actions would obviously have a higher priority than eitherof the two new actions. If the robot did not see any green or
red objects, it would continue to roam around pseudorandomly in search of them.
There are many other possible blocks set that can be
designed to enrich the repertoire of the robot. It can detecta hungry state, which is equivalent to a battery low sensory
signal, and then search for a battery charger station before
continuing to roam its environment. Another possible set
can be found on simulating concentrated and distracted
tensions based on the principles of Piaget’s theory how 0-2
year old children perceive new objects of interest [3].

The development and testing of Robotic-Control Blocks(RCB) has been quite successful thus far. Since the blocks
adhere to psychodynamic architecture and substitute Pulsed
Para-Neural Networks, they are a suitable hardware test
bench for ATR Artificial Brain Project. The basic RCBs set
worked as expected. By adding the Obstacle Avoidance Set
and Meta-Sensing Set on the run, it was shown that a
number of new tensions and behaviors can be added. The
practically unlimited scalability is the most important virtueof RCB approach. Furthermore, the blocks are selfcontained, operate in real-time, and do not require a PC.
The development should be considered a step in the correct
direction but more research and development in other fields
is still necessary before achieving the ultimate goal of
emergent thought. When a bigger block set is created andevolution is incorporated into the system, the covered
functionalities will deserve to be moved into a futuregeneration evolvable hardware. Regardless, if implemented
as dedicated chips and enclosed in aesthetic cases, RCBs
right now may be used in classroom as an inexpensive aid
free of tedious programming.
Acknowledgments. This research was a part of the Research of
Human Communications sponsored by the National Institute ofInformation and Communications Technology of Japan (NICT).
A. Stefanski’s work was made possible thanks to the Coop Japan
Program. The authors also thank Dr. Marek Syrzycki and Dr.
Shahram Payandeh for their invaluable critical review andsuggestions.

Figure 6. Robotic-Control Blocks Set prototype robot

RCBs can also have advanced behavior-basedarchitectures with the possibility of many complex
functions [8]. Different types of learning schema can be
implemented such as genetic algorithms and fuzzy
behavioral control. The agent can expand to accommodate
dozens of different sensors and tensions which can bedischarged through various behaviors by means of actuators.
These advanced blocks may allow for functions that varyfrom voice generation and recognition to cognitive learningand evolution. Future advanced blocks will only have basic
rules coded into them, so-called instincts, and will evolve
over time to into complex functions.
4 Concluding remarks
After the completion of the first prototype set, somechanges and future developments to the current block sets
became evident and are possible. First of all, since theblocks were developed on through-hole prototype boards
and wire connections were used, some of the connections
are fragile. The power, ground, and clock headers are©ISAROB 2005

designed to be irreversible; however, proper irreversible
headers should be added for the signal lines. Using a single
voltage to power all devices would also simplify the design.
This could be accomplished by using only a 5V or 3.3V
power supply and then use charge pumps for higher
voltages when necessary. Once the design for each andevery block is finalized completely, the RCBs should betransferred to printed circuit boards or a CMOS IC which
will make them more durable and aesthetically appealing.

694

REFERENCES
[1]

[2]

[3]

[4]

[5]
[6]
[7]
[8]

Lund H.H. “Intelligent Artefacts”, Proceedings of the EighthInternational Symposium on Artificial Life and Robotics, Japan, p.
I-11-14, 2003.
Buller A., Hemmi H., Joachimczak M. , Liu J., Shimohara K., andStefanski A. ATR “Artificial Brain Project: Key Assumptions andCurrent State”, Ninth International Symposium on Artificial Lifeand Robotics (AROB 9th '04), Beppu, Japan, p. 166-169, 2004.
Liu J. and Buller A “Tension and Conflicts: Towards a Pleasure
Seeking Artifact”, Proceedings, 5th IFAC Symposium on Intelligent
Autonomous Vehicles, Lisboa, Portugal, 2004, to appear.
Buller A., Harada Y., Joachimczak M., Lee S.-I., and Tuli T.S.,“Tension-driven behaviours of a mobile robot - Early experimental
results”, Proceedings of the Eighth International Symposium on
Artificial Life and Robotics, Japan, p. 510-512, 2003.
Best B., “Basic Neuron Physiology”, http://www.benbest.com/
science/anatmind/anatmd1.html, Accessed: December 2003.
Martin, F.G. Robotic Explorations: A Hands-on Introduction to
Engineering, Prentice Hall, 2001.
Brooks R. Flesh and Machines: How Robots will Change Us,
Pantheon Books, 2002.
Arkin, Ronald C. Behavior-Based Robotics, MIT Press, 2000.

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Human-robot communication through a mind model basedon the Mental Image Directed Semantic Theory
Masao Yokota
Fukuoka Institute of Technology
yokota@fit.ac.jp

Masato Shiraishi
Genci Capi
Fukuoka University of Education Fukuoka Institute of Technology
siraisi@fukuoka-edu.ac.jp
capi@fit.ac.jp
MIDST as framework for CMM, several significant
postulates for the basis of CS, formalization ofcommunication, and their implementation on theintelligent system IMAGES-M.

Abstract
The Mental Image Directed Semantic Theory (MIDST) has
proposed a methodology for integrated multimedia information
understanding, for example, cross-media translation. This paper
describes a multi-agent model of human mind based on MIDST
and its application to human-robot communication.

2. Multi-agent model of human mind

The authors have proposed a prototype model of humanmind consisting of Stimulus, Knowledge, Emotion andResponse processing agents as shown in Figure1 [1]. Thisis a functional model of human central nervous system
consisting of the brain and the spine.

1. Introduction

On the way to grow up from infant to adult, people
would sometimes encounter curious but instantly
understandable sentences such as S1-S4. This curiosity
perhaps comes from their apparently unscientific contents
while such understandability perhaps comes from oureveryday perceptive experiences in space and time.
(S1) Time passes swiftly (or slowly).
(S2) It is the longest day.
(S3) The Andes Mountains run south and north.
(S4) The road sinks to (or rises from) the basin.
In near future, this kind of human mental phenomenon
may lead to a certain barrier preventing humans androbots from comprehensible communication by natural
language. This is because both entities can be equippedwith sensors, actuators and brains of different
performances and their vocabularies may well begrounded on quite different sensations, physical actions ormental actions. And in turn such a situation may bringinevitably different kinds of semantics to them, so called,“Natural Semantics (NS)” for humans and “Artificial
Semantics (AS)” for robots.
The authors have been trying to develop such amethodology that can integrate NS and AS into a certain“Compatible Semantics (CS)” and that ultimately can leadto such a “Compatible Mind Model (CMM)” that is
intended to organize CS autonomously [1]. The CMM is
one kind of the multi-agent models [2]. Its mostdistinctively remarkable point is that it works bycomputing mental phenomena representations so called‘Locus formulas’ based on the Mental Image Directed
Semantic Theory (MIDST) [3], whose validity has been
proven by the successful results of several versions of theintelligent system IMAGES [3], [6], [7], [9].
In this paper are presented a multi-agent model ofhuman mind aimed at CMM, a brief description of©ISAROB 2005

St

Kn

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.
Figure 1. Multi-agent model of human mind.
The basic performances of the agents are as follows.
(1) Stimulus processing agent (St) receives stimuli
from W and encodes them into mental images (i.e.
encoded sensations) such as “I sensed somethingoily.” (if verbalized in English.)(2) Knowledge processing agent (Kn) evaluates
mental images received from the other agentsbased on its memory (i.e. knowledge), producing
other mental images such as “It is false that theearth is flat.”(3) Emotional processing agent (Em) evaluates mental
images received from the other agents based on its
memory (i.e. instincts), producing other mental
images such as “I like the food.”

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

concepts 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 S6
must reflect the motion of the observer’s attention [4].
(S5) The bus runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gt,k)∧bus(y)(3)(S6) The road runs from Tokyo to Osaka.
(∃x,y,k)L(x,y,Tokyo,Osaka,A12,Gs,k)∧road(y)(4)(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 against 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.
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 andcombined 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]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’.
(∃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 collapsing. 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 [5]. If not, real-time coordination of multiple
sensors and actuators would be impossible. ‘Mimicking’may be a good support for this hypothesis.
As easily imagined, if an attribute space corresponds toone of human senses, then its loci associated with certain
words belong to NS, otherwise to AS.

3. MIDST as framework for CMM

MIDST has modeled mental images as “Loci inAttribute spaces” [3], [7]. 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. The performance of ‘Attributespace’ is the model of ‘Atomic performance’ introducedin 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” 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), where the values ‘p’ and ‘q’ arerelative 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 S5
is a temporal event and the ranging or extension of the‘road’ by S6 is a spatial event whose meanings or©ISAROB 2005

4. Formalization of communication

At first, we formalize a piece of information (I) as a setof messages (m’s) in the expression (7).
(7)
I={m1, m2, …, mn}
In turn, a message (m) is defined in the expression (8),where D, S, R and B mean the duration, sender(s),receiver(s) and the body of the message, respectively.
m=(D, S, R, B)(8)
The body (B) consists of the two elements shown in theexpression (9), where E and T mean the event referred to

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

and the task requested or intended by the sender,
respectively.
B=(E, T)
(9)
For example, each item of the message m0: “Fetch me
the book from the shelf, Tom” uttered by Jim during thetime-interval [t1, t2] is as follows:
mo=(D0, S0, R0, B0), B0=(E0, T0),
D0=[t1, t2], S0= “Jim”, R0= “Tom”,
E0= “Tom FETCH Jim BOOK FROM SHELF”,
and T0= “realization of E0”.
The authors have found that there are almost uniquecorrespondences between the kinds of tasks (T’s) and thetypes of sentences as shown in Table 1, which are very
useful for computation.

•⋅…•⋅L(v,x1,cm-1,cm,A32,Gs,k2) )Π L(v,x1,Sweet,Sweet,A29,Gt,k3)
Π L(v,x1,Soft,Soft,A24,Gt,k4)
Π L(v,x1,/,/,A30,Gt,k5)
ΠL(v,x2,Noisy,Noisy,A31,Gt,k6)∧ candy(x1) ∧ room(x2) ∧ C={c1,c2,…,ci} ∧ #(C) >1
E2 ⇔ (∃e1,e2,k7) L(E1,M,e1,e2,B04,Gt,k7) ∧ e2>e1.
The special symbols and their meanings in theexpressions above are:‘X→c Y’ =‘If X then consecutively Y’, ‘R’=‘Robbie’,‘M’=‘Masato’, ‘C’=‘the total set of colors’,‘#(X)’=‘cardinal number of set X’, ‘A29’=‘taste’,‘A24’=‘touch’, ‘A30’=‘smell’, ‘/’=‘absence of value’,‘A31’=‘sound’, ‘A32’= ‘color’, and ‘B04’= ‘happiness(=degree of happiness)’
According to Table1, Robbie’s task (T) is only to makeE1 come true where each atomic locus formula isassociated with his actuators/sensors. By the way, theunderlined part of E1 represents the spatial distribution ofcolors over the candy referred to by the word ‘colorful’.
Of course, Robbie believes that he will become happier tohelp Masato, given by expression (12) where ‘B03’ is‘trueness (=degree of truth)’and ‘KB‘ is a certain standardof ‘believability’. That is to say emotionally, Robbie likes
Masato. Therefore, this example is also very significant
for intentional sensing and action of a robot driven bylogical description of its belief.
(∃p)L(R,E,p,p,B03,Gt,KB) ∧ p>KB
∧ E = E1 →c E2(12)For constructing a plausible CMM it is most essential
to find out functional features of human mind and toformalize them as postulates that rule the performances of
CMM and form the basis of CS [1]. APPENDIX showssome of such postulates and examples of dialog
processing by IMAGES-M based on them.

Table 1. Sentence types and Tasks.
Sentence type (Examples)
Task (T)Declarative
To believe E.
(It is ten o’clock now.)
Interrogative[A] To reply whether E is([A] Is it ten o’clock now?
true or false.
[B] What time is it now?)[B] To reply what makes E
true.
Imperative
To realize E.
(Show me your watch.)

5. Human-robot communication

The authors have planned to implement IMAGES-M[6] on real robots as a mind model. One of the mostsignificant works of robots equipped with IMAGES-M isto help people by performing dialogs with them. For
example, assume such a scenario as follows:…A human ‘Masato’ and a humanoid robot‘Robbie’ encounter at the terrace in front of the room
where a Christmas party is going on merrymaking.
Masato says “Robbie, please fetch me a colorful sweet
soft scentless candy from the noisy room.” Robbie
replies “OK, Masato.”….
Robbie interprets Masato’s statement as the expression(10) that reads “If Robbie fetches Masato the candy (E1),then consecutively it makes Masato happier (E2),” or as
its logical equivalent, the expression (11), reading “It is
not the case that Robbie fetches Masato the candy andconsecutively it does not make Masato happier.” Both theexpressions are adopted in MIDST as the canonicalconceptual structures of an imperative sentence.
E1 →c E2(10)~(E1•~E2)
(11)
where
E1 ⇔ (∃x1,x2,k1,…,v,C) (L(R,R,M,x2,A12,Gt,k1)•(L(R,R,x2,M,A12,Gt,k1)ΠL(R,x1,x2,M,A12,Gt,k1)))
Π(L(v,x1,c1,c2,A32,Gs,k2)•
L(v,x1,c2,c3,A32,Gs,k2)©ISAROB 2005

6. Discussions and conclusions

The mind model proposed here is much simpler than
Minsky’s [2] but the locus formula representation canwork for representing and computing mental phenomena
fairly well as shown in APPENDIX. One of the mostimportant problems to be solved is how to realize theatomic performances corresponding to attribute spaces,
including the meta-function ‘conscience’. In order tosolve this problem, we will consider the application ofsoft computing theories such as neural network, genetic
algorithm, fuzzy logic, etc. in the future.

References[1] Yokota, M. & Shiraishi: “A multi-agent mind model
for comprehensible communication between humans
and robots”, Reports of CSL, FIT, 18, pp.1-9, 2004.

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[2] Minsky, M: The society of mind, Simon and Schuster,
New York, 1986.
[3] Yokota, M. et al.: “Mental-image directed semantic
theory and its application to natural language
understanding systems’’, Proc. of NLPRS’91,
pp.280-287, 1991.
[4] Rybak, I.A., et al: “A model of attention-guided
visual perception and recognition”, Vision Research,
38, pp.2387-2400, 1998.
[5] Prinz, W.: “A common coding approach to perception
and action”, In Relationships between perception andaction (Neumann, O. and Prinz, W. eds.), pp.167201, Springer-Verlag, 1990.
[6] Hironaka, D., Oda, S., Ryu, K., and Yokota, M.:“Mutual Conversion of Sensory Data and Texts by
an Intelligent System IMAGES-M’’, Proc. of the 8thInternational Symposium on Artificial Life andRobotics (AROB ’03), pp.141-144, 2003.
[7] Yokota, M. and Hironaka, D.: “Cross-media
translation based on Mental Image Directed
Semantic Theory toward more comprehensible
communication between humans and robots”, Proc.
of AINA ’04 IEEE, Fukuoka, 2004.
[8] Brooks, R. A.: “A robust layered control system for amobile robot”, IEEE Journal of Robotics andAutomation, RA-2, pp.14-23, 1986.
[9] Oda, S., Oda, M.and Yokota, M.: “Conceptual
Analysis and Description of Words for Color andLightness for Grounding them on Sensory Data’’,Trans. of JSAI, 16-5-E, pp436-444, 2001.

H: What did Tom know about Mary?
S: He knew that she went to Tokyo.
[Dialog 04]
Postulate 04: “Intentional performances are necessarily
accompanied by decisions in humans.”
H: Tom sold his book to Mary.
H: Did Tom decide to sell his book?
S: Yes, he did.
[Dialog 05]
Postulate 05: “ Desire precedes decision in humans.”
H: Tom decided to sell his book.
H: What did he want?
S: He wanted someone to buy his book.
[Dialog 06]
Postulate 06: “For humans, a desire for something is abelief of becoming happier with it.”
H: Tom wants to go to Tokyo.
H: Does Tom believes to become happier
if he goes to Tokyo?
S: Yes, he does.
[Dialog 07]
Postulate 07: “Some emotion can coexist with another inhumans.”
H: Tom loves Jane.
H: Whom does he like?
S: He likes Jane.
[Dialog 08]
Postulate 08: “A physical object has never two values ofan attribute.”
H: Tom melted the ice (into water) and drank it.
H: Did he drink the ice?
S: No. He drank the water.

APPENDIX

Examples of postulate-based dialog processing by
IMAGES-M, where ‘H’ and ‘S’ mean a human’s and
IMAGES-M’s utterance, respectively.

[Dialog 01]
Postulate 01:“Sensation precedes perception
humans.”
H: Tom heard Mary sing “Hey, Jude”.
H: Did he sense any sounds?
S: Yes, he did.

in

[Dialog 09]
Postulate 09: “Communication is not transfer but
duplication of information.”
H: Tom said to Mary that his mother was sick.
H: Who knew that Tom’s mother was sick?
S: Tom and Mary did.

[Dialog 02]
Postulate 02: “There are sensor-specific pieces ofknowledge in humans.”
H: Tom knows Mary by sight.
H: Is he familiar with her?
S: No, he isn’t.

[Dialog 10]
Postulate 10: “A spatial event is reversible.”
H: The path sinks to the brook.
H: Does the path rise from the brook?
S: Yes, it does.
H: The roads meet at the city.
H: Do the roads separate at the city?
S: Yes, they do.

[Dialog 03]
Postulate 03: “Perceived matters are memorized as facts
belonging to knowledge in humans.”
H: Tom saw Mary move to Tokyo.

©ISAROB 2005

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The Case for Radical Epigenetic Robotics
Alexander I. Kovács
Grad. Univ. Advanced Studies
Nat. Inst. of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku
101-8430 Tokyo, Japanaik@grad.nii.ac.jp

Haruki Ueno
Grad. Univ. Advanced Studies
Nat. Inst. of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku
101-8430 Tokyo, Japanueno@nii.ac.jp

Abstractfrom standard task-centered methodologies. In a taskcentered approach one starts with a certain cognitive
task for a robot—say, wall-following, map-learning,
gesture recognition, playing soccer, six-legged walking,learning of movement by imitation, object perception
and learning, etcetera—and then tries to write developmental programs [1], evolve controllers [2], design
behaviors [3], formulate knowledge to reason about[4]or somehow come up with suitable sensor-effectormappings in order to achieve this specific task. While
there is nothing wrong with this approach, one could
argue that it (a) has a high risk of making many taskspecific assumptions and (b) leaves unexplored possible ”control” mechanisms.
Radical epigenetic robotics does not try to replaceor compete with such task-centered approaches. It iscomplementary by trying to chart new territory; territory likely not explored by common approaches forreasons (a) and (b). In the next section we elaborateon why (a) and (b) are so common and why they canbe problematic. In section 3 we take a quick look at
the different flavors of robotics out there to position
our approach and loosely define some important terms(such as ”radical” and ”epigenetic”). In section 4 weoutline our proposal and in section 5 we elaborate on
cognitive substrates. We conclude the paper with a
discussion of some difficulties of our approach.

We propose a way to study cognitive robotics
whereby one completely departs from a task-centeredmethodology. The new approach, ”radical epigenetic
robotics”, tries to chart new territory, likely not explored by task-centered approaches because there one
starts with a certain cognitive task for a robot andthen tries to come up with suitable ”control” mechanisms in order to achieve this specific task. Arguably,
this approach has a high risk of making many taskspecific assumptions and of leaving unexplored possible mechanisms. To avoid these problems, radical epigenetic robotics starts not with an outside cognitivetask, but from the inside, with the ”control” mechanism, which we call ”cognitive substrate”; the designof this ”cognitive network” is the focus of research, resultant cognitive competencies of a robot are tested,
ex post facto.
Keywords Epigenetic robotics, cognitive substrate, designer’s dilemma

1

Introduction

The aim of this paper is to propose and justify away of studying the fabrication of cognizers (in theform of robots) that jettisons one of the central assumptions in the field of cognitive robotics, viz., that
one has to start from the cognitive competency that
one wants to model. Only then, the assumptions goes,can one try to understand what mechanism to use to
make progress. We turn things around: we start withthe mechanism and then study what cognitive competencies result. We argue here, that this approach—if
properly construed—is not at all foolish or blind (thatis lacking a tangible goal).
Our approach, tentatively called ”radical epigenetic
robotics” (to be explained below), completely departs©ISAROB 2005

2

Problems with Task-Centered Approaches[W]e must admit the likelihood that top-down reverse
engineering will simply fail to encounter the rightdesigns in its search of design space [5, p. 258]
Task-centered approaches—i.e. approaches thatstart with a certain cognitive task to be modelled—torepeat, can be said to (a) have a high risk of making

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many task-specific assumptions and (b) leave unexplored possible ”control” mechanisms. Why is this so
and what are the problems?
Regarding (a), there are of course good reason forsimplifying assumptions. As Dennett [6, p. 308] puts
it, ”AI research, like all other varieties of research on
this huge topic [understanding how the brain works AIK], must make drastic oversimplifications in orderto make even apparent progress.”
However, such assumptions are often implicit andsince they are made on a case-by-case basis this leads
to many possibly incompatible models. An often
voiced critique of artificial intelligence is that it works
under the unjustified assumption that all the models
in its different subfields will eventually be put together
to result in truly intelligent systems that can, say, pass
Harnad’s Total Turing Test [7]. The critique is basedon precisely the fact that many researchers work under different simplifying and implicit assumptions thatcan lead to utterly incompatible models.
As far as (b) is concerned, one could argue that byhaving a certain task in mind, one is likely to choose
ways of achieving this task by utilizing techniques thatwill obviously or most likely work. In such a methodology, there are good reasons not to try out many (possibly much simpler) mechanisms for, at first sight, they
may just not look appropriate for the task at hand; andso, as the epigraph states, one may ”simply fail to encounter the right designs.”3

to be ”beneficiary of a longish period of infancy” [11,p. 157] in which certain cognitive structures develop
and organize, rather than being fixed by the designer.
Our proposal shares this emphasis on epigenetic development, i.e., the development determined primarily
by interaction rather than genes (that is a prior design), in the sense of Piaget [9]. Whatever the name,all approaches just mentioned are task-centered. The
point of departure, and novelty, of our approach is that
we believe that only by shifting the focus from specifictasks to appropriate, plausible ”control” mechanisms(see below) can the the problems associated with taskcentered approaches be avoided. This shift of focus to
an inside-out approach is what we mean by ”radical”.
Dennett’s quote above continues: ”There are many
strategies of simplification, of which [...] five, while
ubiquitous in all areas of mind / brain research, areparticularly popular in AI. [...] Many of the bestknown achievements of AI have availed themselves of
all five [...] strategies of simplifications [...]. Some critics hostile to any efforts in cognitive science enabledby these strategies, but there is no point in attempting
to ’refute’ them a priori [emphasis added - AIK]. Since
they are strategies, not doctrines or laws or principles,
their tribunal is ’handsome is as handsome does.’” So,rather than further argue against such simplifications
we leave it at having justified our approach via (a) and(b) and take it (again) with Dennett [6, p. 309]: ”onemight just adopt some rival strategy or strategies, andlet posterity decide which are the most fruitful.”

Oh No, Not Yet Another Robotics!
4

There are now a large number of different approaches to cognitive robotics. One finds (the original) cognitive [4], behavior-based [3], developmental[1], cognitive developmental [8], epigenetic [9], evolutionary [2], and adaptive neuro-robotics [10]. As it
stands, the boundaries between them are not clear-cut,
but what all these approaches, in one way or another,
are addressing is how to get more out of the robots
than was (and could have been) designed for; that is
to have ”control” mechanisms that do more than justcontrol (whence the quotes).
The original cognitive robotics [4] was concernedwith sense-plan-act style cognition where the planningcomponent was based on logical programming languages, such as Golog. Cognition here was construed
as reasoning about X, where X could be anything fromgoals, to perceptions, to actions, and so forth. Logicist cognitive robotics ignores learning and development setting it apart from the other approaches mentioned, which acknowledge that cognitive robots have

©ISAROB 2005

Radical Epigenetic Robotics

To avoid problems (a) and (b), radical epigenetic
robotics proposes to not start with a task-specification(outside), but focus on the inside mechanisms first.
The five strategies of simplification mentioned above
by Dennett are, in short, as follows:• ignore both learning and development;• ignore how isolated subcomponent under study
might be attached to the larger system;• hope that scaling-up from toy problem to real domain will be a straightforward extrapolation;• bridge various gaps in one’s model with unrealistic
stopgaps; and• avoid the complexities of real-time, real-world coordination.

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Our research program tries to find new mechanismsthat precisely don’t make any of these assumptions,thats what we mean by appropriate. Since we aspires
to avoid these simplifications, clearly, something else
has to give. That something is that we will initiallyhave to make do with the fabrication of rather simple
proto-cognition. However, by definition, an appropriate mechanism will, e.g., scale well, make additions of
new components easy, not introduce makeshifts whose
fixing turns out problematic.
To be plausible, the mechanisms we investigate try
to avoid as many known problematic assumptions as
possible, such as e.g. regarding encodingist represtentation [12]. So, to be plausible, they must, e.g., beradical constructivist [13], interactivist [12], and there
are other criteria. They must also offer a solution towhat we call the Designer’s Dilemma. Since we aretalking about fabricating cognizers, qua artifact, there
must be something that the engineer designs. We now
know that neither behaviors nor knowledge are theright level we can design for cognition (this assertionis irrespective of whether we use machine learning ornot) because the designer just cannot anticipate (andhence not accommodate) an infinite number of cases.
The Designer’s Dilemma forces us to focus on a level
where we can (in principle) have complete knowledge,that is, not the cognitive task but the level of the cognitive substrate, as we call appropriate and plausible
mechanisms for the fabrication of veritable cognizers.

5

perception, etc.). And if one looks at the literature on
so-called complex adaptive systems one cannot help
but notice the cognitively laden vocabulary used totalk about these systems. Though these parallels havebeen noticed and also lead to interesting applications,more general theoretical work is outstanding. We can,nevertheless, already guess at what our cognitive substrate might look like: a self-organizing, synergetic
network of loosely-coupled anticipating self-sustained
oscillatory elements.
The reader might now object, How is this different
from research into artificial neural networks (ANN);
isn’t that just such an approach where you start with amechanism (the inside) and then see what you can dowith it (the outside)? The quip reply to this objection
is: maybe, but nobody ever put ANN’s in robots justto study ”what happens”. Besides, ANN’s wouldn’t
be under such heavy fire from workers pointing out
what ANN’s cannot do to realize whole cognizers ifthey were even a suitable tool.
Whether ANN’s (whatever kind you look at) haveserious short-comings that rule them out as cognitive
substrate (and they do) is besides the point. However,
how to ”hook up” the sensors and effectors of a robot
with an ANN (whatever kind) to produce some form of
proto-cognition is precisely the point of contention. Of
course, you could just as well take ANN’s and embark
on the research program we propose. This hasn’t been
done yet and it is an open problem, whether any protocognition would be producible. That is, of course, if westart with a network which hasn’t been already trained
for a certain task, for in radical epigenetic robotics,
there are no a priori tasks to train an ANN for.

Cognitive Substrates

We shall call an appropriate and plausible ”control”
mechanism cognitive substrate. By cognitive substrate
we mean, roughly: the layer underlying or bringing
about cognitive capabilities; so we could call the animal brain a ”neurobiological cognitive substrate”. The
volume in design space of ”control” mechanisms that
we are specifically targeting is located where one finds
what Varela et al. [14] have called ”cognitive networks”.
It would now be rather foolish to blindly try to findsuch mechanisms such that if implemented on a target
robot architecture would lead to ”interesting” behavior or what Beer calls minimally cognitive behavior[15]. We therefore urge to base the development ofsuch cognitive substrates on a solid theoretical foundation, on a ”general theory of cognitive systems”. Apartfrom the animal brain, biologist have also for a while
been speaking of the immune system as a ”biologicalcognitive network” [16] with—though very much simpler [17]—similar capabilities to the brain (memory,©ISAROB 2005

6

Conclusion
Is this optimistic prospect an illusion? Is thisbottom-up project as hopeless as trying to build atower to the moon? You can’t get there from here,say [...] the skeptics. Don’t even try. [18, p.196]

The radical epigenetic robotics approach proposed
could perhaps be express by stating: ”Let the robot
think what it wants and do what it wants”. As thus,
the approach has a number of obvious disadvantages
compared to other approaches and will likely face difficulties (and because of that, perhaps outright rejection). For example, since we start with a (theoretically motivated) cognitive substrate, and hopefullysome predictions made by the theory as to the expected behavior of the system, the question remains
how to evaluate resultant behavior of a robot. What

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counts as proto-cognition and how can it be verified tobe veritable, i.e., attributable to the cognitive robot
itself, and not some human designer. However, thisproblem of evaluation is an highly interesting problem
by itself. Perhaps what is needed are new sciences ofcognitive robot psychology and ethology.
A second problem (though not of the approach assuch) is that the ”general theory of cognitive systems”
on which to base our proposed cognitive substrates
doesn’t yet exist. However, there are hints in the literature, such as a criterion differentiating cognitive from
non-cognitive systems [16, 17]—we might want to call
”Hershberg-Efroni-criterion”—which states, that ”ina cognitive system the capabilities of the system are
not preordained merely by the plan of the system butneed interaction with their environment to define themexactly.”
We are in an early phase of developing this new
approach, but think it is timely to start debate.

[8] M. Asada, K.F. MacDorman, H. Ishiguro, et al.
Cognitive developmental robotics as a newparadigm for the design of humanoid robots.
Robotics and Autonomous System, 37(1):185–193,
2001.
[9] C. Balkenius, J. Zlatev, C. Brezeal, et al., editors.
Proc. 1st Int. Workshop on Epigenetic Robotics.
Lund, Sweden, 2001.
[10] T. Ziemke. The construction of ’reality’ in the
robot: Constructivist perspectives on situated artificial intelligence and adaptive robotics. Foundations of Science, 6(1):163–233, 2001.
[11] D.C. Dennett. The practical requirements formaking a concious robot. In Dennett [18], chapter 9, pages 153–170.
[12] M. H. Bickard and L. Terveen. Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution. Elsevier Science,
New York, NY, 1995.

Acknowledgements
AIK was supported by the Joint Studies Program of
The Graduate University for Advanced Studies and an
NII Fellowship Grant provided by Sumitomo ElectricIndustries, Ltd.

[13] E. von Glaserfeld. Radical Constructivism: A
Way of Knowing and Learning. Falmer Press,
London, 1995.
[14] F. Varela, A. Coutinho, B. Dupire, and N.Vaz.
Cognitive networks: Immune, neural, and otherwise. In A. Perelson, editor, Theoretical Immunology, Part II, SFI Series on the Scienceof Complexity, pages 359–375. Addison-Wesley,
Redwood City, NJ, 1988.

References[1] J. Weng. Developmental robotics: Theory andexperiments. Int. J. of Humanoid Robotics,
1(2):199–236, 2004.

[15] R. A. Beer. Toward the evolution of dynamicalneural networks for minimally cognitive behavior.
In P. Maes, M. J. Mataric, J.-A. Meyer, et al., editors, From Animals to Animats 4: Proc. FourthInt. Conf. on Simulation of Adaptive Behavior,
pages 73–76, Cambridge, MA, 1996. MIT Press.

[2] S. Nolfi and D. Floreano. Evolutionary Robotics.
MIT Press, Cambridge, MA, 2000.
[3] R.A. Brooks. Intelligence without reason. A.I.
Memo 1293, MIT, AI Lab, April 1991.
[4] R. Reiter. Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical
Systems. MIT Press, Cambridge, MA, 2001.

[16] U. Hershberg and S. Efoni. The immune systemand other cognitive systems. Complexity, 6(5):14–
21, 2001.

[5] D.C. Dennett. Cognitive science as reverse engineering: Several meanings of ”top-down” and”bottom-up”. In Dennett [18], chapter 16, pages
249–259.

[17] U. Hershberg. Proposing a new focus for the
study of natural and artificial cognitive systems.
In C.G. Prince, Y. Demiris, Y. Marom, et al.,editors, Proc. 2nd Int. Workshop on Epigenetic
Robotics, Edinburgh, Scotland, 2002.

[6] D.C. Dennett. Cognitive ethology: Hunting forbargains or a wild goose chase. In Dennett [18],chapter 21, pages 307–322.

[18] D. C. Dennett, editor. Brainchildren: Essays on
Designing Minds. Bradford Books, 1998.

[7] S. Harnad. Minds, machines and Searle. Journalof Theoretical and Experimental Artificial Intelligence, 1:5–25, 1989.

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Development of a robotic surgical manipulator for Minimally Invasive Surgery
Ho-Seok Song
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Taejon, Korea 305-701
hosek@kaist.ac.kr

Jong-Ha Chung
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Taejon, Korea 305-701
jongha@imhp.kaist.ac.kr

less scarring and less likelihood for incisional
complications. Since the late 1980’s, minimally invasive
surgery has gained widespread acceptance because of thesuch advantages. However there are significant
disadvantages which have, to date, limited theapplications for these promising techniques [1]-[3]. For
example the standard laparoscopic instruments used inmany minimally invasive procedures do not provide thesurgeon the flexibility of tool placement found in open
surgery. As the instrument slide, twist and pivot throughthe trocar (the point at which instruments enter the bodywall), they are four–degree–of-freedom manipulators.
Conse- quently the surgeon can reach points within athree-dimensional volume but cannot fully control
orientation. Most current laparoscopic tools have rigid
shafts, so that manipulation of delicate and sensitive canbe difficult while manipulating these long-handled tools
from outside the body [3]. Additionally, the fact that theport in the patient’s abdomen (trocar) acts as a laterally
restrictive pivot point for the body of the positioning
apparatus complicates the proper positioning of thesurgical tools. Because entry portal (trocar) is embeddedin abdominal wall, it cannot be perfectly fixed. This
allows the movement of entry portal which acts as afulcrum and it leads to the lack of dexterity andsensitivity of endoscopic tools [4]. Moreover trocar
makes the direction of the surgeon’s hand motion reverseat the instrument tip. The video monitor is often locatedon the far side of the patient, and the difference inorientation between the endoscope and the monitor
requires the surgeon to perform a difficult mental
transformation between visual and tool coordinate
frames. Impaired contact force reception by friction andabsence of distributed tactile information also become
problems. Although many abdominal operations can be
performed laparoscopically at this moment in time,
performance of complex minimally invasive surgery isin the hands of a limited number of experts. Therefore,
researchers have started to develop new tools forlaparoscopic surgery to minimize the unsatisfactory
aspects of the process [5]. The launch of robotic
telemanipulation system heralds this development.
Reduced dexterity and impaired visual control wereconsidered the major burdens of endoscopic surgery andinitial attempts in developing robotic endoscope support
systems aimed at enhancing the surgeon’s control of thescope. AESOP® (Computer Motion, Inc.) andEndoassist® (Armstrong Healthcare, Inc.) arerepresentative works among laparoscope support
systems. While developments in imaging systems clearly

Abstract
Minimally Invasive Surgery (MIS) is surgery of thechest, abdomen, spine and pelvis, done with the aid of aviewing scope, and specially designed instruments.
Benefits of minimally invasive surgery are less pain, lessneed for post-surgical pain medication, less scarring andless likelihood for incisional complications. Since thelate 1980’s, minimally invasive surgery has gainedwidespread acceptance because of the such advantages.
However there are significant disadvantages which have,to date, limited the applications for these promisingtechniques. The reasons are limited degree-of-freedom,
reduced dexterity and the lack of tactile feeling. To
overcome such disadvantages many researchers haveendeavored to develop robotic systems. Even thoughsome robot aided systems achieved success andcommercialized, there still remain many thing to be
improved. In this paper, the robotic system which can
mimic whole motions of a human arm by adding
additional DOF is presented. The suggested design is
expected to provide surgeons with improved dexterity
during minimally invasive surgery.
Keywords— minimally invasive surgery, articulated
manipulator, and robot aided surgery

1. Introduction
Minimally Invasive Surgery (MIS) is surgery of thechest, abdomen, spine and pelvis, done with the aid of aviewing scope, and specially designed instruments. In
these procedures, a slender imaging prove is typicallyintroduced via a puncture incision. The surgical site isviewed through a videoscope equipped with a miniature
video camera. A CRT screen displays the resultant
camera output. In the abdomen, carbon dioxide is
pumped in to create viewing a working room. Tools are
fed through additional puncture incisions using trocars.
The benefits of minimally invasive surgery are many.
Traditional surgery often requires a lengthy hospital stay
and weeks of recovery. With minimally invasive surgery,many procedures require one to two days or less in thehospital and recovery time is generally shorter. That
usually means that patients can get back to their normal
routines quicker. The faster recovery is possible because
there are only a few small incisions requiring a stitch or
two instead of a large incision through the skin andmuscles. Other benefits of minimally invasive surgery
are less pain, less need for post-surgical pain medication,©ISAROB 2005

Jung-Ju Lee
Dept. of Mechanical Engineering
Korea Advanced Institute of
Science and Technology,
Taejon, Korea 305-701
jjlee@mail.kaist.ac.kr

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progressed, dexterity problems remained a crucial
problem. In the early 1990s, the concept of a masterslave tele-manipulator was developed. This concept
required the surgeon to control a manipulation systemfrom a master console remote from the patient. A
computer uses computing power to support the surgeon’s
dexterity. The surgeon moves two master devices madeto resemble surgical instruments at the console, and eachmotion is translated to the robotic arm which scale down
the movements at the end of the instruments inside thepatient’s body. The robotic slave arm follows allcommands of the master arm in a natural way,comparable to manipulation in open surgery. Many
researchers have developed robot aided minimallyinvasive surgery systems. Among them, da Vinci®
(Intuitive Surgical, inc.) and ZeusTM (Computer Motion,Inc.) telemanipulation systems received FDA clearance.
The major advantage of these newer master-slave robotic
systems is the introduction of extra degrees-of-freedom
at the end of the instruments, allowing surgeons tomanipulate in a manner similar to that of open surgery.
The ZeusTM offers five DOF and da Vinci® offers six
DOF by using the Endowrist® system. In addition, theunnatural opposite response of the instruments iscorrected by the robotic telemanipulation systems.
Tremors and trocar resistance are eradicated by the manmachine interface. The digital processing allows thescaling down of the surgeon’s hand movements to alevel where micro-vascular procedures are feasible. The
ergonomic and reduced fatigue features will be a great
advantage [5].

direction. The most up-to-date robotic MIS systems havesix-degrees-of-freedom; four at the entry portal and twoat the end of tool. These, so to speak, resemble thehuman arm which just has wrist and shoulder and noelbow. Some researchers reported that if we could
develop either mechanical or electromechanicalteleoperators which enable surgeons to move a MIS
system in a manner analogous to an open instrument, we
could potentially reduce the time of current laparoscopic
procedures by at least 15% and we could perhaps alsoenable surgeons to perform procedures which are
currently too difficult [6]. This result shows that the
robotic system which can mimic whole motions of ahuman arm by adding additional DOF may have more
powerful usages. This is the first and underlying premiseof our design. The second premise is that the entry portal,
trocar, which acts as a fulcrum is having bad effects on
the dexterity and the repeatability of surgery tool. The
most serious reason is that the fulcrum does not be
firmly fixed. The most movements of conventional tools
for minimally invasive surgery depend on that throughthe trocar and very few movements which are noteffected by the motion of the trocar is achieved. Thus themain design concept of this research is to add two DOF
rotational joint at the surgery tool so that this added joint
function as a human elbow (Fig.1). By adopting thismechanism, the surgery tools can behave like a humanarm because the type of degree-of-freedom of the trocar(3-rotations and 1-translation) is exactly same that of ashoulder and the type of degree-of-freedom of tool tip(2-rotations; perpendicular to the longitudinal axis) andadded joint (2-rotations; one is perpendicular and theother is parallel to the longitudinal axis) are same that ofa wrist and an elbow respectively. Moreover the added
joint is not affected by the movement of the trocar
because it is placed inside the abdomen during surgery.
Thus the space where the surgery tool can moveindependently to the trocar is enlarged and the dexterity
and the repeatability are enhanced.

2. Design Concepts
Previously described robotic tele-manipulation
systems potentially offer great benefits for minimallyinvasive surgeries. However there still remain several
points to be improved in the aspect of dexterity. In open
procedure, the surgeon has unlimited flexibility inpositioning his body, elbow, wrist and fingers; theoperative field may be approached from various
Trocar entry: 4 DOF ( 3rotation & 1-translation)
Abdominalwall

2 DOF : 2-rotation

ShoulderWrist

Additional joint with 2-rotations

Fig 1. Design Concept©ISAROB 2005

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3. Design Requirements
There are several design requirements which should
be satisfied for robotic tools to be used at minimallyinvasive surgery. Typical properties are size, force which
can be produced at the tool tip, speed, repeatability,
dexterity, workspace, weight, and convenience tooperators.
Size : The surgery tool is inserted into the patient’s
abdomen through a trocar, so that the width of atool should not exceed the size of conventionallyused trocar. The size of currently used trocar israrely above 10mm. Thus we will design thesurgery tool with 10mm diameter.

Fig 2. Differential Drive

Force : It is generally known that the amount offorce needed for fine motion tasks such as
suturing is roughly 10N at the gripper [3]. Thus
we set the lower limit of required force as 10N.

(gear 3) is output device. When input gears rotate tosame direction, the output gear is turned on the axis of
input gear. When input gears rotate to opposite directions,the output gear is turned on the longitudinal axis. This is
the exactly same motion with a human elbow. The
suggested design shows 45-degrees motion range of
output gear at each rotational axis. The wrist part shouldbe able to be manipulated with two axes which areperpendicular to longitudinal axis. To this end, theuniversal joint may be best solution. But universal joint
has a intersect axis which looks like a cross and it is very
difficult to drive such a part by using a wire. To solve
this problem, rotational axes are split apart and are
driven separately. But this design needs one more joint
than that of universal joint and it makes the manipulator
become more complex and pliable. To overcome thisdrawback, the axis for tool tip rotation and the axis fortool opening are joined together and two parts of a tool
tip are driven separately (Fig.3). In this design, theaverage of two rotations (pulley 1 & 2) represents thechange of the orientation of tool end and the difference
between two rotations does the tool tip opening angle.
Thus the orientation and the tool opening angle can becontrolled in one axis.
Because wire-mechanism is only operated whentension-force work on it, wire-driven system requires 2nwires to control n-DOF motion. In our system, we need
6 wires to control 3 DOF motion of tool tip (pitching inwrist, gripping and yawing in hand). However, the space
where driving mechanisms are to be installed isrestricted by size of trocar, and wires must be passed

Speed : In the aspect of speed we need about 3-5 Hz
for the joints to achieve a speed comparable tohuman fingers.
Repeatability : The human’s end repeatability is
known about 1mm. In due consideration ofpractical fabrication process, our design goal for
repeatability is under 0.25mm.
Dexterity : The suggested design has more joint anddegree-of-freedom than conventional robotic
surgery tools, it will inherently shows improved
dexterity. Thus design goal is the maximizationof the dexterity through whole workspace byrearranging kinematic parameters.
Workspace : As written at dexterity, workspace isalso enlarged by adopting the additional joint.
Thus maximization of not workspace but
dexterous workspace is our design object.
Convenience to operators : Originally this parameteris the most important point to be consideredduring the design process. Thus the link length ofsurgery tool is determined by continuous
simulation and consultation with surgeons.

4. Design in detail
The main idea of suggested design is to add the
additional joint which behaves like an elbow. In humanarm, an elbow joint moves with two rotational degreesof-freedom; one is perpendicular to the longitudinal axis
and the other is parallel to the axis. To adopt this type
motion, the differential mechanism is used (Fig.2). The
differential mechanism is composed of three bevel gears
whose axes intersect mutually through a common point
at right angle. Two gears are input (gear 1 & 2) and one©ISAROB 2005

Pulley 2

Pulley 1
Tool tip

Fig 3. Universal joint and suggested design
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Cable C5 and C6 form two sides of a single cable CSthat engages pulley P7.
The movement of wrist and fingers acts like following.
If motors M1 and M4 rotate in opposite direction,
capstan A will rotate for gripping and yawing. Likewise
if motors M2 and M3 rotate in opposite direction,
capstan B will rotate. If motors M1 and M4 rotate in thesame direction, motors M2 and M3 rotate in the same
direction, but in opposition to M1 and M4, then the wrist
will pitch about axis B.
The surgery tool is composed of previously describedparts and is shown in Fig.5. It is expected to make thesurgery tool be applied more various tasks.

5. SummaryFig 4. Schematic diagram for cabling

This paper presents the design of the dexterous
manipulator for minimally invasive surgery. The
capability of the suggested surgery tool is enhanced byadopting additional joint which acts like a human elbow
joint. The improved manipulability may contribute toperforming complex tasks during surgery andpopularization of minimally invasive surgery.

through axle of bevel gear of differential drive.
Therefore, technique that can reduce number of wire isrequired. Consequently, only 4 wires are required to passthrough axle of the upper bevel gear by adopting
following technique.[7]As shown in fig.4, this system is composed of 2
capstans, 7 pulleys and 4 motors. Cables C1 and C4form two sides of a continuous loop L1. Cable C1 ofloop L1 engages proximal pulley 5, drive shaft of motor
M1, intermediate pulley P1, and driven capstan B. Loop
L1 returns from capstan B as cable C4 and engages
intermediate pulley P4, drive shaft of motor M4, andproximal pulley P5. The cables C1 and C4 are fixed tocapstan A in order to pull it.
In the same manner, Cables C2 and C3 form two sides
of a continuous loop L2. Cable C2 of loop L2 engages
proximal pulley 6, drive shaft of motor M2, intermediate
pulley P2, and driven capstan A. Loop L2 returns fromcapstan A as cable C3 and engages intermediate pulleyP3, drive shaft of motor M3, and proximal pulley P6.
The cables C2 and C3 are fixed to capstan B.

Acknowledgements
We gratefully acknowledge the financial support of the
Korea Science and Engineering Foundation (HWRSERC).

References[1] US Patent 006594552, “Grip Strength with Tactile
Feedback for Robotic Surgery”[2] R. Mukherjee, G. Song and R. Satava, “AnArticulated Manipulator for Enhanced Dexterity in
Minimally Invasive Surgery,” Proc. IEEE Eng.
Med. Bio., pp.220-222, 1996
[3] S.S. Sastry, M. Cohn and F. Tendick, “Milli-robotics
for Remote, Minimally Invasive Surgery,” Robotics
and Autonomous Systems, Vol. 21, pp.305-316,
1997[4] R. D. howe and Y. Matsuoka, “Robotics for Surgery,”Annu. Rev. Biomed. Eng., pp.211-240, 1999[5] J. Ruurda, T. Vroonhoven and I. Broeders, “Robotassisted Surgical Systems: A New Era in
Laparoscopic Surgery,” Ann. R. Coll. Surg. Engl.
Vol. 84, pp. 223-226, 2002
[6] A. J. Hodgson, R. A. Pantazopol, M. D. Visser, S. E.
Salcudean and A. G. Nagy, “Assessing Potential
Benefits of Enhanced Dexterity in Laparoscopic
Surgery,” Proc. IEEE/EMBS, pp.1966-1969, Oct.
1997[7] A. J. Madhani, “Design of Teleoperated Surgical
Instruments for Minimally Invasive Surgery,”
Doctoral Thesis, pp.121-124, Feb. 1998

Fig 5. overall shape of surgical tool

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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0.14
1st order Volterra kernel
0.12

1.5
Actual output
Output by use of up to 3rd Volterra kernels
Output by use of 1st Volterra kernel

0.1

0.08

1
g1(τ)

0.06

0.04

0.5
y(t)

0.02

0

0

-0.02

-0.04
0

5

10

15

20

25

30

35

40

45

-0.5

50

τ

)* $9 ", )  A, D2, # 2  6 < ) ,)+2 ,) 

3rd order Volterra kernel(τ3=1)

g3(τ1,τ2,τ3=1)

0.04
0.03
0.02
0.01
0
-0.01
-0.02
-0.03
-0.04
16
14
12
0

10
2

4

8
6
τ1

6
8

10

4
12

14

τ2

2
16 0

)* 59 ", )  ,) D2, # 2  6     H
0< ) , )+2 ,) ©ISAROB 2005

710

-1
0

10

20

30

40

50

60

70

80

t

)* ;9 + ) ",> , 2 ,, >),  2(
2 , ,, 3+ D2, # 2 ) , )+2 ,) 

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005)* %9 48)+ , 3    +, >), " #2 )* .9  ,2 ,+ "2#©ISAROB 2005

711

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005
25
1st order Volterra kernel
20

15

g1(τ1)

10

5

3rd order Volterra kernel0

g3(τ1,τ2,τ3=0)

-5

0.015

-10

0.005

0.01

0
-0.005

-15
0

5

10

15

20

25

30

35

40

45

-0.01

50

τ1

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35
30
0

25
5

10

20
15

τ1

τ2

15
20

25

10
30

35

5
40 0

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2nd order Volterra kernelg2(τ1,τ2)

0.04
0.03
0.02
0.01
0
-0.01
-0.02
-0.03

0

2

4

6

8

τ1

10

12

14

16

18

20 0

2

4

6

8

10

12

14

16

18

20

τ2

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1
output by use of Volterra kernels
actual output
0.8
0.6
0.4

[V]

0.2
0
-0.2
-0.4
-0.6
-0.8
-1
100

120

140

160

180

200

t

)* 0'9 + ) ",> , , 2 ,, >),, ,,  22 , 3+ D2, # 2 )   3
   +, ,+©ISAROB 2005

712

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

An Optimal Capturing Trajectory Planning for a Moving Object
Byoung-Suck Choi
Dep. of Electronics Engineering
Pusan National University
Busan, Republic of Korea

Jang-Myung Lee
Dep. of Electronics Engineering
Pusan National University
Busan, Republic of Korea

Abstract

researches on the trajectory planning, while many papers
on the position estimation and trajectory control are
published. The importance of driving trajectory planningwhich reduces driving distance and time has not beenstudied closely[4].
This paper proposes a single curvature trajectory fora precise motion of a mobile robot and it is applied forthe trajectory of a mobile robot to capture a moving
object. Based on the kinematics modeling of a mobile
robot, a theoretical driving model is proposed; thedriving characteristics of the single curvature trajectory,
that is, driving time, distance, and error have beenmeasure and analyzed through the simulations and realexperiments. In chapter 2, driving characteristics of amobile robot have been analyzed through kinematics
analysis. The single curvature planning is described inchapter 3 concretely. In chapter 4, the capturing
algorithm following the single curvature trajectory is
introduced with theoretical formulars. The comparison
between a single curvature trajectory and a double
curvature trajectory in terms of tracking error, time, anddistance has been shown in chapter 5. The real capturingexperiments are also demonstrated in the chapter.
Chapter 6 concludes this paper.

An optimal capturing trajectory for a moving object
is proposed in this paper based on the observation that asingle-curvature path is more accurate than double- or
triple-curvature paths. Traveling distance, tracking time,
and trajectory error are major factors considered indeciding an optimal path for capturing the moving object.
That is, the tracking time and distance are minimized
while the trajectory error is maintained as small as
possible. The three major factors are compared for the
single and the double curvature trajectories to showsuperiority of the single curvature trajectory. Based upon
the single curvature trajectory, a kinematics model of amobile robot is proposed to follow and capture themoving object, in this paper.

Keywords Single curvature trajectory; Mobile robot;
Capturing; Moving object

1 Introduction
From 1970’s, ‘Robot’ has come close to our daily
lives. More precise and fast control became possible
with the development of integrated circuits, sensors,
artificial intelligence, image processing, and computer
technologies. As the result, intelligent robots which
recognize changes of environment and decide theirreactions automatically, appeared in daily lives[1][2].
There are two typical robots: a robotic manipulator
and a mobile robot. The robotic manipulators that has afixed base, have been utilized for precise assembly
operations. However their workspace is limited within asmall volume. On the contrary, workspace of a mobile
robot is not limited to a certain area. However, in thecontrol of the mobile robot, position control accuracy,
velocity and acceleration limits, obstacle recognition,and trajectory planning must be considered carefully[3].
Therefore researches on a mobile robot can be
globally classified into four categories: mobile robot
navigation itself, trajectory planning, position estimation,and driving control. Trajectory planning of a mobile
robot aims at providing an optimal path from an initial toa target position. Generally there are three major factors
to be considered in the trajectory planning: the driving
time, distance, and error. Note that these factors are notindependent but closely related to each other. Therefore
the trajectory planning aims at the selection of best
trajectory in view of all the factors. There are not many

©ISAROB 2005

2 Kinematics Modeling andCharacteristics of a Mobile Robot

Moving

To form a trajectory for a mobile robot to follow thedesired path, kinematics analysis which shows therelation between robot control variables and robot
position and velocity needs to be done previously.

2.1 Kinematics analysis of a mobile robot
As shown in Fig. 1-(a), a mobile robot withdifferential driving mechanism has two wheels on thesame axis, and each wheel is controlled by anindependent motor. Let us define v L as the velocity ofleft wheel, v R as that of the right wheel, and l as the
distance between the two wheels. The robot motion canbe determined by the two wheel velocities, v L and vR ,and the linear and angular velocities of the mobile robot
can be described in terms of v L and v R as follows[5]:v + vL(1)
v1 = R2
2( v R − v L )
(2)
v2 =
l

713

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

In this case, ICC will be determined by the velocity
ratio between the two wheels’. Fig. 3 illustrates ICC
along with the robot’s velocity and position. There exists
proportional relationship between the wheel velocity andthe distance from the wheel to the ICC, which isrepresented asll(5)vL : vR = R − : R + .
2
2
Equation (5) can be simply represented as

Kinematics model of a mobile robot with differential
driving mechanism can be described as Fig. 1-(b). On
the two dimensional X − Y Cartesian coordinates,
position of the mobile robot is described by x R (t ) andy R (t ) while the orientation is represented as θ R (t ) .
Y

v1
vL

vR

θR

R=

yR

v2

( xR , yR , θ R )
l

l
2

l
2

xR
(b)

Fig. 1. Kinematics model of a mobile robot.
Then x& R (t ) and y& R (t ) represent the linear velocity
of a mobile robot while θ&R (t ) represents the angularvelocity. The velocity vector of the mobile robot isdefined as(3)
P& = x& R y& R θ&R T
where P = [x R y R θ R ] T .
Now the kinematics model of the mobile robot canbe represented as[6]⎡cosθ R 0⎤⎡v ⎤ .
(4)&
P = ⎢⎢ sin θ R 0⎥⎥ ⎢ 1 ⎥v⎢⎣ 0 1⎥⎦ ⎣ 2 ⎦[(6)Note that the rotation radius of the mobile robot isdetermined by the values of left and right wheel
velocities. When the robot is following a straight line,
R = ∞ and v R = v L . When v R ≠ v L , the robot follows acircular trajectory with a certain rotation radius.
ICC

X

(a )

l ⎛ vR + vL ⎞ .
⎜⎟2 ⎜⎝ v R − v L ⎟⎠

vL

ω

vR

]

R

vL

d

A

vR

Fig. 3. ICC of a mobile robot
In Fig. 3, when the mobile robot is moving from A
where the robot is located on ( x R , y R , θ R ) at time = t
to B where the position is on ( x ′R , y ′R , θ R′ ) at time =t + δ t , the coordinates of ICC can be determined as(7)
ICC = [ xR − R sin(θ R ) , yR + R cos(θ R ) ]Also the mobile robot position, ( x ′R , y ′R , θ R′ ) , at
time= t + δ t , can be expressed in terms of position of
ICC and angular velocity, ω , as follows:

2.2 Driving Principle of a Mobile Robot
Motion states of the mobile robot with differential
driving mechanism are changing according to the twowheel velocities. A robot with multiple wheels rotatesalong a rotation center instantaneously, and this rotation
center is defined as ICC (Instantaneous Center ofCurvature). As it is shown in Fig. 2-(a), ICC locates on
the cross-section point of the extension-lines of thewheel centers. For a mobile robot with differential
driving mechanism, as shown in Fig. 2-(b), ICC can belocated any point on the wheel axis since the two wheel
axes are on the same line[5].

⎡ x ′R ⎤ ⎡cos(ω δ t ) − sin(ω δ t ) 0⎤ ⎡ x R − ICC X ⎤ ⎡ ICC X ⎤⎢ y ′ ⎥ = ⎢ sin(ω δ t ) cos(ω δ t ) 0⎥ ⎢ y − ICC ⎥ + ⎢ ICC ⎥ .(8)
Y ⎥
Y ⎥
⎢
⎥⎢ R
⎢ R⎥ ⎢⎥⎦ ⎢⎣ ω δ t ⎥⎦⎢⎣θ R′ ⎥⎦ ⎢⎣001⎥⎦ ⎢⎣
θR

Now, the total distance, d , and the rotation angle,

ϕ , of the mobile robot movement from location A to B
can be obtained as follows:t+δ t
t+δ t v + v
LRd=∫v1 dt = ∫
dt
t
t
2
t+δ t

ICC
(b)

Fig. 2. Center of instantaneous rotation.

©ISAROB 2005

(9)(v L + v R ) dt
d ∫(10)
ϕ= = t(v R − v L )Rl (v L + v R )Using these equations, when the rotation radius,
distance of movement, and rotation angle of the mobile
robot are planned previously, the desired linear andangular velocities, v L , v R , and ω can be obtained
when it makes a curved motion[5].

ICC
(a)

( x ′R , y ′R , θ R′ )( xR , yR , θ R )l2

Kinematics analysis aims at the proper velocity
assignment to each wheel to drive the mobile robot to adesired position and orientation[7].

B714

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

rotation radius and driving speed.

3 Single-Curvature Trajectory
3.1 Curvature

L2

Curvature, k , is defined as ratio of ∆θ to ∆s , when amobile robot is rotating from a point, P, to Q as it isillustrated in Fig. 4. The curvature is defined as[8]
dθ
∆θ
(11)
k = lim=ds∆s∆s → 0
Rotation radius can be defined as the inverse of thecurvature as(12)
If k > 0 , then ρ = 1 / k .
As it can be clearly recognized from Eq. (11), the total
travel distance along the curve, ∆s , is the length of thearc and is proportional to the curve radius. Since
curvature, k , is inverse proportional to ∆s , curvature
k is inverse proportional to the radius of the curve. If
k = 0 , then the radius of the curve, in other words,
rotation radius becomes infinite. That is, k = 0 implies
a straight line which is a circle with the infinite radius[5].

L1
R _ ideal
R _ exp

ICC

B1

A

B2
Error

Fig. 5. Driving error of a mobile robot on a
curved path.
Fig. 6 shows the error characteristics according to thedriving radius and speed with a real mobile robot. The
speed of right wheel is kept constant, while that of left
wheel was changed to make a curved motion of themobile robot. With the increase of the left wheel velocity,
the driving error is increased as well. Also note that thedriving error becomes large with the small rotation
radius even though the speed is same. From the analysisof Fig. 6, it can be concluded that the driving error of amobile robot becomes larger with the smaller rotation
radius and with the higher speed while it is driving along
the curved path[4].
100

∆s
∆θ

Error rate rotation radius (%)

P

Rotation - radius 0.3 m

Q

Fig. 4. Curvature.
When the mobile robot is moving along the curved
trajectory, the rotation radius affects severely on thedriving error. Generally the mobile robot has smaller
error along a straight line path ( k = 0) , than the
curved ( k ≠ 0) . Theoretically, the curved trajectory canbe calculated with the Eq’s (5)~(8) assuming the purerolling and non-slipping conditions. Practical driving
may have some differences from the theoretical values.
When the mobile robot is following a curved path, there
exist centrifugal and centripetal forces all together. The
friction force between the surface and the wheels acts ascentripetal force to ICC and keeps the curved motion of
the mobile robot. Under the ideal conditions, the driving
error becomes zero without the slippage. However,practically, there always exists the driving error causedby the slippage. From the following equation, it can beformulated that the centrifugal force is a function ofrotation radius and velocity:
mv 2
(13)
F=
r
Fig. 5 illustrates the driving situation of a mobile robot,
starting from A and following a circular curved path. In
the ideal condition, the estimated position of robot
becomes B1. However, practically the robot arrives at
B2 by the driving error. So far there are lots ofresearches to reduce the driving error caused by the©ISAROB 2005

R = 0.9 m

80

R = 0.6 m
60

R = 1.2 m

40

20

0

0.01

0.03

0.05

0.07

0.09

0.11 0.13

0.15

0.17

0.19

0.21 0.23

Velocity of left wheel (m/s)

Fig. 6. Driving errors in terms of speed androtation radius

3.2 Single curvature trajectory
The single curvature trajectory keeps the same curvature
while the double curvature trajectory changes the driving
direction and curvature at the point of inflection.. That is,
the points of inflection exist at several locations. While amobile robot is moving along the trajectory , the wheel
velocities of the mobile robot need to be changed
whenever the rotation radius and driving direction arechanged according to Eq. (6).
For the same travel distance, the single curvature
trajectory has the biggest rotation radius, while theothers have varying radius with smaller values.
Therefore it can be predicted that when the mobile robot
is traveling along the single-curvature trajectory, it may
have the least tracking error. To verify the fact, the715

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

v L > v R . The velocities can be related to the accelerations
as follows:t(14-a)
v L = a L (τ )dτ = a L t

tracking errors are going to be recorded and analyzed
through the real experiments. Before the realexperiments, pre-simulations are performed to show therotation angles, traveling distance, trajectory, etc.

∫v = ∫ a (τ )dτ = a t
0
t

R

2
0

2
0

0

2

6

4

0

2

4

R

(14-b)R

As it can be noted from the relations between thevelocities and accelerations, the velocity is proportional
to the acceleration, that is, a L > a R . The second segment
is a straight line along which the robot follows from( xline1 , yline1 ) to ( xline 2 , y line 2 ) . At the third part of the

4
Y (m)

Y (m)

4

0

6

X (m)

X (m)

path, the mobile robot estimates the position, ( xobj , y obj ) ,(a) Single curvature(b) Double curvatureand velocity of the moving object using a CCD camera
attached on its body, and follows the single-curvature
trajectory to capture the moving object precisely.

Fig. 7. Single-curvature and double-curvature
trajectories.
Table 1. Computed single-curvature and doublecurvature trajectories
Single Curvature

Double Curvature

Initial Position

Pi=(0, 0, 90o)Pi=(0, 0, 90o)

Final Position

Pf=(5, 4, 109.6o)

Pf=(5, 4, 90o)

7.18 m

7.18 m3.75 m1.88 m109.65o109.65o, 109.65o(3.75, 0)(1.87, 0)(3.13, 4)( xline1 , yline1 )Path - 1
Path - 2( ICC x 2 , ICC y 2 )r2PathDistance(m)CurveRadius(m)RotationAngle(deg)ICC
CoordinatesvL

ϕ1( xline 2 , y line 2 )( ICC x1 , ICC y1 )( xobj , y obj )Path - 3

Fig. 8. Capturing trajectory of a moving object

5 Experiments

Simulation results the total travel distance is same forthe single-curvature and double-curvature trajectories.
However, the rotation radius is double for the singlecurvature trajectory. In more detail, the rotation radius
for the single-curvature trajectory is 3.75m, while that of
double-curvature is 1.875m, which has the point of
inflection at (2.5, 2). As the result of close observation ofFig. 6, it can be concluded that the single curvature
trajectory has less tracking error than the doublecurvature.

5.1 Experimental system
Experiments were performed in the researchlaboratory building. The floor was flat and slippery.
A. Moving object
The moving object does not need any specificcharacteristics. Therefore it is implemented as a simple
and small mobile robot. To estimate the position of themoving object easily, a red color patch is attached on theobject. The color information of the patch is obtained by
the CCD camera on the mobile robot and is utilized toestimate the position of the moving object.

4 Capturing Algorithm
When a mobile robot is tracking to capture a moving
object, the decision of tracking trajectory is a very
important factor for a successful operation. To minimize
the tracking error, a single curvature trajectory is
selected and the mobile robot is planned to follow the
trajectory. Fig 8 shows the mobile robot path to capture amoving object along the single-curvature trajectory. The
total path is composed of two single-curvature and astraight-line trajectories. The left-wheel velocity andacceleration of the robot are defined as vL and aL ,respectively. At the first part of the path, the robot needs
to rotate clock-wise along the circular trajectory, the leftwheel velocity is higher than the right-wheel velocity,©ISAROB 2005

vR

ϕ2

r1

B. Mobile Robot
The mobile robot utilized for the experiments has a
two DOF active vision camera to recognize the objects
and surrounding environments. To enable theautonomous navigation, several sensors including rategyroscope sensors are used for the mobile robot. The
hardware specifications of the mobile robot aresummarized in Table 2.

Table 2. Specifications of the mobile robot

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Item

Specifications

Size (mm)660 * 430 * 465 (L*W*H)

Weight (kg)19.5 kg

Distance between
Wheels (mm)

400 mmDiameter of Wheel

210 mmTable 4. Rotation radius, driving error, time, anddistance along the single-curvature trajectoryv1
R_realError
Time
Distance
(m/s)

(m)

(m)

(s)

(m)

0.1

3.88

0.13

74.0

7.31

0.2

4.13

0.38

39.5

7.54

0.3

4.37

0.62

28.8

7.75

5.2 Experimental results and discussions

0.4

4.61

0.86

24.0

7.98

In the first experiment, the single-curvature anddouble-curvature trajectories are compared in terms of
tracking error, traveling distance, and tracking time withthe same mobile robot for a moving object. Figure 9shows the real experimental environment. The
experimental results are summarized in Table 4 and 5.
Figure 10 shows tracking error, traveling distance, andtracking time as a graph according to the realexperimental values.

0.5

4.98

1.23

21.5

8.32

Table 5. Rotation radius, driving error, time, anddistance along the double-curvature trajectoryv1
R1_real R2_real
Error
Time Distance
(m/s)

(m)

(m)

(m)

(s)

(m)

0.1

2.02

2.06

0.51

77.2

7.67

0.2

2.25

2.25

1.02

41.6

8.08

0.3

2.47

2.38

1.44

32.1

8.12

0.4

2.85

2.89

2.43

28.8

8.80

0.5

3.12

3.15

3.01

26.2

9.33

Table 3. Computed velocity of the mobile robot
for each trajectory
Doublecurvature
Trajectory(r=1.875*2m)

vL

vR

vL

vR

(m/s)

(m/s)

(m/s)

(m/s)

v1 (m/s)Time(s)

0.1

72.8

0.105

0.095

0.089

0.111

0.2

37.9

0.211

0.189

0.179

0.221

0.3

26.9

0.316

0.284

0.268

0.332

0.4

21.9

0.421

0.379

0.357

0.443

0.5

19.3

0.527

0.473

0.447

3.5
Driving error (m)ListsSinglecurvature
Trajectory(r=3.75m)

3

Double curvature

2.5
2
1.5

Single curvature

1
0.5
0
0.0

0.1

0.2
0.3
Rotation radius (m)

0.4

0.5

(a) Comparisonof driving- error

0.553

6

Driving time (s)90

0.5 m/s
0.4 m/s
0.3 m/s
0.2 m/s
0.1 m/s

5
4
3
Y[m]

2

80
70
60
50
40

Double curvature

30

1

20
10

0

0

Single curvature

0.1

-1
-1

0

1

2

3
X[m]

4

5

0.2

6

0.3
Rotation radius (m)

0.4

0.5

(b) Comparisonof driving- time

Driving distance (m)(a) Single Curvature

0.5 m/s

7

0.4 m/s

6
0.3 m/s
0.2 m/s

50.1 m/s

4
3
Y[m]

2

0

(b) Double Curvature

0

1

2

3

4

5

6

Double curvature

Single curvature

4
3
2

0.1

0.2

0.3
Rotation radius (m)

0.4

0.5

(c) Comparisonof driving- distanceX[m]Fig. 10. Comparison of single- and doublecurvature driving characteristics

Fig. 9. Real experiments of the mobile robot
Driving©ISAROB 2005

7
6
5

1
0

1

-1
-1

10
9
8

The second experiment is performed to show thetracking and capturing of a moving object by the mobile717

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

mobile robot. For the trajectory planning of a mobile
robot in a curved path, superiority of the single curvature
trajectory is verified based on the experiences andtheoretical backgrounds. To practically show theeffectiveness of the proposed algorithm, several
experiments are performed with other trajectories, for an
example, double-curved trajectory. Since the mobile
robot using only dead-reckoning sensors aggregates the
position estimation error while it is navigating, an error
correction algorithm is necessary to support a precise
and prompt control performance. Frequent error
correction processes degrade the driving performancecritically and may cause unstable operations. Providing a
trajectory which causes less error for the mobile robot isvery effective for the precise and fast curved motion. To
improve the driving accuracy of the mobile robot,
intelligent position estimation schemes and driving
control algorithms are necessary to develop further as
future research works.
-------------------------------------------------------------------This work was supported (in part) by the Ministry of
Science and Technology, Korea, under the University
Region Research Center Project

robot. As it is shown in Fig. 8, the mobile robot follows
two curved paths and a straight path to capture a moving
object. Figures 12 and 13 show the real trajectories
followed by the mobile robot. In this experiment, themobile robot’s velocity is 0.1m/s, while the moving
object has the velocity of 0.05m/s. To minimize thetracking error of the mobile robot, the maximum
allowable acceleration was kept as 0.05m/s2 . The initial
position of the mobile robot was (1, 2, 90o), and themoving object was initially located at (0, 2, 25.5o) before
the experiment. The first curved path has rotation radiusof 1.5m. When the mobile robot followed this path, it
had tracking error of 0.6m. Along the second straightline path, the mobile robot moved 2.8m, and byfollowing the third curved path with rotation radius of
1m, the mobile robot hits the moving object to capture.
The total traveling distance of the mobile robot was
6.91m, the the tracking error was 1.4m. The total time tocapture the moving object was 71.2seconds.

Fig. 11. Capturing operation along the singlecurvature trajectory

References[1] M. W. Spong and M. Vidyasagar, Robot Dynamics
and Control, John Wiley & Sons, 1989.
[2] K. Hirai, M. Hirose, Y. Haikawa and T. Takenaka,“The Development of Honda Humanoid Robot,”
Proc. IEEE Int. Conf. on Robotics and Automations,pp. 1321-1326, 1998.
[3] Jong-Woo Moon, “A Study on Kinematic Correction
and Trajectory Tracking ofWheeled-Mobile
Robots with 2 D.O.F,” Department of Electronic
Engineering Graduate School of Kyung Hee
University, Seoul, Korea, 2000.
[4] Yong-Jun N, “A Study on the reduction of Navigation
error for Curvilinear navigation of Mobile Robotusing Neural Network,” Department of Electronic
Engineering Graduate School of Chonnam National
University, Kwangju, Korea, 2002.
[5] G. Dudek and M. Jenkin, Computational Principles ofMobile Robotics, Cambridge university press, 2000.
[6] W. E. Dixon, D. M. Dawson, E. Zergeroglu and A.
Behal, Nonlinear Control of Wheeled Mobile
Robots, Springer, 2003
[7] W. E. Dixon, M. S. de Queiroz, D. M. Dawson, and T.
J. Flynn, “Controlling Mobile Robots in Distributed
Intelligent Sensor Network,” IEEE Trans. On
Control Systems Technology, vol. 12, no. 1, Jan.
2004.
[8] S. I. Grossman and W. R. Derrick, Advanced
engineering mathmatics, HarperCollins Publishers,
2000.

(7.3, 3.09)

ϕ1 = 117.4 o

r 2 = 1m

ϕ 2 = 51or1 = 1.5m(7.75, 2.25)( 2.5, 2)(1, 2)( 2, 0)Fig. 12. Tracking and Capturing Trajectory

e1 = 0.6 m

Capturing trajectory (Real)

Capturing trajectory (ideal)

Error = 1.4 m(2.5, 2)

(1, 2)(2, 0)

Fig. 13. Tracking error

6 Conclusions
This paper demonstrated optimality of a single
curvature trajectory in capturing a moving object by a

©ISAROB 2005

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Proposing a Passive Biometric System for Robotic Vision
M. Masudur Rahman, Seiji Ishikawa
Department of Mechanical and Control Engineering
Kyushu Institute of Technology, Japan
Email: rahman@ss10.cntl.kyutech.ac.jp

ear have been chosen individually as the biometriccomponents [4, 6]. However, the present study merges
the eigenfaces and eigenears for developing a passive
multimodal biometric system where face and ear are thebiometric component.
In this paper, we propose and develop a prototype
passive biometric system using eigenears and eigenfaces.
The images of the ears and faces are collected from asingle image of a particular person, and the databases of
eigenears and eigenfaces via the PCA algorithm areproduced. New type of biometric data (ear or face) isverified using the databases, and personal verifications
are done judging the match-record. Since the proposed
study extracts face and ear from the same image, thesystem does not require the application of multiple
cameras. Or, we can also employ a ceiling camera for
extracting such images. To the best of our knowledge,
this is a new method in the biometric systems.
We have presented a brief formulation of the proposed
biometric system. In the experiment, we have presented
three different results for comparing the face, ear andmultimodal biometrics. A discussion and concluding
remarks are also placed at the end of this study.

Abstract:
We present a passive biometric system to the detection
and identification of human faces and ears and develop amultimodal biometric system using eigenfaces andeigenears. A new technique of tracking a human face andan ear from the same image of a particular person is
proposed that provides us to use a single camera fordeveloping the proposed biometric system. The proposedsystem uses the extracted face and ear images to develop
the respective feature spaces via the PCA algorithm
called eigenfaces and eigenears, respectively. A new faceand ear can be characterized by calculating the Euclidean
distance between the classes of eigenfaces or eigenears
and the new face or ear, respectively. Eighteen persons’faces and ears are employed for developing the databasesof the eigenfaces and eigenears, and new faces and ears
taken from various sessions of the same persons are
employed for the identification. The proposed
multimodal biometric system shows promising results
than individual face or ear biometrics investigated in theexperiments.
1. Introduction
Many of security companies are seeking a suitablebiometric system that can create its database and identify
targeted person passively. Such a system is especially
important for robotic vision where subject’s cooperation
or direct interaction of human and robot may not always
be available. In some cases, a subject’s involvement may
cause severe threat to the biometric systems especiallyfor identifying a suspect or a terrorist. In addition, an
automatic personal identification system based solely ona particular biometric component such as fingerprint orface is often not able to meet the security satisfaction [1].
We need such a system where acquisition of biometric
database and its verification could be developed without
a subject’s concern, and the system performance will
satisfy the security requirement. A new multimodal
biometric system based on eigenfaces [2] and eigenears[3] is introduced in this paper. We develop a passive
multimodal biometric system where subject’s concern is
not necessary. The proposed system can be implemented
for developing a passive identification system employing
a humanoid robot and it can be of a suitable application
to an airport security system.
In the past, various studies have been done inbiometric systems. However, the area of passive and/or
multimodal biometric systems is quite new in biometricresearch. We have seen some of studies where face and©ISAROB 2005

2. Eigenfaces and Eigenears
PCA is a very powerful technique for selecting
appropriate features or to reduce the dimension in which
variation in the dataset is preserved. Thus the

EXTRACTION
Face or ear image

TRAINING
Creating eigenears or
eigenfaces

IMAGE
PROCESSING
Cropping the faceand ear images

TESTING
Projecting new faceand ears

NORMALIZATION
Normalizing size &Illumination

DECISION
Successful hit

Figure 1: Flow chart of the system’s algorithm.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

classification is done in a lower dimensional space called
the eigenspace, which is just the space defined by theprincipal components or the eigenvectors of the data set.
In case of face or ear images, the principal
components are called eigenfaces or eigenears,
respectively. The proposed system performs various
procedures that are shown in Figure 1. In the extraction
stage, acquired training face and ear images are cropped
to a specified size from the same image of a particularsubject. Image processing step includes geometricnormalization and masking. Different masks can be used
to avoid the unnecessary parts of the images. In theverification stage, it consists of two stages; training stage
and testing stage. In the training phase, the eigenears andeigenfaces are developed choosing the appropriate
eigenvectors. In the testing phase, the decision of theidentification is taken based on the Euclidean distance
between the classes of eigenfaces or eigenears and newfaces or ears, respectively.

covariance matrix corresponding to the original face or
ear images and they are faces or ears like theirappearances.
2.2 Classifying new Faces or Ears using Eigenfaces orEigenears
After creating the eigenfaces or eigenears,
classification of new images becomes a patternrecognition matter. A new face or ear image (x) is
transformed into its eigenface or eigenear component
projected onto the eigenfaces or eigenears by thefollowing way,

wk = ekT (x − ψ)for k = 1, 2,..., k ′ . This describes a set of point-by-point
image multiplication and summations. The weight fromthe vectors Ω = [ w1 , w1 ,..., wk ' ] that describes theTcontribution of each eigenface or eigenear in
representing the input face or ear image treating the
eigenfaces or eigenears as a basis set of face or ear
images [4]. Calculating an Euclidian distance is thesimplest way to classify the new face or ear class asfollows,

2.1 Calculating Eigenfaces or Eigenears
Let a face or ear image I(x,y) be a two dimensional N
by N array of intensity or a vector dimension N2. Let thetraining set of face or ear images be

x1 , x2 , x3 ,... xM(1)

d k = (Ω - Ωk )The average face or ear of the set is defined by

1 M
ψ=
∑ xn
M n =1class. A face is classified as belonging to class k whenthe minimum d k is in the defined threshold limit of

(2)

1 n
∑ φi φiΤM i =1εk . Otherwise, the new face or ear is defined as‘unknown’. The unknown face or ear can be used fordeveloping further database.

3. Experimental Results(3)

Three different issues are considered for performing thisstudy. We have initially developed two individual
biometrics using face and ear separately. Finally, amultimodal biometric system is developed merging theface and ear biometrics.
As we have described in the Section 2 and Figure 1,
there are three main steps involved in developing the
proposed system: (a) extraction of images,
pre-processing and normalization, (b) creating eigen
databases for ears and faces, and (c) verification. We
have employed the databases of university of Notre
Dame [9] which is composed of five different sessions
and imaging conditions. The database contains fivedifferent variations based on the sessions collected in the
database. We employ one set for creating the databasesof eigenears and eigenfaces, and four other variations areused for the verification. In the preprocessing step, we
have used only the side faced-images for obtaining the(right) ears and faces together. Fig. 2 shows 5 person’s
images out of total 18 employed in this study. Some ofimage samples used in the experiments are shown in Fig.
3 along with extraction procedures of the ear and face

= Xn XTnwhere, X is the new image set obtained by thesubtraction result between each image and φi . This setof very large vectors is then subject to principal
component analysis that seeks a set of k orthogonal
vectors or the principal components ei and their

λ i which best describes the
distribution of data. The vectors ek and scalars λ k areassociated eigenvalues

the eigenvectors and eigenvalues, respectively, of thecovariance matrix. The N dimensional space defined by
all the eigenvectors of C is reduced via the principal
component analysis [8]. One may find a simple method
to select the eigenvectors proposed by Turk and Pentland
2[2]. As a result, the chosen K ( 1 ≤ k << N ) eigenvalues
λk (k=1,2,…,K) and corresponding eigenvectors ek are
obtained. The k-dimensional vector defined by the
eigenvectors ek is called an eigenface or eigenear.
Because these vectors are the eigenvectors of the©ISAROB 2005

(5)

where Ω k is a vector describing the kth face or ear

Each face or ear differs from the average by the vector
φi = xi − ψ . A covariance matrix can be obtained by

C=

(4)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

best performances of each system are also placed inTable 1.

4. Discussion
The present study has aimed to develop a multimodal
biometric system for personal identification that can also
work passively. Experimental results have shown that theconcept of tracking the ear and face from the same imageof a person has received better performances over the
many available methods [4-7]. Although there was nointention to make any recommendation in favor of any
particular biometric systems, it is observed that facebiometrics has performed better than ear biometrics. The

Figure 2: Some of models employed in the experimentout of total 18.

Table 1: Experimental setup
Fig. 3. Procedures of extracting ear and face images(upper), and image variations used in the experiments(lower).
area (upper row). In the Fig. 3 (lower row), 5 images of aparticular subject taken from 5 different sessions areshown. The extraction of faces and ears are yet not
automatic, as we need some manual interruption for
selecting the exact size of the ear and face. The images
are cropped to a specific size with just containing theface and ear. Finally, the original size of the cropped
images is reduced to a size of 32x32 pixels for the sakeof efficiency. Now, the images are normalized forminimizing the lighting effect.
We have developed the databases of eigenears andeigenfaces of 18 subjects as described in the section 2.
Performance of the verifications is obtained based on
successful hits by calculating the distance within theprescribed threshold. In case of multimodal biometricsystem, if the system recognizes any one of the ear orface of a particular person successfully, we consider thecorrect identification of the subject. Detail ofexperimental dataset is placed in Table 1. We have also
obtained the classification results on various issues thatare placed in Fig. 4, Fig., 5, Fig. 6 and Fig. 7.
Performance based on eigenvectors, eigenvector vs. CPU
time, and performance based on day variations are shownin Fig 4, Fig. 5 and Fig. 6, respectively. Fig. 7 shows
the performance of the proposed multimodal system
based on required eigenvectors. It has shown thatmultimodal biometrics has better performance overindividual face and ear biometrics. In comparison
between face and ear biometrics, face biometrics has
shown better performance than ear biometrics. However,
ear biometrics has shown better performances in case of
CPU time and day variations. Ear biometrics has
absolutely no effect on day variations. We haveobtained the best recognition rate of 94.44% by
multimodal biometric system on 40 eigenvectors. The©ISAROB 2005

Biometrics

Total images
5x18=90

Test
Variations
4

Best
Perform.
88.88 %

FaceEar

5x18=904

77.77%

Multimodal

2x5x18=180

494.44%

Figure 4: Experimental results on eigenvectors vs.
recognition rates.

Figure 5: Experimental results on CPU time vs.
eigenvectors.

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

multimodal biometric using eigenfaces and eigenears canperform better than either one alone. We have seen thesame results in case of using face and fingerprint or faceand voice [1]. However, use of face and ear has the best
combination for developing a passive biometric system.
This research may open up some new multimodal
biometric systems with merging two or more biometricsystems such as face and gait or face, ear and gait.
Since the proposed biometric system can detect andidentify human face and ear passively, it can be of asuitable application in a robotic vision field. A robot canbe a part of security systems in an airport for security
checking.
The present study has some limitations on several
factors, e.g., occlusion, use of appropriate mask, etc. The
occlusion may sometimes greatly influence to the
proposed system. Fig. 8 shows a simple example whereocclusion problem makes impossible to use the ear as abiometric component. Although face biometric can be
implemented in this particular case, a multimodal systemcannot be developed due to absence of ear image.
There are some extensive works on extracting face and
ear images using different masking. Although use of suchmasks needs some manual work, better results can beobtained in different imaging conditions [3,9]. We needsome further studies with considering the mentioned
factors such as occlusion by facial hair or partiallycovered ear by long hair, and different masking.

Figure 6: Experimental results on recognition rate vs.
day variations.

5. References
1. L. Hong and A. Jain: “Integrating Faces andFingerprints for Personal Identification”, IEEE Trans.
on PAMI, Vol. 20, No. 12, pp. 1295-1307 (1998).
2. M. Turk and A. Pentland: “Eigenfaces for
Recognition”, J. of Cognitive Neuroscience, Vol. 3, No.
1, pp. 71-86 (1991).
3. K. Chang and K. W. Bowyer: “Comparison andCombination of Ear and Face Images in
Appearance-Based Biometrics”, IEEE Trans. on PAMI,
Vol. 25, No. 9, pp. 1160-1165(2003).
4. M. Turk and A. Pentland: Face recognition Using
Eigenfaces, Proceedings of the CVPR, pp.
586-591(1991).
5. L. Sirovich and M. Kirby: “Low Dimensional
Procedure for Character of Human Faces”, J. of Opt.
Society of America, Vol. 4, No. 3, pp. 519-524(1987).
6. B. Victor, K. Bower and S. Sarkar: “An Evaluation ofFace and Ear Biometrics”, Proceedings of the ICPRCDROM Version (2002).
7. M. Burge and W. Burger: “Ear Biometrics in
Computer Vision”, Proc. of the Int’l Conf. on Pattern
Recognition, CD ROM (2002).
8. R. C. Gonzalez and P. Wintz: Digital Image
Processing, Addison-Wesley Publishing Company, Inc.
(1992).
9. P. J. Flynn, K. W. Bowyer, and P. J. Phillips:“Assessment of time dependency in face recognition:
An initial study,” Audio and Video-Based Biometric
Person Authentication, pp.44-51 (2003).

Figure 7: Multimodal biometric system. Experimental
results on eigenvectors vs. recognition rate.

(a)

(b)

Figure 8. Occlusion problems in extracting face and ear
images. Extraction of (a) face is possible but (b) ear is
not possible due to long hair. Images of Britney Spears
collected from the Internet

results presented in this paper differ slightly with thepaper by Burge and Burger [7] where they have showed
ear-based biometrics better than face biometrics.
Variations on the ears and faces may give us different
results as described by Chang and Bower [3]. Our results,
on the other hand, do support the conclusion that a©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Human Motion Recovery by Mobile Stereoscopic Cameras
Tan, J. K.*, Yamaguchi, I.*, Ishikawa, S.*, Naito, T.**, Yokota, M.**
*Department of Control Engineering, Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japan
Email: {etheltan, ishikawa}@cntl.kyutech.ac.jp**Department of Periodontology and Endodontology, Kyushu Dental College
Manazuru 2-6-1, Kokurakita, Kitakyushu 803-8580, Japantechniques that can capture wide range motions are
obviously better for wide spread of the technique.
We have already proposed a shape recovery techniqueof 3-D non-rigid objects based on multiple
calibration-free cameras [2,3]. It employs a factorization
method [1] with an extended measurement matrix [2] that
contains spatiotemporal information on the object’s
deformation. Since the technique necessitates cameras tobe fixed around the object concerned, it can only deal withthe motions/ movements in a limited space. Thisdisadvantage can be overcome by the employment ofmultiple mobile cameras with much more flexibility inimage taking.
The idea of our approach is to devise a way of creatinga measurement matrix that should be a full matrix whose
entries are all known. Once a full measurement matrix is
given, it can be factorized into a camera orientation
matrix and a shape matrix [1]. The shape matrix gives
information on a 3-D shape/motion of the object
concerned. This paper proposes a multiple mobile
cameras system as a most convenient way of taking
images of an object moving in a wide range. The method
is described and some experimental results are shown.
Precision of the created 3-D model is as well explainedand discussion is given.

Abstract
A method is described for reconstructing 3-D object
models, particularly human models, based on multiple
calibration-free mobile cameras and a computer. Existing
optical 3-D modeling systems must employ calibrated
cameras that are set at fixed positions. This inevitably
gives constraints to the range of movement with object
motions. In the proposed method, multiple mobile
cameras take images of an object moving widely and its
3-D model is reconstructed from the obtained video
image streams. Advantages of the proposed method
include that the employed cameras are calibration-free
and that the method claims at least two cameras,
stereoscopic cameras, to be employed, which offers a
simple system. The theory is described and theperformance is shown by the experiment on 3-D humanmotion modeling. Precision of the 3-D model is also
shown with discussion.
Keywords: Stereoscopic cameras, mobile cameras,
motion capture, motion recovery, computer vision.

1. Introduction
Three-dimensional shape recovery techniques ofdeformable objects have attracted researcher especially in
a computer vision community over the last decade.
Particularly human motion recovery has become popular,
since it has various potential applications such as creatinga human model in video games or in a virtual reality space,motion analysis in various sports, traditional dances orskills preserving in an electronic museum, etc.
Stereoscopic vision is, as is well known, a popular
technique for performing such 3-D shape recovery. But it
is not very convenient particularly for outdoor use
because of camera calibration. Alternatively motion
recovery employing magnetic sensors is also a common
technique. It restricts motion range of the subject,however. Optical measurements with non-contact©ISAROB 2005

2. A measurement matrix for mobile cameras
The measurement system proposed in [2,3] places
cameras at fixed positions like existent 3-D measurement
systems commercially available. This inevitably restricts
the motion of a person whose 3-D model is the present
concern. An efficient way of taking images of such amoving object is to let cameras move along with theobject. The idea of moving a camera is normallyemployed when static objects are concerned for 3-D
shape recovery [1]. The technique presented in this paper
allows camera motion to widely moving non-rigid aswell as rigid objects.
We suppose that F cameras take images of an object

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be written as

by changing their locations L times. If we denote a
measurement matrix of the F cameras at location l(l=1,2,…,L) by Wl, a general form of an overallmeasurement matrix W is given by the following;0 
W1 0 0
 0 W2 0 0 
W =
.
0
0 O 0 



0
0
0
W
L


(

Wl = Wl R(3)

Since matrix WlR (l=1,2,…,L) only contains common
static feature points, they are collected into a single
Rmatrix W by(1)

W1R  R
W 
WR = 2 .
 M W R  L Here matrices Wl (l=1,2,…,L) are placed at the diagonal
positions of W and the remaining entries are all vacant.
The matrix having this form cannot be further processedin terms of factorization [1].
It should as well be noted that cameras cannot becalibrated in this technique because of their mobility.

(4)

Applying factorization to the above full matrix WR, wehave

W R = M ⋅SR ,(5)
where matrix S contains the 3-D coordinates of all thechosen static feature points. Camera orientations at
location l (l=1,2,…,L) are also obtained by matrix M. Let
us denote the camera orientation matrix M byR

3. Mobile cameras system I
Ncan be done in the context of factorization method.
To go further, we proposed a mobile frame system [5] inwhich cameras are placed fixed on a mobile frame. We
placed the world coordinate system on the mobile
cameras, instead of placing it on the ground where themobile cameras move. Then the circumstance isequivalent to that the world moves around the cameras inplace of the cameras moving in the world. This meansthat all the sub-matrices Wl (l=1,2,…,L) can be aligned in
the same rows as follows;
W ′ = (W1 W2 L WL ) .
(2)

M = (M 1

M2 L ML ) .
T(6)Then the 3-D coordinates of moving feature points arethen calculated by

S N = M + ⋅W N .

(7)

Here SN is a matrix composed of SlN , M+ is a matrix
whose diagonal elements are Ml+, and WN is a matrix
whose diagonal elements are WlN.
In this way, the measurement matrix of Eq.(1) can be
decomposed as

Equation (2) provides a full measurement matrix and it
can be factorized according to [2,3], and finally obtained
shape matrix S giving 3-D coordinates of the motion of
the object relative to the cameras.

W = M ⋅S

(8)
and all the feature points registered in matrix W recover
their 3-D positions. They are given by

4. Mobile cameras system II
In this section, we describe a technique [6] how to takeimages and recover shape of a widely moving object,
employing independently moving cameras. In thetechnique, the camera orientations are all different
through the camera movement from location l=1 to l=L.
Therefore the cameras need calibration to obtain 3-D
information of the object. But unlike existent 3-D
recovery techniques that perform camera calibration
using 3-D tools, the proposed technique doesn’t employ
such tools for the calibration. Instead, it employs rigid
points that are observed in the captured image streams.
The proposed procedure is described in the following.
At the position l (l=1,2,…,L), the cameras are required
to observe common feature points on static objects(referred to as static feature points hereafter) other than
the feature points on non-rigid objects (referred to as
moving feature points hereafter). Let the sub-matrix
containing static feature points be denoted by WlR and the
sub-matrix containing moving feature points be denotedby WlN. Then the sub-matrix Wl at position l in Eq.(1) can©ISAROB 2005)

Wl N .

S = (S R | S N ) .

(9)
Precision of recovered 3-D shape is evaluated byprojecting the obtained 3-D shape back onto the originalvideo images [2]. We multiply the derived matrices M
and S to obtain 2-D positions of recovered feature points
on each image plane. Let us denote the result of themultiplication by Ŵ , i.e.,Ŵ = M ⋅ S .
(10)
The precision is then computed by evaluating thedifference between W and Ŵ .
5. Experimental results
In the performed experiment, a subject moved aroundin the yard and its motion was taken images by two
mobile cameras, denoted by CL and CR, respectively. The
cameras were connected to video transmitters and thevideo images were sent via the transmitter and the724

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remote places of interest where time compensation bytedious camera calibration may cause loosing important
shots of the event of interest. In the proposed system, one
only have to bring two cameras or more to such a placeand just start taking images of the event there. If one
sends the taken images back to the lab by a video
transmitter or even internet, for example, the event canbe modeled in a 3-D way in the lab. This means that a
dynamic event at any distant place can be modeled in a
3-D way by the proposed technique. This pattern of
employment of the technique may have various
applications in future.

receivers to two PCs in a distant room. Sampling interval
was 0.1 second and 100 images were sampled. Thus 10
seconds motion was processed for recovery. The chosenfeature points are 24 for static feature points and 17 formoving feature points. In this particular experiment, thesubject’s motion was controlled so that the two cameras
could observe the 24 static feature points during all theobservation time.
Video images, obtained from cameras CL and CR, ofthe subject’s motion are partly shown in Fig.1. The resultof 3-D recovery is given in Fig.2. In both figures, thetime proceeds as indicated by arrows. The precision of
the recovery was computed and the recovery error was
1.75% for static feature points and 1.71% for moving
feature points with respect to the left camera, and 1.72%
for static feature points and 2.12% for moving featurepoints with respect to the right camera. Thus we have
1.8% of the average recovery error. Figure 3 is theillustration of the precision of the experiment. The
recovered images shown by broken lines are superposed
onto the original image shown by solid lines. As we haveachieved very small recovery errors, it is actually noteasy to see the difference between them.

This study was partly supported by JSPS
KAKENHI (14580450), which is greatly acknowledged.

References[1] C. Tomasi, T. Kanade, “Shape and motion fromimage streams under orthography: A factorization
method”, Int. J. Computer Vision, Vol.9, No.2,
pp.137-154, 1992.
[2] J. K. Tan, S. Ishikawa, “Human motion recovery by
the factorization based on a spatio-temporalmeasurement matrix”, Computer Vision and Image
Understanding, Vol.82, No.2, pp.101-109, 2001.
[3] J. K. Tan, S. Ishikawa, “Recovering 3-D motion ofteam sports employing uncalibrated video cameras”,
IEICE Trans. on Information and Systems, Vol. E84D, No.12, pp.1728-1732, 2001.
[4] Y. Chen, G. Mediomi, “Object modeling by registration of multiple range images”, Image and Vision
Computing, Vol.10, No.3, pp.145-155, 1992.
[5] T. Tabusa, J. K. Tan, S. Ishikawa, “Recovering
human motions by mobile video cameras andfactorization”, Proc. of Asian Conf. on Computer
Vision, 2002.
[6] J. K. Tan, I. Yamaguchi, T. Tabusa, S. Ishikawa, S.
Hirokawa, “Various image taking strategies for 3-D
object modeling based on multiple cameras”, Proc.
of Int. Conf. on Image Processing, 2487-2490, 2004.

6. Conclusions
We have shown a technique for 3-D human motion
recovery employing mobile cameras. In this technique, atleast two cameras are used for the recovery. Therefore it
can be called mobile stereoscopic vision by mobile
stereoscopic cameras. Camera calibration is done using
some representative points observed in images. This iscompletely different from existent techniques that use
3-D tools for the calibration. In this sense, our technique
is easier to use especially for those who are not very
familiar with shape/motion recovery.
The precision of the recovery or the recovery error
was satisfactorily small, 1.8%, in the performedexperiment, which is comparable to existent techniques
based on fixed stereo cameras. At the moment, we
employ orthographic projection as a lens model.
Changing the model to week perspective projection, e.g.,
may result in better precision. This remains for the next
step of the study.
Since the proposed mobile cameras system does notneed ordinary camera calibration, it can be employed in©ISAROB 2005

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(a)

(b)

(a)

Fig. 2. Recovered 3-D motion: (a) CR, and (b) CL.

Fig. 1. Video images of a human in motion:(a) CR, and (b) CL .

CR

CLFig. 3. Precision of the recovered 3-D model.

©ISAROB 2005

(b)

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

A High-speed Human Motion Recovery Based on Back Projection
M. Uchinoumi*, J. K. Tan*, S. Ishikawa*, T. Naito**, M. Yokota***Department of Control Engineering, Kyushu Institute of Technology
Sensuicho 1-1, Tobata, Kitakyushu 804-8550, Japan
Email: {etheltan, ishikawa}@cntl.kyutech.ac.jp**Department of Periodontology and Endodontology, Kyushu Dental College
Manazuru 2-6-1, Kokurakita, Kitakyushu 803-8580, Japan

expensive products. These drawbacks prevent theexistent motion recovery techniques from being popularamong various users who need motion recovery for theirfamiliar subjects such as evaluation of the effect of
exercises in rehabilitation.
In this paper, a novel technique is presented for 3-D
human motion recovery based on back projection ofsilhouette images. It contains some accelerated
computational techniques that contribute to high-speed
motion recovery. They include (1) simple architectureby the employment of a server-client system forcomputation of back projected images, (2) use of a lookup table to realize quick search for the voxels within theobject concerned, and (3) use of difference images forvolume recovery. Experiments are performed employing
the proposed technique. The implemented system
contains a host computer as a server and fourcamera-computer units as clients mutually connected via
a LAN. Some results are shown on the recovery. The
achievement of this study and further issues to bechallenged are finally discussed.

Abstract
This paper describes a novel technique for high-speed
human motion recovery. For a 3-D human motion
recovery, stereoscopic vision is a normal technique aswell as the employment of the magnetic field. But theycannot escape from the difficulty of marker tracking. In
this paper, a novel motion recovery technique is
proposed based on back projection of silhouette images.
This technique has some advantages over others that it
does not employ markers and that it has a simple
architecture. In the performed experiment, the proposed
motion recovery technique is implemented on a system
containing a LAN, a host computer and four pairs of acamera and a computer and it achieves high-speed
human motion recovery.
Keywords: Motion recovery, human motion, backprojection, silhouette, real-time.

1. Introduction

2. Shape recovery by back projection

Three-dimensional human motion recovery is an
attractive field of study and recently it has gained muchattention among various communities related to digital
image processing. It is, for example, employed for
producing 3-D characters in video games. Evaluating
3-D motions in sports or in rehabilitation is as well an
important application field.
An optical motion recovery is a technique that
captures images by cameras and recovers 3-D motion
sequences of a human by a computer. Existent major
motion recovery techniques are divided into two
categories; one with marker tracking [1,2] and the otherwithout markers [3]. In the former, 3-D positions of themarkers attached on a human body recover by geometriccomputation. But automatic marker tracking remains abottleneck of the technique. On the other hand, motion
recovery without markers has an advantage over theformer in its high-speed nature in processing. But it still
suffers from heavy computational load, resulting in©ISAROB 2005

The idea of back projection is simple. Suppose one
takes an image of an object by a camera. Subtraction of abackground image from the acquired image yields amask image of the object after some steps of image
processing. The mask image is equivalent to a silhouetteof the object from the camera orientation. Then one isable to imagine a cone defined by connecting the centerof the camera lens and every point on the contour of thesilhouette on the image plane, i.e., an image hull. Clearly
the object exists within the cone. If one takes many
images of the object from different orientations, each
image defines a cone and the original object is obtained
from the intersection of all the cones. This is the idea ofback projection. See Fig.1 for intuitive explanation ofback projection. It is obvious that concave portions of
the object concerned are lost in this recovery technique,since they cannot be observed on the silhouette from any
orientations.

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(3) Use of difference images for successive volume
recovery, i.e., the 3-D area at sample time t is producedby the back projection of those pixels in the region thatreceived displacement between the image frame at t-1
and the image frame at t.

Image hull

3.1 Distributing computational load by parallelarchitecture
In the back projection technique, a larger number ofimage taking cameras can realize higher precision ofrecovered motion. To speed up the computation, a
server-client system is employed, when implementing
the technique. Cones Vi (t ) (i=1,2,…,n) are produced
at client computers i (i=1,2,…,n), respectively, and they
are transferred through a LAN to a server, where theintersection by Eq.(2) is performed to yield V (t ) .
One of the advantages of a server-client system is that
it is a distributed computation system. If one needs to
get more precise shape of the object concerned, one only
has to connect more number of camera and client pairs
to the system. This doesn’t increase the overall
computational load much, as the computation is done inparallel. Too many clients, however, cause the increase
in data transfer time in the LAN, which may givenegative influence on high-speed computation.

Camera lens centre

Fig. 1. Explanation of back projection.

At sample time t (t=1,2,…,T), a silhouette image
I i (t ) (i=1,2,…,n) of a human in motion is madefrom the image frame Iˆi (t ) by subtracting theBbackground image I i , i.e.,

I i (t ) = Iˆi (t ) − I iB

(1)Image I i (t ) contains a figure part and a ground. Itthen receives binarization. The resultant image is again
denoted by I i (t ) in which a human region denoted by
Ri (t ) remains as a figure with the value 1. The groundis given the value 0. The region Ri (t ) is thenprojected back into the 3-D space to yield a volumetricregion (or a cone) Vi (t ) . These cones Vi (t ) (i=1,2,…,n) are collected and intersected to obtain a common
region by

V (t ) = ∩ Vi (t )i=1,..., n

3.2 Listing correspondence between pixels and voxels
In the back projection method, the camera system
employed must be done calibration in advance. Supposethat a voxel X k ≡ ( X k , Yk , Z k ) in the XYZ world
coordinate system be projected onto the pixel
x ij ≡ ( xij , y ij ) of the ith camera’s image plane. Then,as their relation, we have xij ≡ xij ( X k ,Yk , Z k )(2)

The idea of the intersection is explained in Fig.2 as a
2-D case, in which (a-c) three camera-client pairs
produce cones and (d) their common part is extracted.
This procedure is repeated for t=1,2,…,T to obtain theset {V (t ) | t = 1,2,..., T } , which gives the recovered
3-D motion sequence.
In order to use this technique, all the observing
cameras must be calibrated in advance, since the backprojection is performed employing direct linear
transformation [4].

(a)

(b)

(c)

(d)

3. Strategies for high-speed computation
In order to realize high-speed shape recovery, someideas are employed in the computation of backprojection:(1) Use of a server-client system to distributecomputational load to client computers;(2) Use of a look up table (LUT) to realize quick search
for those voxels in the 3-D space whose projection ontoa camera image plane is a single pixel within thesilhouette in the image plane;©ISAROB 2005

Fig. 2. The idea of back projection and intersection.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

andyij ≡ y ij ( X k ,Yk , Z k ) by direct linear
transformation method [4].
Employing these relations, every voxel in the
XYZ-coordinate system is projected onto thecorresponding single pixel on the image plane. It is
obvious that, given a pixel x ij on an image plane, thevoxels on the line Lij passing through the point x ij
and the lens center of the ith camera are all projected
onto the pixel x ij . So this is normally many–to-one
correspondence.
Let us denote the set of voxels on the above line Lij
byFor all x ij such that x ij is an element ofRi ( x ,t ) , and for all X such that X is an elementof S ij , let Vi ( X ,t ) = 1 .

Then the pairs { x ij , S ij } (i=1,2,…,n; j=1,2,…,m) arestored into a table called a LUT. Here m is the numberof the pixels in the image plane of camera i (i=1,2,…,n).
The elements of the set S ij are calculated in advance,
given the XYZ world coordinate system.
Once a silhouette image I i (t ) is given, those pixels
within the figure region Ri (t ) are found by scan on
the image plane. Since the voxels corresponding to the
pixels in Ri (t ) are known from the LUT, these voxels
are marked 1 in the 3-D space yielding the volumetricregion Vi (t ) . The regions Vi (t ) (i=1,2,…,n) are all
sent to the server to receive the procedure given by
Eq.(2).

4. Experimental Results
We have implemented the proposed technique in asystem, which consists of four PCs and a single PC(CPU: Pentium, 1.7-3.2GHz) employed as clients and aserver, respectively, and a 100Mbps LAN combining
them each other. The clients are connected to respective
digital cameras settled in a room. The cameras arecalibrated in advance employing the DLT method. A
2m×2m×2m space in front of the cameras are digitized
into 80×80×80 voxels in this particular experiment. A
person acts various motions within the cubic space. The
motions recover in a 3-D way by the proposed techniqueand the recovered motion is shown in the PC display.
In this paper, the result of a batting motion is shown.
The experimental environment is shown in Fig.3 and therecovered 3-D motion is given in Fig.4.
The process time for the recovery with each sample
time was approximately 91 msec in average. It includes
the time for the recovery calculation at the clients, thetime for data transfer between the clients and the server,
and the time for the intersection calculation anddisplaying the result at the server. This signifies thatvideo images were processed every three frames. In this

(4)

(5)

S i (t ) = {x | I i ( x, t ) = 1}

(6)

Then the following three cases are applied to create
successive cones. Here the number of the elements inthe set A is denoted by n(A).
Case 0:t=1:©ISAROB 2005)

the difference is smaller than the silhouette itself. The
voxels are deleted from Vi (t − 1) that correspond to
the pixels whose gray value is –1, and the voxels areadded to Vi (t − 1) that correspond to the pixels whosegray value is 1, yielding Vi (t ) . In case 2, thedifference is larger than the silhouette itself and Vi (t )is created directly from a silhouette image I i (t ) .
In this way, the region Vi (t ) is created. Once
Vi (t ) (i=1,2,…,n) are collected at the server, the entire
volumetric region V (t ) is computed and the entire
motion is given as the set {V (t ) : t = 1,2 ,...,T }.

We further define the following two sets of pixels;}dCase 0 is the initial situation, i.e., t=1, and the region

If the human motion concerned is not acted very fast,
the difference of volumetric regions Vi (t ) andVi (t − 1) , and hence the difference of silhouette images
I i (t ) and I i (t − 1) , is not very large. Then creationof Vi (t ) can be done by creating only the different
part between the silhouette images I i (t ) andI i (t − 1) . If the motion is fast, this strategy may not bevery effective and Vi (t ) had better be made directly.
Selection of the strategies depends on the magnitude ofthe difference.
Let us denote Vi (t ) by Vi ( X , t ) and Ri (t ) byRi ( x ,t ) in order to emphasize the voxels and pixels inthem, respectively. Let us also denote a difference imageddby I i (t ) ≡ I i ( x , t ) , i.e.,

{

(

)

Vi (1) is created directly from a silhouette image
I i (1) . There are two strategies for t=2,3,…,T. In case 1,

3.3 Employment of difference images

S id (t ) = x | I id ( x , t ) = ±1dCase 2: n S i (t ) ≥ n(S i (t ) ) for t=2,3,…,T:
For all x ij such that x ij is an element ofRi ( x ,t ) , and for all X such that X is an elementof S ij , let Vi ( X ,t ) = 1 .

S ij = { X k ≡ ( X k , Yk , Z k ) | k = 1,2,..., K ij } . (3)I id ( x , t ) = I i ( x , t ) − I i ( x , t − 1) .

(Case 1: n S i (t ) < n(S i (t ) ) for t=2,3,…,T:
Let Vi ( X ,t ) ≡ Vi ( X ,t − 1) .
d
For all x ij such that I i ( x ij ,t ) = −1 , and for all
X such that X is an element of S ij , let
Vi ( X ,t ) = 0 .
dFor all x ij such that I i ( x ij ,t ) = 1 , and for all
X such that X is an element of S ij , let
Vi ( X ,t ) = 1 .

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order to speed up the computation, (i) distributed
computation architecture was employed in order to build
a volumetric region (a cone) in a parallel way, (ii) listsof correspondence between voxels in the world
coordinate system and pixels in image planes was
prepared for high-speed back projection, and (iii)difference images between successive frames wereeffectively used for less computational load of backprojection. Performance of the proposed technique wasexperimentally shown.
The presented technique has an advantage over thoseexistent techniques based on markers tracking in that it
doesn’t have to use markers. This makes the techniquemuch simpler than such existent techniques, sincemarkers tracking problem is still an issue of difficulty.
Instead, the present technique cannot escape fromcamera calibration like other shape/motion recovery
techniques, except for [2]. Ideally a technique that
employs neither markers nor camera calibration for therecovery is desirable for wide spread of a motion
recovery technique to various users who don’t havemuch related knowledge. Such a technique still remains
unknown.

Fig. 3. Experimental environment.

References[1] R. C. Gonzalez and P. Wintz, Digital Image
Processing, Addison-Wesley, 1987.
[2] J. K. Tan, and S. Ishikawa, “Deformable shaperecovery by factorization based on a spatiotemporalmeasurement matrix”, Computer Vision and Image
Understanding, Vol 82, No. 2, pp. 101-109, May, 2001.
[3] K. Tomiyama, et al., “A dynamic 3D objectgenerating algorithm from multiple viewpoints using thevolume intersection and stereo matching methods”, The
Journal of the Institute of Image Information andTelevision Engineers, Vol 58, No. 6, pp. 797-806, 2004.
(in Japanese)[4] R. Shapiro, “Direct linear transformation for threedimensional cinematography”, Res. Quart., Vol. 49, pp.
197-205, 1978.

Fig. 4. Recovered batting motion.
way, high-speed human motion recovery has beenrealized.

5. Conclusions
A technique was proposed to perform high-speed 3-D
human motion recovery based on back projection ofsilhouette images provided from multiple cameras. In©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Auto-Correlation Probabilistic Relaxation
Matching Method
Xin Wang1 , Masanori Sugisaka2, Jin Wang3
1

Graduate School of Engineering,
The University of Tokushima, Tokushima 770-8506, Japan2

Department of Electrical and Electronic Engineering,
Oita University, Oita 870-1192 Japan

3
College of Textile and Garments,
Hebei University of Science and Technology, Shijiazhuang 050031 China

Abstract

The feature based matching problem can generally be fall into three categories: matching points,
curves, and areas. Point based matching is, fora location in one image, to ånd the displacementthat aligns this location with a matching location in
the other image [5][7][12]; curve-based matching isby analysis of the similarity and compatibility between curves in diãerent images [2][3][4]; area based
method yield a dense disparity map by matchingsmall image patches as whole with respect to geometry, textures [6][8][10][11]. Theoretically, point
correspondences, which matches points with a certain amount of local information, are the robust
way. Traditional point based approaches, however,
have two foundational diéculties when applied tomore general scenes. First, they usually assumed
known camera geometries for stereo matching, so
less point's relation in one image considered. Second, the similarity constrains are seriously required.
We depict the attributes of a target by its signiåcant energy points. Our idea to understand a target
is from its points of this target to its lines, fromits points to its lines and areas. This is related tomanipulate our knowledge database in our further
works. If the proposed auto-correlation approach isused alone, it also gives very satisåed results [14].

The statistics of the neighborhood gradients in animage can yield useful descriptions of target features. This paper presents a new approach, which
is iterative and begins with the detection of all potential correspondence pairs, on auto-correlation relaxation algorithm by using those features to match
image clips. Each pair of matching points is assigned a number representing the probability of its
correspondence. The probabilities are iteratively recomputed to get global optimum sets of pairwise relations. This algorithm could be found wide applications of matching video frames, industrial detection
recognition and so on.

1. Introduction
Image matching is the problem of ånding correspondence points in two or more images of the same
scene, traditionally. Recently, many researches also
show their interest in image stream, which is large
amount of information for 3-D image reconstruction
from 2-D images.
Two image points p and p0 match if they result
from the projection of the same physical point P in
the scene, a property that is often approximated bya similarity constraint requiring, for example, p and
p0 to have similar intensity or color. The desired output of an image correspondence algorithm is a disparity map, specifying the relative displacement ofmatching points between images. The image correspondence problem is inherently under constrained
and further complicated in image stream and also by
the fact that the images typically contain noise. Traditional approaches thus either try to only recover a
subset of matches, or make additional assumptions.

©ISAROB 2005

2. Auto-correlation model
Let I(X)(also denoted as I), X=fxi jxi 2X ; X ö Rn g,
be the image function in an image frame. Given theshift of X as ÅX, the auto-correlation function isdeåned as:
f (X)
XÄ
Å2
I(X) Ä I(X + ÅX)
=
w

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
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where X presents the global position in the working
window w . According to the Taylor expansion, inthe case of Xö R2 , we rewrite the elements in X as
x, y, the item I(X + ÅX) has:I(X + ÅX)= I(X) + Ix(X)Åx + Iy (X)Åy+; ÅÅÅô I(X) + rI(X)(ÅX)t(2)

where Ix =@I(X)=@x, Iy =@I(X)=@y. Substituting
the above approximation (2) into Eq.(1), thenFig.1: An original image.

f (X)
XÄ
Å2
rI(X)(ÅX)t
=

ì
í 2
X
Ix Ix Iy
(ÅX)t
=
(ÅX)
Ix Iy Iy2w†!
X í I 2 Ix Iy ìx= (ÅX)(ÅX)t
Ix Iy Iy2wÄÅ= (ÅX) Q(X) (ÅX)t
w

(3)
(4)

I(X) is smoothed as I(X) by a Gaussian G. Thenwe write Q(X) as rI(rI)t , build up a transform
relation H(X) in a local window about X.
Xàâ
H(X) = T (X)ÉrI(rI)tX ö (Gx ÉI)2 (Gx ÉI)(Gy ÉI)õ= T (X)É(Gx ÉI)(Gy ÉI) (Gy ÉI)2

Fig.2: Distribution of the global correlation about Fig.1.

points xm and yn can be considered potentially corresponding if their distance satisåes the assumptionof maximum velocity,(5)

where G is with standard deviation one, Gx =@G/@x,
Gy =@G/@y. T (X) is a weight mask to weight thederivatives over the window. In (5), I=I(X), there
are relations @I/@x=@/@xÉI, @/@xÉ(GÉI)=(@/@xÉG)*I=@G/@xÉI. H(X) captures the local structure. The eigenvectors of this matrix are the principal curvatures of the auto-correlation function. We
consider a cost function M(X) :M(X) / H1 + H2

(6)

where H1 , H2 are the determinant and trace of
H(X) respectively. We can get some image energy
points, called interest points, by (5) and (6). An
example of M(X) is illustrated in Fig.1 and Fig.2.

(7)where Dmax is the maximum distance a point may
move in the time interval between two consecutive
images. Two correspondences of points xm yn andxk yl are termed consistent ofjcmn Ä ckl j î Ddif(8)

where Ddif is a pixel distance deviation. Consistency of corresponding point pairs will increases theprobability that a correspondence pair is correct.
We Determine the sets of interest points Am ö A0m ö
Ii (x), An ö A0n ö Ij (x), and construct a data structure as follows:[xm ; (cm1 ; Pm1 ); (cm2 ; Pm2 ); . . . ; (N oV É; N oP É)]
where Pmn is the probability of correspondence ofpoints xm and yn , N oV É, and N oP É are special
symbols indicating that no potential correspondence
was found.
(0)
We initialize the probabilities Pmn of correspondence based on local similarity {if two points correspond, their neighborhood should correspond as
well:
í
ì
1
(0)
(0)
1 Ä P(NoV É ;NoP É)
(9)
Pmn =
1 + kp !mn

3. Probabilistic relaxation algorithm
Assume that there be two local windows A0m ö I(X)and A0n ö I(X + ÅX). Let Am =fxm g be the set of
all interest points in the årst starting image that is
input state space, and An = fyn g the interest points
in the second image that is output state space. Let
cmn be a vector connecting points xm and yn (thusyn =xm +cmn ). Let the probability of correspondences of two points xm and yn be Pmn . Two©ISAROB 2005

jxm Ä yn j î Dmax

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

(0)where P(N oV É;NoP É ) is the initialized probability ofno correspondence, kp is a constant and
!mn =

X

[Im (xm Ü Å x) Ä In (yn Ü Å x)]2
Åx(10)

Å x deånes a neighborhood for image match testing{ a neighborhood consists of all points (x + Å x),
where Å x is deåned as a symmetric neighborhood
around x.
We iteratively determine the probability of correspondence of a point xm with all potential points yn
as a weighted sum of probabilities of correspondenceof all consistent pairs xk yl , xk are neighbors of xm
and the consistency of xk yl is evaluated according
to xm , yn . A quality qmn of the correspondence pairis deåned as
X X (sÄ 1)(sÄ 1)
=
Pkl
(11)
qmn
k

Fig.3: Stating clip from image stream.

lwhere s denotes an iteration step, k refers to all
points xk that are neighbors of xm , and l refers to
all points yl ö An that form pairs xk yl consistent
with the pair xm yn .
The probabilities of correspondence are updated
for each point pair xm , yn .
(s)(sÄ 1)(sÄ 1)= Pmn(ka + kb qmn)
P^mn

Fig.4: Local window selected.

this is the reason why Fig.6 gives the larger window in it. The frames should be referred to thesame center. Comparing Fig.5 and Fig.6, we canånd 8 pairwise relations are detected from A0m andA0n . Fig.7 and Fig.8, the images zoomed in, show us
surprising good results.
From Fig.3 to Fig.8, the entire experimental images are in the form of gray. In the case of color
image, color images can bring us more detail image energy distribution and less similarity than grayones. So this method is also recommended to thecolor case.

(12)

where ka and kb are preset constants. They deal
with the convergent speed of Pmn . Normalize
(s)
P^mn
(s)
Pmn
= P (s)
^
j Pmj

(13)

Those interest points that hold high probabilities
that obviously diãer from those interest points without correspondences. Repeat (11) (12) and (13) un(s)til the Pmn >Pthr (threshold) is found for all points
xm , yn .

5. Conclusions
This paper presents a new matching approach without camera calibrations. The matching principle
is based on the probabilistic relaxation under local
image energy constraints. This algorithm can ånd
contributions to image classiåcation [14] and object
tracking [7][15][16]. We also recommend this technique to visual based intelligent navigation.

4. Experiments and discussions
To show availability of the presented approach, experiments are executed by matching image stream.
A starting image is given in Fig.3. To illustrate
the method easily, we have made an 11-points-based
contour model as shown in Fig.3. Details how tomake this contour can be found in [7][15][16], but
beyond scope of this paper. As shown in Fig.4, one
small window is selected in our experiment. Because the experiment is to match image stream, thesecond frame for matching should take into account
this displacement with the respect to time t, and©ISAROB 2005

Acknowledgements
Part of this research was done while Xin Wang wasat the Department of Electronics, Oita Institute ofTechnology.

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B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Fig.5: Matching results of Fig.4.

Fig.7: Fig.5 is zoomed in.

Fig.6: Matching results of the clip following Fig.4.

Fig.8: Fig.6 is zoomed in.

[8] D. A. Forsyth, J. Ponce, Computer Vision, Prentice Hill, USA, 2003.
[9] B. J°
ahne, Image processing for scientiåc applications, CRC Press, New York, 1997.
[10] M. Brown, D. Terzopoulos, Real time computer
vision, Cambridge University Press, 1994.
[11] H. Zhang, S. Roth, it Camera aided robot calibration, CRC Press, Boca Raton, 1996.
[12] L. Torresani, C. Bregler, \Space-time tracking," Computer Vision - ECCV 2002, Part I,
pp. 801-812, 2002.
[13] W. Kropatsch, R. Klette, F. Solina, and R. Albrecht, Theoretical foundations of computer vision, Springer Wien, New York, 1996.
[14] X. Wang, M. Sugisaka, W. Xu, \Recognizing
static target in video frames taken from moving platform" Proc. of the Int. Conference onControl, Automation, and System，Korea，pp.
223-227, 2003.
[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. 373-376, 2004.

References[1] D. Scharstien, \Stereo Matching with NonlinearDiãusion," International Journal of Computer
Vision, vol.28, no.2, pp. 155-174, 1998.
[2] 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.
[3] L. Robert, and O. Faugeras, \Curve-based ster
-eo: Figural continuity and curvature," Proc.
CVPR'91, pp. 57-62, 1991.
[4] A. Kumar, A. Tannenbaum, and G. Balas, \Optical çow: a curve evolution approach," IEEE
Trans. Image Processing, vol.5, no.5, pp. 598610, 1996.
[5] R. Green, D. Gray, and J. Powers, Artiåcial
Vision, Academic Press, London, 1997.
[6] 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.

[16] 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.

[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.

©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Segmentation and Object Recognition for Robot Bin Picking Systems
R. Nagarajan+, Sazali Yaacob*, Paulraj Pandiyan+, Hema C.R.+, Shamsudin Amin++, Marzuki Khalid++, M. Rizon*+

Artificial Intelligence Group
School of Engineering and Information Technology
Universiti Malaysia Sabah, 88999 Kota Kinabalu, Sabah, Malaysia++
Center for Artificial Intelligence and Robotics
Faculty of Electrical Engineering
Universiti Teknologi Malaysia Kuala Lumpur, Malaysia*School of Mechatronic Engineering
Northern Malaysia University College of Engineering
01000 Kangar, Perlis, Malaysia
E-mail: rizon@kukum.edu.my

Abstract
segments belonging to the object border and all internalangles [4]. A regional growth is a technique that starts at
the known pixel points and extends to all neighbouring
pixels that are similar in gray level, colour texture, or otherproperties in order to form a complete region. Using thedifference chain code in contour encoding is another wayof recognizing partially occluded object. An object shapeis identified by the system through the detection of
selected discrete feature segments in the contour code
instead of attempting to search for a complete boundary[5]. Image segmentation can be considered as a clustering
process in which the pixels are classified to specificregions based on their gray-level values and spatialconnectivity.

This paper presents a vision based segmentation andobject recognition system for bin picking applications.
Objects in the bin are usually placed in a disorganizedmanner inside the bin. A method to segment the topmost
object from the bin image is presented using thresholdderived from the histogram of the bin image. Once theobject is segmented its singular value features are
extracted for object recognition. Object recognition isaccomplished using asimple feed forward neuralnetwork. The proposed method is capable of segmenting
occluded object as well as recognizing complex objects.
Keywords: Bin Picking, Object Recognition, Occluded
Object Segmentation, Singular Value Decomposition.

1

We propose a vision based segmentation and object
recognition method for occluded objects using
thresholding, singular value decomposition and Neural
Networks. Section 2 briefly introduces singular value
decomposition. Section 3 describes the methodology ofsegmentation that includes feature extraction and a neuralnetwork architecture for object recognition. The
experimental results and conclusion derived from thiswork are given in section 3 and 4 respectively.

Introduction

In bin picking robot systems, objects to be picked areplaced inside a bin in an unorganized pile. Any visionsystem attached to a bin picking robot must be able toanalyze the image of the bin and be able to locate all theparts seen as well as identify them correctly. Research on
bin picking systems had started in the 80s’ but most bin
picking publications consider only non-occluded objects.
Some researches pick up an object and analyse the object
for pose determination and recognition using database of
the object images, while others rely on sensors other thanthe vision sensors[1],[2],[3]. We present here a vision
based system to isolate and recognise the object to bepicked. If all the parts are separated from each other, thenidentifying the parts is relatively easy. However when theparts are partially occluded more than one object will bemerged into a single blob making it difficult to identify
each one of them.

2

The Singular Value Decomposition [SVD] is a widelyused technique to decompose a matrix into several
component matrices, exposing many of the useful andinteresting properties of the original matrix [6]. Any ‘m x
n’ matrix a (m >= n) can be written as the product of a ‘m
x m’ column-orthogonal matrix u, an ‘m x n’ diagonal
matrix w with positive or zero elements, and the transposeof an ‘n x n’ orthogonal matrix v [7]:The image segmentation that divides the image intomeaningful sub regions is an indispensable step in image
analysis. One approach of solving the problem ofidentifying objects is by detecting and measuring all©ISAROB 2005

Singular Value Decompositiona = u w vt

735

… (1)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

3.1

where
⎡ w1
⎢
⎢
w = ⎢ .
⎢
⎢ 0
⎢⎣ 0

0
w1

.
.

0
0

.
0

.
.

.
w n −1

0

.

0

0 ⎤
0 ⎥⎥
. ⎥
⎥
0 ⎥
w n ⎥⎦

Bin image segmentation involves identifying the top mostobject from the cluster of objects in the bin for picking up.
Since all the objects are partially occluded except the
topmost object, separating the topmost object can be doneusing the grey value of the object. A histogram of the bin
images displays the grey levels of the image. Pixels ofgrey levels higher than a threshold value relate to the
topmost object. The threshold value is determined bytaking log of the magnitudes using equation (4) for 40
maximum values.

(2)

and
u t u = vv t = I

Bin Image Segmentation

(3)

where

w1 , w 2 ,..., w n − 1 w n ≥ 0 ,T = [log10 (COUNTS a)] iand ‘t’ is the transpose.

where

The diagonal elements of matrix ‘w’ are the singularvalues of matrix ‘a’ which are non-negative numbers.
The singular values obtained by the SVD of an image
matrix is an algebraic feature of an image which
represents the intrinsic attributes of an image[6].
3‘T’ is the threshold‘i’ is the Gray scale value‘COUNTS a’ is the Gray value countMethodology

Images of the occluded objects in the bin are captured
using a Pulnix TM 6702 CCD machine vision camera.
Direct bin lighting of the bin is avoided to reducebrightness and albedo effects. The captured images are of
size 640 x 480 pixels and are monochromatic. To
minimise the processing time and to improve theefficiency of the system without significant loss of
information of the objects, the images are resized to 128 x
96 pixels. The resized images have to be further processedusing filtering techniques to identify the borders of theobjects and to smooth the intensity of the object region.
First the image is filtered using a regional filter and a mask.
This masked filter filters the data in the image with the
2-D linear Gaussian filter and a mask the same size as theoriginal image for filtering. This filter returns an image
that consists of filtered values for pixels in locations where
the mask contains 1's, and unfiltered values for pixels in
locations where the mask contains 0's. The above processsmoothens the intensity of the image around the objects.
The resultant image is passed through a median filter
which defines the object edges. The median filter
performs median filtering of the image matrix in two
dimensions. Each output pixel contains the median value
in the M-by-N neighbourhood around the corresponding
pixel in the input image. The filter pads the image withzeros on the edges, so that the median values for the points
within [M N]/2 of the edges may appear distorted. The
M-by- N is chosen according to the dimensions of theobject. In our experiment, a 4 x 4 matrix was chosen. The
resulting filtered image is then subjected to segmenting
techniques as detailed in the following section.

The maximum value is chosen experimentally to getsatisfactory results for all images. The grey images areconverted to binary images by applying the thresholdderived from the histogram using equation (4). The Figure
1 shows the bin images before and after segmentation.

(a) Bin images(a)(b) Segmented images showing the top most object

Figure 1 Bin Image Segmentation

3.2©ISAROB 2005

(4)

736

Object Feature Extraction

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

used to implement the weight updating.
The topmost object extracted from the segmentation
process varies in orientation and pose as the objects in thebin are not placed in an organised pile. It now becomes
essential to recognise the object irrespective of its
orientation. To recognise the object, features of the object
in various poses and orientation are extracted to train aneural network to recognise the object. In the feature
extraction process the edge images of the segmented
object is extracted using a canny edge detector. The edge
image matrix is decomposed using singular value
decomposition to extract the singular value. Edge features
minimise the computational time and pocess optimum
information of the object for recognition. Figure 2 showsthe edge images of object with different orientation andpose.

Object 1
Singular
Values.
.
.

NeuralNetwork

Object 2

Figure 3 A Feed Forward Neural Networks for Object
Recognition

w1 j (new) =w1 j (old), j=1,2,3,…,p (5)w12j + w22 j + ... + wnj2

where n - number of input units

Figure 2 Edge images of object

p - number of hidden units
The singular value features extracted from the edge
images are the object features; singular values below 1
are ignored since they h are sometimes due to noise in theimage and do not relate to the object under consideration.
Twenty five such significant features of the object is fed toa neural network to train the network for object
recognition.

3.3

A sum squared error criteria as defined by equation (6) isused as a stopping criteria while training the network. The
sum squared tolerance defined in equation (6) is fixed as
0.01. The network is trained by the conventional backpropagation procedure. The cumulative error versus epoch
plot of the trained neural network is shown in Figure 4.
The cumulative error is the sum squared error for eachepoch and is given by:-

Neural Network Architecture

The Neural Network architecture shown in Figure 3
consists of three layers. 25 singular values are fed to the
network as input data. The hidden layer is chosen to have
13 neurons and the output consists of 2 neurons which
represent the two objects to be recognised. The hidden andinput neurons have a bias value of 1.0 and are activated bybinary sigmoid activation function. The choice of initial
weight will influence whether the network reaches a
global minimum of the error and if so how quickly it
converges. It is important that the initial weights must not
be large in order to avoid the initial input signals to eachhidden or output unit falling in the saturation region. On
the other hand, if the initial weights are too small, the net
input to a hidden or output unit will be close to zero which
also causes extremely slow learning [8]. Hence the initial
weights for the above Network are
randomized
between-0.5 and 0.5 and normalized. The initial weights that areconnected to any of the hidden or output neuron arenormalized in such a way that the sum of the squared
weight values connected to a neuron is one. This
normalization is carried out using equation (5) which is©ISAROB 2005p

Sum squared error =m∑∑ (t − y )p =1 k =1

k

2

k

where

t k is the expected output value for the kth neuron,y k is the actual output value for the kth neuron,m is the total number of output neurons, andp

737

is the total number of input neurons.

(6)

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4

Experimental Results

4 Conclusion
A vision based segmentation and object recognition
method is presented. The method is experimentally
verified and results presented are satisfactory. The
proposed method however was found to be ineffective forobject images with albedo effects and over brightness.
Optimal lighting is essential to derive satisfactory results.
The proposed method has successfully segmented
partially occluded objects. Methods to improve thesegmentation results are under study.

In the experimental study two sets of bin images are
acquired for two different objects. Only similar objects
are placed in the bin at a time. The neural network is
trained with 60 percent of the data and tested with 116
image data. Table I lists the training parameters of the
Neural Network. The proposed method is found tosuccessfully classify
93 % of the tested images. Figure
4 shows the Cumulative error versus epoch plot of theneural network. At each iteration, an objective function
is minimized to find the best location for the clusters.

Acknowledgement
The authors thankfully acknowledge the funding [Code.
No. 04-02-10-0024-PR001] received from IRPA throughUTM and UMS.
References[1]Ayako Takenouchi, Naoyoshi Kanamaru and Makoto
Mizukawa, Hough-space-based Object Recognition
Tightly Coupled with path planning for Robust andfast Bin-picking, Proceedings of International
Conference on Intelligent Robots and Systems,
Canada, October 1998, pp 1222 – 1229.
[2]Kohtaro Ohba, Katsushi Ikeuchi, Recognition of theMulti Specularity Objects for Bin-picking Task,
Proceedings IROS, IEEE October 1996 pp 1440 –1447.
[3]Yoshimasa YANAGIHARA, Toshiro KITA, Parts
Picking in Disordered Environment, IEEE
International Workshop on Intelligent Robots andSystems 1991, Osaka , Japan pp 517-522.
[4] Paulo Lopes, Eugenio Oliveira. Identification ofPartially Occluded Objects Using Object Chain Code.
Proceedings of IEEE/RSJ International Conferenceon Intelligent Robots and Systems Yokohama, Japan
1829-1834 July 1993
[5]Peter W.M. Tsang, P.C.Yuen. Recognition ofPartially Occluded Objects. IEEE Transactions on
Systems, Man and Cybernetics Vol. 23, No.1 January
February 1993[6]Zi-Quan Hong, Algebraic Feature Extraction of
Image for Recognition IEEE Transactions on Pattern
Recognition Vol. 24 No. 3 pp 211-219, 1991.
[7] David S. Watkins, Fundamentals of Matrix
Computations. John Wiley & Sons 2001.
[8]S.N. Sivanandam, M.Paulraj, Introduction to
Artificial Neural Networks Vikas Publishing House,
India. 2003
[9] Laurene Fausett, Fundamentals of Neural Network
Architecture and Applications, Prentice Hall, USA.
1990

Figure 4 Cumulative error versus Epoch Plot

Table I – Training Parameters of NN

Parameters

ValuesNo. of input neurons

25

No. of Output neurons

2

No. of hidden neurons

13

Bias value

1.0

Tolerance

0.01

Percentage Classification

93%

Maximum Epoch9331©ISAROB 2005

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

Stereo Camera based Artificial Vision for Blind through Hearing
G.Balakrishnan+ G.Sainarayanan+
R. Nagarajan+
Sazali Yaacob*
M. Rizon*+
AI Research Group, School of Engineering and Information Technology
Universiti Malaysia Sabah, Locked Bag No. 2073, 88999, Malaysia*School of Mechatronic Engineering
Northern Malaysia University College of Engineering
01000 Kangar, Perlis, Malaysia
E-mail: rizon@kukum.edu.my
obstacle in the path was time consuming and similar tohaving a long cane. But in recent years, due toadvancement in the development of high-speed
computers and sensory devices, attempts to design
sophisticated equipment for the vision substitution are in
progress.

Abstract
In this paper, a design of real time methodology forobject identification and image sonification applied tovision substitution for the blind is presented. The
proposed methodology includes intelligent image
processing, stereopsis and image sonification modules.
The hardware system consists of stereo digital video
cameras as vision sensors, stereo headphone and a laptop
computer. The images captured from stereo cameras areprocessed to obtain isolated object image. The stereopsis
module performs area based stereo correspondence over
the object image and calculates the disparity. The object
is given a preference based on its distance and position
using fuzzy rule base for collision free navigation byblind user. Finally the information is conveyed to theblind through stereo sound in the form of musical tones.
In the proposed algorithm distance information of theobject is determined with less computation compared toconventional area based stereo correspondence method.
Experiments were conducted in an indoor environment,
and the results obtained are promising.

The concept of using video camera as vision sensor
had been introduced in Peter Meijer’s portable system,
the vOICe [4]. The image captured is scanned from left
to right direction for sonification or sound generation.
The top portion of the image is converted into highfrequency tones and the bottom portion into low
frequency tones. The loudness of sound depends on thebrightness of the pixel. In Peter Meijer’s work,
image-processing efforts to enhance the object properties
are not undertaken. The sound produced from theunprocessed image will contain more information of the
background rather than object. This may be the reason
for the blind user having difficulties to distinguish theobject and the background through sound fromearphones.

Keywords:

Navigational Assistance for Visually Impaired(NAVI) [5] makes use of single camera and the captured
image is resized to 32 X 32 and the gray scale of theimage is reduced to 4 levels. The image is differentiatedinto objects and background. The objects are assigned
high intensity and the background is suppressed. Herethe processed image is converted into stereo soundwhere the amplitude of the sound is directly proportional
to intensity of image pixels, and the frequency of soundis inversely proportional to vertical orientation of pixels.
Both in vOICe and NAVI the distance between the user
and the obstacle cannot be obtained directly by the users.
The distance is one of the important aspects for collision
free navigation for blinds. In order to incorporate thedistance information, stereo cameras are used in thiswork. The manner in which human beings use their twoeyes to see and perceive the three-dimensional world has
inspired the use of two cameras to model the world inthree dimensions.

Vision substitution, Fuzzy inference system, Stereopsis,
Blind navigation

1

Introduction

Human navigation consists of two distinct
components: sensing the immediate environment for
impediments to travel (e.g., obstacles and hazards) andnavigating to remote destinations beyond theimmediately perceptible environment. Informations
about way finding are passed to humans through themost sophisticated sensory system, the vision system.
The visually impaired are at a considerable disadvantage,as they often lack the needed information for bypassing
obstacles and hazards. According to World HealthOrganization census, around 180 million people
worldwide are visually disabled, of those 40 to 45
million are totally blind [7]. This population is expected
to double by the year 2020.

2

During the past three decades, several researchers
have introduced devices called Electronic Travel Aids
(ETAs) [6], that use sensor technology to assist andimprove the blind users mobility in terms of safety andspeed. In early type of ETAs, the users had to scan theenvironment constantly and continuously. Locating an©ISAROB 2005

Experimental Setup

A prototype system is designed for this work. The
hardware model constructed for this vision substitution
system has a sunglass fitted with two mini video cameras,
stereo earphone and a laptop computer. The two cameras
are displaced from each other by a distance of 5 cm. Both

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

detector is an optimum edge detector [2]. Figure 3 showsthe edge features of the right camera image extractedusing canny edge detector. Similar processing isundertaken for left camera image also.

the cameras are adjusted to same focus byexperimentations. Figure 1 shows the experimental setup
used in this work. When the sunglass is worned by theblind user, the stereo cameras capture the scene infront of
the user.

1
2
3

Figure 3: Edge Image of Right Stereo Image
In this work a region enclosed within edges is
considered as an object. It is realized that edge detection
alone is not enough to extract the closed boundary of an
object from the image. Therefore further processing isundertaken to connect the edges to form a meaningful
object. Edge linking process is required to assemble
these edges into meaningful edges. Morphologicaloperation using dilation [1] is proposed to link thebroken edges. The region within the closed boundary is
considered as an object. The intensity of pixels in theregion is enhanced to higher level using flood fill
operation. Each object in the image is labeled. It is foundthat some of the extraneous edges still exist after flood
fill operation. Hence noise removal operation is
performed to remove extraneous edges present in theimage without eliminating the desired objects. Erosionand dilation operations are undertaken to smooth theobjects [1]. Thus image with only objects will beobtained. Figure 4 shows the image obtained after flood
fill and noise removal method.

(a)
1 – Laptop Computer
2 – Sunglass fitted with stereo camera
3 – Stereo headphone
Figure 1: Prototype system

3

Image Processing Methodology

In this paper, objects or obstacles are identified andare assigned preference based on their position anddistance. The first step in this methodology is the image
acquisition, in which scene in front of blind is captured
using both cameras simultaneously. The image captured
from camera is a color image of size 352 x 288.
Processing the image with original size will increase
computation time. This work involves real timecomputation. So the computation time is critical and has
to be minimized. Therefore pre-processing is undertaken
to reduce the computation time, where both the left andright stereo images are converted to gray scale intensity
image and resized to 64 x 64. Figure 2 shows the
preprocessed left and right image. The main task
involved here is to identify and assign preference to theobjects. In this work, the objects in both the left and rightcamera images are identified by locating its edges.

Figure 4: Right Image with only objects

3.2

(a)

Binary images of both left and right camera image
with only objects are derived from the previous section.
This binary image is mapped with the resized gray scaleintensity image and new gray scale intensity image with
only objects is derived. These processes are preformed
for both left and right camera images to obtain two grayscale images with only objects. Figure 5 shows the left
and right images with only objects.

(b)

Figure 2: Resized Gray Scale Intensity Image(a) left image (b) right image

3.1

Image Post Processing

Edge detection is one of the important human vision
properties as it has the ability to recognize the object
boundaries. The goal of edge extraction is to provide
useful structural information about object boundaries.
Canny edge detector is used for edge detection because
this method uses two thresholds to detect strong andweak edges, and includes the weak edges in the output
only if they are connected to strong edges. Canny edge

©ISAROB 2005

Image Object Isolation

Figure 5: Grayscale Image with Only Objects of Left andRight Camera Images

740

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

4

centroid have to be determined. Based on thesecharacteristics, the ratio of the object area lying within
the central region can be calculated. But with these
inputs, devising an algorithm to compute object
preference is very difficult. In order to overcome thisuncertainty fuzzy logic is applied. Four main
characteristics are measured for object preference
assignment. They are size of an object, Euclidean
distance between the object centroid and image centroid,
ratio of objects lying within the center of an image andthe distance through disparity of an object [1]. These
characteristics are applied as input to fuzzy logic
algorithm. Each characteristic is expressed using threemembership functions namely low, medium and high.
Membership functions are expressed using a trapezoidal
curve. The output is object preference, which has four
trapezoid curve memberships. The defuzzification is
performed using centroid method. 81 rules are formed
based on four inputs.

Stereo Vision Methodology

After obtaining the isolated object image, thedisparity has to be determined in order to calculate thedistance between the blind user and the obstacle. The
concept of stereopsis is employed here. Given two
camera images, if it is possible to identify the image
locations that correspond to the same physical point inspace from both the camera images, then it is possible todetermine its three-dimensional location. In order tocalculate the disparity correspondence or matching of
two images has to be done. In this work, area based
correspondence method is applied over two isolated
object images. To decrease the total computation time,the maximum disparity is bounded to a certain range andthus avoids examining an entire row for each pixel. Since
only the isolated object images are used forcorrespondence, the mismatch error is less and also thecomputation time is reduced when compared toconventional area based and feature based techniques.

From the fuzzy output the objects are given different
intensity values based on their preference. The object
with very high preference is symbolized by white
intensity and the object with least preference issymbolized with very dark gray intensity. The object
preference based on fuzzy rule based system is shown in
Figure 7.

From the disparity map obtained, it is observed thatin some areas, the disparity value within the object varies
due to some mismatch. For uniform disparity value in an
object, histogram is used. In a histogram, disparity valuethat occurs most within the object is found and thatparticular value is assigned to all pixels within the object
Figure 6 shows the disparity map obtained after
assigning uniform value.

Figure 7: Object Preference Based on Fuzzy Output
The fuzzy output image is then converted to stereo soundusing the following sonification methodology.

Figure 6: Disparity Map

5

6

Object Preference

The effective audible frequency range extends fromabout 20 Hz to around ten thousand hertz, although it
depends entirely on the individual. The audible range isdivided into octaves. An octave is really a frequency
range from a frequency f1 to f2 such that f2 is twice thatof f1 in terms of cycles or hertz. The human ear islogarithmic and is sensitive to frequency octaves. The
audible frequency is then comprised of many octaves.
The f1 can be any number but f2 is double the f1. Even afrequency range from 20 Hz to 40 Hz is defined as anoctave [3].
In most of the western musical instruments thefrequencies are arranged in such a manner that they arein a geometric series. That is, the frequency deviation
between any key and the key immediately to its left is aconstant, the constant being equal to the twelfth root of
two or 1.059. Even though there is a degree of freedom
for selecting the range of an octave (whether it is from
240 to 480 Hz or 254 to 508 Hz etc.), the western music
defines a standard octave called the Middle A octave

A Real time environment contains more than one
object. If all objects are given same preference, then theblind user finds it difficult to identify those objects,
which are obstacles in their path for navigation. So eachobject has to be assigned some priority for collision freenavigation. In this work objects are given preference
based on two important conditions. One is the distance ofthe object from the user and another one is the position of
the object. In human vision system, the eyes mostly
concentrate on a particular object rather than the
background, which gets less focus. The object of interest
will be usually at the center of the sight. In this paper, thecenter of the image is considered as the center of sight.
Any object that is located in the central region with highdisparity will be considered for high preference andobjects located outside the central region with low
disparity will be less preferred.
In order to check whether the object is located in thecentral region, object characteristics such as size and©ISAROB 2005

Image Sonification

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

starting from 440 Hz.
With the help of the above concept, musical tones
can be incorporated for image sonification. By
experimentation it is found that octave frequency of 440
Hz to 880 Hz produce pleasing music. In the developed
method, this octave frequency of 440 Hz to 880 Hz is
selected. With this octave, 12 musical notes aredeveloped. Let f (1,..,12) be the 12 octave frequencies.
The music pattern generated is given by
M ( j ) = sin (2πf ( j )t )j=1,2,…,12
(1)
where
M(j) is the musical note generated for f(j) frequency and‘t’ varies from zero to desired total duration of theacoustic information.
Different musical tones are generated with the
combination of these notes. In this work three notes are
combined to form musical tones. Four half steps between
first and second note and three half steps between second
and third note define major chords. Here eight tones
including some major chords are generated using thesenotes. The image to be sonified is resized to 32 X 32 for
reducing the computation time. Musical tones are
assigned in such a way that high frequency tones
occupies the top portion of the image and low frequency
tones are assigned to lower portion of the image. So eachpixel in an image is assigned with samples of musicaltones based on their position in the image.
The conversion of image into sound involves taking
one column at a time starting from left most one andgenerating sound pattern for that column. The soundpattern generated is given by∑ I (i, j)M (i, j)

Wearable Computer
HelmetStereo Camera
Stereo Headphone

Figure 8: Future Wearable Prototype

7

Conclusions

Distance information is one of the most importantcriteria for blind navigation. In this work stereo cameras
are used to find the distance information and theinformation is conveyed to the blind user throughmusical tones. Experiments were conducted in an indoor
environment, and the results obtained are promising. The
computation time of this method is also fast compared toconventional area based methods. There are some errors
due to stereo mismatching. In the near future, the work
will be continued towards achieving higher accuracy,
minimizing the hardwares and testing the system withthe blind people.
Acknowledgment: Authors wish to thank Ministry of
Science, Technology and Innovation, Malaysia forfunding the research through Universiti Malaysia Sabah
under IRPA code: 03-02-10-0043/EA0041.

Reference32

S(i) =

i = 1,2,..,32(2)[1] Balakrishnan G, Sainarayanan G, Nagarajan R,
Yaacob S, “Blind Navigation Using Stereopsis With
Fuzzy Rule Base”, International Conference on
Artificial Intelligence in Engineering andTechnology, Malaysia, pp. 232-237, 2004.

j =1

where S(i) is the sound pattern for column i of the image
I(i,j) is the intensity value of (i,j)th element, M(i,j) is the
samples of musical tone for (i,j)th pixel.
The sound pattern from each column is appended toconstruct the sound for the entire image. The scanning of
picture is performed in such a way that stereo sound is
produced. In this stereo type scanning, the sound patterns
created from the left half side of the image is given to left
earphone and sound patterns of right half side to rightearphone simultaneously. The scanning is performed
from leftmost column towards the centre and from rightmost column towards the centre.
Since the sound produced are differentiated into left
and part, objects that lie to the left of the user willproduce sound only in the left earphone and the object
that lies to the right will be heard only in the rightearphone. Different tones are produced for different
shapes. Hence the sound pattern generated by thissonification method is able to differentiate objects basedon its position, shape and distance. The most advantageof this method is that since musical tones are used, thesound generated will be pleasing to the user andcontinuous use will not fashion loss of interest. Prototype
has been designed to minimize the hardware and using
the system in outdoor environment also. Figure 8 shows
the design of future prototype.

©ISAROB 2005

[2] Canny J, “A Computational Approach to Edge
Detection”, IEEE Trans. Pattern Analysis andMachine Intelligence, Vol. 6, pp. 721-741, 1984.
[3] Elliott R, “Fundamentals of music”, New Jersey:
Prentice-Hall.
[4] Meijer P, “An Experimental System for Auditory
Image Representations”, IEEE Transactions on
Biomedical Engineering, Vol 39, pp 112-121, 1991.
[5] Sainarayanan G, “On Intelligent Image Processing
Methodologies Applied to Navigation Assistance for
Visually Impaired”, Ph. D. Thesis, University
Malaysia Sabah, 2002.
[6] Wong F, Nagarajan R, Yaacob S, Chekima A,“Electronic travel aids for visually impaired – A
guided tour”, Proceedings of Conference inEngineering in Sarawak, Malaysia, pp 377-382,
2000.
[7] World Health Organization, Internet site address:
http://www.who.int//mipfiles/2400/AllenFoster.pdf

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The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

AUTHORS INDEX
Notation of session name:
PT: Plenary Talk; SS: Special Session; GS: General Session; IS: Invited Session or Organized Session

A

B

C

D

K. Aihara

PT-1

J.S. Heo

IS8-3

S. AlSehaim

GS14-4

T. Hioki

GS2-4

G. Balakrishnan

GS25-7

M. Hirasawa

IS2-1

J.C. Balasuriya

IS7-3

Y. Hirata

IS5-3

T. Bando

GS15-5

S. Hirose

GS10-4

Z. Bunicki

GS1-1

W-S Hong

GS11-2

G.. Capi

GS1-3

K. Imamura

GS22-3S-P Cheon

GS21-4

H. Inooka

GS8-3

B-S. Choi

GS25-1

H.Inoue

GS16-2

H-S.Choi

IS8-4

S. Ishii

GS21-5

T-Y Choi

IS8-1

H. Isu

GS18-3

R-S. Dong

IS12-2J. ItoGS23-4

S. Ito

IS3-3

I

E
F

G

H

X. FanGS10-5Y. Y. Fan

GS14-1

R. FreundGS17-2

H. Fujii

IS11-2

T. Fukuda

IS3-4
K. Izumi

IS7-4
IS7-5

B.G. Jang

IS8-5

GS13-1

T-S. Jin

GS3-1

M. Goan

IS10-2

J.H. Johnson

GS10-3

G. Gomez

GS5-1

I. Juarez-Campos

GS2-5

R. GotoGS12-1

D-Y. Jung

IS9-1

T. GotoGS1-5

H. Kadota

GS19-2

H. Harada

GS8-4

K. Kamimura

IS2-4

H. Harada

GS4-3

D. Katagami

IS11-1

M. Harada

IS6-2

T. KatoGS1-6

T. Hashimoto

GS3-5

J. Katsuki

GS21-1

GS1-7

T. Kawahara

IS6-5

T. HashizumeGS22-5

T. Kawauchi

GS14-3

Y. HashizumeGS2-3

J-S. Kim

IS9-4

E. Hayashi

GS4-1

Y-T Kim

GS11-5

J

K

IS9-2

GS22-2
H. Kinjo©ISAROB 2005

743

IS1-1

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

L

M

N

Y. Kinouchi

GS6-3

M. Nakanishi

GS7-3

Y. Kita

GS17-3

K.Nakano

GS16-1

T. Koga

GS18-1

K. Nakayama

GS12-2

N. Kogawa

GS5-3

K. Nakazono

IS1-3

T. Kohno

IS5-2

S. Namba

GS13-2

M. Konno

GS3-3

T. Nawata

GS13-3

A.I. Kovacs

GS24-4T-M-H Nguyen

GS7-4

T. KubikGS17-1

K. Nishikawa

GS6-4

GS20-3

S. Nishio

IS6-1

M. Kubo

IS12-1

S. I. Nishimura

GS17-4

M-D LeGS15-2

T. Nishizono

GS12-3

H.C. Lee

GS19-4

W. Nistico

GS8-2

M. LeeIS10-4

Y. Nojima

GS18-5

L-W Lee

GS11-3

S. Omatu

IS3-1

M.K. Lee

GS11-6

W-S. Lee

IS9-5

M. Obayashi

GS23-3

Y. LeeGS4-2

K. Ohnishi

GS7-2

J. Li

GS24-1

M. Onodera

GS9-4

Z. Li

GS18-4

N. Oshiro

IS1-4

H. H. Lund

Special Session

Y-H Otake

IS5-4

K. Makino

GS9-2

C. Pathirana

IS7-1

N. Masuda

GS22-4

H. Masugami

IS6-3

O

IS3-2

P

IS7-2
I. Pejman

GS15-6

M.M. Rahman

GS25-2

V. RoseGS21-6

S. Sagara

GS23-1

IS11-3

M. Saito

IS2-3

S. Matsuzaki

GS3-4

P. Sapaty

GS2-1

T. Mizoi

IS6-4

S. Satou

GS15-1

M. Motoki

GS20-4

K. Shimada

GS1-2

E. Muhando

IS1-2

H-B. Shin

IS9-3

H. Nagai

IS11-4

H-S. Song

GS24-5

R. Nagarajan

GS25-6

A. Stefanski

GS24-2

Y. NakamaIS2-2

K. Sugawara

GS4-5

Y.Nakamichi

GS4-4

M. Sugisaka

GS11-1

A. Matos

GS21-3Q

K. Matsumoto

GS6-1

R

T. Matsumoto

GS15-3N. Matsumoto

IS10-1©ISAROB 2005

S

744

The Tenth International Symposium on Artificial Life and Robotics 2005(AROB 10th ’05),
B-con Plaza, Beppu, Oita, Japan, February 4-6, 2005

TU

GS11-4H-U Yoon

IS8-2

GS15-4S-J. Yoon

IS12-3

Y. Sugita

GS20-1

T. Yoshida

GS12-4

S. Sunahara

GS19-3

S. Yoshimura

GS21-2

R. Suzuki

GS13-4Y. G. Zhang

Special Session

S.V. Suzuki

IS10-3

Y.X. Zhong

GS7-1

Y. Suzuki

GS10-1

H. ZhaoGS23-2

M. Tabuse

IS2-5

J.K. Tan

GS25-3

T. Takemoto

IS5-1

S. Tensho

GS16-3

K. Tokuda

GS18-2

M. Tomimasu

GS1-4

Y. Tominari

GS8-1

K. Tomizawa

GS3-2

T. Torigoe

GS20-2

Y. Toyozawa

GS24-6

Y. Tsuboi

GS9-1

M. Uchinoumi

GS25-4

H.UedaGS5-2

T. Umeno

GS22-1

H. UmeoGS6-2

K. Umezaki

GS2-2

K. Uosaki

GS9-3

J. Wang

GS19-1

S. Wang

GS10-2

X. Wang

GS25-5

T. YamadaGS20-5

H. Yamamoto

GS14-2

H. Yanagimoto

IS4-1

Z

V
W

X
Y

IS4-2
T. Yokoi

IS4-3
IS4-4

M.Yokota©ISAROB 2005

GS24-3745