PROCEEDINGS OF
THE EIGHTEENTH
INTERNATIONAL SYMPOSIUM ON
ARTIFICIAL LIFE AND ROBOTICS(AROB 18th ’13)

Jan. 30-Feb. 1, 2013
Daejeon Convention Center, Daejeon, KOREA

Publication Date: January 30, 2013
ISBN 978-4-9902880-7-5

Proceedings of the Eighteenth International Symposium on

ARTIFICIAL LIFE AND ROBOTICS(AROB 18th ’13)

January 30 - February 1, 2013
Daejeon Convention Center, Daejeon, Korea

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Artificial Life and Robotics
We invite you to publish your paperin this journal full of inventive scientific findings and state-of-the art technologies.
Two types of papers from the world areI.

Contributed papers

II.

Recommended papers presented at the International Symposium on Artificial Life and Robotics(ISAROB),

Contribution fee:
4 pages or Under￥19,950 / page (¥19,000 + 5 % consumption tax: only in Japan)

Extra page charge￥16,800 / page (¥16,000 + 5% consumption tax: only in Japan)(Pages are counted on double-columns basis as they appear in the journal.)
Shipping Cost:
domestic￥735 / delivery (¥700 + 5 % consumption tax)

overseas￥3,000 / delivery

We deliver every corresponding author (or sponsor) one copy of journal in which his/her paper is included. We
would like to thank authors for their support in the form of contribution fee payable before publication, whichfunds the production of this journal contracted by Springer Japan.
Order:
I. Price for co-authors¥ 5,250 (¥5,000 + 5 % consumption tax: only in Japan) per copy (for every co-author)
If necessary, please order extra copies for co-authors to the Journal Office.
II. Subscription fee
domestic

overseas

Annual subscription rate

¥21,000*/year¥20,000/year

Shipping Costs

free¥2,000/year*including consumption tax

III. Purchase price for single issue¥ 8,400 (¥8,000 + 5 % consumption tax: only in Japan) per issue.
AROB Journal Office : journal@isarob.org© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Artificial Life and Robotics
Our Review System
For contributed papers,
1. Duration:
Two months is our target time of the whole review process, however, we are trying our best to make it asshort as possible.
2. Review results:
Every contributed paper will be reviewed by three assigned reviewers with score ranging from 1 (highestmark) to 4 (lowest mark);
1. publish with no revision,
2. publish with minor revisions,
3. possibly publish with revision and re-review,
4. reject.
Review results, a total score of the three evaluation scores, would be read as;.
3 or 4: accepted and to be published as it is,4, 5, or 6

: accepted and to be published after minor revisions,7, 8, or 9

: acceptable but subject to rewriting and re-review,

10, 11, or 12: rejected

For recommended papers,
The recommended papers are ones that the chair of each session and reviewers read before the symposium thatyear and recommend to AROB Journal Office using the same evaluation score above. Therefore, the quality ofrecommended papers is apparent at the time of recommendation.
We express our heartfelt gratitude to chairs and reviewers of ISAROB for theirsupport and sincere voluntary work without which this journal never exists.

AROB Journal Office : journal@isarob.org© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Artificial Life and Robotics
We invite you to advertise your company
in this international journal financed by ISAROB (International Symposium on Artificial Life and Robotics )and produced by a renowned publisher, Springer Japan.
Merits:・Connectivity with ambitious academic circles in the rapidly-advancing fields,
・Expanded publicity through toll electronic circulation by Springer-Japan,
・Reasonable advertising fee
1 page = ¥ 90,000, 2 pages = ¥ 150,000(Fee+ 5 % consumption tax: only in Japan)
For further details please contact

AROB Journal Office
AROB Secretariat
International Society on Artificial Life and Robotics
A-101, 8-7 Hatakenaka, Oita, 870-0856, Japan
Tel: +81-97-594-0181
Fax: +81-97-547-9242
Email: journal@isarob.org
Home Page：http://isarob.org/© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

THE EIGHTEENTH INTERNATIONAL SYMPOSIUM
ONARTIFICIAL LIFE AND ROBOTICS(AROB 18th '13)
ORGANIZED BYInternational Organizing Committee of International Symposium on Artificial Life and Robotics (AROB)

CO-OPERATED BY
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)
The Institute of Systems, Control and Information Engineers (ISCIE)The Institute of Electronics, Information and Communication Engineers (IEICE)IEEE Japan Council (IEEE Robotics and Automation Society Japan Chapter)
Japan Robot Association (JARA)
Institute of Control, Robotics and Systems (ICROS, Korea)
Chinese Association for Artificial Intelligence (CAAI, China)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

GENERAL CHAIR
Ju-Jang Lee (KAIST, Korea)
Masanori Sugisaka (Alife Robotics Co., Ltd., Japan)

PROGRAM CHAIR
Hiroshi Tanaka (Tokyo Medical & Dental University, Japan)

VICE CHAIRJohn L. Casti (International Institute for Applied Systems Analysis, Austria)
Yingmin Jia (Beihang University, China)
Henrik Hautop Lund (Technical University of Denmark, Denmark)Hiroshi Tanaka (Tokyo Medical & Dental University, Japan)
Yong-Guang Zhang (Academia Sinica, China)

ADVISORY COMMITTEE CHAIR
F. Harashima (Tokyo Metropolitan University, Japan)

ASSISTANT GENERAL CHAIRMAN
Takao Ito (Ube National College of Technology, Japan)

ADVISORY COMMITTEE
Bruce Eisenstein (Drexel University, Former IEEE President, USA)
Robert Fischl (Drexel University, USA)
Toshio Fukuda (Nagoya University, Japan)
Fumio Harashima (Tokyo Metropolitan University, Japan)
Paul Kalata (Drexel University, USA)
Moshe Kam (Drexel University, IEEE President, USA)
Hidenori Kimura (RIKEN, Japan)
Paul Oh (Drexel University, USA)
Masayoshi Tomizuka (University of California Berkeley, USA)

INTERNATIONAL ORGANIZING COMMITTEE
Hussein Abbass (University of New South Wales, Australia)
Kazuyuki Aihara (The University of Tokyo, Japan)
Takaya Arita (Nagoya University, Japan)
Christopher L. Barrett (Virginia Polytechnic Institute and State University, USA)
Mark A. Bedau (Reed College, USA)
Malachy Eaton (University of Limerick, Ireland)
Joshua M. Epstein (The Johns Hopkins University, USA)
Adam Grzech (Wroclaw University of Technology, Poland)
Hideki Hashimoto (Chuo University, Japan)
Yoshiteru Ishida (Toyohashi University of Technology, Japan)
David John Glyndwyr James (Coventry University, UK)
Takashi Kohno (The University of Tokyo, Japan)
Kazuo Kyuma (Mitsubishi Electric Corporation, Japan)
Man Hyung Lee (Pusan National University, Korea)
Kenneth James Mackin (Tokyo University of Information Sciences, Japan)
Fumitoshi Matsuno (Kyoto University, Japan)
Masatoshi Nakamura (Research Institute of Systems Control, Japan)
Marion Oswald (Vienna University of Technology, Austria)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Steen Rasmussen (University of Southern Denmark, Denmark)Thomas S. Ray (University of Oklahoma, USA)
Shinichi Sagara (Kyushu Institute of Technology, Japan)
Katsunori Shimohara (Doshisha University, Japan)
Hideyuki Suzuki (The University of Tokyo, Japan)
Jerzy Świątek (Wroclaw University of Technology, Poland)Charles Taylor (University of California-Los Angeles, USA)
Toshio Tsuji (Hiroshima University, Japan)
Jiwu Wang (Beijing Jiaotong University, China)
Keigo Watanabe (Okayama University, Japan)
Kazuko Yamasaki (Tokyo University of Information Sciences, Japan)
Masao Yokota (Fukuoka Institute of Technology, Japan)
Changshui Zhang (Tsinghua University, China)

INTERNATIONAL PROGRAM COMMITTEE
Richard K. Belew (University of California-San Diego, USA)
Shyi-Ming Chen (National Taichung University of Education, Taiwan)
Young Im Cho (The University of Suwon, Korea)
Mo-Yuen Chow (North Carolina State University, USA)
Fengzhi Dai (Tianjin University of Science & Technology, China)
Yueyue Fan (University of California-Davis, USA)
Takayasu Fuchida (Kagoshima University, Japan)
Ivan Godler (The University of Kitakyushu, Japan)
Sung Hyun Han (Kyungnam University, Korea)
Noriyasu Homma (Tohoku University, Japan)
Zunaidi bin Ibrahim (TATi University College, Malaysia)
Seiji Ishikawa (Kyushu Institute of Technology, Japan)
Takashi Iwamoto (Mitsubishi Electric Corporation, Advanced Technology R&D Center, Japan)
Kiyotaka Izumi (Saga University, Japan)
Jeff Johnson (The Open University, UK)
Sanjay S. Joshi (College of Engineering University of California, USA)
Hiroshi Kinjo (University of the Ryukyus, Japan)
Kentarou Kurashige (Muroran Institute Technology, Japan)
Hee-Hyol Lee (Waseda University, Japan)
Yong Liu (University of Aizu, Japan)
Kathryn Elizabeth Merrick (University of New South Wales and ADFA, Australia)
Chris Mi (University of Michigan-Dearborn, USA)
Kazushi Nakano (The University of Electro-Communications, Japan)
Tomoharu Nakashima (Osaka Prefecture University, Japan)
Walter Nistico (University of Dortmund, Germany)
Harutoshi Ogai (Waseda University, Japan)
Shigeru Omatu (Osaka Institute of Technology, Japan)
Luigi Pagliarini (Technical University of Denmark, Denmark)Rolf Pfeifer (University of Zurich-Irchel, Switzerland)
Mohd Rizon (University of Malaysia Perlis, Malaysia)Peter S. Sapaty (National Academy of Sciences of Ukraine, Ukraine)
Jeong-Yon Sim (Kangnam University, Korea)
Ken Sugawara (Tohoku Gakuin University, Japan)
Mikhail Svinin (Kyushu University, Japan)
Ivan Tanev (Doshisha University, Japan)
Hiroshi Umeo (Osaka Electro-Communication University, Japan)
Katsuji Uosaki (Fukui University of Technology, Japan)
Mutsumi Watanabe (Kagoshima University, Japan)
Hidekazu Yanagimoto (Osaka Prefecture University, Japan)
Ikuo Yoshihara (University of Miyazaki, Japan)
Yong Yu (Kagoshima University, Japan)
Tao Zhang (Tsinghua University, China)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Huailin Zhao (Shanghai Institute of Technology, China)

LOCAL ARRANGEMENT COMMITTEE
Sung Hyun Han (Kyungnam University, Korea)
Jang Myung Lee (Pusan National University, Korea)
Jin Bae Park (Yonsei University, Korea)
Jeong Yon Shim (Kangnam University, Korea)

HISTORY
The AROB was founded in 1996 under the support of Science and International Affairs Bureau, Ministry of
Education, Culture, Sports, Science and Technology, Japanese Government. Since then, the symposium organizedby the AROB has been held every year at B-Con Plaza, Beppu, Japan except AROB 5th ’00 (Oita) and AROB
6th ’01 (Tokyo). The eighteenth symposium will be held on January 30–February 1, 2013, at Daejeon Convention
Center, Daejeon, Korea.

OBJECTIVE
This symposium will bring together researchers to discuss development of new technologies concerning artificial
life and robotics based on computer simulations and hardware designs of state-of-the-art technologies, and toshare findings on how advancements in artificial life and robotics technologies that relate to artificial intelligence,
virtual reality, and computer science are creating the basis for exciting new research and applications in various
fields.

COPYRIGHTS
Accepted papers will be published in the proceeding of AROB and some of high quality papers in the proceeding
will be requested to re-submit their papers for the consideration of publication in an international journal
ARTIFICIAL LIFE AND ROBOTICS. All correspondence related to the symposium should be addressed to
AROB Secretariat.

AROB Secretariat
AROB Secretariat
International Society on Artificial Life and Robotics
A-101, 8-7 Hatakenaka, Oita, 870-0856, Japan
Tel: +81-97-594-0181
Fax: +81-97-547-9242
Email: arobsecr@isarob.org
Home Page：http://isarob.org/© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

GENERAL SESSION TOPICS
Artificial intelligence
Artificial life and complexity
Artificial living
Control techniques
Human‐machine cooperative systems
Human‐welfare robotics
Image processing
Intelligent control & modelingLearning
Mobile vehicles
Multi agent system
Neural networks
Pattern recognition
Robotics
Virtual reality

ORGANIZED SESSION TOPICS
Applications and Theories of Advanced Technology
Artificial Life and Control
Bio-inspired Theory and Applications
Biomimetic Machines and Robots
Complexity and Diversity
Computer-Supported Learning Systems
Control in Mechatronics and Power Electronics
Control of Mechatronic System
Facial Expression Recognition, Speech Synthesis of Emotions, and Music Recommendation
Information Technology for Environmental ResearchIntelligent control and applications
Intelligent control and implement
Intuitive Human-System InteractionKansei Engineering and its Applications© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

MESSAGES
Ju-Jang Lee
General Chair of AROB
Professor, KAISTThe Eighteenth International Symposium on Artificial Life and Robotics (AROB) will be held in Daejeon, Korea
from Jan 30th to Feb. 1st, 2013. This year’s Symposium will be held amidst the high expectation of the increasingly
important role of the new interdisciplinary paradigm of science and engineering represented by the field of artificial
life and robotics that continuously attracts wide interests among scientist, researchers, and engineers around theglobe.
Since the time of the very first AROB meeting in 1996, each year, distinguished researchers and technologists fromaround the world are looking forward to attending and meeting at AROB. AROB is becoming the annual excellent
forum that represents a unique opportunity for the academic and industrial communities to meet and assess the latest
developments in this fast growing artificial life and robotics field. AROB enables them to address new challenges,
share solutions, discuss research directions for the future, exchange views and ideas, view the results of appliedresearch, present and discuss the latest development of new technologies and relevant applications.
In addition, AROB offers the opportunity of hearing the opinions of well known leading experts in the field
through the keynote sessions, provides the bases for regional and international collaborative research, and enables toforesee the future evolution of new scientific paradigms and theories contributed by the field of artificial life androbotics and associated research area. The twenty-first century will become the century of artificial life andintelligent machines in support of humankind and AROB is contributing through wide technical topics of interest
that support this direction.
It is a great for me as the General Chairman of the 18th AROB 2013 to welcome everyone to this important event.
Also, I would like to extend my special thanks to all authors and speakers for contributing their research works, theparticipants, and the organizing team of the 18th AROB.
Looking forward to meeting you at the 18th AROB in Dajeon, Korea and wishing you all the best.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Masanori Sugisaka
General Chair of AROB
ALife Robotics Corporation, Ltd. , Japan

It is my great pleasure to invite you all to the Eighteenth International Symposium on Artificial Life and Robotics(AROB 18th ‘13).
The symposium from the first (1996) to the Thirteenth (2008) were organized by Oita University and Nippon Bunri
University (NBU) and the Symposium from the Fourteenth (2009) to the Seventeenth (2012) were organized by
Nippon Bunri University (NBU) and ALife Robotics Corporation Ltd. under the sponsorship of the Science andTechnology Policy Bureau, the Ministry of Education, Science, Sports, and Culture (Monbusho), presently, theMinistry of Education, Culture, Sports, Science, and Technology (Monkasho), Japanese Government, Japan Society
for the Promotion of Science (JSPS), The Commemorative Organization for the Japan World Exposition ('70), Air
Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AFOSR/AOARD),
USA. I would like to express my sincere thanks to not only Monkasho, JSPS, the Commemorative Organization for
the Japan World Exposition ('70), AFOSR/AOARD but also Japanese companies (Mitsubishi Electric Corporation,
Advanced Technology R&D Center, Oita Gas Co., Ltd., ME System Co. LTD, and Sanwa Shurui Co., LTD.) for
their repeated supports.
This symposium is organized by International Organizing Committee of AROB and is sponsored by ALife
Robotics Co., Ltd., and technically co-sponsored by IEEE Robotics and Automation Society Japan Chapter, JARA,
SICE, ISCIE, IEICE and CAAI (P.R. China).
I hope that the new technologies presented in this symposium bring happiness to human society and will facilitate
the establishment of an international research Institute of Artificial Life and Robotics.
Welcome and enjoy your stay in Daejeon.

© ISAROB 2013P-7

1F-101: Room A
1F-102: Room B

1F-103: Room C
1F-104: Room D© ISAROB 2013

1F-105: Room E
1F-105+1F-106: Room F

Conference Rooms

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Daejeon Convention Center 1F

1F-107: Computer Room
1F-108: VIP Meeting RoomThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

© ISAROB 2013

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16:00 – 17:15

15:30 – 16:00

13:00 – 15:30

Cair: T. Yamaguchi

GS14
Robotics I

OS: Organized Session

OS9
Information Technology for
Environmental Research

H.H. Lund

GS: General Session

PS: Poster Session

GS1
Artificial intelligence

Coffee Break

Chair

Invited Session on Playware (Room F)

Opening Ceremony (Room F)

Chair: K. Naitoh

OS4Complexity and Diversity

12:35 – 13:00

Chair: K. Watanabe

OS3
Biomimetic Machines and Robots

Lunch

Chair: K. Higuchi

OS6
Control in Mechatronics and PowerElectronics

Room C

Registration (Registration Desk)

Room B

11:45 – 12:35

10:00 – 11:45

09:00 –

Room ATime Table (January 30, 2013)

Chair: C.-W. Hung

OS11Intelligent control and implement

Chair: K.-L. Su

OS10Intelligent control and applications

Room DThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

© ISAROB 2013

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15:45 – 17:00

15:30 – 15:45

GS9
Learning

OS: Organized Session

Chair: T. Hattori

PS: Poster Session

Chair: H.-H. Lee

OS1
Artificial Life and Control

GS: General Session

OS13
Kansei Engineering and its
Applications

Coffee Break

GS2
Artificial life and complexity

Coffee Break

14:15 – 14:30

14:30 – 15:30

Invited Speech (Room F)
Dr. Peter S. Sapaty

13:15 – 14:15

GS10
Mobile vehicles

Lunch

12:15 – 13:15

GS4
Control techniques

Poster Session

GS5
Human-machine

11:15 – 12:15

GS15
Robotics II

Coffee Break

GS8Intelligent control & modeling

Room C

Registration (Registration Desk)

Room B

11:00 – 11:15

10:00 – 11:00

09:00 –

Room ATime Table (January 31, 2013)

GS11
Multi agent system

GS7
Image processing

GS12
Neural networks

Room DThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

© ISAROB 2013

10:30 – 11:30

10:15 – 10:30

08:30 – 10:15

08:00 –

GS13
Pattern recognition

Chair: K.J. Mackin

OS5Computer-Supported
Learning Systems

Room A

P-11

Room C

GS: General Session

GS3
Artificial living

Coffee Break

Chair: Y. Yoshitomi

OS8
Facial Expression
Recognition, Speech
Synthesis of Emotions, andMusic Recommendation

Registration (Registration Desk)

OS: Organized Session

GS16
Robotics III

Chair: S. Sagara

OS7
Control of Mechatronic
SystemRoom BTime Table (February 1, 2013)

Chair: M. Yokota

11:00-11:30
Applications and Theories ofAdvanced Technology

PS: Poster Session

Chair: T. Ito

OS12Intuitive Human-System
Interaction

GS6
Human-welfare robotics

Room E

Chair: K. Yamamori

9:45-10:45
Bio-inspired Theory andApplications

will end at 09:30

GS17
Virtual reality

Room DThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Technical paper index
January 30 (Wednesday), 13:00-15:30
Room F
Invited Session on Playware
Chair: Henrik Hautop Lund (Center for Playware, Technical University of Denmark, Denmark)ISP1 Effect of Playful Balancing Training - A Pilot Randomized Controlled Trial
Henrik Hautop Lund, Jari Due Jessen (Centre for Playware, Technical University of Denmark, Denmark)ISP2 Engaging through her eyes Embodying the perspective of a robot companion
Patrizia Marti (Social, Political and Cognitive Science Department, University of Siena, Italy)
Jelle T. Stienstra (Department of Industrial Design, Eindhoven University of Technology, Netherlands)
ISP3 Fable: Socially Interactive Modular RobotArnþór Magnússon, Moises Pacheco, Mikael Moghadam, Henrik H. Lund, David J. Christensen (Technical
University of Denmark, Denmark)
ISP4 bioToys: biofeedback device for physiotherapy using building blocks
Tomoya Shimokakimoto, Asaki Miura, Kenji Suzuki (University of Tsukuba, Japan)

January 31 (Thursday), 13:15-14:15
Room F
Invited Speech
IS1 Future Technology and Market Developments for Unmanned Maritime Vehicles
Peter S. Sapaty (National Academy of Sciences of Ukraine, Ukraine)

January 30 (Wednesday), 10:00-11:45
Room A
OS6 Control in Mechatronics and Power Electronics
Chair: Kohji Higuchi (The University of Electro-Communications, Japan)
Co-Chair: Kazushi Nakano (The University of Electro-Communications, Japan)
OS6-1 Robust Digital Control for Interleave PFC Boost Converter
Yuto Adachi, Kohji Higuchi, Tatsuyoshi Kajikawa (The University of Electro-Communications, Japan)
Tomoaki Sato (C&C SYSTEMS CENTER Hirosaki University, Japan)
Kosin Chamnongthai (King Mongkut's University of Technology Thonburi, Thailand)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

OS6-2 Synthesis of continuous-time dynamic quantizers for LFT type quantized feedback systems
Kenji Sawada, Seiichi Shin (The University of Electro-Communications, Japan)
OS6-3 Optimal scheduling of automatic guided vehicle transportation system based on MLD system modeling
Kenji Sawada, Seiichi Shin (The University of Electro-Communications, Japan)
OS6-4 Direct Multivariable PI Controller Tuning from Closed-Loop Response Data
Yosihiro Matsui, Hideki Ayano (Tokyo National College of Technology, Japan)
Kazushi Nakano (The University of Electro-Communications, Japan)
OS6-5 On inhibition of premature convergence in Genetic Algorithms for mobile robot control
Satoshi Shintaku, Kazushi Nakano (The University of Electro-Communications, Japan)
OS6-6 Digital PI control for Interleave PFC boost converter
Yuto Adachi, Kohji Higuchi, Tatsuyoshi Kajikawa (The University of Electro-Communications, Japan)
Tomoaki Sato (Hirosaki University, Japan)
Kosin Chamnongthai (King Mongkut's University of Technology Thonburi, Thailand)

Room BOS3 Biomimetic Machines and Robots
Chair: Keigo Watanabe (Okayama University, Japan)
Co-Chair: Akimasa Otsuka (Tokyo University of Science, Yamaguchi, Japan)
OS3-1 A new solution to the SLAM problem by using an unscented smoother
Keigo Watanabe, Shoichi Maeyama (Okayama University, Japan)
Satoshi Funamoto (Shin Nippon Koki Co., Ltd., Japan)
OS3-2 Kinodynamic motion planning and control using anisotropic damping forces
Kimiko Motonaka, Keigo Watanabe, Shoichi Maeyama (Okayama University, Japan)
OS3-3 Application of variable search space GAs to fine gain tuning of model-based robotic servo controller
Akimasa Otsuka, Fusaomi Nagata (Tokyo University of Science, Yamaguchi, Japan)
OS3-4 Propulsive force analysis of a pectoral fin for rajiform type fish robots from fluid dynamic aspects
Masaaki Ikeda, Shigeki Hikasa, Keigo Watanabe, Isaku Nagai (Okayama University, Japan)
OS3-5 Underactuated control for a blimp with four-propellers by a logical switching method
Yoshikazu Nakamura, Keigo Watanabe, Isaku Nagai (Okayama University, Japan)
OS3-6 Continuous shape-grinding experiment based on model-independent force / position hybrid control method with
spline approximation
Akira Yanou, Mamoru Minami, Hiro Tanimoto (Okayama University, Japan)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Room COS4 Complexity and Diversity
Chair: Ken Naitoh (Waseda University, Japan)
Co-Chair: Hiroshi Tanaka (Tokyo Medical and Dental University, Japan)
OS4-1 Catastrophic chaos theory: predicting the edge between health and death
Ken Naitoh, Hirofumi Inoue (Waseda University, Japan)
OS4-2 Supercomputing of transition to turbulence in pipe with adit flow
Tsuyoshi Nogami, Takahiro Tobe, Ken Naitoh (Waseda University, Japan)
OS4-3 Stock market dynamics derived from a cognitive bias
Moto Kamiura, Yu Murata (Tokyo Denki University, Japan)
OS4-4 Fusion visualization of surface and volume on AVS/Express
Hideo Miyachi (CYBERNET SYSTEMS CO.,LTD., Japan)
Koji Koyamada, Naohisa Sakamoto (Kyoto University, Japan)
OS4-5 Autonomous polymer actuators
Yusuke Hara (The National Institute of Advanced Industrial Science and Technology (AIST), Japan)
OS4-6 Cyanobacterial circadian clock is nullified under low temperature via Hopf bifurcation
Yoriko Murayama, Hideo Iwasaki (Waseda University, Japan)
Hiroshi Kori (Ochanomizu University, Japan)
Takao Kondo (Nagoya University, Japan)
Hiroshi Ito (Kyushu University, Japan)
OS4-7 Molecular robotics for artificial cell-model construction
Shin-ichiro M. Nomura, Yusuke Sato (Tohoku University, Japan)
Kei Fujiwara (JSPS Research Fellow, Japan)

Room D
OS10 Intelligent control and applications
Chair: Kuo-Lan Su (National Yunlin University of Science and Technology, Taiwan)
Co-Chair: Chia-Nan Ko (Nan Kai University of Technology, Taiwan)
OS10-1 Adaptive learning neural networks for system identification of a magneto-rheological damper
Chia-Nan Ko, Guan-Yu Liu, Yu-Yi Fu (Nan-Kai University of Technology, Taiwan)
Pi-Yun Chen (National Chin-Yi University of Technology, Taiwan)
OS10-2 Identification of RBFNs with SVR under censored data
Yu-Yi Fu, Chia-Nan Ko, Jhih-Cheng Chang (Nan Kai University of Technology, Taiwan)
Yu-Fen Fu (China University of Technology, Taiwan)
Kuo-Lan Su (National Yunlin University of Science and Technology, Taiwan)© ISAROB 2013

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OS10-3 Drift algorithm second order sliding mode control for a synchronous reluctance motor
Wen-Bin Lin (National Yunlin University of Science and Technology and Far East University, Taiwan)
Huann-Keng Chiang, Yi-Chang Chang (National Yunlin University of Science and Technology, Taiwan)
OS10-4 Development of the multi-detection system using multi-sensor fusion algorithms
Ying-Yao Ting, Huan-Sheng Wang (National Kaohsiung First University of Science of Technology, Taiwan)
OS10-5 Design a feasible docking station for mobile robots
Yi-Lin Liao, Kuo-Lan Su, Jr-Hung Guo (National Yunlin University of Science and Technology, Taiwan)
Li-Chun Lai (National Pingtung University of Education, Taiwan)
OS10-6 A PSO method for time-optimal motion planning of a mobile robot
Li-Chun Lai, Sheau-Wen Lin, Chia-Hung Shih (National Pingtung University of Education, Taiwan)
Kuo-Lan Su,(National Yunlin University of Science and Technology, Taiwan)
Chia-Nan Ko (Nan Kai University of Technology, Taiwan)
OS10-7 The Implementation of Driving anti-sleep Safety warning system
Yi-Yu Lu, Kuo-Kung Chen, Po-Yu Chen, Wen-Bin Lin (Far East University, Taiwan)

January 30 (Wednesday), 16:00-17:15
Room A
OS9 Information Technology for Environmental Research
Chair: Takashi Yamaguchi (Tokyo University of Information Sciences, Japan)
Co-Chair: Kenneth J. Mackin (Tokyo University of Information Sciences, Japan)
OS9-1 Study on the efficient use of satellite image data analysis system
Hayao Mori, Seiji Tsukamoto, Takashi Yamaguchi, Masaki Hanada, Jong Geol Park, Eiji Nunohiro (Tokyo
University of Information Sciences, Japan)
OS9-2 Administrative division based data segmentation for autonomous paddy field classifier modelingTakashi Yamaguchi, Shinya Iwasaki, Kazuma Mori, Kenneth J. Mackin, Masaki Hanada, Eiji Nunohiro (Tokyo
University of Information Sciences, Japan)
OS9-3 Analysis of seasonal variability of methane over global land area
Jonggeol Park, Sooyoung Park, Eiji Nunoriho, Takashi Yamaguchi (Tokyo University of Information Sciences,
Japan)
Youngjoo Kwak (International Centre for Water Hazard and Risk Management (ICHARM), Japan)
OS9-4 Estimating a floodwater from MODIS time series and DEM data using the self-organizing map
Youngjoo Kwak, Kazuhiko Fukami (International Centre for Water Hazard and Risk Management (ICHARM),
Japan)
Jonggeol Park (Tokyo University of Information Sciences, Japan)© ISAROB 2013

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Room B
GS14 Robotics I
GS14-1 A simple wall-climbing mechanism for a window cleaning robot
Takafumi Soeda, Nobuhiro Okada, Kazuo Kiguchi (Kyushu University, Japan)
GS14-2 Topological Graph Based Boundary Coverage Path Planning for a Mobile Robot
Bong Keun Kim, Hideyuki Tanaka, Yasushi Sumi (Intelligent Systems Research Institute, AIST, Japan)
GS14-3 Arc/line segments extraction from unknown indoor environment with laser sensor
Rui-Jun Yan, Jing Wu, Ming-Lei Shao, Ji-Yeong Lee, Chang-Soo Han (Hanyang University, Korea)
GS14-4 Study on the development of an open motion control platform for a differential mobile robot based on ROS
Jiwu Wang, Fangbo Liao (Beijing Jiaotong University, China)
Sugisaka Masanori (Alife Robotics Corporation Ltd., Japan)

Room CGS1 Artificial intelligence
GS1-1 A proposition of addition and integration of q-values in Q-Learning
Kathy Thi Aung, Takayasu Fuchida (Kagoshima University, Japan)
GS1-2 Recipe search engine inspired by ant colony's foraging
Jinwook KIM, Kazuyuki Ito (Hosei University, Japan)
Katsunori Tachibana (NEC Communication Systems, Ltd., Japan)
GS1-3 Phase Scaling Effect of Adiabatic Quantum Computation in Deutsch-Jozsa Problem (withdrawal)
Shigeru Nakayama (Kagoshima University, Japan)
Ichiro Iimura (Prefectural University of Kumamoto, Japan)
Gang Peng (Huizhou University, China)
GS1-4 Constructing Obstacle-Based Triangularized Roadmap
Jeong Hyeon Wang, Ju Jang Lee (Korea Advanced Institute of Science and Technology, Korea)

Room D
OS11 Intelligent control and implement
Chair: Chung-Wen Hung (National Yunlin University of Science and Technology, Taiwan)
Co-Chair: Kuo-Hsien Hsia (Far East University, Taiwan)
OS11-1 Motion planning of the multi-robot based chess game
Yung-Chin Lin (National Yunlin University of Science and Technology, WuFeng University of Science ofTechnology, Taiwan)
Yi-Lin Liao, Cheng-Yun Chung, Kuo-Lan Su (National Yunlin University of Science and Technology, Taiwan)© ISAROB 2013

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OS11-2 An Indoor Autonomous Surveillance Robot via Humanoid Vision System
Kuo-Hsien Hsia (Far East University, Taiwan)
Shao-Fan Lien, Juhng-Perng Su, Wei-Yi Ciou (National Yunlin University of Science and Technology,
Taiwan)OS11-3 Autonomous robot localization based on 3D pose estimation approach (withdrawal)
Chien-Chou Lin, Ben-Zhong Lin, Chang-Hong Wu (National Yunlin University of Science and Technology,
Taiwan)OS11-4 An ultra-high speed motor driver with hybrid modulations
Chung-Wen Hung (National Yunlin University of Science and Technology, Taiwan)
OS11-5 A current balance strategy for multi channel interleaved power converters
Jhih-Han Chen, Chung-Wen Hung (National Yunlin University of Science and Technology, Taiwan)

January 31 (Thursday), 10:00-11:00
Room A
GS8 Intelligent control & modelingGS8-1 Modeling and robust decoupling control for hypersonic scramjet vehicle
Xiaofeng Su, Yuefei Jiang, Yingmin Jia (Beihang University, China)
GS8-2 Flocking control of multi-agent systems in a limited space
Sharu Jiang, Yingmin Jia, Shichen Long (Beihang University, China)
GS8-3 Inverse kinematics in Hyper-redundant robot using Adaptive Neural Network
Chatklaw Jareanpon (Polar lab, Department of Computer Science, Faculty of Informatics, Mahasarakham
University, Thailand)
GS8-4 Reduction of Impact when a Humanoid Robot Lands on the Ground
Homare Fujieda, Shingo Okamoto, Jae Hoon Lee (Ehime University, Japan)

Room B
GS15 Robotics II
GS15-1 Study on the remote and high accurate measurement of micro-cracks on the bottom of a bridge
Jiwu Wang, Shaodong Do, Fangbo Liao (Beijing Jiaotong University, China)
Masanori Sugisaka (ALife Robotics Co., Ltd., Japan)
GS15-2 The small eye and tongue module for an android robot head
Dongwoon Choi, Dongwook Lee, Duckyeon Lee, Byeong-kyu Ahn, Hogil Lee (Korea Institute of Industrial
Technology, Korea)
GS15-3 Design of a robotic arm's behavior in imitation of animal consciousness: development of altruistic behavior
Kyoko Tanaka, Eiji Hayashi (Kyushu Institute of Technology, Japan)© ISAROB 2013

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GS15-4 Network Based Multiple Mobile Robots with Subsumption Architecture Supporting Swarm Behaviors
Maki K. Habib (American University in Cairo, Egypt)
Fusaomi Nagata, Akimasa Otsuka (Tokyo University of Science, Yamaguchi, Japan)
Keigo Watanabe (Okayama University, Japan)

Room C
GS5 Human-machine cooperative systems
GS5-1 Design of a user support system for event addition based on route search using schedule information
Hiroki Imamura, Hiroyuki Nishiyama (Tokyo University of Science, Japan)
GS5-2 The study of walking control with plural cylinder leg robots
Jihao Cao, Feifei Zhang, Masanori Ito, Takumi Yaginuma (Tokyo University of Marine Science andTechnology, Japan)
GS5-3 The Ground Reaction Force generation algorithm for tracking the wearer's
Sukwoo Lee, Jongwon Lee (University of Science and Technology, Korea)
Hyogon Kim, Jaeho Jang, Sangdeok Park (Korea Institute of Industrial Technology, Korea)

Room D
GS12 Neural networks
GS12-1 Epilepsy Diagnosis Using PSO based ANN
Nesibe Yalçın, Cihan Karakuzu (Bilecik Şeyh Edebali University, Turkey)
Gülay Tezel (Selçuk University, Turkey)
GS12-2 The effect of the internal parameters on association performance of a chaotic neural network
Shun Watanabe, Takashi Kuremoto, Masanao Obayashi (Yamaguchi University, Japan)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS12-3 An estimating method for missing values by using multiple SOMs
Yuui Kikuchi, Nobuhiro Okada, Yasutaka Tsuji, Kazuo Kiguchi (Kyushu University, Japan)
GS12-4 Proposal and Evaluation of information gathering methods that considers the MANET's load in a desaster
Keisuke Gohara, Hiroyuki Nishiyama (Tokyo University of Science, Japan)

January 31 (Thursday), 11:15-12:15
Poster Session
PS1-1 Application of the actor-critic method to a robot using state representation based on distance between
distributions
Manabu Gouko (Tohoku Gakuin University, Japan)
PS1-2 Color Halftoning Using the Error Diffusion Method by the Edge Adjustment and the False Colored Limit
Kazuya Kadoya, Ken-ichi Tanaka (Meiji University, Japan)© ISAROB 2013

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PS1-3 The Three Optimization Methods of Searching The Effective Combination Dither Matrix
Shohei Niinuma, Kenichi Tanaka (Meiji University, Japan)
PS1-4 Inverse halftoning of color image using unsharp mask
Hiroki Matsuo, Ken-ichi Tanaka (Meiji University, Japan)
PS1-5 Development of mobile robot navigation system using simplified map based on place recognition
Satoshi Yamanaka, Kazuyuki Morioka (Meiji University, Japan)
PS1-6 Robots from printer: the prospect of using printed electronics technologies for producing robots and creating
objects of electronic artificial life
Sergey N. Orlov, Evgeny S. Gornev, Nikolay A. Zaytsev, Sergey I. Yanovich, Andrey G. Krasnikov, Ilya A.
Khomyakov (Molecular Electronics Research Institute JSC, Russia)
PS1-7 Teaching method for the industrial dual-arm robot
Taeyong Choi (KIMM, Korea)
PS1-8 Real-time 3D-Shape Reconstruction System in Intelligent Space Based on Networked Vision Sensors
Takashi Ishimaru, Kazuyuki Morioka (Meiji University, Japan)
PS1-9 Using a GA-based extension recognized method for fault diagnosis in car engines
Meng-Hui Wang, Pi-Yun Chen (National Chinyi University of Technology, Taiwan)
PS1-10 Design of a rehabilitation robot suit with hardware-based safety devices: Proposal of the basic structure
Yoshihiro Kai, Satoshi Kitaguchi (Tokai University, Japan)
Wenlong Zhang, Masayoshi Tomizuka (University of California, Berkeley, USA)
PS1-11 Through-Wall Imaging for a Metallic Cylinder by Dynamic Differential Evolution
Chung-Hsin Huang (Taipei College of Maritime Technology, Taiwan)
Chi-Hsien Sun, Ping-Chieh Chiang (National Taiwan University of Science and Technology, Taiwan)
Lung-Fai Tuen, Ching-Lieh Li, Pin-Ru Lai (Tamkang University, Taiwan)
PS1-12 Analysis of Effects of Exercises on Middle-aged and Elderly Arthritics by Thermal Imagery and Automatic
Auxiliary Diagnosis
Chin-Lun Lai, Tung-Lin Yang (Oriental Institute of Technology, Taiwan)
Tsung-Ching Lin (Far Eastern Memorial Hospital, Taiwan)
PS1-13 Enhancing electrical communication through reconstructed nanowire electrodes for implantable enzymatic
biofuel cell (EBFC)
Deby Fapyane, Soo-Jin lee, In seop Chang (Gwangju Institute of Science and Technology (GIST), Korea)
DuHyun Lim, Jou-Hyeon Ahn (Gyeongsang National University, Korea)
PS1-14 Object tracking based on adaboost classifier and particle filter
Chin-Lun Lai, Li-Yin Lee (Oriental Institute of Technology, Taiwan)
PS1-15 A metaheuristic for huge scale quadratic assignment problems
Hatsumi Nakaura, Fubito Toyama, Hiroshi Mori, Kenji Shoji, Juichi Miyamichi (Utsunomiya University,
Japan)
PS1-16 Competition-based Particle Swarm Optimizer for solving Numerical Optimization Problems
Sheng-Ta Hsieh, Bo-Han Chen (Oriental Institute of Technology, Taiwan)© ISAROB 2013

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PS1-17 Elitist Differential Evolution for solving Numerical Optimization Problems
Sheng-Ta Hsieh, Huang-Lyu Wu, Tse Su (Oriental Institute of Technology, Taiwan)
PS1-18 Artificial Bee Colony Algorithm with Crossover Strategy for Global Numerical Optimization
Sheng-Ta Hsieh, Jhih-Sian Chen (Oriental Institute of Technology, Taiwan)
PS1-19 Microwave Imaging of Dielectric Cylindrical Target Using Dynamic Differential Evolution and Self-Adaptive
Dynamic Differential Evolution
Ching-Lieh Li, Lung-Fai Tuen, Huang-Wei Wong (Tamkang University, Taiwan)
Chi-Hsien Sun (National Taiwan University of Science and Technology, Taiwan)
Chung-Hsin Huang (Taipei College of Maritime Technology, Taiwan)
PS1-20 Application of Natural Language Processing for Information Retrieval
Su Mei Xi (Shandong Polytechnic University, China)
Dae Jong Lee (Chungbuk National University, Korea)
Young Im Cho (Suwon University, Korea)

January 31 (Thursday), 14:30-15:30
Room A
GS4 Control techniques
GS4-1 Self-triggered optimal control based on optimization with prediction horizon one
Koichi Kobayashi, Kunihiko Hiraishi (Japan Advanced Institute of Science and Technology, Japan)
GS4-2 H∞ controller with graphical LMI region profile for gantry crane system
Mohd Zaidi Bin Mohd Tumari, Muhammad Salihin Bin Saealal, Mohd Riduwan Bin Ghazali, Yasmin Binti
Abdul Wahab (Universiti Malaysia Pahang, Malaysia)
GS4-3 Optimized Walking Control of a Biped Walking Robot Considering Theory of a Pendulum
Hisashi Koike, Jae Hoon Lee, Shingo Okamoto (Ehime University, Japan)
GS4-4 Sliding mode control of a quad rotor helicopter using nonlinear sliding surface
Bambang Sumantri (Toyohashi University of Technology, Electronic Engineering Polytechnic Institute of
Surabaya, Indonesia)
Naoki Uchiyama, Shigenori Sano, Yuma Kawabata (Toyohashi University of Technology, Japan)

Room B
GS10 Mobile vehicles
GS10-1 Development of Outdoor Mobile Robot for Human Following Navigation
Jae Hoon LEE, Kei OKAMOTO, Masumi OUE, Shingo OKAMOTO (Ehime University, Japan)
GS10-2 Parameter Estimation Experiment and Development of Decentralized Caster Modules
Kazuyoshi Ohara, Jae Hoon Lee, Shingo Okamoto (Ehime University, Japan)© ISAROB 2013

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GS10-3 Parameter optimization for decoupling controllers of 4WS vehicles
Mingxing Li, Haijing Xu, Yingmin Jia (Beihang University, China)
GS10-4 A method of sharing for common coordinate system by using relative position among the swarm
Koji Makino (Tokyo University of Technology, Japan)

Room CGS2 Artificial life and complexity
GS2-1 Non-uniform Cellular Automata based on Open-ended Rule Evolution
Noritsugu Sughimura, Reiji Suzuki, Takaya Arita (Nagoya University, Japan)
GS2-2 A Procedure for Constructing Social Network Using Web Search Engines: The Case for Japanese Automotive
Industry
Yuchi Kubota, Reiji Suzuki, Takaya Arita (Nagoya University, Japan)
GS2-3 Statistical Analysis for Price Changes of Carbon European Union Allowances
zeyu zheng, Kazuko yamasaki (Tokyo University of information sciences, Japan)
Jun Yang (Chongqing University, China)

Room D
GS7 Image processing
GS7-1 A describing method of latency tolerant hardware for a pure ANSI-C/C++ based high-level synthesis technology
Akira Yamawaki, Seiichi Serikawa (Kyushu Institute of Technology, Japan)
GS7-3 A Research on State Recognition in Wide Area by Aerial Images Analysis
Ganwen Jiang, Masayuki Kashima, Kiminori Sato, Mutsumi Watanabe (Kagoshima University, Japan)
GS7-4 Semantic Segmentation in Manhattan-like Environments from 2.5D data
Sven Olufs, Markus Vincze (University of Technology / Automation and Control Institute, Austria)

January 31 (Thursday), 15:45-17:00
Room A
GS9 Learning
GS9-1 Cell-production parts layout by virtual factory system using reinforcement learning and MTM
Hidehiko Yamamoto, Tsuyoshi Sugimoto, Takayoshi Yamada (Gifu University, Japan)
Masahiro Nakamura (Lexer Research Inc, Japan)
GS9-2 Gait motion of a six legged real robot employing associatron
Masashi Sakai, Tomo Ishikawa, Masato Hashimoto, Tomo Ishikawa, Koji Makino, Jin-Hua She, Yasuhiro
Ohyama (Tokyo University of Technology, Japan)© ISAROB 2013

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GS9-3 Modified Neural Q-Learning for Two-Wheeled Inverted Pendulum balancing Control
Yeon-Seob Kim, Ju-Jang Lee (Korea Advanced Institute of Science and Technology, Korea)
GS9-4 Reinforcement learning for dynamic environment: a classification of dynamic environments and a detection
method of environmental changes
Masato Nagayoshi (Niigata College of Nursing, Japan)
Hajime Murao, Hisashi Tamaki (Kobe University, Japan)

Room B
OS13 Kansei Engineering and its Applications
Chair: Tetsuo Hattori (Kagawa University, Taiwan)
Co-Chair: Yoshiro Imai (Kagawa University, Japan)
OS13-1 On a relation between feeling Impression and 3PACF of sound signal
Yusuke Kawakami, Tetsuo Hattori (Kagawa University, Japan)
Hiromichi Kawano (NTT Advanced Technology, Japan)
Tetsuya Izumi (Micro Technica Co., Ltd., Japan)
OS13-2 Color image arrangement by elastic transform based on histogram matching
Daisuke Kutsuna, Tetsuo Hattori, Yusuke Kawakami, Haruna Matsushita, Yoshiro Imai (Kagawa University,
Japan)
Tetsuya Izumi (Micro Technica Co., Ltd., Japan)
OS13-3 Face identification insensitive to facial expression in the crowded people scene
Qingyu Shu, Tetsuo Hattori, Saki Masunari (Kagawa University, Japan)
Tetsuya Izumi (Micro Technica Co., Ltd., Japan)
Hiromichi Kawano (NTT Advanced Technology, Japan)
Bahram Javidi (University of Connecticut, USA)
OS13-4 Kansei Engineering based evaluation for distance learning on distributed multiple servers
Yoshio Moritoh (Kagawa Junior College, Japan)
Yoshiro Imai, Tetsuo Hattori (Kagawa University, Japan)
OS13-5 An application of Kansei Engineering to community-based collaboration
Masatoshi Imai (Kagawa Junior College, Japan)
Yoshiro Imai, Tetsuo Hattori (Kagawa University, Japan)

Room COS1 Artificial Life and Control
Chair: Hee-Hyol Lee (Waseda University, Japan)
OS1-1 A Micro-Simulator for Traffic Signal Control Based on A Modified Cellular Automaton Traffic Flow Model
Hu Jin, Chengyou Cui, Hee-hyol Lee (Waseda University, Japan)© ISAROB 2013

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OS1-2 Real-Time Stochastic Optimal Control for Traffic Signals of Multiple Intersections
Chengyou Cui, Hee-hyol Lee (Waseda University, Japan)
OS1-3 Decouple PID Control of Compact Binary Power Generation
Kun-Young Han, Lu Li, Hee-Hyol Lee (Waseda University, Japan)
OS1-4 Decouple Sliding Mode Control of Compact Binary Power Generation
Kun-Young Han, Hee-Hyol Lee (Waseda University, Japan)

Room D
GS11 Multi agent system
GS11-1 Evaluation of efficiency of the symmetry bias in grammar acquisition
Ryuichi Matoba, Hiroki Sudo, Shingo Hagiwara (Toyama National College of Technology, Japan)
Satoshi Tojo (Japan Advanced Institute of Science and Technology, Japan)
GS11-2 The Evolution of Pre-play Communication in the Interactive Minority Game
Keita Nishimoto, Reiji Suzuki, Takaya Arita (Nagoya University, Japan)
Ivan Tanev, Katsunori Shimohara (Doshisha University, Japan)
GS11-3 A new decision-making system of an agent based on emotional models in multi-agent system
Shogo Watada, Masanao Obayashi, Takashi Kuremoto, Shingo Mabu (Yamaguchi University, Japan)
Kunikazu Kobayashi (Aichi Prefectural University, Japan)
GS11-4 Design of a user-support system for finding the other person talking on smart Phones
Hiroyuki Nishiyama, Fumio Mizoguchi (Tokyo University of Science, Japan)

February 1 (Friday), 08:30-10:15
Room A
OS5 Computer-Supported Learning Systems
Chair: Kenneth J. Mackin (Tokyo University of Information Sciences, Japan)
Co-Chair: Takashi Yamaguchi (Tokyo University of Information Sciences, Japan)
OS5-1 Development of entertainment based learning features in programming learning support system
Eiji Nunohiro, Masanori Ohshiro, Takashi Yamaguchi (Tokyo University of Information Sciences, Japan)
OS5-2 Programming learning support systems focused on structures of programming language and code
Masanori Ohshiro, Takashi Yamaguchi, Eiji Nunohiro (Tokyo University of Information Sciences, Japan)
OS5-3 Individual student support system for teacher and TAs using mobile devices in exercise classes
Masaki Hanada, Takashi Yamaguchi, Yasuo Nagai (Tokyo University of Information Sciences, Japan)
OS5-4 Research on XP approach using The Personal Software Process Practices
Daisuke Yamaguchi (Toin University of Yokohama, Japan)© ISAROB 2013

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OS5-5 Non-event driven graphics API for programming education
Kenneth J. Mackin (Tokyo University of Information Sciences, Japan)
OS5-6 An Education of Social Media Literacy to High School Students in Social Media Times
Yoshihiro Kawano, Takuya Miya, Naoki Furuya (Tokyo University of Information Sciences, Japan)
Yuka Obu (Unicast Inc., Japan)
OS5-7 Syntax structure based typing tool for source code writing
Takashi Yamaguchi, Shinya Iwasaki, Kazuma Mori, Kenneth J. Mackin, Masaki Hanada, Eiji Nunohiro (Tokyo
University of Information Sciences, Japan)

Room B
OS7 Control of Mechatronic System
Chair: Shinichi Sagara (Kyushu Institute of Technology, Japan)
Co-Chair: Masahiro Oya (Kyushu Institute of Technology, Japan)
OS7-1 Adaptive steering controller for vehicles with driving/braking force distribution
Bo Zhou, Masahiro Oya, Jinxin Zhuo, Panfeng Shu (Kyushu Institute of Technology, Japan)
OS7-2 Oscillation Control of a Contact Scanning Scanners System
Hideki Wada (Sin-Nippon Nondestructive Inspection co., Japan)
Hiroyuki Koga, Masahiro Oya (Kyushu Institute of Technology, Japan)
Katuhiro Okumura (Fukuoka Industrial Technology Center, Japan)
OS7-3 Rollover Prevention Control of Driver-Heavy Vehicle Systems
Yusuke Suetake, Masahiro Oya, Pangfeng Shu, Jinxin Zhou (Kyushu Institute of Technology, Japan)
OS7-4 Development of a dual-shaft propeller thruster equipped with rotational speed sensor for UVMS control
Radzi Bin Ambar, Shinichi Sagara, Takuya Yamaguchi (Kyushu Institute of Technology, Japan)
OS7-5 Control of a dual arm underwater robot
Shinichi Sagara, Radzi Bin Ambar, Kenichi Imaike (Kyushu Institute of Technology, Japan)

Room C
OS8 Facial Expression Recognition, Speech Synthesis of Emotions, and Music
Recommendation
Chair: Yasunari Yoshitomi (Kyoto Prefectural University, Japan)
Co-Chair: Taro Asada (Kyoto Prefectural University, Japan)
OS8-1 Speech synthesis of emotions using vowel features of a speaker
Kanu Boku, Taro Asada, Yasunari Yoshitomi, Masayoshi Tabuse (Kyoto Prefectural University, Japan)
OS8-2 A system for facial expression recognition of a speaker using thermal image processing and feature vector spacecharacteristics
Taro Asada, Yasunari Yoshitomi, Yuu Nakanishi, Masayoshi Tabuse (Kyoto Prefectural University, Japan)© ISAROB 2013

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OS8-3 Facial expression recognition of a speaker using thermal image processing and reject criteria in feature vector
space
Yuu Nakanishi, Yasunari Yoshitomi, Taro Asada, Masayoshi Tabuse (Kyoto Prefectural University, Japan)
OS8-4 Music recommendation hybrid system for improving recognition ability using collaborative filtering andimpression words
Saya Yoshizaki (Works Applications Co., Ltd., Japan)
Yasunari Yoshitomi, Taro Asada (Kyoto Prefectural University, Japan)
Chikoto Koro (ISI Software Corp., Japan)

February 1 (Friday), 08:30-09:30
Room D
GS17 Virtual reality
GS17-1 Estimating the clothes characteristics with image and depth sensors for developing virtual fitting room
Yuka Matsuba, Hiroyuki Funaya, Kazushi Ikeda (Nara Institute of Science and Technology, Japan)
Jehan Jung, Rammohan Mallipeddi, Minho Lee (Kyungpook National University, Korea)
GS17-2 Spatial operation using skeletal recognition for virtual 3D work space
Yusuke Saito, Hiroyuki Nishiyama (Tokyo University of Science, Japan)
GS17-3 Breast Cancer Palpation Training System Using Five-Fingered Haptic Interface Robot and GPGPU
Takamitsu Kawai, Tetsuya Mouri, Takahiro Endo, Haruhisa Kawasaki (Gifu University, Japan)
GS17-4 ALife approach for body-behavior predator-prey coevolution: body first or behavior first?
Takashi Ito, Marcin L. Pilat, Reiji Suzuki, Takaya Arita (Nagoya University, Japan)

February 1 (Friday), 08:30-10:15
Room E
GS6 Human-welfare robotics
GS6-1 A study on the communication system using electrooculogram signals for persons with disabilities
Sou GO, Mingmin Yan, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)
GS6-2 A study on the electric wheelchair hands-free control system using the laser range scanner
Takayuki Murata, Hiroki Tamura, Koichi Tanno (University of Miyazaki, Japan)
GS6-3 On driving assistance using forearm vibrotactile feedback for wheelchair drivers
Aydin Tarik Zengin, Kazuki Nabekura, Hiroshi Okajima, Nobutomo Matsunaga (Kumamoto University, Japan)
GS6-4 Measurement and analysis of upper-limb essential tremor motion for tremor suppression with an exoskeleton
robot
Yosuke Ono, Kazuo Kiguchi, Nobuhiro Okada (Kyushu University, Japan)© ISAROB 2013

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February 1 (Friday), 10:30-11:30
Room A
GS13 Pattern recognition
GS13-1 Simultaneous Recognition of Multiple Actions using CHLAC features
Kyosuke Masumitsu, Takayasu Fuchida (Kagoshima University, Japan)
GS13-2 Hand gesture recognition using subunit-based dynamic time warping
Yanrung Wang, Atsushi Shimada, Rin-ichiro Taniguchi (Kyushu University, Japan)
Takayoshi Yamashita (OMRON Corporation, Japan)
GS13-3 Mathematical modeling of mirror based passive moiré target for orientation sensing
Md. Mahbub Hasan, Satoshi Tanemura, Kenbu Teramoto(Saga University, Japan)
GS13-4 A study about interactive musical editing system for automatic piano： Inferring performance expression byconsidering tempos
Tomohiro Inoue, Eiji Hayashi (Kyushu Institute of Technology, Japan)

Room B
GS16 Robotics III
GS16-1 A precision position arrangement of the SCARA robot by H∞ robust control
Yasuyuki Hirofuji, Feifei Zhang, Masanori Ito (Tokyo University of Marine Science and Technology, Japan)
GS16-2 Hybrid control with adaptive and state feedback control for Robot Hand
Kazuma Funahashi, Hiroki Goto, Feifei Zhang, Masanori Ito (Tokyo University of Marine Science andTechnology, Japan)
GS16-3 Hexa-Quad Transformation Control for Hexapod Robot Based on Support Polygon Pattern
Addie Irawan, Yee Yin Tan, Mohd Syakirin Ramli, Mohd Riduwan Ghazali (University Malaysia Pahang,
Malaysia)
Kenzo Nonami (Chiba University, Japan)
GS16-4 Design of a special cam-based high speed manipulator system
Jing Wu, Rui-Jun Yan, Kyoosik Shin, Chang-Soo Han (HanYang University, Korea)

Room C
GS3 Artificial living
GS3-1 A Real-Coded Genetic Algorithm Taking Account of the Weighted Mean of the Population
Naotoshi Nakashima, Yuichi Nagata, Isao Ono (Tokyo Institute of Technology, Japan)© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

GS3-2 Diesel Engine Fuel Injection Control with DDVC Hydraulic System
Xing Jin, Feifei Zhang, Masanori Ito, Kyoko Narukawa (Tokyo University of Marine Science and Technology,
Japan)
GS3-3 The Complex Network Study of Money and CO2 Emission Flows between Industrial Sectors in Asian Countries
using Input-Output Table
Xiang Gao, Takeshi Fujiwara, Kousuke Yoshizawa, Shuhei Miyake, Zeyu Zheng, Naoko Sakurai, Kazuko
Yamasaki (Tokyo University of Information Sciences, Japan)
GS3-4 On a serendipity oriented recommender system based on folksonomy
Hisaaki Yamaba, Michihito Tanoue, Kayoko Takatsuka, Naonobu Okazaki, Shigeyuki Tomita (University ofMiyazaki, Japan)

February 1 (Friday), 9:45-10:45
Room DOS2 Bio-inspired Theory and Applications
Chair: Kunihito Yamamori (University of Miyazaki, Japan)
OS2-1 Diffusion model analysis of OneMax problem
QinLian Ma, Kiminobu Koga, Kunihito Yamamori, Makoto Sakamoto, Hiroshi Furutani (University ofMiyazaki, Japan)
Yu-an Zhang (Qinghai University, China)OS2-2 Study of factor IX gene using regional structure
Hiroshi Furutani, Kenji Sueyoshi, Kenji Aoki, Kunihito Yamamori, Makoto Sakamoto (University of Miyazaki,
Japan)
OS2-3 Remarks on Recognizability of Four-Dimensional Topological Components
Makoto SAKAMOTO, Makoto NAGATOMO, Tatsuma KUROGI, Satoshi IKEDA, Masahiro YOKOMICHI,
Hiroshi FURUTANI (University of Miyazaki, Japan)
Takao ITO, Yasuo UCHIDA (Ube National College of Technology, Japan)
Tsunehiro YOSHINAGA (Tokuyama College of Technology, Japan)
OS2-4 Power Consumption Reduction by Dynamic Core-counts Control with Power Gating
Kazuyuki Tamoto, Kunihito Yamamori, Masaru Aikawa (University of Miyazaki, Japan)© ISAROB 2013

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February 1 (Friday), 11:00-11:30
Room D
OS14 Applications and Theories of Advanced Technology
Chair: Takao Ito (Ube National College of Technology, Japan)
OS14-1 Bottom-Up Pyramid Cellular Acceptors with n-Dimensional Layers
Makoto SAKAMOTO, Tatsuma KUROGI, Makoto NAGATOMO, Satoshi IKEDA, Masahiro YOKOMICHI,
Hiroshi FURUTANI, (University of Miyazaki, Japan)
Takao ITO, Yasuo UCHIDA (Ube National College of Technology, Japan)
Tsunehiro YOSHINAGA (Tokuyama College of Technology, Japan)
OS14-2 Homogeneous Systolic Pyramid Automata with n-Dimensional Layers
Makoto SAKAMOTO, Tatsuma KUROGI, Makoto NAGATOMO, Satoshi IKEDA, Masahiro YOKOMICHI,
Hiroshi FURUTANI, (University of Miyazaki, Japan)
Takao ITO, Yasuo UCHIDA (Ube National College of Technology, Japan)
Tsunehiro YOSHINAGA (Tokuyama College of Technology, Japan)

February 1 (Friday), 10:30-11:30
Room E
OS12 Intuitive Human-System Interaction
Chair: Masao Yokota (Fukuoka Institute of Technology, Japan)
Co-Chair: Kaoru Sugita (Fukuoka Institute of Technology, Japan)
OS12-1 Automatic identification of 3-D shape of head for wig manufacture
Yonghu Zhu, Yoshiaki Adaniya, Kazuhiro Tsujino, Cunwei Lu (Fukuoka Institute of Technology, Japan)
OS12-2 Retrieval method for augmented reality objects based on color impression
Kazunori Nakamura, Kaoru Sugita, Masao Yokota (Fukuoka Institute of Technology, Japan)
OS12-3 Preparatory data analysis for customer servicing interface of Buddhism statue ordering system
Tzu-Hsuan Huang, Kaoru Sugita, Masao Yokota (Fukuoka Institute of Technology, Japan)
OS12-4 Toward multimodal user interface for intuitive interaction with Buddhism statue ordering system
Tzu-Hsuan Huang, Kaoru Sugita, Masao Yokota (Fukuoka Institute of Technology, Japan)© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Effect of Playful Balancing Training – A Pilot Randomized Controlled Trial
Henrik Hautop Lund

Jari Due Jessen

Centre for Playware, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
hhl@playware.dtu.dk
www.playware.dk

development in the child. In play, the child always
behaves beyond his average age, above his daily
behavior; in play it is as though he were a head taller thanhimself. As in the focus of a magnifying glass, playcontains all developmental tendencies in a condensed
form and is itself a major source of development.” [4].

Abstract
We used the modular playware in the form of modular
interactive tiles for playful training of communitydwelling elderly with balancing problem. During shortterm play on the modular interactive tiles, the elderly
were playing physical, interactive games that werechallenging their dynamic balance, agility, endurance,and sensor-motoric reaction. A population of 12 elderly(average age: 79) with balancing problems (DGI averagescore: 18.7) was randomly assigned to control group or
tiles training group, and tested before and after
intervention. The tiles training group had statisticalsignificant increase in balancing performance (DGI
score: 21.3) after short-term playful training with themodular interactive tiles, whereas the control groupremained with a score indicating balancing problems andrisk of falling (DGI score: 16.6). The small pilot
randomized controlled trial suggests that the playfulinteraction with the modular interactive tiles has asignificant effect even after a very short time of play. The
average total training time to obtain the statisticalsignificant effect amounted to just 2h45m.

We believe that this quality of play may translate to manydifferent groups of people. However, often accounts forthe potential benefit of play e.g. on cognitive learning or
physical abilities remain of a descriptive nature.
Therefore, in this study, we engage in making aquantitative study of the effect of play, and we performthe study with a user group with whom play is often not
attributed. In particular, to make such a study, ourobjective became to test for dynamic balancing ofcommunity-dwelling elderly as a result of short-term
training playing with a specific playware, namely themodular interactive tiles [5].

Equipment - Modular Interactive Tiles
For the effect test of playful training, we used themodular interactive tiles as the training equipment. The
modular tiles have been described before in details, e.g.
[5, 6]. This is a fully distributed system made up of anumber of tiles that can connect to each other to form asurface, on which people can play different games, either
by themselves or by competing with each other. Each tile
contains a microprocessor, battery, IR communication, an
FSR sensor, and 8 coloured LEDs in a circle. The FSR
sensor can sense a step or a hit on a tile, and the LEDs
can shine up in different coloured patterns. This is used
to create interactive games on the modular interactive
tiles. Currently, there are more than 20 different games
for the modular interactive tiles. The games used for theintervention are described later.

Introduction
Research into playware [1] and modular playware [2]puts emphasis on the design of (modular) intelligent
hardware and software that creates play and playful
experiences. Often, it is believed that such playware canmediate other actions, for instance actions such as social
interaction and physical movement. It has been outlined
how the playware may act as a play force that pushes theuser into play dynamics [3]. When you are in such play
dynamics, you may feel transformed from the normal
state of being and feel as if forgetting about time andplace. Sometimes we may feel being able to perform
more or better when in play, which is interesting if such
performance may have a desired side effect. Indeed,
Vygotsky puts it like “Play creates a zone of proximal© ISAROB 2013

The tiles can easily be taken apart and put together tocreate different forms, which will demand different

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

pivoting during walking, and stair climbing (each ability
is scored 0-3). A DGI score lower than 19 points has
been associated with impairment of gait and fall risk [7,8].

interaction patterns by the users when playing the games,
e.g. the tiles can be changed from a 3*3 square to ahorse-shoe shape in less than a minute.

DGI was collected before and after a 2-months period,
during which the tiles training group performed anaverage of 12.5 group training sessions on the modular
interactive tiles. On average, each individual performed
13 minutes of playful training on the modular interactive
tiles at each of the group sessions (Fig. 2). The control
group continued their normal daily activities during the
2-months period.
Figure 1: Left: The interior of the modular interactive tiles.
Right: Assembling of a tile play field.

Intervention
We prepared a small randomized controlled trial as apilot study of dynamic balancing of elderly training withmodular interactive tiles, with the objective to evaluate
the effect of short-term playful training.
Twelve community-dwelling elderly people (averageage: 79 (66-88)) with balancing problems were recruitedfrom the waiting list for voluntary balancing training
amongst elderly in Lyngby-Taarbaek Municipality in
Copenhagen, Denmark. The population of elderly withbalancing problems was randomly assigned to control
group (CG) or tiles training group (TTG). The random
assignment was done by lottery tickets picked by a third
person blinded to the intervention.

Figure 2: The group session for playful training in the tiles
training group.

The training sessions with the elderly group was plannedin cooperation with physiotherapists from LyngbyTaarbaek Municipality. In order to secure a valid result, a
protocol for the intervention was designed, in which 4
games were chosen that the elderly should try out: “Color
Race”, “Final Countdown”, “Island” and “Concentration
Color” (see Fig. 3 for the protocol). “Color Race” is agame where one of the tiles will light up in red. When
stepped on, the tile will go black and another will lightup. The player needs to step on as many tiles as possible
within 30 seconds. In “Final Countdown” all the tiles willlight up in purple and each tile will start counting downby turning off one of its 8 LED’s every second. When theplayer steps on a tile, the countdown will restart. If any of
the tiles gets all its LED’s turned off, the game is lost.
“Island” can be compared to the children game “TheFloor is Made of Lava”. Here, the player needs to movefrom green tile to green tile or island to island, without
stepping on the other tiles. Once stepped on, the tile will
start counting down and the player has to go on to thenext before the tile turns off. This game willautomatically adjust the speed according to the player’s
performance. The last game “Concentration Color” is a

The population from both the tiles training group and thecontrol group was tested for dynamic balancing using the
Dynamic Gait Index test before (pretest) and after(posttest) the intervention. The tests were performed bytwophysiotherapists
fromLyngby-Taarbaek
Municipality, who were both blinded to the intervention
and who did not take part in the intervention.
The Dynamic Gait Index (DGI) contains 8 different testsof balancing. The DGI was developed by ShumwayCook and Woollacott [7] as a clinical tool to assess gait,balance and fall risk, and it is viewed as an especiallysensitive task, since it evaluates not only usual steadystate walking, but also walking during more challenging
tasks. The eight abilities assessed are: steady-state
walking, walking while changing gait speed, walking
while moving the head vertically and horizontally,walking while stepping over and around an obstacle,© ISAROB 2013

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memory game where all tiles turn white but they arehiding a colour ‘underneath’. When stepped on, a tile willshow the color it is “hiding”. The object of the game isthen to find all the matching color pairs (red, green, blue,
yellow, purple, light blue).

of “Island” and 1-2 rounds of “Concentration Color”
depending on the time available (see Fig 3).

Results
The population of elderly with balancing problems wastested with the DGI, and had an average score of 18.7. In
the DGI test, below 19 is viewed as the threshold forhaving balancing problems and risk of falls. The
population was randomly assigned to control group or
tiles training group. Data were analyzed for statisticalsignificant differences between the control group and thetiles training group, and for increase of mean score on
DGI.
After the random division into the two groups, thecontrol group and the tiles training group did not differ atbaseline (DGI mean score: 18.3 vs. 19.0). After the 2months period, there was significant difference in change
of DGI score period with the control group decreasing
DGI score by 9.3% and tiles training group increasing
DGI score by 12.3% on average. A two way repeatedmeasures ANOVA (Student Newman-Keuls method)resulted in no statistical significant differences atbaseline. Also, there was no statistical significant
difference in the control group’s performance over time.
There was statistical significant increase in performanceof the tiles training group over time (p<0.05). Also, there
was statistical significant difference between the control
group and the tiles training group after intervention(p<0.05). DGI mean score after intervention was 16.6 forthe control group, and 21.33 for the tiles training group,or an average decrement of 9.3% for the control group
and an increase of 12.3% for the tiles training group.

Figure 3: The training protocol for the tiles training group.

The score for control group and intervention group ispresented in table 1.

The training group was put into two groups where eachgroup had one hour of training. The group was placedaround the tiles that to begin with were placed in a groupof 3x3 tiles. This setup of the tiles was changed before
playing the game “Concentration Color” where theplatform was changed to a “horse shoe” formation.

Table 1. Results of the DGI pre-test and post-test after twomonths of the 12.5 sessions training with modular interactive
tiles.

Control group
Training group

The sessions was built up of small “rounds” of play,where each participant played two minutes (though only
1,5 minutes for the game Island) of each game before
having a small break while the rest of the group played.

Post-test
16.6
21.3

Improvement
-9.3%
12.3%

SignificanceNS
P<0.05The community dwelling elderly with balancing
problems seemed to be at high risk of falling if not
subject to any training, whereas those who performed
training increased their dynamic balancing abilities. A
DGI score of <19 is associated with impairment of gait
and fall risk [7, 8], so the statistical significant difference

Each training session was organized like this: 2 rounds of“Color Race”, 2 rounds of “Final Countdown”, 2 rounds© ISAROB 2013

Pretest
18.3
19.0

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between DGI score of 16.6 of the control group and DGIscore of 21.33 of the tiles training group is important.

References[1] H. H. Lund, T. Klitbo, and C. Jessen, C. Playware
Technology for Physically Activating Play, Artificial
Life and Robotics Journal, 9:4, 165-174, 2005.
[2] H. H. Lund, and P. Marti. Designing Modular
Robotic Playware. In Proc. of 18th IEEE Int. Symposium
on Robot and Human Interactive Communication (RoMan 2009), IEEE Press, 115-121, 2009.
[3] C. Jessen and H. H. Lund. On play forces, play
dynamics, and playware. Unpublished manuscript.
[4] L. S. Vygotsky, L.S. Mind in society:Thedevelopment of higher mental processes. Eds. and trans.
M. Cole, V. John-Steiner, S. Scribner and E. Souberman.
Cambridge, MA: Harvard University Press, 193035/1978.
[5] H. H. Lund. Modular Robotics for Playful
Physiotherapy, in Proceedings of IEEE Int. Conference
on Rehabilitation Robotics, IEEE Press, 571-575, 2009.
[6] C. B. Nielsen and H. H. Lund. Adapting Playware to
Rehabilitation Practices. International Journal ofComputer Science in Sport, 11:1, 2012.
[7] A. Shumway-Cook and M. Woollacott. Motor
Control: Theory and Applications. Baltimore, MD:
Wilkins & Wilkins; 1995.
[8] A. Shumway-Cook, M. Baldwin, N. L. Polissar, andW. Gruber. Predicting the probability for falls incommunity-dwelling older adults. Phys Ther.
1997;77:812–819.

During the sessions the elderly expressed an increase in
their abilities and endurance in normal day activities. At
the posttest one participant expressed that others had told
her that they could see and feel an increase in her general
movements, endurance and balance.

Discussion and Conclusion
Despite the limited sample set, the trend of the pilot study
is clear: there is statistical significant effect from shortterm playful training with the modular interactive tiles.
The community dwelling elderly with balancing
problems seemed to be at high risk of falling if not
subject to any training, whereas those who performed
training increased their dynamic balancing abilities. A
DGI score of <19 is known to signify risk of falling, so
the significant difference between DGI score of 16.6 ofCG and DGI score of 21.33 of TTG is important, as it
indicates that it may be possible to ensure that such a test
population may reduce risk of falling by training on themodular interactive tiles.
The study also shows how playful hardware (playware)
can be used to create a quantifiable effect on people byusing play as a motivator for engagement and training.
The study has certain, clear limitations. First andforemost, the study was performed as a pilot study with
only 12 subjects. A larger randomized control trial (RCT)should be performed based upon the indications of thissmall pilot study. Despite such limitations, it isinteresting that the effect of training with the modular
interactive tiles amongst the community-dwelling elderlywith balancing problems was obtained after just 13
minutes of training per session for an average of 12.5
training sessions. Hence, the average total training time
to obtain this statistical significant effect amounts to justaround 2h45m, which is very little training time.

Acknowledgement
The work is partly sponsored by the project Patient @Home. The authors would like to thanks participatingelderly in the intervention group and control group,
therapists from Lyngby-Taarbaek Municipality, andcolleagues from the Center for Playware, DTU.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013Engaging through her eyes
Embodying the perspective of a robot companion
Patrizia Marti1, Jelle T. Stienstra12
1Social, Political and Cognitive Science Department, University of Siena, Italy
Department of Industrial Design, Eindhoven University of Technology, Netherlands(Tel: +390577234743, Fax: +390577234754)

2

patrizia.marti@unisi.it, j.t.stienstra@tue.nl
Abstract: In response to a change in the use of computers and interactive technologies, traditional Human-Computer
Interaction concepts of usability, efficiency and productivity have progressively been enriched with other concepts such as
curiosity, empathy, playfulness and affection [1]. Korhonen et al. [2] state that the acceptance of a product depends not only on
its utilitarian properties but also on non-utilitarian ones including playfulness. However, even if there seems to be nearconsensus on the importance of designing interactive systems beyond rational and functional requirements, the way in which
this can be achieved is still an open research issue. In this paper we describe our design approach to develop an embodied andplayful mobile interface to control a robot companion in a smart home environment.
A major challenge of the research is to engage an older person in rich, empathic and playful interaction with a robot to
encourage a prolonged, subtle, and stimulating effect beyond the initial encounter [3]. This challenge is explored through thedesign of innovative concepts of playful interaction embodying the perspective of the robot companion.
Keywords: embodied interaction, graphical user interface. playful interaction,
In the following we first describe the theoreticalframework that inspired our design. Later we illustrate the
GUI and the interaction modalities. Finally we discuss thecharacteristics of the playful experience associated toembodying the robot’s perspective. For doing this, we apply
the Playful Experience Categories as defined by Korhonen
et al. [2]. These researchers studied the pleasures of playwith the aim to understand the underlying fundamental
elements of pleasure or play and inform designers. Theydeveloped a framework elaborating and expanding themodel defined by Costello and Edmonds [5] who derived
thirteen categories of pleasure: Creation, Exploration,
Discovery, Difficulty, Competition, Danger, Captivation,
Sensation, Sympathy, Simulation, Fantasy, Camaraderieand Subversion.
Since these categories focus on pleasurable playfulinterfaces in interactive artworks, the model has been
expanded by Korhonen et al. to address the design ofutilitarian products that elicit playful experiences. Moredesign practical notions to achieve playful interaction canbe found in the work of Bekker et al. [6]. Together withdirect motivational feedback and open-endedness Bekker et
al. [7] use social player-interaction patterns as one of theirthree design values for creating playful interactions. The
social player-interactions exploited in their approach is bycollaborative and competitive richness of interaction.
The whole framework is fully illustrated in the final
section of the paper and applied to our concept of embodied

1 INTRODUCTION
Embodied interaction describes how meaning ininteraction emerges from a bodily and contextual
experience. It refers to the way our perception of physicaland social phenomena develops in interplay with the world
around us. Dourish defines embodied interaction as “thecreation, manipulation and sharing of meaning throughengaged interaction with artefacts” [4] (p. 126). Since its
original definition, the concept of embodied interaction has
inspired a number of design approaches considering“embodiment” as the bridge between the physical anddigital. In particular this concept emphasises theopportunities of action that the physical world offers andthat should not be neglected in the design of digitalinteractive products.
With this approach as a source of inspiration for thedesign, this paper explores the possibilities of embodied
interaction in controlling a robot companion, namely CareO-Bot, by an older person in the home environment.
Following the principles of designing for embodiment, agraphical user interface (GUI) has been developed
exploiting perspective taking to engage in playfulinteraction modalities with the robotic system.
In this context, the concept of playful interaction
explores and expands the range of interaction modes
between the digital and the physical enabled by the GUI.

© ISAROB 2013

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(Care-O-Bot and user(s)) and physical states of the tangible
objects and environment.

perspective taking. The objective is to evaluate if theapplicationofembodiedprinciples(continuity,intersubjectivty, contextuality and personalization), might
help in engendering playful and engaging interactions.

2.1 Interaction dynamics
Through a tablet interface, the person can see andexecute action-possibilities that can be performed by orwith the robot at the moment of interaction. The actionpossibilities are organized by relevance. In time, theelderly's usage of action-possibilities will influence theirrelevance with respect to a specific context of use.

2 DESIGN
A context dependent and personalized interaction
capitalized within perspective taking is one of the conceptsof social behaviour that we have been developing in the
project. It emerges in an active interplay in context where
the elderly takes on the robot’s point of view by looking
through its eyes. The concept is based on the notion ofintersubjectivity, a phenomenological notion thatemphasizes that shared contextual activity and consensus isessential in shaping empathic relationships. Our graphicaluser interface containing context depending actionpossibilities, is further build upon philosophicalperspectives derived from ecological psychology [8] andphenomenology of perception of Merleau-Ponty [9] Anaffordance, in Gibsonian sense, is an action-possibility thatis enabled by our bodies action-possibilities in relation to
those of the 'bodies' in the environment. In the context ofthe graphical user-interface we approach action-possibilities
in the relation the robot has towards objects in theenvironment.
By extending the reciprocal character of affordancewith the subjective experience of context following
Merleau-Ponty's thought, we approach action-possibilities
in context depending yet embodied manner. Thus actionpossibilities are relations between actors and objects orenvironment. In this case the Care-O-Bot towards objects
and environment, therefore the graphical user-interface
requires information about states of the objects to behandled by the Care-O-Bot, about the states of the Care-OBot himself, the environment as well as the states of theuser and its unique approach to its world.
In other words we require a larger picture of context.
Lets say, an action-possibility ‘making coffee with sugar’
requires to know where the user is located (to bring thecoffee to), whether the user is thirsty (not to provide coffee
over and over again). It further needs from the environmentand objects involved whether there are empty and cleancups, as well as sufficient coffee, a clean coffee machineand so on. The likelihood of an action-possibility is also
defined by previous preferences and rituals between the
robot and the elderly, that can hold in interaction in the longterm. We thus utilize desires and factual states of the actors© ISAROB 2013

Fig. 1. Seeing through the eyes of the robot, including
context depending action possibilities, varying in size
depending on their likelihoods.
While selecting a desired action-possibility throughclicking the Care-O-Bot will start performing. The elderly
persons can see how the task is performed from the Care-OBots eyes. On completion of the task, the displayed actionpossibilities are updated as the context has changed throughthe task executed by the Care-O-Bot (e.g. after closing thedoor, ‘closing the door’ is most likely not actionableanymore and has been substituted by another actionpossibility namely ‘open the door’).
The robot-view displays what the Care-O-Bot is looking
at. This view is covered with a mask indicating a clear
vision in the centre and cloudy one outside the centre. As if
looking through the eyes of the robot.
While looking the action-possibilities are displayed on
the actual objects in the environment. In other words what
is behind the robot can not be selected. In order to turn the
robot, to look around, the robot can be controlled with aswipe movement. A direct action-perception loop is appliedin the interaction, while swiping has started, the visiondisplayed on the screen starts moving (even before therobot does) using the full image that is used. In other words,

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competition against oneself, opponent or system). The
elderly person can together with the robot achieve things
that would be impossible without its help. The experienceof Completion, finishing and closure, in relation to an
earlier task or tension as category is thus addressed in thecollaborative making things work. The nature of thiscollaborative relationship is of Fellowship (the experienceof friendship, fellowship, communality or intimacy) and
Nurture (the experience of nurturing, grooming or
caretaking)
Collaborative Discovery (the experience of discoveringa new solution, place or property) and Exploration (theexperience of exploring or investigating a world,
affordance, puzzle or situation) can be considered as corevalues of our contextual action-possibility interface design.
The embodiment of context, achieved through the eyes of
the robot enables the older person to discover actionpossibilities and thereby the opportunities the robot
provides within the independent live of the elderly person.
Seeing what is possible by collaboration, and exploring theworld that is shared between elderly person and robot
highly provokes playful interaction.
Expression (experience of creating something or
expressing oneself in a creative fashion) might occur when
action-possibilities emerge through use. But till thishappens, action-possibilities are straightforward embodied
in the actual possibilities the robot has with its
environment. This further limits the Fantasy (the experienceof make-believe involving fantastical narratives, worlds orcharacters) one can have when not considering the
explorative element of finding action-possibilities byplaying with the eyes.
Sensation (a meaningful sensory experience) is
stimulated as the robot is part of the direct world of theelderly person. Even though the movements of performing
action-possibilities are stiff and ‘robot’-like, the robot is
actually there to be part of life. The graphical-user-interface
as a tablet filled with ‘representational action-possibility
labels’ is flat and does not allow for meaningful sensory
experience)
The category Simulation, an experience of perceiving a
representation of everyday life, is rooted in gaming where
things are to be done that can normally not be done. This
category profoundly contradicts our approach as we intend
to achieve an embodiment within a direct interaction withthe real world. Therefore we attempt to bypass any
representation and utilize the direct sensorial world as it is

the mask hides part of the view, which will appear on directinteraction.

Fig. 2. By swiping, the elderly person can look through theeyes of the robot and see around. It allows for exploring
action-possibilities.
The robot-view contains a perspective from the Care-OBot on the environment as elaborated before. This isextended with certain expressions of how the robot feels.
When the Care-O-Bot is sleepy (due to performing manytasks) the vision (and thereby mask) will express this to theuser besides the ‘tired’ movement of the executed task,
mimicking blinking eyelids.

3 DISCUSSIONAs anticipated in the introduction, we conclude ourpaper by discussing the Playful Experience Categories
applying the framework by Korhonen et al. [2]. Our aim isto evaluate if embodied interaction realised through theapplication of continuity, intersubjectivty, contextuality andpersonalization principles, might help in engendering
playful and engaging interactions.
The categories Captivation (the experience of forgetting
one’s surroundings), Control (the experience power,
mastery, control or virtuosity) and Challenge (theexperience of having to develop and exercise skills in achallenging
situation)areclosely
relatedtoCsikszentmihalyi’s flow theory [10]. He describes theexperience of being in the Flow as being constituted by abalance between one’s skills and the challenge; a thin line
between boredom and difficultness. Even though we
consider captivation to be an objective, evidence of suchachievement is yet to be found. As we do not intend to vary
the challenge, we hope to achieve this experience throughthe engagement or embodiment of context the elderly
person has with seeing through the eyes of the robot.
Our GUI design deliberately chooses collaboration overCompetition (the experience of victory-oriented© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

person with insights of what is possible for the robot to doin a continuous flow of interaction.

to the elderly person and robot and not address digital
abstractions.
Subversion, the experience of breaking social roles,
rules and norms, might be achieved as meaning emerges ininteraction. Though we did not specifically design for thismatter.
Sympathy, described as an experience of sharing
emotional feelings, is addressed in our embodied
perspective interface. Perhaps we have to be more nuancedin this as could argue that the robot does not have feelings,
we consider a limitation of an action-possibility informedby the robots properties a feeling of the robot. In otherwords, in case the robot is not able to perform a difficult
task informed by a low battery and overused motors, thegraphical-user-interface does reflect these inner states by
not showing action-possibilities.
The Thrill category, defined as an experience of thrill
derived from an actual or perceived danger or risk, theEroticism category (the experience of sexual pleasure or
arousal), the Sadism category (the experience of destructionand exerting power over others), Relaxation (the experienceof unwinding, relaxation or stress relief. Calmness duringplay), and the Suffering category (experience of frustration,
anger, boredom and disappointment typical to playing) arebeing purposely avoided. The Suffering category, in ourview, contradicts with the Captivation category in which theabove mentioned experiences are ought to be avoided by adynamic challenge that matches the elderly person’s skills.
The playful interaction design values by Bekker et al [7]as introduced before can be found in our work as well. The
direct motivational feedback design value is addressed via
continuous-sustained action-perception loops as well as thecollaborative achievement of action-possibilities. Here thus
we approach the social player-interaction pattern byinteractions of collaborative nature between elderly person
and robot. The open-endedness design value is provided by
the context and the action-possibilities of the robot itself,
yet this can be seen as a limiting factor more than an
opening. Open-endedness concerns the opportunity tocreate interaction paradigms in contrast to predefined rules
and objectives. To come forth to this design value not only
the meaning should emerge within interaction between
elderly person and robot but also the growth of actionpossibilities from the robots side.
The novelty of the approach relies on an innovative
concept of interaction, based on perspective taking. The
robot control is addressed via the resonation of older person
and robot through shared viewpoints; providing the older© ISAROB 2013

ACKNOWLEDGEMENTS
The research leading to these results has received
funding from the EU-FP7 ACCOMPANY project under
grant agreement n°287624. We thank Ernesto Di Iorio forhis support with implementing the GUI and Michele
Tittarelli for his collaboration with the graphical design.

REFERENCES[1] Marti, P., Overbeeke, C.J. (2010), “Il bellodell’interazione”. In S. Bagnara, M. Mattioda (ed) Index:
Un percorso di specializzazione in Interaction Design
Experience, Franco Angeli, Milano, 2010, ISBN 978-88568-3206-8.
[2] Korhonen, H. Montola, M. Arrasvuori, J. (2009),
Understanding Playful User Experience Through Digital
Games In Designing Pleasurable Product and Interfaces,
DPPI09, 13- 16 October 2009, Compiegne University ofTechnology, Compiegne, France.
[3] Stienstra J.T., Marti P., (2012), Squeeze Me: Pleasegently. Proceedings of the 7th Nordic Conference on
Human-Computer
Interaction
NordiCHI
2012,
Copenhagen, 14-17 October 2012, pp. 746-750.
[4] Dourish P. (2001), Where The Action Is: The
Foundations of Embodied Interaction. MIT Press, 2001.
[5] Costello, B. Edmonds, E. (2007), A Study in Play,
Pleasure and Experience Design. In Designing Pleasurable
Products and Interfaces conference, DPPI’07, 2007 (ACM
Press), pp.76-91.
[6] Bekker M.M., Sturm J and Eggen B. (2010), Designing
playful interactions for social interaction and physical play.
Personal Ubiquitous Comput. 14, 5 (July 2010), 385-396.
DOI=10.1007/s00779-009-0264-1[7] Bekker M.M., Sturm J, Barakova E (2008), Designing
for social interaction through physical play, In: Extended
abstracts of conference on fun and games 2008, pp 62–67[8] Gibson, J.J. (1986), An ecological approach to vis
ual perception. London, UK: Lawrence Erlbaum Associates.
[9] Merleau-Ponty, M. (1962), Phenomenology of Perc
eption (C. Smith, Trans.). New York: Humanities Press.
(Original work published in 1945)[10] Csikszentmihalyi, M. (1990), Flow: The Psychology
of Optimal Experience. New York, NY: Harper & Row.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fable: Socially Interactive Modular RobotArnþór Magnússon, Moises Pacheco, Mikael Moghadam, Henrik Hautop Lund, and David Johan Christensen
Technical University of Denmark, Center for Playware, DTU Electrical Engineering, Elektrovej, building 325, DK-2800 Kgs.
Lyngby, Denmark{arnma, mpac, mikm, hhl, djchr}@elektro.dtu.dk
Abstract: Modular robots have a signiﬁcant potential as user-reconﬁgurable robotic playware, but often lack sufﬁcient sensing forsocial interaction. We address this issue with the Fable modular robotic system by exploring the use of smart sensor modules thathas a better ability to sense the behavior of the user. In this paper we describe the development of a smart sensor module which
includes a 3D depth camera, and a server-side software architecture featuring user tracking, posture detection and a near-real-timefacial recognition. Further, we describe how the Fable system with the smart sensor module has been tested in educational and playfulcontexts and present experiments to document its functional performance.
Keywords: human-robot interaction, modular robots, playware, vision

1 INTRODUCTION

2 RELATED WORK

Fable is a modular robotic platform under development,
aimed at enabling non-expert users to design and develop socially interactive robotic creatures from various types of modules. A user can create their own Fable robots by assemblingits modules into some conﬁguration and programming it withthe desired behavior based on a simple programming language.
We explore the use of smart sensors modules, which are neededto achieve social interaction between a human and a robot. The
smart sensor enables the robot to sense the user’s behavior andrespond accordantly. Smart sensors modules could for example
be cameras or microphones combined with appropriate processing and mechanical encapsulation able to recognize the face or
voice of the user. Mixing smart sensors and modularity introduces a set of challenges due to high bandwidth and processing
beyond what a low power microcontroller, as typically used inmodular robots, can handle.
This paper presents the design and tests of a smart sensor
module for the Fable platform. The module contains a 3D camera, Asus Xtion Pro Live (similar to Kinect by Microsoft) andis equipped with connectors for compatibility with other Fable
modules. The smart sensor module and corresponding softwarearchitecture provides the user with a simple programming interface for user tracking, posture detection and facial recognition.
Our working hypothesis is that smart sensor modules will enable
non-expert users to construct socially interactive and playful Fable robots and that this will be more motivating for the user thana system with no or only primitive sensors.
In the rest of this paper we ﬁrst describe related work in Sec.
2. The Fable system is described in Sec. 3 and the software architecture for smart sensor modules is described in Sec. 4. Sec. 5
describes tests performed to study the system performance andthe use of the system in an educational and an playful context.

© ISAROB 2013

Modular robots are comprised of independent robotic modules that can be attached and detached from one another, being
able to construct various robot morphologies depending on the
scenario [1]. Some modular robotic systems are able to selfreconﬁgure [2, 3] which allows the robots to shift their own
shape by rearranging the connectivity of their parts to adapt tocircumstances [4] [5], form needed tools [6] or furniture [7].
In this paper we explore user-reconﬁgurable robots, which allow non-expert users to physically construct their own robots
from different types of modules (such as passive, actuators andsensors). This type of robots are particular suited for applications such as rapid robot prototyping [8], play [9, 10], rehabilitation [11, 12], composing music [13], and education (e.g. Mobot,
Barobo, Inc. or LEGO Mindstorms). The modularity and openendedness of these systems motivates the user to be creative,
reﬂect, and iterate on their creations whereby they learn, train,
or simply enjoy themselves.
In this paper we are considering modular robots for playfulsocial interaction. Social interaction is being widely utilized fornon-modular robots [14]. The interactive and animated iCat [15]is a type of user interface for controlling various media in a morenatural way. Probo [16] [17] is a huggable robot designed to
improve the living conditions of children in hospitals as a teleinterface for entertainment, communication and medical assistance. MeBot [18] is a telerobot that allows people in different places to feel present and to allow for social expressions,
not only from video and audio, but also expressive gestures andbody pose. Playware technology [19] has also been developed asa mediator for playful social interaction for over long distances.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

3 FABLE: MODULAR ROBOTIC PLATFORM
3.1

Concept

The vision of the Fable project is to build a novel modular
robotic platform, which enables non-expert users to assemble innovative robotic solutions from user-friendly building blocks. A
robot is assembled by connecting two or more modules together
where each module provides functionality to perform a speciﬁc
task, such as sensing users, environment or actions, moving ormanipulating its surroundings. With an easy-accessible softwaretool-kit provided, the user can create custom functionality andeventually we plan to make it easy to share their creations online with other users to try out, for inspiration, or as a starting
point for their own creations. The objective is to develop thisplatform to enable users to realize their own innovative ideas.
3.2(a) Humanoid.

(b) Four legged robot.

Hardware(c) Snake.

The Fable is a heterogeneous chain-based modular robotic
system which consist of various modules, such as different types
of joint, branching and termination modules [20]. Joint modules are actuated robotic modules used to enable locomotion andinteraction with the environment. Branching modules connects
several modules together in tree-like conﬁgurations. Termination modules may add structure, a visual expression, additional
sensors, or actuators (e.g. grippers or wheels).
Every module, depending on its type has one or more mechanical magnetic connectors. The connectors are designed toallow rapid and solid attachment and detachment between modules. Further, the connectors are scalable to allow modules ofdifferent sizes to be combined and designed to allow neighborto-neighbor communication. Flanges mechanically lock the connection against twisting and bending and only allow disconnection by pulling on the axis perpendicular to the connecting surfaces.
Each module will be equipped with an onboard battery andan electronic board with an Atmel Atmega2561 microcontroller.
Each board has four IR channels for communicating with neighboring modules and in addition, have the possibility to connect
a ZigBee chip for wireless communication between modules ora PC. All modules have an onboard accelerometer and a gyroscope. The actuator modules have in addition a connector
to power and can control several daisy chained AX-12A Dynamixel servos. These custom made electronic boards are in theprocess of being integrated into the system. Meanwhile we haveused a compatible embedded system, CM-510, for centralized
control of the modules.
The Fable system is still being extended with new module
types to allow a wide range of different robots to be created.
Fig. 1 shows examples of humanoid, snake and walking robots
constructed with the current Fable system.

© ISAROB 2013

Fig. 1: Example conﬁgurations with the Fable system.

3.3

Sensor Module Design

The Fable system is designed to include primitive and smart
sensors:
Primitive Sensors sense simple signals, often from the environment, that can be processed by the small 8-bit microcontroller, also controlling the module, to extract meaningful low-level features (e.g. brightness, distance and temperature).
Smart Sensors sense simple or complex signals and process
them to extract higher-level information about the user, therobot or its environment. Possible example includes, emotion categorization based on audio signals, gesture recognition based on accelerations, and posture detection based on
3D depth signals.
The design of smart sensor modules should preferably befully embedded to make the robot system self-contained and independent of external devices. This is however challenged by
the limited space, processing power and battery capacity which
can be embedded inside a module. Another option is to ofﬂoad
the sensor outputs to a server through a wireless transport tohave maximum processing power, but that also introduce challenges, such as the capability to transfer large amount of data
wireless, potential delays in the system, and a higher hardware
complexity. At this stage of the development we have chosen touse a server-based solution with a software architecture that canbe ported to an embedded device in the future. The smart sensor module presented in this paper is therefore connected using

10The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013a tether to a server for processing and feature extraction. This
high-level information is then offered as services to the modules wirelessly and can be accessed by the user application (asexplained in Sec. 4).

<<hardware>>

Camera4 FABLE SOFTWARE ARCHITECTURE

Server (ROS)

The section describes the software architecture of the Fable
system with special focus on the server-side architecture to process smart sensor signals and provide them as services. The architecture is split into three main components: the user computer, the embedded device(s), and the server (see the diagramin Fig. 2.).

CameraRecognition

4.1

#1
User
Tracker

User Computer Architecture

To enable non-expert users to program their own Fable robots,
we provide the LOGO programming language for user application development. LOGO is an educational programming language coming from Seymour Papert’s constructionism tradition[21] and gives a more natural English-like syntax than C (e.g.
no braces). We use a restricted version of LOGO, called PicoLOGO [22], which has a reduced instruction set and limited datatypes. An application is created by the user, which is then compiled into a byte-code representation and uploaded through a serial connection or a USB ﬂash drive. This architecture makes it
simple for the user to program the robot and easy for us, the developers, to add new Fable speciﬁc primitives to the PicoLOGO
instruction set.
Listings 1.1 depicts an example LOGO program where the3D depth camera is utilized to detect the angle of the left elbow
joint of a user and maps it to a speciﬁc motor of the robot. Notethat the symbol ’;’ starts a comment line and that the program
will start on the function onstart.

Embedded Connection

Embedded Device (ASE)ROS ConnectionAPI

LOGO Virtual Machine

Listing 1.1: Example PicoLOGO application.

User application(byte code)

constants [[userId 1][left_elbow 0][motorId 1]]

User Computer

to mirror_elbow; Define a variable
let [angle 0]Compiler

Serial/USB Key

User application(source code); Assigns the variable with elbow angle
make "angle get_joint_angle userId left_elbow

Fig. 2: Diagram of software architecture.

; Set the motor position to angle
set_motor_pos motorId :angle
wait 1end© ISAROB 2013

#2
PostureDetection#3
User
Recognition

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

to onstart; Requests for left elbow data
use_user_joint left_elbow; Infinite loop
forever [mirror_elbow]
end

4.2

Embedded Architecture

Every module is controlled by an embedded device containing a small microcontroller. The microcontroller runs the
Assemble-and-Animate framework (ASE) [23]. ASE is a ﬂexible and extendible control framework targeted for modular
robots, it provides high level of abstraction, libraries of algorithms, components and an asynchronous event-driven system.
A virtual machine (VM) able to execute PicoLOGO code compiled into byte-code [22] is included as a process in ASE. Hence,
the user application is executed by the VM, which may call functions that use smart sensor services from the server-side. The
VM abstracts many of the system resources away, such as low
level details of sensors, motors and distributed processing. This
enables users to more quickly develop applications and uploadthem to the robot.
4.3(a) Red dots showing joint
positions available for eachuser.

Fig. 3: Image showing joint positions and angles available foreach user.

These features form the basis for other components for further
processing but are also offered as a service for the user application layer. An example application we have developed with thiscomponent is a Fable robot conﬁgured as a humanoid torso withthe 3D camera module on top. The robot then mimics or mirrors
the users shoulders and elbow movements with its two degree of
freedom joint modules (the application is an extension of Listings 1.1).

Server Architecture

The server architecture is designed to process signals fromsmart sensors and offer them as services to the user application. We utilize the Robot Operating System (ROS) [24] on theserver, offering graph node structure, to create loosely coupled
components which allows for task abstraction and code re-use.
ROS provides a messaging framework for communication between components and also offers access to large sets of software libraries such as OpenCV and many community createdcomponents. The components role can vary from receiving sensor inputs, extract features or offering services to the user application running on the modules. The following subsections will
describe some of these components and example applications.

Posture detection component An efﬁcient way for humans tocommunicate is verbally, but in many cases it is supported bybody language, such as body posture, gestures, facial expression
and eye movements. Body language can provide clues to attitude
or state of mind of a person, or simply to give commands ordescriptions.
To enable such a communication between a robot and theuser, we implemented a posture detection system where therobot can detect pre-deﬁned postures stored in a database. A
posture is a set of labeled angles, where each angle representsa joint on the user, gathered from previously described user
tracking component. The user’s posture is continuously being
compared to the database, detection is considered when the Euclidean distance between two sets are below a certain threshold,and then an event is triggered. This component #2 can be seenin Fig. 2.
This functionality allows for a simple interaction between
users and the Fable system. As an example we have developed

User tracker component The robot must be able to sense users
and their movements in order to enable interaction based on
body language.
As a basis for tracking and detecting users we utilize OpenNI1
user tracker module and Asus Xtion Live Pro (similar to Kinectfrom Microsoft). It allows for user detection and tracking by
providing coordinates for all major limbs of the body as well
the angles of the joints. The component is placed on the server,as seen in Fig. 2, component #1.
1

OpenNI (Open Natural Interaction) is a framework created for sensor devices published under LGPL license. http://openni.org© ISAROB 2013(b) Showing different angles
of arms and legs that could be
calculated from the joint positions.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

5 TESTS 

5.1

The Fable system is designed to enable the construction of
playful interactive robots. As a proof-of-concept, a pilot user test
was performed with a 6 year old girl who interacted with a simple upper humanoid torso. The robot was pre-programmed witha LOGO application and pre-assembled from Fable modules.
The robot was equipped with a smart sensor module, the 3D
depth camera, directly connected to a server which was running
the software architecture described in Sec. 4. An embedded controller, the CM-510, was running the LOGO application to control the robot’s actuators based on the services provided by theserver. Speciﬁcally, the LOGO application would use the user
tracking service to mirror the girl’s movement of hers shoulders
and elbows. Two special postures, identiﬁed with the posturedetection service, would start and end application.
We observed the girl’s reactions during the pilot test (seeFig. 5). She was given the basic instructions about how a speciﬁc posture would trigger the mimicking. As soon as the robot
started moving she reacted with positive excitement. Soon she
realized the connection between the movement of her hands andthe robots. As the play progressed she tried to go beyond thelimits of the movements the robot can perform, for example,
try to make the robot clap its hands and dance, but still appearing to be enjoying herself. In the end (after around 5 min.) she
started to complain about her tired arms, as she had been moving
them the whole time. The system was not glitch-free; there were
occurrences where an unintended posture was detected, which
resulted in ending the mimicking. Overall, this pilot-test conﬁrmed that it was possible with the system to construct robotic
application that would trigger play dynamics and interaction between a child and the robot. Further testing is needed to explore
this in greater detail.

  

  


  

 ! "$

%!

Fig. 4: Facial recognition process.

another mimic application, again with the modules conﬁgured
into a humanoid torso and the 3D camera module on top. In thisgame the robot does a certain posture with its arms and thenthe user mimics that posture, when the robot detects the correct
posture it makes a new one, and so on.

5.2
Facial recognition component A facial recognition service(see component #3 in Fig. 2) is provided in order to make therobot’s behavior dependent on which person it is interactingwith. For example a user developing an interactive robot application might want the robot to greet and speak the name of theuser’s family members whenever it sees them.

Educational User Test

One of the objectives of the Fable system is that it can be used
as an educational tool. By tinkering with constructing robots, theuser will be motivated to learn something about programming,
mathematics, robotics, sensors, actuators, etc. in order to build
better robots.
To test the Fable system’s potential as an educational platform
we have performed a user test with seven high school students.
The students had little or no programming experience. They participated in a 2-hour programming exercise where they should
create an application for a robot conﬁgured as a humanoid torso
using the LOGO programming language. The robot should do
a posture with its arms and the user is supposed to perform thesame posture, then the robot does another posture and so on.
This would require the participants to create functions, loops anduse pre-deﬁned functions that involve the robot moving its arms

The facial recognition is performed in several steps, depicted
in Fig. 4. First, the users face is extracted from an image withhelp from the user tracker component. Keypoints are extracted
from these images with the FAST [25] algorithm, which arethen given a description with the SIFT [26] algorithm. The descriptors are compared to the database with Fast library approximate nearest neighbor (FLANN) [27] matcher and on a matchan event is sent. OpenCV’s implementations of the algorithms
were used.

© ISAROB 2013

Playful User Test

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013100

Detection rate [%]

80

60

40

20

0

(a)

k

for

(b)

ls

rre

ba

n
k
ut
up
ard
ow t_fors_orw nds_ns_dbes_fo ha
nd
nd
ha
ha

nd

ha

Y

X

l

tota

Fig. 6: Showing individual detection rates by postures, while therightmost (red bar) shows the average detection rate.

80
70

(d)

60
Detection rate [%](c)Fig. 5: The Fable robot mimicking a 6 year old girl.

50

40

in certain positions and detecting users and their postures. A
manual was supplied with the problem broken down into smaller
steps and a presentation was given explaining the tools to program, compile, and upload the code to the robot.
We observed that the students quickly realized how to use thetools, while the actual programming, understanding functions
and loops had a small learning curve. Most grasped the concepts quickly while others needed some extra help. By studying
the manual they learned how to create functions for the different postures that the robot was supposed to perform. Some parts
required extra effort, e.g. the part of detecting a user’s posture.
We observed that the students seemed highly motivated to understand the programming in order to make the robot do as requested. Clearly the students learned something about robotics
and programming from this exercise. By letting the students deﬁne their own projects and create robots that makes sense tothem, we anticipate that it will motivate the students to work
concentrated for long time periods. In this process we hope thatthe students will learn useful skills, but the extent to which theFable system allows for such open-ended creation and learning
is a topic which we will explore more in future work.
5.3

20
10
0
dadi marel david finnur arnthor loftur haddi runi moises total

Fig. 7: Showing individual detection rates by subjects, while therightmost (red bar) shows the average detection rate.

posture was detected or not. The camera was placed about 3
meters from the subject at 1.5 meters height.
Fig. 6 shows the detection rates for each posture performed,
while Fig. 7 depicts the detection rate per user. The accuracy(true positives / total detection) of performed postures was 95 %,where we observed false positives being recorded while theusers were moving to the ﬁnal posture. Note that one posture(hands_forward) had a very low detection rate. It turned outthat since the hands were directed straight to the camera, it madethe camera unable to correctly detect the positions of the hands.
If the camera would have been placed lower this would not haveaffected the test. In summary the posture detection system isgenerally fairly capable to detect between the nine different postures, but it varies highly depending on the particular user. Further work is needed to make the posture detection component
more robust.

Posture Detection

A test was performed in order to determine the detection
rate of the posture detection algorithm. 9 test subjects, all maleranging from ages 25-35, were asked to perform a set of nine
postures twice. No feedback was given from the system if a

© ISAROB 2013

30

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013a robot and thereby learn about programming and robotics. In
conclusion we anticipate that the Fable system equipped withsmart sensor modules is a step towards a new type of userreconﬁgurable robotic playware that motivate its users to be creative and learn while creating socially interactive robotic applications.
In future work a more integrated/embedded version of thesmart sensor module will be developed (not tethered to theserver). Further, the developments of new smart sensor modules are being explored and more thoroughly tested. In addition,
the Fable system is being improved to be more user-friendly andinclude more module types.

0.082
0.080
0.078
0.076
Time [s]

0.074
0.072

0.070
0.068
0.066
0.0640

50

100

150
200
Images in database

250

300

7 ACKNOWLEDGEMENTS

Fig. 8: Average time to match a face to database with varying
database size.

5.4This work was performed as part of the “Modular Playware
Technology” project funded by the Danish National Advanced
Technology Foundation. Many thanks to Brian Silverman who
is the developer of the PicoLOGO virtual machine and compiler
used to program the Fable robot.

Facial recognition

The facial recognition systems detection rate is highly dependent on the number of images in the database and specially thevariation of image types, such as different illumination, face angles and distances. As the system is supposed to work in nearreal-time the speed of detections is a crucial factor and is therefore tested here in terms of the number of images in the database.
The test was performed on a database containing faces in different scales, distances and orientation of 5 subjects, all maleranging from the ages 25 - 35. Ten random images were incrementally added to the database at a time until 260 images had
been reached, each time comparing 50 random images to thecurrent database while recording the average time.
Fig. 8 depicts the average time to match a face to the databasewith varying database size. As one can see the average time isalmost constant at 0.072 s or approximately 14 faces/sec, which
is sufﬁcient for most purposes. The corresponding detection rate
is 75.8 % and the accuracy is 99.3 % (true positives / (true +false positives)). We anticipate that these results are sufﬁcientfor practical applications utilizing facial recognition but this has
yet to be tested.

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Transactions on, 7(4):431 –441, dec. 2002.
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Design of the atron lattice-based self-reconﬁgurable robot.
Auton. Robots, 21(2):165–183, September 2006.
[6] K. W. Gilpin, A. N. Knaian, and D. L. Rus. Robot pebbles:
One centimeter modules for programmable matter throughself-disassembly. IEEE, 2010.
[7] A. Sproewitz, A. Billard, P. Dillenbourg, and A. J. Ijspeert.
Roombots-mechanical design of self-reconﬁguring modular robots for adaptive furniture. IEEE International Conference on Robotics & Automation (ICRA), pages 4259–
4264, 2009.

6 CONCLUSION
This paper presented a smart sensor module with corresponding server-side software architecture for the Fable modular
robotic system. The sensor module is based on a 3D depth camera providing user tracking, posture detection and a near-realtime facial recognition. We tested the functional performanceof these services and found them sufﬁcient for our purpose although there is still room for improvements. Further, a pilot user
test was described that demonstrated a playful and interactive
robot build from the system. In addition, we described a user
test with high-school students who used the system to program© ISAROB 201315

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[22] Playful
inventioncompany.
http://www.picocricket.com/picopeople.html.
Developers of PicoLOGO.
[23] D. J. Christensen, U. P. Schultz, and M. Moghadam. The
assemble and animate control framework for modular reconﬁgurable robots. In Proceedings of the IROS Workshop on Reconﬁgurable Modular Robotics: Challenges of
Mechatronic and Bio-Chemo-Hybrid Systems, 2011.
[24] M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote,
J. Leibs, E. Berger, R. Wheeler, and A. Ng. Ros: an opensource robot operating system. In ICRA workshop on open
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[25] E. Rosten and T. Drummond. Machine learning for highspeed corner detection. In Computer Vision ECCV 2006,
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[27] M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm conﬁguration. In International Conference on Computer Vision Theory and Application VISSAPP’09), pages 331–340. INSTICC Press,
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[8] A. Lyder, R. F. M. Garcia, and K. Stoy. Mechanical designof odin, an extendable heterogeneous deformable modular
robot. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’2008),
pages 883–888, Nice, France, September 2008.
[9] H. S. Rafﬂe, A. Parkes, and H. Ishii. Topobo: a constructive assembly system with kinetic memory. In Proceedingsof the SIGCHI conference on Human factors in computing
systems, CHI ’04, pages 647–654, New York, NY, USA,2004. ACM.
[10] E. Schweikardt and M. D. Gross. roblocks: a robotic construction kit for mathematics and science education. In
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USA, 2006. ACM.
[11] P. Marti, L. Giusti, and H. H. Lund. The role of modular
robotics in mediating nonverbal social exchanges. IEEE
Transactions on Robotics, 25(3):602–613, June 2009.
[12] C. B. Nielsen and H. H. Lund. Adapting playware to rehabilitation practices. In Serious Games - Theory, Technology & Practice: Proceedings GameDays 2011, 2011.
[13] H. H. Lund, N. K. Bærendsen, J. Nielsen, C. Jessen, andK. Falkenberg. Robomusic with modular playware. In
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[14] C. L. Breazeal. Designing sociable robots. The MIT Press,
2004.
[15] A. J. N. van Breemen. Bringing robots to life: Applying
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CHI2004 Workshop on Shaping Human-Robot Interaction- Understanding the Social Aspects of Intelligent Robotic
Products, Vienna, 2008.
[16] K. Goris, J. Saldien, I. Vanderniepen, and D. Lefeber. The
huggable robot probo, a multi-disciplinary research platform. Communications in Computer and Information Science, 33 CCIS:29–41, 2009.
[17] J. Saldien, K. Goris, S. Yilmazyildiz, W. Verhelst, andD. Lefeber. On the design of the huggable robot probo.
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robotic platform for socially embodied presence. In Proceedings of the 5th ACM/IEEE international conference on
Human-robot interaction (HRI ’10), pages 15–22, 2010.
[19] H. H. Lund and T. Thorsteinsson. Social playware for mediating tele-play interaction over distance. Artiﬁcial Lifeand Robotics, 16:435–440, 2012.
[20] M. Pacheco, M. Moghadam, A. Magnusson, B. Silverman,
H. H. Lund, and D. J. Christensen. Fable: Design of amodular robotic playware platform. In Proceedings ofIEEE International Conference on Robotics and Automation, 2013. (to appear).
[21] S. Papert. Mindstorms: children, computers, and powerfulideas. Basic Books, Inc., New York, NY, USA, 1980.

© ISAROB 201316

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

bioToys: biofeedback device for physiotherapy using building blocks
Tomoya Shimokakimoto1, Asaki Miura1, and Kenji Suzuki1,2
1 Artificial Intelligence Laboratory, University of Tsukuba, Japan
2 Japan Science and Technology Agency, Japan{shimokakimoto, asaki}@ai.iit.tsukuba.ac.jp, kenji@ieee.org
Abstract: In the field of physiotherapy for children with impaired motor functions or congenital loss of limbs, physical therapists (PT) assist children to recover motor functions or to adapt to the use of artificial limbs controlled by Electromyography.
However, children easily give up training to use artificial limbs because it becomes rapidly boring. Also, PT are required toperform many operations on the biofeedback system. It is important for children to feel excited about therapeutic activities andthe system used should be easy to handled by the PT. In this research, we propose a building block based biofeedback toys
called “bioToys”. This system enables the use of biological or physiological signals in input blocks and generates various outputs. This building block system also allows users to program biofeedback systems. We show that the developed system is capable to control toys and to record muscle activities in real time.
Keywords: Playware, biofeedback, Tangible User Interfaces, toy, children, education.

1 INTRODUCTION

becomes boring after a while or become they just dislike it.
Also, biofeedback systems require the PT to perform many
operations including setting of instrumentation, parameters
tuning, and maintenance of prosthesis. In addition, it isneeded to observe the patient’s activity, check the output
from the biofeedback system, and instruct the patient thenext action to be performed all at the same time. Then, it isalso important for children to feel excited with the therapeutic activities to keep their motivation and this causes
strong affects the therapeutic achievements. Since thetherapist needs to handle the system and paid less attention
to the therapeutic activity itself and also it is difficult toobserve the child activity while handling the system.
Many works have reported about the benefit of tangibleuser interfaces [4] which offer educational benefits to children. Especially, building blocks system have been a proposed as a tool for learning programing language [5,6,7].
These building blocks systems allows user to freely build
system, and the system produces outcome under the combination of properly built blocks with a particular function.
We consider that building blocks systems are an effective
education tool for children to learn about system procedure,
and also could encourage child to learn in voluntary andcreative manners. .
In this study, we first introduce a building blocks likebiofeedback toys called “bioToys” as shown in Fig.1 .
Promising applications include a creative tool for physiotherapy. In this application, this system is designed for users
to understand the causality between biosignal and system

Electromyography (EMG) biofeedback is one of themethods of physiotherapy for augmented motor function[1]. EMG biofeedback therapy is widely used in the training for patient with impaired motor function or congenital
loss of limbs. Physical therapists (PT) assist patient to recover the motor function and several biofeedback devices
have been proposed so far such as presenting a waveformon the LCD display (i.e. MyoBoy, Ottobock, Inc.), converting EMG to the sound tone [2], or displaying the shape andbrightness on the specific muscle on the body skin [3].
Also, these treatments are used for the training of EMG
prosthesis. Although the rehabilitation trainings generally
consist of simple tasks such as picking up and moving
small objects,, some adults patients endure this training
because of their strong motivation to go back to their daily
life. However, children often refuse the training because it

Blocks・Shape Supporter
・Battery・Input・output

EMG
Heart pulse

motor control
・forward，
backward，stop

LED control
・brigtness,
ﬂash patternFig. 1. Concept of bioToys© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Battery Block

Bluetooth
Block

Sensor Block

ParameterBlockillumination
Block

Motor Block

Sensor Block

Fig. 3. Network structure of blocks

Fig. 2. Overview of the developed blocks.

tions. These blocks are performed to obtain and transmit
data to other blocks. Wireless receiver block is implemented to obtain biosignal or physical motion data form thewearable sensor via Bluetooth connection. Since biosignals
and human motions are measured on body skin, each receiver block is paired with a wearable sensor. It then receives those messages from a pair of signal processing
block and broadcasts to other blocks in the network. Parameter Tuner block has a gain adjuster for tuning data forthe output. (iv) Action Blocks interpret the message fromother blocks and convert the signal for providing several
actions such as motion, vibrations, sounds and illuminations.

output in an intuitive manner, and could encourages children to self initiate playing and training. Also it is easy for
the PT and children to handle it.

2 BUILDING BLOCKS SYSTEM
2.1 Device Specification
Figure 2 shows an overview of the developed blocks.
We implemented the system on the DUPLO(LEGO Group)and four types of block devices are constructed such as
shape supporter, battery, input and output. These block-typedevices are modified from the normal blocks and are added
two connectors. On the upper side, each connecting parts
has two electrodes. The inner and outer pins are used as a
power and ground lines, respectively. On the lower side, in
opposite, the connecting hole is used as a ground and theouter as a power. Since these electrodes are designed likecoaxial socket, the devices shape are almost the same as theone of normal blocks and they can connect not only tomodified block devices but also to regular DUPLO® blocks
in a traditional way.
On the proposed system, power to each block is provided by a single master supply from the battery block.
Recharging only the battery block is therefore adequate formaintenance. In order to reduce the number of cables between the blocks and also increase the flexibility of the system, power line communications have been used to share
information between the blocks. Therefore, although thedeveloped blocks have an electrical connection and function, physical connection between blocks are not limited.
This mechanism allows the user to use and handle the developed blocks in the same way as normal building blocks.
Table 1 shows an example of block types and their functions. The developed blocks are classified in four different
types. (i) Link Block is used to connect or disconnect thecircuit and also used to determine the physical shape. Normal blocks are used to create shapes and separate electricalconnections between blocks. Bridge blocks are used not
only to create the shape but also to provide electrical connectivity and data communication between blocks throughthe in-built circuit between the upper and lower side. (ii)Processing block equipped with an electronic circuit withmicrocontroller (LPC1113/302, NXP) has different func-© ISAROB 2013

Bluetooth
Block

2.2 System Configuration
Figure 3 shows an example of a network structure between the connected blocks. Note that data communication
is solely done on a power line. This network topology is abus-network. When the battery block is switched on, blocks
connected to the power line start to work according to theirfunctions. Any blocks connected to the network broadcast
data and share the circuit by serial communication. These
data consist of a header, data length, its own specific type,signal data value/command and CRC. To avoid conflicts orTable. 1. Example of the block type
Category

Specific type Functions

Link Block

NormalDisconnection between blocks

BridgeConnection between blocks

Power Block

Battery

Supply voltage to the blocks
and monitoring power line

Processing
Block

Signal
Processing

AD conversion, processing of
sensor signal and transmitting
to received block

Wireless
Reciever

Receiving data from sensor andtransmitting data to power line

Parameter
Tuner

AD conversion of a volumevoltage and transmitting data to
power line

Motor

Get data form power line andinterpret the data then gen-erate
output for driving each element

Action Block

LEDs
Sound

18

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Measurement

Signal processing

Flexor EMG

Low pass
FilterExtensor EMG

Low pass
FilterTrain Control
Integration

IEMG

Action1: only forward(using single signel)
Action2: only forward(using both signels)

Integration

IEMG

Action3: Forward andbackword

Fig. 4. Train system flow
Action1: only forward(using single signel)
IEMG

Back side

PWM
modulation

Motor Control

Front side

> Threshold
Compere withThresholdFig. 6. Overview of the train system
3

Action2: only forward(using both signels)
Flexor IEMG

PWM
modulation

Extensor IEMG

2

Voltage[V]1.5
1Action3: Forward and backword

Td

PWM
modulation> Threshold

Forwardmotor control

0.5

Backward
motor control

0
0

1

2

3

4

5
Time[s]

6

7

8

9

10

Fig. 7. IEMG, threshold and generated output Waveforms> Threshold

Fig. 5. variations of the train action

ues are above the threshold (Action2)(See Figure 6). On theother hand, when each set of blocks are assembled on eachside of the roof, the train moves frontward or backward
when either data value are above the threshold (Action3).
These biofeedback systems are designed to manipulate
blocks by simply arranging and rearranging them without
consideration about the block order with the aid of communication on the battery line.

a crunch of the bandwidth, signal processing blocks compute AD converting data to meaningful and low frequency
data depending on measurement signal feature before
transmission. For example, EMG signals are obtained fromthe electrodes attached with a human skin and sampled at a
rate around 1kHz. The resulting Integrated EMG(IEMG)
signals are transmitted at a rate around 100Hz. On the other
hand, in the case of changing parameters such as the parameter tuner, a rate of about 10Hz would be sufficient.
When action blocks obtain data from the power line, thespecific type of these data are checked and processed.

3 Experiment
3.1 System Evaluation
In order to evaluate the performance of the proposedsystem, we conduct an evaluation experiment to verify thelatency of the system. We prepared a basic system that iscomposed by a EMG wearable sensor, a wireless receiver, aparameter tuner and an action blocks. In this experiment,
the biosignal data are transmitted at 1kHz and the parameter
tuner block is transmitted data at 10Hz. Figure 7 shows ascene during the experiment. The train is controlled according to the measured EMG signal attached with the elbow of
the subject. We measured Td[s] is the system latency sixteen
times and the averaged result is 0.454s. This includes a timerequired for wireless communication, and converting powerline communication. We consider that this latency does notcause a problem for inferring the causality between bodily
action and train movement.

2.3 Action Blocks and Block Assembly
We have developed the train-toy system based on motor
action block. Figure 4, 5 and 6 show the system overview
and data flow. The train has two kinds of connectors, theconnectors placed at the front and back sides on the roof areassigned for forward and backward motor control, respectively.. A set of a battery, block wireless receiver and parameter tuner are assembled to the train on the different
side of the roof, the train moves forward or backward according to the given block assembly. In a simple case, whendata values are above the given threshold determined by theparameter tuner (Action1). Then, when another block set isadded, the train moves forward when either of the data val-

© ISAROB 2013

Threshold

2.5> Threshold

Extensor IEMG

IEMGMotor Control> Threshold

Flexor IEMG

System output

19

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

made the participant realize that he still does not control thetwo muscles separately. The system showed the possibility
to help therapist to monitor the children’s physiologicalactivity while playing with toys.

4 CONCLUSIONSIn this paper we proposed a unique biofeedback toys
called bioToys based on normal and modified building
blocks. In the field of physiotherapy for children, therapists
are struggling to design therapeutic programs and methods
according to the response of patients. They encounter the
problem that some children easily give up training to use
prosthesis because such training become boring after awhile, since the rehabilitation training contents comprise
only simple tasks.
We developed special building blocks that can be used
in a traditional way, although they are embedded with afully functioned electronic circuit. We verified that theseblocks can be utilized for a biofeedback training that enables the use of biological or physiological signals in processing blocks and can generates various actions. The system
are constructed by arranging and rearranging these building
blocks easily in real time. Further investigation will also
include the design of different types of blocks.

Fig. 8. The scene in the physiotherapy using train system

flexor muscle IEMG
extensor muscle IEMG

3

voltage[v]

2.5
2
1.5
1
0.5
0
-0.5
0

5

10

15

Time[s](a)
flexor muscle IEMG
extensor muscle IEMG

3

voltage[v]

2.5
2
1.5
1
0.5
0
-0.5
0

5

10

15

Time[s](b)

Fig. 9. Flexor and extensor IEMG (a) healthy participant(b) the child with congenital loss of left arm

REFERENCES[1] Wolf S. L. (1983), Electromyographic biofeedback applications to stroke patients. A critical review. Physical
Therapy, 63(9): 1448–1459[2] Tsubouchi, Y., and Suzuki, K. (2010), Biotones: A
Wearable Device for EMG Auditory Biofeedback, Proc.
of Annual Intl. Conf. of the IEEE EMBS, pp. 6543-6546[3] Igarashi, N., Suzuki, K., Kawamoto, H., and Sankai, Y.
(2010), Biolights: Light Emitting Wear for Visualizing
Lower-Limb Muscle Activity, Proc. of Annual Intl.
Conf. of the IEEE EMBS, pp. 6393-6396.
[4] Hiroshi, I., Brygg, U. (1997), Tangible Bits: Towards
Seamless Interfaces between People, Bits and Atoms,
Proc. of the ACM CHI, pp. 234-241.
[5] Lund.H.H. (2003), Intelligent Artefacts, Proc. of 8th
Intl. Symp. on Artificial Life and Robotics, pp. 11-14.
[6] Horn, M.S., Jacob, R.J.K. (2007), Designing tangible
programming languages for classroom use, Proc. of the
1st Intl. Conf. on Tangible and Embedded Interaction,
pp.159-162.
[7] Schweikardt, E., Gross, M.D. (2008), The robot is the
program: interacting with roBlocks, Proc. of the 2nd
Intl. Conf. on Tangible and Embedded Interaction,
pp.167-168.

3.2 Case-Study: Therapeutic Activities for Children
We used the train system with a child suffering from acongenital loss of the left arm.Children with forearm impaired motor functions or a congenital loss of limbs aretrained to generate proper EMG signals for the use of prosthesis limb and hand. The participant is being trained to use
flexor and extensor muscle for opening and closing theprosthesis hand. We describe such training can be achieved
using the proposed biofeedback system. Before this experiment, the participant were not likely to wear cosmetic
artificial hand and he was uncooperative even for the EMG
measurement.. We then introduced a prototype setup as
shown in Figure 8, which shows the scene in therapeutic
activity using the train system. We observed that the participant was motivated and willing to play with the train
toy. He was more cooperative to have its EMG measured.
Physiotherapist attended to the session switched the actionmode, and measured the EMG signals.
The proposed system is capable not only to control thetrain but also to record muscle activities in real time. Figure
9 shows the flexor and extensor IEMG wave form. We
asked the participant to move the flexor and extensor muscles in turn during playing with the toys. The train system© ISAROB 201320

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A Micro-Simulator for Traffic Signal Control Based on
A Modified Cellular Automaton Traffic Flow Model
Hu Jin1, Cheng-You Cui1, and Hee-Hyol Lee1
1

Graduate School of Information, Production and Systems, Waseda University, Japan(Tel: 81-93-692-5164, Fax: 81-93-692-5164)
1
kinko@akane.waseda.jp

Abstract: In recent years, many traffic signal control methods have been proposed to reduce traffic jams. However, theexperiments of the methods were difficult to perform in real road networks. In order to prove the effectiveness of the methods,
traffic simulations by using a traffic simulator were required. Although various traffic simulators have been developed, themost of simulator was not specified for simulation of traffic signal control. A micro traffic simulator for simulation of traffic
signal control is introduced in this paper. The micro-simulator is based on a Cellular Automaton (CA) model, which is a modelof artificial life. Furthermore, the CA traffic model is modified according to features of traffic flows at intersections in order toreproduce the traffic situation of urban area. The usability of the proposed CA traffic model is evaluated through the analysis ofrelationship between traffic density, average speed, and traffic flows. Moreover, the micro-simulator is applied to evaluate
traffic signal control methods.
Keywords: Traffic model, Cellular Automaton, Traffic Flow, Simulation.
that are similar to those we see in everyday life. The
proposed CA urban traffic model is built based on the
SchCh model (highway traffic model).

1 INTRODUCTION
In recent years, many traffic signal control methods
have been proposed to reduce traffic jams. However, theexperiments of the methods were difficult to perform in realroad networks. In order to prove the effectiveness of themethods, traffic simulations by using a traffic simulator
were required. On the other hand, many traffic simulators
were developed such as the TRANSIMS, SUMO, and
VISSIM and so on. However, the most of simulator was not
specified for simulation of traffic signal control. A micro
traffic simulator for simulation of traffic signal control isintroduced in this paper. The micro-simulator is based on a
Cellular Automaton (CA) model, which is a model ofartificial life. Furthermore, the CA traffic model is modified
according to features of traffic flows at intersections inorder to reproduce the traffic situation of urban area. The
usability of the proposed CA traffic model is evaluatedthrough the analysis of relationship between traffic density,
average speed, and traffic flows. Moreover, the microsimulator is applied to evaluate traffic signal control
methods.

2.1 Road setting and movement rule
In the proposed CA traffic model the default size of acell is 7.5m, and an example is shown in Fig.1. The lane
number is determined according to the real road and, rightturn only lane also considered in the model.

7.5[m]

Fig. 1. Cell setting of road

2.2 Rules of Inflows and outflows
A vehicle will be created according to the inflow rate,
and the travel direction will be determined according to theratio of different travel direction. Here, the travel directionis the road’s number, and a vehicle pass the intersection tomove designated road according to the travel direction. An
example is shown Fig.2.

2 MODIFIED CELLULAR AUTOMATON
TRAFFIC FLOW MODEL
Cellular Automaton (CA) models are conceptuallysimple, thus a set of simple CA rules can be used to produce
complex behavior. Through the use of powerful computers,
these models can encapsulate the complexity of the realworld traffic behavior and produces clear physical patterns© ISAROB 2013

The rules of inflows as follows:
Rule 1: if inR< rand(),than create a vehicle.
Rule 2: if Dird<rand()<Dird+1,
than set the travel direction to d.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

else stop.
Step 5: move to parallel lane
if the parallel cell j, j+1, j-1,j-2 are empty,
than move the vehicle to cell j+1.
else stop.

Here, inR is the inflow rate, Dird is the ratio of the travel
direction, rand() is random number within [0, 1]. The inR
and Dird is determined according the equation (1), (2) withthe measured traffic data. In the equations, k and m is thecycle number, Ct is the cycle length, Ln is the lane number,d is the different road number, I is the traffic volume of
inflows, O is the traffic volume of outflows.

Here, Pacc is determined by equation (3). P0 is the set
value; Td is the traffic density of the road.

m = k -1

∑I m
m =1
InR = m = k -1
∑Ctm • Ln

Pacc = P0 - Td • rand () • 0.1

(1)

(3)

m =1

m = k -1

∑Od

Dird =∑O

If the travel direction of a vehicle is right-turn, thevehicle moves to the right turn exclusive lane, even when
there is no obstacle. As an example, the rule of movements
is shown in Fig.3.

m

m =1
m = k -1

(2)
m

m =1

Rule1
If InR < rand() ,then create a car
Rule 2
If Dir(d-1)< rand() < Dird
then set travel direction to dd=3

3

Speed rule 1
If cell i+1 is empty, then move car 1 cell
Speed rule 2
If cell i+2 is empty and P_acc<rand()then move car to cell i+2

Right turn only rule
If the direction of car is right turnthen, move to right turn only lane

3
3

2

1

1

2

d=2

2

1

2

Rule2

?

Rule1

Lane change rule
If there is a car in front cell,If the neighboring cell j, and j-2,j-1,j+1 are empty

d=1

Fig. 3. Movements
Fig. 2. Inflow and outflow

3 MICRO SIMULATOR
2.2 Rules of movements
In proposed CA traffic model, a vehicle can be
accelerated up to maximum speed, and can change the roadlane. In addition, particular rule will be used at the rightturn only lane. Specific rules are as follows:
Step 1: check cell
if the cell i+1 is empty,
than go to step 2;
else go to step 4.
Step 2: move one cell
Move the vehicle to i+1 cell.
if i+2 is the empty,
than go to step 3,
else stop.
Step 3: random accelerate
if Pacc < rand(),than move to cell i+2
else stop.
Step 4: check multi-lane
if the road is the multi-lane
than go to step 5.

© ISAROB 2013

A micro traffic simulator is developed based on themodified CA traffic flow model. The procedure of thesimulator is shown Fig.4.
Step 1: Initialization
To set the intersection, and road length, lane number,inflow rate, ratio of different travel direction at eachroad. The traffic signal of each intersection can beobtained by using the empirical equations (Webster’s
formulations), also it can be changed by an interface.
Step 2: initial parameterTo set the simulation time (St), set the count time ofeach intersection (Ct), and set the cycle (k) of eachintersection. Here, each intersection has respective Ct
and k.
Step 3: Step check
If reach the simulation time, than go to step 15,
else continue next step.
Step 4: CA (1/2)
Move the vehicles according to CA rules (in/outflows)
Step 5: CA (2/2)

22The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Move the vehicles according to CA rules (movements)
Step 6: Change signals
To change the signal phases of each intersection,according to the Ct with the parameters of traffic
signals.
Step 7: Record 1
To record moved cells
Step 8: Cycle check
If reach the cycle time, than go to step 10,
else continue next step.
Step 9: CountTo counts simulation time (St) and cycle time (Ct) of
each insertion, go to step 3.
Step 10: Record 2
To record traffic volume data of each intersection, andcycle number, intersection number.
Step 11: Calculation of Signal
To calculates the traffic signal of next cycle. If there
no calculation algorithm is implemented the pre-timed
signal will be used continued.
Step 12: Traffic signal reset
Reset the traffic signals of intersection.
Step 13: Parameter reset
Set the Ct to 0, and count the cycle k.
Step 14: Record 3
To record updated traffic signals.
Step 15: Record 4
To record PI(Performance Index). The PI will becalculated with the recorded data.

Start

Initialization

St=1 ; Ct=1 ; k=1

St<Stime

N

Y
Output traffic
data

In/Out cell settingMove vehicles
Calculate traffic signal
Change signals
Reset traffic signal
Output traffic
data
Ct=0 ; k++N
Ct<Ctime

Output traffic
signal dataY
St++ ; Ct++

Output Performance
Index

Stop

Fig. 4. Procedure of micro traffic simulator

The frame of initialization and simulation are shown in
Fig.5 as an example.

4 SIMULATION
To evaluate the usability of proposed CA traffic model,
two kinds of the simulation were carried out. The firstsimulation is that analyze the relationship between traffic
density, average speed, and traffic flows. Here, a diagram ofrelation between the traffic flux and the traffic density
called the fundamental diagram of traffic flow. Generally,
the fundamental diagram of traffic flow has somecharacteristics as follows;
1) When the density is 0, flow will also be 0, since
there are no vehicles on the road.
2) When the number of vehicles gradually increases
the density as well as flow increases.
3) When more and more vehicles are added, itreaches a situation where vehicles can’t move.
This is referred to as the jam density or the

Fig. 5. Initial settings, Frame of simulator, and file structure© ISAROB 2013

23

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Traffic flow [pcu/h]

maximum density. At jam density, flow will be 0
because the vehicles are not moving.
On the other hand, the average speed generally
maintains 30-40[km/h], and when the average speeds
smaller than 20[km/h] the road will be considered as atraffic jam in urban area
Fig.6 shows the fundamental diagram of proposed CA
traffic model, and the Fig.7 is the relation between traffic
density and speed, the Fig.8 is the relation between traffic
flow and speed.

The second simulation is comparison of the actual
traffic flow data and simulated traffic data by using the
micro simulator. The results are listed in table 1. Thesefigures and the table can prove that, the modified CA trafficmodel can reproduce the traffic flows of urban areas very
well.

3500
3000
2500
2000
1500
1000
500
0
0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Density

1

Fig. 6. Fundamental diagram of traffic flow
Fig. 9. Example of simulations

Mean speed [km/h]

50
40

The micro simulator has been applied to evaluate
several traffic signal control methods. Examples of thecrossroad intersection and multi-way intersection are shownin Fig.9.

30
20
10
0

5 CONCLUSION0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Density

1

40

A micro traffic simulator is built up based on a modified
CA traffic model. Through simulations the usability of the
proposed CA traffic model is evaluated. As the future work,
first is to improve the GUI, second is to add the vehicle
category.

30

REFERENCES

20[1] K. Nagel, M. Schereckenberg, “A Cellular automaton
model for freeway traffic”, Journal de Physique I, Vol.2,
No.12, pp.1868-1870, 1996.
[2] Michał MACIEJEWSKI, “A comparison of
microscopic traffic flow simulation systems for an urbanarea”, Transport Problems: an International Scientific
Journal, Vol.5, No.4, pp.27-38, 2010.
[3] Pedro Fernandes, Urbano Nunes, “Platooning ofAutonomous Vehicles with Intervehicle Communications in
SUMO Traffic Simulator”, 13-th International IEEE
Annual Conference on Intelligent Transportation Systems,
pp.1313-1318, 2010.

Fig. 7. Relation between traffic density and speed]

Mean speed [km/h]

50

10
0
0

1000

2000
3000
Traffic flow [pcu/h]

4000

Fig. 8. Relation between traffic flow and speed
Table 1. Comparison of traffic flows
Traffic
flows

ActualData [pcu/h]

By simulator
Mean(10times)

In

2,325

2,362.7

0.7 %

Out

2,229

2,212.9-1.6 %© ISAROB 2013

Mean
error

24

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Real-Time Stochastic Optimal Control for
Traffic Signals of Multiple Intersections
Cheng-You Cui1, Hee-Hyol Lee1
1

Graduate School of Information, Production and Systems, Waseda University, Japan(Tel: 81-93-692-5164, Fax: 81-93-692-5164)
1
cuichengyou@ruri.waseda.jp

Abstract: Traffic congestion has become a serious problem with exponential increase of vehicles recently. In urban area,
almost all of traffic jams occur at intersections. In such cases, traffic signal control is a reasonable method to reduce the traffic
jams. Traffic signal control can be divided into two types: one is off-line (Pre-timed) control and the other is on-line (Adaptive)
control. In the pretimed control, empirical formulas were used to calculate the traffic signals off-line using historical traffic
data, but it cannot handle the variation of traffic flows. The adaptive control can overcome this limitation by adjusting thetraffic signals on-line in the various traffic flows. The adaptive control can also be divided into two types: centralized anddistributed systems. However, the centralized system requires the extensive data processing and computational time tocalculate optimal traffic signals. On the other hand, the distributed system can achieve a real time control. In this paper, a realtime stochastic optimal control method of traffic signal is proposed. A modified Cellular Automaton (CA) traffic model andBayesian Network (BN) model are used to predict the traffic jams. In addition, H-GA-PSO algorithm is used to search optimal
traffic signals based on the stochastic model. The H-GA-PSO algorithm is a modified Hierarchical Particle Swarm
Optimization (H-PSO) method based on Genetic Algorithm (GA). Finally, the effectiveness of the proposed method is shown
through simulations at multiple intersections using a micro traffic simulator.
Keywords: Traffic signal, Stochastic Optimal Control, Cellular Automaton Traffic model, H-GA-PSO algorithm.
traffic signals to minimize traffic-jam probabilities using an
optimal probabilistic model. Finally, the effectiveness of the
proposed method is shown through simulations at multiple
intersections using a micro traffic simulator.

1 INTRODUCTION
Traffic jam has become a serious problem withexponential increase of vehicles recently. In urban area,
almost all traffic congestion occurs at intersections. One of
the ways to solve this problem is road-expansion, but it isdifficult to realize in urban areas. In such cases, traffic
signal control is a reasonable method to reduce traffic-jam.
Traffic signal control can be divided into two types. One is
off-line (Pre-timed) control and the other is on-line(Adaptive) control. In the pre-timed control, Webster’s
formula is used to calculate the traffic signals off-line using
historical traffic data, but it cannot handle the variation of
traffic flows. On the other hand, the adaptive control canovercome this limitation by adjusting the traffic signals online in the various traffic flows. SCOOT and SCATS havebeen implemented on urban traffic networks using thecentralized systems. However, the centralized systems
require the extensive data processing and computationaltime for calculating optimal traffic signals. The distributed
system can achieve the real time control.
In this paper, a real time stochastic optimal control
method of traffic signal is proposed. A modified Cellular
Automaton (CA) traffic model and Bayesian Network (BN)model are used to predict the probabilistic distributions ofstanding vehicles of the intersection roads. In addition, ahierarchical H-GA-PSO is proposed to search the optimal© ISAROB 2013

2 STOCHASTIC MODEL FOR TRAFFIC JAM
2.1 Prediction of probabilistic distribution forstanding vehicles at intersection
In order to predict the traffic jam probability, a Bayesian
Network (BN) model is built to predict probabilistic
distributions of standing vehicles at crossroad according to
the relationship between traffic flows and standing vehicles.
IRk
ORk

OFk

IFk

Sk
OLk

ILk

ORk

S k -1OFk

OLk

S k +1

Sk

IRk

IFk

ILk

Fig. 1. Crossroad and BN model

25

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Here, we consider a crossroads as shown in Fig.1. The
random variables of the inflows and the outflows of thecrossroad and the standing vehicles between the twointersections are represented as nodes. The Bayesian
network model of the standing vehicles is shown in Fig.1.
The number of the standing vehicles of k-th cycle can becalculated as equation (1). The probabilistic distribution of
the standing vehicles is obtained by summing over all joint
probability distribution of the other variables.

Step2: Using the observed traffic data of before the k-th
cycle to calculate the probabilistic distribution of inflow

∑P( I
i

(1)

S k = S k -1 + IRk + IFk + ILk - ORk - OFk - OLk

P( S k ) = ∑∑∑∑∑∑∑P( S k , S k -1 , IFk , ILk(2)

S k -1 IFk ILk IRk OF k OLk ORk

k -1= i) and outflow ∑iP(Ok -1 = i) .

Step3: Using the estimated traffic flow data andprobabilistic distribution to calculate the prior probabilitiesof traffic inflow and outflows according to the equations (5)– (8).
γPˆ ( I ke ) = P( Iˆke-1 ) ( Iˆke-1 ≠Iˆk )
(5)
E -1
γ.n
Pˆ ( I ke ) = P( Iˆke-1 ) +( Iˆke-1 = Iˆk )
(6)
E -1
γ
Pˆ (Oke ) = P(Oˆ ke-1 ) (Oˆ ke-1 ≠Oˆ k )
(7)
E -1
γ.n
Pˆ (Oke ) = P(Oˆ ke-1 ) +(Oˆ ke-1 = Oˆ k )(8)
E -1
γ = 5%e ∈ (0,1,2, E)

, IR k , OF k , OLk , ORk )P( S k , S k -1 , IFk , IL k , IR k , OF k , OLk , ORk ) =P( S k | S k -1 , IFk , IL k , IR k , OF k , OLk , ORk )Here, the CA traffic model is built based on the SchChmodel (highway traffic model). The rules of movements forthe vehicles on road network are shown in Fig.3 as an
example. A vehicle can be accelerated up to maximum
speed (Maximum speed = move 2 cell; 1cell=7.5m/1 step
[1sec]) when there is no obstacle. According to theconditions of the road, the speed can be changed randomly.
In multi lanes road, a vehicle can move to parallel lanes. If
the direction of travel is right turn, the vehicle moves to theright turn exclusive lane.

× P( S k -1 | IFk , IL k , IR k , OF k , OLk , OR k )× P( IFk | IL k , IR k , OF k , OLk , OR k )× P( IL k | IR k , OF k , OLk , ORk )(3)

× P( IR k | OF k , OLk , ORk )× P(OF k | OLk , OR k )× P(OLk | ORk )× P(ORk )With the chain rule, the joint probabilistic distribution isrepresented as the product of conditional probability as
equation (3). Then, according to the d-separation and therelationship between traffic flows and standing vehicles theequation (2) can be represented as

⑤

4

(4)

2

3

?

2

②

Reset direction of travel by
new percentage

, P( ILk ) P( IR k ) P(OF k ) P(OLk ) P(ORk )

?

5

③

P( S k ) = ∑∑∑∑∑∑∑P( S k ) P( S k -1 ) P( IFk )S k -1 IFk ILk IRk OF k OLk ORk

1 . If InR<rand(), the create a car
2. Set direction of travel according to theprobability by the oversexed data

④

5

①

InR: inflow rate ; rand(): random number (0,1)

Fig. 2. CA traffic model
In order to calculate the probabilistic distribution of thestanding vehicle at intersection road, the prior probabilitiesof traffic inflow and outflows must be updated. Generally,
the priori probability of the traffic flows is variable under
the different traffic signal. Hence, we use the CA trafficmodel to estimate the traffic flow of the different traffic
signals. And then, according to the estimated traffic flows
the priori probabilistic distributions will be updated. The
updating process is as following:
Step1: Using the CA traffic model to estimate the traffic
inflow I ke and outflow Oke of the different traffic signal of
the k-th cycle.

© ISAROB 2013

2.2 Traffic jam probability optimization problem
The objective of proposed method is to search an
optimal traffic signal to minimize the traffic jam
probabilities. Here, we formularized the traffic jam
probability optimization problem as equation (9) according
to the probabilistic distributions of the standing vehicles atcrossroad intersection.
Sk =+∞

Sk =+∞

S k = S max

S k = S max

F (t k1 , t k2 ,...,t ki ) = a • ∑Pˆmain ( S k ) + β • ∑Pˆ minor ( S k )(9)Subject to
t

26

n
min

≤t ≤t
n
k

n
max

i = 1,2,...,nThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Here, t kn is the time length of signal phase n, and t nmin

Search space

Search space

n
max

and t
indicates the adjustable range of the signal phasen, Pˆmain ( S k ) is the probabilistic distributions of the mainroad Pˆ ( S ) is the probabilistic distributions of the
minor

gbest
lbest

lbest

Lmin

xi

lbest

k

minor road, a and β are weight value.

xijGroup j

3 H-GA-PSO ALGORITHM AND REAL-TIMECONTROL

Fig. 4. Structure of H-GA-PSO

In order to achieve the real-time control, an H-GA-PSO
algorithm is proposed based on the Hierarchical-PSO. In HGA-PSO a particle is influenced by its own so far best
position and by the best position of the particle that is
directly above it in the hierarchy. All particles are arranged
in a tree that forms the hierarchy so that each node of thetree contains exactly one particle. Fig.3 shows the structureof H-GA-PSO.

Initialize particles using
Initialization rule

Update velocity, position
using new Eqs. (10-13)
Reset selected particle position
using Genetic Algorithm
Traffic data

CA traffic model

BN model+∞predicted traffic jam probability∑P(S )

DetectorhkhS kh = Smax

gbest

itr ++

Calculate fitness of each particle
using Eq. (9)High hierarchy…

…

Group 1…

Update gbest, pbest, lbest

Group j…

YGroup J…

itr<itrmax…

N

Low hierarchy

gbest = optimal traffic signal

Traffic light

Fig. 3. Structure of H-GA-PSO algorithmv ijitr +1 = wl • v ijitr + c l1 • rand () • ( pbest ijitr - x ijitr )+ c l 2 • rand () • (lbest iitr - x ijitr )

k ++

(10)

Fig. 5. GA operation and initialization of particles(11)

The H-GA-PSO algorithm is used to search a opti
mal traffic signal with traffic jam probability optimalmodel. The procedure is shown in Fig.5.

+ c l 3 • rand () • ( gbest - x )
itr

itr
ij

x ijitr +1 = x ijitr + v ijitr +1
v ijitr +1 = wh • v ijir + c h1 • rand () • ( pbest ijitr - x ijitr )+ c h 2 • rand () • ( gbest itr - x ijir )x ijitr +1 = x ijitr + v ijitr +1(12)

4 SIMULATION(13)
To prove the effectiveness of the proposed method, a si
mulation was carried out with a micro-traffic simulator. The
micro-traffic simulator is developed based on the CA trafficmodel. We assume a road network and detector systems.

In the H-GA-PSO, the particles in low hierarchy are
updated using equations (10) and (11), and the particles inhigh hierarchy are updated using equations (12) and (13).
In equation (10), lbest is the position with the best fitness
found so far by the corresponding group.
Also, Genetic Algorithm (GA) and a rule of particle’s
initialization are used to avoid the local minimum problem.
The GA is used to update a selected particle’s position andbasic concepts of initialization for particles are shown in
Fig.4. The crossover processing is used to exchange twoparticles from different group. The mutation processing isused to reset a particle around gbest or lbest. The initial
particles can keep a certain distance by using the rules.

© ISAROB 2013

Fig. 6. Frame of micro simulator

27

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The road network of the experiment includes 16
intersections, and all traffic data of the intersections can beobserved by the detector systems. The simulator frame isshown in Fig.6. The conditions of the traffic flows and road
networks are shown in table 1. Each pre-timed signal of theintersections are calculated according to the Webster’s
formula.

Mean Delay time (sec)Propsed method
30
20
10
0
0

10

Table 1. Conditions of traffic flowsRoad
direction of ravelLane
lengthstraight right left

Direction
West
to
East

80
[cell]

3

90%

4%

6%

East
to
West

80
[cell]

3

90%

4%

6%

South
to
North

70
[cell]

2

60%

10%

30%

North
to
South

70
[cell]

2

60%

20%

20%

* pcu :

passenger car unit ;

No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Time (sec)Phase2Phase 3

REFERENCES[1] F. V. Webster and B. M. Cobbe, Trafﬁc Signals,“Road Research Technical Report 39”, Her Majesty’s
Stationery Office (London), 1958.
[2] Dennis I. Robertson and R.David Bretherton,“Optimizing Networks of Traffic Signals in Real Time The
SCOOT Method”, IEEE Transactions on VehicularTechnology, Vol.40, No.1, pp.11-15, 1991.
[3] A. G. SIMS and K. W. DOBINSON, “The Sydney
Coordinated Adaptive Traffic (SCAT) System Philosophy
and Benefits”, IEEE Transactions on Vehicular Technology,
Vol.29, No.2, pp.130-137, 1980.
[4] K. Nagel, M. Schereckenberg, “A Cellular automaton
model for freeway traffic”, Journal de Physique I, Vol.2,
No.12, pp.1868-1870, 1996.
[5] J. Kennedy, R, Eberhart, “Particle Swarm
Optimization”, Proceedings IEEE International Conference
on Neural Networks, pp.19842-1948, 1995.
[6] Janson S., Middendorf M., “A hierarchical particle
swarm optimizer and its adaptive variant”, IEEE
Transactions on Systems, Man, and Cybernetics, Vol.35,
No.6, pp.1272-1282, 2005.
[7] Cheng-You Cui, Hee-Hyol Lee, “Distributed Traffic
Signal Control Using PSO Based on Probability Model for
Traffic Jam”, Intelligent Autonomous Systems 12 Advancesin Intelligent Systems and Computing, Vol. 193, pp. 629639, 2013.

Phase 4

30
0
11

16

21 26
Cycle

31

36

41

Mean speed (km/h)

Fig. 7. Optimal Traffic signals of intersection1
Propsed method30Pre-timed control

20
10
0
0

10

20

30
40
Time (min)5060

Fig. 8. Comparison of mean speed© ISAROB 201360

A real-time traffic signals stochastic optimal control was
proposed based on the optimization model of traffic-jam
probability and the H-GA-PSO algorithm to calculate theoptimal signals. Through a simulation with the micro
simulator the effectiveness of the proposed method wasshown.

60

650

5 CONCLUSION

90

1

30
40
Time (min)By the proposed the total delay time is decreased about
18.3% compared with pre-timed control, and the result isshown in Fig.9. And, the comparison of mean speed ofproposed method and pre-timed control is shown Fig.8. By
the proposed method, the mean speed is increased.

1 cell=7.5m

Phase 120

Fig. 9. Comparison of mean delay time

Inflows(pcu/h)
1300
1100
1400
1500
1400
1300
1000
900
600
400
500
500
400
500
500
600

The traffic signals of the intersection 1 suggested by the
proposed method are shown in Fig.6. Compared with pretime signals, the signals by the proposed method are
updated according to the changing traffic flows.
120

Pre-timed control

40

28

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Decouple PID Control of Compact Binary Power Generation
Kun-Young Han1, Lu Li, and Hee-Hyol Lee
Graduate School of Information, Production and Systems, Waseda University, Japan(Tel: 81-93-692-5164, Fax: 81-93-692-5164)
1kyhan@akane.waseda.jp

Abstract: A compact binary power generation using low-temperature difference thermal energy can be produce electric powerby utilizing low temperature difference between hot water and cold water. In this study, a reduced transfer function model ofwhole transfer function model for the compact binary power generation pilot plant is constructed. A decouple PID control
system is also designed to control the power generation pilot plant using a pseudo diagonalization, gershgorin bands, andmulti-stage pre-compensators.
Keywords: power generation, renewable energy, evaporator, condenser, non-interacting, pre-compensator, PID

whole system consists of seven components: evaporator,condenser, separator, regenerator, turbine part (turbine andabsorber), reducing valve, and working fluid pump.

1 INTRODUCTION
A compact binary power generation is to convert
thermal energy into electric power using temperature
difference between heat and cooling source such as heat ofhot springs, or waste heat of factories, and ocean thermalenergy, which are enormous quantitatively. Thermal power
plant that uses fossil fuels such as oil, coal, and natural gas,
but the compact binary power generation is anenvironment-friendly and semi permanent energy source. In
this respect, the compact binary power generation using
renewable energy has been attracted. However, thetemperature difference of compact binary power generation
system is much lower than that of the other commercialized
steam power plants. Furthermore, controlled variables andmanipulated variables of the system interact on each otherin general. If a control system is designed without
consideration of such case, it is possible to make worse thecontrol performance, because a control loop exerts harmful
influence to other control loop through the interaction. For
that reason, an appropriate control system is required toobtain the stable electric power.
In this paper, a reduced transfer function model ofwhole transfer function model is built for a power
generation pilot plant and a decouple PID control system isdesigned to control the plant.

1)Experimental conditions:
The pressure of the inlet and outlet of the turbine aremeasured by using amount of hot water as step input. At
that time, Amount of cold water and working fluid is fixed.
Next, amount of cold water is used as step input to measure
the pressure of inlet and outlet of the turbine. At that time,amount of hot water and working fluid is fixed. The
sampling time is 3[sec], the number of data is 100 samples.
A compact binary power generation system is modeled
based on measured data.
2.2Transfer function models based on step response
The transfer function models for the compact binary
power generation system using low-temperature difference
are drawn by approximate methods using 1st time delaytransfer function and 2nd order transfer functions is used toexpress oscillating component.
1) Transfer function model for evaporator
A block diagram of an evaporator is shown in Fig.2.The
working fluid and hot water are used as input in theevaporator. The transfer function models of the evaporator
are drawn as shown in Eq.(1)-(3).
1
15(1)
GEv 1( s ) =×
0.81s + 1
s 2 + 0.02 s + 15
0.087515
G Ev 2( s ) =
×
e -s
(2)
2
5s + 1s + 0.02 s + 15
77.2
6
G Ev 3( s ) =
×
e -s
(3)
1.1s + 1
s 2 + 0.07 s + 6

2 MODELING OF COMPACT BYNARY POWER
GENERATION SYSTEM
2.1System structure and experiment Conditions
The block diagram of the power generation pilot plantby low- temperature difference is illustrated in Fig.1. The© ISAROB 2013

29

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.1.Block diagram of whole system
For the total system, two inputs-two outputs transfer
functions model that is regarded revolutions per minute of
hot and cold water pump as manipulated values is modeledby using each of components of transfer function models.
Fig.5 shows the block diagram of two inputs-two outputs
transfer function model.
Fig.2.Block diagram of the Evaporator
In order to verify the transfer function models, theoutputs from the evaporator of transfer function model arecompared with the real data measured from theexperimental plant. The simulation results are shown in
Fig.3, Fig4respectively.

Fig.5.Transfer function model of two inputs two outputs
2.3Reduced order modelObtained the transfer function models have a high order.
In this paper, two inputs-two outputs transfer function
model is represented as follows.
- 0.002411 s 3 + 0.004986 s 2 - 0.003498 s + 0.001086
Gp11( s) = 4
s + 0.6342 s 3 + 0.3208 s 2 + 0.05245 s + 0.002724
0.1791 s 3 + 0.08403 s 2 + 0.02325 s + 0.004264
s + 0.4987 s 3 + 0.2156 s 2 + 0.02792 s + 0.00116- 0.0005017 s 3 + 0.001038 s 2 - 0.0007492 s + 0.0002311
Gp 21( s) =s 4 + 0.6331 s 3 + 0.319 s 2 + 0.05208 s + 0.002703
0.188 s 3 + 0.07508 s 2 + 0.02937 s + 0.001672(4)
Gp 22( s ) = 4
s + 0.4868 s 3 + 0.2062 s 2 + 0.02276 s + 0.0007496

Gp12( s ) =Fig.3.Input of working fluid, and outputs of hot water andworking fluid for evaporator

4

The simulation results of the reduced transfer functions
are compared with transfer functions that are calculated byconnection of the overall transfer functions as shown in
Fig.6 and Fig.7, respectively.

Fig.4.Input of hot water, and outputs of hot water andworking fluid for evaporator
3) Transfer function models for additional components
A condenser, separator, regenerator, turbine part (turbine
and absorb), reducing valve, and working fluid pump aremodeled[1].
4) Deduction of total transfer function model for two
inputs- two outputs system© ISAROB 2013

Fig.6.Input of cold water, outputs of working fluid,and cold water for condenser

30

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.11.Block diagram of Decouple PID control system

Fig.9.Inverse nyquist array and gershgorin bands by Gc1
The origin is still included in the gershgorin bands asshown Fig.9 so that the multi-stage connection isconsidered [3].

Fig.7.Input of cold water, outputs of working fluid, andcold water for condenser

3 MUlTI-STAGECONNECTED NONINTERACTING PRE-COMPENSATOR

3.2Multi-stage connection of Non-interacting precompensators
New pre-compensators Gc1, Gc2 are designed to remove
the interaction by using the multi-stage connection method
and they are connected to three-stage series. Where Gc1, Gc2
are designed at w= 0.241, w= 0.211, respectively.
The pre-compensators are calculated as
é2.9742 - 0.1376ù
Gc ( s ) = Gc 0 ´ Gc1 ´ Gc 2 = ê(7)úë0.0482 - 0.2590û

3.1Pre-compensator based on generalized pseudodigonalization method and diagonal dominanceFor the transfer function matrix Gp(s), if Gc0 is
expressed by Eq.(5), there is no interaction each other infrequency region at nearby s=0 from G(0) = Gp(0)Gc0=I[2].

é3.8813 - 6.3958ù
Gc 0 = Gp(0) -1 = êúë0.1488 - 0.6936û

(5)

The inverse nyquist array and the gershgorin bands ofthe system G(s)=Gp(s)Gc0 are shown in Fig.8.

The inverse nyquist array and the gershgorin bands areshown in Fig.10.

Fig.8.Inverse nyquist array, gershgorin band of Gp(s) Gc0
A pre-compensator Gc1is designed at w= 0.241 because
the origin is included in a large number of gershgorin bands.

é - 0.9998 - 0.9903ù
Gc1 = êú
ë - 0.0222 - 0.1386û

© ISAROB 2013

Fig.10.Inverse Nyquist array and Gershgorin bandby Gc0Gc1 Gc2
The gershgorin bands do not include the origin any
longer, so the diagonal column dominace is realized by 3stage series connection.

(6)

31

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

4DESIGN OF DECOUPLE PID CONTROLSYSTEM
The goal of this control system is to design a controller
to make the pressure difference between the inlet and outletof turbine by 0.3[MPa]. The decouple PID control systemof two inputs-two outputs system which controls thepressure at inlet-outlet for the turbine was designed byusing pre-compensators as shown Fig.11.
4.1Design of PID control system
For the controlled system Gp(s), PID parameters aredesigned by using the ultimate sensitivity method. The PID
parameters and the result of PID control are shown in
Table1and Fig.12, respectively.
Table1.PID Parameters
Hot water pump
Cold water pump
Kp
5.136
-120
Ti
16.2
0.261
Td
4.05
0.065

Fig.13.Result of decouple PID control
The control performance is improved by using 3-stage
series connection of pre-compensators.

5CONCLUSION
The transfer function models of each of components for
a compact binary power generation pilot plant using lowtemperature difference thermal energy were built and they
were integrated into the whole system. The reduced ordertransfer function model of two inputs-two outputs system
which controls the pressure at inlet-outlet for the turbine
was built, and the decoupling PID control system wasdesigned. Also, the effectiveness of the transfer function
models and the decoupling PID control system wasconfirmed through the simulation.

REFERENCES[1] K-Y Han, ARITA M, H-H Lee, ”Decoupling PID
control System for Power Generation by Low-Temperature
Difference”, International conference on Humanizedsystems, pp.306-311,2012.
[2] H-H Lee, W-K Choi, J-Y Song, S-G Lee, Akizuki K,
Noninteracting PID Control of a Fluid Temperature andLiquid Level Interacting System, Trans. IEE of Japan,
vol.119-C. no.8/9, pp.1035-1041,1999.
[3] H-H Lee, Nagamachi M, W-K Choi, J-Y Song,Miyazaki M, Akizuki K, Noninteracting Multi-Stage
Noninteracting PID Control by Pre compensators with
Series-Parallel Connection, Trans. IEE of Japan,
vol.123,no.1, pp.43-49,2003.

Fig.12.Result of PID control
4.2Design of Decouple PID control system
When the non-interacting pre-compensator Gc is usedby three pre-compensator, the PID parameters of thedecoupling control system are shown in Table2.
Table2.PID Parameters
Hot water pump
Cold water pump
Kp
2.11
600
Ti
15.87
14.15
Td
3.97
3.54
The modified PID parameters by trial and error and theresult of the decoupling PID control system are shown in
Table3 and Fig.13, respectively.
Table3.Modified PID Parameters
Hot water pump
Cold water pump
Kp
0.00710.845
Ti
0.1914
22.54
Td
1.3945
0.012© ISAROB 201332

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Decouple Sliding Mode Control of Compact Binary Power Generation
Kun-Young Han1, Hee-Hyol Lee
Graduate School of Information, Production and Systems, Waseda University, Japan(Tel: 81-93-692-5164, Fax: 81-93-692-5164)
1kyhan@akane.waseda.jp

Abstract: This paper presents a Decouple Sliding Mode Control(DSMC) of a compact binary power generation using lowtemperature difference thermal energy. First, a state equation model based on the transfer function model is deduced. Then, a
Sliding Mode Control system with decoupler is designed to control the pressure difference between inlet and outlet of a turbine
by high pressure steam of working fluid. The validity of the proposed model is confirmed by a comparison between simulationand experimental result and the control simulation results show the effectiveness of the decouple Sliding Mode Control.
Keywords: power generation, low temperature thermal energy, optimum servo system, decoupling control,
sliding mode control(SMC), decoupling Sliding Mode control(DSMC)
domain.This paper deals with a modeling and designing
control system in time domain. A state equation model
based on a transfer function model is developed and a
Decouple Sliding Model Control system is also designed tocontrol the pressure difference between the inlet and outletof turbine to keep 0.3[Mpa] by the high pressure steam ofworking fluid. The simulation model is comparedexperimental data that measured from the compact binary
power generation pilot plant and the control simulation
results show the effectiveness of the DSMC.

1 INTRODUCTION
A compact binary power generation uses thetemperature differential between hot and cold waters to
produce electric power. In the closed cycle concept, asecondary working fluid(e.g., ammonia or Hydrochlorofluoro-carbon) is vaporized and recondensed in aclosed loop to drive a turbine. The working fluid isvaporized by hot water that is pumped by hot water pumpand passed through heat exchangers. This working fluid
expands, emerging as a high pressure steam which derives aturbine. The vapor is condensed by passing it through asecond set of heat exchangers containing cold water
pumped by cold water pump.
The compact binary power generation is an effective
method of power generation, which has small impact onenvironment and can be semi-permanently utilized.
Therefore, if the power generation using enormous thermalenergy is commercialized it is expected that huge change
can be brought to energy supply. However, compared withthe thermal power generation using fossils or nuclear
energy, whose temperature difference between heat source
and cooling source is very low. Also, this kind of the system
controlled variables and manipulated variables interact oneach other in general.Therefore, for an efficiency and stable
operation of the compact binary power generation plant, arobust control system that is suitable for the environmental
factor or disturbance is required[1].
In our recent study, a transfer function model and acontrol system of the compact binary power generation wasdeveloped to control the pilot plant in frequency© ISAROB 2013

2MODELING OF COMPACT BYNARY POWER
GENERATION SYSTEM
The schematic diagram of the compact binary power
generation pilot plant using low temperature difference
thermal energy is illustrated in Fig.1. A feature of this pilot
plant uses ammonia/water mixed working fluid.
2.1Experiment Conditions·

Hot water pump: 60[Hz], Cold water pump: 60[Hz],Working fluid pump: 70[Hz].

·Sampling period: 3 [sec], measured data: 100
samples.

·

These data was measured under a steady sate ofcompact binary power generation pilot plant

2.2two inputs-two outputs system state equation modelIn our previous study[2], the transfer function model isalready drawn as shown Eq.(1). It was modeled by using
approximated method using 1st and 2nd order time delaytransfer function.

33

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.1.Schematic diagram of compact binary power generation system
0.04351.6
éù
e- 0.01s
e- s úéy ù ê
3 + 1.5s 2 + 5.1s + 1
3 + 1.99s 2 + 11.09s + 1
éu ù
5
s
11
s
1
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ê ú=ê
y
0
.
01
1
.
9
u
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.
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s
e
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ê 3
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3
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.
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s
+
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.
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s
+
1
.
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s
+
0
.
1
15
s
+
1
.
15
s
+
15
.
01
s
+
1
ë
û

(1)
Where, y1,y2 are the pressure of inlet and outlet of
turbine and uh,uc are the manipulated value of hot and cold
water respectively. In this paper, a state equation model
based on two inputs-two outputs transfer function model isdrawn by using matlab as shown in Eq(2).
The simulation results of the state equation model arecompared as shown in Fig.3, Fig.4, respectively.
Fig.3.Input of cold water, outputs of inlet of turbineé -0.463 -1.2481 0.3076 ù
é-0.0009 -0.3899 ùêúêúA = ê 0.9677 0.4546 0.0315 ú , B = ê 0.0025 0.1181 ú
ê- 0.0095 - 0.0012 - 0.0639 úê 0.0081 - 0.1584 ú
ëê
ûú
ëê
ûú
é- 0.0713 - 0.0655 0.7009 ù
é0 0 ù
C=ê
ú, D=ê
ú
êë- 0.3024 - 0.2924 0.8361úû
êë0 0 úû3DECOUPLE SLIDING MODE CONTROL
Sliding Mode control is a robust control scheme basedon the concept of changing with structures states of thesystem in order to obtain a desired response[3]. The biggest
advantage of SMC is its robustness to variation inparameters, external disturbance and modeling errors,
therefore, SMC with a decoupler is designed in this study.

(2)

3.1EquivalentControlSystem&Switching Hyper
PlaneDesign
Let us consider as follows system,dx(t ) = ( A - B F ) x(t ) + B G u(t ))dty (t ) = C x(t )(3)where, x ∈ R 3 , u ∈ R 2 , y ∈ R 2
The error and switching hyper plane is defined as Eq.(4).
e(t ) ≡ r (t ) - y (t )

ψ 1 ≡ S1e + S 2 edt

òFig.2.Input of hot water, outputs of inlet of turbine© ISAROB 2013

34

(4)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.4.Block diagram of DSMC
When the state is restricted on a hyperplane, theequivalent control input Ueq can be obtained by Eq.(5)when S1 ≠ 0 ,CBGu eq = S1-1 S 2 e + r& ( A - BF )x

3.2Sliding Mode Controller Design
Sliding mode controller is designed by using eventuallyhierarchy control law for the state of system to reach toswitching hyper plane from arbitrary initial state x0.

(5)

Let us define the Lyapunov function V with respect to Y1
as Eq.(12),Also, the equivalent control input is obtained by Eq.(6)when CBG is nonsingular matrix.
u eq = (CBG ) -1 ( S1-1 S 2 e + r& - My ) (r = m)

1
V ≡ ψ 1T ψ 1
2(6)& of the V is then given
the condition of derivative time V
by Eq.(13),Where, M satisfy,C(A - BF ) = MC

V& < 0This condition result in Ψ 1 belong to the switching hyper
plane.
& is rewritten as Eq.(14),
Derivative time V(8)& = Ψ t ( Se + r& - My ) - Ψ T CBu& = ψT Ψ
V
1
1
1
1

(9)

where, eventually hierarchy control input u, the equivalent
control input as a linear control term, the unit input as anonlinear control input and k1as scalar gain is defined asEq.(15),u eq
u ≡ u eq + k1
(15)
u eq

In this paper, matrix M is obtained as Eq.(9).

é 0.2194 ´ 10 -16
M=ê-16
ë 0.1935 ´ 10

0.4922 ´ 10 -16 ùú0.1332 ´ 10 -16 û

Substituting for Ueq from Eq.(6) into Eq.(4) gives

e& = S1-1 S 2 e- Se& is rewritten as Eq.(16),Substituting Eq.(15) into Eq.(14), V& = ψ T ( Se + r& - My ) - ψ T CBG (U + k u eq
V
1
1
eq
1
u eq

(11)

= k1

A Switching matrix S can be designed by setting an
arbitrary eigenvalues.

© ISAROB 2013(14)

where, · is norm.
(10)

where, let S1 = I , S 2 = S , then

e& =

(13)

(7)

Eq.(7) may be rearranged as Eq.(8) by multiplying thetranspose matrix of C.
M = C( A - BF )C' (CC' ) -1

(12)

35

Ψ 1T CBGu eq
u eq

(16)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Hence, the switching condition scalar gain k1 is obtainedas Eq.(17).

k1

≤
>

0 : if Ψ 1T CBGueq

≤
>

0

(17)

3.3DSMC Control Simulation Result
A simulation is carried out using MATALB to verify theperformance of the designed DSMC as shown in Fig.4.The
results of DSMC are compared with SMC. In this paper, S2,
K1 is configured 1000, 0.01 respectively. The goal of thiscontrol system is to make maintain the pressure difference
between the inlet and outlet of turbine by 0.3[Mpa].
Fig.5 shows the comparative result of outputs.

Fig.7.The result of switching condition
Fig.8 shows the comparative result of outputs of k1.

Fig.5.The result of outputs
Fig.6 shows the comparative result of control value.

Fig.8.The result of k1

4CONCLUSION
The state equation for two inputs-two outputs for acompact power generation pilot plant using low
temperature difference is drawn. DSMC is designed tocontrol the pressure at inlet-outlet of the turbine.
Also, the validity of the state equation model and theeffectiveness of decoupler are confirmed through thesimulation.

REFERENCES[1] Kun-Young Han,Hee-Hyol Lee, “Neuro PID control
of power generation using a low temperature gap”,
Artificial Life and Robotics, Vol.16.No.2,pp.178-184,2011.
[2] K-YHan, ARITAM,H-HLee,”Decoupling PID
control System for Power Generation by Low-Temperature
Difference”, International conference on Humanizedsystems,pp.306-311,2012.
[3] H-HLee, SHOJI.F, TSURUOKAH, MIYAZAKI M,
AKIZUKI K,“Frequency-ShapedSliding Mode Control
Using Output Feedback and Application to Follow-up
Control of Process System” ,SICE, vol.38,no.2, pp.186192,2002.

Fig.6.The result of control value
Fig.7 shows the comparative result of switchingconditions.

© ISAROB 2013

36

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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    &   , ,The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013       ,    / +  /    &>, ,  /& !   ,  /
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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013 4 ,       / & !              / / & ! ,  #& 5      


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%     #& 1 /    

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 ,        # 1#        

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3:6 E&   (5AA7)    * + F/# H  .    *   &        B #      @H5;
,  & B (4@<A)           E    ?, >,3;6 & &D&© ISAROB 201340

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013   
    ½
  ¾
 ¿½
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 !  "#  ##$

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 %     #  ,   -  .%           '          /01# -'        % 2               '               #         %    ,                #     %               %      %   #)                          &    (   /31# +  ,      ' '             *  /41#
    '          '      '                #      %         '   %      '     #   '        -    +   -   (# -    (       &                %         &#  & 5   6                /7 81#   &      $       # %         9           #  %     © ISAROB 2013

 

 

   )   -   (   .%
    /:1#         %   04    #   3<=0     %    47 %
     0       # >  3<=0     =:3 '  9     %%
     #             %    8:0         %   #
>              & ' '      %      ==    # +      %             '  , # )   07=7    #  &     ,       '        44 %  /81#  &    7:0         %              !

41

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013% 0#   .    
A  
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56 +   
5 6  #

5 6       (! 0 D E 8 D  &Ü Ü 576

)  2         #               % '          2  %  , ,   -              #







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


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





Û

5 6       (!  8 D  &#© ISAROB 2013-  '     ,   9  576   #  Ü '          *  5*6 ,      #     Û     Û 
%              9  546  
 % 9  5:6#

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Ü

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 


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%$                 %            %    &#+     &   C %           %     %        %     #  2         %     







 Û Ü  


 

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           '    ' * @      %   /=1# A C       '   5 6             5 6      # )    '   ,           # > 

506

42

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013 *         + 5  
6# +     *        (      %                 +# ) '       F    >      5F>6  #1

True Positeve Rate

0.8

1

0.6

Self-Prediction
Act-Prediction

0.4

0.8

True Positive Rate

0.2
0.6

0
0

0.4

0.2

Self-Prediction
EGF-Prediction

0
0.2

0.4
0.6
False Positive Rate

0.8

1 0 ' F>                   (   #     ' *       #     ' 
F>          %  % *  +   5+6   # '               '   C # 3    ' F>   +   #            '     ' *     #          
 %        #
 4    ' F>   @+   #    '  *         @+
  #         *  +
  #   2              '        #    

/01     A#.#  # ( # *# -'  G#
A   +#.#  ?##   .#?#!  -   .% ! 3;;<  #       53;;=6#/31    #  #   -#! F    +    +  +  %       & +   -    (# + 2  @   F%   53;;<6 840E847/41 +  G# H G# # #  -#!
+       .   -    ( %  *   # A     0=           ?     A 
 @  ?       *#B:::576
53;036 7B:E7<4)            '     '                # +    '                      %     %               #             © ISAROB 20131

'           C # -'                 '        #   ,           
    '             ' ## 0# F>                   (   #     '  *      
  #     *  +   #

 

0.8

# 3# F>                   ( +   #     '  *      +   #     *     #

0.2

0

0.4
0.6
False Positive Rate

/71   (#   (##!    (   (   #    50=<<6 8;8E80</81 H   #  (# #   .# # .  # )#
  G  G#! ?    9     -  &# (   #  50=<86 4B4:E4B8;43

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

1

True Positive Rate

0.8

0.6

0.4

Self-Prediction
Gla-Prediction

0.2

0
0

0.2

0.4
0.6
False Positive Rate

0.8

1

# 4# F>   @+   #     ' *       
@+   #     *  +   #/:1 -    (   .% ! + %                     &#     53;;76#/B1 I (# (  ###  +##! +  G       A  @  50===6/<1 I (#  G# *  #A#! G    
   (   + ( (  A  @  53;;76/=1   #!    * @ A  +   G      * @    A  @  50===6© ISAROB 2013

44

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Remarks on Recognizability of Four-Dimentuonal Topological Components
Makoto Sakamoto1 , Makoto Nagatomo1 , Tatsuma Kurogi1 , Takao Ito2 , Yasuo Uchida2
Tsunehiro Yoshinaga3 , Satoshi Ikeda1 , Masahiro Yokomichi1 , and Hiroshi Furutani1
1Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN

2
3

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

Dept. of Computer Science and Systems & Electronic Enginieering, Tokuyama College of Technology, Shunan 745-8585,JAPAN

Abstract

three-dimensional arrays[2,5]. In[2], interlocking components was proposed as a new topological propertyof three-dimensional digital pictures : Let S1 and S2
be two subsets of the same three-dimensional digital
picture. S1 and S2 are said to be interlocked when
they satisfy the following conditions:

The study of four-dimensional automata as thecomputational model of four-dimensinal pattern processing has been meaningful. However, it is conjectured that the three-dimensional pattern processing
has its our difficulties not arising in two- or threedimensional case. One of these difficulties occurs inrecognizing topological properties of four-dimensional
patterns because the four-dimensional neighborhood
is more complicated than two- or three-dimensionalcase. Generally speaking, a property or relationshipis topological only if it is preserved when an arbitrary’ rubber-sheet ’ distortion is applied to the pictures .
For example, adjacency and connectedness are topological ; area, elongatedness, convexity, straightness,
etc. are not. In recent years, there have been many interesting papers on digital topological properties. For
example, an interlocking component was defined as a
new topological property in multi-dimensional digital
pictures, and it was proved that no one marker automaton can recognize interlocking components in athree-dimensional digital picture. In this paper, wedeal with recognizability of topological components
by four-dimensional Turing machines, and investigate
some properties.

(1) S1 and S2 are toruses,(2) S1 goes through a hole of S2 ,(3) S2 goes through a hole of S1 .
By the way, the question of whether processing
four-dimensional degital pattern in much difficult than
three-dimensional ones is of great interest from thetheoretical and practical standpoints both. In recent
years, due to the advances in many application areas
much as computer animation, motion image processing, and so both, the study of four-dimentional patternprocessing has been of crucial importance. Thus, it isvery interesting to deal with the geometrical propertises such as interlocking components and cavities ina four-dimensional digital picture.
The interlocking of S1 and S2 in a four-dimentional
tape is illustrated in Fig.1. This relation may be considered as a chainlike connectivity.
It is proved that no one marker automaton can recognize interlocking components in a three-dimensional
digital picture in [2]. In this paper, we investigate
recognizability of topological properties such as interlocking components by three-dimensional Turing machines.

KeyW ords : digital geometry,interlocking component, one marker automaton, three-dimensional automaton, topological component, Turing machine.

1

Introduction

Digital geometry has played an important role incomputer image analysis and recognition[3]. In particular, there is a well-developed theory of topological properties such as connectedness and holes for
two-dimensional arrays[4]. On the other hand, threedimensional information processing has also becomeof increasing interest with the rapid growth of computed tomography, robotics, and so on. Thus it has
become desirable to study the geometrical properties such as interlocking components and cavities for© ISAROB 20132

Preliminaries

Definition 2.1. Let Σ be a finite set of symbols.
A four-dimensional tape over Σ is a four-dimensional
array of elements of Σ. The set of all four-dimensional
tapes over Σ is denoted by Σ(4) . Given a tape x ∈ Σ(4) ,for each j(1≤j≤4), we let lj (x) be the length of x along
the j th axis. When 1≤ij ≤lj (x) for each j(1≤ j≤4), letx(i1 , i2 , i3 , i4 ) denote the symbol in x with coordinates

45

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where
(1) Q is a finite set of states;(2) q0 ∈Q is the initial state;(3) F ⊆Q is the set of accepting states;(4) Σ is a finite input alphabet (♯∈Σ/ is the boundary
symbol);࡮࡮ ࡮(5) {+,−} is the pair of signs of presence and absenceof the marker; and(6) δ: (Q×{+,−}) × ((Σ∪{♯}) × {+,−})→
2(Q×{+,−}) ×((Σ∪{♯})×{+,−})×{east,west,south,
north,up,down,future,past,no move}) is the nextmove f unction, satisfying the following: For any
q,q ′ ∈Q, any a,a′ ∈ Σ, any u,u′ ,v,v ′ ∈ {+,−}, and any d ∈ {east,west,south,north,up,down,future,past,no move},if((q ′ ,u′ ),(a′ ,v ′ ),d)∈δ
((q,u),(a,v))
then
a=a′ ,and′ ′(u,v,u ,v )∈{(+,−,+,−),(+,−,−,+),(−,+,−,+),(−,+,+,−),(−,−,−,−)}.

‫ޓ‬VKOGCZKU
VJGVJCZKU

Fig. 1: Interlocking components in a four-dimensionaltape.

(i1 , i2 , i3 , i4 ), as shown in Fig. 2. Furthermore, we
definex[(i1 , i2 , i3 , i4 ), (i′1 , i′2 , i′3 , i′4 )],when 1≤ij ≤i′j ≤lj (x) for each integer j(1≤j≤4), as thefour-dimensional tape y satisfying the following :(i) for each j(1≤j≤4), lj (y)=i′j − ij + 1;(ii) for each r1 ,r2 ,r3 ,r4 (1≤r1 ≤l1 (y), 1≤r2 ≤l2 (y),1≤r3 ≤l3 (y), 1≤r4 ≤l4 (y), y(r1 , r2 , r3 , r4 )=x(r1 +i1 − 1, r2 + i2 − 1, r3 + i3 − 1, r4 + i4 − 1).
x(1, 1, 1, l4 (x))
up

north
west
l3 (x)

down

east
south

x(l1 (x),l2 (x),l3 (x),l4 (x))

Definition 2.3. Let Σ be the input alphabet of 4N M1 M . An extended input tape x̃ of M is any fourdimensional tape over Σ×{+,−} such thatx(1, 1, 1, 1)
north
west
l3 (x)

up
down

east
south

l1 (x)࡮࡮࡮l2 (x)

future(i) for each j(1≤j≤4), lj (x̃)=lj (x),x(l1 (x),l2 (x),l3 (x),1)(ii) for each i1 (1≤i1 ≤l1 (x̃)), i2 (1≤i2 ≤l2 (x̃)), i3 (1≤i3 ≤l3 (x̃)), and i4 (1≤i4 ≤l4 (x̃)), x̃(i1 , i2 , i3 , i4 ) =x(i1 , i2 , i3 , i4 , u) for some u ∈ {+, −}.

time axis
l2 (x)

l1 (x)(the 4th axis)
past

Definition 2.4. A configuration of 4-N M1 M =(Q, q0 , F, Σ, δ) is an element ofFig. 2: Four-dimensional input tape.

((Σ ∪ {♯}) × {+, −})(4) × (Q × {+, −}) × N 4 ,where N denotes the set of all nonnegative integers. The first component of a configuration c =(x̃,(q, u),(i1 , i2 , i3 , i4 )) represents the extended input
tape of M . The second component (q, u) of c represents the extended state. The third component

Definition 2.2. A four-dimensional nondeterministic one-marker automaton 4-N M1 is defined by thesix-tuple
M = (Q, q0 , F, Σ, {+, −}, δ),© ISAROB 2013

We call a pair (q,u) in Q×{+,−} an extended state,
representing the situation that M holds or does nothold the marker in the finite control according to thesign u = + or u = −, respectively. A pair (a,v) inΣ × {+,−} represents an input tape cell on which themarker exists or does not exsit according to the sign
v = + or v = −, respectively.
Therefore, the restrictions on δ above imply the following conditions. (A) When holding the marker, M
can put it down or keep on holding. (B) When notholding the marker, and (i) if the marker exists on thecurrent cell, M can pick it up or leave it there, or (ii)
if the marker does not exist on the current cell, M
cannot create a new marker any more.

46

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 20133

(i1 , i2 , i3 , i4 ) of c represents the input head position.
If q is the state associated with configuration c, thenc is said to be an accepting conf iguration if q is anaccepting state. The initial conf iguration of M on
input x is

Simulationoffour-dimensional
one-marker automata by fourdimensional Turing machines

In this section, we show the algorithms describedin the previous section are optimal in some sense. We
can get the following Theorems any using the same
technique as in the proof of Lemmas 6.2 and 6.3 in [6].

−IM (x) = (x , (q0 , +), (1, 1, 1, 1)),−

where x is the special extended input tape of M
such that x− (i1 , i2 , i3 , i4 )=(x(i1 , i2 , i3 , i4 ), −) for each
i1 , i2 , i3 , i4 (1≤i1 ≤l1 (x̃),1≤i2 ≤l2 (x̃),1≤i3 ≤l3 (x̃),1≤i4 ≤
l4 (x̃)). If M moves determinately, we call M a f ourdimensional deterministic one-marker automaton
4-DM1 .

Theorem 3.1. T o simulate 4 − DM1 ’s, (1) SV 4N T M ’s require Ω(m3 log(m3 )) space and (2) SV 43
3
DT M ’s require 2Ω(m log(m )) spacespace(m≥1).
Theorem 3.2. T o simulate 4-N M1 ’s,

Definition 2.5. A seven-way f our-dimensional
T uring machine is defined by the six-tuple(1) SV 4-N T M ’s require Ω(m6 ) space, and
6(2) SV 4-DT M ’s require 2Ω(m ) space(m≥1).

M = (Q, q0 , F, Σ, Γ, δ),
where

4

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

In this paper, we show that interlocking components are not recognized by any space-bounded fourdimensional Turing machines.
First of all, we consider a four-dimensional input
tape T3 that is 7 units in thickness. So, for some m,
T3 ={ (i1 ,i2 ,i3 ,i4 ) | 1≤i1 ,i2 ,i3 ≤ m+2,1≤i4 ≤7 }.
Fig.3(a)represents T3 . Now we define two different
5×5×5 patterns as shown in Fig.3(b)(c). Then we
consider an arbitrary n-by-n matrix of those 5×5×5
patterns (see Fig.3).

(3) F ⊆Q is the set of accepting states;(4) Σ is a finite input alphabet (♯∈Σ/ is the boundary
symbol);(5) Γ is a finite storage-tape alphabet (B∈Γ is theblank symbol); and(6) δ⊆(Q×(Σ∪{♯})×Γ)×(Q×(Γ−{B})×{east,west,
south,north,up,down,future,no move}×{right,left,
no move}).
If M moves determinately (nondeterminately), wecall M a seven-way f our-dimensional deterministic(nondeterministic) T uring machine SV 4-DT M(SV 4-N T M ).

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮
࡮࡮࡮

Let L: N →R be a function. A seven-way fourdimensional Turing machine M is said to be L(m)space bounded if for all m≥1 and for each x withl1 (x)=l2 (x)=l3 (x)=l4 (x)=m, if x is accepted by M ,then there is an accepting computation path of M on
x in which M uses no more than L(m) cells of the storage tape. We denote an L(m) space-bounded SV 4DT M (SV 4-N T M ) by SV 4-DT M (L(m)) (SV 4N T M (L(m))).

࡮࡮࡮

m+2

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

࡮࡮࡮

m+2

࡮࡮࡮
࡮࡮࡮

m+2

࡮࡮࡮

m+2
m+2
m+2

࡮࡮

࡮



time axis

Definition 2.6. Let T (M ) be the set of fourdimensional tapes accepted by a machine M , andlet £[4-DM1 ]={T |T (M ) for some 4-DM1 M }. £[4N M1 ], etc. are defined in the same way as £[4-DM1 ].



a



(the 4th axis)

Fig. 3: Four-dimensional input tape including interlocking components T3 [2].

We can easily derive the following theorem by using
ordinary technique[6].
Theorem 2.1. F or any f unction L(m) ≥ log m3 ,£[SV 4-N T M (L(m))]⊆Uc>0 £[SV 4-DT M (2c(L(m)) )].

© ISAROB 2013

Recognizability of interlocking components in four-dimensional images

Then, we can get the following lemma from Lemma
2.1 in [2].

47The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Lemma 4.1. 4-DM , cannnot recognize interlocking
components of an arbitrary given digital picture.

[4] A.Rosenfeld, Picture Languages, Academic Press,
New York(1979).

From Theorem 3.1 and Lemma 4.1, we can get thefollowing.

[5] A.Rosenfeld, Three-dimensionl digital topology,
Information and Control 50:119-127(1981).
[6] M.Sakamoto, Three-dimensional alternating Turing machines, P h. D. T hesis, Y amaguchi
U niversity (1999).

Theorem 4.1. Interlocking
componentsarenot accepted by any SV 4-DT M (L(m))(SV 4N T M (L(m)))
f oranyf unctionL(m)m3 logm3
suchthat
limm→∞ [L(m)/2]=0(limm→∞ [L(m)/m3 log m3 ] = 0)(m≥1).
Next, we can get the following lemma by using atechnique similar to that in the proof of Lemma 2.1 in[2].
Lemma 4.2. 4-N D1 cannnot recognize interlocking
components of an arbitrary given digital picture.
From Theorem 3.2 and Lemma 4.2, we can get thefollowing.
Theorem 4.2.
Interlocking components
are not accepted by any SV 4-DT M (L(m))(SV 4-N T M (L(m)))
f oranyf unction
m6
L(m) such that limm→∞ [L(m)/2 ] = 0(limm→∞ [L(m)/m6 ]=0)(m≥1).

5

Conclusion

In this paper, we dealt with recognizability of topological components by four-dimensional automata,and showed that interlocking components are not recognized by any space-bounded four-dimensional deterministic or nondeterministic Turing machines. By theway, what is the situation for a two or three marker
automata, or for alternation (see [1])? This question
seems very intersting. We will investigate the problem
in further papers.

References[1] A.K. Chandra , D.C. Kozen, and L.J. Stockmeyer,
Alternation, J. of ACM , 28(1):114-133(1981).
[2] A.Nakamura and K.Aizawa, Detection of ic interlocking components in three-dimensional digital
pictures, Imformation Sciences 40:143-153(1986).
[3] A.Rosenfeld and K.C. Kak, Digital Picture Processing, Academic Press, New York(1976).

© ISAROB 2013

48

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Power Consumption Reduction by
Dynamic Core-Counts Control with Power Gating
Kazuyuki TAMOTO1, Kunihito YAMAMORI2, Masaru AIKAWA3
1

Graduated school of Engineering, University of Miyazaki
2
Faculty of Engineering, University of Miyazaki
3
Technical Center, Faculty of Engineering, University of Miyazaki(Tel: +81 985 58 7589, Fax: +81 985 58 7589)
2

yamamori@cs.miyazaki-u.ac.jp

Abstract: Power Gating (PG) technology is known that it reduces static power consumption by leakage current. Linux
operating system on multi-core processor evenly assigns processes to each core. Linux scheduler assigns processes to the core
which has the fewest number of processes even if the core is idle. It makes an idle core in PG state change to active state, andavoids reducing power consumption. In this paper, we propose a method to reduce electric power consumption by effective useof PG. Our method modify linux kernel to change number of active cores in system dynamically, and apply PG to idle cores
immediately. The number of active cores is changed according to the system load. Experimental results show that our proposed
method reduces about 8kWh electric power consumption from the original kernel with keeping performance.
Keywords: Power Management, Power Gating, Linux Kernel
cores to avoid interrupting PG state. As a result, power
consumption of CPU is reduced. Our method also changes
the number of active cores according to current load ofsystem to keep system performance.

1 INTRODUCTION
Recent computers are required low electric power
consumption in terms of thermal problem, running cost, andenvironmental problems. Fine process rule improves
performance of CPU, and reduces total electric power
consumption. However, increasing of leakage current
becomes as a large problem. Leakage current is a flow
through insulator of transistor when transistor is in off state,
and it causes unnecessary power consumption. So many
researchers try to reduce it.
To reduce electric power consumption of CPU, Clock
Gating[1] (CG) is used in long time. As shown in Figure 1,
CG technology stops to supply a clock signal for a CPU
core and reduces power consumption. CG is easy to
implement and efficient to reduce power consumption.
However, leakage current remains in the clock circuit.
Recently, Power Gating[2] (PG) is attracted in attention. PG
technology stops to supply electric power for cores asshown in Figure 2. Thus, PG prevents to appear leakage
current and reduces power consumption by leakage current.
PG is now replacing with CG, and implementing in manykinds of CPUs.
CG and PG can work when cores are in idle state only.
Current linux system on multi-core processor evenly
assigns processes to each core. A new process is assigned tothe core which has the fewest number of processes even if
the core is in idle state. It means PG cannot work effectively.
In this paper, we propose a method to use PG
effectively by modifying linux kernel on multi-coreprocessor. Proposed method dynamically changes thenumber of active cores in order to load of linux system, andimmediately apply PG for idle cores. While the system load
is low, our method does not assign a new process to the idle

© ISAROB 2013

Fig. 1. A model of Clock Gating

Fig. 2. A model of Power Gating

2 RELATED WORKS
Some works have been reported for effective use of PG
to reduce electric power consumption. When CPU switches
its state from active/PG to PG/active, additional electric
power is consumed. It means that frequently change ofCPU state affect power consumption. Kimura et al.[3] pointout that additional electric power depends on CPU
temperature, and they proposed a method that changes CPU
state according to CPU temperature when processes
frequently stay in WAIT state.
When CPU switches its state from active to PG, CPUmust save the internal conditions such as program counter

49

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

to low level memory, and it takes additional time. Otomo etal.[4] propose a method that CPU can quickly switch from
active to PG by use of an additional memory. By using of
the additional memory, CPU does not need to save its
internal conditions to low level memory.
These studies assume to use original CPU or FPGA,
there are few researches for general CPUs.

3.3 Load of CPU
Our method changes the number of active coresaccording to the load of system. Thus, we implement a new
function to get the load of system in linux kernel.
Equation (1) defines the load of the system. Linuxkernel manages CPU time as the sum of system time 𝑡𝑠 ,user time 𝑡𝑢 , idle time 𝑡𝑖 and others 𝑡𝑜 . The load of k-th
core is defined as the ratio of active time to total time.

3 IMPROVEMENT OF LINUX KERNEL
𝐿𝑜𝑎𝑑𝑘 =

We modify a linux kernel to achieve a system that
effectively uses PG on general multi-core processor. The
system periodically watches load of CPU. When the load ofsystem is low, kernel turns a CPU core off, and applies PG
immediately. Meanwhile the load of system becomes high,our method wake core in PG state up. Thus, the system
reduces power consumption with keeping system
performance.

(1)

3.4 PG management through ACPILinux kernel manages CG and PG by C-states of ACPI
[6]. C-states show power states when CPU core is in idle
state. C-states consist of four level states, C0 means that thecore is in active state, and C3 means in PG state. The corein deep level consumes low electric power. Meanwhile, thecore in deep level needs large latency when the corechanges its state from PG to active. Figure 4 illustrates
relations between electric power consumption and latency
in each C-state level. While a core is in idle state, systemchanges the state from C0 to C1, C2, and C3. If a process is
assigned to a core, the core returns to C0.

3.1 Environments
We implement proposed method in linux kernel 2.6.32[5], and evaluate our method on Intel core i7 Nehalemarchitecture processor [6] which equips PG technology.
Hyper-threading and DVFS is not used.
3.2 Dynamic Core-Counts Control
Linux kernel 2.6.32 controls on/off of core by bitmask
and scheduling domain. If a bit in bitmask is 0,
corresponding core is in off state. Scheduling domain
manages cores with hierarchical group. If a process should
move other core, it is preferentially moved to the corebelonging to the same group. Figure 3 illustrates a conceptof scheduling domain of hyper-threading dual core
processor. If a process in core0 should move, preferential
destination is core2. When system switches on/off of a core,
those groups are reconstructed. In this paper, all coresbelong to the same group.
Our proposed method controls the state of cores bybitmask, and reconstructs group of scheduling domain. We
make original daemon process that watches the load ofsystem and changes the number of active cores at fixedinterval. In linux kernel, the lowest number of active core isone. Thus, core0 is always online, and our daemon process
always works on core0.

Fig. 4. Power stat and latency of each state
To reduce electric power consumption on multi-core
CPU, it is important to keep the idle core in C3 state.
However, linux scheduler evenly assigns a process to thecore with the fewest number of processes, so C3 state isfrequently interrupted. To avoid this situation, we logicallyremoved the idle core from system as described in Section
3.2 since linux operating system cannot detect the logicallyremoved core, no process is assigned to the removed core,
and the core can keep C3 state. On the other hand, theremaining cores have to work harder because the number ofcores is decreased, and it may make electric power
consumption of remaining cores increase. If the load ofsystem is high, this increasing of electric power
consumption can ignore because the cores work hard andthey already consume all the required electric power. If theload of system is low, increasing of electric power
consumption of remaining cores may become a problem
since logical removing of a core prevents remaining coresstaying C1, C2 or C3.
Here we investigate the relations between the system

Fig. 3. Scheduling domain of dual core processor with
Hyper-threading technology© ISAROB 2013

𝑡𝑠 + 𝑡𝑢 + 𝑡𝑜
× 100(%)
𝑡𝑠 + 𝑡𝑢 + 𝑡𝑖 + 𝑡𝑜50

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013high load leads poor performance, system should increase
the number of active cores.
Threshold to turn the core on/off is not clear. Since
early switching from off to on leads high electric power
consumption, this switching is desirable near to 100% of
system load. Figure 5 and Figure 6 shows that the averageload of system is increased when a core is logicallyremoved because the processes assigned to the removed
core are distributed to other remaining cores. Therefore thethreshold to turn the core on/off should be decided by theload of remaining cores. Suppose 𝑁 denotes the number ofremaining cores, the average load is defined by Equation(2).

load and electric power consumption according to thenumber of active cores. Figure 5 and Figure 6 show thattransition of load and power consumption of each numberof active cores when initial average load is about 40%, 30%,
20% and 10% in four active cores. In both figures, bars
denote the electric power consumption corresponding to theleft vertical axis, and lines denote the average load of thesystem corresponding to the right vertical axis.

𝐴𝑣𝑔𝑙𝑜𝑎𝑑 =∑ 𝐿𝑜𝑎𝑑𝑘(𝑁 ≥ 2)𝑁−1(2)

When the 𝐴𝑣𝑔𝑙𝑜𝑎𝑑 becomes near to100%, it means thesystem performance may down. In the following
experiments, we use 𝐴𝑣𝑔𝑙𝑜𝑎𝑑 = 90% to switch the corestate on/off.

4 EXPERIMENTS AND DISCUSSIONS
Fig. 5. Average load and power consumption when initialload is 40% and 30%.

4.1 Experimental conditions
We evaluate electric power consumption and execution
time by comparing our proposed method with original linux
kernel 2.6.32. Our modified kernel watches the load ofsystem at every two seconds, and decides to make a core
remove or return. Test programs change 𝐴𝑣𝑔𝑙𝑜𝑎𝑑 at
random by forking small programs in many times, so
system changes the number of active cores. We measureelapse time until the test programs have finished. Moreover,
we evaluate performance of a floating-point matrix
calculation under the modified kernel.
4.2 Results and Discussion
Figure 7 shows electric power consumption at every
second of proposed method and original kernel. The
horizontal axis is seconds, and vertical axis is actual power
consumption of whole system. Figure 7 says that our
method globally reduces power consumption. At the
beginning, the number of active cores is one in our
proposed method. Since our proposed method uses only a
few cores for several seconds from the beginning, electric
power consumption stays low. As shown in Figure 7,
electric power consumption of our kernel is lower than thatof the original kernel after 109 seconds since our kernel
finishes test programs faster than the original kernel. Figure
7 also shows that our method is effective in the case which
the electric power consumption by original kernel is morethan 165W. Our kernel employs a hard threshold to decide
on/off of a core, the curve of our kernel sometimes waves
around 40 seconds and 65 seconds.
Table 1 shows mathematical total electric power
consumption by our proposed kernel and the original kernel
calculated from Figure 7. As shown in Table 1, our method
can reduce about 8kWh from original kernel.
Table 2 shows executing time of test programs. It shows
our method is faster than original system. It is because

Fig. 6. Average load and power consumption when initialload is 20% and 10%.
Figure 5 shows that power consumption is clearly
reduced when turn CPU cores off although the load of
remaining cores is increasing. This means that logicalremoving of cores gives us reducing electric power
consumption even if it increases the load of the remaining
cores when the initial load of system is high. On the other
hand, reducing of electric power consumption is not clear
when the initial load of system is low as shown in Figure 6.
But electric power consumption continues to be down even
if the initial average load is 10%.
From experimental results, power consumption is
actually reduced when cores are turned off.
3.5 Threshold for core state switching
Figure 5 and Figure 6 show that system can run withfewer cores even if load is moderately high. However, if© ISAROB 2013

51

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Table 3. Processing time of floating-point calculation
Number of
Original(sec)
Proposed(sec)Processes
1
133.26
139.68132.99134.81
2
133.99141.09migration mechanism of linux kernel. Linux kernel keeps
load balance through this migration mechanism to move theprocess from busy core to idle core. This migration itself
becomes overhead for execution of processes. Our test
program forks many small processes, and the kernel assigns
these processes into online cores. Since our kernel makessome cores stay offline, migration over cores is rare caseand it reduce overhead by migration. Therefore execution
time on our kernel becomes shorter than that of the original
kernel.

5 CONCLUSIONSIn this paper, we propose a method to reduce electric
power consumption on linux servers. Our method
dynamically manages the number of active cores, andimmediately applies PG for idle cores. In addition, ourmethod logically removes the idle cores from the system,
and makes no process assign into idle cores to keep the corein PG state. When the system load is over threshold, ourmethod return the cores in PG state to active. Experimental
results show that our method copes with both low electric
power consumption and good performance for the system
executing many small processes with high load. It remains
future works to employ “soft” threshold to stable electric
power consumption and improve algorithm for core stateswitching.

Fig. 7. Comparison with electric power consumption

REFERENCES

Table 1. Average of power consumption
Original(kWh)Proposed(kWh)578.22570.30[1] Wu Q, Pedram M, Wu X (1997), Clock-Gating and its
application to low power design of sequential circuits.
Custom Integrated Circuits Conference, 1997,
Proceedings of the IEEE 1997, pp.479-482[2] Pakbaznia E, Fallah F, Pedram M (2008), Charge
Recycling in Power-Gated CMOS Circuits. ComputerAided Design of Integrated Circuits and Systems,
Vol.27, Issue.10, pp.1798-1811[3] Kimura K, Kondo M, Amano H (2011), Fine Grain
Power Gating Control Adapting to a Change in Core
Temperature with Operating System, Vol.2011-ARC195 No.30, pp.1-8[4] Otomo T, Kurihara K, Teranishi Y (2012), Highreliabillity, Low-power and High-performance
Implementation by Memory Redundancy and Power
Gating, Vol.2012-ARC-199 No.14,pp.1-8[5] http://www.kernel.org/[6] Intel(R) Turbo Boost Technology in Intel(R)Core(TM) Microarchitecture (Nehalem) Based
Processors (2008) Intel White Paper

Table 2. Execution time of test programs
Original(sec)
Proposed(sec)109.20106.11
We also evaluate execution time of floating-point
calculation by inverse matrix calculation whether ourmodified kernel affects the performance of scientificcalculation. Table 3 shows the execution time of floatingpoint inverse matrix calculation. “Number of processes”
denotes the number of floating-point calculation processsimultaneously executed. Table 3 says that our kernel isabout 3% slower than the original kernel. It is because thedaemon process to decide on/off of the core described in
Section 3.2. This daemon process always stays in core0. In
the case of one floating-point calculation is executed, only
the core0 is the active core. The core0 has to execute
daemon process at every two seconds, it disturbs floatingpoint calculation. When we throw two floating-point
calculations simultaneously, the one finishes about 6
seconds faster than the other. It means that the processassigned into core0 is affected by the daemon process. The
number of active cores is the same between the original
kernel and our modified kernel, so the electric power
consumption is almost the same.
From the experiments, our method is suitable for thesystems that execute many small processes or threads withhigh load. In that case, our method copes with both low
electric power consumption and faster execution.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A new solution to the SLAM problem by using an unscented smoother
Keigo Watanabe1 , Satoshi Funamoto2 , and Shoichi Maeyama1
1

Department of Intelligent Mechanical Systems, Division of Industrial Innovation Sciences,
Graduate School of Natural Science and Technology, Okayama University
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
Tel & Fax: +81-86-251-8064
E-mail: {watanabe, maeyama}@sys.okayama-u.ac.jp
2
Shin Nippon Koki Co., Ltd., 4-1, Kita-Kyuhoji-Machi 2-chome, Chuo-ku, Osaka, Japan

Abstract: As a solution to the SLAM problem, the extended Kalman filter or unscented Kalman filter (UKF) is often used
up to now. In the case of an offline use of estimated results, a fixed-interval smoother is available and it is expected to give
much more high accuracy. In this paper, a solution to the SLAM problem is proposed with the unscented Rauch-Tung-Striebel(RTS) smoother (URTSS) and several experimental results are given to show the improvement of the estimation accuracy due tothe present method. In particular, the superiority of our method over the conventional UKF based method is demonstrated byevaluating the estimated accuracy of both methods through some simulations using a mobile robot.
Keywords: SLAM, Unscented Kalman filter, Unscented RTS smoother, Mobile robots.
1

INTRODUCTION

smoother (URTSS) with UT in order to apply it to a modelthat has strong nonlinearities.
The SLAM problem is one of nonlinear estimation problems so that the URTSS is expected to contribute the improvement of the accuracy on this problem. Therefore, in thispaper, the solution to the SLAM problem with the URTSS is
proposed and the experimental results are given to demonstrate the effectiveness of the present method by evaluating
the estimated accuracy through some simulations.

For the solution to the Simultaneous Localization and
Mapping (SLAM) problem [1], [2], two kinds of information
are generally used for the estimation method. One is the selfposition obtained from internal sensors such as encoders andinertial sensors. This information is called “control inputs”.
The other is relative position to the landmarks obtained fromexternal sensors such as camera, laser range finder, etc. This
information is called “measurements”. However, the bothinformation include noises and the exact position is not acquired. Therefore, probabilistic methods to integrate the bothare applied for localization and map building.
Up to now, as a solution to the SLAM problem, extended
Kalman filter (EKF) that is one of probabilistic estimation
methods, is mainly used. However, the EKF has two problems: one is less-accuracy in the case of the system with any
strong nonlinearity because of linearization by Taylor expansion, and the other is that the derivative of model must be
calculated for linearization. To overcome these weak points,
Julier et al. [3] proposed a novel method so-called Unscented
Kalman Filter (UKF). The UKF based on using Unscented
Transform (UT) excludes the derivative of model to improve
the problems of the EKF and can estimate the state of nonlinear systems accurately.
The SLAM solutions using the EKF and the UKF areavailable online estimation. That is, it is possible to estimate
the latest self-position and the optimal map while capturing
the data. On the other hand, it is sometimes considered that
there needs a precise trajectory and map information after
completing the exploration. For instance, in the case of the
exploration of planetary or disaster environments, the offline
estimation is applicable.
In the case of offline use of estimation results, a fixedinterval smoother is available and expected to give muchhigher accuracy. A Rauch–Tung–Striebel (RTS) smoother,which is one of fixed interval smoothers proposed by Rauchet al. [4], was applied to various applications. Recent years,
Terejanu et al. [5] and Särkkä [6]proposed an unscented RTS© ISAROB 2013

2

URTSS–SLAM

The objective in the URTSS–SLAM is to simultaneously
estimate the self-position and direction of the robot and thelandmark position, which are described in a composite statevector xt such as
xt =(x, y, θ, μ1,x , μ1,y , . . .
μj,x , μj,y , . . . , μN,x , μN,y )T(1)

where x, y and θ are the robot position and the direction (orazimuth), which are called the posture collectively in this paper. μj,x and μj,y denote the absolute position of the j-thlandmark, and the N denotes the number of landmarks.
The action model used here and the measurement model
are respectively described by
xt = g(ut−1 + ρt−1 , xt−1 )z t = h(xt ) + εt

(2)
(3)

where ut−1 is the “control” input. The action model transfers the state xt−1 to xt through the action function g andthe control input is affected by the noise ρt−1 . The measurement model produces the measurement z t through themeasurement function h and this model is also affected by
the noise εt .
In this paper, the estimate for xt is represented by using
the mean mt and the covariance P t . The URTSS–SLAM is
manly composed by the following two-steps:

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Table 1. Simulation conditions
Meaning
Simulation time
Sampling interval
Control noise const.
Measurement noise
S.D. for relative distance
Measurement noise
S.D. for relative angle

Variableandparameter
T
Δt
a1
a2
a3
a4

Value

Unit

76
0.5
0.02
0.02
0.02
0.02

[s]
[s]

σr

0.05

[m]

σφ

2.0

[deg]

Fig. 1. Simulation environment
1. Using the UKF-SLAM algorithm for the time interval[0 T ], memorize the estimates for the self-position anddirection of the robot and for the landmark position.
2. Re-estimate the states using the URTSS–SLAM algorithm backward in time from time T .
The UT technique and both the UKF–SLAM and the
URTSS-SLAM algorithms are not described here, but seee.g., Terejanu et al. [5] and Särkkä [6] for the detailed explanations.
3Fig. 2. Motion Model

The robot posture is defined as follows. Let us consider arobot moving on a plane, and its posture is described byT
p= x y θ(4)SIMULATIONS

Simulation experiments are presented to check the estimation accuracy of the proposed method, where the present
URTSS–SLAM is compared with the conventional UKF–
SLAM.

where x and y are the robot position, and θ is the azimuth of the robot. Assume that the robot moved from
pt−1 to pt . Then, the odometry returns noisy p̂t−1 =
T
T


x̂t−1 ŷt−1 θ̂t−1 and p̂t = x̂t ŷt θ̂t . The control input can be obtained as a noisy value that includes
noises as shown in Fig. 2, which is given byT
(5)ût−1 = δ̂r1, t−1 δ̂l, t−1 δ̂r2, t−1

3.1 Simulation conditions
Assume that the robot navigates with a constant velocity
along the path depicted in Fig. 1, where the robot moves fromthe coordinate (0, 0) counterclockwise and the landmarks are
assigned every 1.3 [m] in a 4 [m]×6.5 [m] rectangular area.
The estimation accuracy is evaluated with 100 trials, where
the action and measurement models are given by the previous sections. It is assumed that the measurement rage of thelandmarks is restricted such as ±120 [deg] measured fromthe centerline of the direction of movement and 4 [m] in maximum detection distance. Furthermore, following the resultsof Julier et al. [3] and Kim et al. [7], the parameters used in
the UT were set to α = 0.002, β = 2, and κ = 3 − n, where
note that other simulation conditions are shown in Table 1.

Here, each component of the control input ût−1 is as follows:δ̂r1, t−1 is the amount of rotation before moving, δ̂l, t−1 is theamount of movement, and δ̂r2, t−1 is the amount of rotation
after moving. ût−1 can be then calculated as below:⎞⎛
atan2(ŷ t − ŷt−1 , x̂t − x̂t−1 ) − θ̂t−1
ût−1 = ⎝(x̂t − x̂t−1 )2 + (ŷt − ŷt−1 )2 ⎠ (6)θ̂t − atan2(ŷt − ŷt−1 , x̂t − x̂t−1 )3.2 Action modelAn odometric model [8] is used for the action model,
where the input to the model is the amount of the robot posture change obtained from the odometry. Note that the odometry is a measurement rigorously, so that velocity information
should be added to the state, if the odometry is used as a measurement. If it is regarded as an input in general, then it ispossible to reduce the number of states. The present paper
follows such an approach.

© ISAROB 2013

Equation (2) representing the action model is reduced toxt = g(ut−1 + ρt−1 , xt−1 )= g(ût−1 , xt−1 )⎞⎛δ̂l, t−1 cos(θt−1 )⎜ δ̂l, t−1 sin(θt−1 ) ⎟⎟= xt−1 + ⎜⎠⎝δ̂
r1, t−1 + δ̂r2, t−1
0

54

(7)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 3. Measurement modelin which ρt−1 is the action noise defined by⎞22
N (0, a1 δr1,
t−1 + a2 δl, t−1 )
2
2
⎠ (8)
ρt−1 = ⎝N (0, a3 δl,2 t−1 + a4 (δr1,
t−1 + δr2, t−1 ))2
2
N (0, a1 δr2, t−1 + a2 δl, t−1 )⎛Fig. 4. Estimated robot paths and landmarks (UKF–SLAM)

where a1 to a4 are the parameters that determine the amountof noises.
3.3 Measurement model
A rangefinder model is used for the measurement model.
This model uses the measurements consisting of the relative
distance r between the robot and a landmark, and the relative
angle φ. When defining the robot posture as (x, y, θ)T , thej-th landmark position as (μj,x , μj,y ), and the measurements
as z = (r, φ), the measurement model in Eq. (3) can be
rewritten by (see Fig. 3)
z =h(x) + ε
(μj,x − x)2 + (μj,y − y)2h(x) =
atan2(μj,y − y, μj,x − x) − θε=N (0, σr2 )N (0, σφ2 )(9)Fig. 5. Estimated robot paths and landmarks (URTSS–
SLAM)

where σr denotes the standard deviation for the relative noiseand similarly σφ denotes one for the relative angle.
3.4 Results and considerations
Figures 4 and 5 show the estimation results of one trial
for both methods, where in the figures the red dot denotes
the landmark position, the black solid line is the movement
trajectory of the robot, the light-blue broken line denotes theodometry value, and the blue × and solid line are the estimation results. For the estimation of self-position, it is foundthat both the URTSS-SLAM and UKF-SLAM methods havea high accuracy, compared to a method of using only theodometry. In particular, the estimate produced by using only
the odometry displays a large deviation around the terminal
point, whereas the both estimation methods give good estimates that reach the goal point. This is attributed to the factthat the both methods estimate the self-position by using theinformation on landmarks. It is seen that they also give all
the landmark positions, i.e., the correct map estimate.
Figures 6 to 8 shows the time variation of the rms error
in each axis of the robot posture. The broken line denotes
the rms error for the UKF–SLAM, whereas the solid line is
the rms error for the URTSS–SLAM. The rms error for the
URTSS-SLAM is smaller in most of time durations than thatfor the UKF-SLAM. It is seen from Fig. 6 that the rms error
in x-coordinate is gradually improved in its accuracy for the© ISAROB 2013

latter half of estimation: i.e., its accuracy recovers closely tothat of around t = 10 [s].
Table 2 shows the time average of the rms error for eachaxis of the self-position. It is seen that the URTSS-SLAM
has about 5.6 [%]smaller rms error in the average of eachaxis, compared to the UKF-SLAM, so it is claimed that an
improved accuracy has been obtained for the self-position estimate in the SLAM problem.
Figures 9 and 10 shows the rms error for the landmark position. It is found from Fig. 9 that the rms error is relatively
large because the landmark is estimated with less number ofmeasurements at start, but it becomes gradually small andTable 2. Simulation results of localization
Algorithm
UKF–SLAM
URTSS–SLAM

55

x [mm]
36.23335.289RMS error
y [mm]
θ [rad]23.145 0.018659
21.159 0.017063

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 6. RMS error for x coordinate

Fig. 9. RMS error for landmark location (UKF–SLAM)

Fig. 10. RMS error for landmark location (URTSS–SLAM)
Fig. 7. RMS error for y coordinate
Table 3. Simulation results of landmark locations
converges as time goes and the number of measurements increases. Two rms errors, related to the landmarks denotedby blue and yellow lines, become large once at about 10 [s]and 12 [s], decrease at about 53 [s], and after that they converge to small values. This is based on the fact that the robot
measured those landmarks several times at about 10 [s] and12 [s], moved out from the measurable range, returned tothe original position at about 53 [s], and finally started tore-measure such landmarks. The rms error of the URTSS–SLAM is small and constant approximately, as seen from
Fig. 10, because from the property of the URTSS the landmarks are estimated by using all the information for the estimation interval.
Table 3 shows the estimation error for the landmarks,
where a landmark error was extracted when the minimum
estimation covariance was achieved, and was also averagedby the number of landmarks and trials. The estimation error of the landmarks with the URTSS–SLAM is small about
6.8 [%], compared to that with the UKF-SLAM, so that it isclaimed that the map accuracy has been improved, as well as
the estimation accuracy of the self-position for the robot.
4

Algorithm
UKF–SLAM
URTSS–SLAM

error of landmark
locations [mm]41.04938.254due to the UKF–SLAM by the URTSS. Then, the estimation accuracy of the proposed URTSS–SLAM and the conventional UKF–SLAM was compared through the simulation
experiment. As a result, compared to the conventional technique, the estimation accuracy for both the self-position andthe map was improved, so it can be said that the URTSS–SLAM is effective in an off-line SLAM problem.

REFERENCES[1] Durrant-Whyte H, Bailey T (2006), Simultaneous localizationand mapping Part I. IEEE Robotics & Automation Magazine
13(2): 99–110[2] Durrant-Whyte H, Bailey T (2006), Simultaneous localizationand mapping Part II. IEEE Robotics & Automation Magazine
13(3): 108–117[3] Julier SJ, Uhlmann JK, Durrant-Whyte HF (1995), A new
approach for filtering nonlinear systems. Proc. of American
Control Conference, pp. 1628–1632

CNCLUSIONSIn this paper, the URTSS has been applied to the SLAM
problem and a URTSS–SLAM has been proposed. The
URTSS–SLAM is the technique of having aimed at the improvement in accuracy by reprocessing the estimation result[4] Rauch HE, Tung F, Striebel CT (1965), Maximum likelihood
estimates of linear dynamic systems. AIAA Journal 3(8):1445–1450[5] Terejanu G, Singh T, Scott PD (2007), Unscented Kalman filter/smoother for a CBRN Puff-based dispersion model, Proc.
of the 10th Int. Conf. on Information Fusion[6] Särkkä S (2008), Unscented Rauch–Tung–Striebel smoother.
IEEE Trans. on Automatic Control 53(3), 845–849[7] Kim C, Sakthivel R, Chung W (2008), Unscented fastSLAM:
A robust and efficient solution to the SLAM problem. IEEE
Trans. on Robotics 24(4), 808–820[8] Thrun S, Burgard W, Fox D (2005), Probabilistic Robotics.
The MIT Press

Fig. 8. RMS error for θ coordinate© ISAROB 2013

56

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Kinodynamic motion planning and control using
anisotropic damping forces
1 Kimiko Motonaka, 1 Keigo Watanabe and 1 Shoichi Maeyama
1 Department of Intelligent Mechanical Systems,

Division of Industrial Innovation Sciences,
Graduate School of Natural Science and Technology,
Okayama University,
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
E-mail: k.motonaka@usm.sys.okayama-u.ac.jp; fwatanabe, maeyamag@sys.okayama-u.ac.jp
Tel&Fax: +81-86-251-8064
Abstract: We deal with gain optimization in kinodynamic motion planning by using NADFs (Nonlinear, Anisotropic, Damping
Forces) and HPF (Harmonic Potential Field). The NADFs are a kind of controller which has been proposed by Masoud, and it
has the effect of canceling the force in unnecessary direction (e.g. any external force or the inertial force). The NADFs have
two gains, but optimizing them was not mentioned at all. In this paper, it is assumed that a mass with Kinodynamic Motion
Planning is controlled in simulation. The effectiveness of NADFs is verified by comparing the control that uses only the gradientof HPF with the control that introduces the NADFs. At the same time, we apply a method called “Clamping Control” to theaforementioned situation, and accomplish a reliable convergence. It is reported through experiments that there exists a possibilityof accomplishing more accurate motion planning by optimizing the gains of NADFs.
Keywords: Motion planning, Kinodynamic planning, Potential field
1

INTRODUCTION

object is confirmed according to the change of the gains. We
also prove the efficacy of introducing the NADFs in the environment where an external force exists. It is found in simulations that the NADFs are able to cope with the appliedexternal force, but its posture diverges sometimes after thecontrolled object reaches the target point. Therefore, we apply a method called “Clamping ontrol” to the aforementioned
situation, and accomplish a reliable convergence. “Clamping control” is a control method proposed by Masoud, whichmakes an attractive force in around the target point to confine
the controlled object to the target point. It is reported throughthe experiments that the NADFs can be applied in an environment where external forces exist; the convergence time andtrajectory are changed by tuning the gains; and there exists
a possibility of accomplishing accurate motion planning by
optimizing the gains.

Kinodynamics is a discipline dealing with the kinematics
and dynamics simultaneously. Kinodynamic motion planning aims at synthesizing the kinematic motion planning likeobstacle avoidance, and the dynamic motion planning which
is needed in actual control. In this research, we deal withgain optimization in kinodynamic motion planning by using
NADFs (Nonlinear, Anisotropic, Damping Forces) and HPF(Harmonic Potential Field). An HPF is a smooth potential
field which has no stationary points [1]. It is guaranteed thata kinematic trajectory, which is generated by using the gradient of HPF, can reliably reach a target point. Thus, if acontrolled object moves along the gradient of HPF, then suchan object can reliably reach the target point. However, its trajectory deviates from the kinematic trajectory in the consequence of the inertia or an external force, even if a controller
directly gives the controlled object the gradient of HPF as
the control input. Then, we apply the NADFs to a controller.
The NADFs is a kind of controller which has been proposedby Masoud [2], and it has the effect of canceling the force in
unnecessary direction (e.g. any external force or the inertial
force). By applying NADFs, the controlled object can reachthe target point quickly without being affected by external
forces or inertial forces. The NADFs introduced by Masoudhas two gains, but he did not at all mention optimizing thesegains. In this paper, it is assumed that a point mass with Kinodynamic Motion Planning is controlled in simulation. The
effectiveness of NADFs is verified by comparing the control that uses only the gradient of HPF with the control that
introduces the NADFs. At the same time, by changing thegains manually, the trend in the behavior of the controlled© ISAROB 2013

2 MOTION PLANNING USING HPF
Kinodynamic motion planning using NADFs is based on
the motion planning using HPF. In the motion planning using
HPF, a controlled object is controlled by control input basedon the gradient of HPF. HPF is generated from the boundary
information on the environment. In what follows, a generation method of HPF and the motion planning using only thegradient of HPF are described.
2.1

Generation method of HPF and the calculation ofgradient vector
An HPF is a smooth potential field which has no stationary
points. For generating an HPF, an environment is at first segmented into small grids, and the potential at each grid (i j ) is
calculated. In initial states, potential is set to 1 for the bound

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

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Fig. 1. Normalized gradi- Fig. 2. Path generation byusing gradient of HPF
ent field
Fig. 3. The clamping controlof obstacles and the environment, and set to 0 for an intended
target point. Then, to compute the potential in each grid, thefollowing equation is calculated iteratively:
U (i j ) =

1
4

;

;

(Uij 1 + Uij +1 + Ui 1j + Ui+1j )of the current position, ;rV (x), and the damping force linearly proportional to the velocity, ;b  x_ . This damping force
increases in proportion to increasing of the damping coefficient or velocity, and suppresses the acceleration of a point
mass.

(1)

Note that, U (i j ) is the value of the potential at an (i j ) gridin the environment field. By calculating Eq. (1) iteratively,
the surface of the generated HPF becomes smooth, because
the average of potentials in the four neighborhood grids issaved as the potential at the center grid.
Then, a gradient in each position is calculated by using thegenerated HPF. Fig. 1 shows gradient vectors calculated from
HPF. Note that these vectors have been normalized by thegrid size. The position circled in Fig. 1 shows the configured
target point. Using this gradient vector, a kinematic trajectory
can reliably reach a target point from anywhere in the field
while avoiding obstacles. Letting V (x) be a potential value
in position x = x y ]T , a gradient on the surface of HPF
in position x can write ;rV (x). Using gradient ;rV (x),a kinematic trajectory can be generated from the following
equation:
x_ = ;rV (x)(2)

3 MOTION PLANNING USING NADFS
The method described in previous section, which is basedon only the gradient of HPF, does not work well under anenvironment where an external force exists. Masoud [2] proposed a control method that can cancel out the effect of external forces by combining the gradient of HPF with NADFs.
The following equation is the controller proposed by Masoud[2]:u = ;bd  h(x x_ ) ; k  rV (x)(4)

where bd and k denote positive constant gains, and h(x x_ )is the proposed NADFs, which is given by(x x_ ) =h

2.2

Control of a point mass based on the gradient of
HPF
When controlling an object having a mass, dynamics including object mass, inertial force and external force should
be considered. Koditschek et al. [3] proposed a method fordesigning a control input that considers dynamics by adding
both the gradient of HPF and a damping force linearly proportional to the velocity. In this method, when controlling apoint mass with 1 [kg], the control input u is given as follow:
(3)

(5)

4 CLAMPING CONTROLThe control using NADFs can guide a controlled object
to a target point while cancelling external forces. However,where b denotes a damping coefficient and also is positive
constant. In Eq. (3), control input consists of the gradient© ISAROB 2013

 V (x)T V (x)T i_ +_  (rV (x)T x_ )
nT xn

x
krV (x)kkrV (x)kHere, n is a unit vector orthogonal to rV and (rV (x)T x_ )is the unit step function. The function
takes 0 if
rV (x)T x_ is negative, and otherwise takes 1. The function (rV (x)T x_ ) is prepared to check the consistency between the direction of current velocity of the controlled onject and the direction of gradient of HPF. If the directionof the current velocity matches the gradient direction, thenrV (x)T x_ takes a positive value and (rV (x)T x_ ) takes
1, thus, a force toward the current velocity direction is increased. By contrast, if their directions are not matched, thenrV (x)T x_ takes a negative value and (rV (x)T x_ ) takes
0, thus, a force canceling the current velocity is added. At
the same time, a force toward the gradient direction is added,
and the controlled object is returned to the kinematic trajectory. Thus, this method is also applicable to the control of an
object in an environment where external forces exist.

As an example, Fig. 2 shows the calculated kinematic trajectories from four configured start positions to the target point.
Configured start positions A to D and the target point wereset as shown in Fig. 2. It is confirmed from Fig. 2 that a kinematic trajectory using the gradient of HPF can reach a target
point from anywhere in the field while avoiding obstacles.

u = ;b  x_ ; rV (x)h58The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 201345

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Fig. 4. Simulation environment[kg], so that the equation of motion with the controller basedon only using the gradient of HPF is given by

x = ;b  x_ ; rV (x)after reaching to the target point, the controlled object sometimes cannot maintain any states and therefore it oscillates
and/or diverges. Therefore, we apply “clamping control”.
Fig. 3 shows the schematic structure of the clamping control.
The clamping control is applied in a circle area as shownin Fig. 3. The center point of the region is a target point
and its radius is  . In this area, if a controlled object moves
toward the target point, then the clamping control is not applicable. By contrast, if a controlled object moves against
the target point in the area, then the clamping control adds
a force (xT ; x) in a direction toward the target point, andconverges a controlled object on the target point. Appending
the clamping control to the control input based on NADFs
given in Eq. (4), it follows thatu = ;b  h(x x_ ) ; k  rV (x) ; kC  FC (x x_ )dSimilarly, one based on using the gradient and NADFs andthat based on additionally applying clamping control are
given as follows:

x = ;b  h(x x_ ) ; k  rV (x)dx = ;b  h(x x_ ) ; k  rV (x) ; kC  FC (x x_ )d(6)Here, the unit step function ( ; jxT ; xj) is set tocheck whether the controlled object is in the region, and(x_ T (xT ; x)) is prepared to check whether the directionof current velocity of the controlled object consists with thedirection of the target point.

SIMULATION EXPERIMENTIn this section, we made a simulation experiment for comparing three methods as described above (using gradient of
HPF only, applying NADFs, and combining NADFs andclamping control). Fig. 4 shows an experimental environment, where there are two obstacles. In this experiment, it
is aimed at guiding a point mass with 1 [kg] from the initial
position x0 (40 40) to the target point xT (10 10). The trajectory drawn in Fig. 4 is a kinematic trajectory calculated
from the gradient of HPF. This kinematic trajectory is set toan ideal trajectory.
5.1

A point mass control in the absence of external
forces
At first, we compared three different types control in theabsence of external forces. The point mass has the mass of 1

© ISAROB 2013

(9)
(10)

In this simulation, the gains k and kC are set to k = 1
and kC = 10 constantly, and we compared the results whenchanging the gains b and bd .
Figs. 5–7 show the trajectory of the point mass and theconvergence time with each controller when the gains b andbd are set to 0.1, 1.0 and 10.0. The trajectory of the point
mass came closer to the kinematic trajectory (Fig. 4) in proportion to increasing gains. This is because the controller
based on only the gradient of HPF suppresses the velocity
with an increase in gain b. On the other hand, in the case of
the controller including NADFs, a force canceling the velocity except the matched direction of the gradient is increased
with an increase in gain. As a result, the both controllers
can guide the mass point to a target point with high accuracy. As long as looking at the trajectory, there exist no big
difference between the controller using NADFs and that notusing NADFs, but there is a big difference in convergencetime. In Fig. 5, the convergence time is increased particularly with an increase in gain b. By contrast, in Fig. 6,
convergence time is shorter with an increase in gain bd . Thisis because the NADFs consider the direction of the velocity
and suppress unnecessary force only. In addition, when using the controller including the clamping control (Fig. 7), thetrajectory and the convergence time are almost the same as
those using NADFs, but the overshoot was suppressed after
the point mass reached the target point.
In this experiment, it is confirmed that all controllers canguide the point mass to the target point by tuning the gains b
and bd . In particular, the controller using NADFs can guidethe point mass quickly with a high accuracy. Moreover, we
also confirmed that the overshoot is suppressed by applying
the clamping control.

where kC denotes a positive constant gain related to theclamping control and FC is clamping control given by
FC (x x_ ) = (xT ; x)  ( ; jxT ; xj)  (x_ T (xT ; x) (7)

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(8)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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Next, assume that external forces fg = 4 4]T exist in theenvironment. Behavioral models of the point mass with eachcontroller are as follows, respectively:

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Fig. 8 shows the trajectory of the point mass using eachcontroller when the gains b and bd are set to 1.0, 5.0 and
10.0. As shown in Fig. 8(a), the controller using only thegradient of HPF cannot suppress the external forces, and thepoint mass entirely deviated from the route. By contrast, thecontroller including NADFs suppressed the external forces,and guided the point mass to the target point by amplifying
the gains (Fig. 8(b)). However, the point mass could not keep
the state at the target point. The controller including NADFs
and clamping control can guide the point mass, and kept thestate at the target point (Fig. 8(c)). In this experiment, it isconfirmed that the controller based on NADFs can also apply
in the environment where external forces exist, and a controlled object can keep its state at the target point by adding
the clamping control .

Fig. 6. Simulation results using formula (9)in static environment (gains bd = 0:1, 1:0 and 10:0)

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6 CONCLUSIONSIn this paper, we have compared three controllers: thecontroller by using only the gradient of HPF; the controller
including NADFs; and the controller including NADFs andclamping control. At the same time, by changing the gains
manually, the trend in the behavior of the controlled object isconfirmed according to the change of the gains. As a result,kinodynamic motion planning by using NADFs can guide thecontrolled object to the target point quickly, and it is robust
against external forces. Moreover, by applying the clamping
control, the secure convergence to the target point was able
to be realized. In future work, we will optimize gains for
NADFs by using GA and validate the associated behavior.

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REFERENCES

40[1] Jan V (2007), Navigation of mobile robots using potential fields and computational intelligence means. Acta
Polytechnica Hungarica, 4(1):63–74.

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[2] Masoud A (2010), Kinodynamic motion planning. IEEE
Robotics & Automation Magazine, 17(1):85–99.

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[3] Elon R, Daniel E K (1995), Exact robot navigation using
artificial potential fields. IEEE Trans. on Robotics andAutomation, 8(5):501–518.

(c) using formula (13)Fig. 8. Simulation results where external forces exist(gains b bd = 1:0, 5:0 and 10:0)© ISAROB 201360

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Application of variable search space GAs to ﬁne gain tuning ofmodel-based robotic servo controller
A. Otsuka1 , and F. Nagata2
Tokyo University of Science, Yamaguchi, Japan(Tel: 0836-88-3500 Fax: 0836-88-3400 )1

otsuka a@rs.tus.ac.jp 2 nagata@rs.tus.ac.jp

Abstract: In this paper, genetic algorithms with a variable search space function are proposed for ﬁne gain tuning ofa resolved acceleration controller which is one of model based robotic servo controllers. Genetic algorithms proposedin this paper have a variable search space function which is activated if the optimal solution is not updated for ﬁxed
generations. The function is terminated if the optimal solution is updated, or if the optimal solution is not updated
within certain generations. This proposed method is evaluated through a trajectory following control problem insimulation. Simulations for sine curve trajectories are conducted using the dynamic model of PUMA560 manipulator.
The result shows the improvement of optimal solution and its convergence.
Keywords: Fine Gain Tuning, Genetic Algorithms, PUMA560.

1Introduction
F(θ,θ )At designing a control system, feedback gains tuning is the complicated problem. Conventionally, thegains have been experimentally and instinctively tuned
by trial and error based on known model information
and an operator’s skill. For articulated-type industrial
robots, this problem tends to become more complicated.
Recently, the computing power is increased signiﬁcantly,
so that the gains in a control system can be numericallytuned through computer simulations by using the target
dynamic model. Gain tuning problem is a typical nonconvex optimization problem, so that genetic algorithms
have been applied to eﬃciently solve this problem. For
example, Deris et al. [1] considered the stabilization of
an inverted pendulum which can be controlled by moving a car in an intelligent way. Ya and Meng [2] proposed a genetic algorithm-based optimized hybrid controller which is suitable for controlling both linear andnonlinear systems. Also, Nagata et al. [3] proposed aneﬀective ﬁne gain tuning method for a computed torquecontroller, in which genetic algorithms were applied toobtain more suitable feedback gains after manual turning process.
Genetic algorithms proposed in this paper have avariable search space function which is activated if the
optimal solution is not updated for ﬁxed generations.
The function is terminated if the optimal solution is updated, or if the optimal solution is not updated withincertain generations. The propose method is evaluatedthrough a trajectory following control problem using
sine curves. Simulations are conducted by using the
dynamic model of PUMA560 manipulator. Two typesof the desired trajectories are designed with sine curves
on x-y plane and x-z plane. The results of the simula-^ (θ)J -1(θ)
MJ (θ)

Manipulatorθθ^(θ,θ )+G^ (θ )H

J (θ )

Fkine(θ)
Fig. 1. Block diagram of resolved acceleration control
tions showed the improvement of optimal solution andits convergence. The simulation environment used iscalled Robotics Toolbox and was developed by Corke [4].

2

Robotic servo controller

The dynamic model of an industrial manipulator
without friction torque is generally given by
M (θ)θ̈ + H(θ, θ̇) + G(θ) = τ(1)where M (θ)∈ ℜ6×6 , H(θ,θ̇)∈ ℜ6×1 and G(θ) ∈ ℜ6×1
are the inertia term, Coriolis/centrifugal force term andgravity term in joint space, respectively. θ=[θ1 , ..., θ6 ]T ,θ̇ ∈ ℜ6×1 , θ̈ ∈ ℜ6×1 , are the vector of joint angular position, velocity and acceleration. τ ∈ ℜ6×1 is the joint
driven torque vector. Fig. 1. shows the block diagramof the resolved acceleration controller, which is known
as one of model based robotic servo controllers. WhereF (θ,θ̇)∈ ℜ6×1 is the frictional torque term. Desired position, velocity and acceleration in Cartesian coordinate
system must be given to the reference of the servo system in order to apply the controller to a trajectory fol1

© ISAROB 2013

τ61

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Velocity gains

Position gains

Initial configuration
Evaluation

Evaluation

1 1 0 1 0 0 1 0

0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0

8 bits

16 bits

Fig. 2. Example of chromosome of an individual
Table 1. Initial parameters of genetic algorithms
100
6
Tournament selection and elitism
Uniform crossover (rate=87.5%)
Random mutation (rate=1.0 % )50 ≤ Kvi ≤ 220
1000 ≤ Kpi ≤ 35000
200

Yes

Change totemporarysearch range

Optimal solution
is updated
No

Yes

Optimum solution
is not updated for
10 generations
No

Resume tonormalsearch range

Selection

Crossover

Crossover

Mutation

Mutation

Genetic algorithms with
fixed search range

Genetic algorithms withvariable search range

If K v and K p are selected based on Eq. (4), the stability is guaranteed and the errors convergence to zero.

3

Fine gain tuning by genetic algorithms
In this section, genetic algorithms are used to optimize K v and K p . In order to apply genetic algorithms, K v and K p are transformed into a digit binary
string as chromosome. Diagonal elements of them areencoded into 8 bits and 16 bits codes respectively, sothat an individual chromosome is composed of 144 (=8× 6+16 × 6) bits as shown in Fig. 2.. A populationhas 100 individuals and each individual in initial population is generated within the expectable values. The
parameters of GAs are deﬁned as shown Table 1. In
our study, variable search space GAs are proposed andapplied. Fig. 3. shows the ﬂowchart of the proposed
variable search space GAs, in which if the optimal gain
is unchanged for 10 generations then the search space isvaried. This means that the search spaces are redeﬁned
temporarily based on the optimal gains at the generation.

where the hat symbol means the estimated term obtained by computing inverse dynamics via recursive
Newton-Euler formulation. x ∈ ℜ6×1 consists of position vector [x, y, z]T and orientation vector [α, β, γ]T .
ẋ ∈ ℜ6×1 and ẍ ∈ ℜ6×1 are the velocity and acceleration vectors, respectively. xr , ẋr and ẍr are
the desired vectors of position, velocity and acceleration, respectively. θ, θ̇, x and ẋ are actual controlled values. Fkine(θ) is the function of forward kinematics given by x=F kine(θ). K v =diag(Kv1 , ..., Kv6 ).
K p =diag(Kp1 , ..., Kp6 ) are the feedback gains of velocity and position respectively, each of which is set to a
positive deﬁnite diagonal matrix. M̂ (θ), Ĥ(θ, θ̇) andĜ(θ) are called nonlinear compensation terms, which
act on the cancellation of nonlinearity of manipulator. The nonlinear compensation terms are eﬀective toachieve a stable trajectory following control. If the estimate error dose not exist at all, then the second ordererror equation of this system described in Eq. (3) isderived from Eqs. (1) and (2).

Kpimin = Kpibest − 10000(5)

Kpimax = Kpibest + 10000(6)

Each individual is evaluated through the following evaluation function.
v
u 500
uX
Ev = t||e(k)||(7)k=1

where k is the sampled discrete-time number in simulation. Note that, this evaluation function also represents
the position errors.

(3)

where e = xr − x, ė = ẋr − ẋ, ë = ẍr − ẍ. Accordingly,
a nominal response is obtained by adjusting K v andK p suitably considering the following equation basedon critically damped condition.
p(4)
Kvi = 2 Kpi(i = 1, ..., 6)© ISAROB 2013

Optimum solution
is not updated for
10 generations
No

End

Fig. 3. Flowchart of proposed genetic algorithms

lowing control problem. The resolved acceleration controller generates the joint driven torque as given byhτ = M̂ (θ)J −1 (θ) ẍr + K v {ẋr − ẋ}i+K p {xr − x} − J̇ (θ)θ̇ + Ĥ(θ, θ̇) + Ĝ(θ)(2)

ë + K v ė + K p e = O

Yes

Selection

Total 144 bits

Population size
Number of elite
Selection
Crossover
Mutation
Search space of Kvi
Search space of Kpi
Maximum generation

1000 generations
No

Yes

4
4.1

Simulations

Desired trajectory

In this section, to evaluate the eﬀectiveness of the
proposed method, simulations are carried out by us-

62

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Sine curve on x−z plane

Sine curve on x−y planezx0.20 m0.50 m

x

×

×

-4
10

0.80

-2
10
0.00

x - trajectoryy - trajectory

-0.05

-0.10

m0.60

y

m
y

x

0.20 m

no
it
is
o
P0.40

0.50 m

no
tii
so
P

-0.15

-0.20

-0.25

0

ing the dynamic model of PUMA560 manipulator on
MATLAB system [4]. First of all, in order to apply the
resolved acceleration control method to PUMA560 manipulator, the desired trajectory in Cartesian coordinate
system must be prepared. Discrete values of desired position, velocity and acceleration are generated for eachsampling period in advance. Four type trajectories areprepared to be compared for each and set as below.
Case 1.

z = 0.1sin(10πx)(8)Case 2.
Case 3.
Case 4.

z = 0.1sin(20πx)y = 0.1sin(10πx) − 0.15
y = 0.1sin(20πx) − 0.15

(9)
(10)
(11)

3

4

5

F (θ, θ̇) = BG2r θ̇ + Gr τ c {sign(θ̇)}(13)

where B ∈ ℜ6×6 is the coeﬃcient matrix of viscous friction at each motor, Gr ∈ ℜ6×6 is the reduction gear
ratio matrix which represents the motor speed to joint
˙ is the Coulomb friction torquespeed, and τ c {sign(θ)}˙ is positive/negative,
appeared at each motor. If sign(θ)+−
then τ c = τ c /τ c = τ c , respectively. The friction parameters of PUMA560 manipulator such as B, Gr , τ +cand τ −
c are technically available to engineers[4].
It is conﬁrmed through preliminary simulations that
this friction term, which works as disturbance as shownin Fig. 1., causes undesirable error even in criticallydamped condition. Also, in actual robot systems there
inevitably exists the modeling error with respect toM̂ (θ), Ĥ(θ,θ̇) and Ĝ(θ). That is the reason why theﬁne gain turning after manual gain tuning is important
for robotic servo system in order to realize more accurate trajectory following control.

4.3

Result and discussions

Fig. 6. shows the minimum Ev values at every generation searched by the proposed GAs. It is observedthat optimal solutions of each case are almost converged
before 50 generations. In Fig. 7., the curved line is critical damping condition, and the plotted points are theoptimal gains of each case. It is observed that mostof gains lie on under-damping condition. Table 2 tabulates each best ﬁtness Evbest which is obtained using
the four optimal gains.
At ﬁrst, Evbest in case of using the trajectories in xy and x-z planes are compared. Table 2 indicates that

Dynamic model

In this simulation experiment, it is assumed that thefriction is composed of viscous friction and Coulomb
friction. Therefore, robotic dynamic model considering
friction can be rewritten by© ISAROB 20132

F (θ,θ̇) is composed of viscous friction and Coulomb friction and given by

Fig. 4. is the overview of the desired manipulator movement. The desired positions given by Eq. (8) and (9)are designed with sinusoidal waves on x-z plane. Also,
the desired positions given by Eqs. (10) and (11) are
generated with sinusoidal waves on x-y plane. As an
example, Fig. 5. illustrates the desired trajectory of
Eq. (10) in x and y directions. The position x of desired
trajectory is generated with a 4-1-4 order polynomial
equation. The initial and ﬁnal position/orientation vectors are set to xinit = (0.28, −0.15, 0.00, 0.00, π/2, 0.00),
xf in = (0.78, −0.15, 0.00, 0.00, π/2, 0.00), respectively.
In this simulation, we set them such that the total
simulation time is 5.00 sec, the sampling width is 0.01
sec, the acceleration and deceleration time is 1.00 sec.
To obtain satisfactory and safe control performance withavoiding singularities, K v and K p are roughly tuned
with trial and error, considering the critically damped
condition.

M (θ)θ̈ + H(θ, θ̇) + G(θ) + F (θ, θ̇) = τ

1Time sFig. 5. Desired trajectory using Eq. (8)Fig. 4. Illustration of desired trajectory

4.2

-0.30

0.20

(Case 3 and Case 4)(Case 1 and Case 2)

Table 2. Best ﬁtness comparison for each case
Evbest(12)

63Case 1
0.0630Case 20.0874

Case 30.0672

Case 40.1147

z10

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

0.18

2.0

Case 1Case 2
Case 3
Case 4

0.16
0.14
s
se
nt
if 0.12tse
b 0.10

0

40

80

120

160

x
z

0.5

f
o
r
o
rr 0.0enioits
o -1.0
p
-2.0

200

0.0

-0.5

y10
0

2.0

Case 1Case 2
Case 3
Case 4

3

4

5

×10-4

×10-21.0

x
y

0.5

10000

‫ܭ‬௣

20000

-2.0

30000

0.0

-0.5

0

1

2

3

4

5

f
o
r
ro
re
n
io
its
o
p

-1.0

Time sFig. 9. Position error in Case 3

Fig. 7. Best gains and critical damping condition

posed GAs are applied for a trajectory following control
based on several sine curves using dynamic model of
PUMA560 manipulator. It has been conﬁrmed that the
proposed GAs eﬀectively can reduce the generations forconvergence and search better optimal gains.

the position error of Case 3 was larger than Case 1. The
similar tendency was observed from Evbest of Case 2 andCase 4. The reason is supposed that only three joints
must be driven to track the desired trajectories given by
Eq. (8) and (9), whereas all joints must be driven when
Eq. (10) and (11) are applied.
Next, Evbest in case of using two kinds of frequencies
are further compared. Evbest of Case 1 is smaller thanthe one of Case 2. The reason is supposed that the desired velocity of Case 1 is slower than the one of Case 2.
In other words, the desired trajectories shown in Fig. 4.
are calculated within the range 0.28 ≤ x ≤ 0.78, so thehigher the frequency becomes, the longer the moved distance becomes. The similar tendency can be observed
between Case 3 and Case 4. Fig. 8. and Fig. 9. are
two examples of simulation when Evbest shown in Table
2 are applied.

References[1] S. Deris, S. Omatu and K. Kitagawa, “Stabilization of
inverted pendulum by the genetic algorithm, ” Proceedings of 1995 IEEE International Conference on Systems,
Man and Cybernetics, pp. 4372–4377, 1995.
[2] L. S. Ya and J. E. Meng, “Optimal hybrid control oflinear and nonlinear systems, ” Proceedings of the 40th
IEEE Conference on Decision and Control, pp. 1669–
1674, 2001.
[3] F. Nagata and K. Watanabe, “Fine Gain Tuning for
Model-Based Robotic Servo Controllers Using Genetic
Algorithms, ” Advances in Computer Science and Engineering, Vol. 2, No. 3, pp. 201–219, 2008.

Conclusions[4] P. I. Corke, “A robotics toolbox for MATLAB, ” IEEE
Robot Automation Magazine, Vol. 3, No. 1, pp. 24–32,
1996.

In this paper, genetic algorithms with a variablesearch space function has been proposed for ﬁne gain
tuning of a resolved acceleration controller. The pro-

© ISAROB 2013

m
y

f
o
r
ro
re 0.0nioits
o -1.0
p

0

-1.0

x

100

5

2

m 1.0

‫ܭ‬௩ 200

0

1

f
o
r
o
rr
e
n
o
it
is
o
p

Time sFig. 8. Position error in Case 1

Fig. 6. Evolutionary histories of best ﬁtness

300

m
z

x

generation

400×10-21.0

m 1.0

0.08
0.06

×10-4

64

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Propulsive force analysis of a pectoral fin for rajiform type fish robots from fluid
dynamic aspects
Masaaki Ikeda1 , Shigeki Hikasa1 , Keigo Watanabe1 , and Isaku Nagai1
1

Okayama University, Okayama, Okayama 700-8530, Japan(Tel: +81-86-251-8064, Fax: +81-86-251-8064)(ikeda-m@usmm.sys.okayama-u.ac.jp)

Abstract: In this paper, we analyze the propulsive force generated from pectoral fins for a rajiform type fish robot from fluid
dynamic aspects. A pectoral fin of the rajiform type fish robot is constructed by multiple fin-rays, which move independently,
and a film of pushing water. Then, the propulsive force of the fish robot is analyzed from the momentum of the fluid surroundingfor every fin between fin-rays. The total propulsive force for one pectoral fin is the sum of these momenta. The propulsion speedof a fish robot is determined from the difference of the propulsive force generated from pectoral fins, and the resistance force thatthe fish robot receives from the water when moving forward. The effectiveness of the calculated propulsive force is examinedthrough numeric simulation results.
Keywords: Fish robot, Rajiform type swimming, Propulsive force analysis
1

INTRODUCTION

type fish robot, and examine the usefulness of the proposal
technique by calculating the propulsive force by a pectoral
fin. Then, the effectiveness of the diving method proposed
here is checked by numeric simulations.

Oceanographic investigation for investigating mineral resources at sea bottom and an aquatic life is carried out actively. Autonomous (unmanned) underwater vehicles called
AUVs are needed for such oceanographic investigation [1][2]. Furthermore, an application to search activities in the
shoal, where the introduction of any AUV is not easy, is also
attractive. The conventional AUV has a common mechanism for propulsion using screw propellers. The underwater propulsion using screw propellers is good at performing
a long-distance cruise and keeping a maximum speed. On
the other hand, any screw propeller is not suitable for sudden
acceleration or rapid turning.
When observing underwater living things, they hide themselves from an underwater foreign enemy, or have captured
foods in the crevice of reefs, and manipulate sudden acceleration and rapid turning easily [3]. Fishes are in the aquatic
life that can manipulate such things. It is known [4] thatfishes can produce propulsive forces by oscillating the various kinds of fins and the body. The propulsion inducedby oscillating fins and the body can change the propulsive
force and direction easily by changing the oscillatory situation. Fishes are divided into some types, depending on every
motion that produces propulsive forces. Among such motion types, a ray such as Manta is classified into a rajiform
swimming type. Such type fishes can control underwater position and posture by using only one pair of right and leftpectoral fins. Therefore, since there are few portions to be
operated, compared to other swimming types, and it need notnecessarily operate all fins cooperatively, it is easy to realize
mechanical reappearance and mimicking in motion.
In this paper, we propose a diving method for a rajiform© ISAROB 2013

2

RAJIFORM TYPE FISH ROBOTA rajiform type fish robot has a central float and a pectoral
fin on each side, as shown in Fig. 1. The float is designed sothat the specific gravity of the fish robot may be set to 1. A
pectoral fin has six structures, each of which is called a finray. The fin-ray has mounted a fin-ray driving unit, and eachfin-ray can be driven independently. The film for pushing out
water is mounted between fin-rays. The control circuit andpower supply for controlling the pectoral fin of the fish robot
are arranged by expanding electric wiring to the outside ofwater.
2.1

Fin-ray driving unit

The fin-ray driving unit is composed of a waterproofedbox and a gear part, as shown in Fig. 2. The waterproofedbox has one RC servo-motor in inside. The transfer part ofdriving force to a fin-ray is waterproofed by taking out thedriving force of a motor using a stan-tube. A gear part isused to change the rotation axis from a motor 90 deg, and it
is realized using a bevel gear.
2.2

Waveform with fin-rays

A pectoral fin of the rajiform type fish robot generates a
propulsive force by driving the fin as a traveling wave. Whenthe form of the traveling wave formed at a pectoral fin is asinusoidal wave form, the angular velocity ωk of the k-th fin

65

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fin

Fin-ray

Float box

Fin-ray driving unit

Fixed

Traveling wave

Front

380 mmDivingdirection

sive force obtained from the traveling wave, the propulsive
force to the diving direction occurs, according to the resistance force that the fixed partial fin receives from water. The
propulsive force to the diving direction generated in this way
is used in order that this fish robot may dive.

650 mmFig. 1. Rajiform type fish robot

4

Waterproofed box

Fig. 2. Fin-ray driving unit

ray from a front side is expressed by the following equation:
2πt2πθmaxcos+ (k − 1)φ
(1)
ωk =
T
T
Here, t [s] is time, θmax [rad] is amplitude, T [s] is a period,and φ [rad] is the phase difference between adjacent fin-rays.
The waveform of a sinusoidal wave is changeable by changing these values.

4.1 Propulsive force between fin-rays
A pectoral fin has several fin-rays. Any two adjacent finrays and the fin existing between them are considered. The
forces which this partial fin receives from water are shownin Fig. 4 as fx and fy . The way of the water on the considered partial fin is set as control volume (CV). Assume thatthe stream of the circumference of CV flows in from A1 sidein Fig. 4, and flows out of A2 side. The momenta for the
inflow and outflow per unit time are calculated from the area
and the flow velocity that flows into CV, and the area and the

DIVING METHOD

A diving method is proposed for a rajiform type fish robot
using a forward propulsion speed. As shown in Fig. 3, twofront fin-rays and the partial fin between them are fixed,
where the fixed partial fin tilts with a fixed angle ψ. The
propulsive force to the forward direction in this fish robot
is obtained by making a traveling wave using other remaining fin-rays. When the robot moves forward by the propul-© ISAROB 2013

ANALYSIS OF PROPULSIVE FORCEIn this section, the equation for calculating the forward
propulsion speed and diving speed of a rajiform type fishrobot is derived. First, the equation for the forward speed of a
rajiform type fish robot is derived from a forward propulsive
force and a resistance force. Next, the equation for diving
speed of a rajiform type fish robot is derived from the resistance added to the fixed partial fin. Two assumptions are set
to derive these equations. One assumption is that the wholebody of this fish robot is in static water. This means that thewave or the vortex does not occur in the water around this fishrobot. When a rajiform type fish robot moves propulsively,
the fish robot receives a resistance force by wave or vortex
generated from itself, and a profile drag force with water by
the profile. However, it is difficult to analyze the resistanceforce by wave or vortex. Therefore, the resistance force bywave and vortex is included by setting a profile drag coefficient as a larger value. The other assumption is to set the
profile drag coefficient for this fish robot to 2.0. This value
was decided based on the fact that the profile drag coefficientin a cube was 1.07, and that the maximum of a profile dragcoefficient was 2.0.

Motor

3

Reaction force
from water
Fig. 3. Diving motion

Front

Fin-ray Bevel gear

ψ66

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Traveling wave

However, the forward propulsive force receives a constraint
by the number of fin-rays n for making a traveling wave.
Therefore, when π/φ > n, π/φ is set to n. The resistanceforce Dx that a rajiform type fish robot receives from water
is the sum of the profile-drag force for the body and the oscillating fin, and the resistance force for the fixed fin. Dx is
expressed by the following equation:

Control volume

A2
ψ
A1

Dx =

fx
fy

Fig. 4. Generated forces by two fin-rays

where α, β and γ are defined respectively as follows:

flow velocity that flows out of CV. The force that the considered partial fin receives from water is calculated from thedifference between the momenta, which are produced when
the surrounding water of CV flows in and flows out. The
mean angular velocity of a fin-ray ω, the speed of a travelingwave u, the slant of a partial fin ψ, the inflow area A1 and theoutflow area A2 of CV are expressed respectively by
=

u

=

ψ

=

A1

=

4θmax
T
ωa
θmax sinφ
Lsin(θmax sinφ)atanaL2 sin(θmax sinφ)/2A2=

A1 cosψ

fy

=

(3)
(4)
(5)
(6)

=

ρ(vx − u)2 A1 (1 − cosψ)2

ρ(vx − u) A2 sinψ(7)

4.2 Forward velocity
A rajiform type fish robot has a pectoral fin in right andleft, respectively. Only the surface projected on the directionof motion of a traveling wave pushes water, and generates
propulsive force. Therefore, the forward propulsive force Fx
generated from one pectoral fin of a fish robot is 2π/φ times
the force to the motion direction fx generated from between
fin-rays. Thus, Fx is expressed by the following equation:© ISAROB 20132π
ρ(vx − u)2 A1 (1 − cosψ)
φ

=

β

=

γ

=




1
π
CD S1 + 2A1 (1 − cosψ)(1 − )2φ
4π
A1 (1 − cosψ)uφ
2π− A1 (1 − cosψ)u2φWhen a rajiform type fish robot advances in the diving direction, the profile-drag force Dy which it receives is expressedby the following equation:(8)

where ρ is the density of water.

Fx =



α

4.3 Diving speed
Since fin-rays of a fish robot are oscillated symmetricallywith respect to the horizon, it is considered that the average
propulsive force to the diving direction generated by the pectoral fin is 0. Therefore, the propulsive force to the diving
direction is only a propulsive force to the dive direction generated by the fixed partial fins. In other words, the propulsive
force Fy to the diving direction is the sum of fy ’s generated
by the fixed partial fins. Since the fixed partial fins may not
make a traveling wave, u that is a speed of a traveling wave
is 0. From equation (8), Fy is expressed by the following
equation:
Fy = 2ρvx2 A2 sinψ
(12)

(2)

where a is a distance between fin-rays and L is the length ofa fin-ray. fx which is the force of the forward direction, andfy which is the force of the dive direction are expressed by
the following equations:
fx

(10)

where the projected area of the propulsion direction is S1 .
When the forward propulsive force Fx and the resistanceforce Dx are equal, the forward propulsion speed vx is expressed by the following equation:−β + β 2 − 4αγ
(11)
vx =
2α

Fin-rayω1
CD ρvx2 S1 + 2ρvx2 A1 (1 − cosψ)
2

Dy =

1
CD ρvy2 S22(13)Here, the projected area to the diving direction is S2 . Whenthe propulsive force Fy and the resistance force Dy to thediving direction are equal, the diving speed vy is expressedby the following equation:

4vx2 A2 sinψ
(14)
vy =
C D S2(9)

67

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

5

NUMERICAL SIMULATION

When a rajiform type fish robot is diving by the method
proposed in Section 3, the propulsion speed is changed by
the phase difference φ [rad] between adjacent fin-rays, theamplitude θmax [rad] and the period T [s], associating withthe oscillating fins. Change of a diving speed is examinedby a numeric simulation by fixing two design parametersand changing one of the three above parameters using theequation derived in Section 4. Other parameters are set ton =4, CD =2, L =150 [mm], a =60 [mm], ρ =1 [g/cm3 ],S1 =28000 [mm2 ] and S2 =250000 [mm2 ].
The result of a numeric simulation is shown in Fig. 5.
Fig. 5(a) shows the change of diving speed when changing θmax while T =2 [s] and φ =0.52 [rad] (=30 [deg]).
Fig. 5(b) shows the change of a diving speed when changing T while θmax =0.52 [rad] and φ =0.52 [rad]. Fig. 5(c)shows the change of a diving speed when changing φ whileθmax =0.52 [rad] and T =2 [s]. Fig. 5(a) shows that the diving
speed of a fish robot becomes so speedy that the amplitudeof the oscillating fin is large. Fig. 5(b) shows that the diving
speed of a fish robot is so fast that the period of the oscillating
fin is small. In Fig. 5(c), the diving speed of a fish robot is
the fastest, when the phase difference between fin-rays is approximately set around 0.69 to 0.87 [rad] (=40 to 50 [deg]).
This is because it becomes π/φ = n at the time of π/φ > n,so it is restricted as the forward propulsive force of the fishrobot stated in Section 4.2. Since the rajiform type fish robot
used in this paper is n=4, a diving speed displays the maximum at the time of phase difference 0.79 [rad] (=45 [deg]).
Therefore, this result is appropriate.
6(a) Diving speed vs. maximum amplitude(b) Diving speed vs. cycle

CONCLUSION(c) Diving speed vs. phase difference between fin-rays

In this paper, we have proposed a diving method for the
rajiform type fish robot. The diving speed obtained by the
proposed diving method is calculated from the propulsive
force obtained from a pectoral fin, and the resistance force
that the fish robot receives from water. Furthermore, the effectiveness of the calculated propulsive force is examined bynumeric simulation results. In the future, the effectivenessof the proposed technique will be confirmed by experiments
using an actual rajiform type fish robot.

Fig. 5. Diving speed[3] Ynag S, Qiu J, Han X (2009), Kinematics modeling andexperiments of pectoral oscillation propulsion robotic
fish. Journal of Bionic Engineering 6: 174–179[4] Zhou C, Low KH (2012), Design and locomotion control
of a biomimetic underwater vehicle with fin propulsion,
IEEE/ASME Transactions on Mechatronics 17(1): 25–
35

REFERENCES[1] Yuh J (2000), Design and control of autonomous underwater robots: a survey. Autonomous Robots 8(1): 7–24
[2] Watanabe K and Izumi K (2008), Skilful control for underactuated robot systems: from the ground to the air andunderwater. Proc. of the 2nd International Conference on
Underwater System Technology: Theory and Applications 2008 (USYS ’08), pp. 4–5© ISAROB 2013

68

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Underactuated Control for a Blimp with Four-Propellers
by a Logical Switching Method
Yoshikazu Nakamura1, Keigo Watanabe1, and Isaku Nagai1
1Department of Intelligent Mechanical Systems, Division of Industrial Innovation Sciences,
Graduate School of Natural Science and Technology,
Okayama University,
3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
E-mail: nakamura-y@usm.sys.okayama-u.ac.jp; {watanabe, in}@sys.okayama-u.ac.jp
Tel&Fax: +81-86-251-8064

Abstract: Although most of existing airships employ control methods by combining propellers and rudders, such a control
approach has the problem that the maneuverability is deteriorated if their traveling speed is slow. In this research, “X4-Blimp”
controlled by only four propellers is proposed. Since the X4-Blimp can control its position and attitude by regulating the output
of four propellers, it can realize high maneuverability, irrespective of its traveling speed. However, it is not easy to control the
X4-Blimp, because it is an underactuated system. This paper proposes a method for controlling the X4-Blimp by switching twocontrollers, one of which is constructed by combining models that include nonlinear terms and models that only include linear
terms, where those are separated from the derived dynamic model. The effectiveness of the proposed method is verified by somesimulations.
Keywords: X4-Blimp, Underactuated control, Switching control
1

INTRODUCTION

In recent years, unmanned aerial vehicles are being expected to be used for the vegetational observation and theinformation collection of disaster sites [1]. Especially, airships that can float by its own buoyancy are attractive, because they are effective in energy consumption. Small airships called “blimp” have been developing to make the management easy. Although most of existing airships employ
control methods by combining propellers and rudders, sucha control approach has the problem that the maneuverability is deteriorated if their traveling speed is slow because theairflow received by rudders is weakened. In this research,“X4-Blimp” is propsed as a blimp controlled by only four
propellers without any rudders, and the objective aims at controlling it.
This paper is organized as folllows: the proposed X4Blimp is explained in section 2. A dynamic model is derived in section 3 and partial underactuated controllers aredesigned in section 4. Logical switching rules are created in
section 5, simulaton results are shown in section 6 and theconclusion is drawn in secton 7.
2

Fig. 1. Definition of the coordinates
2.2 Definition of the coordinates
In general, when rigid body motion is considered, the center of gravity of the rigid body is discussed as the representative point. However, the X4-Blimp motion is considered withthe center of the gondola as a representative point in this paper. A definition of coordinates is shown in Fig. 1, and therobot coordinate C is defined such that the origin is the centerof the gondola, positive X-axis is set as the forward directionof the airframe, positive Y-axis is set as the right direction of
the airframe, and positive Z-axis is set to be downward perpendicular to the airframe. Similarly, the world coordinate
E is a right-handed coordinate where positive z-axis is set tobe vertically downward. The center position of the gondola
is represented by ξ = [x, y, z]T in the world coordinate, andthe rotational angles for roll, pitch, and yaw in the robot coordinate system are represented as φ, θ and ψ respectively, thenthe attitude of the gondola is represented by η = [φ, θ, ψ]T .
A rotation matrix R to transform the robot coordinate to theworld coordinate is derived as follows:⎤⎡cθcψ sφsθcψ − cφsψ cφsθcψ + sφsψ(1)
R = ⎣ cθsψ sφsθsψ + cφcψ cφsθsψ − sφcψ ⎦
−sθ
sφcθ
cφcθ

OVERVIEW OF THE X4-BLIMP

2.1 Structure of the X4-Blimp
The X4-Blimp proposed in this research is composed ofan envelope, a gondola and propellers as shown in Fig. 1.
The envelope is filled with helium gas to balance airframe
mass with the buoyancy. The envelope form is a spheroid
to decrease air resistance for traveling direction. The gondola includes batteries and controllers, and it is suspended
from the envelope. The gondola form is a rectangular solid
to maintain the space for the controllers etc. and simplify acalculation of the moment of inertia. The four propellers areattached on up, down, left and right sides of the gondola withthe same distance from the center of the gondola.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

mation, the dynamic model is partially linearized such that
ẍ
ÿ
z̈
φ̈
θ̈
ψ̈

w1
tan ψw1− tan θ sec ψw1
w2
w3
w4

=
=
=
=
=
=

(7)

Then, the inputs are transformed as follows

Fig. 2. Concept of the proposed controller

where cA is cos A and sA is sin A.
3

DERIVATION OF DYNAMIC MODEL

φ̈
⎪
⎪
⎪
⎪
θ̈
⎪
⎪
⎩
ψ̈

=
=
=
=
=
=

(cos θ cos ψu1 )/m(cos θ sin ψu1 )/m(− sin θu1 )/m(θ̇ψ̇(IY − IZ ) + u2 )/IX
(φ̇ψ̇(IZ − IX ) − Jp ψ̇Ω + lu3 )/IY(φ̇θ̇(IX − IY ) + Jp θ̇Ω + lu4 )/IZ

(2)

where the mass of the airframe is m, the moment of inertia
for each axis is represented by IX , IY and IZ respectively,
the moment of inertia of the propeller is Jp and Ω = ω2 +
ω4 −ω1 −ω3 . When four propellers are numbered from 1 to 4
in the clockwise from the upper propeller and the direction ofrotatinal velocity of each propeller is positive if it is defined
as clockwise. And the input u1 of translational motion, theinput u2 of roll motion, the input u3 of pitch motion and the
input u4 of yaw motion are represented by
u1
u2
u3
u4
4

=
=
=
=

b(ω12 + ω22 + ω32 + ω42 )d(−ω22 − ω42 + ω12 + ω32 )b(ω12 − ω32 )b(ω22 − ω42 )

(8)

w2

= (θ̇ ψ̇(IY − IZ ) + u2 )/IX

(9)

w3

= (φ̇ψ̇(IZ − IX ) − Jp ψ̇Ω + lu3 )/IY

(10)

w4

= (φ̇θ̇(IX − IY ) + Jp θ̇Ω + lu4 )/IZ

(11)

z11
z12
z21

=
=
=

h1 = x
Lf h1 = ẋLg1 Lf h2 = tan ψz22=

Lf Lg1 Lf h2 =

z31
z32

=
=

h2 = yLf h2 = ẏψ̇cos2 ψ

(12)
(13)
(14)
(15)
(16)
(17)

Then, the inputs are transformed as follows
v1

=

v2

=

w1

(18)

1
2 tan ψ 2
w4 +
ψ̇cos2 ψ
cos2 ψ

(19)

From the above results, a chained from is derived by
z̈11
z̈21
z̈31

(3)
(4)
(5)
(6)

=
v1
=
v2
= z21 v1

(20)

To apply a method in Xu and Ma [3] to Eq. (20), it is rewritten for state variables such as
ẋ1
ẋ3
ẋ5

DESIGN OF PARTIAL UNDERACTUATEDCONTROLLERS

Since the system of the X4-Blimp represented by the dynamic model of Eq. (2) is an underactuated system withfour inputs and 12 states, it is different to realize underactuated control. As shown in Fig. 2, two partial underactuated controllers for a model with 4 inputs 10 states are designed by combining a controller for a 2-input/4-state partial
model with a controller for a 2-input/6-state partial model.
The whole system is controlled by switching these two partial
underactuated contollers. To perform a chaind from transfor-© ISAROB 2013

= cos θ cos ψu1 /m

The partial underactuated controller 1 is designed from a 2input/6-state partial model for x, ψ and y, and from a 2input/4-state partial model for φ and θ. The partial underactuated controller 2 is designed from a 2-input/6-state partial
model for x, θ and z, and from a 2-input/4-state partial modelfor φ and ψ. When a chained form transformation in [4] isapplied, the 2-input/6-state partial model for x, ψ and y isdenoted by

A dynamic model of the X4-Blimp is derived by refering to X4-AUV studied in Watanabe et al. [2], the dynamic
model of the X4-Blimp is derived as
⎧
ẍ
⎪
⎪
⎪ ÿ
⎪
⎪
⎪
⎨ z̈

w1

= x2 ,= x4 ,= x6 ,

ẋ2
ẋ4
ẋ6

= v1
= v2
= x3 v1

Then the control input v1 is denoted by
v1 = −(s1 + s2 )x2 − s1 s2 x1(21)

where s2 >s1 > 0. To control the underactuated system, acoordinate transformation is performed to design a controller
based on a discontinuous model:xi
zi = xi (i = 1, 2, 3, 4), zi =(i = 5, 6)
(22)
x1

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The Eq. (22) is rewritten as follows
z˙1
z˙2

= z2
= −(s1 + s2 )z2 − s1 s2 z1

(23)
(24)

Ż3−6

= (A1 + A2 (t))Z3−6 + Bv2(25)

where Z3−6 = [z3 , z4 , z5 , z6 ]T . Here, A1 , A2 (t) and B are
denoted by⎤⎡⎤⎡
0 1 0 00
⎢ 0 0 0 0 ⎥⎢ 1 ⎥
A1 = ⎣, B=⎣
0 0 s1 1 ⎦0 ⎦20
s1 0 0 s1
⎡
⎤
0
0 0
0
0
0 0
0 ⎥
⎢
A2 (t) = C ⎣0
0 −1 0 ⎦−(s1 + s2 ) 0 0 −1

Fig. 3. Structure of energy regions
The 2-input/4-state partial model for φ and ψ is derived by alinear feedback such aswhere C = zz21 + s1 . The controllability of [A1 , B]
is confirmed. A controllable matrix is represented as[B A1 B A21 B A31 B]. It is regular because s1 >0. Since
A1 +BL is controllable, the feedback gain L = [l1 , l2 , l3 , l4 ]is calculated to make matrix A1 +BL as the Hurwitz matrix
by the pole placement method. The control input v2 is denoted by
v2 = LZ3−6 = l1 z3 + l2 z4 + l3 z5 + l4 z6=−(s1 + s2 )ẋ − s1 s2 x

v2=

l1 tan ψ + l2yẏψ̇+ l3 + l4
2
cos ψ
x
x

In this way, the controller for the 2-input/6-state partial modelfor x, ψ and y is designed. Next, the controller for the 2input/6-state partial model for φ and θ is designed by a linear
feedback such asw3= −k3 θ − k4 θ̇

(30)(k3 , k4 > 0)=−(s1 + s2 )ẋ − s1 s2 x

v2

=

l1 (−

© ISAROB 2013

=
=

=
=

π1 (E1 ) =π2 (E1 ) =BASED SWITCHINGψ2 + y2θ2 + z 2

(35)
(36)

√

1 − e− E1
2π1

(37)
(38)

In Fig. 3, the partial underactuated controller 1 is used on
the region R1 , whereas the partial underactuated controller 2
is used on the regionR2. Considering an overlapped region,
switching rules are decided as follows:
Rule 1 :If 0 < E2 ≤ π1 (E1 ) then st = y
Rule 2 :If π1 (E1 ) < E2 < π2 (E1 ) and st−1 = y then st = y
Rule 3 :

z
ż
tan θ
θ̇
) + l2 (−) + l3 + l4
cos ψ
cos ψcos2 θxxv1− cos ψ cos2 θ · v2 − 2 tan θ · θ̇2

REGIONIn Fig. 3, a two-dimensional plane is represented by E1 andE2 , and hysteresis like boundary lines π1 and π2 to separate
the energy plane are represented respectively by

The control inputs based on the chained form transformation
is denoted by
w1
w3

(34)

E1
E2

The partial underactuated controller 1 for a model with 4 inputs and 10 states is designed by combining the controllers
for x, ψ and y with the controller for φ and θ.
Similarly, the partial underactuated controller 2 is designed by combining the controller for the 2-input/6-statepartial model for x, θ and z with the controller for the 2input/4-state partial model for φ and ψ. When the partial
model for x, θ and z is transformed to a chained form, theinput transformation is denoted byv1= −k3 ψ − k4 ψ̇ (k3 , k4 > 0)

METHOD

(28)

(29)

w4

Switching the two partial underactuated controllers for 4
inputs 10 states is considered to control an underactuatedsystem with 4 inputs 12 states. However, if input chattering phenomena occur when controllers are switched, an excessive burden is placed on motors. Therefore, a switching
method[5] that has multiple boundary regions is used to prevent the chattering phenomena.
The energy is defined from the errors of generalized coordinates. Since the state x is doubly generated from the setof (x, ψ, y) and (x, θ, z), and similarly the corresponding
attitude angle φ is also doubly generated from the set of (φ,θ) and (φ, ψ), the errors for the stabilization to the origin are
directly represented by ψ, y, θ and z because both partial underactuated controllers always stabilize the state x and theangle φ to the origin. Then, the energy based on the errors isdefined as follows:(27)= −k1 φ − k2 φ̇ (k1 , k2 > 0)(33)

5 ENERGY(26)

w2= −k1 φ − k2 φ̇ (k1 , k2 > 0)

The partial underactuated controller 2 for the model with 4
inputs and 10 states is designed by combining the controller
for x, θ and z with the controller for φ and ψ.

Thus, since it can be stabilized to the origin, the control inputs for the chained form are derived as follows
v1

w2

(31)
(32)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Table 1. Parameters for the X4–Blimp
Parameter
m
l
IX
IY
IZ

Description
Mass
Distance
Roll Inertia
Pitch Inertia
Yaw Inertia

Value
0.464
0.1
0.03770.06240.0309Unitkg
mkg · m2kg · m2kg · m2

Fig. 5. Controlled attitudes

Fig. 4. Controlled positions
If π1 (E1 ) < E2 < π2 (E1 ) and st−1 = z then st = z
Rule 4 :If π2 (E1 ) < E2 then st = z
where st represents the controller used for each rule. Whenst =y, the partial underactuated controller 1 is used, whereas
when st =z, the partial underactuated controller 2 is used.
st−1 represents the controller used before one-sampling time.
According to this switching rule, the partial underactuatedcontroller 2 is used to control the state z, if the error energy
on z becomes large when using the partial underactuated controller 1 to control states except the state z. Similarly, the partial underactuated controller 1 is used to control the state y,if the error energy on y becomes large when using the partial
underactuated controller 2 to control states except the state y.
It should be noted that, in this switching rule, the chattering
phenomena are unlikely to occur because an overlapped region between the boundary lines π1 and π2 exists to switch
the controllers.
6

Fig. 6. Energy trajectory
point G. Switching of controllers occurs at the point P and
the state variables are changed suddenly, if the energy trajectory exceeds the boundary line π1 . Thus, it is confirmed thatthe positions and attitudes of the X4-Blimp can be stabilized
by switching the two partial underactuated controllers.
7 CONCLUSION
In this paper, an underactuated controller has been proposed for stabilizing an X4-Blimp, where two partial underactuated controllers were designed from the derived dynamic
model, and switching rules for switching two such controllers
were constructed by applying the conventional logical rules
based on hysteresis-like switching boundaries. The effectiveness of the proposed method was verified by the simulation.
For future work, it needs to confirm the effectiveness of thisapproach on real robot experiments.

SIMULATIONREFERENCES

This simulation is intended to verify that the state variables related to the position and attitude of the airframe
converge to the origin by switching the two partial underactuated controllers using the switching rules created inprevious section. The initial state of X4-Blimp is q0 =[1, 0.5, 1, π/18, π/4, π/4]T , and the goal state is qr =[0, 0, 0, 0, 0, 0]T . The physical parameters used for simulation are shown in Table 1. The feedback gains k1 =0.8,
k2 =1.2, k3 =0.6, k4 =0.7, s1 =1/40, s2 =0.45, l1 =−0.18,
l2 =−0.68, l3 =−1.74 and l4 =−38.7 are for the partial underactuated controller 1, whereas the feedback gains k1 =0.8,
k2 =1.6, k3 =1/30, k4 =0.7, s1 =1/50, s2 =0.45, l1 =−0.09,
l2 =−0.48, l3 =−0.68 and l4 =−21.3 are for the partial underactuated controller 2.
It is found from Fig. 4 that the positions, i.e., the states x,y and z converge from the initial positions to the goal positions. Similarly, it is seen from Fig. 5 that all the attitudes
φ, θ and ψ converge to the desired angles. Fig. 6 shows theenergy trajectory, where it starts from the point S. It is foundthat the controller 2 was switched to the controller 1 at the
point P and the energy finally converges to the origin at the© ISAROB 2013

[1] Kawabata K, Hada Y, Asama H (2006), Robotics research related to lighter than air aircraft. Journal of Robotics Societyof Japan 24(8): 901–905[2] Watanabe K, Izumi K, Okamura K, Rafiuddin S (2008), Discontinuous underactuated control for lateral X4 autonomous
underwater vehicles. Proc. of the 2nd International Conference on Underwater System Technology: Theory and Applications 2008 (USYS ’08) Paper ID 14[3] Xu WL and Ma BL (2001), Stabilization of second-order nonholomic systems in canonical chained form. Robotics and Autonomous Systems 34: 223–233[4] Laiou MC, Astolfi A (2004), Local transfomations to generalized chained forms. Proc. of the 16th International Symposium on Mathematical Theory of Networks and Systems[5] Hespanha JP, Morse AS (1999), Stabilization of nonholonomic intergrators via logic-based switching. Automatica 35:
385–39372

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Continuous shape-grinding experiment based on model-independent force /position hybrid control method with spline approximation
Akira Yanou1 , Mamoru Minami1 and Hiro Tanimoto1
1

Graduate School of Natural Science and Technology, Okayama University, Japan(Tel: +81-86-251-8924; E-mail: yanou@suri.sys.okayama-u.ac.jp)

Abstract: Based on the analysis of interaction between manipulator’s hand and working object, a model representing
the constrained dynamics of robot is first discussed. The constraint forces are expressed by an algebraic function ofstates, input generalized forces, and constraint condition, and then a decoupling control method of force and position ofmanipulator’s hand tip is proposed. In order to give the grinding system the ability to adapt to any object shape beingchanged by the grinding, we added estimating function of the constraint condition in real time for the adaptive force /position control, which is indispensable for our method instead of not using force sensor. This paper explores whether
the performance of the proposed controller is independent of alloy work-piece models or not. The experimental result isshown in order to verify the feature of the decoupling control of force and position of the tip.
Keywords: Continuous shape-grinding, Force / position hybrid control, Spline approximation

1. INTRODUCTION

Many researches have discussed on the force controlof robots for contacting tasks. Most force control strategies are to use force sensors [1],[2] to obtain force information, where the reliability and accuracy are limited
since the work-sites of the robot are filled with noise andthermal disturbances, reducing the sensor’s reliability.
On top of this, force sensors could lead to the falling of
the structure stiffness of manipulators, which is one of themost essential defects for manipulators executing grinding tasks. To solve these problems, some approaches using no force sensor have been presented [3]. To ensure thestabilities of the constrained motion, those force and position control methods have utilized Lyapunov’s stability
analysis under the inverse dynamic compensation[4],[5].
Their force control strategies have been explained intelligibly in books[6] and recently interaction control for
six-degree-of-freedom tasks has been compiled in a book[7]. The problem to be solved in our approach is thatthe mathematical expression of algebraic constraint condition should be defined in the controller instead of themerit of not using force sensor. In order to make the performance of proposed controller be independent of target
work-piece model, grinding task requires on-line estimation of changing constraint condition since the grindingis the action to change the constraint condition in nature.
In this paper, we estimate the object’s surface using thegrinder as touch sensor. In order to give the system theability to grind any working object into any shape, we focus on how to update the constraint condition in real time,
obtaining the result that spline function is best for on-line
shape estimation. Based on the above preparation we explored a continuous shape-grinding experiment to evaluate the proposed shape-grinding system, which aims for
grinding to desired shape without force sensor.

Fig. 1 Grinding robot systemand examined the problem of the control of the biped locomotion constrained in the frontal plane. Their purpose
was to control the position coordinates of the biped locomotion rather than generalized forces of constrained dynamic equation involved the item of generalized forces of
constraints. And the constrained force is used as a determining condition to change the dynamic model from constrained motion to free motion of the legs. In this paper,
the grinding manipulator shown in Fig. 1, whose endpoint is in contact with the constrained surface, is modeled according (1) with Lagrangian equations of motion
in term of the constraint forces, referring to what Hemami
and Arimoto have done:
∂L
d ∂L
(
)−(
) = τ + Jc T (q)Fn − Jr T (q)Ft , (1)dt ∂ q̇∂qwhere, Fn is the constrained force associated with constraint C, Ft is the tangential grinding force, Jc and Jr
satisfy;
∂C
∂C
∂C ∼
∂C
Jc =
/∥
∥=
∥,
Jr / ∥
∂q
∂r
∂r
∂r
∼
∼T
∂r
J r=
,
J Tr = J r ṙ/ ∥ ṙ ∥,∂q
r is the l position vector of the hand and can be expressed
as a kinematic equation

2. MODELINGr = r(q).

2.1 Constrained Dynamic Systems
Hemami and Wyman have addressed the issue of control of a moving robot according to constraint condition© ISAROB 2013(2)

L is the Lagrangian function, q is l(≥ 2) generalized coordinates, τ is l inputs. The discussing robot system does
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But the constrained condition C (j) (j = 1, 2, · · ·, d − 1)changed by the previous grinding is hard to define as an
initial condition. So we define
C (j) = y − f (j) (x) = 0

where, y is the y position of manipulator’s end-effector
in the coordinates Σw depicted in Fig. 2 and we assume
C (1) is known, that is to say, f (1) (x) is initially defined.
f (j) (x) is the working surface remained by i-th grinding. And f (j) (x) is a function passing through all points,(x1 , f (j) (x1 )), (x2 , f (j) (x2 )), · · ·, (xp , f (j) (xp )), theseobserved points representing the (j)-th constraint condition obtained from the grinding tip position since we proposed previously the grinding tip used for the touching
sensor of ground new surface. Here we assume f (j) (x)could be represented by a polynomial of (p − 1)-th order of x. Given the above p points, we can easily decide
the parameters of polynomial function y = f (j) (x). If
the current constrained condition can be got successfully,
which means the current working surface f (j) (x) can bedetected correctly, the distance from the current working
surface to the desired working surface which is expressedas ∆h(j) shown in Fig. 2 can be obtained easily.

Fig. 2 Model of shape-grinding
not have kinematical redundancy. C is a scalar functionof the constraint, and is expressed as an equation of constraints
C(r(q)) = 0,
(3)(1) can be derived to beT
M (q)q̈+H(q, q̇)+G(q) = τ +J T
c (q)Fn −J r (q)Ft ,(4)

where M is an l × l matrix, H and G are l vectors. The
state variable x is constructed by adjoining q and q̇: x =(xT1 , xT2 )T =(q T , q̇ T )T . The state-space equation of thesystem areẋ1 = x2 ,ẋ2 = −M −1 (H(x1 , x2 ) + G(x1 ))+M −1 (τ + J Tc (x1 )Fn − J Tr x1 )Ft ), (5)or the compact form is given as ẋ = F (x, τ , Fn , Ft ).
Using the inverted form of combination from (3) and ẋ =F (x, τ , Fn , Ft ), Fn can be expressed as (this part had
been detailedly introduced in [10] by us)
Fn = Fn (x, τ , Ft ),(6)∆h(j) (xi ) = f d (x) x=xi − f (j) (x) x=xi△

where, a(x1 , x2 ) is a scalar representing the first term in
the expression of Fn , and A(x1 ) is an l vector to represent the coefficient vector of τ in the same expression.
ẋ = F (x, τ , Fn , Ft ) and (6) compose a constrained system that can be controlled, if Fn = 0, describing the unconstrained motion of the system. Substituting (7) into
(5), the state equation of the system including the constrained force (as Fn > 0 ) can be rewritten as

Fnd (xi ) = k ′ ∆h(j) (xi )
(j)

(12)

3. FORCE / POSITION CONTROLLER
3.1 Controller using Estimated Constraint Condition
Reviewing the dynamic equation (1) and constraint
condition (3), it can be found that as l > 1, the numberof input generalized forces is more than that of the constrained forces. From this point and (7) we can claim that
there is some redundancy of constrained force between
the input torque τ , and the constrained force Fn . Thiscondition is much similar to the kinematical redundancyof redundant manipulator. Based on the above argument
and assuming that, the parameters of the (7) are known
and its state variables could be measured, and a(x1 , x2 )and A(x1 ) could be calculated correctly, which meansthat the constraint condition C = 0 is prescribed. As aresult, a control law is derived and can be expressed as{}+Tτ = −A (x1 ) Fnd −a(x1 , x2 ) − A(x1 )J r Kt Fndẋ1 = x2 ,ẋ2 = −M −1 [H(x1 , x2 ) + G(x1 ) − J T
c (x1 )a(x1 , x2 )]TT+M −1 [(I − J Tc A)τ + (J c A − I)J r Ft ],(8)
Solutions of these dynamic equations always satisfy theconstrained condition (3).
2.2 Shape-grinding
In the past, we did the continuous shape grinding simulations[10] to try to extend the grinding ability of ourgrinding robot[9]. Now in this paper, the continuous
shape grinding experiment which has been done by the
proposed force sensorless force/position control method
will be introduced.
To make the grinding task to be different from the former flat grinding experiment[9], we want to grind thework-piece into the one with different kinds of shapes, forexample, grinding the flat surface into a curved one, justlike Fig. 2. In Fig. 2, we can find that the desired working surface is prescribed (it can be decided by us.), whichmeans the desired constrained condition C d is known, so© ISAROB 2013(11)

In this case, we can obviously find that the desired constrained force should not be a constant. It should bechanged while ∆h(j) changes. So we redefine the de(j)sired constrained force Fnd as a function of ∆h(j) with′constant k , shown as follows:= a(x1 , x2 ) + A(x1 )J Tr Ft − A(x1 )τ , (7)C d = y − f d (x) = 0(10)+(I − A+ (x1 )A(x1 ))k,(13)

where it is assumed that Ft = Kt Fn ≈ Kt Fnd . Kt is
an empirical coefficient, I is a l × l identity matrix, Fnd
is the desired constrained forces, A(x1 ) is defined in (7)and A+ (x1 ) is the pseudoinverse matrix of it, a(x1 , x2 )is also defined in (7) and k is an arbitrary vector which isdefined as(9)74

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013}
∼T {k =J r (q) K p (r d − r) + K d (ṙ d − ṙ) ,(xiÄ1 ; yiÄ 1 ) (xi ; yi )(14)(x0 ; y0 )j Ä th grinding surf ace

link 2+(I − Â (x1 )Â(x1 ))k,mc −1 ∥

Fig. 3 Situation of known constraint surface model(xiÄ1 ; yiÄ 1 )(xi ; yi )(x0 ; y0 )mc −1 ∥

∂ Ĉ
∂ Ĉ
△
∥{(
)M −1 } = Â(x1 )∂r∂qf j (xi ) = yi =?

link 1

Fig. 4 On-line estimation model
3. When beginning to work, the initial condition of theend-effector is known and it has touched the work object.
4. The chipped and changed constraint condition can beapproximated by connections of minute sections.
Some relations between position value and time value
are written here, in this section, you’d better remember
these relations because it will help you understand theconcept of “on-line estimation method”.
xi−1 = x(ti−1 ) = x(t0 + (i − 1)∆t),xi = x(ti ) = x(t0 + i∆t),
xi+1 = x(ti+1 ) = x(t0 + (i + 1)∆t).

(18)
(19)
(20)

Before on-line estimation method is introduced, let’s
take a look at the situation of known flat constraint surface. For example, just like the grinding surface shownin Fig. 3, the expression of this surface is straight linear
equationf j (xi ) = yi = 0.5173(i = 0, 1, 2, 3...n),(21)

and point (xi , yi ) is the current position of grinding
robot’s end-effector. As a result, points before (xi , yi )have been already ground by grinder when t <= t0 +i∆t.
In the next moment, when time ti+1 = t0 + (i + 1)∆t,constraint condition

(16)

j
Ci+1
= y − f j (xi ) = 0

(17)

(22)

can be used for calculation of deriving torque τ . And
also, grinder will move to next point (xi+1 , yi+1 ) withno hesitation driven by the input torque τ . By “nohesitation”, it means on this known surface, grinder
has nowhere to go but point (xi+1 , yi+1 ), since thiswhole grinding surface f j (xi ) = yi = 0.5173(i =0, 1, 2, 3...n) is determined obviously. However, we
all know that the grinding surface on work-piece afterground will turn into some kind of irregular shape that nomathematic equation can express. What should we do tojobtain the future constraint condition Ci+1
if the grinding
surface is unknown? Like the situation shown in Fig. 4,
the grinding surface is not a simple straight line or somecurve line which can be defined and expressed by somecertain curve equation, after current time ti = t0 + i∆t,where should the grinder go? Grinding robot has no idea
since input torque τ cannot be derived without constraint
jcondition Ci+1
. To solve this problem, we consider thatsome kind of on-line estimation function should be utilized to imitate the unknown grinding surface, in order to

It can be found from (7) and (15) that the constrained
force always equals to the desired one explicitly if theestimated constraint condition equals to the real one, i.e.,
C = Ĉ and Ft = 0. This is based on the fact that force
transmission is an instant process. In the next section, we
will introduce an estimation method which is used to getĈ in current time.
3.2 On-line Estimation Method of Constraint
Now shape-grinding method is given to be solved inour research. But how to estimate the unknown constraint
surface is the nodus and key point. Here, an unknown
constrained condition is assumed as following,
Assumptions:
1. The end point position of the manipulator during performing the grinding task can be surely measured and updated.
2. The grinding task is defined in x − y plane.
© ISAROB 2013

(xd ; yd )j Ä th grinding surf ace

link 2∂ ∂ Ĉ∂ Ĉ
∂ Ĉ∥{−[ ()q̇]q̇ + ()M −1 (h + g)}
∂r
∂q ∂q
∂q
△

(xi+1 ; yi+1 )grinder(15)= â(x1 , x2 )f j (xi ) = yi = 0:5173

link 1

where K p and K d are gain matrices for position and thevelocity control by the redundant degree of freedom ofA(x1 ), r d (q) is the desired position vector of the endeffector along the constrained surface and r(q) is thereal position vector of it. (14) describes the 2-link rigid
manipulator’s arm compliance, we have to set K p andK d with a reasonable value, otherwise high-frequency
response of position error will appear. The controller presented by (13) and (14) assumes that the constraint condition C = 0 be known precisely even though the grinding operation is a task to change the constraint condition.
This looks like to be a contradiction, so we need to observe time-varying constraint conditions in real time byusing grinding tip as a touch sensor. The time-varying
condition is estimated as an approximate constrained
function by position of the manipulator hand, which isbased on the estimated constrained surface location. The
estimated condition is denoted by Ĉ = 0(in this paper,“ ˆ ” means the situation of unknown constraint condition). Hence, a(x1 , x2 ) and A(x1 ) including ∂ Ĉ/∂qand ∂/∂q(∂ Ĉ/∂q) are changed to â(x1 , x2 ) and Â(x1 )as shown in (16) and (17). They were used in the later
experiments of the unknown constrained condition. As
a result, a controller based on the estimated constrained
condition is given as}{+Tτ̂ = −Â (x1 ) Fnd −â(x1 , x2 ) − Â(x1 )J R Ft+

(xd ; yd )(xi+1 ; yi+1 )grinder75

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

fij (xiÄ 1 ) = yiÄ 1(xiÄ 1 ; yiÄ 1 )^Time interval that C
i+1 is usedjfij (xi ) = yi

Ii = [xiÄ 1 ; xi ]

ti

tiÄ1

(xi ; yi )

ti+1

t

(xiÄ1 ; yiÄ 1 )

0
j0
fi+1
(xi ) = fij (xi )(xi ; yi )(xi+1 ; yi+1 )Ii+1 = [xi ; xi+1 ]Ground surface

grinderFig. 5 Fitting by quadratic spline curve

Unground surface

Current position of grinderjobtain an unknown constraint condition Ĉi+1, which canbe used to calculate the input torque τ̂ .
Therefore, now let’s take a look at Fig. 4, in current
time ti = t0 +i∆t, end-effector of grinding robot is at position (xi , yi ), so far, point (xi−1 , yi−1 ) and point (xi , yi )have become known because they were just ground by
the grinder in the moment ti−1 = t0 + (i − 1)∆t andti = t0 + (i)∆t and the information of point (xi−1 , yi−1 )and (xi , yi ) can be derived through the position of robot’s
end-effector. Now building an estimation function going
through these two points, for example, a quadratic spline
functionfij (xi )Fig. 6 Expansion of interval between point (xi−1 , yi−1 ) and point(xi+1 , yi+1 ) on the on-line estimation model

spline function. With the estimation function the next
point (xi+1 , yi+1 ) can easily be found to be known, andthen this point can be the position where grinder should
go in the next moment when ti+1 = t0 +(i+1)∆t, At thesame time, this imitative function can be used as the online estimation function to obtain the unknown constraint
conditionjĈi+1= y − fij (x)= y − [αi (x − xi−1 )2 + βi (x − xi−1 ) + γi ] = 0,(xi ≤ x ≤ xi+1 )(30)= fspline (xi ) = yi
= αi (x − xi−1 )2 + βi (x − xi−1 ) + γix ∈ [xi−1 , xi ](i = 0, 1, 2, 3...n), (23)during the period when grinder goes from point (xi , yi ) topoint (xi+1 , yi+1 ), which means in this unknown intervalon the grinding surface, the future unground part (xi , yi )to (xi+1 , yi+1 ) can be ground by this on-line estimation
method based on the information obtained from alreadyground part (xi−1 , yi−1 ) to (xi , yi ). So, in the situationof unknown constraint surface, using this on-line estimation method point to point, the problem for grinding robotthat it doesn’t know where it should go in future time canbe solved theoretically.

we can figure out the coefficients αi , βi and γi uniquely
according to the information of points (xi−1 , yi−1 ),(xi , yi ) and derivation at point (xi , yi ) as follows.
Firstly, let fij (xi ) satisfy the following conditions
shown in Fig. 5.
(A) Go through two ends of the interval
yi−1 = fij (xi−1 )(24)

yi = fij (xi )(25)

4. EXPERIMENTIn this section, we verify the feature of the proposed
controller (13). In the previous papers[11],[12] we havealready confirmed the ability in on-line shape measurement based on spline approximation (on-line estimationjof the constraint condition Ĉi+1) and continuous shapegrinding. Whereas alloy model of work-piece to be
ground has been just one type, and the performance ofproposed force / position controller has not been confirmed for various types of alloy models with different
hardness. In other words, it is necessary to confirm
whether its performance is independent of alloy modelor not. Therefore this section shows the experimental results of model-independent force / position hybrid control
by using three types of alloy models with different hardness. Fig. 2 shows the experiment’s grinding task. In
Fig. 2, we can find that the desired surface is known (itcan be determined by us, here we use (31) as this desired
surface)(B) First-order differential of the spline polynomials
are equal at the end-point of adjoined function.
′

′

j
fi+1
(xi ) = fij (xi )(26)

From the relation among (23), (24), (25) and (26), we canobtain:γi = yi−1 , (i = 1, 2, · · ·, n)(27)
βi+1 = 2ui − βi , (i = 1, 2, · · ·, n − 1)(28)

βi+1 − βi, (i = 1, 2, · · ·, n − 1)(29)2hiyi −yi−1. The aboveWhere, hi = xi − xi−1 , ui =himentioned result can update the constrained conditionaljexpression Ĉi+1
step by step.
Making an expansion of the interval between point(xi−1 , yi−1 ) and point (xi+1 , yi+1 ) on the grinding surface which is shown in Fig. 6, we can see the first halfof grinding surface before the current position - point(xi , yi ) is shown by black line, which means this part has
been already ground, and second half after point (xi , yi )is shown by break point line, which means this part has
not been ground yet. Now let’s pay our attention on theinterval between point (xi , yi ) and point (xi+1 , yi+1 ),which means this part has been estimated by quadraticαi =© ISAROB 2013

f d (xi ) =0.5173 + 0.015 cos(3 × 5πxi −(0.0[m] ≤ xi ≤ 0.2[m])

π
) [m]
2
(31)

and also the initial flat surface is known as f 1 (xi ) =0.5173[m]. Here we notice that although the initialconstraint surface f 1 (xi ) and desired constraint surface
f d (xi ) are known already, those functions f j (xi ) who
76

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 11 Desired constraint force Fnd (left) and real constraint force

Fig. 7 Desired constraint force Fnd (left) and real constraint force

Fn measured by force sensor (right) (Alloy type: A5083P)

Fn measured by force sensor (right) (Alloy type: S45C)

Fig. 8 Grinding position xi (left) and its velocity ẋi (right) (Alloy

Fig. 12 Grinding position xi (left) and its velocity ẋi (right) (Alloy

type: S45C)

type: A5083P)

were executed for three types of alloy models with different hardness. From the experimental results, it is foundthat the proposed controller can decouple force and position control for continuous shape-grinding independentof alloy models. As future work, the relation between
hardness of alloy model and shape to be ground will be
explored in order to utilize in many robotic control fields.

Fig. 9 Desired constraint force Fnd (left) and real constraint force
Fn measured by force sensor (right) (Alloy type: A2017P)

REFERENCES[1]

Fig. 10 Grinding position xi (left) and its velocity ẋi (right) (Alloy[2]

type: A2017P)

can express the constraint working surfaces between
f 1 (xi ) and f d (xi ) are unknown. Therefore, we utilize
the quadratic spline function to estimate them by meansof f j (xi ) = fspline (xi ). The initial constraint surface
to be ground is defined as (x, y) = (0.0, 0.5173) ∼(0.2, 0.5173)[m] in time 5.0[s], and the desired velocity
along the surface is 0.04[m/s]. The desired force Fnd isj
set as Fnd(xi ) = k ′ ∆hj (xi ). k ′ is set to be 666 andj∆h (xi ) = f d (xi ) − f j (xi ) indicates the distance between the current surface and desired surface as shownin Fig. 2. Fig. 7 and Fig. 8 give the experimental result
for alloy type S45C with Vickers hardness 170-195 [HV].
The result of Fig. 9 and Fig. 10 is for alloy type A2017P
with Vickers hardness 125-130 [HV]. And the result of
Fig. 11 and Fig. 12 is for alloy type A5083P with Vickers hardness 80-90 [HV]. One trial takes 5 [s] and thenumber of trials is 10 in these experimental results. So
the experiment of each alloy type takes 50 [s]. Fig. 7,
Fig. 9 and Fig. 11 show desired constraint force Fnd andreal constraint force Fn measured by force sensor. Fig.
8, Fig. 10 and Fig. 12 show grinding position xi andthe velocity ẋi . From these figures, it can finds that the
proposed controller (13) can decouple position and force
control independent of alloy models although the resultsof real constraint force and grinding velocity are affectedby grinding.

[3]

[4]

[5]

[6]
[7]
[8]

[9]

[10]

[11]

5. CONCLUSIONS[12]

In order to verify the feature of the proposed forcesensorless force/position hybrid control, the experimentsof the proposed force/position hybrid control method© ISAROB 2013

77

M.H.Raibert and J.J.Craig: Hybrid Position/Force Control of
Manipulators, Trans. of the ASME, J. of Dynamic Systems, Measurement and Control, Vol.102, pp.126-133, June, 1981.
T. Yoshikawa: Dynamic Hybrid Position/Force control of Robot
Manipulators — Description of Hand Constraints and Calculation of Joint Driving Force, IEEE J. on Robotics and Automation,
Vol.RA-3, No.5, pp.386-392, 1987.
L. Whitcomb, S. Arimoto, T. Naniwa and F. Osaki, Experiments
in Adaptive Model-Based Force Control, IEEE Control Systems
Society, Vol.16, No.1, pp.49-57, 1996.
D.Wang and N.H. McClamroch: Position and Force Control for
Constrained Manipulator Motion: Lyapunov’s Direct Approach,IEEE Trans. Robotics and Automation, Vol.9, pp.308-313, 1993.
Jaydeep. Roy and Louis L. Whitcomb: Adaptive Force Control
of Position/Velocity Controlled Robots: Theory and Experiment,
IEEE Transactions on Robotics and Automation, Vol.18, No.2,
April, 2002.
B. Siciliano, L. Villani, Robot Force Control, Kluwer Academic
Publishers, U.K., 1999.
C. Natale, Interaction Control of Robot Manipulators, Springer
Tracts in Advanced Robotics, Germany, 2003.
Hooshang Hemami and Bostwick F. Wyman: Modeling andControl of Constrained Dynamic Systems with Application to
Biped Locomotion in the Frontal Plane, IEEE Trans. on Automatic Control, Vol.AC-24, No.4, pp.526-535, 1979.
Takeshi Ikeda, Mamoru Minami, Position/Force Control of a
Manipulator by Using an Algebraic Relation and Evaluations byExperiments, The 2003 IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM2003), Proceeding CD-ROM paper
No.159, 503-508, 2003
Mamoru Minami, Weiwei Xu, Shape-grinding by Direct Position / Force Control with On-line Constraint Estimation, 2008
IEEE/RSJ International Conference on Intelligent Robots andSystems, Acropolis Convention Center, Nice, France, Sept, 2226, 2008. pp. 943-948
Hiro Tanimoto, Mamoru Minami, Akira Yanou and Masaki
Takebayashi, Continuous Shape-Grinding Experiment based on
Constraint-combined Force / Position Hybrid Control Method,
Proceedings of 2012 IEEE/SICE International Symposium on
System Integration (SII2012), 2012.
Hiro Tanimoto, Mamoru Minami, Akira Yanou and Masaki
Takebayashi, Constraint-combined Force / Position Decoupling
Control Experiment for Shape-Grinding, The 2013 IEEE International Conference on Robotics and Automation (ICRA 2013)(to be submitted)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Catastrophic chaos theory: predicting the edge between health and death
Ken Naitoh1 and Hirofumi Inoue1
1

Waseda University, 3-4-1 Ookubo, Shinjuku, Tokyo 169-8555, Japan(Tel: 81-3-5286-3265)
1
k-naito@waseda.jp

Abstract: Recently, we proposed a macroscopic model for explaining the reason why organs such as a blast cyst, ectodermal
endoderm, mesoderm, heart, and hand generate at about sevenfold cell divisions during the morphogenetic process and the biochemical standard clock such as circadian one. (Naitoh, AROBJ 2008, 2012, JJAIM 2011) The present standard clock model
derived logically with experimental observations is described by a nonlinear differential equation predicting time-evolutions ofsix macroscopic molecular groups: three gene groups and three enzyme groups, which include acceleration and depression
factors. In our previous reports (Naitoh, Proc. JSST, 2011, J. of Physics, 2012), we also find that the fundamental network
pattern of neurons, which has been a mystery for a long time, will be dominated by the equation. Here, the macroscopic model
extended for describing also aging processes shows various types of cycles and reveals the physical condition for determining
whether or not living beings such as the human beings can survive after getting ill. It is stressed that, after getting ill, living
systems with too fast generation of information molecules such as various genes result in the death, whereas too fast
production of enzymes leads to regeneration. This may also explain an essential feature underlying cancerous process.
Keywords: Health, Death, Catastrophe, chaos.
basic molecular instrument regulating the biological rhythm
common to the cell cycle, proliferation and differentiationinduced by the stem cell cycle, neural pulse, neural network,and circadian clock. In order to generate the standard clock,at least two more inhibitory molecules (molecular groups)
of information and function should be added to depress amonotonous increase in DNA. [2-5, 10,11,12,13].

1. INTRODUCTION
Our previous reports [1,2] clarified the minimum excitatory
network of chemical reactions necessary for biological selfreplication, which is the origin of life such as primitivebacteria and archaea. The minimum system as a closed loop
has four types of macroscopic molecular groups: two
information groups x11 and x12 and two functional
molecular groups x21 and x22. (Fig. 1) It should be stressedthat only three molecular groups are necessary for realizing
the self-organizing replication, i.e., x11, x12, and x22,
because x21 can be automatically generated by these threegroups. As collision of two molecules generally occurs witha high probability in nonliving molecular pool,
simultaneous collision of three molecules or impact of third
molecules on two connecting molecules are possible with acertain probability, although simultaneous collision of fourmolecules is relatively difficult. Only three molecules ofx11, x12, and x22 led to the emergence of living beings.
We can topologically see the symmetric and asymmetriccircles of reaction networks in Fig. 1. [1,2] Fusion ofasymmetric and symmetric size ratios of molecules(bipolarity of sizes of 1:1 and about 2:3) [1,4,5,7] will
naturally result in the fusion of asymmetrical andsymmetrical network patterns (bipolarity of topology).
Complementary pairs of RNA such as double-strand RNA(dsRNA), i.e., only one molecular group, may form thesimplest excitatory cycle. However, information and acatalytic function are undetached in this type of system,
because each strand of dsRNA has both of them. This leads
to the fact that the dsRNA and DNA suitable for stabilizing
information is not conducive to the production of various
functions for inducing multi-cellar systems having complex
geometries. Thus, living beings select the detachment of
information and function. [13]

x22
close
close
replicatex11

x12
replicate

close

close
replicatex21

Fig. 1 Four-stroke molecular engine for living
systems as minimum biological cycle
Here, we define x13 and x23 as the other molecular groups
for inhibitory factors repressing reactions. These twogroups are incorporated in the four groups of x11, x12, x21,
and x22, because today’s cells, the morphogenetic
processes of multi-cellar systems, and neural systems use
negative controllers such as Oct-4 and SOX2 for producing
tissues and organs [6]. This leads to a macroscopic model
having six types of molecular groups, or in other words, asix-stroke engine (Fig. 2). We can describe the densities ofx

the six molecular groups ij at generation N after themother cell generation in the morphogenetic process, by thefollowing equations.

2.MORPHOGENETIC PROCESSThe main temporal mystery is the standard clock, i.e., the© ISAROB 2013

replicate78

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

x1Ni +1 − x1Ni = α i1 x1Ni ⊗ x 21N
x2i

N +1

, (i = 1,2,3)− x 2i = α i 2δ ( x1i − ξ i x 23 ) ⊗ x 22
NNN

amount of total energy inside the neurons.
There are two sides in Fig. 3, one for inputs and the otherfor outputs, which correspond to information and functional
molecules in Eq. 1 and Fig. 3. The upside-down topology of
inputs and outputs in Fig. 3 will also be possible.
The most important point is that the present equation (Eq.
1) and Fig. 3 describe the essential physics underlying the
network of neural cells and the molecular network inside aneuron, whereas the Hodgkin-Huxley (H-H) model [8]describes only the outer electrical quantities such as
electron flow and voltage for a single neuron. [It should be
added that some variations modified from the network
pattern in Fig. 3 and Eq. 1 are also possible, by varying thearbitrary constants and also by adding more molecular
types except for x13 and x23.]
It should be stressed that the circadian clock of about
24-25 hours are also seven times the fundamental
temperature oscillation of about 3.5 hours. [9]Equation 1 is an ordinary differential one that eliminatesspatial variations of quantities, because the spatial diffusionof molecules and cells is relatively fast in comparison withtemporal oscillations and also because a lot of molecules
move between cells.
Moreover, emphasis is placed on the fact that the cycles ofboom and bust appearing in economic and social systems
are also the seven-beat on average, because economic
systems are produced by human brains. Flux and reflux ofcompanies and capital can also be clarified by the present
analysis. [2], (i = 1,2,3)

N

(1)

xij
where xij ⊗ x km denotes the smaller value amongand xkm and also where δ (x ) and α ij denote thelarger value of x or 0, i.e., max (x, 0) and constant ofreaction probability, respectively.[2-5] Statistical mechanics
inevitably leads to the mathematical form on the right-handside in Eq. 1, because of collision probability.
Numerical solutions for Eq. 1 show about a sevenfold beatcycle of densities for molecular groups on average, whilevarying the parameters in Eq. 1 results in four- to ten- foldbeat cycles.
An important point is that the actual morphogenetic
processes show about seven-beat cycles of moleculardensities [2-5].
x22
close
replicate

close
x12

x11

close

replicate

x23

x13

replicatereplicateclose

replicate

close

close

replicate

4 MODEL EXTENDED WITH TOTAL ENERGY
LIMIT
The model (Eq. 1) in the previous sections was derived
under the assumption of an infinite energy supply. However,energy supplied for molecular networks, cell colonies,
organs, neural networks, or economic systems will belimited, because the surface-to-volume ratio of each systemdecreases according to an increase in the number ofmolecules, cells, neurons, or populations, leading to acondition of insufficient energy. Thus, a new energy
restriction term should be added to Eq. 1, which results in
Eq. 2 [3, 7].

x21

Fig. 2 Six-stroke molecular engine including depression
effectPulse inputs from outerx22
Provoked
pulse
Provoked
pulse

Activate

Provoked
pulse
Activatex12

x11

Activatex23

x13

Depress

xij

Activate
Provoked
Pulse

Activate
Provoked
pulse

Provoked
pulse

Activatex21− xij = α ij ( x1 j − β ij x23 ) ⊗ x2 i - ε ij [ xij ]q ,xij ≥ 0,

N

N

N

N

N

x1 j − β ij x23 ≥ 0. (i = 1 − 2, j = 1,−3)
N

N

(2)

Outputs
to outerwhere q > 2 is set in case that the symbol ⊗ is defined as
product, whereas q > 1 if the symbol ⊗ means smaller

Fig. 3 Six cellar network for the brain system

value among two.
Let us solve the time-dependent process including
morphogenesis and aging processes by using Eq. 2.
Numerical solutions for the equation extended with totalenergy limit show a transition to sick situation such as
cancer in the aging process of the human beings including
the brain, i.e., a mysterious transition from chaotic
oscillation at 2nd stage (from 80 to 200 generation) toperiodic one at 3rd stage (after about 250), while the

3. NEURAL TOTWORK
Equation 1 will also reveal the standard topology of thecortical neural circuit (network), the integration mechanismof brain functions, the neural system for muscle control,
and the chemical reaction network inside a neuron [3,5,7].
Figure 3 shows the standard pattern for neural networks,
which includes inputs and outputs. For the network, six
variables of xij (i=1-2, j=1-3) are redefined as the activationlevel of neurons, related to the density of molecules and© ISAROB 2013

N +1

79

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013vibration amplitude keeps a constant level. (Fig. 4)

Robotics, 2012.
[8] Hodgkin A L and Huxley A F, A quantitative descr
iption of membrane current and its application to conduction and excitation in nerve. J. of Physiology. 117, 500-544, 1952.
[9] Bear M F, Connors B W, and Paradiso M A,Neuroscience, Lippincott Williams & Wiklins Inc., USA, 2
007.
[10] K. Naitoh. The universal bio-circuit: underlying
normal and abnormal reaction networks including cancer,
Proc. of European IFMBE Conference, IFMBE
Proceedings 37, pp.247-250, Budapest, 2011.
[11] K. Naitoh. Onto-oncology: a mathematical physics
underlying the proliferation, differentiation, apoptosis, andhomeostasis in normal and abnormal morphogenesis andneural system. Proc. of 15th Nordic and Baltic Conf. on
Biomedical Engineering and Biophysics, IFMBE
proceedings 34, Denmark. Pp.29-32, 2011.
[12] K. Naitoh. Onto-biology: clarifying also the standard
clock for pre-biotic process, stem-cell, organs, brain, andsocieties, Proc. of CBEE, Singapore. 2009.
[13] K. Naitoh, Onto-biology, Aritificial Life and Robotics, 2010.

Fig. 4.Transition to sick condition as the 3rd stage in the
aging process of the human being including the brain.
5. DEATH OR REGENERATION
Here, we further examine the mysterious equation (Eq. 2)
by varying the parameters in detail. Increasing values of
α11 and α 21 result in very rapid decreases of densities forsome molecular groups: biological catastrophe. An
important point is that there are two essential patterns of thecatastrophic processes: death and regeneration fromasphyxia. (See Figs. 5 and 6) Figure 5 demonstrates theregeneration from asphyxia, whereas Fig. 6 shows the death,because densities of molecular groups cannot increase again.
The critical condition for determining whether or not theliving system comes back is related to the densities D1, D2,
and D3 of information groups x11, x12, and x13 just after
the crash occurred. If the densities of D1, D2, and D3 arezero, the death comes.

Fig. 5 Recoverable catastrophes appearing in the solution
for Eq. 2.

6. CONCLUSION
The present model clarifies the edge between the death andregeneration after becoming ill. Excessive replication of
information molecules leads to the death of information
system, which results in the death of the whole system.

α11 = 1.0, α12 = 1.0, α13 = 1.0, α 21 = 3.2, α 22 = 1.0, α 23 = 1.0, q = 2.0, ε ij = 1.0 × 10 −13
1.00E+14
1.00E+12© ISAROB 2013

Numer of molecules

References[1] Naitoh K Stochastic Determinism. Artificial Life andRobotics, 13, pp10-17. 2008. (also in K. Naitoh. Ontobiology: Inevitability of Five Bases and Twenty Aminoacids. Proc. of 13th Int. Conf. on BioMedial Engineering,(ICBME), Singapore. Springer-Verlag (2008)).
[2] Naitoh K Morphogenic economics. Japan J. of
Industrial and Applied Mathematics, Vol. 28, No.1, 2011.
[3] Naitoh K Onto-neurology. Proc. of JSST2011, Int. Conf.
Modeling and Simulation Technology, Tokyo, pp.322-327,
2011.
[4] Naitoh K (2011) Proc. of 15th Nordic and Baltic Conf.
on Biomedical Engineering and Biophysics, Denmark.
[5] Naitoh K Spatiotemporal structure: common tosubatomic systems, biological processes, and economic
cycles. J. of Physics, Conf. Ser. 344, 2012.
[6] Takahashi K, Yamanaka Cell 126, pp663-676., 2006.
[7] Naitoh K. Hyper-gourd theory. Artificial Life and1.00E+10

D1

1.00E+08

D2

1.00E+06

D3

1.00E+04

R1
R2

1.00E+02

R3

1.00E+00
0

100

200

300

400

500

Generation

Fig. 6 Unrecoverable catastrophe seeing in the solution of
Eq. 2 in case of very high increase of information proteins.
α11 = 2.0, α12 = 1.0, α13 = 1.0, α 21 = 1.0, α 22 = 1.0, α 23 = 1.0, q = 2.0, ε ij = 1.0 × 10 −1380The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Supercomputing of transition to turbulence in pipe with adit flow
Tsuyoshi Nogami1, Takahiro Tobe1, and Ken Naitoh1
1

Waseda University, 3-4-1 Ookubo, Shinjuku, Tokyo 169-8555, Japan(Tel: 81-3-5286-3265)
1
k-naito@waseda.jp

Abstract: Transition from laminar flow to turbulence often occurs in closed pipes such as pathologic blood vessels andartificial systems such as micro-tubes. While varying disturbances entering at pipe inlet or at heart pump, the transition points
in space from laminar flow to turbulence in closed pipe are solved by using the weakly-stochastic Navier-Stokes equation anda finite difference method proposed previously by us (Naitoh and Shimiya, 2011), although the previous numerical simulations
and instability theories based on the deterministic Navier-Stokes equation could never indicate the transition point in closed
tunnel. The most important point of our approach is a philosophical method proposed for determining the stochasticity level,
which is deeply related to boundary condition. Here, we qualitatively clarify the relation between the transition point andamount of adit on solid wall, because living systems exchange water and molecules through the wall of blood vessel. A
mysterious feature obtained is that a larger amount of additional adit at the inlet may result in laminalization of the boundary
layer.
.
Keywords: Transition, Turbulence, Drag, Adit.
disturbances, which suggests the possibility of a stochastic
Navier-Stokes equation. [19]1. INTRODUCTION
Our previous reports [19-22] showed that weakly
Reynolds experimentally showed the transition tostochastic field equations averaged in a mesoscopic window
turbulence in pipe flow 100 years ago.[1] However,(MW) smaller than that for continuum mechanics will lead
traditional stability theories based on deterministic
to solutions to various transient and critical problems still
continuum mechanics [2, 3, 4, 5] cannot indicate the criticalunsolved even numerically. An important study [19-22] isReynolds number, i.e., the transition points in closed pipethat we could solve the transition point in space in theflow for various inlet disturbances, although a great deal oftransition to turbulence in pipes while varying velocity
effort has been exerted over the years to reveal thefluctuations at the inlet. Thus, in this report, we apply thetransition points in pipe flow using experimental,
new numerical model based on the weakly-stochastic
theoretical, and computational approaches.
Navier-Stokes equation to the pipe flow including adit on
Experimental researches related to puffs and slugs [6, 7, 8,
wall. This is important, because adit on wall appear in
9] have yielded important information for clarifying thevarious flows such as artifact test inside wind tunnel, blood
early stage of the transition process around the criticalflow, and fuel cell.
Reynolds number. However, these previous studies could
not clarify clearly whether or not the stochasticity coming
2.METHODOLOGYfrom molecular fluctuations influence the transition point in
It is stressed that actual pipe flow shows a transition topipes, because the spatial resolution was not sufficient.
turbulence, although traditional linear instability analysis
Recent mathematical and physical theories [10, 11] havebased on a deterministic governing equation such as therevealed important aspects about the early stage of thepotential equation and Navier-Stokes equation with thetransition in pipes. Numerical computations of pipe flow
divergence of velocity of
have also been tried using the deterministic Navier-Stokes
equations [12, 13, 14, 15]. However, the influence of inlet
disturbances on the transition point in space and the critical∂uDˆ = i = 0
Reynolds number in pipe flow cannot be analyzed by these∂xi
approaches.
shows no transition in closed pipes, even if large
There are also some theoretical researches for the
disturbances are input.
mesoscopic regime lying between molecular dynamics and
The early laminar boundary layer in pipes is thinner thancontinuum mechanics. [16, 17, 18] However, the role oftheKolmogorov scale [25]. Then, there are no actual flows
stochasticity for the nonlinear unsteady phenomena inwithout
inlet disturbances. Thus, they lead to an
multi-dimensional space such as those described by theinhomogeneous
and wavy velocity distribution at theNavier-Stokes equation are still mysterious.
starting point of instability inside the very early laminar
This has led to the possibility that averaging theboundary layer that is thinner than the Kormogolov scale.
phenomenon in a relatively large window for the continuum
Let us consider the smallest size of vortex in fullyassumption eliminates the instability driven by small
turbulent flow, i.e., the Kolmogorov scale. This is on thephysical fluctuations in the mesoscopic regime. This mayorder of 1000 times as large as the mean free path of
be true because our previous computations in a straight andmolecules in high Reynolds number flows. [25] Thus, theclosed pipe using numerical disturbances close to themolecular discontinuity produces stochastic fluctuations ofrandom number generator have qualitatively showed the
0.1% in density and velocity in the Kolmogorov-scaletransition in space for various Reynolds numbers and inlet
vortex. Even if the deterministic Navier-Stokes equation is© ISAROB 2013

81

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

numerically solved using a fine grid system on the
Kolmogorov scale, this stochasticity is not evaluated. This
stochastic level is also close to the ratio between the inlet
disturbance and main flow in wind tunnels. It is alsoemphasized that stronger inlet disturbances produce shortertransition points, which apparently come from the smaller
minimum scale of the fluctuation in the thin boundary layer.
For these reasons, deterministic models such as thedeterministic Navier-Stokes equations based on thecontinuum assumption are essentially defective for solving
the transition. Thus, the mesoscopic window size (Dmw)should be identical to this minimum scale of the weakfluctuation in the thin boundary layer.
To say more, an asymmetric distribution of numericalerrors in space similar to a random force induced verticalasymmetric flows in many previous computational studies,
which include the Karman vortex streets. [4, 12, 13, 29]This fact also supports the inevitability of stochastic terms.
It was also confirmed previously that the mean velocity
profiles after the transition point computed by the stochastic
Navier-Stokes equation [19-22, 26] agreed well with theexperimental data in Refs. 27 and 28. The early stage of thetransition for a low Reynolds number of 6,000 in our
computations also shows a two-dimensional path ofparticles (corresponding to the well-known Squire theorem),
although relatively high Reynolds numbers suddenly
generate three-dimensional flow.
In this section, we first show the concrete formulation forthe stochastic Navier-Stokes equation related to boundary
condition and the computational results performed with afine grid system having high resolution and accurate
random force, while there is no adit on wall.
The definition of physical quantities such as fluid velocityru (= u1 , u2 , u3 )andrc (= c1 , c2 , c3 )densityρinmolecular

dxmw and Vmw denote the length scale for
integration and control volume
with the diameter Dmw,rrespectively, while ρ and u imply the corresponding
values averaged in Dmw and ρ ' and u' are stochastic
wherefluctuations indeterminate. [19,21,22]
A mesoscopic averaging window size, smaller than thatfor the continuum assumption, yields a stochastic
compressible Navier-Stokes equation in a non-conservative
form in the case where specific heat and viscosity
coefficient have constant values, written as the governing
equation below,⎡ ∂u⎢ i 1 D⎡ f1 ⎤ ⎢ ∂x + ρ Dt ρ⎢ ⎥ ⎢ i⎢ ⎥Du∂p1 ∂ 2ui−F ≡ ⎢ f2 ⎥ = ⎢ i +2⎢⎢ ⎥ ⎢ Dt ∂xi Re ∂x j
⎢ ⎥ ⎢2⎣⎢ f 3 ⎦⎥ ⎢ DT − Dp − 1 ∂ T
Dt Pe ∂xi 2⎢⎣ Dt⎤⎥ ⎡ε 1 ⎤
⎥ ⎢ ⎥
⎥ ⎢ ⎥
⎥ = ⎢ε 2 ⎥
⎥ ⎢ ⎥
⎥ ⎢ ⎥
⎥ ⎢ ⎥
⎥ ⎣ε 3 ⎦⎥⎦(6a)where ui, p, ε i (i = 1 − 3) , t, Re, and Pe denote thedimensionless quantities of velocity components in the idirection, pressure, random forces, time, Reynolds number,and Peclet number, respectively.
Equation (6a) is transformed to Eq. (6b), which is a multilevel formulation. [29]
⎡ f1 ⎤ ⎡ Dˆ + 1 D ρ
ρ Dt
⎢ ⎥ ⎢
⎢ ⎥ ⎢
2
⎢ f 2 ⎥ ⎢ Dui + ∂p − 1 ∂ ui⎢
2
⎢ ⎥⎢ Dt ∂xi Re ∂x j
F ≡⎢ ⎥=⎢ 2⎢ f3 ⎥∂ p∂⎢ ⎥ ⎢ 2 + ρ Dˆ + Φ
⎢
∂
t
∂
x
⎢ ⎥
i
⎢ ⎥ ⎢ DT Dp 1 ∂ 2T
⎢ ⎥ ⎢
−
−
⎣ f 4 ⎦ ⎢⎣ Dt
Dt Pe ∂xi 2

velocityr
and physical space x (= x1 , x2 , x3 ) in Eqs. (3)and (4) leads to the deterministic Navier-Stokes equation,
when the window size for averaging is large on the basis ofcontinuum assumption. [2]r rr

ρ = m ∫ f (c , x , t )dc ,

(3)

r r r
r
m ∫ c f ( c , x , t ) dc
r
u =

ρ

(6b)
∂
u
where Dˆ (= i ) denotes the divergence of velocity and
∂xi

(4)
r r
where m, f (c , x , t ), and t denote molecular weight,
probability density function, and time, respectively, whilerc denotes the molecular speed.
Here, we redefine physical quantities such as fluid
velocity and density in the mesoscopic window size (Dmw)
smaller than that for deriving continuum mechanics.
Averaging should be done in the Dmw, which is theminimum scale dominating the flow phenomenon, Lms. As
a result, we obtainr ralso the equation forf 3 is the spatial derivative of that off 2 , while the third term on the left-hand sideΦ=∂u∂1 ∂ 2 ui
[u j i −]∂xi∂x j Re ∂x j 2

denotes the spatial derivative of the convection andviscosity terms in f 2 . Four variables of D̂ , ui , p ,r r

ρ = m ∫ ∫ f (c , x , t )dc dx mw / Vmw = ρ − ρ ' andand T can be solved by the four equations in Eq. (6b).
The second term on the left-hand side of the energy
conservation
law in Eq. (6b) can approximately be(eliminated for low Mach number conditions.

r r r
r r
m ∫ ∫ c f (c , x , t )dc dx mw / Vmw rrru == u − u'ρ

© ISAROB 2013⎤⎥ ⎡ε 1 ⎤
⎥ ⎢ ⎥
⎥ ⎢ ⎥
⎥ ⎢ε 2 ⎥
⎥ ⎢ ⎥
⎥=⎢ ⎥
⎥ ⎢ε ⎥⎥ ⎢ 3⎥⎥ ⎢ ⎥⎥ ⎢ ⎥⎥ ⎢⎣ε 4 ⎥⎦⎦82

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

In this study, the initial velocity distribution is set to bethat of the potential flow, i.e.,u 1 ( t = 0 ) = Uo = 1(7)
u 2 (t = 0 ) = u 3 (t = 0 ) = 0

the other a special numerical disturbance close to the actual
random number.
The second approach uses numerical errors coming fromthe iteration method for the matrix calculation, because therounding error due to the iteration method such as the SOR
method [30] is close to the random number generator,
although truncation errors distorting the phenomenon
should be eliminated.
One reason why previous attempts to compute thetransition point in space have not succeeded [12, 13] is thatat each point, while the initial pressure and temperature areconstants.
The inlet boundary condition is given as

u1 = Uo + δ = 1 + δ , u 2 = u3 = 0

(8a)

ε

and the outlet boundary condition as∂
ui = 0∂n

the criterion of 1 in Eq. (6b) was set to zero based onmathematics [30], not on fluid physics taking into accountindeterminacy and molecular discontinuity. Evaluations of
ε in previous reports [30] were too small. Most of theprevious studies on computational fluid dynamics havecontrolled only truncation errors, whereas the approach
proposed here also provides physical control of rounding
error.
The numerical algorithm used here is based on a multilevel formulation [29] that can simulate bothincompressible and compressible flows. The momentum
conservation law and the second derivative of the pressure
equation in the formulation clarify the mathematicalstructure that the governing equation varies from an elliptic
type to a hyperbolic type as the Mach number increases.
Thus, the numerical algorithm is extended from the Marker
and Cell (MAC) [31], SIMPLE [32], ICE [33], and CUP
methods [34]. Details of the numerical discretizations andnumerical algorithms are described in Refs. 29 and 34.
Computational results computed on mean velocity,
turbulence intensity, and transition point agree well with theexperimental data reported by Laufer, while the present
approach also reveals the critical Reynolds number around
1,500 - 2,500. [19-22, 26](8b)where Uo , δ , and n denote the dimensionless inlet
velocity, dimensionless inlet disturbance, and the direction
normal to the outlet, respectively.
A no-slip boundary condition(8c)ui = ois imposed at the solid walls of the pipe.
in Eq. (6b). Stronger inlet
Next, let us consider ε1disturbances result in a smaller characteristic scale, because
they lead to more inhomogeneous velocity distributions at
the starting point of instability inside the laminar boundary
layer.
Two types of indeterminacies, in the boundaries and inner
regions, should be set to the same level. [19, 20, 21, 22, 26]Thus, the relationN∑ ε / N = Ce δ
1

n =1

(9)
is used with an arbitrary constant Ce and the number ofgrid points N. In this report, C e is set to be of the order of
1.0. [Comparisons with computations and experiments on
the transition point in space and the theoreticalconsiderations based on statistical mechanics including the
Liouville equation brings that CX should be about 1.0.]
Stochastic terms ε

2

4. TRANSITION TO TURBULENCE IN A ST
RATGHT PIPE WITH ADIT

, ε 3 , and ε 4 in Eq. (6b) should also

Here, we examine the relation between the transition pointin space and flow amount injected from solid wall into astraight pipe. Figure 1 qualitatively shows that larger
amount of adit from the wall results in later transition pointin space. This may be used for reducing the drag force.

be added to the momentum conservation law, the Poisson
equation, and the energy conservation law averaged in themesoscopic averaging window (MW). However, in thisstudy, ε , ε 3 , and ε 4 are set to be 0.0, because their2

9. CONCLUSION

role is similar to small variations of Reynolds and Peclet
numbers in space and time, resulting in less influence on
the transition point than in the case of the massconservation law. This is clear from the fact that even the
turbulence viscosity model based on the RNG theory [23]has less influence on the transition point, when the model isused in numerical computations. The stochastic terms
ε , ε 3 , and ε 4 have relatively weak influence on the

The present result obtained by computation should bechecked by comparing with experiments.
REFERENCES
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the Royal Society of London, 35, (1883), 84-99.
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New York 1964).
3. Aris R., Vectors, tensors, and the basic equations of fluid
mechanics (Dover, New York 1989).

2transition phenomenon, because those in momentum andenergy conservation laws can also be dissipative, while
mass with ε cannot essentially be dissipative.
1

There are two ways to calculate the random force term of
ε 1 : One involves the use of a random number generator and© ISAROB 2013

83

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of transonic and low Reynolds number airfoils. AIAA J, 25,
10, 1347 (1987).
25. Pope S.B., Turbulent Premixed Flames. Ann. Rev. Fluid
Mechanics, 19, 237 (1987).
26. Naitoh K., Noda A., Kimura S., Shimiya H., andMaeguchi H.
Transition to Turbulence andLaminarization clarified by Stochastic Determinism.
Proceedings of 8th International Symposium on
Engineering Turbulence Modeling and Measurements(ETMM8). Marseille, 775 (2010).
27.Laufer J., Investigation of turbulent flow in a twodimensional channel, NACA Technical Note 2123, 68(1950).
28.Kuan C.L. and Wang T., Experimental Thermal andFluid Science, 3, 157 (1996). Also in Zhou D. and T. Wang
T. Experimental Thermal and Fluid Science, 12, 338.
(1996).
29.Naitoh K. and Kuwahara K., Large eddy simulation anddirect simulation of compressible turbulence andcombusting flows in engines based on the BI-SCALES
method. Fluid Dynamics Research, 10, 299 (1992).
30.Roache P.J., Fundamentals of Computational Fluid
Dynamics, (Hermosa Publishers, New Mexico1972).
31.Harlow F.H. and Welch J.E., Physics of Fluid, 8, 12(1965).
32.Harlow F.H. and Amsden A.A., J. of Computational
Physics, 8, 197 (1971).
33.S.V. Patanker and D.B. Spalding, International J. of Heatand Mass Transfer, 15, 1787 (1972).
34.Yabe T. and Wang P.Y., J. of Phys. Soc. Japan, 60, 2105,(1991).

4. Tatsumi T., Fluid dynamics, (Baifukan, Tokyo 1982).
5. Landau, E.D. and Lifshitz, E.M., Fluid Mechanics. 2nd
Edition, (Butterworth-Heinemann Elsevier, Oxford 2004).
6. Wygnanski I.J. and Champagne F.H. On transition in apipe. Part 1. The origin of puffs and slugs and the flow in aturbulent slug. J. of Fluid Mechanics, 59-2. 281. (1973).
7. Hof B., Westerweel J., Schneider T.M. & Eckhardt B.
Finite lifetime of turbulence in shear flows. Nature 443,
59. (2006).
8. Nishi M., Ünsal B., Durst F. and Biswas G. Laminar-toturbulent transition of pipe flow through slugs and puffs,
Journal of Fluid Mechanics. 614. 425. (2008).
9. Kanda H. and Yanagiya T. Hysteresis Curve in
Reproduction of Reynolds' Color-Band Experiments, J. of
Fluid Engineering. Transaction of ASME, 130. 051202.
(2008).
10. Faisst H. and Eckhardt B., J. Fluid Mechanics, 504, 343.
(2004).
11. Eckhardt B., Schneider T.M., Hof B., and Westerweel J.
Annual Review of Fluid Mechanics. 39, 447. (2007).
12. Moin P. and Kim J., J. Fluid Mech. 118, 341. (1982).
13. Kawamura T. and Kuwahara K., K. Direct simulation ofa turbulent inner flow by finite-difference method. AIAA
paper 85-0376 (1985).
14. Kanda H. and Shimomukai K.. Numerical study ofpressure distribution in entrance pipe flow, J. of Complexity.
(2009).
15. Orlandi P. Time evolving simulations as a tentative
reproduction of the Reynolds experiments on flowtransition in circular pipes. Physics of fluids. 20, 101516.
(2008).
16. Haken H. Synergetics, an Introduction: Nonequilibrium
Phase Transitions and Self-Organization in Physics,
Chemistry, and Biology, 3rd rev. enl. ed. New York:
Springer-Verlag, 1983.
17. Kitahara K., Nonequilibrium statistical physics,
Iwanami, 1997.
18. Funaki T. and Otobe T. Scaling limits for weakly pinned
random walks with two large deviation minimizers. J. Math.
Soc. Japan, 62 (2010), 1005—1041.
19. Naitoh K. and Shimiya H. Stochastic determinism.
Japan Journal of Industrial and Applied Mathematics,
Vol.28, No.1, pp.3-14. (2011). [(also in Proceedings of 5thInternational Conference on Computational Fluid Dynamics,(ICCFD5), 2008].
20. Naitoh K., Maeguchi H., Ryu K., Matsushita S..
Stochastic Determinism. The 60th Nat. Cong. ofTheoretical and Applied Mechanics. (2011)
21. Naitoh K. Stochastic determinism: revealing the critical
Reynolds number in pipes and fast phase transitions.
Proceeedings of ISAIF, Brussels, 2011.
22. Naitoh K, Ryu k, Matsushita S, Tanaka S, Kurihara M,
Marui M, Weakly-stochastic Navier-Stokes equation andshocktube experimenys: revealing the Reynolds’ mystery inpipe flows. AIAApaper 2012-2689, 2012.
23.Yakhot V. and Orszag S., Renormalization Group
Analysis of Turbulence. I. Basic Theory. J. of Scientific
Computing, 1, 3. (1986).
24. Drelaand M. and Giles M.B., Viscous-inviscid analysis© ISAROB 2013

0.25%
Computation (y/D=0.06)
injection point x=0

Amount of injection air

0.20%

0.15%

0.10%

0.05%

0.00%
0

5

10

15

20
25
30
Dimensionless transition point

35

40

45

50

Fig.1 Relation between adit amount and transition point,
which is computed by the present approach.

84

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Stock market dynamics derived from a cognitive bias
Moto Kamiura1* and Yu Murata1
1

Tokyo Denki University, Japan*kamiura@mail.dendai.ac.jp

Abstract: Econophysics and behavioral economics give two new directions to economics. In econophysics, Non-Gaussianityof a distribution on stock market returns and generating mechanisms of that have been researched. Behavioral economics gives
some approaches to human economic behaviors derived from cognitive biases. In these two approaches, we can findcontrastive views on markets: i.e. “collective and individual”. Connecting the two approaches, we can see a new aspect onmarket dynamics. In this study, we present a cognitive experiment which simulates human prediction of stock market returns,
which follow an intermediate distribution between a Gaussian and a Cauchy’s. Stock market dynamics may be based on not
only self-organization of collective traders but also cognitive processes of individuals. Each of individuals can generate nonGaussian distributions on the predicted prices. As the result of a cognitive experiment, we obtained distributions which aresimilar to the ones of stock market returns.
Keywords: Abducive reasoning, Overgeneralization, Distribution feature.

1 INTRODUCTION

The above facts suggest that non-Gaussianity of stock
market returns is derived from overgeneralization on timeseries of stock dynamics. A cognitive bias generating theovergeneralization is a subject of behavioral economics,
and non-Gaussianity of stock market returns is a subject of
econophysics. The two subjects have to be discussed in theview to connect them.
In this study, we present a cognitive experiment which
simulates human prediction of stock market returns, which
follow an intermediate distribution between a Gaussian anda Cauchy’s. Stock market dynamics may be based on not
only self-organization of collective traders but also
cognitive processes of individuals. This study focuses on
the latter: i.e. the ability of individuals generating nonGaussian distributions. The experiments were done by 142
volunteers, using Windows PCs and application programsfor the cognitive experiments. Each subject watches one’s
monitor displayed a graph of random walk time series,
operates one’s mouse and input a predicting value on thegraph. In the result, we obtained distributions which aresimilar to the ones of stock market returns.

Econophysics and behavioral economics give two new
directions to economics. Non-Gaussianity of a distribution
on stock market returns and generating mechanisms of thathave been researched in econophysics, which had beenpresented by Mantegna and Stanley [1]. Some important
ideas in econophysics are inspired by statistical mechanics
and complex systems science: i.e. phase transitions of amarket system and self-organization of market traders [2-4].
On the other hand, behavioral economics, which evolved
from prospect theory presented by Kahneman and Tversky[5], gives some approaches to human economic behaviors
derived from cognitive biases.
In these two approaches, we can find contrastive views
on markets: i.e. “macroscopic and microscopic” or“collective and individual”. Econophysics describes market
dynamics as collective phenomena. It focuses on interaction
structures of traders and on statistical features such as a sortof distributions of stock market returns. Behavioral
economics researches human reactions to some stock
trading situation: i.e. its research object is an individual andone’s behaviors. Connecting the two approaches, we cansee a new aspect on market dynamics.
In our previous researches [6-7], we studied relations
between abduction (or abductive reasoning, which is a sortof heuristic reasoning) and parameter estimation withovergeneralization. The estimates based on the “simulated
abduction” follow non-Gaussian distributions, which havefeatures as intermediate distributions between a Gaussian
and a Cauchy's. The intermediate feature is also in a
distribution of stock market returns.

© ISAROB 2013

2 ABDUCTIVE REASONING
AND ITS MATHEMATICAL MODEL
Conventional formal logic, which is equal to deduction,is based on transitivity of entailment. The simplest model isa syllogism: i.e. A→B and B→C imply A→C. On the other
hand, whole of human reasoning includes not only
deduction which is secure reasoning but also abduction
which is insecure and creative reasoning [8-10]. Abduction

85

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

overgeneralization, although some apparent irrationality isin it as well as in abduction.
In the present study, we have a conjecture: i.e. nonGaussianity of stock market returns would be induced fromovergeneralization for market data series. In the following
sections, we check non-Gaussianity of real market data
(Nikkei 225), and we present a cognitive experiment tostudy causes of the non-Gaissanity.

is a type of reasoning which has the following form: i.e. A→C and B→C imply A→B. Three types of reasoning,deduction, induction and abduction, are coordinated by
C.S.Peirce [8]. The three types can be represented by SawaGunji diagrams [6,7,10].

Major premise

Minor premise

3 NON-GAUSSIANITY ON STOCK DYNAMICS
A blue line of Fig.1 is a histogram of returns xt=xt－xt-

Conclusion

1 of Nikkei225 closing price xt . The histogram consists ofFig. 1. Sawa-Gunji diagram

7006 daily data from 4/January/1984 until 3/July/2012.
A Sawa-Gunji diagram (Fig.1) is a triangle with three
arrows which correspond to a minor premise, a major
premise and conclusion. Each of the three types ofreasoning is represented by an operation in which one
arrow induced from the other two arrows.
In our previous works [6-7], we show a correspondence
between abduction based on a Sawa-Gunji diagram andgeneralized parameter estimation on a numerical function:i.e. when transitivity of a syllogism is equated with that of asystem y = f (x; a), abduction in which a minor premise isinduced from a major premise and a conclusion is translated
into estimation of a parameter a. It is not constrained bysome conditions for conventional parameter estimation. The
generalized parameter estimation, which has multi-aspectaccording to constraints, can be “simulated abduction” on anumerical system.
An example of simulated abduction is incomplete AR(p)parameter estimation based on the small number of data N.
Under N → ∞ , the incomplete estimation results inconventional estimation, and the estimated parameters
follow a Gaussian distribution. The Gaussianity disappears
under N → p+1, and the distribution approaches
asymptotically to Cauchy distribution. Under p+1< N <<∞,
we obtain intermediate distribution between a Cauchy’s anda Gaussian.
Conventional parameter estimation on a system y=f(x;a)
is a kind of mathematically appropriate generalization forgiven data x and y. On the other hand, incomplete
parameter estimation like the above can be summed up as akind of overgeneralization for given data. Avoiding
overgeneralization
andpursuing“appropriate”
generalization are based on mathematical and technologicalrequirements. Human beings would have ability of© ISAROB 2013

Nikkei225 data
Nikkei225

0.01

Gaussian (μ=0, σ=145)
Cauchy (α=0, β=115)

f(x)
0.001

0.0001

50

500

x

5000

Fig. 2. Distributions of returns of Nikkei225 closing
price, a Gaussian and a Cauchy’s.
Note that Fig.2 is double logarithmic. A red line and agreen line of Fig.1 are a Gaussian distribution (=0, =145)
and a Cauchy distribution (=0, =155). The peaks of thatare fitted to the peak of the Nikkei225 histogram, which ischaracterized as intermediate distributions between a
Gaussian and a Cauchy's. This fact induces motivation of a
cognitive experiment described in the next section: i.e. does
prediction of an individual generate the intermediate
distribution?

4 COGNITIVE EXPERIMENT
Patzelt, et al. [11] show that distances between a moving
target and a mouse-driven cursor on a PC monitor follow a
power law distribution by a cognitive experiment. In thepresent study, we propose a cognitive experiment, which isinspired by the Patzelt’s experiment, simulates a process ofpseudo stock price predicting. In the experiment, each86

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013subject uses application software with the following GUI
on a PC (Fig.3).

1.00E-02

Experimantal Resultf(⊿x)
Gaussian (μ=0, σ=145)
1.00E-03

Cauchy (α=0, β=115)f(x)1.00E-04

1.00E-05
50

500

x

5000

Fig. 4. The distribution of the differences between thepredicted prices and the indicated prices.

Fig. 3. The GUI of application software for theexperiment on stock price predicting

6 CONCLUSION
The application indicates just random work time series pt+1
= pt + t where t follows a Gaussian distribution with the
S.D. =145 and the initial price is p-25 = 9839. For the
subject, we explain the time series as a daily chart on an
artificial stock market, and we instruct them to input a
predicting price of the next day via the slider of theapplication. A subject can see 20 days data of the timeseries on the application window. (S)He iterates through100 times (i.e. 100 days) to move the slider, to decide theprediction price and to push a “next” button to go to thenext day. The predicted prices { xt | 0 ≦ t ≦ 100 } do not
influence the indicated time series.

Econophysics and behavioral economics give two new
directions to economics. In these two approaches, we canfind contrastive views on markets: i.e. “macroscopic andmicroscopic” or “collective and individual”. Connecting thetwo approaches, we can see a new aspect on market
dynamics. In this study, we presented a cognitive
experiment which simulates human prediction of stock
market returns, which followed an intermediate distribution
between a Gaussian and a Cauchy’s. Stock market
dynamics may be based on not only self-organization ofcollective traders but also cognitive processes of individuals.
This study focuses on the latter: i.e. each of individuals cangenerate non-Gaussian distributions on the predicted prices.
As the result of a cognitive experiment, we obtained
distributions which are similar to the ones of stock market
returns.

5 EXPERIMENTAL RESULT
A total of 142 men and women in their twenties
participated in the cognitive experiment. Each person
iterated 100 predictions in the experiment therefore we
obtained 14200 data on the predicted prices xt and thedifference xt=xt－pt-1, where 1 ≦ t ≦ 100 and pt-1 is anindicated price as a value on the random walk time series. A
blue line in Fig.4 shows a histogram of the differencext=xt－pt-1. A red line and a green line are a Gaussian
distribution and a Cauchy distribution which are the samein Fig.2. The histogram of the difference in Fig.4 is alsocharacterized as an intermediate distribution between them.

© ISAROB 2013

REFERENCES[1]R.N.Mantegna & H.E.Stanley (1999) Introduction to
Econophysics: Correlations and Complexity in FinanceCambridge Univ. Press.
[2]V.M.Eguíluz&
M.G.Zimmermann(2000)
Transmission of Information and Herd Behavior: An
Application to Financial Markets, Vol.85, No.26, pp.56595662.
[3]D.Sornette (2002) Why Stock Markets Crash:
Critical Events in Complex Financial Systems, Princeton
Univ. Press.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[4]M.Bartolozzi, D.B.Leinweber & A.W.Thomas (2005)
Self-organized criticality and stock market dynamics: anempirical study, PhysicaA, Vol.350, pp.451–465.
[5]D.Kahneman & A.Tversky (1979) Prospect Theory:
An Analysis of Decision under Risk, Econometrica, Vol.47,
No.2, pp. 263-291.
[6]M.Kamiura (2010) Implication of Abductiton:
Complexity Without Organized Interaction. Computing
Anticipatory Systems, AIP Conf. Proc., Vol.1303, pp.60-67.
[7]M.Kamiura (2012) Abduction as Incomplete
Parameter Estimation, Triple C, Vol.9, No.2, pp.494-501.
[8]C.S.Peirce (1955) Chap.11, Philosophical Writings ofPeirce, Dover Publications.
[9]J.Finlay & A.Dix (1996) An Introduction to
Artificial Intelligence. UCL press, London.
[10]K.Sawa & Y-P.Gunji (2010) Dynamical Logic
Driven by Classified Inferences Including Abduction, AIP
Conf. Proc., Vol.1303, pp. 52-59.
[11]F.Patzelt, M.Riegel, U.Ernst & K.Pawelzik (2007)
Self-organized critical noise amplification in human closed
loop control, Frontiers in Computational Neuroscience,
Vol.1, Article 4.

© ISAROB 201388

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fusion visualization of surface and volume on AVS/Express
Hideo Miyachi1, Koji Koyamada2, and Naohisa Sakamoto,2
1

CYBERNET SYSTEMS CO., LTD., Japan
2
Kyoto University, Japan(Tel: 81-3-5297-3799, Fax: 81-3-5297-3637)
1

miyachi@cybernet.co.jp

Abstract: Due to the innovation of super computer technology, the output of simulation by using such high performancecomputers has complexity and the data size is going to be too large. That makes difficulty to visualize the output for theevaluation and the analysis. To solve the issue, we have started a project sponsored by Japan Science and Technology Agency(JST) in which we develop a new visualization system named "Fusion Visualization" on a commercial software package
AVS/Express. It will provide the volume rendering visualization with the surface rendering for conventional visualization
methods. In this paper, we will present the plan and some preliminary visualization outputs by using the prototype system.
Keywords: visualization, super computing, volume rendering
assumed that the inside of a cell is full of the light emission
particles. The density of the particles is defined by the cell’stransparency value. High transparency cell has a probabilityof small number of particles and low transparency cell has aprobability of large number of particles.

1 INTRODUCTION
A volume rendering has an advantage that is smaller
memory cost than a surface rendering. However, most of
volume rendering methods requires the sorting of objects
when the scene includes semi-transparency cells. It makes adifficulty of the parallel processing. To avoid the drawback,
we have developed a sort free volume rendering method
called Particle Base Volume Rendering (PBVR).[1] It solved
the parallel issue, but it brought about a new problem. That
was a difficulty of combining with surface rendering.
Then, we have started a project to develop new
visualization software which can apply a volume renderingand a surface rendering to a scene. In this paper, we
introduce the project plan and some outputs by the
prototype version.

Volume data
ボリュームデータ
視線（レイ）
Ray
サンプリング点
sampling point

Eye point

スクリーン
ScreenPixel
ピクセル

Fig.1 Ray casting volume rendering

2 Particle Base Volume Rendering
A volume rendering is an image generation method thatcan be applied to scalar value defined at volume cells. In atypical method: ray casting method, a ray is extended froma pixel on a screen and sampling points are generated on theray. Then, color and opacity values at each sampling point
are calculated from the scalar value by a transfer function.
Finally the color and opacity values on the pixel arecalculated by integrating values along the ray (Fig.1).
Within this method, the integration has to beaccumulated from the back to the front if one or more semitransparency volume cell exist. It brings about theperformance down especially to unstructured grid data.
To overcome the drawback, we have proposed a newmodel to volume rendering shown in Fig.2. In our model, it© ISAROB 2013

Fig.2 Light emission particles model
Particles generation and volume rendering are
performed repeatedly. It is known that the ensemble

89The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

figure includes a teapot and a hydrogen molecule. The
teapot is shown by a surface rendering and the hydrogen isshown by a volume rendering.

average of all images is approaching to the correct image.
We called this method PBVR. This method does not require
the sorting of cells, but requires much computer cost in
proportion to the number of rendering. However, thenumber is controllable. Users can select the number of
repeat according to the image quality needed.

3 Integrating PBVR to conventional surface renderingThere are two ways to integrate volume rendering tosurface rendering. The first is a method of carrying out avolume rendering after a surface rendering. The color and Z
value which the surface rendering generated are used as an
initial value of volume rendering. The second isvoxelization. A volume rendering is carried out afterconverting surfaces to volume. Most of cases are using thefirst way.
Figure 3 shows an integration result of surface andvolume rendering by a commercial software package:
AVS/Express[2].

Fig.4 Ambiguous depth judging
As far as seeing this picture, it seems that the teapot islocated in front of the molecule. It is misunderstanding
which comes from the ambiguity of a depth judging.

4 Our goal and the implementation plan
We are going to implement a fusion visualization inwhich PBVR is integrated to conventional surface
rendering. The goal of our project is to make a base of asustainable product. Therefore, we are planning to
implement the new visualization method on AVS/Express
which has been maintained by CYBERNET SYSTEMS
CO., LTD.(Tokyo, Japan) and .Advanced Visual Systems
Inc.(MA, USA). The user interface is shown in Fig.5.

Fig.3 Integration of surface and volume rendering
The volume data in Fig.3 includes a lobster CT-scan
image. A volume rendering visualizes the shape of thelobster and a surface rendering visualizes the density profile
on a cross section and the external edges of the volume data.
It may seem perfect, but it is not guaranteed the correct
rendering when the visualization includes semitransparency surface and volume.
Figure 4 is a case that the conventional method makes
an ambiguous visualization about depth judging. This

© ISAROB 2013

Fig. 5 User interface of AVS/Express

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The visualized data is the same as Fig.4. The
conventional method could not visualize it with the correct
depth judging. On the other hand, PBVR can visualize itcorrectly. The teapot and hydrogen are located in the samearea.

AVS/Express is designed by object orientation.
Rectangle icons in Fig.5 represent modules. Lines between
icons represent data flow. A set of the modules and the lines
is called network that means a visualization program. For
example, figure 5 illustrates the program to visualize Fig.3.
Read Field loads the lobster volume data from a disk. And
bounds, orthoslice and volume render visualize the externaledge, the cross section profile and the lobster volume
rendering respectively. Uviewer3D gives us mouseinteraction to rotate, scale or translate the object. We plan toimplement our fusion visualization as modules in it.
Within the first year (Oct 1st 2012 - Mar 31st 2013), we
will develop the prototype version. It works on
AVS/Express and visualize surface and volume data by
OpenGL surface rendering and PBVR respectively. In thesecond year (Apr 1st 2013 – Mar 31st 2014), we will
evaluate the prototype. We are looking for the evaluation
partners who need large scale visualization or complex
visual data mining. The points of the evaluation are as
follows.
(1)
Ease of use of a user interface(2)
Performance and efficient memory use(3)
Image quality of fusion visualization
In the final year (Apr 1st 2014 – Mar 31st 2015), we
will enhance the software to respond the comments from
evaluators.

5.2 Visualization of ocean data
Another trial result is shown in Fig.7. It shows aoceanographic data set simulated by the MRI Multivariate
Ocean Variational Estimation (MOVE) System developed
by Japan Meteorological
Agency (JMA)
andMeteorological Research Institute (MRI).

streamlines

temperature profile

Korea

Japan
Pacific ocean

(a)

(b)

5 Preliminary results by early prototype
5.1 Correct depth judging
Figure 6 shows the visualization result by our early
prototype.

(c)
Fig.7 A fusion visualization trial result(a) Volume rendering (b) Streamlines(c) Fusion visualization
This picture Fig.7 (c) includes both surface renderingand volume rendering. The streamlines representing the

Fig. 6 Correct dept judging by our early prototype© ISAROB 201391

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013velocity profile (Fig.7 (b)) are shown by surface renderingand the temperature profile (Fig.7 (a)) is shown by volume
rendering. The transfer function should be more optimized
for the picture, but the early prototype does not haveinteractive interface to adjust it. That point will be one of
our future works.

4 CONCLUSION
We have proposed a fusion visualization of surface andvolume rendering for large scale visualization. We havedeveloped an early prototype and showed some trial resultsby it. We will finish the first version for evaluation until theend of March 2013 and evaluate it in 2014 with voluntaryevaluators. In the spring of 2015, the fusion visualization
system will work with commercial software: AVS/Expresswith easy-to-use interface.

Acknowledgment
This work was partially supported by a project ASTEP<AS2415031H>: “The research and development of
Fusion Visualization technology“, of Japan Science andTechnology Agency (JST). We would like to thank Dr.
Hiromichi Igarashi from Japan Agency for Marine-Earth
Science and Technology (JAMSTEC) for giving us theoceanographic datasets and Mr. Takashi Uenaka forsupporting to our visualization.

REFERENCES[1] Naohisa Sakamoto, Takuma Kawamura, Koji
Koyamada,, ” Improvement of particle-based volume
rendering for visualizing irregular volume data sets”,
Computers & Graphics, Vol.34, No.1,2010, pp.34-42, 2010
[2] C. Upson, T. Faulhaber, D. Kammins, D. Laidlaw, D.
Schlegert, J. Vroom, R. Gurwitz and A. Van Dam. ： The
Application Visualization System: a computational environment
for scientific visualization, IEEE Computer Graphics andApplictaions, Vol. 9, No. 7, July. pp. 30-42., 1989

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Autonomous polymer actuators
Yusuke Hara1
1

Nanosystem Research Institute, NRI, National Institute of Advanced Science and Technology, AIST,
Central 5-2, 1-1-1 Higashi, Tsukuba 305-8565, Japan1(Tel.: +81-29-861-9318; Fax: +81-29-81-6236)
1
y-hara@aist.go.jp

Abstract: In this paper, in order to design the autonomous polymer actuators for soft robots and microfluidic devices, theeffect of the molecular structure of the polymer chain on the self-oscillating behavior and the oscillation period wasinvestigated by utilizing two-types of polymer chains; one is consist of N-isopropyl acrylamid (NIPAAm) and Ru catalyst of
the BZ reaction, the other has NIPAAm and Ru catalyst, Acrylamide-2-methylpropanesulfonic acid (AMPS) with anioniccharge as a solubility control site. As a result of the measuring the transmittance self-oscillation, it is clarified that the selfoscillating behavior is much affected by the molecular structure of the polymer chain. Moreover, this paper demonstrated theeffect of the initial concentration of the three substrates of the Belousov-Zhabotinsky (BZ) reaction on the period of theaggregation-disaggregation self-oscillation.
Keywords: BZ reaction, molecular robot, soft actuator, oscillation period, self-oscillating polymer chain
In this paper, in order to design the autonomous polymer
actuators for soft robots and microfluidic devices, the effectof the molecular structure of the polymer chains on the selfoscillating behavior and the oscillation period wasinvestigated by utilizing two-types of polymer chains; oneis consist of N-isopropyl acrylamid (NIPAAm) and Ru
catalyst of the BZ reaction, the other has NIPAAm and Ru
catalyst,
Acrylamide-2-methylpropanesulfonicacid(AMPS) with anionic charge as a solubility control site.
As a result of the measuring the transmittance selfoscillation, it is clarified that the self-oscillating behavior ismuch affected by the molecular structure of the polymer
chain. Moreover, it is demonstrated the effect of the initialconcentration of the three substrates of the BZ reaction on
the period of the aggregation-disaggregation self-oscillation.

1 INTRODUCTION
The self-oscillating polymer system can cause theaggregation-disaggregation self-oscillation synchronizedwith the periodically solubility change of the metal catalyst
moiety in the polymer. [1-3] The cyclic solubility changeof the metal catalyst is induced by the BelousovZhabotinsky (BZ) reaction. The BZ reaction is wellknown as an oscillating reaction accompanying
spontaneous redox oscillations. [4-6] The overall process
is the oxidization of an organic substrate by an oxidizing
agent in the presence of the catalyst under acidic conditions.
The solubility of the Ru catalyst changes with the oxidant
state. In order to convert the cyclic solubility change of
the Ru catalyst into the aggregation-disaggregation selfoscillation,thepoly(Nisopropylacrylamide)(poly(NIPAAm)) covalently bonded to the Ru catalys was
synthesized. As a result, the polymer chain caused theaggregation-disaggregation self-oscillation under theconstant temperature conditions with the coexistence of theBZ substrates other than the metal catalyst. Therefore, thedriving environment of the polymer chain was strongly
restricted in the BZ reaction field. In my previous studies,in order to modify the driving environment, the molecular
structure of the self-oscillating polymer chain was modified.
As a result, the novel self-oscillating polymer chain cancause the aggregation-disaggregation self-oscillation onlyin the presence of malonic acid. [7] That is because thenovel self-oscillating polymer chain has both a pH control
and an oxidant supply sites in the self-oscillating polymer
chain. In the process of this study, the self-oscillating
polymer system with a negatively charged acrylamide-2methylpropanesulfonic acid (AMPS) has a lot of potentials
to control the self-oscillating behavior. [8-12] By utilizing
this potential, the self-oscillating polymer system can cause
an on-off switching of the self-oscillation by controlling theexternal temperature and a viscosity self-oscillation underacid-free condition.

© ISAROB 2013

2 EXPERIMENTAL SECTION
Polymerization of poly(NIPAAm-co-Ru(bpy)3)
poly(NIPAAm-co-Ru(bpy)3) (Figure 1) was synthesizedby utilizing N-isopropyl acrylamid (NIPAAm) andRu(bpy)3 monomer (ruthenium (4-vinyl-4’-methyl-2,2bipyridine)bis(2,2’bipyridine)bis(hexafluorophosphate))and 2,2’-azobisisobutyronitrile (AIBN) as an initiator byradical polymerization in an ethanol solution under a total
monomer concentration of 20 wt% at 60°C. The feed
composition (wt%) was as follows; NIPAAm : Ru(bpy)3 =10 : 90. The resulting reaction mixture was dialyzed
against water for 4 days followed by ethanol for 3 days, andthen freeze-dried.
Polymerizationofpoly(NIPAAm-co-Ru(bpy)3-coAMPS)
Using NIPAAm, Acrylamide-2-methylpropanesulfonicacid (AMPS), Ru(bpy)3 monomer and AIBN,
poly(NIPAAm-co-Ru(bpy)3-co-AMPS) (Figure 2) was
synthesized by radical polymerization in a mixture ofethanol and water (1:1 wt/wt%) under a total monomer

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

concentration of 20 wt% at 60 °C. The feed composition(wt%) was as follows; NIPAAm : Ru(bpy)3 : AMPS =40 :10 : 50. The resulting reaction mixture was dialyzed
against water for 4 days followed by ethanol for 3 days, andthen freeze-dried.

Figure 1. Chemical structure of poly(NIPAAm-coRu(bpy)3)

Figure 2. Chemical structure of poly(NIPAAm-coRu(bpy)3-co-AMPS).
Measurement of transmittance self-oscillations.
The self-oscillating polymer solutions were prepared bydissolving the polymer (0.5 wt%) into an aqueous solution
containing the three BZ substrates (nitric acid (HNO3),
sodium bromate (NaBrO3) and malonic acid (MA)). The
transmittance self-oscillations of the polymer solutions
were measured under constant temperature (18 °C) andstirring condition. In order to detect the transmittancechange which is based on the autonomous aggregationdisaggregation change, 570-nm wavelength was used.
This is because 570-nm wavelength is the isosbestic pointof the reduced and oxidized states of Ru(bpy)3. [3, 7-9]
The time course of the transmittance at 570 nm wasmonitored by a spectrophotometer.

3 RESULT AND DISCUSSION
Figure 3 shows self-oscillating behaviors of the poly
(NIPAAm-co-Ru(bpy) 3 -co-AMPS) (Figure 3(A) and 3(B)) and the poly(NIPAAm-co-Ru(bpy)3) (Figure 3(C) and 3(D)) solutions in the different concentrations of sodium bromate ([NaBrO3] = 0.4 and 0.5 M) at 18 °C under the fixed concentration of malonic acid ( [MA] =0.1M ) and nitric acid ( [HNO3] = 0.3 M).
As shown in Figure 3(A) and 3(B), the amplitude of the tran
smittance self-oscillation for the AMPS-containing poly
mer solution is significantly stable. In contrast, the amplitude of the self-oscillation for the poly(NIPAAm-co-Ru(bpy)3) is not constant.

© ISAROB 2013

Figure 3. Oscillating profiles of transmittance at 18 °C
for 0.5 wt% poly(NIPAAm-co-Ru(bpy)3-co-AMPS) ((A)
and (B)) and poly(NIPAAm-co-Ru(bpy)3) ((C) and (D))
solutions in the fixed nitric acid and malonic acidconditions ([HNO3] = 0.3 M and [MA] = 0.1 M ): (A)[NaBrO3] = 0.4 M, (B) [NaBrO3] = 0.5 M, (C) [NaBrO3] =0.4 M, (D) [NaBrO3] = 0.5 M.

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That is because the self-oscillating polymer chain wit
hout the AMPS moiety is easy to aggregate in the pol
ymer solution because the solution containing three BZ
substrates has the high ionic strength. On the other
hand, the AMPS-containing polymer solution with the low polymer concentration is hard to aggregate due tothe repulsive force of the AMPS moiety in the polyme
r chain. In my previous studies, in the polymer concen
tration above 1 wt%, the poly(NIPAAm-co-Ru(bpy)3-coAMPS) chain is easy to aggregate due to the electrost
atic attractive force among the polymer chains. [12-13]
This is because the polymer chain has the cationicRu(bpy)3 and the anionic AMPS moieties in the polym
er chain. As shown in Figure 3(C) and 3(D), the am
plitude of the self-oscillation gradually increased with t
ime.

This behavior indicates that the aggregated polymer chains gradually disaggregated in the self-oscillating behavior.
Figure 4 and 5 showed the logarithmic plots of theperiod against the initial concentration of one substrateunder fixed the other two BZ substrates at the consta
nt temperature (T = 18 °C). As shown in Figure 4 a
nd 5, all the logarithmic plots had a good linear relati
onship. Therefore, the period [T(s)] of the transmittan
ce self-oscillation can be expressed as a[substrate]b wh
ere a and b are the experimental constants and brackets assign the initial concentration. As shown in Figure
4 and 5, there are no saturation points in this experi
mental condition. In the case of the nonthermorespons
ive polymer chain, the period of the self-oscillation have the saturation point at the [HNO3] = 0.3 M. [14]Figure 4. Logarithmic plots of period T (in s) for 0.5
wt% poly(NIPAAm-co-Ru(bpy)3-co-AMPS) solution vs i
nitial molar concentration of one BZ substrate at a con
stant temperature (T = 18 °C) under fixed concentratio
ns of the other two BZ substrates: (A) [NaBrO3] = 0.3
M and [HNO3] = 0.3 M; (B) [MA] = 0.1 M and [HNO3] = 0.3 M; (C) [MA] = 0.1 M and [NaBrO3] = 0.
3 M.

Figure 5. Logarithmic plots of period T (in s) of 0.5
wt% poly(NIPAAm-co-Ru(bpy)3) solution vs initial mol
ar concentration of one BZ substrate at a constant tem
perature (T = 18 °C) under fixed concentrations of theother two BZ substrates: (A) [NaBrO3] = 0.3 M and[HNO3] = 0.3 M; (B) [MA] = 0.1 M and [HNO3] =0.3 M; (C) [MA] = 0.1 M and [NaBrO3] = 0.3 M.

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As shown in Figure 4(C) and 5(C), the, the b value of thepoly(NIPAAm-co-Ru(bpy)3) is much larger than that ofpoly(NIPAAm-co-Ru(bpy)3-co-AMPS). In the case of thepoly(NIPAAm-co-Ru(bpy)3-co-AMPS) solution (Figure
4(C)), the aggregation-disaggregation self-oscillation do notoccur in the condition under the [HNO3] = 0.1 M.
Moreover, in the case of the poly(NIPAAm-co-Ru(bpy)3co-AMPS) solution (Figure 4(B)), there are no aggregationdisaggregation self-oscillation in the condition under the[NaBrO3] = 0.1 M. These results indicate that theconcentration range of the three BZ substrates for the
AMPS-containing polymer solution where the selfoscillation occur is much narrower than that for thepoly(NIPAAm-co-Ru(bpy)3) solution. In addition, as shownin Figure 4(C) and 5(C), the period has the different aspect
as compared to the conventional-type poly(NIPAAm-coRu(bpy)3) gel [15]. In the case of the poly(NIPAAm-coRu(bpy)3) gel, when increasing in the concentration of
nitric acid, the period increased. However, in general, theperiod of the self-oscillation decreased with increasing theinitial concentration of the BZ substrates because of theincrease in the collision frequency among the BZ substrates.
Therefore, I consider that the tendency of the Figure 4(C)and 5(C) is more natural tendency

REFERENCES[1] Ishiwatari T, Kawaguchi M, Mitsuishi, M (1984),
Oscillatry reactions in polymer systems, J. Polym. Sci. Part.
A: Polm. Chem, 22, pp. 2699-2704.
[2] Yoshida R, Takahashi T, Yamaguchi T, Ichijo H(1996), Self-oscillating gel, J. Am. Chem. Soc. 1996, 118,
pp. 5134-5135.
[3] Yoshida R, Sakai T, Ito S, Yamaguchi T (1996), Selfoscillation of polymer chains with rhythmical solubleinsoluble changes, J. Am. Chem. Sci. 124, pp. 8095-8098.
[4] Zaikin A.N, Zhabotinsky A.M (1970), Concentration
Wave propagation in two-dimensional liquid-phase selfoscillating system, Nature, 225, pp.535–537.
[5] Field, R.J, Burger M (1985),Oscillations andTraveling Waves in Chemical Systems; John Wiley &Sons: New York, NY, USA, 1985.
[6] Field, R.J, Noyes R.M (1974), Oscillations inchemical systems. IV. Limit cycle behavior in a model of areal chemical reaction. J. Chem. Phys., 60, pp.1877–1884.
[7] Hara Y, Yoshida R (2008), Self-oscillating polymer
fueled by organic acid. J. Phys. Chem. B, 112, pp.8427–
8429.
[8] Hara Y, Yoshida R (2005), Self-oscillation of polymer
chains induced by the Belousov-Zhabotinsky reaction underacid-free conditions. J. Phys. Chem. B, 109, pp. 9451–9454.
[9] Hara Y, Yoshida R (2009), Damping behavior of
aggregation-disaggregation self-oscillation for a polymer
chain, Marcomol. Rapid Commun. 30, pp.1656–1662.
[10] Hara Y, Yoshida R (2005), Control of oscillating
behavior for the self-oscillating polymer with pH-control
site. Langmuir, 21, pp. 9773–9776.
[11] Hara Y, Yoshida R (2008), A viscosity selfoscillation of polymer solution induced by the BZ reaction
under acid-free condition. J. Chem. Phys. 128, 224904.
[12] Hara Y, Jahan R.A (2011), Influence of temperatureand the BZ substrate on aggregation-disaggregation selfoscillation of a polymer chain, Key Engineering Materials,
467-469, pp.1472-1477.
[13] Hara Y, Jahan R.A (2011), Self-oscillating behaviorsof negatively charged polymer chain induced by theBelousov-Zhabotinsky reaction, Advanced Materials
Research, 181-182, pp.206-211.
[14] Hara Y, Jahan R.A (2011), Influence of initialsubstrate concentration of the Belouzov-Zhabotinsky
reaction on transmittance self-oscillation for anonthermoresponsive polymer chain, Polymers, 3, pp.330–
339.
[15] Yoshida R, Onodera S, Yamaguchi T, Kokufuta E(1999), Aspects of the Belousov-Zhabotinsky reaction inpolymer gels, J. Phys. Chem. A, 103, pp.8573-8578.

4 CONCLUSION
In this study, in order to design the autonomous polymer
actuators for soft robots and microfluidic devices, the effectof the molecular structure of the self-oscillating polymer
chain on the self-oscillating behavior and the oscillation
period was investigated by utilizing the poly(NIPAAm-coRu(bpy)3-co-AMPS) and the poly(NIPAAm-co-Ru(bpy)3)
solutions.
As a result, the amplitude of thepoly(NIPAAm-co-Ru(bpy)3) is not stable due to theaggregation property of the polymer chains. On the other
hand, the amplitude of the poly(NIPAAm-co-Ru(bpy)3-coAMPS) is significantly stable. This result indicates that
the AMPS-containing polymer chain is hard to aggregate
originating in the repulsive force of the AMPS moiety inthe polymer chain in the low polymer concentration. In
addition, this paper demonstrated the period of the twotypes of self-oscillating polymer solutions.
The
concentration range of three BZ substrates for thepoly(NIPAAm-co-Ru(bpy)3) solution where the selfoscillation occur is much wider than that for thepoly(NIPAAm-co-Ru(bpy)3-co-AMPS) solution.

5 ACKNOWLEDGMENT
This work is supported also by Grants-in-Aid(KAKENHI) (No. 24656178) for Challenging Exploratory
Research. In addition, this work was carried out under the
auspices of the New Energy and Industrial Technology
Development Organization (NEDO) of Japan under the
Industrial Technology Research Grant Program in 2011.
Moreover, this work was supported by a Grant-in-Aid for
Scientific Research on Innovative Areas "Molecular
Robotics" (No. 24104003) of The Ministry of Education,
Culture, Sports, Science, and Technology, Japan.

© ISAROB 2013

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Cyanobacterial circadian clock is nullified under low temperature
via Hopf bifurcation
Yoriko Murayama1, Hiroshi Kori2, Takao Kondo1, Hideo Iwasaki1, Hiroshi Ito4
1

Graduate School of Sciences and Engineering, Waseda University, Japan
2
Division of Advanced Sciences, Ochanomizu University, Japan
3
Division of Biological Science, Graduate School of Science, Nagoya University, Japan
4
Department of Design, Kyushu University, Japan(Tel: 81-92-553-4535)
4

hito@design.kyushu-u.ac.jp

Abstract: One of the key characteristics of all circadian rhythms is that the free-running period remains stable under arelatively broad range of ambient temperatures, referred to as “temperature compensation” of the period. Outside of the rangeof temperature compensation, circadian clocks stop running and are arrested at a certain phase. Based on bifurcation theory,
Hopf bifurcation and saddle-node bifurcation are plausible scenarios of circadian arrhythmia at low temperature. We focusedon a biochemical circadian oscillation, KaiC phosphorylation rhythms, which can be reconstituted in a test tube by mixing thethree protines , KaiA, KaiB, and KaiC in the presence of ATP. The KaiC phosphorylation rhythm in vitro is the simplest
circadian oscillation to observe directly and precisely dynamics of circadian oscillator. We found that the phenomena ofnullification of KaiC phosphorylation rhythm by low temperature was explained by theory of Hopf bifurcation.
Keywords: circadian rhythms, cyanobacteria, Hopf bifurcation
oscillation can be reconstituted in a test tube using only
three clock proteins, KaiA, KaiB, and KaiC [4]. The KaiC
phosphorylation rhythm in vitro (hereafter referred to as“ in vitro clock ” ) satisfies the criteria of circadianrhythms [4,5] and disappears below a certain criticaltemperature (around 20 ºC: [6]).

1 INTRODUCTION
Biological rhythms are the physiological oscillation inliving things. The oscillation with a 24 hours period iscalled circadian rhythms in biology and extensively studied.
Awake-sleep cycle, nyctinasty movement of leaves, andperiodical gene expressions are well examined in thisresearch area.
One of the shared characteristics among all circadianrhythms is that the free-running period remains stable for a
relatively broad range of ambient temperatures, referred toas “temperature compensation” of the period. Temperature
at which temperature compensation is effective typicallylies well within the physiological range, that is, the range
permissive for growth. Interestingly, outside of the range oftemperature compensation, circadian clocks stop runningand are arrested at a certain phase [1, 2]. Althoughessentially identical results have been found in variousorganisms, it remains unclear if temperature stimuli affectthe circadian clock directly or indirectly through such as
metabolic changes.
In this presentation, we will focus on why circadianrhythms cannot be observed at low temperature conditions.
Cyanobacterium, Synechococcus elongatus PCC 7942 is thesimplest organism that exhibit circadian rhythms. The
cyanobacterial circadian timing requires neither de novotranscription nor translation [3], and the post-translational© ISAROB 2013

Fig.1 Two types of bifurcation for arrhythmia

2 BIFURCATION FOR ARRHYTHMIA
There are two typical scenarios for nullification of rhythmic
phenomena according to bifurcation theory [7] (Fig.1, 2).
One is called Hopf bifurcation and the other is called
Saddle-node bifurcation on circle. The behavior of selfsustained oscillators (limit-cycle oscillators) depends on the97

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0.8

type of bifurcations. In the case of Hopf bifurcation, theamplitude of the oscillations can be altered and then theamplitude monotonically approach to 0 around bifurcation
point. Below bifurcation point, self-sustained oscillator can
not sustain its amplitude and is changed into a damping
oscillator.
On the other hand, the amplitude does not change
significantly in the case of Saddle-node bifurcation on
circle. When a control parameter is altered, the period canbe lengthened because the phase progress at a certain phasebecomeｓ lower. The period of the oscillation approaches
to infinity around the bifurcation point. Below critical point,
stable and unstable fixed points are created on the circle.
Cell cycle is known as the famous example of the
scenario of Saddle-node bifurcation on circle[8]. By
inducing gene expressions involved in cell cycle (e.g.
Cyclin), cell cycle can be arrested at a certain stable fixed
point on circle. The fixed point is called “check point” inthe research area of cell biology.

0.6
0.4
0.2
0.6
0.4
0.2

Period (h)Amplitude

24.8 ºC0
0.6

Ratio of P-KaiC

0.4
0.2
0

20.5 ºC

0.6
0.4
0.2
0

19.5 ºC

0.4
0.2
018.6 ºC

0.6
0.4
0Saddle-node
Bifurcation

17.0 ºC

0.6
0.4
0.2
0

24

14 ºC
24

48

72

96

120

Incubation time (h)
24

Tc

0.2
Tc

Tc

0.6

0.2

Hopf
Bifurcation

29.9 ºC

048

Tc

Amplitude

Fig.2 Amplitude and Period for Hopf and Saddle-nodebifurcations
To distinguish the type of bifurcations of arrhythmia ofin vitro clock, we prepared 15 samples mixing the three Kai
proteins with ATP and incubated at various ambient
temperatures. From 30 ºC to the critical temperature (ca.
19ºC), the amplitude of in vitro clock declined
monotonically but its period remained relatively constant(Fig.3). This result indicates the rhythms can be nullified at
low temperature conditions via Hopf bifurcation.

0.1

0

24

15

20

25

30

Period (h)7HPSHUDWXUH Û&

0

Temperature (ºC)

Fig3. Rhythms of in vitro clock at low temperature

3 DAMPING OSCILLATIONS BELOWCRITICAL TEMPERATURE
As stated at the previous section, the damping
oscillation should be observed below critical point. We
chilled samples at 4 ºC and exposed 30ºC pulse to them for
12 hours. Then we transferred the samples to below criticaltemperatures (10ºC – 19 ºC). The damping oscillator was
kicked by high temperature pulse and the rapid damping
rhythms were successively observed (Fig.3). Especially in© ISAROB 201398

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

the case of the lowest conditions, we can not observe
damping oscillation. Instead, no overshooting relaxation(over-damping) was observed. This result also suggests thatcircadian rhythms are abolished by low temperature
through Hopf bifurcation.
Û&Resonance
0.05
0.04

Amplitude0

as a biological clock.

24 36
156 (h)Û& Respective Temperature
0.2

0.03
0.02

Ratio of P-KaiC

0.01

0.4
0
0

18.8 ºC

0.2

24

36

48

Fig. 5 Resonance of in vitro clock
0.4
0.2
36

Overdamping
10 ºC
60

84

5 A SIMPLE MODEAL FOR RESONANCE

108 132 156

To mathematically interpret the resonance phenomena,
we performed a simple simulation. Consider Stuart-Landau
oscillator,𝑧 = 𝑎 + 𝑖𝜔 − 𝑧 ! 𝑧
which is the generalized model near Hopf bifurcation point.
𝑎 is a parameter specifying the distance from Hopf
bifurcation and 𝜔 is the natural frequency (here, 𝜔  wasfixed to 3). This model exhibits self- sustained oscillations
if 𝑎 > 0 and settles down at fixed point if 𝑎 ≤ 0. We
chose parameter sets which can reproduce in vitro
oscillation at 16 ºC and 18 ºC.
Under 16 ºC and 18ºC, in vitro clock exhibits damping
oscillations. We hypothesized that the position ofequilibrium points depends on the ambient temperature.
Then, changing the ambient temperature corresponds toshifting the equilibrium point.

Incubation Time (h)Fig.4 Damping oscillation of in vitro clock

4 RESONANCE BELOW CRITICALTEMPERATURE
Under the critical point of Hopf bifurcation, the limit
cycle oscillator can be changed into damping oscillator.
Linear oscillator or damping oscillator under the external
periodic environment can show resonance phenomena.
When the damping oscillators is exposed to oscillatory
external forces with the period of the natural frequency ofthe oscillator, the amplitude of the oscillator can growsignificantly. In that sense, there is optimal frequency inexternal force, which can grow the amplitude of theoscillators.
We tested if there is optimal frequency of external force
for the damping oscillation of in vitro clock below criticaltemperature. We observed in vitro clock under ambient
temperature cycles of 16.7ºC / 18.7 ºC with a various period(6 hours – 36 hours). Both 16.7ºC and 18.7 ºC are below
critical temperature.
We observed the forced oscillation with large amplitude
when the period of external forces was 26 hours (Fig. 5).
The observed resonant behavior might have a physiologicalmeaning. Cyanobacteria populate most sea or lakes evennear the north pole. The water temperature of cold area
should be kept below critical temperature (19 ºC) for all day.
However, diurnal change of water temperature might act asa periodical external force and grow the amplitude of KaiC
phosphorylation rhythms in a cell. Thus, even under criticaltemperature, KaiC phosphorylation rhythms might function© ISAROB 201312

Period of temperature cycle T (h)Fig. 6 Resonance of SL oscillator
The simulation can successively reproduce theresonance when the period of switching the equilibrium
point (denoted as T) equals to the free-running period of theoscillator (Fig. 6). The periodic orbit on the phase planesurrounded the both equilibrium points. When muchsmaller T was chosen, the system settled down to a mid
point between the two equilibrium points.

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6 CONCLUSIONS
We have showed KaiC phosphorylation circadian rhythms
in a test tube can be nullified via Hopf bifurcation. As
bifurcation theory predicted, we succeeded in observing
damping oscillation of the in vitro clock. We also
succeeded in observing resonance of KaiC phosphorylation
rhythms under low temperature cycles.

REFERENCES[1] Pittendrigh CS (1975), Circadian Clocks: What are
They? In: Hastings JW, Schweiger H-G, editors. The
Molecular Basis of Circadian Rhythms. Berlin: Abakon
Verlagsgesellschaft. pp. 11–48.
[2] Kondo T (1980), Phase progress under low
temperature treatment of the potassium uptake rhythm in a
duckweed, Lemna gibba G3. Plant and Cell Physiology. 21
95-103.
[3] Tomita J, Nakajima M, Kondo T, Iwasaki H (2005),
No transcription-translation feedback in circadian rhythmof KaiC phosphorylation. Science 307, 251–254.
[4] Nakajima M, Imai K, Ito H, Nishiwaki T, Murayama
Y, Iwasaki H, Oyama T, Kondo T (2005), Reconstitution of
circadianoscillationofcyanobacterial
KaiC
phosphorylation in vitro. Science 308, 414-5.
[5] Yoshida T, Murayama Y, Ito H, Kageyama H, Kondo
T (2009), Nonparametric entrainment of the in vitro
circadian phosphorylation rhythm of cyanobacterial KaiC
by temperature cycle. Proceedings of the National
Academy of Sciences USA. 106, 1648-53.
[6] Kitayama Y, Nishiwaki T, Terauchi K, Kondo T(2008), Dual KaiC-based oscillations constitute thecircadian system of cyanobacteria. Genes Development 22,
1513-21.
[7] SH Strogatz (1994), Nonlinear Dynamics and Chaos,
Addison-Wesley, New York
[8] Borisuk M & Tyson J (1998), Bifurcation analysis ofa model of mitotic control in frog eggs. Journal of
Theoretical Biology 195, 69–85.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Development of Entertainment Based Learning Featuresin Programming Learning support System
Eiji Nunohiro, Masanori Ohshiro, and Takashi Yamaguchi
Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba 265-8501, Japan(Tel: 81-43-236-1292; Fax : 81-43-236-1292)
nunohiro@rsch.tuis.ac.jp
Abstract: In this research, we developed a self learning programming support system called CAPTAIN (Computer Aided
Programming Training And Instruction) that stimulates students’ interest and motivation of their study continuously. When
students use CAPTAIN, they feel that they are just like playing their computer games. In order to identify advantage anddisadvantage or to investigate how to improve CAPTAIN, we are now using CAPTAIN for the tutorials of a Java programmingcourse. We developed an improved version of CAPTAIN, a learning programming support system which has the feature ofentertainment. In this paper, we describe the overview of the proposed learning programming support system CAPTAIN3.4
which contains entertainment features, such as interactively showing animation and inciting the students to compete like theirgames. The proposed system has been applied to an actual university programming course and we evaluate the effect of thissystem by comparison between the students’ progress with this system and that without using this system. We then discusspossible future works based on the evaluation.
Keywords: E-learning, Programming, Entertainment Based learning.
In this research, we developed a self learning
programming support system called CAPTAIN (Computer
Aided Programming Training And Instruction) that
stimulates students’ interest and motivation of their study
continuously. When students use CAPTAIN, they feel thatthey are just like playing their computer games. In order to
identify advantage and disadvantage or to investigate how
to improve CAPTAIN, we are now using CAPTAIN for thetutorials of a Java programming course.
In this paper, we describe the overview of the proposed
learning programming support system CAPTAIN ver.3.4(CAPTAIN3.4) which contains entertainment features, suchas interactively showing animation and inciting the students
to compete like their games.
The proposed CAPTAIN3.4 has been applied to an
actual university programming course and we evaluate theeffect of this system, especially the function of competitive
learning function using nickname which is a new functionof CAPTAIN3.4 by comparison between the students’progress with this system and that without using this system.
We then discuss possible future works based on theevaluation.

1 Introduction
It is said that the interests of students taking computer
or information sciences courses in educational institution
are expanding various directions. Their interests do not only
include computer science but also Web design, game, multimedia and so on. In order to teach those students effectively
in a same class, those students in the tutorial classes ofprogramming are classified into small groups depending
upon their initial skill, experience and ability and teachthose tutorial groups separately. However the restriction ofstaff resource, it is often happened that the size of thosetutorial groups is between 40 and 50 which is not ideal to
give a high quality support to every one of the member ofthe students. In conclusion, slow learning students could
not achieve their targets, whereas advanced students finish
quicker, so get bored. In either case, the motivation of thosestudents will be lost. It is therefore required a learning
support system that stimulates every students’ interest andthe motivation of their studies.
For past research on learning programming systems,
one of the methods, suggested, provides spaces in a source
program then students fill program code in the spaces by
instructions provided [1]. For the research on motivational
learning methods, there is a method based on ARCS model[2] [3]. However, we could not find a teaching system,
applied by ARCS model in the literature, which
continuously stimulates students’ interest and motivation.

© ISAROB 2013

2 Purpose of the programming learning support
systemFrom past experience, the low motivation of learners
who have difficulty in programming is strongly related tostumbling in the early stages of programming training. We
considered that a game-based programming learning

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standpoint, this function breaks up a program into puzzle
pieces, and the user must reconstruct the program by
selecting the correct program puzzle pieces in the correct
order. Figure 2 shows a screenshot of the puzzle interface
during programming exercise.

environment that focuses on program structure recognition
and training would stimulate and sustain the learning
motivation as well as improve programming skill. From this,
we proposed a programming training system with gamebased learning features to stimulate and sustain motivation
for beginners and slow learners in programming. We aimed
at developing a learning environment that can expect thefollowing learning effects.

Table 1 main function of CAPTAIN3.4(a) To introduce programming training through a gaming
experience in order to stimulate interest in programming.
(b) To improve understanding of programming structuresand control flow through a gaming experience, insteadof focusing on the language syntax or particularalgorithms.

No1

main function
Nickname function

2

Puzzle styleprogram exercise
function

3

Animation display
function

4

Learning history
display function

5

Learning progress
monitoring function

3 Feature of the programming learning support
system
We have already reported [4]-[9] about CAPTAIN, apuzzle-based programming training system withmotivational learning methods, in which learners create
programs similarly to solving a puzzle game. In thisresearch, we developed learning programming support
system which contains entertainment features andimplemented these functions in CAPTAIN3.4, based on theprevious version of CAPTAIN. The main features of thissystem are shown in Table 1 and the content of eachfunction is described below.

contents
This function monitors the statusesof all students' progresses andevaluates
theirdegreesofachievement and ranks them using
nickname in real time andcontinuously.
This function automatically createspuzzle pieces from the problem
source code according to theprogress and comprehension levelof the learner, and offers the puzzle
based interface for program
exercise.
This function provides userinterface in order to give effective
breaks and to keep the motivationin program exercise.
This function displays individual
learning history with the progress
report and the accuracy rate to givethe sense of accomplishment.
This function provides real-time
progress monitor to display every
learner’s progress status.

3.1 Competitive learning function using nickname(Nickname function)
This function provides a competitive learning
environment using nicknames which is new function of
CAPTAIN3.4. This function aims to introduce the game
nature between students, and improved a concentration skill
such as raising the number of programming exercise which
is solved in a definite period of time. The function assigns
different nicknames to each learner individually in the
beginning of an exercise at each time and displays ranking
using nicknames to all learners and teachers. Each leaner
can know own relative rank compared to other learners inthe same group. Figure 1 shows a screenshot of the ranking
display using nicknames.
3.2 Puzzle style program exercise function
This function implements puzzle-based interface inorder to introduce programming training through a gaming
experience. Programming training from the viewpoint ofprogram structure comprehension and program flow
comprehension can be compared to solving a 2-dimensional
puzzle by logically laying out the puzzle pieces. From this

© ISAROB 2013

Figure 1 Example of ranking list using nicknames.

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3.5 Learning progress monitoring function
This function aims to improve the satisfaction oflearners by providing progress situation of learners toteachers in real time, and allowing teachers to quickly
support the learners who fall behind or repeatedly error on
particular problems. Figure 5 shows the instructor’s
monitoring screen displayed as a Web application. The
monitoring program retrieves updated progress status of alllearners from the database on a short time interval, anddisplays the progress visually on a Web browser. On themonitoring screen, the seating layout of the learners using
CAPTAIN3.4 is shown and user ID and the degree ofachievement are expressed as numerical value and color ineach seat.

Figure 2 Programming training screenshot
3.3 Animation display function
This function aims to give effective breaks and to keep
the motivation by using the visual stimulus such as displayof the animation depending on the learner's exercise resultand progress report in the programming training. Figure 3 isan example screenshot of an animation for a correct answer.

Figure 5 Progress monitoring Web application

4 Application and evaluation of Captain system

Figure 3 Animation for correct answer

4.1 Application
CAPTAIN3.4 was applied in a Java programmingcourse for 2nd year students in the Department ofInformation Systems, Tokyo University of Information
Sciences. In this course, the students were divided into 3classes (advanced, intermediate, introductory) according to
the results of a preliminary test. CAPTAIN3.4 was appliedin the introductory class. The applied programming course
is a half year course consisting of 90 minutes lecture and 90
minutes lab per week. The students recognize grammar at
the lecture and recognize the program structure and theflow of processing by solving programming exercise. Here,the experience about programming education of theteachers is also similar.

3.4 Learning history display function
This function aims to give sense of accomplishment canbe given to the learner by displaying the study history andthe acquired training contents. The learner can confirm theexercise which were answered correctly and correct answer
rate on the progress monitoring screen. Figure 4 shows anexample screenshot of a learner’s progress monitoring
screen.

4.2 Evaluation
We executed the same progress evaluation test (2 midterm exams and 1 final exam) for 3 classes (advanced,
intermediate, introductory) in order to evaluate the learning

Figure 4 Progress monitoring screenshot© ISAROB 2013

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effect by using CAPTAIN3.4. Moreover, the introductory
class was divided into the class using the nickname featureand the class which is not used in order to evaluate theefficient of nickname feature which is main function
supported in CAPTAIN3.4, and an anonymousquestionnaire was taken in the classes. The evaluation
period is from April in 2012 to July in 2012 and theevaluation was applied to student data (21 students in theadvanced, 31 students in the intermediate and 39 students in
the introductory classes) who had attended all mid-term andfinal exams. The flow of programming course andevaluation is shown in Figure 6. The number in ( ) is thenumber of lectures between evaluation.

improvement in programming skill immediately since thedifference of average score for the tests between theadvanced, intermediate and introductory classes was almost
no change.

Figure 7 Transition of average
4.2.2 Questionnaire
An anonymous questionnaire was taken in the
introductory level classes. The questions were chosen toevaluate CAPTAIN system, and student selected from 1 to
5 for each question where 5 was the highest positive reply.
The questionnaire results of the class which uses thenickname function and the class which did not use thenickname function are shown in Table 4.
The results of questionnaire show that the evaluation
result of the class using a nickname function is higher thanthe class which did not use a nickname function in allquestionnaire item. It seems that the student showedinterested in programming by using nickname function.
Figure 6 Flow of programming course and evaluation

Table 4 Results of the questionnairequestionnaire item

4.2.1 Comprehension evaluation test
The Comprehension evaluation test were classified into
the following five skill groups, and the maximum total
points for each test were 35 points.
(a) Basic knowledge for Java programming(b) Program creation (fill in the blanks without answer
group)(c) Program analysis (execution results analysis withoutanswer group)(d) Program creation (fill in the blanks with answer group)(e) Program reconstruction (program reconstruction using
program pieces with answer group)
Figure 7 shows the transition of average score for the
mid-term and the final exam of the advanced/intermediate(CAPTAIN3.4 not used) and introductory (CAPTAIN3.4
used) classes. Figure 7 shows that there was not enough© ISAROB 2013

class
5
27.5

4
50.0

Results(％)
3
2
5.0
7.5

1
10.0

(a) Did you feelthe game like
programming
training?

withnickname
without
nickname

22.2

27.8

11.1

11.1

27.8

(b) Did you feel
the
sense
of
accomplishment
by the display of
your study history?
(c) Did you feelthat greediness forlearning improvedby the display of
your ranking?

withnickname

15.0

50.0

17.5

10.0

7.5

without
nickname

16.7

33.3

5.6

16.7

27.8

with
nickname

17.5

35.0

25.0

12.5

10.0

without
nickname

22.2

27.8

11.1

0.0

38.9

The questionnaire also accepted free comments, inorder to collect other opinions that were not covered in the
fixed questions. From among the free comments, there weremany positive comments as follows.

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References[1] Sugumura Akira, Mine Tsunenori, Shoudai
Takayoshi “Automatic Exercise Generating System That
Dynamically Evaluates both Students' and Questions'Levels(Learning Support)”, IPSJ Journal, Vol.46,No.7,
pp.1810-1818 (2005)[2] Principles of Instructional Design (2005), Robert M.
Gagne, Walter W. Wager, Katharine C. Golas, John M.
Keller, Wadsworth Pub Co.
[3] Wenyong WANG, Mitsuru IKEDA, Fengrong LI(2007), Proposal and Evaluation of the “MotivationOriented” Teaching Method in Programming Education,
Japan Society for Educational Technology, Vol.31, No.3,
pp.349-357 (in Japanese)[4] E.Nunohiro, K.J.Mackin, M.Ohshiro, K.Matsushita,
K.Yamasaki (2007), Implementation of a GA driven
programming training support system, The 12th
International symposium artificial life and robotics, pp.517522[5] E.Nunohiro, K.Matsushita, K.J.Mackin, M.Ohshiro,
K.Yamasaki (2008), Design and development of a puzzle
based programming learning support system with genetic
algorithm, Transactions of Japanese society for informationand system in education, vol.25, No.2, pp.207-213 (inJapanese)[6] Yuji Yoneyama, Kotaro Matsushita, Kenneth J.
Mackin, Masanori Ohshiro, Kazuko Yamasaki, Eiji
Nunohiro (2008), Puzzle Based Programming Learning
Support System with Learning History Management, The
16th International Conference on Computers in Education(ICCE2008), pp. 623-627[7] Yuuko Yamakawa, Masanori Ohshiro, Kotaro
Matsushita,
KennethJ.Mackin,
Eiji
Nunohiro:“Programming Learning Support System ‘CAPTAIN’ with
Motivational Study Model”, The 18th International
Conference on Computers in Education (ICCE2010),
pp.171-175[8] Eiji Nunohiro, Kotaro Matsushita, Kenneth
J.Mackin, Masanori Ohshiro:“Development of Game Based
Learning features in Programming Learning Support
System”, The Seventeenth International Symposium on
Artificial Life and Robotics (AROB 17th '12), February,
2012, ISBN 978-4-9902880-6-8, Proceeding,230-233[9] Masanori Ohshiro, Kotaro Matsushita, Kenneth J.
Mackin, Eiji Nunohiro:“Programming Learning Support
System with Competitive Gaming Using Monitoring andNicknames”, The Seventeenth International Symposium on
Artificial Life and Robotics (AROB 17th '12), February,
2012, ISBN 978-4-9902880-6-8, Proceeding,238-241

Q1: Did you feel the game like programming training?
A1: Although I was poor at programming, I was able toenjoy like the game.
Q2: Did you feel the sense of accomplishment by thedisplay of your study history?
A2: Since this function displayed the mistaken part
intelligibly, it was able to do the given exercise
comfortably.
Q3: Did you feel that greediness for learning improved by
the display of your ranking?
A3: I was able to keep the motivation in order to get 1strank.

5 Conclusion and future works
In this paper, we developed game based learningfeatures in a programming learning support system, to offer
a learning environment that stimulates and sustains the
motivation of the learner during programming training. The
proposed system was applied to an actual college
programming course to verify the effectiveness of the
proposed system.
From the result of questionnaire, we concluded that thedeveloped CAPTAIN system was effective in the viewpointthat the student showed interested in programming.
However, from the result of the transition of average scoreof the mid-term and the final exam for theadvanced/intermediate (CAPTAIN3.4 not used) andintroductory (CAPTAIN3.4 used) classes, it could not beestablished that the proposed method had a positive effectin improvement of the programming skill, andimprovement of the learning features and revision on
method for applying the proposed system is necessary.
There was also another education effect considered thatsome students who were using CAPTAIN got interested indevelopment of an education system. For future works, weplan to improve learning functions which support
improvement in the capability to constitute the flow ofprocessing or programming creation capability withentertainment features.

Acknowledgments
This research was supported by The Ministry of
Education, Culture, Sports, Science and Technology(MEXT), Grant-in-Aid for Scientific Research(C)(24501207).

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Programming Learning Support Systems Focused on Structures ofProgramming Language and Code
Masanori Ohshiro1 , Takashi Yamaguchi1, and Eiji Nunohiro1
1

Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba, Chiba, 265-8501, Japan(Tel: +81-43-236-4667, Fax: +81-43-236-4667)
ohshiro@rsch.tuis.ac.jp

Abstract: The authors have developed a programming training system CAPTAIN (Computer Aided Programming Training And
INstruction). In this training system, each complete program is fragmented randomly into a few lines by the system. Students
sort the lines as an original program similarly to solving a puzzle game. In this paper, we propose an advanced feature for thesystem. In order to write correct programs, students must know important structures of language s syntax. For example, block
syntax is used for significant structures in Java. Therefore, in the new system, a program is divided into block syntax elements.
First, contents of theses elements are empty except for their frame. Students are instructed to place them into correct position
and to fill contents of the block syntax elements. It is expected that students will understand the structures of the programs insuch process and their ability of writing programs will be improved.
Keywords: programming, learning, game, java
1 INTRODUCTION
The authors have developed a programming training system CAPTAIN (Computer Aided Programming Training
And INstruction) and have applied the system in an actual programming course. Fig.1 is a login window of
CAPTAIN[1, 2, 3, 4, 5, 6]. In this training system, learners create programs similarly to solving a puzzle game asfollows. Each complete runnable program is fragmented randomly into a few lines by the system. Users must sort thelines as an original source program (fig.2). The system compiles the source program sorted by the user and checks thecorrectness of it.
In our previous version of the system, exercises are executed in style of a puzzle, sorting lines into correct source
programs as mentioned above. It seems that such a puzzle
method improves students ability of reading source programs and understanding algorithms in the codes. However,it will not improve learners ability for creating and writing
programs. A typical and simple learning method for program
writing is typing programs in a programming editor. But,such a typing method brings many unexpected troubles andneeds sufficient time. On the other hand the puzzle method isadvantageous for lessons with a time limit, for example classin school. Accordingly, we designed a new method for lessonof writing and creating programs in the puzzle-style method.

Fig. 1. CAPTAIN

ical applications of block syntax in Java. These structures
based on the block syntax are important part of programs.
Such block syntax element is a span of tokens enclosed bya pair of parentheses, brackets, and braces. Almost part of asource program written in these languages consists of block
structures. Therefore, in the new system, a program sourceis divided in block syntax elements. At the first, contents oftheses elements are empty except their block frame. Students
are instructed to place them into correct position. In succession, students must fill contents of the block syntax elements.
It is expected that students will understand the structures ofthe programs in such process and their ability of writing programs will be improved.

2 SYNTAX-ORIENTED FRAGMENTATION
In order to write correct source programs, students must
know important structures of language’s syntax. For some
popular programming languages (Java, C, C++, etc.), block
syntax is used as significant structures. Fig.3 shows the typ-

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013class A {int max ( int a, int b ) {// members// statements}}

if( x > 0 ) {for ( i = 0; i < n; i++ ) {// statements// statements}}

else {
try {// statements// statements}}
while ( i < n ) {catch ( Exception e ) {// statements// statements}}

Fig. 2. Each learner must sort fragmented and randomly
listed parts of a program at the lower left panel into the correct order at right lower panel using drag and drop.

Fig. 3. Typical applications of the block syntax in Java.
A1

3 METHOD OF THE FRAGMENTATION2

B2

private String name;3
4

public int getAge() {C1
B4 C2 D1

return age;

C3

}

C4

public void setAge( int age ) {D2

89this.age = age;

10

C6

}

C7

public String getName() {

11
12

B6

13
return name;B7 C8 D5
C9

}

C10

public void setName( String name ) {

14
15
16

this.name = name;

B8 C11 D6

17
18

C12

}

C13

public Person( int age, String name ) { 21

19

B9

20

B10 C14

D7

setAge( age );22

D8

setName( name );

23

}

C15
A3

6

if( age >= 0 ) {
}

D4

5
7

B5 C5 D3 E1

A21

private int age;

B3

The method of syntax-oriented fragmentation besed onblock syntax is displayed in fig.4. Lines of a source code aresymbolized and analyzed using simple syntax parsing. Blanklines in the source program are regard as equivalent to eachother. These blank lines are defined as follows:
blankline = B3 = B6 = B9;These blanklines are ignored in symbolic expressions mentioned below. All lines that contains the same contents are
regard as equivalent to each other and defined as follows:
C3 = D4 = C6 = C9 = C12 = C15 = A3;
A Class ’Person’ is analyzed and described as follows:"class Person"= A1, A2 { B1, B2, B4, B5,
B7, B8, B10 }, A3;
The above symbolic expression describes that a class ’Person’ is defined as sequence of A1, A2, A3. Moreover, theexpression shows A2 is set of B1, B2, B4, B5, B7, B8, B10(the blank lines are ignored). In brief, contents of a pair ofbraces may appear in random order in these symbolic expressions. If some part of contents of such a pair of braces must
be in particular order for some context, we may use a pair ofbrackets as follows:{ B1, [ B2, B3 ] }In this case, two instances shown below are permitted.
B1, B2, B3
B2, B3, B1
In many cases, such necessity of particular order is caused bysemantic context, therefore [ ] may be specified manually.
The expression below describes method ’setAge’ is definedas B5 or a sequence of C4, C5, C6.

© ISAROB 2013class Person {
B1

}

24
25

blankline = B3 = B6 = B9;
C3 = D4 = C6 = C9 = C12 = C15 = A3;“class Person”= A1, A2 { B1, B2, B4, B5, B7, B8, B10 }, A3;“method setAge” = B5 = C4, C5{D2, D3{ E1 }, D4}, C6;

Fig. 4. An example of syntax-oriented fragmentation besedon block syntax.

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(1) An example of an initial state and the first operation.
Pieces of a program
Construction panel"method setAge" = B5
= C4, C5 { D2, D3 { E1 }, D4 }, C6;This expression can be written in two expressions as follows:
D3 = { E1 };"method setAge" = B5
= C4, C5 { D2, D3, D4 }, C6;class Person {

A1

}

A3

private int age;

B1

class Person {}drag and drop
private int age;“class Person”(2) An example of filling a block syntax element.

public Person( int age, String name ) { C13 “ctor Person”
(B10)
}
C15

class Person {setAge( age );D7}this.age = age;

E1

return name;D5

public void setName( String name ) {C10 “method setName”
(B8)
C12

}

private int age;

B9B3(3) An example of a block syntax element correctly filled.

setName( name );D8public String getName() {}C7 “method getName”(B7)
C9

private String name;B2

if( age >= 0 ) {

D2

}

D4

class Person {private int age;
}

C5

B6
this.name = name;D6

public int getAge() {

C1

}

C3

return age;

D1

public void setAge( int age ) {C4 “method setAge”
(B5)
C6

}

“method getAge”(B4)

Fig. 6. An example of typical user operation.
5 CHECKING CORRECTNESS OF RESULTS
Fig.7 is a example of an answer. In order to check the correctness of the answer, the system matching the symbolic expressions extracted from original source code with the symbolic expressions extracted from user’s answer according to
the rules as follows:

Fig. 5. An example of fragments in random order.
The source program is analyzed in the manner mentioned
above. At the next, the fragments are sorted in random order.
It is important that lines of every block syntax are integrated
into one fragment without its contents in this process (fig.5).
By the way, white spaces for indent is omitted in the display
because such indents are unexpected hints.

(1) Blank lines are ignored.
(2) Symbols in { } may appear in random order.
(3) Inside { }, symbols in [ ] must appear in specified
order.

4 USER OPERATION
Fig.6 shows typical user operation. At the first, fragments
are displayed. A user is instructed to place them into correct
position. User can select and move the fragments to construction panel using drag and drop (fig.6-(1)). Especially, contents of every block syntax fragment can be inserted (fig.6(2)). A user can fill and sort fragments in construction panel
any number of times. At the last, a user check the correctnessof the sorted fragments.

© ISAROB 2013For example of fig.4 and fig.7, the first different point is detected as follows:
Original:"method setAge" = B5
= C4, C5 { D2, D3 { E1 }, D4 }, C6;
Answer:"method setAge" = B5
= C4, C5 { D2, D3 { D1 }, D4 }, C6;

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REFERENCES

The different part of these symbolic expressions is underlined. Therefore, the answer is incorrect.
A1

class Person {
B3

1
4

B2
B1
C4

private String name;3

private int age;2

public void setAge( int age ) {8D2if( age >= 0 ) {9return age;

6

B5 C5 D3 D1
}

D4

A2

[1] Nunohiro E, Mackin K, Ohshiro M, Matsushita K, Yamasaki K (2007), Implementation of a GA driven programming training support system, The 12th International symposium artificial life and robotics, pp.517522.

11

C6

}

12

C1

public int getAge() {5

B4 C2 E1

this.age = age;

10

C3

}

C7

public String getName() {[2] Nunohiro E, Matsushita K, Mackin K, Ohshiro M, Yamasaki K (2008), Design and development of a puzzle
based programming learning support system with genetic algorithm (in Japanese), Transactions of Japanesesociety for information and system in education, vol.25,
No.2, pp.207-213.
[3] Yoneyama Y, Matsushita K, Mackin K, Ohshiro M, Yamasaki K, Nunohiro E (2008), Puzzle Based Programming Learning Support System with Learning History
Management, The 16th International Conference on
Computers in Education (ICCE2008), pp. 623-627

7

B9

20
return name;B7 C8 D5

14
15

C9

}

16

C10

public void setName( String name ) {17this.name = name;

B8 C11 D6

18

C12

}

C13

public Person( int age, String name ) { 21B10 C14

19

D8

setName( name );23

D7

setAge( age );

22

}

C15

[4] Yamakawa Y, Ohshiro M, Matsushita K, Mackin K,
Nunohiro E (2010), Programming Learning Support
System ’CAPTAIN’ with Motivational Study Model,
The 18th International Conference on Computers in Education (ICCE2010), pp. 171-175

B6[5] Ohshiro M, Mackin K, Matsushita K, Nunohiro E,
Yamakawa Y (2010), Programming Learning Support
System with Learning Progress Monitoring Feature,
Joint 5rd International Conference on Soft Computing
and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems (SCISISIS
2010), pp.1465-1468

24
13

}

A3

25

“class Person”= A1, A2 { B2, B1, B5, B4, B7, B8, B10 }, A3;“method setAge” = B5 = C4, C5{D2, D3{ D1 }, D4}, C6;[6] Ohshiro M, Matsushita K, Mackin K, Yamaguchi T,
Nunohiro E (2012), Programming Learning Support
System with Competitive Gaming Using Monitoring
and Nicknames, The 17th International symposium artificial life and robotics, pp.238-241.

Fig. 7. An example of an answer.

6 CONCLUSION
We proposed a new feature, syntax-oriented fragmentation for CAPTAIN. It is expected that students may pay much
more attention to structures of source code and syntax of Java
using this new feature. It seems that the feature is useful toknow weak points of students’ because it is easy to detect
where the students made mistakes. We will implement thefeature in CAPTAIN and examine the effect of the feature.

ACKNOWLEDGMENTS
This research was supported by The Ministry of Education, Culture, Sports, Science and Technology (MEXT),
Grant-in-Aid for Scientific Research(C) (24501207).

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Individual Student Support System for Teacher and TAs
using Mobile Devices in Exercise Classes
Masaki Hanada1, Takashi Yamaguchi, and Yasuo Nagai
Tokyo University of Information Sciences, Japan(Tel/Fax: 81-43-236-1224)
1
mhanada@rsch.tuis.ac.jp
Abstract: The paper describes a student support system for a teacher and Teaching Assistants (TAs) in order to support
individual students effectively and satisfactorily in computer exercise classes. In general, a teacher teaches some classes inuniversities and several TAs engage in the activities to support students under guidance from a teacher in charge. Introductionof a student support system for a teacher and TAs is essential in order to support all the students effectively and satisfactorily
by fewer teaching staff. In this research, we develop a system which visualizes the seat location of the questioner and shares
the responses of questions, in order to answer students' questions during the class effectively and satisfactorily. In this paper,
we report the proposed student support system and the results of the system evaluation.
Keywords: Computer exercise classes, Mobile devices, Teacher, Student support system, Teaching assistants

1 Introduction

2 Related Work

The paper describes a student support system for ateacher and Teaching Assistants (TAs) in order to support
individual students effectively and satisfactorily incomputer exercise classes. In general, a teacher teaches
some classes in universities and several TAs engage in theactivities to support students under guidance from teacher
in charge.
Introduction of a student support system for a teacher
and TAs is essential in order to support all the students
effectively and satisfactorily by fewer teaching staff.
In this research, we develop a system which visualizes
the seat location of the questioner and shares the responsesof questions, in order to answer students' questions during
the class effectively and satisfactorily. By using this system,
it is expected that a teacher and TAs are able to support
individual students effectively and satisfactorily.
Moreover, we develop an user-interface in which
students easily ask questions for the exercise. By using thisuser-interface, it is expected that students are able to ask
questions without hesitation.
In this paper, we report the proposed student support
system and the results of the system evaluation.
In Section 2, we present some related work about
education support systems. In Section 3, we describe main
jobs of a teacher and TAs. In Section 4, we describe theproposed individual student support system. In Section 5,
we describe the operation and the evaluation of the
proposed system. Finally, Section 6 gives concluding
remarks and the directions of our future work.

In [1], the authors developed a system which shares the
responses of questions between a teacher and TAs. In [2],the authors developed a system which visualizes thestudents' progresses of an exercise on tablet PCs.
By using these systems, these authors reported thatstudents who do not have a clear understanding were
founded early, and the overall level of understanding in theclass was improved. Based on the result of these early
researches, we develop a new system for a teacher and TAs
in order to support individual students effectively andsatisfactorily.

© ISAROB 2013

3 Main Jobs of a Teacher and TAs in Exercise
Classes
In computer exercise classes (e.g., the programming andthe operating system), the main jobs of teachers are as
follows:(1) Make and prepare educational materials for theclass.
(2) Take students attendance in the class.
(3) Check students' exercise answers.
(4) Answer students' questions during the class.
(5) Support individual students that have insufficientunderstanding and have a problem about an exe
rcise during the class.
(6) Give a grade to each student.
TAs mainly do the jobs from (2) to (5) under guidancefrom a teacher in charge. In particular, the above jobs from(4) to (5) are important for computer exercise classes
because each student has different problems and the110

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By using the user-interface (Fig.1 ①), students are able
to ask a question without hesitation, compared with asking
a question by a show of hands. As a result, a teacher andTAs can support all the students which have insufficientunderstanding and have a problem about the exercise
smoothly.
By providing the entire status to a teacher and TAs
using the tablet PC (Fig.1 ②), if the individual support isneeded (e.g., the question is endemic question), a teacher or
TAs visit and support individual questioners (Fig.1 ③). If
the entire support is needed (e.g., the number of similar
question is large), teacher gives guidance to all the student(Fig.1 ④). As a result, a teacher and TAs support all thestudents effectively and satisfactorily by fewer teaching
staff.

feedback about mistakes must be given at the time themistakes for exercise are made.
In this research, we focus on the jobs of (4) and (5).
The job of (4) means that when a teacher and TAs are
asked a question from a student during the class, theyanswer the question of the student. In this job, a teacher andTAs may be not able to do this job smoothly. This isbecause there is the potential for an increase of students'
questions. The students' questions increase when the
instruction for the exercise is unclear or the understandingof students is insufficiency.
The job of (5) means that a teacher and TAs visit each
student during the class and if a student has insufficientunderstanding and has a problem about a exercise, they
support the student. In this job, a teacher and TAs areoverloaded and may be not able to do this job smoothly.
In this research, we develop a student support system inorder to do these above jobs smoothly.

④ Entire
Supportteacher② Entire status(e.g., the content of the questions andthe seat location of questioners)

4 Proposed Individual Student Support System③ Individual
Support

4.1 Functional Requirements
In order to do the jobs of (4) and (5) smoothly, a teacher
and TAs need to understand the entire status of questions
from questioners (e.g., the content of the questions and theseat location of questioners). If a teacher and TAs are able
to gather the entire status of questions from questioners
quickly, they visit the questioners quickly and support thequestioners smoothly.
Moreover, if a teacher and TAs share the responses ofquestions from questioners, they can support the questioner
smoothly when the asked question is similar to the pastquestion.
From these functional requirements, we develop a
student support system which visualizes the seat location of
the questioner and shares the responses of questions fromquestioners.

TAs

Students① Request for support(e.g., submit a question)

Figure 1 Individual Student Support System

4.3 Processing Flow
The proposed individual student support system iscomposed of a Web client application for students, a client
application for a teacher and TAs and two servers (i.e., Web
server and Database (DB) server).
Fig 2 shows a process flow of the proposed system. A
student selects a question from the question list and thenpushes the submit button (Fig.2 ①). If the question which
the student wants to ask is not registered in advance, thestudent selects other questions (i.e., "etc" on the screen) andpushes the submit button.
The submitted question is sent to the Web and DB
servers and is stored in the DB (Fig.2 ②). Then, thequestion and the seat location of the questioner are sent to
the tablet PC of a teacher and TAs. The client applicationon the tablet PC visualizes the seat location of thequestioner with the question (Fig.2 ③).
After the teacher or TA supports the questioner, theresponder inputs a response of the question on the tablet PC

4.2 Student Support ModelIn this section, we show a system function which
visualizes the seat location of the questioner and shares the
responses of questions from questioners.
The student support model using the proposed
individual student support system is shown in Fig.1. A
student asks a question through the user-interface forquestion. Then, the entire status of questions fromquestioners (e.g., the content of question, the seat locationof questioners, the number of questions and the number ofTAs which is supporting a student) is visualized on tablet
PC of a teacher and TAs.

© ISAROB 2013

Individual Student
Support System

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(Fig.2 ④). The response of the question is sent to theserver and is stored in the DB.
④ Teacher and TAs input a
response of the question into
the tablet PC after supporting
the questioner.

4.5 Client Application for a Teacher and TAs
In the computer exercise class, we assume that a teacher
and TAs have a tablet PC respectively because a teacher
and TAs should support a student individually withcirculating during classes. By using the tablet PC, a teacher
and TAs can view the entire status and input the response of
the question after supporting the questioner.
In the proposed system, client application for a teacher
and TAs is developed as application for Android tablet PC.
Examples of the user-interface (UI) for a teacher andTAs are shown in Fig.4 and Fig.5.
②⑤ The response of thequestion is sent to the server.

③ The entire status ofquestions is sent to the table PC
Web Browser① A student select aquestion and then pushes
the submit button.

② The question is sent tothe servers

Figure 2 Process Flow

4.4 Client Application for Students
In the proposed system, Web browser is used as a client
application to accept a question from a student, because theWeb browser is already installed in many computer
environments and the educational materials are often
constructed as Web contents.
Example of the Web browser user-interface (UI) for
students is shown in Fig.3.

③①
Figure 4 UI for a Teacher and TAs (1)In the left of Fig.4 (Fig.4 ①), the seating map of theclassroom is shown. The seats are displayed as boxes andthe boxes of the current questioners are colored in yellow.
The boxes of the questioners who is currently being
supported are colored in blue. The number shown in theboxes is the number of questions of the questioner. The
number of the current questioners is shown in the upper
right of Fig.4 (Fig.4 ②).
In the lower right of Fig.4 (Fig.4 ③), the list of thequestions and the response is shown. The teacher always
checks the list during the class, and if the number of similar
question is large within the list, the teacher can give
guidance about the question to all the students.
If a teacher or TAs click the seat box in the left of Fig.4(Fig.4 ①), the screen as shown in Fig.5 is displayed.

②①
Figure 3 Web browser UI for students
When a student has a question, the student selects thequestion which the student wants to ask from the question
list (Fig3. ①) and pushes the submit button (Fig3. ②). If
the question which the student wants to ask is not registeredin advance, the student select "etc" and pushes the submit
button. The reasons that the question is listed on the Webbrowser user-interface are as follows:(1) The students make their incomprehensible point
clear by selecting the question from the questionlist.
(2) If the number of similar question is large, theteacher can give guidance about the question to all
the students.

© ISAROB 2013①②③
Figure 5 UI for a Teacher and TAs (2)112The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

willing to ask a question by a show of hands. Second one is
the students who are willing to ask a question using the
proposed system. Therefore, we need to re-establish themanner of the operation of the proposed system according
to requirements of students.

The question and the response are shown in the upperand lower of Fig.5 (Fig.5 ① , ② ) respectively. The
responder submits the response of the question after
supporting the questioner (Fig.5 ③).

5 Operation and Evaluation
The proposed individual student support system isoperated in the three kinds of computer exercise classes
held at 2012 first semester in Tokyo University ofInformation Sciences.
One objective of the proposed system is to prevent theleakage of question and to support as much question as
possible. Therefore, we evaluate the number of the
supported questions using the proposed system, comparedwith no using the proposed system.
The average number of supported questions for one
class is shown in Table.1.
In the computer exercise class for the first grade, theclass is separated into three sub-classes (i.e., Class A, Class
B and Class C). the different three teachers use the sameeducational materials. In one of three sub-classes (i.e.,
Class A), the proposed system is operated. In the other two
sub-classes (i.e., Class B and Class C), the proposed system
is not operated.
In the above classes, the influence by the different
teachers is not avoided. Therefore, one teacher teaches in
the following two classes.
In the computer exercise class for the second grade (i.e.,
Class A), one teacher uses the original educational materials.
In the computer exercise class for the third grade, twoteachers use the same educational materials. The class isseparated into two sub-classes (i.e., Class A and Class B).
To reduce the influence by the difference of teachers, theproposed system is operated every other week in thecomputer exercise classes for the second and third grade.
From the results of the average number of supported
questions for a lesson of one class (Table.1), the average
number of supported questions using the proposed system
is small except the exercise class for the second grade,
compared with no using the proposed system.
The two reasons are considered.
The first reason is that the teacher gives guidance about
the question to all the students because the number ofsimilar question is large.
The second reason is that a teacher or TAs forget to
input the responses of questions to the tablet PC because ateacher and TAs are overloaded.
Moreover, from the questionnaire, we confirm that there
are two types of students. First one is the students who are© ISAROB 2013

Table 1 The average number of supported questions

Exercise class for the first grade
Use of the proposed system
yesClass A
15.50
Class B and C
Exercise class for the second grade
Use of the proposed system
yesClass A
6.40
Exercise class for the third grade
Use of the proposed system
yesClass A
2
Class B
3.33

no
39.06
no
5.80
no
8.33
7

6 Conclusion
In this research, we developed a student support system
in order to support individual students effectively andsatisfactorily. In this paper, we reported the operation andevaluation of the proposed system.
From the results of the evaluation, we confirmed aneffect of the proposed system. However, the proposedsystem needs further modification.
Our future work are as follows :(1) The improvement of the user-interface (UI) for
students, the improvement of the application for ateacher and TAs, and the improvement of themanner of the operation are needed.
(2) Re-examination of the evaluation method including
the score of exam is needed.
(3) Combining the proposed system with educational
materials is needed in next version to support
students more effectively and satisfactorily.

References[1] Masafumi Takeda and Osamu Akizuki,``A lecture
support system with using network environment,`` IEICE
ET, vol.111, no.213, pp.59-63, 2011. (in Japanese)[2] Seigo Yasutome and Takuya Ito,``Web application
for TA activity with Tablet PC,`` JSiSE General Conference,
vol.36, pp.34-35, 2011. (in Japanese)

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Research on XP approach using
The Personal Software Process Practice
Daisuke Yamaguchi1
1:Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama, JAPANyamaguti@intlab.toin.ac.jp

Abstract: In this paper, we propose the PSP Practice Support System that we realize record-keeping supportof flame work performing data acquisition of process flow offered in PSP in other Android carrying e
nd with a software development environment using based on the Multiagent technologies. It is thoughtthat we can tie that we come true with an Android mobile terminal when we perform the convenienc
e that we don’t affect to a software development environment and reference of a document if dated c
onsciousness of flow. So, We can be conscious of process flow in every environment with developmentby this system can transmit programming to specific human among many software processes using Ag
ent’s technology. Applying the proposed method to a personal process remove task, a flexible programming for quality of software.
Keywords: Personal Software Process, Software Engineering, Agile Development, eXtreme Programming

Since interruption time is essentially random, ignoring these times would add a large random error into thetime data and reduce estimating accuracy. In this we
propose that the PSP Practice who realize recordkeeping support an flame-work performing data acquisition of the process flow offered in other Android end
user with a software development environment. It isthought that reference of a document if dated cosciousness of flow when perform the convenience don't affectto a software development environment with an Adroid
mobile terminal. Because, we can be conscious of process flow in every environment with development by
this system can transmit programming to specific human among many software processes using Agent's
technology. The system is also synthesized to do parallel and cooperative proposing internally. Applying the
proposed method to a personal process remove task, aflexible programming for quality of software.

I. INTRODUCTION
Software architecture has emerged as an important
sub discipline of software engineering [1]. PSP support
system is built using this. Moreover, We think that thedata inputted can acquire software development processby sorting out using a user action record table [2].
In this paper, the Personal Software Process (PSP)practice support system that we realize record-keeping
support of flame work performing data acquisition ofprocess flow offered in PSP in other Android carrying
end with a software development environment using
based on the Multiagent technologies [3]. It is thoughtthat we can tie that we come true with an Android mobile terminal when we perform the convenience that we
don’t affect to a software development environment andreference of a document if dated consciousness of flow.
So, We can be conscious of process flow in every environment with development by this system can transmit
programming to specific human among many software
processes using Agent’s technology. Intelligent agentsand multiagent systems are one of the most important
emerging technologies in computer science today [4].
Multiagent systems deal with coordinating intelligent
behavior among a collection of autonomous agents.
Emphasis is placed on how the agents coordinate theirknowledge, goals, skills, and plans jointly to take action
or to solve problems. Constructing the multiagent systems is difficult [5,6].

© ISAROB 2013

II. Android Personal Digital Assistant
The PSP data has record on the framework prescribed by the aforementioned individual specific exercise in support system. Therefore, Record time has beentake many form to conducts based on the PSP base-line
framework. Support System should be record processes
by different development, quality control and large
forms. So, it will be not very worried about an activity
recording a process form that dealt in an experienced
developer of the PSP and working-out of software for along time. However, it increases that kept record of aform about becomes vague of that still pressed by eve-

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ryday various works. In order to perform accurate record, their needs for support system automate and reduce
the work measurement process.
It content is missing records, which it does not exactly the plan. Therefore, support systems is to record
all the work, record and share information, and record
information with other differences between them, what
do you consider that characterize the individual.
This will be an important resource to support theability to provide the information necessary to improve
the process. However, to be commissioned to present
the contents of any process improvement is not help
from the Android devices. The recorded information
itself, are working will consider process to improvement.
An important aspect of this device, by allowing alltimes and there is early detection of leaks of personal
records.

need to determine which technologies and methods
work best for you. PSP write several program using the
evolving process shown Figure 1.
The PSP is not a magical answer to all of your software engineering problems, but it can help you identify
where and how you can improve. However, you must
make the improvements yourself. PSP0 and PSP0.1
hierarchy include introduces process discipline andmeasurement. PSP1 and PSP1.1 hierarchy include introduces estimating and planning. PSP2 and PSP2.1
hierarchy include Introduces quality management anddesign. Team Software Process exists over the PSP hierarchies.
So, This any measure record to support agent consider with using this Agent Learner expanded of PSP
support. A person engaging in a person who experienced PSP and software development for many years is
not very worried about a form record-keeping work.
Record keeping is vague, and what is performed of aperson pressed by a work still increases. Necessity toperform automatically is important in a soldier, remission of an activity and process assay to record an activity precisely.
The first Extreme Programming project was started
March 6, 1996[7]. Extreme Programming is one of several popular Agile Processes. It has already been provento be very successful at many companies of all different
sizes and industries worldwide.
Extreme Programming is successful because it
stresses customer satisfaction. Instead of delivering everything you could possibly want on some date far in the
future this process delivers the software you need as youneed it. Extreme Programming empowers your developers to confidently respond to changing customer requirements, even late in the life cycle.
Extreme Programming emphasizes teamwork. Managers, customers, and developers are all equal partners
in a collaborative team. eXtreme Programming implements a simple, yet effective environment enablingteams to become highly productive. The team selforganizes around the problem to solve it as efficiently as
possible. This case study used to Pair Programming
only. Because, This XP Method improvement of Software Process Time for The PSP0.

Fig.1. PSP Process Evolution

III. Personal Software Process and XP
The PSP is a self-improvement process that helps
you to control, manage, and improve the way you work.
It is a structured framework of forms, guidelines, andprocedures for developing software [2]. Properly used,
the PSP provides the data you need to make and meetcommitments, and it makes the routine elements of yourjob more predictable and efficient.
The PSP's sole purpose is to help you improve yoursoftware engineering skills. It is a powerful tool thatyou can use in many ways. Rather than using one approach for every job, you need an array of tools andmethods and the practiced skills to use them properly.
The PSP provides the data and analysis techniques you© ISAROB 2013

IV. Research tool Digital Assistant for Android
mobile tools at Software Estimate Efficiency
In this section, explain assistant of Software Estimate used to the Android Digital Assistant based on inter115

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nal Agent Learner for Intelligent Agent. Intelligent
Agent techniques give connects in other Intelligent
Agent record data on PSP. Hence, that Intelligent Agentput the Agent Learner on necessary thoughts in Multiagent [8].
In the PSP, engineers use the time recording log tomeasure the time spent in each process phase. In this log,
they note the time they started working on a task, thetime when they stopped the task, and any interruption
time. For example, an interruption would be a phone
call, a brief break, or someone interrupting to ask aquestion. By tracking time precisely, engineers track theeffort actually spent on the project tasks. Since interruption time is essentially random, ignoring these times
would add a large random error into the time data andreduce estimating accuracy.
Since the time it takes to develop a product is largely determined by the size of that product, when using
the PSP, engineers first estimate the sizes of the products they plan to develop. Then, when they are done,
they measure the sizes of the products they produced.
This provides the engineers with the size data they needto make accurate size estimates. However, for these data
to be useful, the size measure must correlate with thedevelopment time for the product. While a line of code
(LOC) is the principal PSP size measure, any size
measure can be used that provides a reasonable correlation between development time and product size. It
should also permit automated measurement of actual
product size.
As for the Android terminal unit, it process takepossible for archiving to convey the information that itis necessary in the PSP activity through a screen to aninternal intelligent agent. Information to convey has
memorized the time that activity content in form archiving was really performed.
When "Record" button be push the Android terminal
unit Screen, in order of Figure 2, Figure 3, Figure 4, andactivity changes screen design. Because a screen designchanges, button of a new context format appears. Agent
express current activity contents sequentially whether it
has been work-in-process hereby whether it is activity
record keeping. An optional category have take theproblems number, work-in-process practicing now. This
work-in-process consists of all seven items that theAgent put activity break in as well as six items established in PSP. About activity cutoff, I step over a day
and can record an activity.

© ISAROB 2013

Fig.2. The Android Terminal Unit for first phase on the
Agent Interface

Fig.3. The Android Terminal Unit for second phase on
the Agent Interface

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and work in a process task on Agent Learning data. We
consider to support system by what cannot edit record
data activity archiving be able to offer the usability as
evidence to test pattern data. But, This model will showat the system in a more natural, unscripted scenario,
involving multiple parts in addition to other forms ofprocess and error type phenomenon. XP Method aim torole-playing Navigators into the time record and work
process think support. But, This control working Interface support Navigator only to be Android Digital Assistant device processing space. Next approach productin to this dual work support system used on the navigation techniques.

REFERENCES[1] Paul Clements, Felix Bachmann, Len Bass, David
Garlan, James Ivers, Reed Little, Paulo Merson, RobertNord, Judith Stafford (2010), Documenting Software
Architectures SECOUND WDITION, Addison-Wesley[2] Watts S. Humphrey (2005), PSP - A Self Improvement Process for Software Engineers, Addison-Wesley[3] Daisuke Yamaguchi, Ayahiko Niimi and Muneo
Takahashi (2010), Improvement of a Software Estimate
Efficiency Centered PSP Practice Support System Using Multiagent Techniques, The Fifteenth International
Symposium on Artificial Life and Robotics 2010,
pp.857-860[4] Weiss, G. (1999), Multiagent Systems, A Modern
Approach to Distributed Artificial Intelligence, the MIT
Press[5] Abul, O., Polat, F., and Alhajj, R. (2000), Multiagent Reinforcement Learning Using Function Approximation, IEEE Transaction on systems, man, and cybernetics-part c: application and reviews, Vol. 30, No. 4,
pp. 485-497.
[6] Khosla, R., and Dillon, T. (1997), Engineering Intelligent Hybrid Multi-Agent Systems, Kluwer Academic
Publishers[7] Kent Beck’s (1996), Extreme Programming,
http://www.cs.usfca.edu/~parrt/course/601/lectures/xp.h
tml[8] Carles Sierra, JohnThangarajah, Lin Padgham andMichael Winikoff (2007), Designing Institutional MultiAgent Systems, AOSE 2006, LNCS 4405, PP.84-103

Fig.4. The Android Terminal Unit for third phase on the
Agent Interface
Actually, I am recordable like Figure 3 when the
Agent stepped over a day. A work to perform in thisinterface, work-in-process is recorded by the operationthat it is easy only pushing it in button sequentially.

VI. CONCLUSION
In this research we were analyses PSP Practice Support System used to of flame work performing data acquisition of process flow offered in PSP by the Android
terminal unit Interface. We were able to evidence user
software working process data. We create agent learnerdata in real time work data beside with agent runningand wrote PSP Practice process type and problem number.
For future works, we will consider methods used onrecord time data for agent learner in machine learningand user experience. It is necessary for us to consider
only a clock through this system about the cause that a
description error to be generated by human error.
Future versions of PSP Practice Support System
based on Android Terminal Unit record of error typecode or fact process from error message of compiler andexecuter to test pattern data. But, This model will showat the system in a more natural, unscripted scenario,
involving multiple parts in addition to other forms ofprocess and error type phenomenon. Now, This system
Interface cannot edit by the record data that evidence© ISAROB 2013

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Non-Event Driven Graphics API for Programming Education
Kenneth J. Mackin
Tokyo University of Information Sciences, Chiba 265-8501, Japan(Tel: 81-43-236-1329, Fax: 81-43-236-1329)
mackin@rsch.tuis.ac.jp
Abstract: Current multitasking window-based operating systems have adopted an event-driven model to support graphics anduser interface control in application programs. But for beginner programmers, the idea of threads and event handling can bedifficult to grasp, thus preventing beginner programmers for creating graphics applications, leaving programmers in the earlystages to create simple text based console applications. In this research, a non-event driven graphics API for programming
education is proposed. By applying the proposed graphics API, programmers can create graphics and real-time user interface
applications by simple state-request method calls not requiring any event handling or event call-back methods. The proposedgraphics API also supports text based console I/O such as print line and line input, so beginner programmers can shift fromconsole based applications to graphics applications without any paradigm changes.
Keywords: BASIC graphics, Graphics programming, programming education
But for beginner programmers, the idea of threads andevent dispatching can be difficult to grasp, leading the
beginner to program something that he/she does understandwhat is happening. This initial barrier prevents beginnerprogrammers from creating graphics applications, leaving
programmers in the early stages of education to create
simple text based console applications which are not event
driven.
Text based console applications have limited value incurrent GUI-centric operating systems, so there is a naturalneed for an intermediate step between text-based
programming and event-based GUI programming to easethe jump between the two paradigms. Also, a GUI program
can be much more useful than a text-based program andcloser to 'real' applications in a GUI operating system
environment, which can greatly improve the motivation andsatisfaction of the beginner programmer during
programming education.
In order to overcome this problem, a non-event driven
graphics application programming interface (API) for
programming education is proposed in this research. By
applying the proposed graphics API, programmers cancreate graphics and real-time user interface applications bysimple state request method calls not requiring any event
handling or event call-back methods. The proposedgraphics API also supports text based console I/O such as
print line and line input, so beginner programmers can shift
from console based applications to graphics applications
without any paradigm changes and minimal code changes.

1 INTRODUCTION
After the introduction of Graphical User Interface (GUI)by Xerox PARC in 1973, computer operating systems have
moved from Character User Interface (CUI) centric designs
to GUI-centric designs. As GUI-centric application
development became popular, GUI widget toolkits wereintroduced, in which 'GUI widgets', or GUI components
such as buttons and text fields, are controlled through anevent-driven programming model.
Current multitasking window-based operating systems,
including UNIX X Windows and Microsoft Windows, haveadopted this event-driven model to support graphics anduser interface control in application programs. Naturally,
most of current programming languages, such as C and
Java, which support graphical user interfaces, use the eventdriven API model.
In an event-driven programming model, an 'event' is anaction that triggers some program code, such as a mouseclick or a key stroke. A program registers which event
triggers which program code, called event-callback or
event-handler. The event-driven model allows the
programmer to concentrate on only the related event for aparticular code, and facilitates independent GUI design andreusable GUI widget programming. In recent programming
languages such as Java, the event dispatching routine,
which receives the event signal from the underlying
operating system, manages the list of event handlers, andcalls the appropriate event-handler for the appropriate event,is hidden altogether from the user, and GUI programmer
simply programs the event-handler and waits for the eventhandler to be called.

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2 NON-EVENT DRIVEN GRAPHICS API

Example 2 below is an example of a simple animation
program.

The proposed non-event driven graphics API, named
Basic Graphics, is implemented as a Java language class.
Basic Graphics receives its design hints from 1980's BASIC
language graphics approach. In particular, many of thegraphics commands are based on F-BASIC by Fujitsu
Limited.
Basic Graphics consists of only 1 public class,jp.ac.tuis.basic.BasicGraphics class. Basic Graphics classconsists of methods to draw text, graphics and images,
retrieve line input from the keyboard, and retrieve keyboard
and mouse states. Additionally Basic Graphics includes
features to play music and beep, as well as create network
connections with other Basic Graphics applications andsend and receive network data. Basic Graphics applications
can be run as a stand-alone Java application or a JavaApplet.
Example 1 below is an example of a hello world
program written in Basic Graphics.

//Example 2
import jp.ac.tuis.basic.*;
public class Animation{
public static void main(String[] args){
BasicGraphics g = new BasicGraphics();for(int i=0; i<40; i++){g.locate(i,10);g.print('X');
g.sleep(200);
g.locate(i,10);g.print(' ');}}}The locate(x,y) method in Example 2 defines the x,ycoordinates of the location of the following print() method.
The program will wait further execution in the sleep()
method for the given milliseconds. In this example, an 'X'
will move from the left of the screen to the right.
Example 3 below is an example of a program to retrieve
keyboard input.

//Example 1
import jp.ac.tuis.basic.*;
public class HelloWorld{
public static void main(String[] args){
BasicGraphics g = new BasicGraphics();g.println("Hello, world!");//Example 3}

import jp.ac.tuis.basic.*;}

public class Type{public static void main(String[] args){

Example 1 will create a window and print "Hello,
world!" on the top of the created window.

BasicGraphics g = new BasicGraphics();
while(true){char key = g.inkey();if(key != 0){
g.print(key);}}}}

The inkey() method returns the key of the pressed
keyboard key. If no key is pressed, then inkey() returns 0(zero).
By using the proposed Basic Graphics API, theprogrammer can draw graphics and retrieve key input
events (and similarly mouse events) without using theevent-driven model. The proposed model relies only on thesimple main method architecture of console based

Fig. 1. Hello world program using Basic Graphics© ISAROB 2013

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application programming which the programmer is familiarwith. Example 3 is also a simplified model of a event
dispatcher, and can be built upon to describe theimplementation of a event dispatching loop later in the
programming education.

3 EXPERIMENTAL USE IN PROGRAMMINGEDUCATION
The proposed Basic Graphics API was introduced to 10
2rd year college students who have had 1 year prior Java
programming education, but could not understand event
handling and were not able to create GUI applications yet.
After a 30 minutes lecture on how to use the Basic Graphics
API, the students were given a graphical programming
assignment (Fig.2) to complete. All participating students
were able to complete the assignments within 60 minutes.
Some of the students were able to create individual
graphical applications on their own (Fig.3,4). The
participating students are gave positive feedback on theusefulness of the Basic Graphics API, it was clear that the
motivation and satisfaction of the students had been
improved.

Fig. 3. Example graphical application of students

Fig. 2. Graphical programming assignment

Fig. 4. Example graphical application by student© ISAROB 2013120

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4 CONCLUSION
In this research, a non-event driven graphics API for
programming education is proposed. By applying the
proposed graphics API, programmers can create graphics
and real-time user interface applications by simple state
request method calls not requiring any event handling or
event call-back methods. The proposed graphics API also
supports text based console I/O such as print line and line
input, so beginner programmers can shift from console
based applications to graphics applications without any
paradigm changes.
The proposed API was introduced to 2nd year college
students with text-based console programming experience
to verify the effectiveness of the proposed method. All
participating students were able to complete the graphicalapplication assignment in the total 90 minutes period, andthe motivation and satisfaction of the students had been
improved.
For future works, a complete self-learning tutorial forusing the Basic Graphics API will be developed, to allow
students to use the training material at their own learning
pace.

REFERENCES[1] Ephraim P. Glinert (ed) (1990), Visual Programming
Environments: Paradigms and Systems. IEEE Computer
Society Press© ISAROB 2013

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An Education of Social Media Literacy to High School Students
in Social Media Times
Yoshihiro Kawano1, Takuya Miya1, Naoki Furuya1, and Yuka Obu2
1

Tokyo University of Information Sciences, Chiba 265-8501, Japan
2
Unicast, Inc., Hitachi 316-0033, Japan(Tel: 81-43-236-1149, Fax: 81-43-236-1149)

1{ykawano@rsch.tuis.ac.jp, s10155tm@edu.tuis.ac.jp, s10140nf@edu.tuis.ac.jp}, 2yuka.obu@gmail.com

Abstract: Social media, such as Twitter and Facebook, has been popularized to our society. In the social media times, many
people become more active between online and offline. Today, personal branding is very important to harness an individualstrong point. We are studying about the personal branding for high school and university students by practical use of social
media. This research aims at a career design of the students by personal branding. Concretely, we gave lectures to high school
students and teachers (junior high school, high school) about social media and personal branding. We conduct surveys about
individual strong points and personal branding for high school students. As the result, we confirmed that they had improved
literacy and understanding of social media. From this survey, we propose a personal branding support service named“Mentors”. In the near future, Mentors may be launched.
Keywords: career design, personal branding, social media literacy
Personal branding brings opportunities to get good human
network and information. This may be good supports toconstruct own career and business. However, personalbranding takes much time until effect appears. Therefore,
the practice of personal branding and social media literacy
in information education at high school class are important
for their career design. However, many high school teachers
cannot teach to students about advantage or risk of social
media effectively, because they are not using social media.
In addition, there is no true answer in how to use social
media, because social media is dependent on discretion ofeach person.
Therefore, when people use social media, people needto have the purpose, and creatively uses of social media are
required. There are few reports of case study about personalbranding of students [6]. In this report, course inclusion
experience about personal branding with social media wascarried out to students who major in internet marketing.
However, the case study which specialized in the social
circumstances in Japan has not been reported. This researchaims at the contribution to “students”, “university”, and“society”, paying attention to the possibility of personalbranding with social media.

1 INTRODUCTION
Today, the employment rate of students in Japan is very
low level by the economic conditions caused by global
depression, and major company intention of many students.
The increase in the early unemployment rate for less than
three years is a serious problem by the mismatch of
adoption between students and companies [1]. People are
required to harness individual strong point for survival in
the society.
Recently, social media, such as Twitter [2] andFacebook [3], has been popularized to our society. In thesocial media times, many people become more active
between online and offline. Then, personal branding (theactivity which asserts own specialty and builds own brand)[4] is very important to harness an individual strong point[5]. In this case, social media literacy is important to use
real name for personal branding.
There are several companies which uses social media
for a recruitment activity as a communication tool withstudents in such circumstances. The purpose of thesecompanies is to employ students with high information
sensitivity who masters these tools, and the student
excellent in communications skills. This new stream iscalled “social recruiting” or “so-katsu (the meaning of thejob hunting activities using social media)”.
We are studying about the personal branding for highschool and university students by practical use of social
media. This research aims at a career design of the students
by personal branding. Here, career is "lifetime products".

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2 SOCIAL MEDIA LECTURE

In this lecture, 29 teachers were divided into six groups.
Each group consists of 4-6 teachers, and discussed about
various theme.

2.1 Purpose
We gave lectures to high school students and teachers(junior high school, high school) about social media andpersonal branding. Purposes of the lectures are as follows.
(1) Basic use of social media and practical use as a toolof information gathering.
(2) Social media literacy and suitable information
disclosure and information transmission.
(3) Students have relation with society positively(participation of events, make human connections).
(4) Discovery of an effective use of social media for the
personal branding of students by active discussions amongprofessors and students.

2.4 Discussions
2.4.1 Discussions of lecture to students
In the lecture of students, their strong points weresurveyed. We classified according to consciousness level ofstrong points. Level 0 is without recognition. Level 1 isonly liking. Level 2 is abstract strong point. Level 3 isconcrete strong point. Level 4 is connected with actual
activity. Fig.1 shows the consciousness level of strong
points of students. About 70 % of students have own strong
points. However, 0 students connected with actual activity.
Our mission is to promote consciousness level of thestudents. Concretely, many students answered as strong
points that continuity (5 answers), creativity (5 answers),
volition (4 answers), imaginary skill (3 answers), curiosity(3 answers), and communication skill (3 answers). And,
there are language, human network, sense of commitment
and leadership as a minor reply.

2.2 Lecture to high school students
In our university, there is cooperation lecture between
neighbor high schools and our university for high school
students. We gave a lecture to students about practice ofsocial media and personal branding [7][8]. Overview of thislecture is as follows.
Date time: August 1, 2012, 10:00-17:00
Number of students: 21
Contents:
 Own strong points
 Social media literacy
 Personal branding Discussions about above theme

In this lecture, 21 students were divided into fourgroups. Each group consists of 4-5 students, and discussed
about various theme.
Fig. 1. The consciousness level of strong points

2.3 Lecture to teachers
Our university also provides a lecture place for renewalof teaching credential. We gave a lecture to teachers about
almost same as the students. Overview of this lecture is asfollows.


Next, we investigated whether high school students
could practice personal branding. It answered that thestudents of 1/3 could practice personal branding, and thestudents of 2/3 answered that they could do conditionally.
The merit of being the high school student whom thestudents answered is as follows.
- Easy to be observed from the society- Easy to gather opinions from young people- Can know about society from the young age- Easy to construct human network- Useful for information gathering

Date time: August 9, 2012, 9:15-16:20
Number of teachers: 29
Contents:
 Own strong points
 Social media literacy
 Information education in the school Personal branding for high school students
 Discussions about above theme© ISAROB 2013

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Personal branding from a young age is very important inrespect of human networking, because it takes long time.
2.4.2 Discussions of lecture to teachers
In the lecture of teachers, their strong points as an
educator to support the students were surveyed. Table 1
shows strong points as a teacher whom the teachers
answered.

strong point is necessary. Then, we propose a personalbranding support service named “Mentors”.
3.2. Overview of Mentors
Mentors is a service which connects mentor and mentee.
Fig. 2 shows overview of Mentors. In Fig. 2, mentee asks
for advice to Mentors, mentor registers own strong points.
Then, Mentors matches mentor and mentee.
The concept of Mentors is “Everyone has face of both
mentor and mentee”. An actual human had better judge an
individual strong point rather than a system judges. The
mind of Mentors are "actually met", "talking various
theme", and "communication after meeting". Then, thefunctions of Mentors are as follows.
- Registration of searching mentor type
- Registration of own strong points- Matching of mentor and mentee

Table 1. Strong point as a teacher
Strong points as a teacher
Answers
Specialty about own subject
21
Human network
10
Student guidance, career guidance
8
Social credibility
7
Sense of commitment
7
Keep youth
6
Communication skill
5
Experience in industries
1

Mentors is similar to Forkwell [9] and Wantedly [10].
Forkwell is skill based networking site for engineers andcreators, and seems to be engineer version LinkedIn. The
engineers can make a personal brand with Forkwell, andthere is mechanism which has own skills evaluated fromother people. Wantedly is matching service to meet others
who has same purpose (project member, party friends etc.).
Mentors specializes in finding the mentor who mentee
wants to meet. Mentors will be developed by Facebook
based Web application, because social graph of Facebook isbased on actual human network. Mentors aims at promotionof human life by understanding their strong points andusing social media effectively.

From Table 1, it is confirmed that the teachers harness
the strong point to education of students. Next, weinvestigated how the teachers could support the students topractice personal branding. The support to personalbranding of the students that the teachers answered is asfollows.
- Actual use of Twitter, Facebook- Case introduction- Observation of the students, interposition- Training of sociality- Improvement of writing skill- Management instruction of personal information- Responsibility to the information- Installation of an opportunity to face oneself- Information moral, literacy education- Instruction corresponding to a trouble
There are many teachers with the volition to student
guidance. The necessity of lecture about social media
literacy and personal branding for the high school students
were confirmed.

Fig. 2. Overview of Mentros

3 PROPOSED SERVICE
3.1 Purpose
From the above discussions, a support service for
personal branding is required all over the world. In personalbranding, it is very important to harness individual strong
point. And, existence of mentors which advises to find own© ISAROB 2013

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4 CONCLUSION
This research aims at a career design of the students by
personal branding with social media, and we gave a lecture
about social media to the students. The contents of thelecture are mechanism about social media, social media
literacy, and personal branding with social media. We
conduct surveys about individual strong points and personalbranding for high school students.
As the result, we confirmed that they had improved
literacy and understanding of social media. For practicaluse of social media, not only an understanding of social
media literacy, but also actual use by each person isnecessity. From this survey, we propose a personal branding
support service named “Mentors”. In the near future,
Mentors may be launched.
Future works are more long time observation about
behavior of students, modeling to find suitable social media
for each person, and evaluation method for teaching
portfolio.

REFERENCES[1] Japan Center for Economic Research (2011), “Theshock of 30 percent of the early unemployment rate for lessthan three years (in Japanese),”http://www.jcer.or.jp/report/econ100/pdf/econ100bangai201
10715.pdf.
[2] Twitter, http://twitter.com/.
[3] Facebook, https://facebook.com/.
[4] Tom Peters (1997), “The Brand Called You,” August
1997,
http://www.fastcompany.com/magazine/10/brandyou.html.
[5] Takashi Ohmoto (2011), Social Media Jissen No
Syo (in Japanese). Ric Telecom, June 2011.
[6] Lyle Wetsch (2011), “Personal Branding with So
cial Media; An Essential Application of Direct Marketi
ng Skills for Today’s Students,” Memorial University of Newfoundland, May 2011.
[7] Y. Kawano (2012), "Social Media Lecture 1st",
http://www.slideshare.net/YoshihiroKawano/social-media-personalbranding201208011.
[8] Yoshihiro Kawano (2012), "Social Media Lecture
2nd",
http://www.slideshare.net/YoshihiroKawano/social-media-personalbranding201208012.
[9] Forlwell, https://forkwell.com/.
[10] Wantedly, https://www.wantedly.com/.
[11] LinkedIn, http://www.linkedin.com/.

© ISAROB 2013

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Syntax Structure Based Typing Tool for Source Code Writing
Takashi Yamaguchi1, Shinya Iwasaki1, Kazuma Mori1, Kenneth J. Mackin1, Masaki Hanada1, Eiji Nunohiro1
1

Tokyo University of Information Sciences, Japan(Tel: 81-43-236-1232, Fax: 81-43-236-1232)
1

tyamagu@rsch.tuis.ac.jp

Abstract: Typing is a fundamental skill for the computer works that has the tasks of text inputting by keyboard. For improving
computer work efficiency, the training of fast typing is very important. In this paper, we proposed a typing tool that presents asource code for the subject word. Proposed application has following three aims: the learning of touch typing, the learning of
the syntax structure of programming language, the learning of source code writing process, and the improvement of continuity
in programming language practice. Our typing tool presents a part of source code for the subject words. The parts of sourcecode are derived by blocks of syntax structure on programming language. These parts are presented by the sequences fromsuper to sub nested blocks. From this source code presentation, the learner can feel the syntax structure and the writing process
of expert programmers.
Keywords: e-learning, programming, typing tool, gameIn our past research, we develop an e-learning system
for JAVA programming [3-8] that focus on the improvement
continuity by ARCS model. Our e-learning system focuseson the programming learning with entertainment. In thispaper, we investigate an application of typing tool for theelementary training of the programming language as
another approach of entertainment e-learning system forprogramming.

1 INTRODUCTION
Typing is a fundamental skill for the computer works
that has the tasks of text inputting by keyboard. For
improving computer work efficiency, the training of fasttyping is very important. Therefore, in an elementary
information technology education, students are often
trained the touch typing that faster typing technique without
using the sense of sight to find the keys. However, the longiterative practices are necessity because the finger
placement of touch typing must be learned andunconsciously used.
For the training of touch typing, the typing tools arecommonly used. The typical typing tool is software productthat presents the subject words to make a learner types thissubject words, and shows the result of typing errors for help
the training of typing. Because the learning of touch typing
is based on the iterative practice, the typical typing tools aredesigned like the computer games to improve the continuityof practice by make the attention and competitive spirit oflearner [1].
The improvement of learning continuity is very
important characteristics for self-learning using e-learning
systems because typical e-learning systems are used for
self-learning. For improvement of learning continuity, John
M. Keller proposed ARCS model that is an instructional
design model by focusing on Attention, Relevance,
Confidence, and Satisfaction [2]. These 4 ARCS modelfeatures are considered to be fundamental features todevelop the design of products and package soft wares that
include the typing tool or game.

© ISAROB 2013

2 PROGRAMMING LEARNING SYSTEM
2.1 puzzle-based programming learning system
In our past research, we develop CAPTAIN (Computer
Aided Programming Training And INstruction) that is an elearning system for JAVA programming. Fig.1 is screenshot
of CAPTAIN client.

Fig.1. Screenshot of CAPTAIN client

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of times that learner hit the back space and delete key iscounted for the typing error summary.
Our typing tool presents a part of source code for the
subject words. The parts of source code is derived byblocks of syntax structure on programming language suchas class definition, method definition, if statement, forstatement, and etc. Typically, this syntax structure can beseen as the indention of source code in generalprogramming languages. Our typing tool presents the partsof source code for each the indention level.
In this research, we defined the source code partition
rules for JAVA language. The details of source code
presentation method are described in next section.

CAPTAIN focuses on the feature of entertainment for elearning system. CAPTAIN has following three major
functions; the puzzle-based programming, the score ranking,score visualization. CAPTAIN trains the ability to read thesource code by puzzle-based programming function andimprove the motivation by score ranking function.
Moreover, the teachers can confirm the learner’s
understanding and individually support their understanding
led by teacher.
The puzzle-based programming is a main function of
CAPTAIN client for training JAVA programming language.
The source code is separated into the multiple source code
parts like the orange colored area in Figure 1. These parts
are presented to the learner with randomly ordering. The
learner drag and drop the source code parts to restore to
original source code. From this system, the learner istrained the ability of source code reading. On the other hand,the ability of source code writing is not improve since thelearner has nothing to write in programming training on
CAPTAIN system. Therefore, we proposed a typing tool
based programming training system in order to improve theability of source code writing.

2.2 Typing tool based programming learning system
In this paper, we proposed a typing tool based
programming training system that presents a source code as
the subject word for each syntax structure. Proposed
application has following four aims: the learning of touchtyping, the learning of the syntax structure of programming
language, the learning of source code writing process, andthe improvement of continuity in programming language
practice. In the scene of programming lecture, the teachers
have a problem that the slowness of programming task
disturbs the learner’s understanding and the progress oflecture. The proposed training system is expected to resolve
this problem by the training of touch typing and sourcecode writing with continuous self-learning.
Figure 2 is a prototype of source code typing tool. The
subject words are shown in the text pain of left side and thelearner writes the words to match the subject words. The
subject words are incompletely shown in initial state. The
subject words are updated when the learner correctly write
the subject words. Moreover, the input error check methodof our typing tool is different from the typical typing tools.
The typical typing tool requires the keyboard input andcheck the input errors for each word. Our typing tool checkthe input errors for each time that line feed is input in orderto check syntax error in the future. Additionally, the number© ISAROB 2013

Fig.2. Prototype of source code typing tool

3 IMPLEMENTATION ON JAVA
In JAVA language, source code can be grouped bynested structure of class and interface definition, fielddefinition, method definition, if statement, for statement,
while and do statement, switch statement. The JAVA sourcecode is partitioned into these nested syntax structures andthese parts are presented by the sequences from super to
sub nested blocks similar to the common writing sequenceof expert JAVA programmer. Therefore, proposed sourcecode presentation method is expected to effective forelementally programming training that requires the learningof the source code writing procedure and the fundamental
syntax structure.

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When the learner completes the initial words writing,the field, constructer and method definitions are newly
presented as 2nd subject words shown as bold words in
Figure 5. After the completion of 2nd subject words, thecomplete source code is presented as 3rd subject words.

/* SAMPLE PROGRAM */import java.io.BufferedReader;import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;import java.util.HashMap;/* SAMPLE PROGRAM */import java.util.Map;

import java.io.BufferedReader;import java.io.File;public class Sample {import java.io.FileInputStream;
public static String DELIMITER = ",";

import java.io.IOException;

private Map<String, String> values;

import java.io.InputStreamReader;import java.util.HashMap;
import java.util.Map;

public Sample(String file) {this(new File(file));}public class Sample {public Sample(File file) {}Fig.4. Initial subject words

values = load(file);}/* SAMPLE PROGRAM */private Map<String, String> load(File file) {import java.io.BufferedReader;<… code …>

import java.io.File;return list;

import java.io.FileInputStream;}

import java.io.IOException;}

import java.io.InputStreamReader;Fig.3. Sample source code

import java.util.HashMap;
import java.util.Map;

For example, the source code presentation process is
explained using an original source code of sample program
shown in Figure 3. Only class definitions and package
definitions are presented for initial subject words like as a
Figure 4. The learner should write to match the bolded
words.
When the learner input the line feed or move cursor toanother line, the typing error was checked and the words
include the error are highlighted in input text pain. In thistime, the number of typographic error is counted for the
result summary. The number of times that learner hit theback space and delete key is separately counted as keyboard
input error in order to separate the result to typographic
error and keyboard input error. The typographic error isconsidered to be occurred by the lack of understanding of
programming language syntax. From this function, thelearner can easily discover the error words in real time, andunderstands that the week point is syntax of programming
language or keyboard input.

© ISAROB 2013

public class Sample {public static String DELIMITER = ",";
private Map<String, String> values;

public Sample(String file) {}public Sample(File file) {}private Map<String, String> load(File file) {}}Fig.5. 2nd subject words

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Yamasaki K, Nunohiro E (2008), Puzzle Based
Programming Learning Support System with Learning
History Management, The 16th International Conference on
Computers in Education (ICCE2008), pp. 623-627[6] Yamakawa Y, Ohshiro M, Matsushita K, Mackin K,
Nunohiro E (2010), Programming Learning Support
System ’CAPTAIN’ with Motivational Study Model, The
18th International Conference on Computers in Education(ICCE2010), pp. 171-175[7] Ohshiro M, Mackin K, Matsushita K, Nunohiro
E,Yamakawa Y (2010), Programming Learning Support
System with Learning Progress Monitoring Feature, Joint
5rd International Conference on Soft Computing andIntelligent Systems and 11th International Symposium on
Advanced Intelligent Systems (SCIS-ISIS 2010), pp.14651468[8] Ohshiro M, Matsushita K, Mackin K, Yamaguchi T,Nunohiro E (2012), Programming Learning Support Systemwith Competitive Gaming Using Monitoring and Nicknames, The 17th International symposium artificial life and robot
ics, pp.238-241.

After the training, the error summary is presented to thelearner. Currently, the summary consists of the number oftypographic error, the number of keyboard input error, andaverage times of key board input. We plan to enhance thesummary based on the syntax check to be able to suggest
the lack of understanding on syntax of programming
language in the future.

4 CONCLUSION
In this paper, we proposed a typing tool based
programming training system that presents a source code as
the subject word for each syntax structure in order to train
the touch typing and the source code writing in continuous
self-learning. Moreover, we develop a prototype of
proposed typing tool based programming training system
for JAVA programming language. This system expected toeffective for training touch typing, understanding syntax
structure, and continuous self-learning.
For future works, we plan to evaluate the effectivenessof our system on the practical use in the self-learning the
programming lectures because it is necessary to separately
evaluate on the effectiveness of touch typing training,
effectiveness of programming training, and the continuityof self-learning.

ACKNOWLEDGMENTS
This research was supported by The Ministry of
Education, Culture, Sports, Science and Technology(MEXT), Grant-in-Aid for Scientific Research (C)(24501207).

REFERENCES[1] M Prensky (2005), Computer games and learning:
Digital game-based learning, New York: McGraw Hill
[2] JM Keller (1987), Development and use of the ARCS
model of instructional design, Journal of instructional
development,Springer[3] Nunohiro E, Mackin K, Ohshiro M, Matsushita K,
Yamasaki K (2007), Implementation of a GA driven
programming training support system, The 12th
International symposium artificial life and robotics, pp.517522.
[4] Nunohiro E, Matsushita K, Mackin K, Ohshiro M,
Yamasaki K (2008), Design and development of a puzzle
based programming learning support system with genetic
algorithm (in Japanese), Transactions of Japanese society
for information and system in education, vol.25, No.2,
pp.207-213.
[5] Yoneyama Y, Matsushita K, Mackin K, Ohshiro M,© ISAROB 2013

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Robust digital control for interleave PFC boost converter
Yuto Adachi1, Kohji Higuchi1, Tatsuyoshi Kajikawa1, Tomoaki Sato2, and Kosin Chamnongthai3
1

The University of Electro-Communications 1-5-1, Chofu-ga-oka,Chofu-shi, Tokyo 182-8585, JapanC&C SYSTEMS CENTER Hirosaki University, Bunkyo-Cho 3, Hirosaki-shi, Aomori 036-8561, Japan
3
King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmond, Tungkru, Bangkok 10140, Thailand(Tel: 81-42-443-5182, Fax: 81-42-443-5183)
2

1

arobsecr@isarob.org

Abstract: In recent years, improving of power factor and reducing harmonic distortion in electrical instruments are needed. In

general, a current conduction mode boost converter is used for active PFC (Power Factor Correction). Especially, an interleave
PFC boost converter is used in order to make a size compact, make an efficiency high and make noise low. In this paper, arobust digital controller for suppressing the change of step response characteristics and variation of output voltage at a loadsudden change with high power factor and low harmonic is proposed. Experimental studies using a micro-processor for controller
demonstrate that this type of digital controller is effective to improve power factor and to suppress output voltage variation.

Keywords: interleave PFC, boost converter, digital robust control, micro-processor
controllers which can cope with nonlinear system orchanging of the models with one controller is proposed.
One is an approximate 2-Dgree-of-freedom (A2DOF)
controller for a current control system and another is a PI
controller. These controller are actually implemented on a
micro processor and is connected to the PFC converter.
Experimental studies demonstrate that the digital
controllers designed by proposed method satisfy the desired
performances and are useful.

1 INTRODUCTION
In recent years, improving of power factor and reducing
harmonic of power supply using nonlinear electricalinstruments are needed. A passive filter and an active filter
in AC lines are used for improving of the power factor andreducing the harmonic. Generally a current conduction
mode boost converter is used for an active PFC (Power
Factor Correction) in electrical instruments. Especially, aninterleave PFC boost converter is used in order to make asize compact, make an efficiency high and make noise low.
In the PFC boost converter, if a duty ratio, a load resistanceand an input voltage are changed, the dynamic
characteristics are varied greatly, that is, the PFC converter
has non-linear characteristics. In many applications of theinterleave PFC converters, loads cannot be specified inadvance, i.e., their amplitudes are suddenly changed fromthe zero to the maximum rating. This is the prime reason ofdifficulty of controlling the PFC boost converter.
Usually, a conventional PI or analog IC controller
designed to the approximated linear controlled object at one
operating point is used for the PFC converter. In the
nonlinear PFC boost converter system, those controllers
are not enough for attaining good performance. In thispaper, the robust controller for suppressing the change ofstep response characteristics and variation of output voltage
at a load sudden change with high power factor and low
harmonic is proposed. An approximate 2-degree-offreedom (A2DOF) method [1] is applied to the interleave
PFC boost converter with the load. The PFC converter is anonlinear system and the models are changed at each
operation point. The design and combining methods of two© ISAROB 2013

2 INERLEAVE PFC BOOST CONVERTER
2.1 State-space model of interleave boost converter
The interleave boost converter shown in Fig. 1 ismanufactured.

Fig.1 Interleave PFC boost converter

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3 DIGITAL ROBUST CONTROLLERS

Fig.1, vin is an input AC voltage, iin is an input ACcurrent, Cin is a smoothing capacitor, Vi is a rectifying andsmoothing input voltage, Q1 and Q2 are MOSFETs or
IGBTs, L1 and L2 are interleave boost inductances, D1 andD2 are interleave boost diodes, C0 is an output capacitor, RL
is an output load resistance, iL is the sum of inductor current,
vac is an absolute value of the input AC voltage and vo is an
output voltage. The inductor currents iL is controlled tofollow the rectified input voltage vac for improved power
factor, reduced harmonics and stable the output voltage.
At some operating point, let vo, iL and µ, be Vs, Is and µs,
respectively. Then the linear approximate state equation of
the boost converter using small perturbations ∆iL = iL-Is ,∆vo= vo-Vs and ∆µ=µ-µs is as follows:
x& (t ) = Ac x (t ) + Bc u (t )y (t ) = C c x (t )3.1 Discretization of controlled object
The continuous system of eq. (1) is transformed into thediscrete system as follows:x d ( k + 1) = Ad x d ( k ) + B d u ( k )3 y (k ) = C d xd (k )whereT
Ad = e AcT , Bd =  e Ac τ Bc dτ, Cd = Cc0

Here, in order to compensate the delay time by A/Dconversion time and micro-processor operation time etc.,
one delay (state ξ1) is introduced to input of the controlled
object. Then the state-space equation is described asfollows:xdt (k + 1) = Adt xdt (k ) + Bdt v(k )(3)y (k ) = C dt xdt (k )

(3)

where

 R0
− L
0
Ac = 
−
1
µ
s

 C0

∫

where

1 − µs  Vs − L 
L0, Bc =  0 
1 
− I s − C0 RL C0  a11 a12 a13 
Lde Ac (Ts − Ld ) ∫ e Acτ Bc dτ  0 =  a21 a22 a23 
0  0
0
0 

b 
 Ts − Ld e Acτ B dτ   11 
 x(k ) c
B dt =  ∫0
 = b21  xdt ( k ) = ξ1 ( k ) 1  1  
C dt = [C c 0] = [1 0 0]
e AcTs
Adt =  0y 
 ∆i (t ) 
1 0 
x(t ) =  L , u (t ) = ∆µ (t ), y =  i , Cc = 

y
v
t
∆
(
)
0 1  o 
 vHere µ is duty ratio. When controlling the current iL of thesum of each phase, R0 (equivalent resistance of inductor) is
R1R2/(R1+R2) and L0 is L1L2/(L1+L2). And ∆iL, ∆vo, ∆µ aresmall-signal variables. And yi,= Δ iL is a small signal
inductor current and yv = Δ vo is a small signal output
voltage.
From this equation, matrix A and B of the interleave
boost converter depends on duty ratio µs. Therefore, theconverter response will be changed depending on theoperating point and other parameter variations. The changesof the load RL, the duty ratio µs, the output voltage Vs andthe inductor current Is in the controlled object areconsidered as parameter changes in eq. (1). Such parameterchanges can be replaced with the equivalent disturbances
inputted to the input and the output of the controlled object.
Therefore, what is necessary is just to constitute the control
systems whose pulse transfer functions from equivalentdisturbances to the output y become as small as possible intheir amplitudes, in order to robustize or suppress theinfluence of these parameter changes.

© ISAROB 2013

[ ]

(4)

3.2 A2DOF digital current controller
The transfer function from the reference input ri’ to the
output yi is specified as follows:(1 + H 1 ) (1 + H 2 ) (1 + H 3 )Wri′yi ( z ) =(z + H 1 ) (z + H 2 ) (z + H 3 )(4)×(z − n1i ) (z − n2i )(1 − n1i ) (1 − n2i )Here Hi, i=1,…,3 are the specified arbitrary parameters,
n1i and n2i are the zeros of the discrete-time controlled
object. This target characteristic Wryi is realizable byconstituting the model matching system shown in Fig.5
using the following state feedback to the controlled object(5).
(5)
v = − Fxdt − Gi ri′
Here F = [f1 f2 f3] and Gi are selected suitably. In Fig.
5, qv and qyi are the equivalent disturbances with which theparameter changes of the controlled object are replaced.

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From eq. (10), (11), it turns out that the characteristics fromr to y can be specified with H1 and the characteristics from
Qi to yi can be independently specified with kzi. That is, thesystem in Fig. 3 is an A2DOF system, and its sensitivity
against disturbances becomes lower with the increase of kzi.
If equivalent conversion of the controller in Fig.3, we
obtain Fig. 4. Then, substituting a system of Fig. 2 to Fig. 4,
A2DOF digital integral type control system will beobtained as shown in Fig. 5. In Fig. 5, the parameters of thecontroller are as follows:

Fig.2 Model matching system using state feedback
It shall be specified that the relation of H1 and H3
become |H1| >> |H3| and n1i ≈ H2. Then Wri’yi can beapproximated to the following first-order discrete-timemodel:

1 + H1
Wri′yi ( z ) ≈ Wmi ( z ) =z + H1(5)

k1 = − f1 −Gk zi,
1 + H1k3 = − f 3 ,k ii = Gi k zi ,k2 = − f 2(12)k ri = Gi
Qi

k zi
z 1

yi

The transfer function WQyi(z) between the equivalentdisturbance Qi = [qv qyi]T to yi of the system in Fig.2 isdefined as(6)
WQyi ( z ) = Wqvyi ( z ) Wqyyi ( z ) (10)ri

The system added the inverse system and the filter tothe system of Fig.2 is constituted as shown in Fig.3.

Fig. 4 Equivalent Conversion of the Robust Digital[

Wryi(z), W Qyi(z)
k zi
1 H1

]

qv

k ri

1

ri

z 1

xdt(k+1)=A dtx dt(k)+B dtv(k)v y(k)=C dtx dt(k)
iL
vo
ξ1

k ii

q yi

yi

k3
k2
k1

Fig. 3 System Reconstituted with Inverse System and F
ilter

Fig. 5 Approximate 2DOF Digital Integral Type Current Control System

In Fig. 3, the transfer function Ki(z) is as follows:k zi(7)
K i ( z) =z − 1 + k zi

4 DIGITAL VOLTAGE CONTROLLER

The transfer functions between ri -yi, qui -yi and qyi -yi of thesystem in Fig.6 are given byz − 1 + k zi
1 + H1(8)yi =
Wsi ( z )ri
z + H 1 z − 1 + k ziWsi ( z )z − 1 + k zi
z − 1 + k ziyi =WQyi ( z )Qi (9)z − 1 + k zi z − 1 + k ziWsi ( z )where(1 + H 3 ) (z − n1i )Wsi ( z ) =(z + H 3 ) (1 − n1i )4.1 Addition of uv and vac to ri
Add the multiplier in front of the reference input ri ofthe current control system. Let the inputs of the multiplier
be vac and uv as shown in Fig. 6. vac is the absolute value ofthe input voltage vin and uv is a new input. This addition isfor making the inductor current iL follow the AC voltage vac.

rv'Here, if Wsi ( z ) ≈ 1 , then eq. (8) and eq. (9) areapproximated, respectively as follows:
1 + H1(10)yi =riz + H1z −1(11)
WQyi ( z )Qiyi =z − 1 + k zi

Gv

vac

kri

uv ri

1

z 1

qv
kii

qyi
yi
xdt(k+1)=Adtxdt(k)+Bdtv(k)v y(k)=Cdtxdt(k)
iL
ξ1
vo
k3
k2

qyv
yv

k1

Fig. 6 Current Control System Added Multiplier© ISAROB 2013132

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Next, the digital PI voltage controller is added to the
input of Fig. 6. Then the digital robust control system
including the A2DOF current controller and the PI voltage
controller is obtained as shown in Fig, 7.

Fig.8 Experimental Results of Steady State Waveform,
at load RL=500Ω by proposed method

Fig. 7 Digital Robust Control System Including the A2DOF
Current Controller and the PI Voltage Controller

4 Expermental Studies
All experimental setup system is manufactured. A
micro-controller (RX) from Renesas Electronics is used forthe digital controller. The digital PI current, and voltage
controllers were implemented on 1 micro-processor.
The design parameters of the A2DOF current control
system have been determined as
H1=-0.999866

H2=-0.6

Fig.9 Experimental Results of Sudden Load Change
from 1kΩ to 500Ω controlling the current for every phase

H3=0.1 kzi=0.5

And the parameters of the PI voltage controller have been
determined as
krv=8

Fig.10 Experimental Results of Steady State Wavefor
ms at load RL=500Ω by usual phase lead-lag method

kiv=0.01_

4 CONCLUSION

The experiment results are shown in Fig. 8, 9, 10. The
experiment result of the steady state at load RL=500Ω by
proposed method are shown in Fig. 8. The input current
waveform and the phase are the almost same as the input
voltage at each load and PFC of the converter at load
RL=500Ω are 0.991 and 0.985, respectively. The
experiment result of the steady state at load RL=500Ω byusual phase lead-lag method are shown in Fig. 10. PFC of
the converter at load RL=500Ω are 0.985. The experimentresult of load sudden change from 1kΩ to 500Ω is shown in
Fig. 6. In Fig. 6, the output voltage variation in sudden loadchange is less than 3V (0.78%). It turns out that the digitalrobust controller proposed is effective practically.

© ISAROB 2013

. In this paper, the concept of controller of non-linear
interleave PFC boost converter to attain good robustness
was given. It was shown from experiments that the
proposed A2DOF digital current controller can attain better
performance.

REFERENCES[1] K. Higuchi, K. Nakano, T. Kajikawa, E. Takegami, S.
Tomioka, K. Watanabe, ”A New Design of Robust Digital
Controller for DC-DC Converters”, IFAC 16th Triennial
World Congress, (CD-ROM), 2005

133

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Synthesis of continuous-time dynamic quantizers for LFT type quantized
feedback systems
Kenji Sawada1 and Seiichi Shin1
1

The University of Electro-Communications, Tokyo, Japan(Tel: +81-42-443-5253; E-mail: {knj.sawada,seiichi.shin}@uec.ac.jp)
Abstract: This paper focuses on analysis and synthesis methods of continuous-time dynamic quantizers for LFT type
quantized feedback systems. Our aim is to find multiple (decentralized) quantizers such that a given linear system is
optimally approximated by the given linear system with the quantizer in terms of invariant set analysis. In the case ofminimum phase systems, this paper clarifies that optimal dynamic quantizers and its performance are parameterized by adesign parameter. Also, an analytical relation between the static and dynamic quantizers will be presented.
Keywords: Quantizer, Continuous-time, LFT

1. INTRODUCTION

quantizer which is applicable for the centralized control
system. Since the sensors and actuators are distributed in
the networked control system, it is natural to implement
multiple (decentralized) quantizers rather than a centralized quantizer for the I/O quantized feedback system. Focusing on the LFT type system that covers various quantized feedback systems, we will provide extension resultsof our early work [7]. As space is limited, this paper concentrates on the minimum phase case. We will clarify theeffectiveness and the limitation of our proposed quantizers. In the case of minimum phase systems, it is clarified
that optimal dynamic quantizers and its performance areparameterized by a design parameter.
Notation: The set of n × m (positive) real matrices
is denoted by IRn×m (IRn×m). The set of n× (positive)+
integer matrices is denoted by INn×m (INn×m). 0n×m+and Im (or for simplicity of notation, 0 and I) denote then × m zero matrix and the m × m identity matrix, respectively. For a matrix M , M T , λ(M ), λi (M ) and λmax (M )denote its transpose, its eigenvalue set, the ith element of
the set λ(M ) and its maximum eigenvalue, respectively.
For a number n ∈ IN+ , n! denotes its factorial. For acomplex number c, Re(c) is its real part. For a vector x,
xi is the ith entry of x. For a symmetric matrix X, X > 0(X ≥ 0) means that X is positive (semi) definite. For afull row rank matrix M , M † denotes its pseudo inverse
matrix which is given by M † = M T (M M T )−1 . For amatrix X, ∥X∥2 denotesits 2-norms.
Finally, we use the)(
A B:= C(sI − A)−1 B + D.
“packed” notation:
C D

The cyber-physical system connects the physical systems with the information systems. In the research fieldof control theory, the control problem of such the system
is one of the most active topics since the system covers
various systems including discrete-valued-signals such as
networked systems, hybrid systems, embed devices with
D/A-A/D converters [1]. For the above challenging problem, it is important to focus on optimality of systems
controlled by the discrete-valued signals. There clearlyexits a difference between control performances of thesystems controlled by the continuous-valued signal andthe discrete-valued signal. Motivated by this, this paper
focuses on the quantized feedback systems including thediscrete-valued signals.
Considering optimality of quantized feedback systems, some existing results have provided optimal dynamic quantizers for the following problem formulation:
When a plant and a controller are given in the usual feedback system, the framework synthesizes a dynamic quantizer that minimizes the maximum output difference between before and after the quantizer insert. In this case,the quantized feedback system with such the quantizer
optimally approximates the original feedback system inthe sense of the input-output relation. The two main typesof dynamic quantizer are discrete-time and continuoustime settings. A number of the dynamic quantizer studies
have been done in the discrete-time setting [2-5]. On theother hand, continuous-time dynamic quantizer is a key
device for recent broadband wireless communication andmobile systems because of lower power and longer battery life compared with discrete-time ones [6]. Also, it
is natural to consider the continuous-time setting in thesense that model uncertainty expressed in continuoustime domain is suitable for robust control of physicalmodel.
Motivated by the above, this paper considers acontinuous-time dynamic quantizer design for quantized
feedback systems. In particular, we consider the LFT(linear fractional transformation) type quantizer feedback
system. Our early work has provides an optimal dynamic© ISAROB 2013

2. PROBLEM FORMULATION
Consider the quantized feedback system which consists of the LTI continuous-time plant P (s) with the state
xp ∈ IRnp , the LTI continuous-time controller C(s)with the state xc ∈ IRnc , and the dynamic quantizers
v1 = Qd1 (u1 ), v2 = Qd2 (u2 ). The systems P (s) andC(s) are given by


(
)[ ]
[ ]
Ap Bp
Ac Bc1 Bc2
v2
zp=  Cp1 0  v1 , u1 =r
u2
Cc Dc1 Dc2
Cp2 0

134

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(b) Usual feedback system

Fig. 2. Midtread type quantization(a) LFT type quantized feedback
system with multiple quantizers

i.e.,Fig. 1. Generalized quantized and unquantized systems

vi = qst (uqi ), uqi := vqi + ui
and the dynamic compensator Qi (s) given by
[
] [
][
]
ẋqi
Aqi Bqi
xqi=, eqi := vi − ui
vqi
Cqi 0
eqi

where zp ∈ IR , r ∈ IR , u1 ∈ IR , u2 ∈ IR ,v1 ∈ IRm1 , and v2 ∈ IRm2 denote the measured output, the exogenous input, the controller output, the plantmeasured output, the plant input, and the controller input, respectively. The continuous-valued signals u1 andu2 are quantized into the discrete-valued signals v1 andv2 because of v1 = Qd1 (u1 ) and v2 = Qd2 (u2 ). For
the above systems, define the following vectors: xg :=[ xTp xTc ]T ∈ IRng (ng := np + nc ), u := [ uT1 uT2 ]T ,v := [ v1T v2T ]T , and matrices:
[
]
[
]
[
]
Ap 0
0
Bp 0
A :=
, B1 :=, B2 :=
,
0 Ac
Bc2
0 Bc1
[
]
[
]
0 Cc
C1 := Cp1 0 , D11 := 0, C2 :=
,
Cp2 0
[
]
[
]
Dc2
0 Dc1
D21 :=, D22 :=
,
0
0 0
q

p

m1

m2

where vi ∈ IRmi , ui ∈ IRmi , and vqi ∈ IRmi . For s = 2,
one gets the i/o type dynamic quantizers. Then the quantizer Qd := diag(Qd1 , ..., Qds ) is realized by the staticT
T T
quantizer Qst := [qst, .., qst
] : IRm → dINm , i.e.,v = Qst (uq ), uq := vq + u

and the compensator Q(s) := diag(Q1 (s), ..., Qs (s)),
i.e.,
[ ]
[
][ ]
ẋq
Aq Bq
xq
=
,
(3)
vq
Cq 0
eq
Aq := diag(Aq1 , ..., Aqs ), Bq := diag(Bq1 , ..., Bqs ),Cq := diag(Cq1 , ..., Cqs )where xq := [ xTq1 , ..., xTqs ]T ∈ IRnq , vq :=T T
T, ..., vqs] ∈ IRm , uq := [ uTq1 , ..., uTqs ]T ∈ IRm[ vq1
and eq := [ eTq1 , ..., eTqs ]T ∈ IRm . Note that qst is of
the nearest-neighbor type toward −∞ with the quantization interval d ∈ IR+ and the initial state is given
by xq (0) = 0 for the drift-free of Qd [2, 3] such as the
midtread type quantizer in Fig. 2.
For the LFT system with G(s) and Qd in Fig. 1 (a)with the initial state x0 = xg (0) and the exogenous signal r ∈ Lp∞ , zp (t, x0 , r) denotes the output of zp at thetime t. Also, for the system in Fig. 1 (b) without Qd ,zp∗ (t, x0 , r) denotes its output at the time t. This paper
considers the following cost function:and the quantizer Qd = diag(Qd1 , Qd2 ). In this case,one gets the linear fractional transformation (LFT) type
quantized feedback system in Fig. 1 (a) where the LTI
continuous-time generalized plant G(s) with the statexg ∈ IRng is represented by

 


ẋg
A B1
B2
xg
 zp  =  C1 D11
0  r  .
(1)
yC2 D21 D22v
As shown above, the LFT formulation in (1) covers thevarious systems. Then this paper considers the LFT type
quantized feedback systems. Also, we assume that thematrix A+B2 (I −D22 )−1 C2 is Hurwitz, that is, the usual
feedback system in Fig. 1 (b) is stable in the continuoustime domain.
For the system G(s), we define the discrete-valued
vector v := [ v1T , ..., vsT ]T ∈ IRm and the continuousvalued vector u := [ uT1 , ..., uTs ]T ∈ IRm , respectively, and consider the dynamic quantizer v = Qd (u)which consists of the multiple dynamic quantizers vi =Qdi (ui ) (i = 1, ..., s) with the state vector xqi ∈IRnqi . The case s > 1 implies that the multiple dynamic quantizers Qdi are distributedly implemented. The
sub-quantizer Qdi consists of the static quantizer qst :IRmi → dINmi with the quantization interval d ∈ IR+ ,© ISAROB 2013

(2)

J(Qd ) :=sup(x0 ,r)∈IRng ×Lp∞z(x0 , r)

where∗z(x0 , r) := max sup |zpi (t, x0 , r) − zpi(t, x0 , r)|it∗and zpi , zpi
denote the ith entry of zp and zp∗ , respectively.

If the quantizer Qd minimizes J(Qd ), the system inFig. 1 (a) optimally approximates the usual system inFig. 1 (b) in the sense of the input-output relation. In thiscase, we can use the existing continuous-time controller
design methods for the system in Fig. 1 (b).
Motivated by the above, our objective is to solve thefollowing continuous-time dynamic quantizer synthesis

135

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

holds. For a given scalar f ∈ IR+ , an optimal solutionof (nqi , Aqi , Bqi , Cqi ) (i = 1, 2, ..., s) and its infimum ofγ{ ∈ IR+ to the problem (OP) are given bynqi = ng , Aq = A, Bqi = Bi
(7)
Cqi = −(C1 Aτi Bi )† C1 (A + f I)τi +1
for every i√= 1, 2, ...., s andd m
inf γ = √ ∥ [ σρ1 . . . σρi . . . σρs ] ∥2 ,
(8)
2 ρ
√
(2τi )!
C1 Aτi Bi ,σρi :=(τi !)2 (2f − ρ)2τi +1

problem (E): For the LFT system composed of (1), (2)and (3) with the initial state x0 ∈ IRng and the exogenous
signal r ∈ Lp∞ , suppose that the quantization intervald ∈ IR+ and the performance level γ ∈ IR+ are given.
Characterize a stable continuous-time dynamic quantizer
Qd (i.e., find parameters (nqi , Aqi , Bqi , Cqi )) achieving
J(Qd ) ≤ γ.

3. MAIN RESULT
Define the following matrices:
D := (I − D22 )−1 , C := DC2 , A := A + B2 C,
[
]
[ ]
A
BCq
B
B := B2 D, A :=, B :=,
0 Aq + Bq Cq
Bq[]
C := C1 0 .

ρ = min{|Re(2λi (A))|, |Re(2λi (Aq + Bq Cq ))|}i

if the matrix Aqi + Bqi Cqi defined in (7) is Hurwitz for
every i = 1, 2, ..., s.
In this paper, we call the quantizer in (7) the decentralized optimal dynamic quantizer Qop
d . Theorem 1 indicates that Qop
andits
achievable
performanceare paramd
eterized by the scalar f . Next, this paper considers therelation between the scalar f and the stability of Qop
d for
the simple case q = m, in addition, presents a tractableadjustable range of f similar to [7]. For the simplicity, we
consider the centralized quantizer case s = 1 and τi = τ .

For the matrix B ∈ IRng ×m and i = 1, ..., s, Bi ∈IRng ×mi denotes the ith block column of B, i.e., B :=[ B1 , ..., Bs ].
Assumption 1: For every i = 1, ..., s, the matrix
C1 Aτi Bi is full row rank where τi ∈ {0} ∪ IN+ is thesmallest integer satisfying C1 Aτi Bi ̸= 0.
In quantizer analysis, by using our early result [7], we
obtain the the optimization problem (Aop):minγ s.t.
]AT P + PA + αP
PB
≤ 0,
4α
BT P
− md
2 Im
[
]
P CT
≥ 0.
C γ 2 Iq

P>0,mini {|Re(2λi (A))|}>α>0,γ>0

[

(4)
(5)

(b) Quantizer Qd with Qeq (s)(a) Quantizer Qd with Qe (s)That is, the performance level γ in (5) evaluates theupperbound of the difference between zp∗ (t, x0 , r) andzp (t, x0 , r) within in invariant set framework, andFig. 3. Equivalent expression of dynamic quantizer Qd
As shown in Fig. 3 (a), an equivalent expression of thequantizer Qd is given by the quantization error Qe andQe (s) := Cq (sI − (Aq + Bq Cq ))−1 Bq . The quantizer
structure in Fig. 3 (a) can be recast as the system with
Qeq (s) := Qe (s) + I in Fig. 3 (b) where the signal e isthe difference e := v − uq and eq = vq + e. For thequantizer Qop
d , then one gets()
(A − B(C1 Aτ B)−1 C1 (A + f I)τ +1 B
Qop
(s)
:=
eq
−(C1 Aτ B)−1 C1 (A + f I)τ +1
I

J(Qd ) ≤ γ
holds. Also, the infimum of γ can be expressed by thefollowing lemma [7].
Lemma 1: Suppose that the quantization interval d ∈IR+ is given. Consider the problem (Aop). The infimumof γ is given by√d m√
inf γ = inf √λmax D(α),
(6)
α 2 α
∫ ∞
T
D(α) :=
Ce(A+α/2I )t BBT e(A+α/2I ) t CT dt,

which is corresponding to the compensator Qeq (s) in
Fig. 3 (b). The inverse system of Qop
eq (s) is given by(∞τ−1∑ (C1 A B) C1 (A + f I)τ +1 Ak B )opQ̃ep (s) =
+I
sk+1

0

α ∈ (0, min{|Re(2λi (A))|}).
i

The problem (Aop) suggests that the quantizer synthesis problem (E) reduces to the following non-convex
optimization problem (OP):min

P>0,Aq ,Bq ,Cq ,ᾱ>α>0,γ>0

k=0= (C1 Aτ B)−1 (s + f )τ Ge (s)where Ge (s) := C1 (sI − A)−1 B. Then we obtain thestability condition of Qop
d .
Theorem 2: The following statements hold. (i) The
transmission poles of Qop
eq (s) consist of both “−f ” andthe transmission zeros of Ge (s). (ii) The optimal dynamic
quantizer Qop
d in (7) is stable if and only if the all transmission zeros of Ge (s) are stable (i.e., the system G(s) is
minimum phase).

γ s.t. (4) and (5)where ᾱ := mini {|Re(2λi (A))|}. That is, if (OP) is
feasible, (E) is feasible and the obtained quantizer is stable. Under some circumstances, we obtain an closed form
solution from (6) as follow.
Theorem 1: Consider the non-convex optimization
problem (OP). Suppose that s > 1 and Assumption 1© ISAROB 2013

136

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The resultant zeros of Ge (s) are −3 and −4. For thequantizer Qop
d in (7), we set d = 10 and f = 10. The
performance isinf γ = 4.23. Fig. 4 shows the time responses of u1 (t), u2 (t), v1 (t), v2 (t) and zp (t) for the
dynamic quantizer in Theorem 1 with the initial statexg (0) = [ −4 0 −1 ]T . The thin lines and the thick lines
illustrate the time responses of the usual feedback system
in Fig. 1 (b) and the quantized feedback system in Fig. 1(a), respectively. The controlled output zp of the dynamic
quantizer does not go to zero. However, we see that thecontrolled output of Fig. 1 (a) approximates that of Fig. 1(b) even if the discrete-valued signals v1 ∈ {−10, 0, 10}
and v2 ∈ {−10, 0, 10} are applied. Not that the larger
value of f not only provides the better approximation performance, but also switches the outputs v1 and v2 , more
quickly.

Denote by βmax (δmax ) the maximum real part of thestable transmission poles (zeros) for the system Ge (s).
From the statement (i) of Theorem 2, one gets the rangeof f as follows:f > min{|βmax |, |δmax |}.

u1(t) and v1(t)Also, the following theorem derived from (6) in Lemma
1 and (8) in Theorem 1 provides an analytical relation
between the quantizers Qd and Qst in the continuoustime domain.
Theorem 3: Consider the problem (OP) and denote by γst and γd upper bounds of J(Qst ) and J(Qd ),respectively.
√In the case of ng = 1,
|A|
γd
inf
= √ , f > |A|
(9)
γst
f
holds.
Since the matrix A is stable, A < 0 holds in (9). Theorem 3 guarantees that the scalar f of the quantizer Qd improves J(Qd ) compared with the quantizer Qst in termsof the infimum of the upper bound ratio of the cost functions. Then the larger value the scalar f is set to be, thebetter approximation the quantizer Qop
d achieves between
the minimum phase systems in Figs. 1 (a) and (b).

4. CONCLUSION
Focusing on continuous-time LFT type quantized
feedback systems, we have proposed the continuoustime dynamic quantizer analysis and synthesis conditions. This paper has concentrated on the case of minimum phase systems, clarified that optimal dynamic quantizers and their performance are parameterized by the design parameter. Also, an analytical relation between thestatic and dynamic quantizers has been presented. Finally, it has been pointed out that the proposed method
is helpful through the numerical example.

10
0
−10
0

REFERENCES
2

4

6

8

10

u2(t) and v2(t)

[1]
10

[2]

0
−10
0

2

4

6

8

[3]

10

zp(t)

10

[4]

0
−10
0

2

4
6
time t [s]

8

10

[5]

Fig. 4. Time responses with Qd
Consider the decentralized I/O quantizer system. The
plant P (s) is the unstable minimum phase LTI system:


[
]
[
]
0 1 0
xp
ẋp
=  −2 1 −2 
, zp = u2 .
v1
u2
4 1 0

[6]

[7]

Its eigenvalues are 0.5 ± 0.866i and its zero is {−2}. The
stabilizing controller C(s) is given by
][
]
[
] [
−3 1
xc
ẋc
=
.
v2
u1
−1 1
© ISAROB 2013

137

P. J. Antsaklis and J. Baillieul, Guest Editors (2007),
Special Issue on the Technology of Networked Control Systems, Proc. IEEE, Vol. 95, No. 1.
S. Azuma and T. Sugie (2008), Optimal dynamic
quantizer for discrete-valued input control, Automatica, Vol. 44, No. 12, pp. 396–406.
S. Azuma and T. Sugie (2008), Synthesis of optimal dynamic quantizers for discrete-valued input
control, IEEE Trans. Auto. Contr., Vol. 53, No. 9,
pp. 2064–2075.
K. Sawada and S. Shin (2011), Synthesis of dynamic quantizers for quantized feedback systems
within invariant set analysis framework, Proc. of
ACC, pp. 1662–1667.
H. Okajima, K. Sawada, N. Matsunaga, and Y. Minami (2011), Dynamic quantizer design for MIMO
systems based on communication rate constraint,
Proc. of IECON, pp. 2497–2502.
K. Matsukawa, Y. Mitani, M. Takayama, K. Obata,
S. Dosho, and A. Matsuzawa (2010), A FifthOrder Continuous-Time Delta-Sigma Modulator
With Single-Opamp Resonator, IEEE J. Solid-State
Circuits, Vol. 45, No. 4, pp. 697-706.
K. Sawada and S. Shin (2012), Synthesis ofcontinuous-time dynamic quantizers for quantized
feedback systems, Proc. of 4th IFAC Conference
on Analysis and Design of Hybrid Systems (ADHS12), pp. 248–253.

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Optimal scheduling of automatic guided vehicle transportation system based on
MLD system modeling
Kenji Sawada1 and Seiichi Shin1
1

The University of Electro-Communications, Japan(Tel: 81-42-443-5253, Fax: 81-42-443-5253)
1{knj.sawada, seiich.shin}@uec.ac.jp

Abstract: This paper proposes an optimal scheduling method of transportation systems in semiconductor manufacturing
within MLD (mixed logical dynamical) modeling framework. We consider an optimal scheduling problem of AGV (automatic
guided vehicle) system transfer problem, which is to control the AGV congestion around the meeting points and the dividing
points of the transportation road in this paper. The problem is recast as an ILP (Integer Linear Programming) problem withinmodel predictive control framework.
Keywords: automatic guided vehicle, MLD modeling, schedulingconsider scheduling problems that focus on all areas of
FAB.
The typical scheduling process on the junctions is asfollows: (i) The prioritized AGV which goes through themeeting point without stopping is automatically determinedby the distance from the meeting point. (ii) When some ofthe other AGVs around the meeting point come close tocolliding with the prioritized one, they continuously slow
down or stop according to their collision avoidance systems.
Such a scheduling procedure does not previously consider
both the positional relation and the number of the vehicles
which may collide, and/or the influence of the AGVs’
stopping and slowing down on other AGVs except themeeting point area. These factors can lead to the AGV
congestion as shown in Fig. 2. Also, it is important toconsider the behaviors of the AGVs around the dividing
point area in front of the meeting point area. If we know inadvance that some AGVs go to the parking area through thedividing point, such the information makes it easier tomaximize the number of the AGVs come out from themeeting point area.

1 INTRODUCTION
Recently, there is a growing need to increase
productivity in semiconductor manufacturing with the
improvement of the producing technology. In
semiconductor fabrication (FAB) in Fig. 1, a few hundredof Automatic Guided Vehicles (AGVs) transpose Front
Open Unified Pods (FOUPs) that store semiconductor
wafers [1].

Fig. 1. Semiconductor fabrication
Nowadays, the size of the wafers will be getting lager(from 300mm to 450mm) and the number of the vehicles ina factory will be increasing. This implies that each of the
AGVs must follow the vehicle in front of one closely andtranspose heavy wafers within a set time. In particular, it isimportant to consider the AGV congestion on thetransportation roads which consist of inter-bays and intrabays since the congestion results in the production delay.
The congestion tends to occur around the junctions between
the bays and/or the transfer equipment of the FOUPs. In
particular, this paper focuses on an optimal schedulingproblem of the meeting points and the dividing points of the
FAB junctions, while the existing results [2-4] tend to© ISAROB 2013

Fig. 2. Congestion on junction
Motivated by the above, our early work considers theAGV congestion problem of the meeting point based on the138The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

𝑑𝑖 (𝑡) ∈ {0,1}, 𝑑𝑖 (𝑡) = 1 indicates that one AGV comes inthrough the corresponding i-th exogenous gate at the t-th
time. Denote by 𝑥𝐿 𝑖 (𝑡) ∈ 𝐑𝑛𝑖 , 𝑢𝐿 𝑖 (𝑡) ∈ 𝐑𝑚𝑖 and𝑑(𝑡) ∈ 𝐑2 the vectors of which the i-th entries are 𝑥𝐿𝑖𝑗 (𝑡) ,𝑢𝐿𝑖𝑗 (𝑡) and 𝑑𝑖 (𝑡), respectively.
According to the above notations, the bay model of themeeting and dividing points can be expressed by the state
space realization. First, the link 𝐿1 and 𝐿2 are given bystate space realization modeling [5] and the model
predictive control framework. As an extension of ourexisting result [5], this paper proposes a mixed logicaldynamical (MLD) modeling [6-8] of the meeting and thedividing points considering the positional relation (the areaof the bay model), the number of the vehicles, and the timeof the AGV congestion. By using the model, in addition,
this paper considers the optimal scheduling problem
considering the AGV congestion within model predictive
control framework. In this case, the problem is recast as an
Integer Programming.
Notation: The set of 𝑛 × 𝑚 real (integer) matrices isdenoted by 𝐑𝑛×𝑚 (𝐍 𝑛×𝑚 ). The set of 𝑛 × 𝑛 diagonal
matrices with the diagonals being 1 or 0 is denoted by 𝐃𝑛 .
𝑂𝑛×𝑚 , 𝐼𝑛 , 𝟎𝑚 and 𝟏𝑚 (or for simplicity of notation, 𝑂,
𝐼, 𝟎 and 𝟏) denote the 𝑛 × 𝑚 zero matrix, the 𝑛 × 𝑛
identity matrix, the 𝑛 × 1 vector whose all elements are
zero and the 𝑛 × 1 vector whose all elements are one,respectively. For a matrix 𝑀, 𝑀 T denotes its transpose.
For a vector 𝑥, 𝑥𝑖 is the i-th entry of 𝑥. diag(𝐷1 , … , 𝐷𝑛 )denotes the block diagonal matrix of matrices 𝐷1 , … , 𝐷𝑛 .
{0, 1}𝑛 denotes the set of n-dimensional vectors, which
consists of elements 0 and 1.

𝑥𝐿 𝑖 (𝑡 + 1) = 𝐴𝐿𝑖 𝑥𝐿 𝑖 (𝑡) + 𝐵1𝐿 𝑖 𝑢𝐿 𝑖 (𝑡) + 𝐵2𝐿𝑖 𝑑𝑖 (𝑡)where 𝑖 = 1,2 and the matrices are given by−1⎡ 1⎢
𝐴𝐿 𝑖 ≔ 𝐼𝑛 𝑖 , 𝐵1𝐿𝑖 ≔ ⎢ 0⎢ ⋮⎣ 0
𝐵2𝐿𝑖 ≔ [1⋯0] T ∈ 𝐑 𝑛 𝑖 .

Second, the link 𝐿3 is given by

0
0
−1
⋱
0

⋯
⋱
…
⋱
1

0
⋮ ⎤
⎥
⋮ ⎥ ∈ 𝐑𝑛𝑖×𝑛𝑖 ,
0⎥
−1⎦

2

𝑥𝐿 3 (𝑡 + 1) = 𝐴𝐿3 𝑥𝐿 3 (𝑡) + � 𝐵13𝐿 𝑖 𝑢𝐿 𝑖 (𝑡)𝑖=15+ 𝐵1𝐿 3 𝑢𝐿 3 (𝑡) + � 𝐵13𝐿 𝑖 𝑢𝐿 𝑖 (𝑡)where the matrices are given by

𝑖=4

−1
0
⋯
0
⎡ 1 −1 ⋱⋮ ⎤⎢⎥𝐴𝐿 3 ≔ 𝐼𝑛 3 , 𝐵1𝐿3 ≔ ⎢ 0
1⋱⋮ ⎥ ∈ 𝐑𝑛3×(𝑛3−1) ,
⎢ ⋮
⋱
⋱ −1⎥
⎣ 0
⋯
0
1⎦
0 ⋯ 0 1⎧ 0 0 ⋯ 0𝑛
⎪ � ⋮ ⋱ ⋱ ⋮ � ∈ 𝐑 3×𝑛𝑖 , 𝑖 = 1,2,⎪
𝐵13𝐿𝑖 ≔ 0 ⋱ 0 1⎨ 0 ⋯ 0 0
𝑛
0 ⋯ 0
⎪� ⋮
� ∈ 𝐑 3×�𝑛𝑖+1� , 𝑖 = 4,5.
⎪ 0 ⋱ ⋱ ⋮⎩ −1 0 ⋯ 0

2 PROBLEM FORMUILATION

Fig. 3. Bay model of meeting and dividing points
Fig. 3 shows a bay model of the meeting and dividing
points. The area surround by the black border is the area ofthe bay model that we focus on. The bay is divided into
several parts 𝑥𝐿𝑖𝑗 by the gates 𝑢𝐿𝑖𝑗 and 𝑑𝑖 similar to ourearlier work. Each AGV behavior is expressed by the gate
opening/closing. In particular, the gate 𝑑𝑖 implies the
behavior of the AGVs that come in from the outside.
We define the notations. The bay model consists of 5
links 𝐿𝑖 (𝑖 = 1, … ,5) as shown in Fig. 3. 𝑥𝐿𝑖𝑗 (𝑡) indicatesthe number of the AGVs on the corresponding j-th area ofthe link 𝐿𝑖 at the t-th time. For example, if one AGV
moves on the 4-th area of the link 𝐿2 at the 2-nd time,
𝑥𝐿24 (2) = 1 holds. 𝑢𝐿𝑖𝑗 (𝑡) = 1 or 0 indicates thecorresponding j-th gate opening or closing of the link 𝐿𝑖 at
the t-th time, respectively. For the exogenous gate
© ISAROB 2013

0

0
−1
1
⋱
⋯

Finally, the link 𝐿4 and 𝐿5 are given by
𝑥𝐿 𝑖 (𝑡 + 1) = 𝐴𝐿𝑖 𝑥𝐿 𝑖 (𝑡) + 𝐵1𝐿 𝑖 𝑢𝐿 𝑖 (𝑡)where 𝑖 = 4,5 and the matrices are given by
1
0
𝐴𝐿 𝑖 ≔ 𝐼𝑛 𝑖 , 𝐵1𝐿𝑖 ≔ �
⋮
0

−1
1
⋱
⋯

0
−1
⋱
0

By defining the vectors as follows:

⋯
⋱
⋱
1

0
⋮
� ∈ 𝐑𝑛𝑖×(𝑛𝑖+1) .
0−1T𝑥(𝑡) ≔ �𝑥𝐿 1 (𝑡)T 𝑥𝐿 2 (𝑡)T 𝑥𝐿 3 (𝑡)T 𝑥𝐿 4 (𝑡)T 𝑥𝐿 5 (𝑡)T � ,T𝑢(𝑡) ≔ �𝑢𝐿 1 (𝑡)T 𝑢𝐿 2 (𝑡)T 𝑢𝐿 3 (𝑡)T 𝑢4 (𝑡)T 𝑢𝐿 5 (𝑡)T � ,we obtain the state space realization of the bay model:𝑥(𝑡 + 1) = 𝐴𝐴(𝑡) + 𝐵1 𝑢(𝑡) + 𝐵2 𝑑(𝑡)
139

(1)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where the matrices are given by 𝑛 ≡ ∑5𝑖=1 𝑛𝑖 , 𝐴 ≔ 𝐼𝑛 ,
𝐵1𝐿
⎡ 1
⎢ 𝑂
𝐵1 ≔ ⎢𝐵13𝐿1
⎢ 𝑂
⎢
⎣ 𝑂
𝐵2 ≔ �

T
𝐵2𝐿
1

𝑂
𝐵1𝐿2
𝐵13𝐿2
𝑂
𝑂
𝑂

T
𝐵2𝐿
2

𝑂

𝑂

𝑂

𝑂
𝑂
𝐵1𝐿3
𝑂
𝑂

𝑂
𝑂

𝐵13𝐿4
𝐵1𝐿4
𝑂

T

� ∈ 𝐑𝑛×2 .

To achieve the property (ii), next we consider thefollowing rules.

𝑂⎤𝑂 ⎥𝐵13𝐿5 ⎥ ∈ 𝐑𝑛×(𝑛+1) ,𝑂 ⎥⎥𝐵1𝐿5 ⎦(a) Capacity of each area: For the areas 𝑥𝐿𝑖𝑗 (𝑡) (𝑖 =1, … ,5, 𝑗 = 𝑛1 , … , 𝑛5 ), each capacity is set to be up to one
AGV. This rule is expressed by the constraint:

3.1 Modeling of AGV scheduling
By using the bay model (4), this paper considers thescheduling problem (I): Suppose that the area of the bay
model, the number of the vehicles, and the time of the AGV
congestion are given. Formulate a scheduling algorithm (i)maximizing the number of the AGVs come out from the areaand (ii) avoiding the AGV collision.
Denote by 𝑀 the time of the AGV congestion (thehorizon length). To achieve the property (i), first, weconsider the following cost function:max

𝐽

𝐺𝑐𝑐 𝑢(𝑡) ≤ 𝐺𝑐𝑐 𝑥(𝑡),𝐺𝑐𝑐⎡𝑂
𝐺𝑐𝑐 ≔ ⎢ 𝑂⎢⎣𝑂

T1]T ∈ 𝐑𝑛𝑖 , 𝑖 = 1,2,0]T ∈ 𝐑𝑛𝑖+1 , 𝑖 = 4,5.

𝟎
1
𝟎
𝟎

𝑂
𝑂
𝐺𝑐𝐿4
𝑂

(6)
𝟎
1
𝟎
𝟎

𝑂
𝑂 ⎤
⎥
𝑂 ⎥,
𝐺2𝑐𝐿5 ⎦

𝐺𝑐𝑐 ≔ diag�𝐺𝑐𝐿1 , 𝐺𝑐𝐿2 , 𝐺𝑐𝑢𝑢3 �, 𝐺𝑐𝑢𝑢3 ∈ 𝐃𝑛3−1 ,𝐺𝑐𝑐 ≔ diag�𝐺𝑐𝐿1 , 𝐺𝑐𝐿2 , 𝐺𝑐𝑐𝐿3 , 𝐺𝑐𝐿4 , 𝐺𝑐𝐿5 �,

𝐺𝑐𝐿𝑖 ∈ 𝐃𝑛𝑖 , 𝑖 ∈ {1,2,4,5}, 𝐺𝑐𝑥𝑥3 ∈ 𝐃𝑛3 .

Also, we consider the throughput difference 𝜃 ∈ 𝐑
between the links 4 and 5, which is expressed by© ISAROB 2013

� ∈ 𝐑2×(𝑛+1) ,This logic indicates that an AGV does not go throughmore than two gates on the corresponding areas at a time.
By using the lemma in [6-8], the above logic can be
expressed by the inequality:𝑑
𝜆𝑖 5 ∈ 𝐑

𝑞 = 𝑞𝑀 = −(𝛬1 + 𝛬2 ).

𝑞𝐿T4

T
𝑔𝑏𝐿
5

𝑢𝐿𝑖𝑗+1 (𝑡) = 1 → 𝑥𝐿𝑖𝑗 (𝑡) = 1, 𝑖 ∈ {1, … , 𝑛𝑖 }, 𝑗 ∈ {4,5}.

If ∑𝑀
𝑡=0 𝑑𝑠 (𝑡) is minimized, the throughput within thetime 𝑡 ∈ [0, 𝑀] of the bay also may increase. Then, we set𝑔𝜃 ≔ �𝟎T

𝑂

𝑢𝐿41 (𝑡) + 𝑢𝐿51 (𝑡) = 1 → 𝑥𝐿3𝑛3 (𝑡) = 1,

𝑑𝑠 (𝑡) = � 𝛬𝑗T 𝑥(𝑡) , 𝛬𝑗 ≔ �𝜆1 𝑗 ⋯ 𝜆𝑛𝑗 � ∈ 𝐑𝑛 .

−𝜃 ≤ 𝑔𝜃T 𝑢(𝑡) ≤ 𝜃,

⋯

T
𝑔𝑏𝐿
4

𝑖=1

We denote byor
the distance
between 𝑥𝑖 (𝑖 ∈ {1, 𝑛}) and 𝑥𝐿4𝑛4 or 𝑥𝐿5 𝑛5 , respectively.
In this case, the sum of the distances from 𝑥𝐿4𝑛4 or 𝑥𝐿5𝑛5
to any vehicles in the bay model at the t-th time (we denoteit 𝑑𝑠 (𝑡)) is expressed by
𝑗

0

0

𝑂

𝑂

𝑢𝑖 (𝑡) = 1 → 𝑥𝑖 (𝑡) = 1, 𝑖 ∈ �1, … , � 𝑛𝑖 − 1�,𝑟 ≔ �0 ⋯ 0 𝑞𝐿T4 𝑞𝐿T5 � , 𝑞𝐿 𝑖 ≔ [0 ⋯ 0 1]T ∈ 𝐑𝑛𝑖+1 .

𝑑

⋯

𝑂

𝑂

3

We consider the “throughput maximization” of the links 4
and 5. In this case, the throughputs of the links 4 and 5 are
the opening and closing times of 𝑢𝐿4𝑛4+1 (𝑡) and𝑢𝐿5𝑛5+1 (𝑡), the weight vector 𝑟 is given by

𝑑

T
𝑔𝑏𝐿
2

(c) Speed condition: The acceleration of AGV aroundthe meeting point can lead to the collision. Then we
consider the logic:𝑞M ∈ 𝐑 , 𝑞 ∈ 𝐑𝑛 and 𝑟 ∈ 𝐑𝑛+1 . (2)2𝑂

𝑔𝑏𝐿𝑖 ≔ [1𝑡=0𝑑
𝜆𝑖 4 ∈ 𝐑

(5)

T
𝑔𝑏𝐿
1

𝑔𝑏𝐿𝑖 ≔ [0𝑀−1T

𝟎 ≤ 𝐺𝑏 𝑢(𝑡) ≤ 𝟏,

𝐺𝑏 ≔ �

T
s. t. 𝐽: = 𝑞𝑀
𝑥(𝑀) + � �𝑞 T 𝑥(𝑡) + 𝑟 T 𝑢(𝑡)�,𝑛(4)(b) Meeting and dividing points: To avoid thecollision at the meeting of two gates 𝑢𝐿1𝑛1 (𝑡) and
𝑢𝐿2𝑛2 (𝑡), we consider the rule that does not allow the twogates to open at the same time. Also, we consider the rulethat does not allow the two gates 𝑢𝐿41 (𝑡)and 𝑢𝐿51 (𝑡) of
the dividing point to open at the same time. These rules are
given by the constraint:

3 SCHEDULING ALGORITHM

𝑢(0),…,𝑢(𝑀−1)𝟎 ≤ 𝑥(𝑡) ≤ 𝟏.

If 𝐺𝑐𝐿𝑖 , 𝐺𝑐𝑢𝑢3 , 𝐺𝑐𝑥𝑥3 are set to be the identity matrix,
the all areas does not allow the AGVs to speed up.
Therefore, the bay model with the control rules (a)-(c)can be expressed by the pair of the linear state space system(1) and the linear inequalities (3)-(6). We consider the(3)
T−𝑞𝐿T5 � ∈ 𝐑𝑛+1 .
140The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013max

initial time t=0. In this case, the problem (I) is recast as thefollowing problem (II): Suppose that the initial position
𝑥(0) ≔ 𝑥0 and the sequence 𝑑(0), 𝑑(1), … , 𝑑(𝑀 − 1) of
the AGVs which come in are given. Find𝑢(0), 𝑢(1), … , 𝑢(𝑀 − 1) maximizing the cost function (2)for the bay model with the control rules realized by�𝑥(𝑡 + 1) = 𝐴𝐴(𝑡) + 𝐵1 𝑢(𝑡) + 𝐵2 𝑑(𝑡),𝐶𝐶(𝑡) + 𝐷𝐷(𝑡) ≤ 𝐸

subject to �𝐶𝑀 𝐵𝑀1 + 𝐷𝑀 �𝑢𝑀 0≤ 𝐸𝑀 − 𝐶𝑀 (𝐴𝑀 𝑥0 + 𝐵𝑀 2 𝑑𝑀 _0 )where the vectors are defined by

𝑢𝑀 0 ≔ [𝑢(0)T 𝑢(1)T ⋯ 𝑢(𝑀 − 1)T ]T ,𝑑𝑀 0 ≔ [𝑑(0)T 𝑑(1)T ⋯ 𝑑(𝑀 − 1)T ]T .

(7)

This problem can be solved by the appropriate solvers
such as Optimization toolbox of MATLAB in [9] and/or
ILOG CPLEX in [10] because of ILP framework.
Numerical examples will be shown in the conference room.

𝑔T0𝜃⎡ 𝜃T ⎤
⎡ 0 ⎤
⎡𝜃 ⎤
−𝑔𝜃 ⎥
⎢
⎢ 𝐼 ⎥
⎢ ⎥
⎢ 𝑂 ⎥
⎢
⎥
⎢𝟏⎥
𝐶 ≔ ⎢ −𝐼 ⎥ , 𝐷 ≔ ⎢ 𝑂 ⎥ , 𝐹 ≔ ⎢ 𝟎 ⎥.
⎢
⎥
⎢𝟏⎥
⎢ 𝟎 ⎥
⎢ 𝐺𝑏 ⎥⎢𝟎⎥⎢ 𝟎 ⎥⎢ −𝐺𝑏 ⎥⎣𝑂 ⎦⎣−𝐺𝑐𝑐 ⎦⎣ 𝐺𝑐𝑐 ⎦

4 CONCLUSION
This paper has proposed a dynamic modeling oftransportation systems in semiconductor manufacturing.
Utilizing our modeling method, we have considered an
optimal scheduling problem focusing on the AGV
congestion at the meeting point of the transportation roadjunctions. As a result, the problem can be recast as an ILP
problem within model predictive control framework.

3.2 ILP based schedulingIn this subsection, we reduce the problem (II) to an ILP
problem by using the model predictive control technique in[6] similar to our early work [5]. For the time 𝑀, we define
vectors as follows:𝑥𝑀 (𝑡) ≔ [𝑥(𝑡)T 𝑥(𝑡 + 1)T ⋯ 𝑥(𝑡 + 𝑀)T ]T ,𝑢𝑀 (𝑡) ≔ [𝑢(𝑡)T 𝑢(𝑡 + 1)T ⋯ 𝑢(𝑡 + 𝑀 − 1)T ]T ,REFERENCES

𝑑𝑀 (𝑡) ≔ [𝑑(𝑡)T 𝑑(𝑡 + 1)T ⋯ 𝑑(𝑡 + 𝑀 − 1)T ]T .

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roblems for AGV Systems, IEEE Trans. Aut. Sci. Eng.,
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automatic guided vehicle system, Proc. of SICE Annual Conference, pp.580-583[6] A. Bemporad and M. Morari (1999), Control ofsystems integrating logic, dynamics, and constraints, Au
tomatica, 35(3), pp.407-427[7] T. M. Cavalier, P. M. Pardalos, and A. L. Soyst
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zation/cplex-optimizer/

In this case, the cost function can be recast as
𝐽 = 𝑄𝑀 𝑥𝑀 (𝑡) + 𝑅𝑀 𝑢𝑀 (𝑡)(8)and 𝑥𝑀 (𝑡) is given by𝑥𝑀 (𝑡) = 𝐴𝑀 𝑥(𝑡) + 𝐵𝑀 1 𝑢𝑀 (𝑡) + 𝐵𝑀 2 𝑑𝑀 (𝑡)where the matrices are defined by

𝑂
𝐼
⎡ 𝐵
⎡𝐴 ⎤
𝑖
⎢
⎢ ⎥
𝐴𝑀 ≔ ⎢ 𝐴2 ⎥ , 𝐵𝑀 𝑖 ≔ ⎢ 𝐴𝐵𝑖
⋮
⎢ ⋮ ⎥
⎢
⎣𝐴𝑀 ⎦
⎣ 𝐴𝑀−1 𝐵𝑖
𝑄𝑀 ≔ [𝑞 T

𝑂
𝑂
𝐵𝑖
⋱
⋯

⋯
⋯
⋱
⋱
𝐴𝐵𝑖

T ], 𝑅
T𝑞𝑀
𝑀 ≔ [𝑟

⋯ 𝑞T

⋯

𝑂
𝑂⎤
⎥
⋮ ⎥,
𝑂⎥
𝐵𝑖 ⎦

𝑟 T ].

Also, the inequality of (7) can be also rewritten as
𝐶𝑀 𝑥𝑀 (𝑡) + 𝐷𝑀 𝑢𝑀 (𝑡) ≤ 𝐸𝑀

(9)where the matrices are defined by𝐶𝑀 ≔ [diag(𝐶, … , 𝐶) 𝑂], 𝐷𝑀 ≔ diag(𝐷, … , 𝐷),
𝐸𝑀 ≔ [𝐸 T⋯

T

𝐸T ] .

By using (8) and (9), we see that the following theorem
holds of the problem (II).
Theorem 1: The problem (II) is equivalent to thefollowing optimization problem (III):given 𝑥0 and 𝑑𝑀 0
find 𝑢𝑀 0 ∈© ISAROB 2013

𝑄𝑀 𝐵𝑀 1 𝑢𝑀 0 + 𝑄𝑀 �𝐴𝑀 𝑥0 + 𝐵𝑀 2 𝑑𝑀0 �𝑀{0,1}𝑛𝑛141

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Direct Multivariable PI Controller Tuningfrom Closed-Loop Response Data
Yoshihiro Matsui1, Hideki Ayano1, and Kazushi Nakano2
1

Tokyo National College of Technology, JapanThe University of Electro-Communications, Japan(Tel/Fax: +81-42-668-5173)

2

1

matsui@tokyo-ct.ac.jp

Abstract: This paper proposes a PI controller tuning method for multivariable plants. The method requires only one set of the
input-output transient data of the plant under closed-loop operation to tune the controller. The data is used to obtain an
appropriate controller parameter by solving a model matching problem of FRIT (Fictitious Reference Iterative Tuning) in time
domain, and the data is also used in frequency domain to confirm if the parameter tuned by FRIT is stable and if the model
matching is achieved. The method is applied to a non-interacting control of a gas turbine engine and its effectiveness is shown
through simulations.
Keywords: CMA-ES, data driven controller tuning, FRIT, non-interacting control
𝒏

1 INTRODUCTION𝒓

In recent years, in order to save the time and the cost totune controller parameters for industrial systems, somedirect controller parameter tuning methods from thetransient data of the plant under closed-loop operation
without modeling the plant have been proposed. The FRIT
(Fictitious Reference Iterative Tuning) proposed by Soumaet al [1] is one of those methods and is expected to be
applied to some practical applications. However the FRIT
does not show how to specify the reference model for themodel matching to tune controller, and the stability of theparameter tuned by the FRIT is no clear. This paper shows
how to resolve the problems using the information of thedata in frequency domain. The method is applied to acontroller tuning for a multivariable plant.

𝒆𝑲(𝝆, 𝑠)

𝒖

𝒚

𝑷(𝑠)

Fig. 1. Closed-loop system for multivariable plant

1

1

𝐾11

1

𝐾21

𝑷

𝐾12
2

2

𝐾22

1

2

𝑛1
𝑛2
2

Fig. 2. Closed-loop system for 2-input 2-output plant
controllers given by (1)~(4).

2 Controller tuning for multivariable plant
2.1 Problem settingThis paper deals with the closed-loop system for
multivariable plant shown by Fig.1. The system consists ofa plant given by a 𝑛 × 𝑛 transfer function matrix 𝑷(𝑠)and a controller given by a 𝑛 × 𝑛 controller matrix
𝑲(𝝆, 𝑠) with the controller parameter 𝝆. And 𝒓, 𝒆, 𝒏, 𝒖
and 𝒚 are the reference vector, the error vector, theobservation noise vector, the input and the output vectors of
the plant, respectively. In order to make the explanationeasier, the closed-loop system for a 2-input 2-output plant
shown by Fig. 2 is used in the following.
The controllers of the system are assumed to be PI© ISAROB 2013

𝐾11 (𝑠) = 𝑘11

𝜏11 𝑠 + 1
𝜏11 𝑠

(1)

𝐾12 (𝑠) = 𝑘12

𝜏12 𝑠 + 1
𝜏12 𝑠

(2)

𝐾21 (𝑠) = 𝑘21

𝜏21 𝑠 + 1
𝜏21 𝑠

(3)

𝐾22 (𝑠) = 𝑘22

𝜏22 𝑠 + 1
𝜏22 𝑠

(4)

The controller matrix consists of (1)~(4) is given by
𝐾 (𝑠) 𝐾12 (𝑠)𝑲(𝝆, 𝑠) = ( 11).
(5)
𝐾21 (𝑠) 𝐾22 (𝑠)142The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

𝒚0 (𝑡) = ( 10 (𝑡), 20 (𝑡))𝑇 of the plant in the closed-loopsystem with the initial controller parameter 𝝆𝟎 .
The parameter 𝝆∗ which makes the transfer function
matrix from 𝒓 to 𝒚 of the closed-loop system be similar
to 𝑻∗ (𝑠) can be obtained by (13) and (14) using some nonlinear optimization methods.

The proportional gains and the integral time constants of
the PI controllers are defined as the controller parameter 𝝆
given by
𝝆 = (𝑘11 , 𝑘21 , 𝑘12 , 𝑘22 , 𝜏11 , 𝜏21 , 𝜏12 , 𝜏22 ).

(6)

The goal of this problem is to find the controller
parameter such that a non-interacting control with stability
is achieved. To achieve this, the loop transfer function
matrix 𝑷(𝑠)𝑲(𝝆, 𝑠) must be a diagonal matrix for noninteracting control and its diagonal elements must haveappropriate gain crossover frequencies to satisfy the
Nyquist stable criterion. Let us assume 𝑳∗ (𝑠) given by (7)to be one of such the loop transfer function matrix.
𝑳∗ (𝑠) = diag[𝐿∗11 (𝑠), 𝐿∗22 (𝑠)]𝑁−1𝝆∗ = arg min ∑ 𝜺𝑇 (𝝆, 𝑡) 𝜺(𝝆, 𝑡)𝝆𝜺(𝝆, 𝑡) = 𝑾(𝑡){𝒚0 (𝑡)If 𝑷(𝑠)𝑲(𝝆, 𝑠) becomes 𝑳∗ (𝑠) by 𝝆 = 𝝆∗ , the transfer
function matrix from 𝒓 to 𝒚 of the closed-loop system is
(8)

∗ (𝑠)
𝑇11
=

𝐿∗11 (𝑠)1 + 𝐿∗11 (𝑠)

(9)

∗ (𝑠)
𝑇22
=

𝐿∗22 (𝑠).
1 + 𝐿∗22 (𝑠)

(10)

where

and

𝑻∗ (𝑡) ∗ 𝒓̃(𝑡)}(14)Here, 𝑁 is the data length of the input-output data, 𝑾(𝑠)is a weighting function diagonal matrix which specifies thefrequency band to emphasis the error vector of the model
matching 𝒚0 (𝑡) 𝑻∗ (𝑡) ∗ 𝒓̃(𝑡) for searching 𝝆∗ , and𝑾(𝑡) and 𝑻∗ (𝑡) are the impulse responses of 𝑾(𝑠) and𝑻∗ (𝑠), respectively.

(7)∗ (𝑠), ∗ (𝑠)]𝑻∗ (𝑠) = diag[𝑇11
𝑇22
,

(13)

𝑡

2.3 Confirmation in frequency domain
It is difficult to specify 𝑻∗ (𝑠) or 𝑳∗ (𝑠) for unknown
𝑷(𝑠). Therefore the information in frequency domain of𝒖0 (𝑡) and 𝒚0 (𝑡) is also used.
When 𝒖(𝑡) = 𝒖0 (𝑡) and 𝒚(𝑡) = 𝒚0 (𝑡) and 𝒏 =0 in
Fig.1, (15) holds in frequency domain.

Therefore, the solution of this problem is to find 𝝆∗ suchthat 𝑷(𝑠)𝑲(𝝆∗ , 𝑠) is as similar to 𝑳∗ (𝑠) as possible.

𝒚0 (𝑗𝜔) = 𝑷(𝑗𝜔)𝒖0 (𝑗𝜔)
= 𝑳(𝝆, 𝑗𝜔)𝒆̃(𝑗𝜔),(15)

𝑳(𝝆, 𝑗𝜔) = 𝑷(𝑠)𝑲(𝝆, 𝑗𝜔)𝐿 (𝝆, 𝑗𝜔) 𝐿12 (𝝆, 𝑗𝜔)= ( 11).
𝐿21 (𝝆, 𝑗𝜔) 𝐿22 (𝝆, 𝑗𝜔)

(16)

where
2.2 Controller tuning by FRIT
In order to find 𝝆∗ , the FRIT is employed. The FRIT
requires only one set of input-output transient data of theplant under closed-loop operation. The fictitious reference𝒓̃(𝑡) = ( 1̃ (𝑡), 2̃ (𝑡))𝑇 given by (11) is used to find 𝝆∗
without additional experiments.
𝒓̃(𝑡) = 𝒆̃(𝑡) + 𝒚0 (𝑡),From (15) and (16), (17) and (18) are derived.
10 (𝑗𝜔)̃1 (𝑗𝜔)(11)

20 (𝑗𝜔)where 𝒆̃(𝑡) = ( 1̃ (𝑡), ̃2 (𝑡))𝑇 is the fictitious error andgiven by
𝒆̃(𝑡) = 𝑲−1 (𝝆, 𝑡) ∗ 𝒖0 (𝑡) .

̃2 (𝑗𝜔)= 𝐿22 (𝝆, 𝑗𝜔) + 𝐿21 (𝝆, 𝑗𝜔)

̃2 (𝑗𝜔)̃ (𝑗𝜔)1̃ (𝑗𝜔)1̃2 (𝑗𝜔)

(17)

(18)

Therefore if we can estimate 10 (𝑗𝜔)/ 1̃ (𝑗𝜔) and∗20 (𝑗𝜔)/ ̃ 2 (𝑗𝜔) with 𝝆 = 𝝆 , and compare them to∗ (𝑗𝜔)∗ (𝑗𝜔),
𝐿11
and 𝐿22
respectively, we can investigate to∗see if 𝐿11 (𝝆 , 𝑗𝜔) ≃ 𝐿∗11 (𝑗𝜔) ,𝐿12 (𝝆∗ , 𝑗𝜔) ≃ 0 ,∗∗∗
𝐿22 (𝝆 , 𝑗𝜔) ≃ 𝐿22 (𝑗𝜔) and 𝐿21 (𝝆 , 𝑗𝜔) ≃ 0 . However̃ (𝑗𝜔) andcannot be
01 (𝑗𝜔)/ 1
02 (𝑗𝜔)/ ̃2 (𝑗𝜔)estimated from 10 (𝑗𝜔) , 1̃ (𝑗𝜔) , 20 (𝑗𝜔) and ̃2 (𝑗𝜔)directly since 10 (𝑡) , 1̃ (𝑡) , 20 (𝑡) and ̃2 (𝑡) are not(12)

𝑲−1 (𝝆, 𝑡) shows the impulse response of the inverse
matrix of the controller transfer function matrix 𝑲(𝝆, 𝑠).
As (11) and (12) show, the fictitious reference 𝒓̃(𝑡) andthe fictitious error 𝒆̃(𝑡) can be obtained only from the
input data 𝒖0 (𝑡) = ( 10 (𝑡), 20 (𝑡))𝑇 and the output data

© ISAROB 2013

= 𝐿11 (𝝆, 𝑗𝜔) + 𝐿12 (𝝆, 𝑗𝜔)

143The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

the fifth state variables. The outputs are the first and thethird state variables. The state matrix 𝑨𝑝 , the input matrix
𝑩𝑝 and the output matrix 𝑪𝑝 are given by (26), (27) and(28), respectively.

absolute integrable and the Fourier transform cannot beapplied to them. To resolve the problem, a band pass filter
proposed by Matsui et al [2] is introduced. The filter isgiven by𝐹(𝑠) =

100𝑇𝑠 𝑠,(100𝑇𝑠 𝑠 + 1)(10𝑇𝑠 𝑠 + 1)

1.41220.0552
0.09270.1133𝑨𝑝 =7.84670.25550000(042.9536
6.308704.22040.75813.333 300.4167
4.4894
0
25.00
0
0
0
33.3333)(19)

where 𝑇𝑠 is the sampling period for 𝒖0 (𝑡) and 𝒚0 (𝑡).
The filter and a correlation method are used for theestimation. The estimated frequency responses of̃ (𝑗𝜔) and 20 (𝑗𝜔)/ ̃2 (𝑗𝜔) are defined as
10 (𝑗𝜔)/ 1
̂𝐿11 (𝝆, 𝑗𝜔) and 𝐿̂22 (𝝆, 𝑗𝜔), and are estimated by (20) and(21), respectively.
𝐿̂11 (𝝆, 𝑗𝜔) =𝐿̂22 (𝝆, 𝑗𝜔) =(20)
𝑪𝑝 = (

𝐹[𝑅𝑦𝑒2 (𝑡)]𝐹[𝑅𝑒𝑒2 (𝑡)]

(21)

𝑁−1

̃ (𝜏 + 𝑁
10𝑓 (𝜏) 1𝑓

0
0

0
0

1
0

0 𝑇
)
1

(27)

1
0

0
0

0
1

0
0

0
)
0

(28)

The input-output transient data sets of the plant to find𝝆∗ were obtained in the simulation for the step reference
response of the closed-loop system with 𝝆𝟎 given by (29),and the data sets were saved as 𝒖0 (𝑡) and 𝒚0 (𝑡) ,respectively. The observation noises 𝑛1 and 𝑛2 whichwere white and independent each other were added in thesimulation, and their means and variances were 0 and
0.0025, respectively. Fig. 3 shows 𝒚0 (𝑡).

Here, 𝐹[∙] denotes the Fourier transform, 𝑅𝑦𝑒1 (𝑡) and
𝑅𝑦𝑒2 (𝑡) are the cross-correlations, and 𝑅𝑒𝑒1 (𝑡) and𝑅𝑒𝑒2 (𝑡) are the autocorrelations. The correlations arecalculated as shown in (22)~(25) using 10𝑓 (𝑡), 20𝑓 (𝑡),
̃ (𝑡) and ̃2𝑓 (𝑡) which are all filtered 10 (𝑡), 20 (𝑡),1𝑓
̃ (𝑡) and ̃2 (𝑡) by 𝐹(𝑠), respectively.
1
𝑅𝑦𝑒1 (𝑡) = ∑00

𝑩𝑝 = (

𝐹[𝑅𝑦𝑒1 (𝑡)]𝐹[𝑅𝑒𝑒1 (𝑡)]

1

𝑡)

(26)

𝝆𝟎 = (1, 0.1, 1,1, 10, 10, 10, 10)

(22)

𝜏=0

(29)

The reference model for the FRIT was given by

𝑁−1

𝑅𝑦𝑒2 (𝑡) = ∑20𝑓 (𝜏) ̃ 2𝑓 (𝜏 + 𝑁

1

𝑡)

(23)

∗ (𝑠)
∗ (𝑠)
𝑇11
= 𝑇22
=

𝜏=0

(30)

where 𝑇𝑑 = 0.1 and 𝜁𝑑 = 0.7. Then

𝑁−1

𝑅𝑒𝑒1 (𝑡) = ∑ ̃1𝑓 (𝜏) ̃1𝑓 (𝜏 + 𝑁

1,
𝑇𝑑2 𝑠 2 + 2𝜁𝑑 𝑇𝑑 𝑠 + 1

1

𝑡)

(24)

𝐿∗11 (𝑠) = 𝐿∗22 (𝑠) =

𝜏=0

1
.
𝑇𝑑2 𝑠 2 + 2𝜁𝑑 𝑇𝑑 𝑠

(31)

𝑁−1

𝑅𝑒𝑒2 (𝑡) = ∑ ̃2𝑓 (𝜏) ̃2𝑓 (𝜏 + 𝑁

1𝑡)

The diagonal elements of 𝑾(𝑠) were given by

(25)

𝜏=0

1
,
(32)
𝑇𝑤2 𝑠 2 + 2𝜁𝑤 𝑇𝑤 𝑠 + 1
where 𝑇𝑤 = 0.05 and 𝜁𝑤 = 0.7.
The parameter to achieve the model matching of (13)was obtained by the CMA-ES proposed by Hansen N [4] asgiven by𝑊11 (𝑠) = 𝑊22 (𝑠) =3 Numerical example
The LV100, which is taken from Hjalmarsson [3], is agas turbine engine modeled as a continuous-time linear
system with five state variables, two inputs and two outputs.
The state variables are the gas generator spool speed, thepower output, the temperature, the fuel flow and thevariable area turbine nozzle. The inputs are the forth and© ISAROB 2013𝝆∗ = (0.434, 0.449, 33.3, 4.59,
0.0935, 0.254, 3.70, 0.295) .

144

(33)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013transfer functions 𝐿∗11 (𝑠) and 𝐿∗22 (𝑠) at the frequencies
less than the gain crossover frequencies, respectively, andthey have enough phase margins. Therefore the reference
models for the model matching and 𝝆∗ were considered
to be chosen and tuned appropriately, respectively. Fig.6
shows that the step reference responses were improved
significantly and a non-interacting control of 1 (𝑡) and∗2 (𝑡) was achieved by 𝝆 .

Figs. 4 and 5 show that although the estimatedfrequency responses of 𝐿̂11 (𝝆∗ , 𝑗𝜔) and 𝐿̂22 (𝝆∗ , 𝑗𝜔) arecontaminated by the noises at the high frequencies, they arevery close to the true loop transfer functions 𝐿∗11 (𝑠) and𝐿22 (𝝆∗ , 𝑠) , they are similar to the specified reference
y1
r1

5

2
1.5
1
0.5
0
-0.5
0

1.5

15

y1

1

r1

0.5
0
-0.5
0

y2
r2
5

Time [s]

10

1.5

15

1
y2

y2

Time [s]

10

y1

y1

2
1.5
1
0.5
0
-0.5
0

Fig. 3. Step reference responses with 𝝆𝟎

0.5

5

5

0

1

10
10
Frequency [rad/s]

4 CONCLUSION

2

A method to improve the weakness of the FRIT using
the information of the experimental data in frequency
domain was proposed. The method is able to show that theadequacy of the reference model for the model matching of
the FRIT and the stability of the controller tuned by the
FRIT. The effectiveness of the method was shown by anumerical example of a controller tuning for a multivariable
system.

Gain [dB]
Phase [deg]

0
-90
0

1

10
10
Frequency [rad/s]

2

10Fig. 4. Bode plots of 𝐿̂11 (𝝆∗ , 𝑗𝜔), 𝐿∗11 (𝑠) and 𝐿11 (𝝆∗ , 𝑠)

REFERENCES

Gain [dB]40[1] Souma S, Kaneko O, Fujii T (2004), A new method ofcontroller parameter tuning based on input-output data –fictitious reference iterative tuning (frit) –, Proc. of IFAC
Workshop on Adaptation and Learning in Control and
Signal Processing 2004, pp.788-794[2] Matsui Y, Kimura T, Nakano K (2010), Plant model
analysis based on closed loop step response data, IEEE
Conf. on Control Applications 2010, pp.677-682[3] Hjalmarsson H (1999), Efficient tuning ofmultivariable controllers using iterative feedback tuning.
Int. J. of Adaptive Control and Signal Processing, pp.553572[4] Hansen N (2006), The CMA evolution strategy: acomparing review, Towards a new evolutionary
computation, Springer, pp.75-102

20-20-40 -1
10

Estimated
Reference
True
0

1

10

0

1

10

10
10
Frequency [rad/s]

2Phase [deg]

180
90
0
-90
-180 -110

10
10Frequency [rad/s]

2

Fig. 5. Bode plots of 𝐿̂22 (𝝆∗ , 𝑗𝜔), 𝐿∗22 (𝑠) and 𝐿22 (𝝆∗ , 𝑠)

© ISAROB 2013

15

10

90

0

Time [s]Fig. 6. Step reference responses with 𝝆∗

Estimated
Reference
True

180

-180 -1
10

10

0

20

-40 -1
10

15

r2

40

-20

10

y2

-0.5
0

0

Time [s]145The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

On inhibition of premature convergencein Genetic Algorithms for mobile robot control
Satoshi Shintaku1 , and Kazushi Nakano1,2
1

Dept. of Mechanical Eng. and Intelligent Systems, The University of Electro-Communications, Japan
2
Dept. of Electronic Eng. , The University of Electro-Communications, Japan(Tel: 81-42-443-5190, Fax: 81-42-443-5183)(shintaku@francis.ee.uec.ac.jp)

Abstract: Methods of Evolutionary Robotics using the evolutionary computation has been applied to design of mobile robot
controllers. Genetic algorithms (GAs), ones of the typical methods in the evolutionary computation, have advantages that hardly
fall into local minima compared to the other optimization algorithms. However the GAs have a big problem of premature
convergence that the variety of the population is reduced, so the searching ability is degraded. In this study, through analysis ofa new individual generation in GAs, we propose two methods of Probabilistic Crossover and Fluctuant Mutation to inhibit thepremature convergence. We apply our proposal methods to benchmark problems in optimization and to controller design of thepeg pushing robot, and demonstrate the effectiveness of our proposed methods.
Keywords: Genetic Algorithms, Premature Convergence, Optimization Problem, Evolutionary Robotics
1

INTRODUCTION

troller cannot be implemented in real time. In robot controller
design using the ER, the premature convergence tends to occur because of the difficulties in the fitness function designand hardware restrictions such as robot sensors. Therefore
GAs approaches that enable to inhibit the premature convergence are required.
In this study, we discuss about the changes in the variety of population by crossover and mutation in GAs using
uniform crossover. We divide the convergence of individuals into two parts; one is “learning convergence” that comesfrom the difference between the fitness of individuals, andthe other is “nature convergence” that occurs independentlyof the fitness of individuals. Next we propose two methods ofProbabilistic Crossover (PC) and Fluctuant Mutation (FM).
The PC is a crossover method for determining the probability
distribution of the allele, and to generate individuals of new
generation according to the probability distribution, so the
PC is a crossover method for putting forward learning convergence. The FM is a method for tetermining the mutation
rate so as to complement the decrease of variance based on
the natural convergence from the variances of the individuals
after crossover.
We apply our proposal methods to benchmark problems
in optimization and to controller design of the peg pushingrobot, and demonstrate the effectiveness of the methods interms of the quality and robustness of solution and the convergence speed of learning.

Many researchers have recently focused on methods ofEvolutionary Robotics (ER) using the evolutionary computation to design controllers for mobile robots [1]. In the ER,
teacher signal consisting of a hypothetical input and a desired
response is not required. So, the ER learns only evaluationof robot’s behavior. A robot controller can be built to achieve
the desired operation adapting to a variety of environments
by the ER. However, the control performance depends highlyon the performance of evolutionary computation itself.
Genetic algorithms (GAs), ones of the typical methods
in the evolutionary computation, used for optimization havebeen made to mimic the process of natural evolution [2].
In the GAs, the population is built from some solutions
called individuals, which are encoded binary like a genome.
The GAs search the optimal solution by alternating between
the evaluation of individuals and genetic operations such as
crossover and mutation.
The GAs have a highly global search ability, which havea big problem of “premature convergence” . If an individual exists which has much higher fitness than others, genetic
information of this individual rapidly spreads throughout the
population, and the variety of the population is reduced. Because the searching ability of the GAs depends on the varietyof the population, the searching ability is degraded by the occurrence of premature convergence, and the population has apossibility of converging to local solutions.
Although there are the methods to increase the number ofindividuals so as to maintain the variety of the population, an
increase in the number of individuals causes to an increase in
the computational effort, so there is a risk that a robot con-

© ISAROB 2013

2

RELATED WORKSThe GAs introduced by J. H. Holland in the 1970s are optimization algorithms that mimic the evolution of life. The

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GAs have the following features: first, the GAs are iterative
calculation methods, which update concurrently multiple solution candidates, secondly, the GAs use unique operations
called “crossover” that encode candidate solutions to binary
data called “genotype” and generate new candidate solutions
by combining genotype, finally the GAs are flexible algorithms that can be applied to a wide variety of problems.

move between the islands once in several generations. Emigrated individuals are the candidates of the optimal solution
in other islands, so they have a higher fitness compared tomutated individuals and are hardly lost by selection. Emigrated individuals are candidates of the solution optimized
the different situation, so they can contribute to the maintenance of variety compared to mutated individuals changed
only a small part of the genome. As a result, the DGAs canobtain a higher fitness than GAs that learn in a single population. But at the same time, because the number of individuals
per island is reduced, the environment of each island makes
it easy to converge the local minimum. Therefore, the improvement of the searching ability in DGAs has never beentheoretically proven yet.
Diploid Genetic Algorithms (Diploid GAs) have two
genotypes and represent a phenotype through a new typecalled “agency-type” [7]. Because the Diploid GAs have thelocus that does not appear in the phenotype, does not affectthe fitness and carry over low fitness genome to the next generation, it is possible to maintain the variety of individuals.
These methods are said to get a higher searching ability
than the GAs. However the parameters that should be set
up are increased, for example, temperature scheduling in the
TDGAs, the number of islands and the timing of immigration in the DGAs, and the methods for converting genotype
to phenotype in the Diploid GAs. In order to search the optimal solution efficiently, it is necessary to determine theseparameters appropriately.

There are two methods for analysis of the GAs, one is
Schema theory and the other is for analysis using Markov
chain. The Schema theory determines the change of a subset of genes (Schema) that are components of each individual in Simple Genetic Algorithms (SGAs) using one-pointcrossover and mutation [3]. The Schema theory shows that
the SGAs can increase exponentially the schema which has
partially favorable information and get globally favorable solutions. However the Schema theory cannot give the convergence to the optimal solution.
The analysis of the Markov chain calculates the probability of discovery of the optimal solution and shows the convergence to the optimal solution in the GAs [4]. From theMarkov chain analysis, the probability of discovery of the
globally optimal solution converges to “1” after calculating
in an infinite time if keeping the storage of the best individual that has been found so far and having the positive mutation rate. However the assumption is independent of themain algorithms of the GAs such as crossover and selection.
It is synonymous with that the probability of discovery of the
optimal solution converges to 1 in random search.
The Schema theory and the Markov chain analysis showthat the GAs get the globally optimal solution after calculating in an infinite time, on the contrary, these cannot show thequality of the solution obtained in a realistic time. So we do
not get exactly how to design many parameters used in theGAs from the Schema theory and the Markov chain analysis.

3

TUANT MUTATION
3.1 Analyses of Genetic Algorithms
In this section, we discuss about the crossover and the mutation of the GAs from the viewpoint of probability theory.
First, we derive an expression for the probability distributionof the allele of a new individual generated by the crossover.
The analyzed GAs consist of the roulette selection and theuniform crossover.
Selecting an individual si as the parent, the probabilityp(si ) that the i-th individual becomes a factor for determining a new individual’s locus is described as

Meanwhile, the GAs have improved its searching abilityof the optimal solution by absorbing the advantage of theother optimization algorithms and creating a new method.
Thermodynamical Genetic Algorithms (TDGAs) selectthe individual of the next generation based on Free Energy
Minimization at Simulated Annealing [5]. The TDGAs cancontrol explicitly the variety of individual by setting temperature schedule and avoid the premature convergence. On theother hand, the TDGAs have the following problems: theTDGAs require more computational time than the SGAs, andthe TDGAs causes the TDGAs convergence that loses the variety of the individual due to approximate calculation of theentropy.

f (si )
p(si ) = PN
i=1 f (si ).

(1)

where N is the number of individuals, and f (si ) is the fitness
for a given si .
The probability p(x0j = 1) that the j-th locus of the generated individual is equal to 1 is described asDistributed Genetic Algorithms (DGAs) are ones of theGAs methods that make some islands that the population isdivided into, and the crossover and evaluation are performed[6]. The DGAs provide the immigration that lets individuals© ISAROB 2013

PROBABILISTIC CROSSOVER AND FLUC-

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3.2 Consideration from analyses of GAs
p(x0j = 1)

=

N
−1
X

Figure 1 shows that the crossover changes the probabilityof the locus in the direction of increasing the fitness. As a result, the individuals converge to the genome having a higher
fitness as the generation repeated. This is defined as “learning convergence”.
Furthermore, Eq. (5) shows that the generation of the individuals reduces the variance independently of the fitness.
It means that the individuals converge independent of the fitness with the generation. This is defined as “natural convergence”.
Figure 1 shows that the difference is small between theprobability distribution of the allele of generated individuals
and the rate of the genome in the parent generation. In otherwords, p(x0 = 1) − p(x = 1) is very small. Thereby, inthe case of converging in a wrong direction, even if F in Eq.
(3) is determined to correct the wrong convergence, there isa risk that the natural convergence cancels a modification oflearning by the learning convergence. This causes the premature convergence of the GAs.
In our study, we propose a probabilistic crossover methodfor determining the probability distribution of the allele ofgenerated individuals in crossover, and also do a fluctuant
mutation for changing the mutation rate according to the variance of the locus. We intend to solve this premature convergence problem.

p(si |xij = 1)

i=0

PN −1

=
=
F

=

i=0 f (si )xij
PN −1
i=0 f (si )p(xij = 1)F
1 + p(xij = 1)(F − 1),ave(f (si )|xij =1 )ave(f (si )|xij =0 ).

(2)
(3)

where xij is the j-th locus of the i-th individual of parent generation, and ave(f (si )|xij =1 ) is the average value off (si ) if xij is equal to 1.
Figure 1 shows some examples of the above equation.

Fig. 1. Change of p(x) from crossover in GAs

3.3 Probabilistic Crossover and Fluctuant Mutation

Next, we discuss about a new population named S to generate the N -th individual from the probability distributionp(x0 ). Here, R[S|x0 = 1] is the rate that the locus is equal to
1 in S, and E[var[S]] represents the expectation of the variance of S. These are represented as
0

R[S|x = 1]=

The probabilistic crossover of our first proposing method
is one of the crossover methods for calculating the probability distribution of the allele of generated individuals:(0p(x = 1) =0p(x = 1),(4)
N −1p(x0 = 1)p(x0 = 0).
E[var[S]] =(5)N
As shown by the above equation, E[var[S]] is smaller thanthe variance of p(x0 ) because of sample variance.
Finally, when the population S is mutated with the probability Pm , we show E[var[S0 ]] as follows: the probability
that locus x∗ representing mutated locus x of S is equal to 1
is calculated by(F ≥ 1) ,1 − (1−p(x=1))
F(8)

and generate individuals according to this probability distribution instead of selecting parent individuals and crossing
over the genome.
Equation (8) is designed so as to be a tangent line to thepoint p(x = 1) = 0 or p(x = 1) = 1 in (2). Figure 2 showssome examples of (8).
In F > 1 we design p(x) to increase an increment of p(x)at around p(x = 1) = 0, the learning convergence is likely tooccur even in the neighborhood of p(x = 1) = 1 or 0 while
the premature convergence is less likely to occur.
The fluctuant mutation is our second method for estimating the reduction of the variance by the natural convergenceand for determining the mutation rate to complement the lost
variance.
Since the effects of the natural convergence are different
in each locus, the mutation rate uses different values in eachlocus. Moreover, the mutation handles only the individualsp (x∗ = 1) = p (x = 1) + Pm (p (x = 0) − p (x = 1)) . (6)Therefore, E[var[S0 ]] is described byµ¶¡¢ N −1
2− 4var[x]E[var[S 0 ]] = Pm − PmN(7)
+ E[var[S]].
From the above equation, we find that the mutation acts ina direction to increase the variance, and that this action works
strongly in the proportion to the variance in prior mutation.

© ISAROB 2013F ∗ p(x = 1) (F < 1)

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generated by the crossover and does not effect the individuals
selected by the elite selection.

4.1.1 Rastrigin function
The aim of each optimization algorithm is to find the minimum value of Rastrigin function:
F (x)

=

10N +

N
X
¡ 2
¢
xi − 10 cos(2πxi )

(11)

i=0

−5

Thus, the fluctuant mutation must complement the natural convergence that occurs in the entire population with themutation only for the individuals generated by the crossover.
So, the mutation rate pm (var[x0 ]) in the fluctuant mutation isdetermined byµ¶r0]
pm (var[x0 ]) = P1c 21 − 14 − N 1−1 N −1var[x. (9)−4var[x0 ]Individuals
GAs
PCGAs
FMGAs
PCFMGAsIn the above equation, if var[x0 ] equals to 0, pm (var[x0 ])will be 0. This does not fulfill the condition that the mutation rate is a positive value in the Markov chain analysis,
and the convergence to the optimal solution is not guaranteed. Hence, by extending Eq. (9) to Eq. (10), the fluctuant
mutation avoids this problem.

24 Pm (var[x0 ] < ²)

(12)

Table 1. Fitness for Rastrigin functionNPcxi < 5.

The Rastrigin function has a global minimum at x = 0
where F (x) = 0, and it has the suboptimal solutions in a
reticular pattern near the global minimum. The fitness function is defined by
F (x)f itness = 1 −.
(13)(20 + 52 )N
Table 1 summarizes the maximum fitness values for the
100-dimensional Rastrigin function at the 1000-th generationin each learning method. These values show the average of
100 trials, and within the parenthesis shows their standard
deviation.

Fig. 2. Change of p(x) from probabilistic crossoverpm (var[x0 ]) =µr
 1 1− 1−≤50
0.919(0.006)
0.957(0.004)
0.939(0.008)
0.778(0.063)

100
0.941(0.005)
0.962(0.004)
0.951(0.005)
0.964(0.004)

200
0.945(0.006)
0.963(0.004)
0.952(0.004)
0.968(0.003)

Figures 3 and 4 show the transitions of the maximum fitness values and their variances in 100 individuals.

¶
var[x0 ]1
N −1 N −1 −4var[x0 ]N(var[x0 ] ≥ ²)
(10)

The use of the fluctuant mutation inhibits the convergenceof the variance of the allele by the natural convergence aswell as the premature convergence.
4

SIMULATION RESULTS
Fig. 3. Transition of fitness for Rastrigin function

4.1 Application to optimization problem
To investigate the performance of the proposed methods,
each optimization algorithm is evaluated through the typical benchmark problems in optimization. The optimization
algorithms to be compared are the GAs using the uniform
crossover and the constant mutation rate as the conventional
method. The GAs using the probabilistic crossover (PCGAs), the GAs using the fluctuant mutation (FMGAs), and
the GAs using both the probabilistic crossover and the fluctuant mutation as the proposed methods. We examine thelearning processes for the cases that the number of elite individuals is given as 30, and the number of original individuals
is given as 50, 100 and 200.

© ISAROB 2013

Fig. 4. Transition of variance of fitness for Rastrigin function

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4.1.2 Schwefel function
The aim of each optimization algorithm is to find the minimum value of the Schwefel function:
F (x) =

−

N ³
X

´
p
xi sin( |xi |)Table 3. Fitness for Rosenbrock function
Individual
GAs
PCGAs
FMGAs
PCFMGAs

(14)

i=0

−500

≤

xi < 500.

(15)

The Schwefel function has a global minimum at x = 420
where F (x) = −418.98N , and it does not have the suboptimal solutions near the global minimum. The fitness function
is defined byf itness = 1 − (F (x) + 418.98N )/840N.

(16)

4.1.3 Rosenbrock function
The aim of each optimization algorithm is to find the minimum value of the Rosenbrock function:
N
−1
X

¡

¢
100(x2i − xi+1 )2 + (1 − xi )2 (17)xi < 2.

(18)

i=0

−2

≤

Table 4 summarizes the maximum fitness values and success rate that the maximum fitness value is greater than 0.95
at the 1000-th generation in each learning method. The maximum fitness value shows the average of 100 trials, and within
the parenthesis shows their standard deviation.

The Rosenbrock function has a global minimum at x = 1
where F (x) = 0, and it has a high dependence between thevariables. The fitness function is defined byp(19)
f itness = 1 − F (x)/3609/(N − 1).

Table 4. Maximum fitness and success rate for robot controller
Alghrithm Maximum Fitness Success Rate
GAs
0.912(0.102)
0.48
TDGAs
0.675(0.160)
0.04
DGAs
0.889(0.079)
0.20
Diploid GAs
0.758(0.186)
0.30
PCFMGAs
0.952(0.076)
0.72

Table 3 summarizes the maximum fitness values for the
100-dimensional Rosenbrock function at the 1000-th generation in each learning method. These values show the averageof 100 trials, and within the parenthesis show their standard
deviation.
4.2 Controller design for peg pushing robot
We simulate the problem of peg pushing robot control as
an application task. The control task is to push the peg toward
the goal by a two-wheel robot. The feedforward neural network (NN) having three layers is used as the robot controller.
The inputs of robot controller are the relative coordinates of© ISAROB 2013200
0.961(0.005)
0.964(0.004)
0.956(0.005)
0.965(0.004)

where, dist(goal, pegstart ) is the initial distance between thepeg and goal, and dist(goal, pegend ) is the final distance between the peg and goal.
We evaluate the fitness values of controllers obtained from
10 different initial positions of the peg.
The optimization algorithms used in this simulation are
the GAs, TDGAs, DGAs and Diploid GAs as the conventional methods and the PCFMGAs as proposed method. The
number of individuals is 100 in each optimization algorithms,
and the number of elite selection is 30 other than the TDGAs,
and the mutation rate is 0.1% in the conventional methods,
and Pm is 0.01% in the FMGAs. In the DGAs, the number of the island is 5, and the migration interval is 20. The
temperature schedule of TDGAs is defined by
!
Ã
t
.
(21)
T (t) = exp −25

Table 2. Fitness for Schwefel function
Individual
50
100
200
GAs 0.83(0.011) 0.88(0.012) 0.89(0.011)
PCGAs 0.95(0.006) 0.93(0.005) 0.95(0.006)
FMGAs 0.91(0.009) 0.92(0.008) 0.92(0.008)
PCFMGAs 0.95(0.006) 0.96(0.006) 0.97(0.005)−

100
0.956(0.005)
0.963(0.005)
0.954(0.005)
0.961(0.004)

the goal and peg, and the outputs are the rotational speed of
the wheel. The number of the hidden layer of the NN is 10.
In this simulation, each optimization algorithm optimizes
the synapse of the NN. The fitness function is defined by¶µdist(goal, pegend )(20)
f itness = exp −dist(goal, pegstart )Table 2 summarizes the maximum fitness values for the
100-dimensional Schwefel function at the 1000-th generationin each learning method. These values show the average of
100 trials, and within the parenthesis shows their standard
deviation.

F (x) =

50
0.941(0.005)
0.959(0.005)
0.943(0.005)
0.946(0.014)

Figure 5 shows the transition of the maximum fitness values.

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is low while the DGAs gain a high fitness. The fact demonstrates that the DGAs do not have the local search ability
while it has the global search ability.
Moreover Fig. 5 shows the potential that the Diploid GAs
gain s higher fitness at more advanced generation. However,it requires longer computational time than the other algorithms. Hence, each algorithm is not suitable for optimization of the robot controller design.
On the other hand, the PCFMGAs of the proposed methods gain the highest fitness and success rate among all the
optimization algorithms. The number of generations requiredin the PCFMGAs nearly equals to that required in the GAs
and DGAs. Hence, the PCFMGAs are not suitable for optimization of the robot controllerdesign.

ODS

6

Fig. 5. Transition of fitness of peg pushing robot

5

5.1 Learning method for benchmark problems
Tables 1 - 3 show that the PCGAs gain higher fitness values than the GAs in all benchmark problems. In contrast, theFMGAs cannot gain higher fitness values than the GAs in the
Rosenbrock function, and the fitness of the FMGAs tends tobe lower than that of the PCGAs in all the benchmark problem. While the PCFMGAs gain the highest fitness values
among the other algorithms in the case of many individuals,
the fitness values of the PCFMGAs are lower than the otheralgorithms in the case of a few individuals.
Figure 4 proves the convergence of the individuals of the
GAs at around 100-th generation. For this reason, the learning convergence does not work well in the GAs, and only
the mutation. Exponentially increasing of the fitness value
in Fig. 3 shows that searching the optimal solution become
heuristic by the mutation.
Figures 3 and 4 show that the PCGAs and FMGAs slowly
reduce the variances of the individuals, and the learning processes according to the convergence of the individuals. The
PCGAs maintain the variances to some extent even if thelearning progresses at the long generations. On the contrary,
the FMGAs lose the variances after the generation. Therefore, the PCGAs gain a higher fitness than the FMGAs in the
final result of learning. Thus, a proper setting of Pm in the
FMGAs can gain a higher fitness.
The PCFMGAs make the convergence of the individuals
very slow, and aquire the high global search ability. As a result, the PCFMGAs gain the highest fitness values among
all the algorithms after the long generation. However the
PCFMGAs are not suitable in the case of a limited numberof generations because of slow convergence.

REFERENCES[1] Toshiyuki Kondo: Evolutionary design and behavior analysisof neuromodulatoryneural networks for mobile robots control: Applied Soft Computing, Volume 7, Issue 1, pp.189202,2007[2] J. H. Holland: Adaptation in Natural and Artificial Systems:
University of Michigan Press, 1975
[3] David E. Goldberg: A Practical Schema Theorem for Genetic Algorithms Design and Tuning: IlliGAL Report, No.
2001017, January, 2001
[4] A. E. Eiben,E. H. L. Aarts and K. M. Van Hee: Global convergence of genetic algorithms: A markov chain analysis: Lecture Notes in Computer Science Volume 496, pp.3-12, 1991
[5] Naoki Mori, Junji Yoshida, Hisashi Tamaki, Hajime Kita
and Yoshikazu Nishikawa: A thermodynamical selection rule
for the genetic algorithm : Evolutionary Computation, 1995,
IEEE International Conference on[6] Xiong Shengwu: A distributed genetic algorithm to TSP: Intelligent Control and Automation, 2002. Proceedings of the
4th World Congress on, pp.1827-1830, volume 3, 2002
[7] Shengxiang Yang: On the Design of Diploid Genetic Algorithms for Problem Optimization in Dynamic Environments:
Evolutionary Computation, 2006. CEC 2006. IEEE Congresson, pp.1362-1369 , 2006

5.2 Learning method for robot controller design
Figure 5 shows that the TDGAs, DGAs and Diploid GAs
do not gain higher fitnesses than the GAs in design of themobile robot controller. Table 5 shows that the success rate

© ISAROB 2013

CONCLUSION

To inhibit the premature convergence which is a problem
in the mobile robot controller design, we proposed the new
crossover method and the method for determining the mutation rate, through the analysis of a new individual generationin GAs. We applied our proposal methods to the benchmark
problems in optimization and to the controller design for thepeg pushing robot, and demonstrated the effectiveness of our
proposed methods.

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Digital PI control for interleave PFC boost converter
Yuto Adachi1, Kohji Higuchi1, Tatsuyoshi Kajikawa1, Tomoaki Sato2, and Kosin Chamnongthai3
1

The University of Electro-Communications 1-5-1, Chofu-ga-oka,Chofu-shi, Tokyo 182-8585, JapanC&C SYSTEMS CENTER Hirosaki University, Bunkyo-Cho 3, Hirosaki-shi, Aomori 036-8561, Japan
3
King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmond, Tungkru, Bangkok 10140, Thailand(Tel: 81-42-443-5182, Fax: 81-42-443-5183)
2

Abstract: In recent years, improving of power factor and reducing harmonic distortion in electrical instruments are needed. In

general, a current conduction mode boost converter is used for active PFC (Power Factor Correction). Especially, an interleave
PFC boost converter is used in order to make a size compact, make an efficiency high and make noise low. In this paper, a PI
digital controller for suppressing the change of step response characteristics and variation of output voltage at a load suddenchange with high power factor and low harmonic is proposed. Experimental studies using a micro-processor for controller
demonstrate that the PI digital controller is effective to improve power factor and to suppress output voltage variation and ismore advantageous to control the current of the sum of each phase in hardware.

Keywords: interleave PFC, boost converter, digital PI control, micro-processor
and not easy to implement because of many switching ofcontrollers. In this paper, a PI digital controller for
suppressing the change of step response characteristics andvariation of output voltage at a load sudden change withhigh power factor and low harmonic is proposed. The PFC
converter is a nonlinear system and the models are changedat each operation point. When controlling the current forevery phase, the interleave PFC boost converter needs threecontrollers, two for interleave current control and one forvoltage control, When controlling the current of the sum ofeach phase, it needs two controller, one for interleave
current control and one for voltage control. The digital PI
controllers are used for these controllers. The design andcombining methods of these PI digital controllers which
can cope with nonlinear system or changing of the models
are proposed. These controller are actually implemented ona micro processor and is connected to the PFC converter.
That is, three or two digital PI controllers are equipped with
one micro processor. Simulations and experimental studies
demonstrate that the PI digital controllers designed suitably
satisfy the desired performances and is more advantageous
practically to control the current of the sum of each phase.

1 INTRODUCTION
In recent years, improving of power factor and reducing
harmonic of power supply using nonlinear electricalinstruments are needed. A passive filter and an active filter
in AC lines are used for improving of the power factor andreducing the harmonic. Generally a current conduction
mode boost converter is used for an active PFC (Power
Factor Correction) in electrical instruments. Especially, aninterleave PFC boost converter is used in order to make asize compact, make an efficiency high and make noise low.
In the interleave PFC boost converter, if a duty ratio, a load
resistance and an input voltage are changed, the dynamic
characteristics are varied greatly, that is, the interleave PFC
converter has non-linear characteristics. In many
applications of the interleave PFC converters, loads cannot
be specified in advance, i.e., their amplitudes are suddenly
changed from the zero to the maximum rating. This is theprime reason of difficulty of controlling the interleave PFC
boost converter.
Usually, a conventional analog phase lead-lag
compensation controller or an analog IC controller designed
to the approximated linear controlled object at one
operating point is used for the PFC converter. Theinterleave PFC converter is difficult to control because of
the nonlinearity and the complicated configuration. In the
nonlinear interleave PFC boost converter system, thoseconventional controllers are not enough for attaining good
performance. So the gain scheduling control using many
controllers for many operating points is applied to PFC
boost converter. This control method is very complicated© ISAROB 2013

2 INERLEAVE PFC BOOST CONVERTER
2.1 State-space model of interleave boost converter
The interleave boost converter shown in Fig. 1 ismanufactured. When controlling the current for every phase,
is1 and is2 are measured and used. And when controlling thecurrent of the sum of each phase, il is measured and used.
When controlling the current for every phase, two current

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2.2 Static characteristics of boost converter
At some operating point of eq. (1), let vo, io and µ, be Vs,
Is and µs, respectively. Then average of output voltage Vs
and inductor current Is at the operating points becomes asfollows:

controllers are required. But when controlling the current of
the sum of each phase, what is necessary is to shift a phasefor the output of one controller 180 degrees, and just tobuild two outputs

Vs =

Is =

Fig.1, vin is an input AC voltage, iin is an input ACcurrent, Cin is a smoothing capacitor, Vi is a rectifying andsmoothing input voltage, Q1 and Q2 are MOSFETs or
IGBTs, L1 and L2 are interleave boost inductances, D1 andD2 are interleave boost diodes, C0 is an output capacitor, RL
is an output load resistance, is1 and is2 are inductor currentsof inductance L1 and L2,, respectively, iL is the sum of
inductor current, vac is an absolute value of the input AC
voltage and vo is an output voltage. The inductor currents is1
and is2 or iL is controlled to follow the rectified input
voltage vac for improved power factor, reduced harmonics
and stable the output voltage. Using the state-space
averaging method, the state equation of the inter leave
boost converter becomes as follows [11]:

1 
V 
L0   io   i 
+1  vo   L0 −
0
RL C0 (2)

1 Vs
RL 1 − µ s

2.3 Dynamic characteristics of boost converter
The linear approximate state equation of the interleave
boost converter using small perturbations ∆io = io-Is , ∆vo=
vo-Vs and ∆µ=µ-µs is as follows:
x& (t ) = Ac x (t ) + Bc u (t )(3)y (t ) = C c x (t )
where

 R0
− L
0
Ac = 
µ
1
−
s

 C0

−

1 − µs  Vs  L 

L0, Bc =  0 
1 
− I s − C0 RLDC C0 − ∆i (t ) y 
1 0 
x(t ) =  o , u (t ) = ∆µ (t ), y =  i , Cc = 0 1 
∆vo (t )  yv 
Here, ∆io, ∆vo, ∆µ are small-signal variables. And yi,=Δio
is a small signal inductor current and yv =Δvo is a small
signal output voltage.
From this equation, matrix A and B of the interleave
boost converter depends on duty ratio µs. Therefore, theconverter response will be changed depending on theoperating point and other parameter variations. The changesof the load RL, the duty ratio µs, the output voltage Vs andthe inductor current Is in the controlled object areconsidered as parameter changes in eq. (1). Such parameterchanges can be replaced with the equivalent disturbances
inputted to the input and the output of the controlled object.
Therefore, what is necessary is just to constitute the control

(1)

 1
 0   + vo L0 + io − 1  µ
 C   0 
0 Here µ is duty ratio. When controlling the current for every
phase, io is is1 or is2, R0 (equivalent resistance of inductor) is
R1 or R2 and L0 is L1 or L2. When controlling the current of
the sum of each phase, io is iL R0 is R1R2/(R1+R2) and L0 is
L1L2/(L1+L2). The boost converter has non-linear
characteristics because this equation has the product of statevariable vo, io and duty ratio µ.

© ISAROB 2013

1V
R0 1 − µ s i
1+(1 − µ s ) 2 RL

The actual measurement results of the static
characteristics of µs to Vs are shown in Fig.2. In Fig.2, itturns out that the boost converter is a non-linear system.
The static characteristic of the boost converter ischanged greatly with load resistances, and it influences the
dynamic characteristics of converter. In addition, the static
characteristics will be changed with input voltage variation
Sasaki [1].

Fig.1 Interleave PFC boost converter

 R0−d  iL   L0=
dt vo   1
 C0

1
1

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

systems whose pulse transfer functions from equivalentdisturbances to the output y become as small as possible intheir amplitudes, in order to robustize or suppress theinfluence of these parameter changes.

3 DESIGN OF DIGITAL CONTROLLERS
3.1 Discretization of controlled object
The continuous system of eq. (1) is transformed into thediscrete system as follows:x d ( k + 1) = Ad x d ( k ) + B d u ( k )3 y (k ) = C d xd (k )
where

[ ]

Fig.2 Discrete-time Controlled Object(4)T
Ad = e AcT , Bd =  e Ac τ Bc dτ, Cd = Cc0
This dicrete-time controlled object is shown in Fig. 2. In
Fig. 2, qv, qyi and qyv are the equivalent disturbances withwhich the parameter changes of the controlled object are
replaced.

∫

3.2 Digital PI current and voltage control system
First, in order to make the input iin follow the input
voltage vac, the current control system is constituted bymaking current is1 and is2 or iL into the controlled output yi
as shown in Fig. 2. The transfer function of the digital PI
controller is as follows:

Gi ( z ) = k ri +

k ii
z −1

Fig.3 Interleave control system with digital PI current
controller and digital PI voltage controller

4 Expermental Studies
All experimental setup system is manufactured. A
micro-controller (RX) from Renesas Electronics is used forthe digital controller. The digital PI current, and voltage
controllers were implemented on 1 Micro Controller.
When controlling the current for every phase, two Gi(z)and Gv(z) are needed. Then each PI controller parameters
for each phase are determined as follow:(5)

Next, a voltage control system is constituted bymaking output voltage vo of the current control system into
the controlled output yv as shown in Fig. 2. The transfer
function of the digital PI controller is as follows:

Gv ( z ) = k rv +

k jv
z −1

k ri = 0.8
kri = 0.5

(6)The experiment results of this case are shown in Fig. 4, 5, 6.
The experiment result of the steady state at load RL=1kΩ
and 500Ω are shown in Fig. 4 and 5. The input current
waveform and the phase are the almost same as the input
voltage at each load and the power factor (PFC) of
converter at load RL=500Ωand 1kΩ are 0.9780 and 0.975,
respectively. The experiment result of load sudden change
from 1kΩ to 500Ω is shown in Fig. 6. In Fig. 6, the output
voltage variation in sudden load change is less than 5V(1.30%). and.994 and 0.972

The parameters kri and kii of eq. (5) are appropriately
decided from the Bode diagram of the transfer function
from ri to yi and from qv, qyi and qyv to yi of the system ofFig. 2.
The parameters krv and kiv of eq. (6) are appropriately
decided from the Bode diagram of the transfer function
between rv and yv yv and from qv, qyi and qyv to yv of thesystem of Fig. 2.

© ISAROB 2013kii = 0.2
kiv = 0.001

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Fig, 4 Experimental Results of Steady State Waveform,
at load RL=500Ω controlling the current for every phase

Fig.7 Experimental Results of Steady State Waveform, atload RL=500Ωcontrolling the current of the sum of eachphase

Fig.5 Experimental Results of Steady State Waveform,
at load RL=1kΩ controlling the current for every phase

Fig.8 Experimental Results of Steady State Waveform,
at load RL=1kΩ controlling the current of the sum of eachphase

Fig.6 Experimental Results of Sudden Load Change
from 1kΩ to 500Ω controlling the current for every phase

Fig.9 Experimental Results of Sudden Load Change from 1kΩ to 500Ωcontrolling the current of the sum of each phase

When controlling the current of the sum of each phase,
one Gi(z) and Gv(z) are needed. Then the PI controller
parameters are as follows:

kri = 1.0
k ri = 0.28

4 CONCLUSION. In this paper, the concept of controller of non-linear PFC
boost converter with DC-DC converter load to attain goodrobustness was given. It was shown from experiments thatthe proposed A2DOF digital controller can attain better
performance.

kii = 0.2
kiv = 0.001

The experiment results of this case are shown in Fig. 7, 8, 9.
From Fig.8, at the light load the harmonic is reduced. PFC
of converter at load RL=500Ωand 1kΩ are 0.994 and 0.972,
respectively. So it turns out that and the PI digital
controllers are more advantageous to control the current of
the sum of each phase in performance and hardware.

© ISAROB 2013

REFERENCES[1] S. Sasaki, and H. Watanabe (2005), Analysis of Multiple
Operating Points for Dynamical Control of Switching
Power Converters (in Japanese). IEIC Technical Report,
pp.33-38

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Adaptive Steering Controller for Vehicles with Driving/Braking
Force Distribution
Bo Zhou, Masahiro Oya, Jinxin Zhuo, Panfeng Shu
Department of Mechanical and Control Engineering, Kyushu Institute of Technology
1-1 Sensui-cho, Tobata-ku, Kitakyushu-City, Fukuoka, Japan(Tel: +81-93-884-3186; Fax: +81-93-861-1159)
k344232s@tobata.isc.kyutech.ac.jp

Abstract: In this paper, we propose an adaptive steering controller with driving/braking force distribution of direct
yaw-moment. To achieve good steering performance, a design scheme for ideal vehicle model is proposed. To consider
the effects of the driver dynamics, in the scheme, numerical simulations including a driver model are carried out and an
ideal vehicle model having good steering performance is designed by using a trial and error method. To realize the goodsteering performance also in the actual vehicles, an adaptive tracking controller is developed so that the behavior of theactual vehicle can track that of the designed ideal vehicle model.
Key word: steering control of vehicle, adaptive tracking control, ideal vehicle model
In this paper, we use the two degrees of freedom
Four-Wheel Steering vehicle model shown in Fig.1. The
explanations of parameters are shown in Table 1. The
motion equation of the vehicle is given as follows. The
T
state variable z (t )  vy   e expresses lateral velocity vy(t)Tand yaw rate  (t ) of vehicle, and u(t )  u f ur M  is inputthat includes steering angle of front and rear wheels andyaw-moment.

1 INTRODUCTION
When a vehicle is running on the low-μ road surface,
it is very difficult to achieve good steering performance.
In recent years, in order to overcome the problem, active
Four-Wheel Steering(4WS) schemes have been proposed[1]-[4]. In the methods, it is assumed that tire lateral forces
are linear with respect to steering angles and saturations
are not occurred. If the saturations of tire lateral forces
are ignored and a steering controller is designed, goodsteering performance cannot be expected. To address the
problem, the control schemes [5]-[6] using Direct
Yaw-moment Control(DYC) have been developed.
However, in these schemes, the saturations of the tire
driving/braking force have not been taken into account.
Caused by the influence of the driving/braking force andthe lateral forces, the saturations of tire forces may occur.
Recently, a scheme[7] has been proposed that can prevent
steering instability caused by the saturations of tire forces.
To achieve the objective, in the scheme, an optimal
distribution of driving/braking force was used and asliding mode controller was employed to guarantee goodrobust performance. However, chattering of control input
will occur. Furthermore, there is the problem that the
influence of the driver characteristics has not been
considered.
In order to improve the steering stability on the low-μ
road surface, we propose a new adaptive steering control
scheme with driving/braking force distribution. By
employing an adaptive control scheme, chattering ofcontrol input does not occur and good robust performancecan be achieved. In the control scheme, the variation of
the driver characteristics is taken into consideration.
Carried out numerical simulations including the driver
characteristics, the effectiveness of the proposed
controller is shown.

M c z(t )   vx 1HKH T  M c D  z(t )  HKu(t )

o

0
2Kr

0
1
1  , H  l f
I z 

1
lr 

(1)

m 0 
 0 vx D , Mc   0 I 
0
0



z

3 CONVENTIONAL METHOD[7]In this section, we explain a distribution method andproblems of conventional control system method.
3.1 Necessity of driving/braking force distribution
If the resultant force vector of driving/braking force X
and lateral force Y remains in the circle of radius μZ
where Z is the vertical load of the tire and μ road surface
friction coefficient, the vehicle can be driven stably. The
circle of radius μZ is called friction circle(see Fig.2) andrepresented by the following equation.
X 2  Y 2  Z

(2)

We defined the new signal μi as the estimated load
X i2  Yi 2
 i .
Z i2(3)

If one of the estimated load μi is greater than the roadsurface friction coefficient μ, the saturation of the tireforces occur. Then, the behavior of vehicle will become
unstable. Therefore, in order to prevent saturation of thetire force, it is necessary to redistribute the forces of tires.

2 VEHICLE MODEL© ISAROB 2013

2K f
K 
 0


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Table 1. Notation of vehicle

y
uf

Y1

y0

X1

Y3

ur



m fs , mrsx0

X i , Yi

vx

Zi



X3

M

Y2

C.G.
tr

Y4

C.G.
m
ms

i , 


X2

X4

M l

lr

l

u f , ur
Iz

f

v
ay
ax

x

l f , lrltr

Fig.1．Vehicle model

K f , Kr
hs

Xi

 Zi



characteristics, we used a predicted driver model [8].
Steering dynamics is given as
Tsc (t )  c (t )  g p y (t )  gi y (t )(7)where Ts is a steering constant including the delay of
muscular system and mechanical systems, Tp is predicted
time and gp, gi are the gain steering. The predicteddeviation y (t ) at the predicted position Pp is given by

Yi

Fig.2．Friction circle
3.2 Optimum minimax distribution
As mentioned in the previous subsection, no saturationof the tire force occurs as long as the following
equation holds.
max i  (4)
To hold the equation, tire forces are redistributed so thatthe estimated load of the tires may become same asfollows.
1  3 , 2  4(5)y (t )  yr (t )  Tp y r (t )(8)where yr (t ) is relative deviation between target lane andvehicle.
4.2 Ideal vehicle model
Based on the design scheme of ideal model proposed in[8], we use the ideal vehicle model given bygv 2 sv y d (t )=L1  2
L vx c (t )
2 s  2 s  (9) g  2 ( s  1)
 d (t )=L  2
L  vx c (t )
(10)2
 s  2 s  where s is Laplace operator, L, L1 represent Laplacetransform and Inverse Laplace transform,  ,  , , g v , gareare positive design parameters.
In the case that commanded steering angle  c (t ) isconstant(steady-state circular), ideal lateral velocityvyd (t) is asymptotic to zero, and ideal yaw rate  d (t ) isconverged to a constant value as lim  d (t )   d . Then, thet 
vehicle will be driven in contact with a circle ofradius vx  d . This is one of the advantages of the ideal
vehicles model given by (9) and (10).
In below, we show a design method of parameters in
the ideal vehicle model mentioned above.
(1) To prevent vibration in the ideal vehicle model,  ssets as 1.
(2) In the passive vehicle, when the speed vx is constant,the steady gain from the front wheel steering angle to the1

3.3 Problems of the conventional method
In the paper[7], a sliding mode controller is proposedto achieve good robust tracking performance for theparameter uncertainty of the vehicle(Fig.3). However, inorder to prevent chattering in the control input, thecontroller was approximated asi(6)sgn( i )  i ,  0(i  1, 2).
ii i i
Reducing the value of τi, control performance can be
improved. However, chattering will occur in the control
input. In addition, the driver characteristics have notconsidered in the control system shown in Fig.3.
4 DESIGN OF IDEAL VEHICLE MODEL
4.1 Driver model
In this paper, in order to design the ideal vehicle modelthat takes into account the variation of the driver steering© ISAROB 2013

center of gravity of vehicle
mass of vehicle
sprung mass
unsprung mass of front and rear wheels
driving/braking force and lateral force of the tire ivertical load of the tire
yaw momentside slip angle of tire and vehicle body center
yaw ratesteering angle of front and rear wheels
yaw moment of inertia of vehicle
longitudinal velocity
lateral acceleration of vehicle center of gravity
longitudinal acceleration of vehicle center of gravity
distance from C.G to front and rear wheels
wheelbasetread
cornering stiffness of front and rear wheels
height of the center of gravity of the sprung weight157

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.3．Block diagram of the conventional scheme
Fig.4．Block diagram of the proposed scheme
Table 2. Nominal values of vehicle parameterm
1070Kg1.033mlf
ms
860Kg
1.657mlr
m fs
120 Kg1.540m
tr
m rs
h
80 Kg
0.542ms
KfKr
29770N/rad41460N/radIz
1507[kgm2]

 (t )  Dz (t )  M c zd (t ).

ˆ (t ) (t )u(t )   H T z(t )  vx1H T zd (t )  

2.10 102
5.25 102  1.8vx (t )2.

(18)
where  is positive design parameter,  is positive definite
matrix, K is the nominal value of conering stiffness Kand ˆ (t ) is the estimated matrix of unknown matrix (t ) .
Fig.4 shows the configuration of the proposed control
system.
(11)

6 NUMERICAL SIMULATIONThe numerical simulations were carried out in order toshow the effectiveness of the proposed controller. In thesimulations, the values of parameters of the vehicle areshown in Table 2. The driver parameters are set as
Ts=0.1s，Tp=1.5s，gp=gi=0.05, and the target lane is acircular lane of radius 400m.
Fig.5(a)，(b) show the responses of acceleration andlongitudinal velocity of the vehicle. The responses of thecontrolled vehicle, the passive vehicle and the ideal
vehicle model are shown in Fig.5(c)-(f). Fig.5 (c) shows
the trajectory of vehicles, Fig.5(d) shows the responses ofdriver's steering angle, Fig.5(e) shows the responses ofyaw rate of vehicles, and Fig.5(f) show responses of therelative deviation between target lane and the vehicle.
As shown in Fig.5(d), in the case of deceleration,
vibration occurs in the responses of the steering angle andthe yaw rate of the passive vehicle. However, in thecontrolled vehicle, the steering angle and the yaw rate
become smooth. From this, it can be expected thatdriver's burdens is improved in the controlled vehicle.
Moreover, as shown in Fig.5(f), the relative deviation
between the passive vehicle and target lane is larger thanthat of the controlled vehicle. From this, it can be seenthat the controlled vehicle is more stable than the passive
vehicle.

We set the parameter g  as g  g p so that the steadyyaw rate gain of the ideal vehicle model becomes thesame as that of the passive vehicle.
(3) Carrying out numerical simulations with the drivermodel and using a trial and error technology, we set thedesign parameters ω, gv, η so that good steering
performance can be achieved for any driver's
characteristics 0.05s  Ts  0.4s,1.5s  Tp  4.5s, 0  g p  1,
0  g i  1.

  5.4, g v  0.25,  0.39

(12)

5 ADAPTIVE TRACKING CONTROLLER
In order to design the adaptive steering controller
which makes behavior of actual vehicle track that of ideal
vehicle model, we define the following tracking error
atz (t ) .
(13)z (t ) = z (t )  zd (t )T

Where zd (t )  v yd  d  represents the state of the idealTz (t )  0 0 , thevehicle model. If we can realize limt 
behavior of actual vehicle can track that of designed ideal
vehicle model.
Error equation of the tracking error z (t ) is given by
M c z (t )  vx1 HKH T z (t )+HK  u(t )  vx1 H T zd (t )  (t ) (t )  ,(t )   M c1HK  ,1© ISAROB 2013

(17)

ˆ
 (t )  (t )  ˆ (t )(t )   z(t )T HK (t )T , 

yaw rate is give byg p 

(16)

The adaptive tracking controller is designed as

(14)
(15)

7 CONCLUSION

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013a[m/s 2 ]vx [m/s]

26

0
24

−2
−4
0

100

200

X [m]
300

22
0

100

200

(a) Deceleration

X [m]
300

(b) Velocity
 c [deg]Y [m]4passive

controlled

1002controlled

passive

Ideal model

0
0

100

200

X [m]
300

0
0

100

(c) Trajectories
 [rad/s]
0.1

X [m]
300

(d) Steering wheel
yr [m]

controlled

0

Ideal model

0.05

controlled

Ideal model−0.2
passive

00

Ideal model
200

100

200

X [m]
300

passive−0.40(e) Yaw-rate

100

200

X [m]
300

(f) Lateral deviation
Fig. 5 Simulation results

In this paper, we propose an adaptive steering controller
with driving/braking force distribution to improve thesteering stability in low-μ road surface. Carrying out
numerical simulations, it has been shown that the control
objective was achieved.

Kumamoto, Automatic path-tracking controller of afour-wheel steering vehicle. Vehicle System Dynamics
Vol. 47, No. 10, October 2009, 1205–1227．
[5] NAGAI Masao, YAMANAKA Sachiko, HIRANO
Yutaka, Integrated Control of Active Rear Wheel Steeringand Yaw Moment Control Using Braking Forces, Trans.
Jpn. Soc. Mech. Eng, 64－622C(1998), 2132－2193.
[6] Sagai shin-ichiro, Sado Hideo, Hori Yoichi, Dynamic
Driving/Braking Force Distribution in Electric Vehicle
with Independently Driven Four Wheels, IEEJ
Transactions D, 120－D－6(2000) , 761－768．
[7] Toshihiro Hiraoka, Osamu Nishihara and Hiromitsu
Kumamoto, Driving/Braking Force Distribution by
Minimax Optimization of Tire workload(Case of Active
Four-Wheel Steering for Zero-sideslip Control), Trans.
Jpn. Soc. Mech. Eng. Vol. 71, No. 704, 2005-4.
[8] Masahiro OYA Yoshiaki ARAKI Trans. Jpn. Soc.
Mech. Eng(C), 75-715(2006-3) No.05-0591.

REFERENCES[1]
J.Ackermann ， T.B¨unte ， and D.Odentha ，Advantages of active steering vehicle dynamic contr．
99ME013,1999.
[2] Willy Klier, Gerd Reimann and Wolfgang Reinelt,
Concept and Functionality of the Active Front Steering
System 2003 SAE International 2004-21-0073．
[3] Toshihiro Hiraoka, Osamu Nishihara, Hiromitsu
Kumamoto, Model Following Sliding Mode Control for
Active Four-Wheel Steering Vehicle ， JSAE Spring
Convention No. 77-03(2003-9), 5-10．
[4] Toshihiro Hiraoka, Osamu Nishihara and Hiromitsu© ISAROB 2013

159

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Oscillation Control of a Contact Scanning Scanners System
Hideki Wada1, Hiroyuki Koga2, Katsuhiro Okumura3 and Masahiro Oya2
1

Shin-Nippon Nondestructive Inspection Co.,4-10-13 Ibori, Kokurakita-ku, Kitakyusyu-shi, Fukuoka, 803-8517 Japan(Tel: 81-93-581-1256, Fax: 81-93-581-2232, E-mail: h-wada@shk-k.co.jp)
2
Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyusyu-shi, Fukuoka, 804-8550 Japan
3
Fukuoka Industrial Technology Center, Norimatu, Yahatanisi-ku, Kitakyusyu-shi, Fukuoka 807-0831 Japan

Abstract: In the inspection of the deterioration state of the structures such as plants, contact scanning type sensors areuseful devices. Using the sensors, we can check deterioration of structure and matter inside pipes during the production process.
However, in case of the high-speed scanning, oscillation occurs in the sensor part due to unevenness and friction on themeasured surface. In this paper, to overcome the problem, we propose an oscillation controller for scanning sensor systems. At
last, to show the effectiveness of the proposed controller, numerical simulations are carried out.
Keywords: The scanning system of a sensor, Contact type sensor, Oscillation control, Accelerometers, Ultrasonic sensor
part with accurate measurements of the oscillation.
However, both methods require expensive measuring
instruments and sensors.
In this research, in order to overcome the problem
mentioned above, we propose a new oscillation controller.
One of the main features of the controller is that cheap
accelerometers are only used for measurements of thesensor part.

1 INTRODUCTION
In the inspection of the deterioration state of thestructures such as plants, the manufacturing inspection of
metals, inspection of the welded junction part and so on,usually, contact type scanning sensor systems [1-5] withultrasonic are used. In case of the high-speed scanning,
oscillation occurs in the sensor part due to unevenness andfriction on the measured surface. If oscillation occurs in thesensor part, measurement accuracy may worsen remarkably
caused by the fluctuation of the ultrasonic incident angle toinspection surface. In addition, the durability of themechanism of inspection systems is also reduced. For thereasons, in the real inspection, a slow scanning speed isused to achieve the measurement accuracy. Moreover, since
the amplitude of oscillation varies according to surface
conditions of materials, the operators need to determine thescanning speed by using a trial and error technology so thatthe occurred oscillation in the sensor part can be reduced in
the permissible level. To address the problem, there are thefollowing two methods. One is to increase the number of
sensors. Another one is to control oscillation of the sensor

2 CONTROL OBJECT AND CONTOROL PURPOSE
Fig. 1 shows the simplified model of a scanning typesensor system. The dynamic equation of this model isexpressed by the following equation. The symbol f (t )denotes the dead zone and shows friction occurring in thesensor part. In addition, explanations of the symbols used in
the following equations are shown in Table.1, and thenominal values of parameters are shown in Table.2.
xt (t )  

xs (t )~
xs (t )
ks

Sensor
part

cs

s

(2)

t

ks ~
 c s ~ m x s (t )  m x s (t )  d
s
s



f (t )  0 ck s ~x s (t )  s ~x s (t )  d

m
m
s
s




c s ~kx s (t )  s ~x s (t )  d
ms
ms



c s ~kx s (t )  s ~x s (t )  d
ms
ms



c s ~kx s (t )  s ~x s (t )  d
ms
ms

(3)
The control objective is to reduce the oscillation of ~xs (t ) .
To achieve the objective, we make the following
assumptions for the controlled object.
A1: The position xt (t) and speed x t (t) of the drive partare measurable.

Fig.1 Simplified Model© ISAROB 2013(1)~
x (t )  f (t )  x (t )Drive partxt (t )ctbx t (t )  t u (t )
mt
mt

160

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A2: The position xs (t) and speed xs (t) of the sensor
part can not be measured.
A3: The acceleration xt (t )and the relative
acceleration ~xs (t ) are measurable.

Table.2 Simulation parameter

3 OSCILLATION CONTROL SCHEMESIn order to simplify development of an oscillation
controller, consider the linearized model of the inspection
system given ascbxt (t )   t xt (t )  t u (t )
(4)
mt
mt
k
~
x (t )   cs ~xs (t )  s ~xs (t )  xt (t )
s
ms
ms

n2
Vd ( s)s  2 n s  n2

5.05

cs
ms

1.88

Az 41 ks 1
ms T 2Az 43 

ks
c 1
1
2 s
 2
ms
ms T T

Az 44 

cs
2
 
ms
T



z (t )  z1

mass of drive part and sensor partgain of input and viscosity resistance of motor

ks , cs

spring constant and damper constantucontrol input

(12)

z2

z3

(13)

z4 

T

(14)

c

c
B21   s  t  1T 2  2T
 ms mt

Table.1 Notation of Board Thickness Scanners

bt , c t



1
 0

0
A   ct   ct  2 , b   bt 

m 
m

 t
 t

 mt
0
0
0 
 0
B

 B21 B22 B23 B24 
0
0
 0



c
c
D   t   t  2   1


m

 t

 mt


2

mt , m s(11)

 s i 1 ~zi  L1 
X s ( s ) (i  1, 2 , 3 , 4)2
 Ts  1Tv d (t )  vtd (t ) vtd (t ) vtd (t )

Ts
T  Ts  (9)
X s (s)   X s (s)Ts  1 Ts  1
 Ts  1 
The right hand side denotes the high frequency signals of~x s (t ) . Using the new signal (7) in (5) and rewriting, we
have

~
xs

(10)

k s 1 cs 1
ms T ms T 2x (t )  A x (t )  B z (t )  D v d (t )  bu (t )  u (t ) (8)position of drive division
relative position of sensor division to drive system

0.9

is defined. Using the state, we have

(7)

xt

, Az 42  2x (t ) T
x (t )  ~xt (t ) ~twhere  n ,  are design parameters and v d is the final
constant speed.

© ISAROB 2013

ct
mt

d

where the design parameter  and T are set so that thetransfer function G s (s ) can become asymptotically stable.
Since the ideal acceleration vtd (t ) of the drive pert
converges to zero, if ~xt (t ) can be designed so as toconverge to zero, then, ~x s (t ) becomes also asymptoticallystable. In order to realize this, a new state

Here, T is the design parameter, and vtd (t ) is the ideal
speed of the drive part given by
2

2518

(5)

where  is a design parameter. By using this control
input, the oscillation can be reduced in a relative position~x s (t ) . However, according to the assumption A2, therelative speed ~x s (t ) is unavailable. Moreover, in fact, xt (t )cannot be used as an input signal.
In order to overcome the problem, the following new
signal is defined.

Vtd ( s) 

ks
ms



(6)

T ~
 Ts

~
xt (t )  xt (t )  L1 
X s ( s)  vtd (t ) Ts  1 Ts  1


0.55

~
~

X s ( s)  Gs ( s) X t ( s)  Vtd ( s)where c s is a small damping constant and k s is a spring
constant.
In the equation (5), if it is assumed that the signal xt (t )can be used as a control input, we may use the controller
given byxt (t )  ~x s (t )

bt
mt

c
k 
c
B22   t  s T 2  2 s T  1
m
m
m
s 
s
 t
ks
cs
k
2
B23  T  2 T , B24  smsmsmsz(t )  Az z (t )  B z x (t )  D z v d (t )

161

(15)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

 0
 0
Az  
 0
 Az 41

1

0

0
0

1
0

Az 42

Az 43

0 
0
0
0 

B z  00 ,
1 
0  T 2 

If the initial states are x (0)  0 and z(0)  0 , there
exists a positive constant  6 independent of the design
parameter  such that

0 
0 
1 

Az 44 x  (t )(16)

0 0 00 
1 
D z  0 0 0  2 T
0 0 0
0 


2

mt 

bt 

V1 (t )  xT (t ) P x  (t )   1vz zT (t ) Pz z (t )x  (t )  A x  (t )  A 1   A 2 x  (t )

1

(19)

z (t )  Az z (t )  Bz x  (t )  Dz v d (t )

(20)

1

0
0

1 
0

 ct

A  
,A
10    2  1  2
 mt


0
 0
A 2   ct 0
 m

t



 vz 

1  5
Pz B z(21)© ISAROB 2013

Qz  0(29)

where 10 is a positive constant independent of the design
parameter  . From (30), it can be seen that the control
system becomes stable. Moreover, it can be obtain that
there exists a positive constant  z independent of thedesign parameter  such that z (t )   z .
In order to analyze the detail control performance of thecontrol system, consider the following positive definite
function V2 (t )  x T (t ) P x  (t ) . Using the relations2
1
2
2  1  3 x  (t ) z (t )  1 x  (t )   3 11 z (t )
4
(31)
2
1
1
2   4 x  (t ) v d (t )  1 x  (t )   3 12
4

(22)
(23)



we obtain
 (t)   
V

(24)

2

2

x2

x  (t)   3 13(32)1
 x 2  1   1  2(33)
2
where 11 , 12 and 13 are positive constants independentof the design parameter  . According to (22),  x 2 is apositive value. Therefore, from (32), we can derive theinequality (26).

T

T



where  8 and  9 are positive constants
independent of the design parameter  . Using (29),(23) and (24), we have(30)V1 (t )   vV1 (t )   110

Where matrixes P and Pz are the positive definite matrix
solutions of the Liapounov equations as

Az Pz  Pz Az  Qz ,

2

1 1
2
  vz  5 z (t )221
 1 x  (t )21
22 1  vz Pz Dz z (t ) v d (t )   1  5 z (t )   1  94
2  vz Pz Bz z (t ) x  (t ) 

2A P  P A  Q , Q  0

2

2 1  4 x  (t ) v d (t )   4 x  (t )   2 8

1  min Q ,  2  2 P A  1  P A  2 , 3  P B ,  4  P D ,  5  min Qz 

72satisfied.



z1



Theorem: In the control system (19)-(21), the design
parameter  is set such that the following inequalities are1
1   1  2   3   4   0
2
1
 z1   vz  5   1  3  0
4

2 1  3 x  (t ) z (t )   3 x  (t )   2 z (t )The stability of the control system (19)-(21) using thecontroller (17) is shown below.

 x1 

x1

The parameter  7 is a positive constant independent of thedesign parameter  . Consider the inequalities(17)  B z (t )   D v d (t )
1

(27)

The time derivative of V1 (t ) satisfies the following relation.
2
2V   x (t )   1  z (t )   1 
(28)

Where  is the design parameter. Using the new statex  (t )  diag[1  1 ]x(t )(18)
we have1(26)  4  6

Proof: Consider the positive definite function V1 (t ) .

where Az is an asymptotically stable matrix. Based on thesystem, we developed the following controller.
 mu (t )  u(t )  k T x(t ) , k T   2 t
bt


2

(25)

162

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

From the theorem, it can be concluded that the absolutex (t ) can be reduced by setting thevalues of ~xt (t ) and ~tvalue of the design parameter  .

0

4NUMERIC SIMULATIONS-1
 5
  10  300

Carrying out numerical simulations, the effectiveness ofthe developed controller is shown. The system (1) and (2)are used as the dynamic equation of the controlled object.
The values of system parameters are shown in Table 2. The
design parameters except  are set as  n  11 ,   1 ,T  0.1 and   60 . The final ideal speed of the drive partis set as v d  5 . Fig. 2 shows responses for the design
parameter   5 ,10, 300 . Fig.2 (a) and (b) show the
responses of the signals ~xt (t ) and ~xt (t ) . Fig.2 (c) shows
the responses of the ideal speed vtd (t ) of the drive part andthe speed of the drive part x t (t ) .
As shown in Fig. 2 (a) and (b), by increasing the valueof  , the maximum values of the absolute values of ~xt (t )~and xt (t ) become smaller. As shown in Fig.2 (a) and (c),when  is set as 300, the absolute value of ~xt (t )become almost zero, and x t (t ) follows vtd (t ) .
Fig.3 shows the responses of the controlled system with
 = 300 and the passive system with a constant input（ u  45.9 ）. As shown in Fig.3, compared with thepassive system, the speed of drive part of the control system
converges to the ideal value 5 m/s quickly. Moreover, as
shown in Fig.3 (b), it can be seen that oscillation occurs in
the passive system but the oscillation can be controlled in
the control system.

0

1
(a) ~
xt (t ) 5
  10  300

-1

2[s]

0

1

(b) ~xt (t )

2[s]

[m/s]

4

vtd (t )

2

0
0

 5
  10  300

12 [s](c) xt (t )Fig.2 Response of ~xt (t ), ~x(t ) t , x(t ) t
102 [m]

[m/s]

4

0

2
Passive
Control005CONCLUSION
In this paper, based on the linearlized dynamic equation,
we proposed the oscillation control technique for thecontact type scanning sensor systems using theaccelerometer. Carrying out numerical simulations for the
nonlinear controlled object, effectiveness of the developed
controller was confirmed. As a result, it has been shownthat the oscillation of the sensor part can be prevented inhigh-speed scanning.
REFERENCES[1] Hideyuki Chikuri ， Morihiro Fukushima ， Noriyuki
Nagahama, Yuko Nishiaki, Tadashi Ishigaki “Industrial
Applied Techniques of Ultrasonic Time of Flight
Diffraction (TOFD) for Various Field Targets”，KOBE
STEEL ENGINEERING REPORTS, Vol. 57, No. 3,
pp38-42, (2007)[2]Shigeki Watanabe，Hideaki Tanaka，Shuichiro Fukuzawa，Kazuya Fujii ”Development of an Inspection Machine© ISAROB 2013

10

0

163

-1

Passive
Control

0
1
1
2 [s]
(a) xt (t )(b) ~xs (t )~
Fig.3 Respnse of xt (t ), xs (t )2 [s]Boiler Tubes of Power Station”，proceedings of JSNDI Fall
Conference 2007 October18-19, Hokkaido Japan,
pp217-218，(2007)[3] Hiroyuki Haga，Yuji Matsuo，Minehiro Nakagawa，Tateyuki Tomitaka，Yuji Nisimura，Koichi Tamura andMasanori Hanaguchi “Development of UltrasonicContinuous Thickness Measurement Equipment for Piping”，
Journal JSNDI，No4，pp189-193，(2010)[4]Hiroyuki
Fukutomi,
Shan
Lin,
Takashi
Ogata ”Ultrasonic Testing to Type IV Damage in Welded
Joints of Boiler Piping Part1 Compact Automatic Inspection
System”， proceedings of JSNDI Fall Conference 2010，
Okinawa, Japan, pp159-160，(2010)[5]Hideki Wada ， Masahiro Oya ， Katsuhiro
Okumura ”Development of a position control scheme forrotating sensor unit attached to in-pipe robot”，Artificial
Life Robotics(2012) Vol.17, No.2, pp317-321

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Rollover Prevention Control of Driver-Heavy Vehicle Systems
Yusuke Suetake 1 , Masahiro Oya 1 , Panfeng Shu1 , Jinxin Zhuo1
1

Kyushu Institute of Technology, Japan(Phone: +81-93-884-3186, Fax: +81-93-861-1159)
E-mail: k344212y@tobata.isc.kyutech.ac.jp
In this paper, we propose an adaptive rollover prevention controller. At first, using a evaluation function,
we propose a design method for an ideal vehicle model achieving good rollover control performanceeven if driver’s steering characteristics vary. Next, an adaptive tracking controller is developed so thatthe behavior of the actual heavy vehicle can track that of the ideal vehicle model.
Keywords: Intelligent Truck & Heavy Duty Vehicle Control
1. INTRODUCTIONzδc +δf xφRecently, to present rollover of heavy vehicles, a lot of rollover
prevention control schemes were developed[1]-[8] . The papers[1]-[7] provided the schemes using the yaw moment force dueto the torque difference between left and right. In the paper[8],the rollover prevention controller was proposed using the active
steering of the front wheel. In the control schemes proposed in[1]-[8], only one control input is used. To prevent the rollover andachieve good steering performance, we have to control four statesof the lateral velocity, the yaw rate, roll angle and the roll rate.
Therefore, using the control inputs more than one, it is expectedthat better control performance can be achieve. To address the
problem , the papers[9]-[10] provided the control schemes using
the lateral force of the front active steering and the yaw moment
force, and the scheme using the active steering force of front
and rear was proposed in [11]. However, there exist problems
in the papers [9]-[11] that the uncertainties of the vehicle systemparameters were not considered. In addition, the variation of
the driver characteristics was also ignored. For example, there
exist large differences in handle operation characteristics between
young drivers and elder drivers. Even for the same driver, if thedriver becomes tired, the handle operation characteristics may vary.
Using the rollover prevention controller ignoring the facts, thedesigned control performance may not be achieved.
In the paper, to overcome the problems, we propose an adaptive
rollover prevention controller. At first, we develop a design
method of an ideal vehicle model achieving good rollover control
performance even if the handle operation characteristics vary.
Next, an adaptive tracking controller is developed so that the
behavior of the actual vehicle can track of the ideal vehicle model.

y

lf

vx

lp

vp

lr

δr

ψ
C.G.
C
.G.

C.G.
C.G.

hs cosφ
kφ

dφ

dφ

PPff
hu

PL

y

kφ
PR

ly

Fig.1 vehicle model

described as







∗
∗
˙

δc = δc (t) + δc (t), u (t) = u(t) + u̇(t)





T
T
T
T
−1

x(t) = [ q p (t), q s (t), µ (t) ] , µ = T µ1 (t)


−1 T

µ1 (t) = Mµ HT Ku {−vx HT q v (t) + bu δc (t) + u(t)} 

ms hs
, (1)
T
−
b g q (t)2
2 2 u s s[m(Ix + ms hs2 ) − m]s hs
[
] 


Ix + m s h1lfMµ = diag
,
I
,
H
=
z


1 −lr
m(Ix + m2s h2s )



−1

HT = HTq s (t) = [ϕ(t) , ϕ̇(t)]T , q p (t) = [vp (t) , ψ(t)]T





0
A1 0
I





0
A =  0 A2 A3  B = 
Mp−1 H T Ku
A4 A5 A6



1

T

b
g
A1 = −vx D , A2 = D −

s
s


Ix + ms h2s



1
T


T
A3 =
b
b
s u

2
Ix + ms hs
.
(2)
−1
−1 T
A4 = −vx Mp H Ku H(I + A1 )



−1
T

A5 = −mm T bu g s (I + A2 )

A6 = −vx−1 Mp−1 H T Ku H − mm T −1 bu g Ts A3 − I 
Mp =[ T T Mµ T] , Ku =[diag][kf , kr ] [

]



0 1
1
0


D=
, bu =, bs =


0
0
0
1

[
]

T
2
2
g s = kϕ ly − ms ghs , dϕ ly , T = I + lp Dẋ(t) = Ax(t) + B (bu δc∗ (t) + u∗ (t))

2. DYNAMIC EQUATION OF VEHICLE
Consider the vehicle model shown in Fig.1. It is assumed thatthe pitch dynamics can be ignored. In addition, it is also assumed
that the front steering angle, the rear steering angle and the rollangle are small. The dynamic equation using the new state can be

In the dynamic equation, δc (t) is the driver’s steering angle input© ISAROB 2013

164

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Table 1 Meaning of Symbols
vx Longitudinal velocity
vp Lateral velocity
ψ Yaw rate
ϕ Roll angle
ϕ̇ Roll rateg acceleration of gravity
m Vehicle mass
ms Sprung masskϕ Spring constantdϕ Damper constant
Ix Roll moment of inertia
Iz Yaw moment of inertia
C.G.Center of gravity
Pf Reference point

hu Height of roll axis over groundhs Height of C.G. over roll axis
lp Distance C.G. to Pf
lf Distance front axle to Pf
lr Distance rear axle to Pf
ly Track width
kf Front cornering stiffness
kr Rear cornering stiffness
δc Driver’s steering input
δf Control input for front wheel
δr Control input for rear wheel
PL Vertical load for left wheel
PR Vertical load for right wheel

Pp
Pf

Fig.2 Predictive driver model
m
ms
hu
hs
lf
lr
ly
lp

Table 2 Nominal value
14300 [kg] kϕ 132100
12487 [kg] dϕ
28905
0.68 [m] Ix
24201
1.15 [m] Iz
34917
3.95 [m] kf
582
4.54 [m] kr
783
1.86 [m] g
9.81
0 [m]

[N/m]
[N·s/m][kg·m2 ][kg·m2 ][kN/rad][kN/rad][m/s2 ]

and u(t) = [δf (t), δr (t)]T is the control steering input for the front
wheel and the rear wheel. Definitions for the parameters of heavy
vehicles are shown in Table 1.
The rollover index R(t) for the roll dynamics is defined asfollows.
PL − PR2ms (hays (t) + ghs ϕ(t))
R(t) ==
PL + PR
mgly



T
T

= cv̇p x(t) + co x(t)





ays (t) = v̇p (t) + vx ψ(t) + lp ψ̇(t) − hs ϕ̈(t)h = hu + hs
2I
m
h
(3)
x s
cTv̇p x(t) =([1 0]µ − vx ψ(t))
2mgly (Ix + ms hs )



2ms

T

co x(t) =
(I
hv
ψ(t)
x
x


mgly (Ix + ms h2s )


2
2

+(ms ghs (hs − h) + hs (hkϕ ly + gIx ))ϕ(t) +hs hdϕ ly2 ϕ̇(t) + [0 hlp (Ix + ms h2s )]µ(t))

Fig.3 Target lane

3. IDEAL VEHICLE MODEL
In order to design an ideal vehicle model in which the variationsof the driver’s characteristics is considered, we employ thepredictive driver model given by
˙ − gi ỹ(t)Ts δ̈c (t) + δ̇c (t) = −gp ỹ(t)where gp ,gi are the steering gains, Ts is the steering time constantand denotes the reaction delay of drivers and mechanical systems.
To explain the predicted distance ỹ(t), the predictive driver modelis shown in Fig.2. The solid line represents a state of the vehicle
and the dashed line represents the ideal state where the vehicle
tracks a target lane. yr (t) is the lateral distance between Pf and thetarget lane，εr (t) = ε(t) − εd is the relative yaw angle between
the vehicle and the target lane, εd is the yaw angle of the target laneand ρ is the curvature of target lane. The predicted distance ỹ(t) is
the lateral distance at the preview point Pp and given by

To represent the rollover index by using the state x(t), we introduce
the state including the signal µ(t). The range of R(t) is 0≤|R(t)| < 1．If |R(t)| = 1，the total vertical loads is loaded onright wheel or left wheel．Then, we can see that vehicles wererollover.
When the values of system parameters are nominal values, thevehicle is called the nominal vehicle. Especially, in case of u(t) =0, the vehicle is called the passive vehicle. Nominal values areshown in Table 2.
For the heavy vehicle (1), we make the following assumptions.
A1
A2
A3
A4
A5

ỹ(t) = yr (t) + Tp y˙r (t)(5)

where Tp is the preview time constant. Drivers steer so that thepredicted distance ỹ(t) can converge to zero.
Using the driver model, and carrying out numerical simulations,
we propose the following design scheme for an ideal vehicle.

The lateral velocity vp (t) of vehicle，the yaw rate ψ(t), theroll angle ϕ(t) and the roll rate ϕ̇(t) are measured．
The distances lf ，lr from the reference point Pf to the front
axle and the rear axle are known．
The vehicle parameters except lf ，lr include uncertainties．
The longitudinal velocity vx is a bounded constant andmeasured.
The matrix A2 is asymptotically stable.
© ISAROB 2013(4)

1 We determine the values of weighting coefficients of the⃝following evaluation function.
)∫( 2
J(t) =
q1 yr (t) + q2 ÿr2 (t) + q3 ε2r (t) dt+(6)
q4 max |yr2 (t)| + q5 max |ÿr2 (t)| + q6 max |R2 (t)|165

2 We design the ideal vehicle model with the control input u∗⃝M (t)given byThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

40, q4 = 10, q5 = 5, q6 = 20 by the trial-and-error approach.
2 Design of an ideal vehicle model⃝
The controller for the ideal vehicle model (7) is developed as
1 T
B P xM (t)(9)
u∗M (t) = −
2ε1

1
T
T

A P + P A − P B B P + ε2 cv̇p cTv̇p + ε3 I = 0. (10)ε1ε = 0.1, ε = 0.2, ε = 0.01
1





(7)

3 Evaluation of conditions⃝
The conditions (8) are checked. As a result of the check, in thecase where the values of J are less than 62, the conditions (8) aresatisfied in the designed ideal vehicle.

where ● denotes matrixes and vectors in which the values of∗
elements of ● are nominal values and δcM(t) is the steering input∗
δcM (t) = δcM (t) + δ̇cM (t) generated by Ts δ̈cM (t) + δ̇cM =−gp ỹ˙ M − gi ỹM . The controller u∗M (t) is designed so that thevalues of the evaluation function (7) become less than that of thepassive vehicle.
3 We check the region of driver’s parameters in which the⃝following conditions are satisfied. In the following equations,yrM (t), εrM (t) denote the lateral distance and the relative yaw
angle in the case when the driver is driving the ideal vehicle model.
}|yrM (t)| < 0.6[m], |ÿrM (t)| < 3[m/s2 ](8)
RM (t)| < 0.85, εrM < 0.2[rad]4 Dicision to end of the iteration⃝
As shown in Fig.4(a) and (b), the obtained region where theconditions (8) are satisfied is large enough compared with thatin the passive vehicle. Then the design of the ideal model iscompleted.

4. ADAPTIVE TRACKING CONTROLLER
In order to develop an adaptive tracking controller so that the
behavior of the actual vehicle tracks that of the ideal vehicle model,
the tracking errors are defined as}
ep (t) = q p (t) − q pM (t), qes (t) = q s (t) − q sM (t)q. (11)
e (t) = µ(t) − µM (t)µWhen |yrM (t)| < 0.6[m], the heavy vehicle doesn’t stray from adriving lane．Moreover，when |ÿrM (t)| < 3[m/s2 ]，we consider
that burdens hardly move.

ep (t)+ µe (t) is also
Moreover, the new tracking error signal z(t) = q
defined. Then, we have(ż(t) = −dz(t) + Mp−1 H T Ku H H −1 u∗ (t))(12)−vx−1 (A1 + I)q p (t) + Θω(t) ,4 If the obtained region in ⃝3 is large enough compared with⃝that in the passive vehicle, the design of the ideal vehicle model
1 〜⃝.
3
is completed. Otherwise, we return to step ⃝Using a concrete example and carrying out numericalsimulations, we show detail explanations of the design steps statedabove．Fig.3 shows the target lane used in the example. Fig.3(a) shows the target lane with the curvature ρ (driving maneuver1). Fig.3 (b) shows the target lane with the curvature ρ andthe angle θ1 (driving maneuver 2). In the driving maneuver 1,
the ideal model is checked for ρ = 1/300[m]〜1/500[m]. In
the driving maneuver 2, the ideal vehicle model is checked forρ = 1/300[m]〜1/500[m] and θ1 = 0[rad]〜π/2[rad]. For thedriver parameters, we check the ideal vehicle model in the regionof Ts = 0.1[s]〜0.4[s], Tp = 1.5[s]〜4.5[s], gp = 0〜1, gi = 0〜1
and the vehicle speed vx = 120[Km/h].
1 Determination of the weighting coefficients⃝
Weighting coefficients are set as q1 = 26, q2 = 10, q3 =© ISAROB 20133

Fig.4 shows the values of the evaluation function J of the ideal
vehicle model and the passive vehicle model. The vertical axis
is the values of J, x axis and y axis are steering gains gi , gp . In
the case where J exceed 62, the values of J are fixed as 62.In
Fig.4, vx , Ts , Tp are set as vx = 120[Km/h], Ts = 0.4[s] andTp = 1.5[s], and the values of the evaluation function J are shownfor the driving maneuver 1 with ρ = 1/300[m] and the driving
maneuver 2 with ρ = 1/300[m] and θ1 = π/2[rad]. It can beseen that the region of the ideal vehicle model where the values of
J become less than 62 is larger than that of the passive vehicle.
For lack of space, the simulation results for the other values ofρ, θ1 , vx , Ts and Tp are not shown. However, it was ascertainedthat similar results to Fig.4 could be obtained.

Fig.4 Evaluate function∗ẋM (t) = AxM (t) + B (bu δcM(t) + u∗M (t))
T
T
RM (t) = cv̇p xM + co xMxM (t) = [ q TpM (t) , q TsM (t) , µTM (t) ]T

2

(
)−1
[ A5 , A6 , I ]Θ = Mp−1 H T Ku H
ω(t) = [ q Ts (t) , µT (t) , ξT (t) ]T








e (t)− µ̇M (t) ξ(t) = (dI +A1 )eq p (t)+(d + 1)I µ

,

(13)


e2 q sM (t)+ A
e3 µM (t) ės (t) = A2 q
es (t)+A3 µ
e (t)+ Aqe2 = A2 − A2 , A
e3 = A3 − A3
A
166



,

(14)

where d is a positive design parameter，Θ is the unknown constantThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.5 Response of the controlled heavy vehicle with nominal parameters.

matrix and ω(t) is the known signal vector.
If the tracking error z(t) becomes asymptotically stable, it can be
ep (t) and µe (t) become asymptoticallyseen that the tracking errors q
stable．Then, from (14) and the assumption, it can be also seenes (t) also becomes stable．Especially, inthat the tracking error q
e2 and A
e3 are zero matrixes，the tracking error
the case where A
es (t) becomes asymptotically stable．
q
Using the adaptive tracking controller given byu̇(t) = −u(t) − βHz(t) + vx−1 H(A1 + I)q p (t) − H Θ̂(t)ω(t), β>0, (15)˙TΘ̂(t) = γz(t)ω (t), γ>0performance can be achieved. Moreover, we proposed an adaptive
tracking controller so that the behavior of the actual heavy vehicle
can track that of the ideal vehicle model even if the actual vehicle
system parameters vary. Carrying out numerical simulations,the effectiveness of proposed rollover prevention controller was
shown.
REFERENCES[1] B. Schofield and T. Hagglund, Optimal Control Allocationin Vehicle Dynamics Control for Rollover Mitigation,
Proceedings of American Control Conference, pp3231-3236[2] L-k Chen, C-f Cheng and M-f Luo, Rollover Prevention
through Model Predictive Direct Yaw Moment Control,
the tracking errors become stable and the tracking error z(t)Proceedings of Advanced Vehicle Control, pp.721-726
converges to zero. For lack of space, the proof is omitted.
[3] S. Solmaz, M. Corless and R. Shorten, A methodology
for the design of robust rollover prevention controllers
for automotive vehicles : Part 1-Differential braking,
5. NUMERICAL SIMULATION RESULTS
Proceedings of IEEE Conference on Decision and Control[4]
B.
Johansson and M. Gafvert, Untripped SUV Rollover
The numerical simulation results are shown to demonstrate theDetection
and Prevention, IEEE Conference on Decision andeffectiveness of the proposed adaptive tracking controller. The
Control,
pp.5461-5466
driver parameters Ts ，Tp ，gp ，gi are set as Ts = 0.4[s]，Tp = 1.5[s]，[5] B. Schofield, T. Hagglund and A. Rantzer, Vehicle Dynamics
gp = 0.3，gi = 0.5. The target trajectory shown in Fig.3(b) is used.
Control and Controller Allocation for Rollover Prevention,
The parameters ρ and θ, are set as ρ = 1/300[m], θ1 = π/2[rad]IEEE International Conference on Control Applicationand the longitudinal velocity is vx = 120[Km/h]. In the passive[6]
S.
Solmaz, M. Akar and R. Shorten, Adaptive Rollovervehicle, if the driver parameters stated above are used, the rollover
Prevention
for Automotive Vehicles with Differential
occurs and it very difficult to stabilize the vehicle.
Braking,
Proceedingsof the 17th IFAC, world CongressFig.5 shows the responses of the ideal vehicle, the controlled
[7]
J.
YOON,
D.
KIM
and K. YI, Design of a rollovervehicle and the passive vehicle. Fig.5 (a) shows the responses of theindex-based
vehiclestability
control scheme, Vehicle System
rollover index, Fig.5 (b) shows the responses of the lateral deviation
Dynamics,
Vol.
45,
No.
5,
459-475
and Fig.5 (c) shows the responses of the relative yaw angle.
[8] S. Solmaz, M. Corless and R. Shorten, A Methodology for
From Fig.5, it can be seen that the rollover occurs in the passive
the Design of Robust Rollover Prevention Controllers forvehicle. On the other hand, the rollover didn’t occur in the
Automotive Vehicles Part 2-Active steering, Proceedings ofcontrolled vehicle. For lack of space, we couldn’t show responses
American control Conference
for the variations of the driver parameters and the vehicle system[9]K.
Feng, H.Tan, M. Tomizuka, Automatic Steering Control
parameters. However, it was ascertained that the good control
ofVehicleLateral Motion with the Effect of Roll Dynamics,
performance could be achieved. It can be concluded that for theProceedingsof American Control Conference, pp.2248-2252
variations of system parameters, the proposed rollover prevention[10]
D.
Odenthal,T. Bunte and J. Ackermann, Nonlinear Steering
controller has good robust performance.
and Braking Control for Vehicle Rollover Avoidance,
European Control Conference[11] C. Cheng and D. Cebon, Improving roll stability of articulated
6. CONCLUSIONS
heavy vehicles using active semi-trailer steering, Vehicle
System Dynamics, Vol. 46, pp.373-388
In this paper, a new design method for an ideal vehicle model
was proposed. It has been shown by carrying out numericalsimulation that in the ideal vehicle, the good rollover control© ISAROB 2013

167

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Development of a dual-shaft propeller thruster equipped withrotational speed sensor for UVMS control
Radzi Bin Ambar

Shinichi Sagara

Takuya Yamaguchi

Department of Mechanical and Control Engineering, Kyushu Institute of Technology
Tobata, Kitakyushu 804-8550, Japan
Email: m584204b@tobata.isc.kyutech.ac.jp
Abstract: Majority of underwater robots utilize single propeller thrusters for navigation. A disadvantage on using a single
propeller thruster is the thrust force generated from a single propeller for reverse and forward thrust is asymmetric due to the
disturbed flow caused by the thruster’s body which may reduce thruster efficiency. Measurement procedures to precisely
calculate propeller’s rotation speed were also not available. To address these problems, this paper proposes a dual-shaft
magnetic coupling driven propeller thruster for underwater vehicle-manipulator system (UVMS) equipped with sensors formeasuring propeller’s rotational speed. Numerical studies and experimental results on the position and orientation control ofthe proposed thruster are presented. Detail comparison of the rotational speed, thrust force and duty-ratio between numericalcalculation and actual experimental measurement results shows the effectiveness of the proposed thruster. The ability todetermine propeller rotation directions is also a major advantage.
Keywords: dual-shaft, thruster, rotational speed, UVMS, control
the propellers and compared it with commercially available
thruster for performance comparison.

1 INTRODUCTION
The main purpose of this research is to design a newunderwater thruster for an underwater vehicle-manipulator
system (UVMS) equipped with a 2-link manipulator which
the authors have been actively carried out [1-3].
To date, a large number of underwater robots utilize
single propeller thrusters for navigation [3-6]. A
disadvantage on using a single propeller thruster is thethrust force generated from a single propeller for reverseand forward thrust is asymmetric due to the disturbed flowcaused by the thruster’s body which may reduce thruster
efficiency. A magnetic coupling driven propeller thrustertransmits torque through an air gap between motor driven
shaft and load shaft (propeller). However, due to magnetic
coupling mechanism, it is difficult to measure precisely theactual rotational speed of the propeller and the thrustgenerated from it. Although there are researchers
demonstrating the relation between propeller’s torquerotational speed and the generated thrust, they utilized
unknown parameters and only focusing on motor driven
propeller thruster without magnetic coupling. Measurement
procedures to specifically calculate propeller’s rotation
speed were also not available.
To address these problems, this paper proposes a dualshaft magnetic coupling driven propeller thruster for
UVMS equipped with sensors for measuring rotational
speed of the propellers. The developed thruster is consistsof two magnetic coupling driven propellers which rotate indirections opposite one another. Magnetic couplingmechanism has been selected, due to the several advantages
such as cushioned start, low power usage and low
maintenance cost. The paper also proposes a method on
calculating the propeller thruster’s thrust force throughmeasuring rotational speed and thrust forces generated from© ISAROB 2013

2 DESIGN METHODOLOGY
2.1 Thruster design

Magnetic coupling
Motor shaft

Load shaft

DC motor

Propeller

Fig. 1. Dual-shaft magnetic coupling propeller thruster
In this section, the developed dual-shaft propeller
thruster driven by co-axial magnetic coupling is described.
Fig.1 shows the developed thruster. Basically, one of themagnets is driven by a motor through a motor shaft. Thismechanism is designed inside a waterproof container. The
other magnets is connected to the propeller via a load shaft.
Two Raboesch 3-blade brass propellers are being used. A
dual-shaft Faulhaber 3863A024C DC motor with planetary
gearheads is being used to drive two sets of MTL-03 coaxial magnetic coupling from Magnetic Technologies. The
motor is controlled by a PIC30F3010 microcontroller. Table
1 shows the physical parameters of the developed thruster.

168

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where R is propeller’s rotational speed (unit: rpm), T issampling period (unit: s), P is number of pulse between asampling period.
To control thruster’s force, the desired rotational speed
which corresponded to the desired thrust force need to be
calculated. The desired rotational speed can be calculatedby the following equation:

Table 1. Physical parameters of thruster
Length383 [mm]
Outer diameter
57 [mm]Weight3130 [g]Propeller pitch
102.5 [mm]Propeller diameter
100 [mm]
Propeller weight
84 [g]

R d i = 0 . 1576 f d i

3− 6 . 075 f d i2+ 112 . 2 f d i + 136 . 7(2)Here, d i is desired thruster number i (i=1, 2), R d is thedesired rotational speed (unit: rpm), f d is the desired
thrust force (unit: N). While the desired input signal whichcorrespond to the desired rotational speed can be calculatedby the following equation:ii

u d i = 0 . 913 × 10 − 7 R d i − 5 . 292 × 10 − 5 R d i
3(3)

× 10 − 5 R d i + 4 . 298 × 10 − 2 R d − 2 . 786
2

(a)
(b)
Fig. 2. Reflective photosensors and metal disk position

Where u d is the desired input signal (duty ratio, unit: %).
iBased on these desired thrust, rotational speed and input
signal calculated in equation (2) and (3), and also rotational
speed measured through reflective photosensors, R i inequation (1), the actual input signal u i for the thruster canbe calculated as below:

2.2 Reflective photosensor
The rotation speed of the propellers can be measured byusing two units of RPR-220 (ROHM Co. Ltd.) reflective
photosensors embedded in both of the motor shaft housings
facing towards the propeller as shown in Fig.2 (a). The
reflective photosensors consists of an infrared emitted diodeand a phototransistor which can detect the reflected infrared
light. The infrared light is reflected by a metal disc which is
attached on the propeller’s shaft, located between thepropeller shaft housing and propeller as shown in Fig.2 (b).
The metal disc will rotate along with the propeller. It is
carved with 8 holes and layered with thin black coloredrubber attached on the back of the discs which is to prevent
infrared reflection. The metal reflective surface and blackcolored rubber surface makes a perfect reflecting and nonreflecting surfaces.

u i = u d i + k P ( R d i − R i ) + k I ∫ ( R d i − R i ) dt

Here, k P is proportional gain and k I is integral gain.

3 EXPERIMENTAL SETUP
3.1 Thruster control system experiment
CablesSinglethrusters

As described in the previous section, propeller’srotational speed can be measured using two units ofreflective photosensors embedded side by side. The sensors
are embedded in such a way that provides two voltage
outputs with the phase differences of 90°. When thepropeller and metal disc rotated, the rotational speed can becalculated by the embedded sensors. Moreover, the voltage
outputs phase differences of 90° provide the ability todetermine the directions of rotation for both propellers. The
metal discs have 8 holes, where 1 rotation of the propeller
produces 32 pulse (11.25°/pulse). The relationship between
number of pulse and rotational speed is expressed with© ISAROB 2013

60 × P
32 × T

Dual-shaft
thrusters

LED

LED

LED

2.3 Rotational speed measurement

R =

(4)

Robot
base

2-LinkManipulator
Z

Joint 1

Joint 2Y

Test Tank

X

CCD
cameraJoints motor
driverThrusters
driver(1)

CCD
cameraFront view

XY-Tracker

D/A
board

ComputerCounterboard

MasterController

Fig. 3. Experimental setup for the developed thruster

169

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Next, Fig.5 and Fig.6 shows experiment results for
position and orientation of UVMS. Based on theexperimental setup in Fig.3, the robot base was controlled
to move 0.15m downward. The results of position andorientation (x, y, z, roll, pitch and yaw axes) from the robot
base using the traditional single propeller thrusters and the
proposed dual-shaft propeller thrusters are shown in Fig.5
and Fig.6 respectively. Fig.5 shows that the commerciallyavailable single propeller thrusters produced large vibration
on x axis during the first 20sec from the start of movement.
Large vibrations also recorded in y and z axes, during thefirst 20sec and 40sec respectively. Orientation of the basealso demonstrated large vibration for the first 40sec.
However, Fig.6 shows that by using the proposed thrusters,
UVMS demonstrated no large vibration occurrence on
position and orientation axes. The above results proved thatthe proposed thrusters design demonstrated good
performance compared to the commercially available
thrusters.

Fig.3 shows an illustration of the experimental setup forthe developed thruster on an UVMS to measure the robot
position and orientation. Two of the proposed thrusters
were attached on the robot as shown in Fig.3, and thencompared with two commercially available single propeller
thrusters. Three LEDs are attached to the robot base, andtheir motion is monitored by CCD cameras. Video signals
from the LEDs are converted into position data by X-Y
tracker and transmitted to PC for calculation of thepositions and orientation of robot base. Robot control isachieved using resolved acceleration control (RAC) methodwith sampling period of T=1/60sec [1-3]. For experiments
using the developed thrusters, the gain for rotational speed
control are set as k P = 0.031, k I =0.0017.

4 RESULTS
Experiments have been carried out on two newly
developed propeller thrusters. Firstly, based on thecalculations of desired thrust, rotational speed and input
signal described in section 2.3, performances comparison
with actual measurements using reflective photosensors on
two proposed thrusters has been implemented. Fig.4(a) and
Fig.4(b) shows the relation between rotational speed, duty
ratio and generated thrust force. Fig.4(a) shows thatmeasurement results of the increased in duty ratio on
propeller’s rotational speed from both thrusters have similar
performances with calculated results. Fig.4(b) shows that anincrease of rotational speed resulting in increase of thrustforce. The measurement results also gave similar
performance with calculation results which demonstrated
the usefulness of the proposed thrusters equipped withreflective photosensors for rotational speed measurements.

5 CONCLUSION
A dual-shaft magnetic coupling driven propeller thruster
for underwater vehicle-manipulator system (UVMS)
equipped with sensors for measuring propeller’s rotational
speed have been proposed. Numerical studies andexperimental results on the position and orientation controlof the dual shaft propeller thruster shows the effectivenessof the developed thruster. The ability to determine propeller
rotation directions is also a major advantage.

REFERENCES[1] S. Sagara et al. (2006), Digital RAC for Underwater
Vehicle-Manipulator Systems Considering SingularConfiguration, J. Artificial Life and Robotics, Vol. 10,
No.2, pp.106-111.

Rotational speed [rpm]

1000
800
600
400
200
0
0

[2] S. Sagara et al. (2010), Digital RAC with a DisturbanceObserver for Underwater Vehicle-Manipulator Systems,
J. Artificial Life Robotics, Vol. 15, No.3, pp. 270-274.

Thruster1
Thruster
1 experiment result
Thruster1
approximation
Thruster
1 calculation
resultThruster2
Thruster
2 experiment result
Thruster2
approximation
Thruster
2 calculation
result

2

4

6

8
10
Thrust [N]

12

14

16

[3] S.Sagara et al. (2012), A master-slave control system
for a semi-autonomous underwater vehicle-manipulator
system. J. Artificial Life Robotics, Vol.16, No.4, pp.
465-468.

18

Duty ratio [%]

(a)
100
90
80
70
60
50
40
30
20
10
0
0

Thruster1
Thruster
1 experiment result
Thruster1
approximation
Thruster
1 calculation
result[4] Yoerger D.R. et al. (1990), The influence of thruster
dynamics on underwater vehicle behavior and theirincorporation into control system design, IEEE J.
Oceanic Eng., vol.15, no.3, pp.167-178.

Thruster2
Thruster
2 experiment result
Thruster2
approximation
Thruster
2 calculation
result

200

400
600
Rotational speed [rpm]800[5] Allen, B. et al. (1997), REMUS: a small low cost AUV;
system description, field trials and performance results,
Proceedings of the IEEE Oceans 97, New Orleans,
USA, pp. 994–1000.

1000

(b)

Fig. 4. (a) Relation between thrust and rotational speed, (b)relation between speed and duty ratio© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

−0.02
−0.04
20

40

0
−0.02
−0.04
0

20

40

−0.02
−0.04
20

40

Pitch [rad]Roll [rad]

0
−0.02
−0.04
0

20

40

0
−0.02
20

40

Yaw [rad]Yaw [rad]

60

20

40

60

20

40

60

20

40

60

20

40

60

40

60

−0.02
−0.04

0
−0.02
−0.04

0
−0.02

0.02
0
−0.02

0.02

0
−0.02
−0.04

0
−0.02
−0.04

20

40

60

0

Time [s]20Time [s]Fig. 5. Experimental results using commercial thrusters© ISAROB 2013

40

0

−0.04
0

60

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Error of y[m]Position z[m][6] Zhang Ming-jun et al. (2009), Development andexperiment of an underwater vehicle test-bed
controlled by rudders and thrusters, Proceedings of theIEEE International Conference on Robotics andBiomimetics (ROBIO), pp.1633-1638.

Fig. 6. Experimental results using proposed thrusters
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2   The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Speech synthesis of emotions using vowel features of a speaker
K. Boku, T. Asada, Y. Yoshitomi, and M. Tabuse
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: {boku, t_asada}@mei.kpu.ac.jp, {yoshitomi, tabuse}@kpu.ac.jp
Abstract: Recently, methods for adding emotion to synthetic speech have received considerable attention in the field of speechsynthesis research. We previously proposed a case-based method for generating emotional synthetic speech by exploiting thecharacteristics of the maximum amplitude and the utterance time of vowels, and the fundamental frequency of emotional
speech. In the present study, we propose a method in which our reported method is further improved by controlling thefundamental frequency of emotional synthetic speech. As an initial investigation, we adopted the utterance of a Japanese namethat is semantically neutral. By using the proposed method, emotional synthetic speech made from the emotional speech of one
male subject was discriminable with a mean accuracy of 90.0% when 18 subjects listened to the emotional synthetic utterancesof “angry,” “happy,” “neutral,” “sad,” or “surprised” when the utterance was the Japanese name “Taro.”
Keywords: Emotional speech, Feature parameter, Synthetic speech, Emotional synthetic speech, Vowel
speaking the vowel, and the fundamental frequency of
emotional speech.
In the second stage, we synthesize the phoneme
sequence uttered by the subject. This stage consists of thefollowing five steps.

1 INTRODUCTION
Recently, methods for adding emotions to synthetic
speech have received considerable attention in the field ofspeech synthesis research [1]-[8]. To generate emotional
synthetic speech, it is necessary to control the prosodic
features of the utterances. Natural language is mainlycomposed of vowels and consonants. The Japanese
language has five vowels. A vowel has a more dominant
impact on the listener's impression than does a consonant,primarily because a vowel has a longer utterance time and alarger amplitude in comparison with a consonant. We
previously proposed a case-based method for generating
emotional synthetic speech by exploiting the characteristicsof the maximum amplitude and the utterance time of thevowels, as obtained by using a speech recognition system,and the fundamental frequency of emotional speech [9].
In the present study, we propose a method in which themethod reported in [9] is further improved by controllingthe fundamental frequency of emotional synthetic speech.
The advantage of our study over reported research [1]-[8] isusage of the vowel feature in emotional speech to generateemotional synthetic speech.

Step 1: For a vowel with a consonant appearing just before
it in synthetic speech with neutral emotion, the totalphonation duration time of the vowel and the consonant istransformed into the time for speech with neutral emotion
by the human subject. The synthetic speech obtained by thisprocessing is hereinafter called “neutral synthetic speech.”
Step 2: For a vowel with a consonant appearing just before
it in synthetic speech with one of the intentional emotionsof “angry,” “happy,” “sad,” and “surprised,” the totalphonation duration time of the vowel and consonant is set
as the value whose ratio to that in neutral synthetic speech
is equal to the ratio of the phonation duration time of thevowel in emotional speech to the phonation duration timeof the vowel in neutral speech.
Step 3: The fundamental frequency of synthetic speech,
obtained by the processing up to Step 2, is initially adjusted
based on the fundamental frequency of the emotional
speech.
Step 4: For a vowel with a consonant appearing just before
it in synthetic speech obtained by the processing up to Step
3, the amplitudes are transformed into final values by twice
multiplying the ratio Maxem Maxne  , where Max em and2 PROPOSED METHOD
In the first stage, we obtain audio data of emotional
speech as a WAV file when a subject speaks with each of
the intentional emotions of “angry,” “happy,” “neutral,”“sad,” and “surprised.” Then, for each kind of emotional
speech, we measure the time of each vowel utterance andthe value of the maximum amplitude of the waveform while© ISAROB 2013

Max ne denote the maximum amplitude of the vowel inemotional speech and that in neutral speech, respectively.
The synthetic speech obtained by the processing of Steps 1
to 4 is hereinafter called “emotional synthetic speech.”

176

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of each vowel in each emotion category as spoken by the
subject.
We performed a principal component analysis (PCA) toreveal the prosodic characteristics of “angry,” “happy,”“neutral,” “sad,” and “surprised” in emotional speech byusing the normalized utterance time and the normalized
maximum amplitude of the first and last vowels as thefeature parameters. Here, normalization of the utterance
time and the maximum amplitude was performed by setting
the mean value for the five emotions to zero and setting thestandard deviation for each of the five emotions to one. Fig.
1 shows the feature vector space expressed by the first andsecond components obtained by PCA for the five kinds ofemotional speech for /taro/.
Then, the fundamental frequency of emotional speechwith each of the intentional emotions of “angry,” “happy,”“neutral,” “sad,” and “surprised” was measured for theJapanese first name “Taro.” The fundamental frequency
was selected because it is one of the best-known featureparameters for speech. As shown in Fig. 2, each emotion
had the characteristic time-dependence of the fundamental
frequency.
Voice Sommelier Neo (premium version; Hitachi
Business Solution Co., Ltd., Yokohama, Japan) [11] was

Step 5: The fundamental frequency of emotional synthetic
speech, obtained by the processing up to Step 4, is further
adjusted based on the fundamental frequency of theemotional speech.
If no consonant appears, the process described in Steps
1 to 5 applies to just the vowel. In the present study, theprocessing described in Step 5 is added to the method
reported in [9].

3 EXPERIMENTS
3.1 Condition
We used a speech recognition system named Julius [10]to save the timing positions of the start of speech, and thefirst and last vowels. A male subject (Subject A) in his 50s
spoke the semantically neutral utterance of the Japanese
first name “Taro” with each of the intentional emotions of“angry,” “happy,” “neutral,” “sad,” and “surprised.” Hisaudio data were recorded as WAV files. We measured theutterance time of the first vowel and the maximum absolutevalue of the amplitude of the waveform while speaking thefirst vowel. For the last vowel, we also measured theutterance time and the maximum absolute value of theamplitude of the waveform. Tables 1 and 2 show thephonation time and the maximum amplitude, respectively,

3
2
1

Table 1. Phonation time of vowels as spoken by the
subject [9]EmotionPhonation time (s) Normalized phonation
categorytime (Ratio to thevalue of “Neutral”)
/a/
/o/
/a/
/o/
Angry
0.030
0.090
1.000
0.310
Happy
0.100
0.240
3.333
0.828
Neutral
0.030
0.290
1.000
1.000
Sad
0.100
0.140
3.333
0.483
Surprised
0.050
0.200
1.667
0.690

-4

1

2

3

4

Fig. 1. Feature space of the first (horizontal) andsecond (vertical) components for /taro/ [9]Table 2. Maximum amplitude of vowels as spoken by
the subject [9]EmotionMaximum
Normalized maximum
category
amplitude
amplitude (Ratio to thevalue of “Neutral”)
/a/
/o/
/a/
/o/
Angry
1216
459
1.332
0.630
Happy
1904
1055
2.085
1.447
Neutral
913
729
1.000
1.000
Sad
587
295
0.643
0.405
Surprised
1408
1256
1.542
1.723

© ISAROB 20130
-3 -2 -1
0
angry
-1
happy
neutral-2
sadsurprised-3

Fig. 2. Fundamental frequencies of waveforms of
Subject A while speaking “Taro”

177

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013used as the speech synthesizer for Steps 1 to 3 in Section 2.
For conversion of the amplitude of each vowel andconsonant described in Step 4 in Section 2, a digital audio
editor was used. Then, the method [12] using resampling
and a correlation function were used for Step 5 in Section 2.
When we applied the method described in Section 2 tothe above case, the mode of Male 1 (bright voice) in Voice
Sommelier Neo was used. In this case, each vowel was /a/and /o/, and then the vowel and the consonant just before
the vowel was /ta/ and /ro/.
The emotional synthetic speech without adding theprocessing described in Step 5 of Section 2 is hereinafter
called “emotional synthetic speech No. 1,” whereas theemotional synthetic speech with adding the processing
described in Step 5 of Section 2 is hereinafter called“emotional synthetic speech No. 2.” The emotional synthetic
speech No. 1 was made only by the method reported in [9]and used for the evaluation of Step 5 of Section 2, which isthe new processing in the proposed method.
The 18 subjects participating in the experiments
consisted of the following: Subjects A and B, two males intheir 50s; Subject C, one male in his 30s; Subjects D, E, F,
G, H, I, J, K, L, M and N, 11 males in their 20s; Subjects O,
P, Q and R, four females in their 20s. The subjects made a
judgment of the emotional category after listening to fiveutterances one by one in the following order: emotional
speech by Subject A, emotional synthetic speech No. 1,
emotional speech by Subject A, and emotional synthetic
speech No. 2.

Fig. 3. Fundamental frequencies of waveforms ofemotional synthetic speech No. 1 of “Taro”

Fig. 4. Fundamental frequencies of waveforms ofemotional synthetic speech No. 2 of “Taro”

Emotioncategory

3.2 Results and discussion
Figs. 3 and 4 show the fundamental frequencies of
emotional synthetic speech Nos. 1 and 2, respectively. The
characteristics of each type of emotional speech shown in
Fig. 2 are more precisely reflected in Fig. 4, which was
obtained by the proposed method, than they were in Fig. 3,
which was obtained by the method reported in [9]. As
illustrated in Fig. 5, differences among the emotional
speech waveforms were observed. To some extent, thedifferences of the waveforms were also reflected in eachemotional synthetic speech. Voice Sommelier Neo used in
Step 3 in Section 2 has some restrictions in controlling thefrequency of synthetic speech, so it was difficult to adjust
the fundamental frequency adequately. The differences
between the waveforms of the emotional speech waveformof “neutral” and the synthetic speech waveform of “neutral”
are shown in Fig. 5. As expected, the emotional synthetic
speech inevitably had some waveform differences incomparison with the corresponding emotional speech.

© ISAROB 2013

Emotional speechof Subject A

Emotional synthetic speech
No.1

No.2

Angry

0.4s

Happy

NeutralSad

Surprised

Fig. 5. Waveforms of emotional speech of Subject A andemotional synthetic speech Nos. 1 and 2

178

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013synthetic speech utterances of “angry,” “happy,” “neutral,”“sad,” or “surprised” for the Japanese name “Taro.”

Table 3 shows the results of the subjective evaluations.
In Table 3, the results for emotional speech were calculated
as an average of the values obtained in two sets of listening
by all 18 subjects. As illustrated in Table 3, the mean
accuracy of emotional speech, emotional synthetic speech
No. 1, and emotional synthetic speech No. 2 was 95.0%,
70.0%, and 90.0%, respectively. The advantage ofemotional synthetic speech No. 2 over No. 1 suggests thatfurther adjustment of fundamental frequency in the
proposed method made a much clearer impression on thesubjects for emotional synthetic speech.

Acknowledgments
We would like to thank all the participants who
cooperated with us in the experiments.

REFERENCES[1] Donna E (2005), Expressive speech: Production,
perception and application to speech synthesis.
Acoustical Science and Technology, 26(4):317–325[2] Iida A, Iga S, Higuchi F, et al (2000), A prototype of aspeech synthesis system with emotion for assisting
communication (in Japanese). Transaction of Human
Interface Society, 2(2): 63-70[3] Katae N, Kimura S (2000), An effect of voice quality
and control in emotional speech synthesis (in Japanese).
Proceedings of the autumn meeting the AcousticalSociety of Japan: Vol. 2. pp.187-188[4] Moriyama T, Mori S, Ozawa S (2009), A synthesis
method of emotional speech using subspace constraints
in prosody (in Japanese). Transaction of Information
Processing Society of Japan, 50(3): 1181-1191[5] Murray IR, Arnott JL (1993), Toward the simulation ofemotion in synthetic speech: A review of the literatureon human vocal emotion. Journal of the AcousticalSociety of America, 93(2):1097–1108[6] Murray IR, Edgington MD, Campion D, et al (2000),
Rule-based emotion synthesis using concatenatedspeech. Proceedings of Speech and Emotion, ISCA
Tutorial and Research Workshop, Newcastle, Northern
Ireland, UK pp.173–177[7] Ogata S, Yotsukura T, Morishima S (2000), Voice
conversion to append emotional impression bycontrolling articulation information (in Japanese).
IEICE technical report, human information processing,99(582):53-58[8] Schröder M (2001), Emotional speech synthesis – A
review. In: Dalsgaard P, Lindberg B, Benner H (eds),
Proceedings of 7th European Conference on SpeechCommunication and Technology, Aalborg, Kommunik
Grafiske Losninger A/S, Vol.1, pp.561–564[9] Boku K, Asada T, Yoshitomi Y et al (2012), In: Roger
L (ed), Software engineering, artificial intelligence,
networking, and parallel/distributed computing 2012,
Springer, Berlin, Germany, pp.129-141[10]Julius development team (2011), Open-source large
vocabulary CSR engine Julius. Retrieved June 23, 2012,
from http://julius.sourceforge.jp/en_index.php?q=indexen.html[11]Hitachi Business Solution Co. Ltd. (2012), Voice
Sommelier Neo. Retrieved June 23, 2012, fromhttp://hitachi-business.com/products/package/sound/voice/index.html[12]Aoki N (2008), Sound programming in C (in Japanese).
Ohmsha, Tokyo, Japan, pp.141-160

Table 3. Results of the subjective evaluation

Recognition(1) Emotional speech
Angry
Happy
NeutralSad
Surprised

Angry
97.2
0
0
0
2.8

Happy
0
94.4
2.8
0
2.8

Input
Neutral
0
0
94.4
5.6
0

Sad
2.8
0
2.8
94.4
0

Surprised
0
5.6
0
0
94.4

(%)
(2) Emotional synthetic speech No.1

Recognition

Input
Angry
Happy
NeutralSad
Surprised

Angry
83.3
0
0
0
16.7

Happy
0
50.0
44.4
0
5.6

Neutral
0
38.8
55.6
5.6
0

Sad
0
0
11.1
88.9
0

Surprised
22.2
5.6
0
0
72.2

(%)
(3) Emotional synthetic speech No.2

Recognition

Input
Angry
Happy
NeutralSad
Surprised

Angry
88.8
5.6
0
0
5.6

Happy
5.6
88.8
5.6
0
0

Neutral
0
0
94.4
5.6
0

Sad
0
0
11.1
88.9
0

Surprised
11.1
0
0
0
88.9

(%)

4 CONCLUSION
We previously proposed a case-based method forgenerating emotional synthetic speech by exploiting thecharacteristics of the maximum amplitude and the utterance
time of the vowels, as obtained by using a speech
recognition system, and the fundamental frequency of
emotional speech. In the present study, we propose amethod in which our reported method is further improvedby controlling the fundamental frequency of emotional
synthetic speech. By using the proposed method, emotional
synthetic speech made from the emotional utterances of one
male subject was discriminable with a mean accuracy of
90.0% when 18 subjects listened to one of the emotional© ISAROB 2013

179

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A system for facial expression recognition of a speaker using thermal
image processing and feature vector space characteristics
T. Asada, Y. Yoshitomi, Y. Nakanishi, and M. Tabuse
Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: t_asada@mei.kpu.ac.jp, yoshitomi@kpu.ac.jp, y_nakanishi@mei.kpu.ac.jp, tabuse@kpu.ac.jp
Abstract: We developed an on-line system for the facial expression recognition of a speaker. In the feature vector space inimage processing, the positions of feature vectors generated with imperfection, which caused misrecognition of facial
expression, tended to be far from the center of gravity of the class to which the feature vectors belonged. In the present study,
to omit the feature vectors generated with imperfection in image processing, a module using reject criteria in the feature vector
space was added to the system for facial expression recognition. We adopted the utterance of the Japanese name “Taro,” which
is semantically neutral, to investigate the improved system. The facial expressions of one subject were analyzed when he
exhibited one of the intentional facial expressions of “angry,” “happy,” “neutral,” “sad,” and “surprised.” By using the facial
expression strength, the position of the test feature vector in the feature vector space is shown.
Keywords: Facial expression recognition, Feature vector space, Reject criteria, Speech recognition, Thermal image processing.

1 INTRODUCTION

2 IMAGE ACQUISITION

Although the mechanism for recognizing facial
expressions has received considerable attention in the fieldof computer vision research, it still falls far short of humancapability, especially from the viewpoint of robustness
under widely varying lighting conditions. One of thereasons for this lack of robustness is that nuances of shade,reflection, and local darkness influence the accuracy offacial expression recognition through the inevitable changeof gray levels. To avoid this problem and to develop amethod for facial expression recognition applicable under
widely varied lighting conditions, we do not use visible ray
images. Instead, we use images produced by infrared rays(IR), which show the thermal distribution of the face [1][17]. We previously developed an on-line system for thefacial expression recognition of a speaker by image
processing [13]. In the feature vector space, the positions offeature vectors generated with imperfection, which caused
misrecognition of facial expression, tended to be far fromthe center of gravity of the class to which the feature
vectors belonged.
In the present study, to omit the feature vectors
generated with imperfection in image processing, a moduleusing reject criteria in the feature vector space was added tothe system [13] for facial expression recognition. As an
initial investigation, we adopted the utterance of theJapanese name “Taro,” which is semantically neutral.

The principle behind thermal image generation is the
Stefan–Boltzmann law, expressed as W  T 4 , where is emissivity,  is the Stefan–Boltzmann constant(=5.6705  10-12 W/cm2K4), and T is the temperature (K).
For human skin,  is estimated as 0.98 to 0.99 [18], [19].
In the present study, the approximate value of 1 was used as for human skin because the value of  for almost all
substances is lower than that of human skin [14].
Consequently, the human face region is easily extracted
from an image by using the value of 1 for  [1]–[17]. In
principle, the temperature measurements by IR do notdepend on skin color [19], darkness, or lighting condition,and so the face region and its characteristics are easily
extracted from a thermal image.

© ISAROB 2013

3 PROPOSED SYSTEM
Fig. 1 illustrates the flowchart of our method [17]. We
have two modules in our system. The first is a module forspeech recognition and dynamic image analysis, and thesecond is a module for learning and recognition. In themodule for learning and recognition, we embedded themodule for front-view face judgment [10]. Some details of
our method are explained in our book [14].
3.1 Speech recognition and dynamic image analysis
We use a speech recognition system named Julius [20]to obtain the timing positions of the start of speech and thefirst and last vowels in a WAV file [8]–[10]. Fig. 2 shows anexample of the waveform of the Japanese name “Taro”; the180

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

vowel, we apply the same procedure used for the firstvowel.
3.2 Learning and recognition
For the static images obtained from extracted image
frames, the process of erasing the area of the glasses,
extracting the face area, and standardizing the position, size,
and rotation of the face are performed according to themethod described in our previously reported study [7]. Fig.
3 shows the blocks for extracting the face areas in a thermal
image having 720  480 pixels. In the next step, we
generate difference images between the averaged neutral
face image and the target face image in the extracted faceareas to perform a 2D discrete cosine transform (2D-DCT).
The feature vector is generated from the 2D-DCT
coefficients according to a heuristic rule [6], [7]. We callthe method used in our previously reported research [8][16] the "conventional" method, in which the facial
expression was recognized by the nearest-neighbor criterion
in the feature vector space by using the training data justbefore speaking and when speaking the phonemes of thefirst and last vowels.

Fig. 1. Flowchart of our method [17]Fig. 3. Blocks for extracting face areas in the thermal image [13]3.3 Proposed method for facial expression recognition
Imperfection in image processing with our method for
facial expression recognition inevitably occurs, and resultsin the misrecognition of facial expression. To overcome thisdifficulty, a method is proposed that uses reject criteria in
the feature vector space by using the training data justbefore speaking and when speaking the phonemes of thefirst and last vowels.
The new algorithm of pattern recognition in the feature
vector space of facial expression recognition is as follows:

Fig. 2. Speech waveform of “Taro” and timing
positions for image frame extraction [8]timing position of the start of speech and the timing rangesof the first vowel (/a/) and the last vowel (/o/) are decided
by Julius. By using the timing position of the start of speech
and the timing ranges of the first and last vowels obtained
from the WAV file, three image frames are extracted froman AVI file at the three timing positions. For the timing
position just before speaking, we use the timing position of
84 ms before the start of speech, as determined in ourpreviously reported study [7]. For the timing position of thefirst vowel, we use the position where the absolute value ofthe amplitude of the waveform is the maximum while
speaking the vowel. For the timing position of the last

© ISAROB 2013

Step 1: For all classes in the initial condition, the following
procedure is performed. Vector gi of the center of gravityof the class i (i  1,2,, n) , where n is the number ofclasses, is obtained. Then, go to Step 2.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

for the class of neutral facial expression is used because inmany cases the value of (a) tends to be too small to accept
the recognized result showing class j . Steps 1 to 6 are theStep 2: For each class i , the following procedure is
performed. The elements of class i are narrowed using
the way that the feature vector having the largest distancefrom vector gi among those in class i is omitted fromthe elements in class i . Then, if the number of feature
vectors of class i at that moment is bigger than half thenumber Ni of feature vectors in the class defined in Step
1, go to Step 3. Otherwise, go to Step 4.
Step 3: For class i , vector gi is updated. Then, return toStep 2.
Step 4: Except the class of neutral facial expression, thethreshold T j of class j is decided as (a) the value of thesame as those in [17]. The facial expression strength FESi
described in Step 7 is used for expressing the position of thetest feature vector in the feature vector space.
3.4 Proposed system
Fig. 4 shows the structure of the proposed system. Figs.
5 and 6 show flow charts of the system during facial
expression learning and recognition, respectively. Figs. 5
and 6 demonstrate the processing shared by threecomputers (PC1, PC2, and PC3) connected by cables toform a local area network for performing the flow chart
shown in Fig. 1. In Figs. 5 and 6, a line with an arrowattached denotes a process order, while a dotted line with anarrow attached denotes a communication from a PC toanother PC in the local area network. The only difference
between Figs. 5 and 6 is the processing in PC3. An NTSC
video signal from a thermal video system is successively
inputted to either PC1 or PC2 to obtain image frames: (i)just before speaking, and speaking (ii) the first vowel and(iii) the last vowel in an utterance. We use the proposedsystem with our previously reported method [10] for frontview face judgment.

maximum distance between vector g j and the feature
vector of classj in the initial condition, under theconstraint

vector

that

gj

among

all

vectorsgi (i  1,2,, n) is the nearest to the feature vector.
Similarly, for the class of neutral facial expression, thethreshold T j of class j is decided as the mean value of(a) and (b), where (b) is the minimum value among thevalues of the distance between vector g j and the feature
vector of classj in the initial condition, under theconstraint

vector

that

gj

among

all

vectorsgi (i  1,2,, n) is not the nearest to the feature vector.
Go to Step 5.
Step 5: The elements of each class i are narrowed using
the constraint that the distance between the feature vectors
and vector gi should not be bigger than threshold Ti . Go
to Step 6.
Step 6: The facial expression is recognized by the nearestneighbor criterion in the feature vector space using (1) thefeature vectors selected by the procedure up to Step 5, (2) the
vector gi . Then, the recognized result showing class i isrejected if the distance between the test feature vector and theselected vector by the vector nearest-neighbor criterion isbigger than the threshold Ti . Otherwise the recognizedresult showing class i is accepted. Then, go to Step 7.
Step 7: The element v i having the shortest distance d ito the test feature vector is found among (1) those selectedby the procedure up to Step 5 for each class i and (2) the
vector gi . The facial expression strength FESi for eachclass i is defined as d s di  , where d s  min d i underiIthe condition that set

I consists of all classes.

Fig. 4. Structure of the proposed system [13]In the present study, the Euclidean distance is used as
the distance in the feature vector space. Special treatment© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 6. Flow chart of the proposed system during
facial expression recognition [13]Fig. 5. Flow chart of the proposed system in learning for
facial expression recognition [13]PC1 in Fig. 4 and Thomson Canopus ADVC-300 for PC2 in
Fig. 4) and input into two computers (PC1 and PC2 in Fig.
4) with the same specification (Dell Optiplex 780, CPU:Intel Core 2 Duo E8400 3.00 GHz, main memory: 4.00 GB,
OS: Windows 7 Professional (Microsoft) with an IEEE1394
interface board (I·O data device 1394-PCI3/DV6)). As PC3
in Fig. 4, we used a computer (Dell Precision T1600, CPU:Intel Xeon CPU E31225 3.10 GHz, main memory: 8.00 GB,
OS: Windows 7 Professional (Microsoft)). The threecomputers (PC1, PC2, and PC3) were connected through arouter (Buffalo WZR-HP-AG300H) with cables. We used

4 EXPERIMENTS
4.1 Condition
The thermal image produced by the thermal video
system (Nippon Avionics TVS-700) and the sound captured
from an Electret condenser microphone (Sony ECM-23F5),
as amplified by a mixer (Audio-Technica AT-PMX5P),
were transformed through an audio and video distribution
amplifier (Maspro Denkoh VSP4) into a digital signal by
two A/D converters (Thomson Canopus ADVC-100 for© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Visual C++ 6.0 (Microsoft) as the programming language.
To generate a thermal image, we set the condition that thethermal image had 256 gray levels for the detected
temperature of 304 to 309 K. Therefore, one gray level
corresponded to 1.95 10-2 K. The temperature range forgenerating a thermal image was decided to easily extract
the face area on the image. We saved the visual and audio
information in the computer as a Type 2 DV-AVI file, inwhich the video frame had a spatial resolution of 720  480
pixels and 8-bit gray levels, and the sound was saved in astereo PCM format, 48 kHz and 16-bit levels.
Subject A, a male wearing glasses, exhibited inalphabetic order each of the intentional facial expressionsof “angry,” “happy,” “neutral,” “sad,” and “surprised,”
while speaking the semantically neutral utterance “Taro.”
Fig. 7 shows examples of the thermal images of subject A.
In the experiment, subject A intentionally kept frontview faces in the AVI files saved as both the training andtest data. From one sample, we obtained three images at thetiming positions of just before speaking and just speaking
the phonemes of the first and the last vowels.
We assembled twenty samples as training data and ten
samples as test data for each facial expression in each case,in which all facial expressions of test data for all subjects
were judged as front-view faces by the method mentioned
in our previously reported paper [10]. For each sample, we
obtained three images at the timing positions of just before
speaking and while speaking the phonemes of the first andthe last vowels.

Just before
Speaking

Angry

Happy

Neutral
NeutalSad

Surprised

Fig. 7. Examples of thermal images of subject A
having each facial expression in speaking [13]Table 1. Recognition accuracy and rejection ratio
Input facial expression
Happy

OutputHappy

Neutral100

Neutral100

OthersRejected

76.0

0.0

Others

10.5
57.9
31.6
59.2
(%)

Table 2. Results of facial expression recognition

4.2 Results and discussion
Table 1 shows the facial expression recognition
accuracy and rejection ratio for each facial expression. The
mean recognition accuracy was 77.2%. The facial
expressions of “others” were more difficult to recognize
than those of “happy,” and “neutral,” which were perfectly
recognized with 100% accuracy (Table 1).
Table 2 shows examples of the results of facial
expression recognition. By using the facial expression
strength described in Step 7, the position of the test feature
vector in the feature vector space is quantitatively shown.
In the present experiment, a subject spoke one word thatwas the semantically neutral utterance “Taro.” The system
recognized the facial expression in speaking each word.
When we are ready to apply the proposed system forrecognizing facial expressions in daily conversation, we
should be able to recognize the facial expression during
speaking for a certain interval, such as sentence by sentence.
This is because we focus on human feeling through facial© ISAROB 2013

In speaking first In speaking lastvowel(/a/)
vowel(/o/)

Inputfacialexpression
Angry
Happy
NeutralSad
Surprised

Facial expression strength
A
H
N
Sa
Su
0.98
0.21
0.13
0.24
0.68

0.57
1.0
0.23
0.56
0.45

0.30
0.26
1.0
1.0
0.24

0.51
0.61
0.27
0.71
0.38

1.0
0.19
0.12
0.22
1.0

NearestSecondnearest

Su
H
N
N
Su

A
Sa
Sa
Sa
A

A=Angry, H=Happy, N=Neutral, Sa=Sad, Su=Surprised

expression and it is difficult for humans to change their
feeling on a word-by-word basis. When we use the training
data for all combinations of the first and the last vowels[11], we can apply the proposed system to a speaker for any
utterance. Though several studies on facial expression
recognition using thermal image processing have beenreported (see references [5]–[17], [21]), only our research[5]–[17] has focused on a speaker. In this paper, we propose
an on-line system for recognizing the facial expression of a
speaker by adding a module using reject criteria in the feature
vector space to our previously reported system [13], which is

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[7] Nakano M, Ikezoe F, Tabuse M, et al (2009), A study
on the efficient facial expression using thermal face
image in speaking and the influence of individual
variations on its performance (in Japanese). J. IEEJ
38(2):156-163[8] Koda Y, Yoshitomi Y, Nakano M, et al (2009), Facial
expression recognition for a speaker of a phoneme ofvowel using thermal image processing and a speech
recognition system. Proceedings of 18th IEEE
International Symposium on Robot and Human
Interactive Communication, Toyama, Japan, Sep 29-Oct
1, 2009, pp. 955-960[9] Yoshitomi Y (2010), Facial expression recognition for
speaker using thermal image processing and speech
recognition system. Proceedings of 10th WSEAS
International Conference on Applied Computer Science,
Appi Kogen, Iwate, Japan, Oct 4-6, 2010, pp. 182-186[10]Fujimura T, Yoshitomi Y, Asada T, et al (2011), Facial
expression recognition of a speaker using front-view facejudgment, vowel judgment, and thermal image
processing. J. Artificial Life and Robotics 16(3):411-417[11]Yoshitomi Y, Asada T, Shimada K, et al (2011), Facial
expression recognition of a speaker using vowel
judgment and thermal image processing. J. Artificial
Life and Robotics 16(3):318–323[12]Yoshitomi Y, Tabuse M, Asada T (2011), SpeechTechnologies. InTech, pp.405-424[13]Asada T, Yoshitomi Y, Tabuse M (2012), A system for
facial expression recognition of a speaker using front‐
view face judgment, vowel judgment and thermal image
processing. J. Artificial Life and Robotics, 17(2):263-269[14]Yoshitomi Y, Tabuse M, Asada T (2012), Image
processing: methods, applications and challenges. Nova
Science Publisher, pp.57-85[15]Yoshitomi Y (2012), Facial expression recognition of
speaker using vowel judgment and features of thermal
face image”, Proceedings of 1st WSEAS International
Conference on Information Technology and Computer
Networks, Vienna, Austria, Nov 10-12, 2012, pp.139-145[16]Nakanishi Y, Yoshitomi Y, Asada T, et al (2012),
Robust facial expression recognition of a speaker using
thermal image processing and updating of fundamental
training-data. J. Artificial Life and Robotics 17, in press[17]Nakanishi Y, Yoshitomi Y, Asada T, et al (2013),
Facial expression recognition of a speaker using thermal
image processing and reject criteria in feature vector
space, In:Sugisaka M (ed), Proceedings of theInternational Symposium on Artificial Life andRobotics (AROB 18th), Daejeon, Korea, Jan 30 - Feb 1,
2013, in press[18]Kuno H (1994), Infrared rays engineering (in Japanese).
Tokyo, IEICE, pp.22[19]Kuno H (1994), Infrared rays engineering (in Japanese).
Tokyo, IEICE, pp.45[20]http://julius.sourceforge.jp/[21]Hernández B, Olague G, Hammoud R, et al (2007),
Visual learning of texture descriptors for facial
expression recognition in thermal imagery. Computer
Vision and Image Understanding 16 (2-3): 258–269

based on our previously reported method [10], [11], which issuperior to our earlier reported method [5]–[8]. Comparedwith the conventional method, the effect of adding reject
criteria in the feature vector space is shown in detail in [17].

5 CONCLUSION
We propose an on-line system for recognizing the facial
expression of a speaker by adding a module using reject
criteria in the feature vector space to our previously
reported system [13]. Using the proposed system, the facial
expressions of a subject were discriminable with 77.2%
accuracy for the facial expressions of “happy,” “neutral,”and “others” when the subject exhibited one of theintentional facial expressions of “angry,” “happy,”“neutral,” “sad,” and “surprised.” By using the facial
expression strength, the position of the test feature vector in
the feature vector space is quantitatively shown. We expect
the proposed system to be applicable for recognizing facial
expressions in daily conversation.
Acknowledgment
This work was supported by KAKENHI (22300077).

REFERENCES[1] Yoshitomi Y, Kimura S, Hira E, et al (1996), Facial
expression recognition using infrared rays image
processing. Proceedings of the Annual Convention IPS
Japan, Osaka, Japan, Sep 4-6, 1996, 2:339-340[2] Yoshitomi Y, Kimura S, Hira E, et al (1997), Facial
expression recognition using thermal image processing.
IPSJ SIG Notes, CVIM103-3, Kyoto, Japan, Jan 23-24,
1997, pp. 17-24[3] Yoshitomi Y, Miyawaki N, Tomita S, et al (1997), Facial
expression recognition using thermal image processing andneural network. Proceedings of 6th IEEE International
Workshop on Robot and Human Communication, Sendai,
Japan, Sep 29-Oct 1, 1997, pp. 380-385[4] Sugimoto Y, Yoshitomi Y, Tomita S (2000), A methodfor detecting transitions of emotional states using athermal face image based on a synthesis of facial
expressions. J. Robotics and Autonomous Systems
31:147-160[5] Yoshitomi Y, Kim SIll, Kawano T, et al (2000), Effectof sensor fusion for recognition of emotional statesusing voice, face image and thermal image of face.
Proceedings of 6th IEEE International Workshop on
Robot and Human Interactive Communication, Osaka,
Japan, Sep 27-29, 2000, pp. 178-183[6] Ikezoe F, Ko R, Tanijiri T, et al (2004), Facial
expression recognition for speaker using thermal image
processing (in Japanese). Trans. Human Interface
Society 6(1):19-27© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Facial expression recognition of a speaker using thermal image processing
and reject criteria in feature vector space
Y. Nakanishi, Y. Yoshitomi, T. Asada, and M. Tabuse
Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: y_nakanishi@mei.kpu.ac.jp, yoshitomi@kpu.ac.jp, t_asada@mei.kpu.ac.jp, tabuse@kpu.ac.jp
Abstract: In our previously developed method for the facial expression recognition of a speaker, the positions of feature
vectors in the feature vector space in image processing were generated with imperfections. The imperfections, which caused
misrecognition of the facial expression, tended to be far from the center of gravity of the class to which the feature vectors
belonged. In the present study, to omit the feature vectors generated with imperfections, a method using reject criteria in thefeature vector space was applied to facial expression recognition. By using the proposed method, the facial expressions of two
subjects were discriminable with 90.0% accuracy for the three facial expressions of “happy,” “neutral,” and “others” when they
exhibited one of the five intentional facial expressions of “angry,” “happy,” “neutral,” “sad,” and “surprised,” whereas these
expressions were discriminable with 78.0% accuracy by the conventional method.
Keywords: Facial expression recognition, Feature vector space, Reject criteria, Speech recognition, Thermal image processing.
In the present study, the approximate value of 1 was used as for human skin because the value of  for almost all
substances is lower than that of human skin [14].
Consequently, the human face region is easily extracted
from an image by using the value of 1 for  [1]-[13]. In
principle, temperature measurements by IR also do notdepend on skin color [15], darkness, or lighting condition,and so the face region and its characteristics are easily
extracted from a thermal image.

1 INTRODUCTION
Although the mechanism for recognizing facial
expressions has received considerable attention in the fieldof computer vision research, it still falls far short of humancapability, especially from the viewpoint of robustness
under widely varying lighting conditions. For example,
nuances of shade, reflection, and local darkness influence
the accuracy of facial expression recognition through theinevitable change of gray levels. To develop a method for
facial expression recognition applicable under widely
varied lighting conditions, we used images produced byinfrared rays (IR), which show the thermal distribution of
the face [1]-[13].
In experiments, imperfections in image processing in
our method for facial expression recognition inevitably
occurred and resulted in misrecognition of facial
expressions. To overcome this difficulty, in the present
study, a method using reject criteria in the feature vector
space is applied to the recognition of facial expression of
two male subjects when exhibiting the intentional facial
expressions of “angry,” “happy,” “neutral,” “sad,” and“surprised.”

3 PROPOSED METHOD
Fig. 1 illustrates the flowchart of our method. We have
two modules in our system. The first is a module for speech
recognition and dynamic image analysis, and the second isa module for learning and recognition. In the module forlearning and recognition, we embedded the module forfront-view face judgment [10]. Some details of our method
are explained in our book [16].
3.1 Speech recognition and dynamic image analysis
We use a speech recognition system named Julius [17]to obtain the timing positions of the start of speech and thefirst and last vowels in a WAV file [8]-[10]. Fig. 2 shows anexample of the waveform of the Japanese name “Taro”; thetiming position of the start of speech and the timing rangesof the first vowel (/a/) and the last vowel (/o/) are decided
by Julius. By using the timing position of the start of speech
and the timing ranges of the first and last vowels obtained
from the WAV file, three image frames are extracted froman AVI file at the three timing positions. For the timing
position just before speaking, we use the timing position of

2 IMAGE ACQUISITION
The principle behind thermal image generation is the
Stefan–Boltzmann law, expressed as W  T 4 , where is emissivity,  is the Stefan–Boltzmann constant(=5.6705  10-12 W/cm2K4), and T is the temperature (K).
For human skin,  is estimated as 0.98 to 0.99 [14], [15].

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 3. Blocks for extracting face areas in the thermal image [18]generate difference images between the averaged neutral
face image and the target face image in the extracted faceareas to perform a 2D discrete cosine transform (2D-DCT).
The feature vector is generated from the 2D-DCT
coefficients according to a heuristic rule [6], [7]. We referto the method used in our previously reported research [8][13], [16], [18], [19] as the conventional method, where thefacial expression is recognized by the nearest-neighborcriterion in the feature vector space by using the training
data just before speaking and when speaking the phonemesof the first and last vowels.
3.3 Proposed method for facial expression recognition
Imperfections in image processing in our method for
facial expression recognition inevitably occurred andresulted in misrecognition of facial expressions. To
overcome this difficulty, in the present study, a method
using reject criteria in the feature vector space of the
training data just before speaking and when speaking thephonemes of the first and last vowels is proposed.
The new algorithm of pattern recognition in the feature
vector space of facial expression recognition is as follows:

Fig. 1. Flowchart of our method

Fig. 2. Speech waveform of “Taro” and timing
positions for image frame extraction [8]Step 1: For all classes in the initial condition, the following
procedure is performed. Vector gi of the center of gravityof the class i (i  1,2,, n) , where n is the number ofclasses, is obtained. Then, go to Step 2.
Step 2: For each class i , the following procedure is
performed. The elements of class i are narrowed using
the way that the feature vector having the largest distancefrom vector gi among those in class i is omitted fromthe elements in class i . Then, if the number of feature
vectors of class i at that moment is bigger than half thenumber N i of feature vectors in the class defined in Step
1, go to Step 3. Otherwise, go to Step 4.
Step 3: For class i , vector gi is updated. Then, return toStep 2.
Step 4: Except the class of neutral facial expression, thethreshold T j of class j is decided as (a) the value of the

84 ms before the start of speech, as determined in ourpreviously reported study [7]. For the timing position of thefirst vowel, we use the position where the absolute value ofthe amplitude of the waveform is the maximum while
speaking the vowel. For the timing position of the lastvowel, we apply the same procedure used for the firstvowel.
3.2 Learning and recognition
For static images obtained from the extracted image
frames, the process of erasing the area of the glasses,
extracting the face area, and standardizing the position, size,
and rotation of the face are performed according to themethod described in our previously reported study [7], [16].
Fig. 3 shows the blocks for extracting the face areas in athermal image having 720  480 pixels. In the next step, we© ISAROB 2013

187

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

maximum distance between vector g j and the feature
vector of classj in the initial condition, under theconstraint

vector

that

gj

among

all

had 256 gray levels for the detected temperature range. As aresult, one gray level corresponded to 1.95  10-2 to
5.04  10-2 K. The temperature range for generating athermal image was decided for each subject to easily extract
the face area on the image. We saved the visual and audio
information in the computer as a Type 2 DV-AVI file, inwhich the video frame had a spatial resolution of 720  480
pixels and 8-bit gray levels, and the sound was saved in astereo PCM format, 48 kHz and 16-bit levels.
Two subjects exhibited in alphabetic order each of thefive intentional facial expressions of “angry,” “happy,”“neutral,” “sad,” and “surprised,” while speaking thesemantically neutral utterance “Taro.” In the present study,
we categorized all of the “angry,” “sad,” and “surprise”
expressions as “others” in making the recognition results.
Subjects A and B were males with glasses. Figs. 4 and 5
show examples of thermal images of the neutral expression
captured in each period for Subjects A and B, respectively.
We captured the thermal images for subject A on June 20,
2006 (hereinafter referred to as First_period_A) (Fig. 6); at
approximately 3:00 pm on January 22, 2010 (hereinafterreferred to as Second_period_A); and at approximately 5:00
pm on January 22, 2010 (hereinafter referred to as
Third_period_A). For subject B, we captured the same
thermal images on June 20, 2006 (hereinafter referred to asFirst_period_B) (Fig. 7); and January 25, 2010 (hereinafterreferred to as Second_period_B). In the present study, weinvestigated several combinations of training and test data
for each subject, as listed in Tables 1, 2, and 3 in Section
4.2. Case-A-1-2 for subject A consisted of the following
cases: (1) thermal images for both training and test data
captured on First_period_A, (2) thermal images for both

vectorsgi (i  1,2,, n) is the nearest to the feature vector.
Similarly, for the class of neutral facial expression, thethreshold T j of class j is decided as the mean value of(a) and (b), where (b) is the minimum value among thevalues of the distance between vector g j and the feature
vector of classj in the initial condition, under theconstraint

vector

that

gj

among

all

vectorsgi (i  1,2,, n) is not the nearest to the feature vector.
Go to Step 5.
Step 5: The elements of each class i are narrowed using
the constraint that the distance between the feature vectors
and vector gi should not be bigger than threshold Ti . Go
to Step 6.
Step 6: The facial expression is recognized by the nearestneighbor criterion in the feature vector space using (1) thefeature vectors selected by the procedure up to Step 5, (2)the vector gi . Then, the recognized result showing class i
is rejected if the distance between the test feature vector
and the selected vector by the vector nearest-neighborcriterion is bigger than the threshold Ti . Otherwise therecognized result showing class i is accepted.
In the present study, the Euclidean distance is used as
the distance in the feature vector space. The class of neutral
facial expression receives special treatment because inmany cases the value of (a) tends to be too small to accept
the recognized result showing class j .

4 EXPERIMENTS
4.1 Condition
The thermal image produced by the thermal video
system (Nippon Avionics TVS-700) and the sound captured
from an Electret condenser microphone (Sony ECM-23F5),
as amplified by a mixer (Audio-Technica AT-PMX5P),
were transformed into a digital signal by an A/D converter(Thomson Canopus ADVC-300) and input into a computer(DELL Optiplex 780, CPU: Intel Core 2 Duo E8400 3.00
GHz, main memory: 3.21 GB, and OS: Windows 7
Professional (Microsoft) with an IEEE1394 interface board(I·O Data Device 1394-PCI3/DV6)). We used Visual C++
6.0 (Microsoft) as the programming language. To generate athermal image, we set the condition that the thermal image© ISAROB 2013

Fig. 4. Examples of thermal images of subject A
having a neutral facial expression captured
just before speaking at each period

Fig. 5. Examples of thermal images of subject B
having a neutral facial expression captured
just before speaking at each period

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Just before
Speaking

thermal images of training data of the facial expressions of“happy” and “neutral” captured on Second_period_A,
thermal images of training data of the facial expressions of“others” captured on First_period_A, and the test data were
captured on Second_period_A. In addition, Case-A-2-3 forsubject A consisted of four cases decided by changing thedata of First_period_A and Second_period_A in Case-A-12 to those of Second_period_A and Third_period_A,
respectively. Case-B-1-2 for subject B consisted of the fourcases defined in the same manner described above as CaseA-1-2 for subject A.
For Case-A-1-2, Case-A-2-3, and Case-B-1-2, wecompared (3) and (4) for investigating the effects of
updating the training data of the facial expressions of“happy” and “neutral”, whereas (1) and (2) were added forreference of thermal images for the training and the test
data captured without intentional intervals.

In speaking first In speaking lastvowel(/a/)
vowel(/o/)

Angry

Happy

NeutalSad

Subjects A and B freely changed their face direction
Surprisedduring the capturing of thermal images for the test data on
Second_period_A and Second_period_B, respectively. We

Fig. 6. Examples of thermal images of subject A captured
on First_period_A and having each facial expression [12]Just before
Speaking

assembled twenty samples as training data and ten samples
as test data for all five facial expressions in all cases of
Case-A-1-2, Case-A-2-3, and Case-B-1-2. All facial

In speaking first In speaking lastvowel(/a/)
vowel(/o/)

expressions of the test data for both subjects were judged as
front-view faces by the method mentioned in our previously

Angry

reported paper [10]. As one sample, we obtained one image
each at the timing positions of just before speaking andwhile speaking the phonemes of the first and last vowels. If

Happy

Julius misrecognized the vowel of the test sample, thecorresponding image was not used for facial expression
recognition. We had four cases of misrecognition for thevowel(s): (i) no misrecognition for the first and last vowels,

Neutal(ii) misrecognition only for the first vowel, (iii)
misrecognition only for the last vowel, and (iv)misrecognition for both the first and last vowels. We

Sadprepared feature vectors of the training data in each of thefour cases.
4.2 Results and discussion
Tables 1, 2, and 3 show the facial expression
recognition accuracy for the case of Case-A-1-2, Case-A-23, and Case-B-1-2, respectively. The total facial recognition
accuracy by the conventional method was (429/550=78.0%),
whereas that by the proposed method was (376/418=90.0%).
Because the time period_ subject of Table 1 (2) is the sameas that of Table 2 (1), the data in Table 2 (1) are not taken

Surprised

Fig. 7. Examples of thermal images of subject B captured
on First_period_B and having each facial expression [12]training and test data captured on Second_period_A, (3)thermal images for training and test data captured on
First_period_A and Second_period_A, respectively, and (4)© ISAROB 2013

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Table 2. Recognition accuracy of Case-A-2-3

Table 1. Recognition accuracy of Case-A-1-2

Table 3. Recognition accuracy of Case-B-1-2

into account in calculating the total recognition accuracy.
The proposed method improved the total facial recognition
accuracy by 12.0%. The reject criteria explained in Section
3.3 were effective for improving facial recognition accuracy,
as shown in Table 3 (3) in particular. The proposed method
effectively judged whether the training data were
acceptable for facial expression recognition at the moment;
as shown in Table 3 (3), the training data should be updated.
The total facial recognition accuracy by the proposed
method under condition (3) in Tables 1 and 2 was(43/71=60.6%), whereas that under condition (4) in Tables
1 and 2 was (81/84=96.4%), which was better than(134/145=92.4%), obtained under the conditions of Table 1(1) and (2) and Table 2 (2). The total facial recognition
accuracy by the conventional method under condition (3) inTables 1, 2, and 3 was (76/150=50.7%), whereas that undercondition (4) in Tables 1, 2, and 3 was (119/150=79.3%),
which was lower than (234/250=93.6%), obtained under theconditions of Table 1 (1) and (2), Table 2 (2), and Table 3(1) and (2). In both the proposed method and theconventional method, updating the training data for thefacial expressions of “happy” and “neutral” remarkably
improved the accuracy of facial expression recognition, as
demonstrated in our previously reported paper [12].

© ISAROB 2013

Though several studies on facial expression recognition
using thermal image processing have been reported (seereferences [1]-[13], [16], [18]-[20]), only our research [5][13], [16], [18], [19] has focused on a speaker. In this paper,

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processing (in Japanese). Trans. Human Interface
Society 6(1):19-27[7] Nakano M, Ikezoe F, Tabuse M, et al (2009), A study
on the efficient facial expression using thermal face
image in speaking and the influence of individual
variations on its performance (in Japanese). J. IEEJ
38(2):156-163[8] Koda Y, Yoshitomi Y, Nakano M, et al (2009), Facial
expression recognition for a speaker of a phoneme ofvowel using thermal image processing and a speech
recognition system. Proceedings of 18th IEEE
International Symposium on Robot and Human
Interactive Communication, Toyama, Japan, Sep 29-Oct
1, 2009, pp. 955-960[9] Yoshitomi Y (2010), Facial expression recognition for
speaker using thermal image processing and speech
recognition system. Proceedings of 10th WSEAS
International Conference on Applied Computer Science,
Appi Kogen, Iwate, Japan, Oct 4-6, 2010, pp. 182-186[10]Fujimura T, Yoshitomi Y, Asada T, et al (2011), Facial
expression recognition of a speaker using front-view
face judgment, vowel judgment, and thermal image
processing. J. Artificial Life and Robotics 16(3):411417[11]Yoshitomi Y, Asada T, Shimada K, et al (2011), Facial
expression recognition of a speaker using vowel
judgment and thermal image processing. J. Artificial
Life and Robotics 16(3):318–323[12]Nakanishi Y, Yoshitomi Y, Asada T, et al (2012),
Robust facial expression recognition of a speaker using
thermal image processing and updating of fundamental
training-data. J. Artificial Life and Robotics, 17(2):263269[13]Yoshitomi Y (2012), Facial expression recognition of
speaker using vowel judgment and features of thermal
face image”, Proceedings of 1st WSEAS International
Conference on Information Technology and Computer
Networks, Vienna, Austria, Nov 10-12, 2012, pp.139145[14]Kuno H (1994), Infrared rays engineering (in Japanese).
Tokyo, IEICE, pp.22[15]Kuno H (1994), Infrared rays engineering (in Japanese).
Tokyo, IEICE, pp.45[16]Yoshitomi Y, Tabuse M, Asada T (2012), Image
processing: methods, applications and challenges. Nova
Science Publisher, pp.57-85[17]http://julius.sourceforge.jp/[18]Asada T, Yoshitomi Y, Tabuse M (2012), A system for
facial expression recognition of a speaker using front‐
view face judgment, vowel judgment and thermal image
processing. J. Artificial Life and Robotics, 17, in press.
[19]Yoshitomi Y, Tabuse M, Asada T (2011), SpeechTechnologies. InTech, pp.405-424[20]Hernández B, Olague G, Hammoud R, et al (2007),
Visual learning of texture descriptors for facial
expression recognition in thermal imagery. Computer
Vision and Image Understanding 16 (2-3): 258–269

we propose a method for using reject criteria in the feature
vector space. The advantage of our method over theconventional method is demonstrated in Tables 1 to 3.
After preparing the training data for all combinations of
the first and last vowels, we could apply our method to a
speaker for any utterance [11], [16].

5 CONCLUSION
We previously developed a method for the facial
expression recognition of a speaker. In the present study, amethod using reject criteria in the feature vector space wasapplied. With the proposed method, the facial expressionsof two male subjects were discriminable with 90.0%
accuracy for the facial expressions of “happy,” “neutral,”and “others” when they exhibited one of the intentional
facial expressions of “angry,” “happy,” “neutral,” “sad,”and “surprised.” The proposed method effectively judged
whether the training data were acceptable for the facial
expression recognition at that moment.
Acknowledgment
This work was supported by KAKENHI (22300077).

REFERENCES[1] Yoshitomi Y, Kimura S, Hira E, et al (1996), Facial
expression recognition using infrared rays image
processing. Proceedings of the Annual Convention IPS
Japan, Osaka, Japan, Sep 4-6, 1996, 2:339-340[2] Yoshitomi Y, Kimura S, Hira E, et al (1997), Facial
expression recognition using thermal image processing.
IPSJ SIG Notes, CVIM103-3, Kyoto, Japan, Jan 23-24,
1997, pp. 17-24[3] Yoshitomi Y, Miyawaki N, Tomita S, et al (1997),
Facial expression recognition using thermal image
processing and neural network. Proceedings of 6thIEEE International Workshop on Robot and Human
Communication, Sendai, Japan, Sep 29-Oct 1, 1997, pp.
380-385[4] Sugimoto Y, Yoshitomi Y, Tomita S (2000), A methodfor detecting transitions of emotional states using athermal face image based on a synthesis of facial
expressions. J. Robotics and Autonomous Systems
31:147-160[5] Yoshitomi Y, Kim SIll, Kawano T, et al (2000), Effectof sensor fusion for recognition of emotional statesusing voice, face image and thermal image of face.
Proceedings of 6th IEEE International Workshop on
Robot and Human Interactive Communication, Osaka,
Japan, Sep 27-29, 2000, pp. 178-183[6] Ikezoe F, Ko R, Tanijiri T, et al (2004), Facial
expression recognition for speaker using thermal image© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Music recommendation hybrid system for improving recognition ability
using collaborative filtering and impression words
S. Yoshizaki1, Y. Yoshitomi2, C. Koro3, and T. Asada2
1Works Applications Co., Ltd., 1-12-32 Akasaka, Minato-ku, Tokyo, Japan
Graduate School of Life and Environmental Sciences Kyoto Prefectural University,
1-5 Nakaragi-cho, Shimogamo, Sakyo-ku, Kyoto 606-8522, Japan
E-mail: yoshitomi@kpu.ac.jp, t_asada@mei.kpu.ac.jp
3
ISI Software Corp., 4-6-17 Honmachi, Chuo-ku, Osaka 541-0053, Japan

2

Abstract: Music therapy for improving recognition ability may be more effective when the favorite music of each person isadopted. In the proposed system, first, the recommendation process using collaborative filtering is terminated when no users in
the reference list have the same preference of recommended music as that of a new user. Then, the second recommendation
process finds the most similar music, from the scores for impression words, to those successfully recommended among music
not recommended up to the moment. The average number of recommended songs for each user by the proposed system was
12.1, whereas that of collaborative filtering was 4.3. The recommendation accuracy of the proposed system was 70.2%,
whereas that of collaborative filtering was 62.1%. The ratings of songs can be added on a user-by-user basis in therecommendation process, and this increased number of cases improves the recommendation accuracy and increases the numberof recommended songs.
Keywords: Collaborative filtering, Music recommendation, Music therapy, Impression word, and Recognition ability.
words (quiet - busy). A user scores the word pairs according
to seven levels, which are then transformed to three levels, as
shown in Table 2 [2]. In a music database, 52 songs wereassigned scores i ( 3  i  3) for each pair of impression
words evaluated by the participants. Fig. 1 shows a flowchartof the music recommendation based on impression words.
When music not recommended to a user has the same values
except “0” as that for at least one recommended music
having a high evaluation by the user on the three-level score
for at least five impression words, the music is treated ashaving a positive evaluation by the user. In contrast, whenmusic not recommended to the user has the same scores
except “0” as that for another music just recommended tothe user and having a negative evaluation by the user on the

1 INTRODUCTION
In Japan, the average age of the population has been
increasing, and this trend is expected to continue. Recently,
music therapy has been used for improving the recognition
ability of people, particularly older people. Music therapy
may be more effective when the favorite music of each
person is adopted. We have been developing music
recommendation methods aimed at improving recognition
ability [1]. However, it is not easy to recommend muchmusic to a user using the initially developed method. To
overcome this difficulty, we proposed a music
recommendation method that combines collaborative
filtering and our initial music recommendation process
based on impression words [2].
For this study, we further improved the method reported
in [2] by adding a function that adds the ratings of songs on
a user-by-user basis in the recommendation process toincrease both the recommendation accuracy and the numberof recommended songs by increasing the number of cases.
We implemented the updated method on a personal
computer and evaluated the proposed system by using
children’s songs, which tend to be familiar to older people.

Table 1. Pairs of impression words [3]quiet - busy
bracing - heavy
easy - uneasy
cheerful - gloomy
refreshing - depressing
happy - sad
comforting - harmful

2 MUSIC RECOMMENDATION METHODUSING IMPRESSION WORDS

calm - elevating
clean - dirty

We use ten pairs of impression words (Table 1) [3]. As an
example, we show the user scores for one pair of impression© ISAROB 2013

magnificent - superficial

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proportion of the same three-level scores except “0” as thatof other music recommended to the user and given a
positive evaluation by the user among the music not yet
recommended to the user. In Fig. 1, the “set of music with asimilarity to the recommended music based on impression
words” (MSRIW) is decided by using at least seven pairs ofimpression words in the case of a negative evaluation.

Table 2. Scores for pairs of impression words quiet – busy [2]Score
Three-level score
impression
32very busy
-1

1
0

busy
slightly busy0

neutral-1slightly quiet

-2

quiet

-3

1

3 PROPOSED SYSTEM

very quiet

Figs. 2 and 3 show two music recommendation methods.
Method 1 (Fig. 2) is collaborative filtering only, andMethod 2 (Fig. 3) is the proposed system, which combines
collaborative filtering and our music recommendation
process based on impression words. In the proposed system,
the recommendation process using collaborative filtering isterminated when the number of users is zero in thereference list of users showing exactly the same evaluation
for the recommended music as that of the user up to thatmoment. Then, the recommendation process performs byfinding out the most similar music, from the viewpoints ofthree-level scores except “0” on impression words, to thatsuccessfully recommended among music not yet
recommended. The proposed system recommends music
stored in the database to user u , as shown in Fig. 3. BothFig. 1. Flowchart of music recommendation by using
impression words [2]three-level score for at least seven impression words, themusic is treated as having a negative evaluation by the user.
In Fig. 1, when none of the not recommended musicreceives a positive evaluation by the user, anotherrecommendation is performed by using the subjective
estimations of all users whose subjective estimations are
stored in the database. It is expressed by “with highest
similarity” in Fig. 1 that the music has the highest© ISAROB 2013

Fig. 2. Flowchart of music recommendation bycollaborative filtering [2]193The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

as 0 when the score is 1 to 3 (“dislike,” “slightly dislike,” or“neutral”).
For programming, we used Visual C++ 6.0 (Microsoft)on a PC (Dell Latitude E6599, CPU: Intel Core 2 Duo
P8700 2.54 GHz, main memory: 4.00 GB, and OS:
Windows 7, Microsoft) for the experiment.

4 PERFORMANCE EVALUATION
4.1 Conditions
Because older people tend to prefer children’s songs [4],we selected a CD described as an anthology of older songs
enjoyed by older people with dementia [5], and then we
selected 52 songs on the CD that were also included in amusic textbook database for elementary schools [6]. To
evaluate the music recommendation methods, all 52 of theselected songs in the database were assigned scoress (1  s  5) by 12 users of different ages (teens: 1,
twenties: 6, fifties: 5). For evaluating the proposed system,
we chose each of the 12 users as user u and put theremaining users in the reference user list UL described inFig. 3. Thus, each user was user u one time and in thereference list 11 times. We used 15 as the value of K in
the evaluations of both Method 1 and Method 2. In addition,
all 52 of the selected songs in the database were assigned
scores i ( 3  i  3) for each pair of impression words by
five subjects of different ages (twenties: 3, forties: 1, fifties:
1). Of the five subjects, the one who was in his fifties was
also one of the users who assigned scores s . The averageof scores i obtained from the five subjects for each pair ofimpression words was used as scores i for the
performance evaluation. The 15 songs having the values
except “0” as the three-level score for one impression word
at most were not recommended in the process ofrecommendation based on impression words because they
did not have distinct characteristics from the viewpoints ofimpression words.
Then, we obtained the result of the music
recommendation for each new user for each method
described in Section 3. To evaluate the two music
recommendation methods described in Section 3, 10 newusers (user Nos. 1 to 10) participated in the experiment
without the updating of the AUSE, which is the set of allusers with subjective estimations in Fig. 3, just before
finishing the recommendation process, and then one user
who had the worst accuracy of recommendation among the
new 10 users was selected for additional recommendations
with the updating of the AUSE. Moreover, 14 older users,

Fig. 3. Flowchart of music recommendation usedin the proposed system
the recommendation process using collaborative filteringand that using the proposed system are terminated when thenumber of recommended songs reaches the upper limit K ,decidedpreviously.
Just
beforefinishingtherecommendation process, the database of users withsubjective estimations of the music is updated by adding the
subjective estimations of the user for whom the proposedsystem recommends music.
In the flowcharts of Method 1 and Method 2 shown in
Figs. 2 and 3, respectively, the estimation of user u forsong mR is set as 1 when the score of user u for songmR is 4 or 5 (“slightly favorite” or “favorite”), and it is set© ISAROB 2013

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Nos. 11 to 24 of different ages (seventies: 2, eighties: 8,
nineties: 4) participated in the experiment with the user-byuser updating for the AUSE.
4.2 Results and discussions
The number of recommended songs for users previously
registered in the AUSE by the proposed system was 15 peruser. In this case, the recommendation process bycollaborative filtering was not terminated because thenumber of users staying in the UL did not become zero.
Therefore, the proposed system recommended the mostsongs under the condition that the upper limit K ofrecommended songs was 15. The mean value of therecommendation accuracy of the proposed system was
93.9%, whereas that of the random recommendation (i.e.,
not using Method 1 and/or Method 2) was 47.9%.
As an example, Table 3 shows the process of the music
recommendation for user No. 8. As shown in Table 3,
Method 2 tended to recommend more music than did
Method 1.

Fig. 4. Performance of music recommendation methods:(a) recommendation accuracy,(b) number of recommended songs

Table 3. Music recommendation process for user No. 8[Method 1]
Order

Recommended
music No.

Acceptance

1

52

2

41

3

50

4

21

5

26

6

23

○
○
○
○
×
×

Recommended

Acceptanceproposed system was 12.1 per user, whereas that of only
collaborative filtering (Method 1) was 4.3 per user. The
mean value of the recommendation accuracy of the
proposed system was 70.2%, whereas that of only
collaborative filtering was 62.1%. In the collaborative
filtering (Method 1), the recommendation process was
terminated because the number of users staying in the UL
became zero. In contrast, in Method 2, the recommendation
process was performed while the recommendation using theimpression words was possible under the condition shownin Fig.3. As compared with only using the collaborative
filtering (Method 1), we could increase the number ofrecommended songs while achieving a better accuracy of
the recommendation than with Method 1 by combining therecommendation based on the impression words with thecollaborative filtering.
Fig. 5 shows the effect of updating the AUSE for user
No. 9. By updating the AUSE, the accuracy of therecommendation improved (Fig. 5(a)) and the number ofrecommended songs also increased (Fig. 5(b)).
Fig. 6 shows the performance of the proposed system,with the updating of the AUSE for users Nos. 11 to 24. User
No. 19 stated that he disliked all children’s songs. As aresult, the accuracy of the recommendation was 0% for user
No. 19. The mean value of the number of recommended
songs for users Nos. 11 to 24 was 11.4 per user. The mean

User No. in UL

1,2,3,4,5,7,8,9,10,11,12
1,2,3,4,5,7,8,9,10,11,12
1,2,3,4,5,7,8,9,10,

12

1,2,3,4,5,7,8,9,10,

12
12

none

[Method 2]
Order

User No. in UL

music No.
1～6

Same as Method 1

7

5

8

17

9

36

10

43

11

18

12

13

13

6

○
○
○
×
○
○
○

Fig. 4 shows the performance of the two methods in theexperiment, where updating of the AUSE was not
performed in the proposed system. The mean value of thenumber of recommended songs by Method 2 in the© ISAROB 2013

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people, particularly those who are older and/or have a
cognitive impairment.

5 CONCLUSION
We propose a music recommendation system thatcombines collaborative filtering and music recommendation
based on impression words. We showed that the proposedsystem was more effective for music recommendation than
the system of only collaborative filtering when used on amusic database composed of children’s songs. The function
by which the ratings of songs can be added on a user-byuser basis in the recommendation process was effective forboth improving the accuracy of the recommendation andincreasing the number of recommended songs.
Acknowledgments
We would like to thank Associate Professor M. Tabuseof Kyoto Prefectural University for his valuable advice andsupport. We would also like to thank all the participants
who cooperated with us in the experiments.

Fig. 5. Effect of updating the AUSE using user No. 9:(a) recommendation accuracy,(b) number of recommended songs

REFERENCES[1] Konishi
H,
YoshitomiY(2011),
Music
recommendation system aimed at improving
recognition ability. In:Sugisaka M (ed), Proceedings of
16th International Symposium on Artificial Life andRobotics (AROB16th), Beppu, Oita Japan, Jan 27-29,
2011, pp.241-244[2] Koro C, Yoshitomi Y, Asada T, Yoshizaki S (2012),
Music recommendation aimed at improving
recognition ability using collaborative filtering andimpression words. In:Sugisaka M (ed), Proceedings of
17th International Symposium on Artificial Life andRobotics (AROB17th), Beppu, Oita Japan, Jan 19-21,
2012, pp.222-225[3] Ohta K, Kumamoto T (2002), A design of music
adjective pairs based on linguistic knowledge (inJapanese) IPSJ SIG Notes 40:97-102[4] Takahashi T (1997), Research report on songs familiar
to people advanced in years (in Japanese) Journal ofJapanese music therapy associate 15(1):68-75[5] Akahoshi T (2009), Good old anthology enjoyable forpeople advanced in years and troubled with dementia.
Kirara shobo (in Japanese)[6] Music textbook database for elementary school by
Kanagawa prefectural education center (in Japanese);
http://kjd.edu-ctr.pref.kanagawa.jp/daizai_music/

Fig. 6. Performance of the proposed system:(a) recommendation accuracy,(b) number of recommended songs
value of the recommendation accuracy for users Nos. 11 to
24 was 59.1%, whereas that without user No. 19 was 63.6%.
The mean value of the recommendation accuracy of the
proposed system was 93.9% for users previously registeredin the AUSE. Moreover, updating of the AUSE waseffective for both the improved accuracy of therecommendation and the increase in the number ofrecommended songs. Therefore, to improve theperformance of the proposed system, we should use theproposed system with updating of the AUSE for more

© ISAROB 2013

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Study on the Efficient Use ofSatellite Image Data Analysis System
Hayao Mori1, Seiji Tsukamoto1,
Takashi Yamaguchi2, Masaki Hanada2, Jong Geol Park2, Eiji Nunohiro2,
1

Graduate School of Tokyo University of Information Sciences, Japan
2
Tokyo University of Information Sciences, Japan(Tel: 81-43-236-1292, Fax: 81-43-236-1292)

1{g10022hm, g12008st}@edu.tuis.ac.jp, 2{tyamagu, mhanada, amon, nunohiro}@rsch.tuis.ac.jp

Abstract: Tokyo University of Information Sciences receives MODIS (Moderate Resolution Imaging Spectroradiometer) data,
one of the sensors equipped by NASA's Terra and Aqua satellites, and researches of the analysis on change of environment as
part of the academic frontier project. For the information infrastructure of this frontier research, we are developing a satellite
image data analysis system to support of web system, a parallel distributed system configuration using multiple PC clusters,
database for MODIS data to open the research results and MODIS data for public use. This paper presents the overview ofsatellite data analysis system and new feature which are the composite of multiple satellite data, and LSM with virtualizationof storage system for satellite data, scheduling for multiple cluster nodes and performance evaluation.
Keywords: Scheduling, Virtualization, MODIS
The satellite image data is extremely large, and direct
manipulation of the large data leads to a high load on boththe processor and network. In order to improve thethroughput and turnaround time of data processing, in the
proposed system the computer nodes are configured fromhigh performance servers and multiple PC clusters,
implemented as a parallel distributed system. The various
analysis applications and the preprocessing of satellite
image data are run on this distributed computer nodes.

1 INTRODUCTIONTokyo University of Information Sciences (TUIS)
receives MODIS (Moderate Resolution Imaging
Spectroradiometer) data from one of the sensors equippedby NASA's Terra and Aqua satellites, and provides
processed data to universities and research institutes as apart of academic frontier project organized by TUIS. The
main research theme of this project is on “the sustainable
development of economic and social structure dependent on
the environment in Eastern Asia”. One of the major fieldsof the research using MODIS data is to analyze
environmental change. In the frontier project, we aredeveloping satellite image data analysis system (SIDAS) to
support the frontier research such as to forecast weatherchange and environmental changes as well as processing
and maintaining the satellite data.

2.2. System configuration
SIDAS consists of a web server, an application server
consisting of multiple PC clusters, and a database server.
Figure 1 shows the overview of SIDAS. The application
server schedules the execution of applications, manages thesatellite data, and controls PC clusters which executeapplications. The user can access the system, request data
analysis and check the results over the network via a Web
browser or other web applications, and the user can also
download satellite image data through the system.
The Web server accepts request of the user and submits
processing of the request to the application server. The
application server transmits the MODIS data used by theprocessing from the MODIS database to the local disk in
the application server. The application server executes
application for the request and returns the results to Web
server.

2 SATELLITE IMAGE DATA ANALYSIS SYS
TEM (SIDAS)
2.1. Overview
This system works as the main platform of the total
satellite image data analysis service, maintains the satellite
image data, and implements satellite image data analysis
applications. The user can access the system over the
network via Web browser or other web applications. Users
can request for data analysis, check the results anddownload satellite image data through the system.

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Table 1. Band information
BAND

Fig. 1. Overview of SIDAS

3 SATELLITE IMAGE DATA ANALYSIS SYS
TEM (SIDAS)
3.1. MODIS data using
MODIS is a multiple band radiometric sensor
instrument aboard Terra and Aqua satellites to monitor theenvironment of a large region of the earth covering a widthof 2,330 km. It measures 36 spectral bands between 0.405
and 14.385 µm with three spatial resolutions (250m, 500m,
1,000m). Table 1 shows the band information of MODIS
data.
Tokyo University of Information Sciences (TUIS) is
receiving MODIS data from three places in Hokkaido,
Tokyo and Okinawa, and received the data 3 or 4 times inthe daytime and 3 or 4 times in the nighttime from eachplace. The observing areas from the three places are from80 degrees to 180 degrees of east longitude and from 0
degrees to 80 degrees of north latitude.











MOD02 (spatial resolution: 500m) is used to create
visualized data from satellite data and to calculate
NDVI, and we use 7 spectral bands between visible
range and short wave infrared range. BAND1 (red invisible region), BAND4 (green in visible region) andBAND3 (blue in visible region) are used to create thevisual information from the satellite data.BAND2(near-infrared region) are mainly used to measure thevegetation index (The correlation is high in thephotosynthesis activity).BAND5, 6, 7 (short wave
infrared) has the feature of temperature and reflection
factors.
MOD03 (spatial resolution: 1KM) has geologicalinformation containing geodetic latitude, longitude,
surface height above sea level, solar zenith, solar
azimuth angles, satellite zenith, satellite azimuth
angles, and a land/sea mask. Mod03 is used to make a
map information from satellite data (MOD02, MOD09,© ISAROB 2013

１

WAVELENGTH(nm)
620～670SPATIALRESOLUTIONS
250m

２841～876250m

３459～479500m

４545～565500m

５1230～1250500m

６1628～1652500m

７2105～2155

500m

KEY USE
Absolute Land
Cover change,
Vegetation
Chlorophyll
Cloud Amount,
Vegetation Land
Cover change
Soil/ Vegetation
Differences
Green
Vegetation
Leaf/Canopy
Differences
Snow/Cloud
Differences
Cloud
Properties,
Land Properties

MOD11, MOD28 and MOD35) according to map
projection.
MOD11 (spatial resolution: 1KM) is used to estimate
the land surface temperature by using reflectance ofland cover. MOD11 is effective data for the heat island
phenomenon analysis of urban areas, soil moisture
conditions analysis and the heat flux analysis.
MOD13 (spatial resolution: 500m) is vegetation index
data after atmospheric correction.
MOD14 (spatial resolution: 1KM) is thermal
Anomalies data includes fire occurrence (day/night).
MOD28 (spatial resolution: 1KM) is Sea Surface
Temperature data. This data is mainly used to analyse
the change of sea surface temperature and the flow ofsea.

3.2. Data format
MODIS data is converted into Raw data for every bandby the database creation processing, so that it may be easy
to use public and processing of the various analysis.
Raw data is a data format near a general graphics format.
In the data part of Raw data, photography data at equalintervals are stored continuously.
Additional information such as position information andtime of MODIS data are stored in another file (header file).

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3.3. Preprocessing of satellite image data
3.3.1 Mosaic processing
Figure 2 shows the example of mosaic processing. The
mosaic process aggregates the converted Raw data sets into
one data set per one orbit. The aggregated data set isgenerated from three data sets respectively received fromthe three stations and is maintained consistency because thethree received data sets are the same data at the same point.

In order to process at the high speed of analysis
processing, efficient employment of a system resource isrequired.
For this reason, we developed LSM (Logical Storage
Manager) which manages the execution status of PC clusterand satellite data stored in each cluster.
LSM includes the scheduling function of PC cluster,and the reuse function of satellite imagery data, anddetermines the PC cluster configuration dynamically which
AP (application program) uses.
4.1. Scheduling function of PC cluster
The purpose of the scheduling function of PC cluster isto reduce the execution time of APs by effective use ofresources such as CPU and local disk of each PC cluster
node. Figure 3 shows an example of the schedulingfunction. LSM manages the system status such as theoperation status of all PC cluster nodes and status of thedata stored on the local disk of each PC cluster node. LSM
dynamically determines the configuration of the PC cluster
nodes (i.e., the number and location of PC cluster nodes) to
execute the AP by considering both the operation status andthe data which the AP uses. Dynamic resource allocation
can be performed with dynamically combining the AP and
the data which the AP uses. The procedure of thescheduling function is shown as follows.

Fig. 2. Example of mosaic processing
3.3.2 Composite processing
When analyzing large area satellite data, one common
problem is noise caused by cloud interference. In order toremove the interference of clouds, the standard solution is
to create a composite data of the same regions during a
selected time span, and to patch together data not coveredby clouds to create a clear image.

①

3.4. Example of the use of MODIS data
One of the purposes of SIDAS is to support researches
which use the MODIS data such as environmental
perturbation analysis and earth surface temperature analysis.
Main researches under development are as follows.
1) To search for fire regions in forests and fields.
2) To search for a set of data, from the satellite data in
TUIS, which is similar image data analyzed by the system.
3) To provide the spatiotemporal analysis of land coverchanges of East Asia.
4) To create the disaster map of East Asian.
5) To forecast NDVI by using auto-regression analysis.
6) To provide statistical and soft computing analysis forland cover estimation.

②③④

The AP requires the number of PC cluster nodes andthe data which the AP uses.
From ①, the assigned number of PC cluster nodes isdetermined.
LSM determines the configuration of the PC cluster
nodes to execute the AP with comparing the datastored on the local disk of each PC cluster node to thedata which the AP uses.
The AP and the data which the AP uses are combined.

4 EXECUTION CONTROL OF APPLICATION
The analysis processing which uses MODIS data uses
large-scale satellite data.
Fig. 3. PC cluster scheduling© ISAROB 2013

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

4.2 Data reuse function
The purpose of the data reuse function is to reduce anamount of transferred data between PC cluster node and theglobal disk by reuse of the data stored on the local disk.
Figure 4 shows an example of data reuse. The procedure of
the data reuse function is shown as follows.
①②③

the number of tasks to be performed on PC
cluster：200

5.2 Evaluation result

LSM assigns the AP to the PC cluster node in which
the data which it uses is stored.
When the data which the AP uses is stored on the local
disk of a running PC cluster node, the data istransferred from the running PC cluster node to a PC
cluster node to execute the AP.
When the data which the AP uses is not stored on thelocal disk of any PC cluster nodes, the data istransferred from the global disk to a PC cluster node
to execute the AP.
Fig. 5. Hit rate versus time

Fig. 6. Comparison of hit rate

Fig. 4. Data reuse

Figure 5 and 6 show the results of the evaluation of the
proposed functions. In the evaluation, 200 tasks are
generated and the proposed functions are evaluated in termsof hit rate. Here hit rate is a rate of the number of data
found on the local disk to the number of the generated tasks.
The processing time is reduced as the hit rate increases.
Figure 5 shows the hit rate and task processing time. The
hit rate under normal distribution is higher than uniform
distribution. Figure 6 shows the hit rate under normal
distribution and uniform distribution.

5 PERFORMANCE EVALUATION
5.1 Evaluation method
The following conditions was used for evaluation of thedata reuse function.
 Assess changes reusability of satellite image data (hitrate)
 Multiple tasks are register to the queue of LSM, andare performed in order.
 Number of clusters and the data used for a single task
is determined by the random number The following types random number was used
 Uniform random number
 Normal random number
 Evaluation condition
 Number of PC cluster：32 units
 local data retention limit of PC cluster：4
 Number of data in the database：128 RAW data

© ISAROB 2013

5.3 CONCLUSIONSIn the satellite image data analysis system, we haveproposed and implemented LSM to improve the processing
performance and the data reusability. We measured the hit
rate of the data that is stored on the local disk of PC clusters
to evaluate the scheduling function and the data reuse
function of LSM.
From the evaluation results, we
confirmed that the processing time was reduced as the hit
rate increases.

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In the future, we aim the efficient use of the system
resources by more improving the scheduling function andthe data reuse function.

Acknowledgments
This research was supported by the Research project ofTokyo University of Information Sciences for thesustainable development of economic and social structure
dependent on the environment in eastern Asia.

REFERENCES[1] Akihiro Nakamura, Jong Geol Park, Kenneth J.
Mackin, Eiji Nunohiro, et al, Development and Evaluationof Satellite Image Data Analysis Infrastructure, The
Sixteenth International Symposium on Artificial Life andRobotics, Proceeding Index OS18-3, (AROB 16th' 2011)[2] Eiji Nunohiro, Jong Geol Park, Kenneth J. Mackin,et al, Development of satellite data analysis system,
Proceedings of the 5th International Conference on
Information (2009), pp.143-146[3] Eiji Nunohiro, Kei Katayama, Kenneth J. Mackin,
Jong Geol Park, Forest and Field Fire Search System using
MODIS Data, Journal of Advanced Computational
Intelligence and Intelligent Informatics (JAC 3, 2007),
vol.11 No.8, pp.1043-1048[4] Kenneth J. Mackin, Eiji Nunohiro, Development ofSatellite Image Searching using Distributed Genetic
Algorithm with Normalized Correlation, The twelfth
International Symposium on Artificial Life and Robotics,
Proceeding Index GS12-7, (AROB 12th' 2007)[5] National Aeronautics and Space Administration(NASA), MODIS Web, http://modis.gsfc.nasa.gov/© ISAROB 2013

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Administrative division based data segmentation forautonomous paddy field classifier modelingTakashi Yamaguchi1, Shinya Iwasaki1, Kazuma Mori1, Kenneth J. Mackin1, Masaki Hanada1, Eiji Nunohiro1
1

Tokyo University of Information Sciences, Japan(Tel: 81-43-236-1232, Fax: 81-43-236-1232)
1

tyamagu@rsch.tuis.ac.jp

Abstract: Monitoring changes in paddy area is important since rice is staple food, and paddy agriculture is a major cropping
system in Asia. The decision trees or stochastic analysis based methods using spatiotemporal satellite sensor data is effective tomonitor paddy area by the remote sensing. On the other hand, it is difficult to apply the same models for different countries andregions. Therefore we applied artificial neural network to classify paddy area in order to automatically generate the classifier.
From the computer simulation, the proposed paddy classifier yielded high classification rate in the data subset of north regionof Chiba. However, the accuracy was corrupted by the difference of annual cycle pattern. Thus, in this paper, we investigate anadministrative division based data segmentation method in order to divide the data set into different groups so that the different
patterns of paddy annual cycle is divided to different groups.
Keywords: artificial neural network, classification, remote sensing
project. For the information infrastructure of this frontierresearch, we are developing a SIDAS to support of web
system, a parallel distributed system configuration using
multiple PC clusters, database for MODIS data to publish
the research results and MODIS data for public use. In thisresearch, we develop the sub-systems of satellite image
preprocessing, the analysis using satellite image and themap visualization in order to publish the research results.

1 INTRODUCTION
Monitoring changes in paddy area is important since
rice is staple food, and paddy agriculture is a major
cropping system in Asia. For monitoring change in land
surface, many satellites were launched and its applications
were researched in the field of remote sensing. However
monitoring paddy area with remote sensing is difficult due
to the temporal change in land surface, and difference of
spatiotemporal characteristics in countries and regions.
In our past research, we applied artificial neural network
to classify paddy areas using moderate resolution sensor
data that includes spatiotemporal information. Our aim is toautomatically generate a paddy classifier in order to create
localized classifiers for each countries and region. From theexperiments, proposed paddy field classifier yielded a goodresult in the classification accuracy. However, theclassification accuracy is depends on the set geographicalarea for the training data sampling. In this paper, weinvestigate an administrative division based data
segmentation method in order to divide the data set into
different groups so that the different patterns of paddy
annual cycle is divided to different groups.
This classified paddy field data is visualized on a web
system that called Satellite Image Data Analysis System(SIDAS). Tokyo University of Information Sciences
receives Moderate Resolution Imaging Spectroradiometer(MODIS) data, one of the sensors equipped by NASA's
Terra and Aqua satellites, and researches of the analysis onchange of environment as part of the academic frontier© ISAROB 2013

2 PADDY FIELD CLASSIFIER
2.1 Paddy Field Classifications on Remote Sensing
Monitoring paddy area with remote sensing is difficult
because a paddy has an annual cycle that can be classified
into three main periods: (1) the flooding and ricetransplanting period, when the land surface is observed aswater; (2) the growing period, when vegetation index isincreased by the vegetative growth; (3) the fallow period,
when land surface is observed as soil. For monitoringchange in land cover, moderate resolution remote sensing iseffective because of the high frequency of these satellites toscan the same area.
In the past research for paddy areas estimation using
remote sensing, decision trees or stochastic analysis based
methods using spatiotemporal information were proposed.
On the other hand, it is difficult to apply the same models
for different countries and regions. In this paper we applied
artificial neural network to classify paddy area using
moderate resolution remote sensing data in order toautomatically generate the classifier.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013t p = g (x p ) .

2.2 Multi-Layered Perceptron Classifier
In this paper, Multi-layered perceptron (MLP) was used.
MLP is a type of artificial neural network that canapproximate complex function by machine learning. MLP
is trained by data set that consists of a feature vector and ateaching signal.
Multi-layered perceptron (MLP) is a type of artificial
neural network (ANN) that can approximate complex
function by machine learning. In this research, we used
MLP shown as Fig. 1 in order to learn a classification
function of paddy field area from the MODIS data set.

t1
f

y1
wk0

Bias

z0

f

z1
wj0

Bias x0

x1

t1
f

f
wj1

x1

f

y1

wk1

tk

wk2

wj2

wji

f

yk

wkj

f

z1

tK

yK

When pth training pattern {xp, tp} is given, BP training
modifies weights for minimizing mean square error E
defined as following expression

E=

f

zm

Teaching Signal

xn

Hidden Layer

Input Layer

Fig.1. MLP Network Structure
MLP consists of 3 layers: input layer with n neurons anda bias neuron, hidden layer with m neurons and a bias
neuron, and output layer with K neurons. Each neuron isconnected with every neuron in the next layer, and eachconnection has a weight value. When an input signal x ={x1,x2,…xn} is given, jth output signal zj of hidden layer’s
neuron and kth output signal yk of output layer’s neuron arecalculated by following expressions: n
z j = f  ∑ w ji xi  , i =0


(1)

 m

y k = f  ∑ wkj z j  ,
 j =0(2)∆w ji (s ) = −λ ⋅∂E+ µ ⋅ ∆w ji (s − 1) , (5),∂w ji∆wkj ( s ) = −λ∂E
+ µ ⋅ ∆wkj ( s − 1) , (6)∂wkj positive if y p > θ.
Output p = negative otherwize(7)

where θ is predefined threshold value.
MLP learning result is unstable from the initialization
problem that MLP learning falls into different local minima
by the initial weight values. For resolving unstableness,
combination with ensemble learning and MLP is commonly
used. Ensemble Learning is a method for improving thestability of machine learning algorithms by using multiple
learners. For ensemble method, bagging method was used[4]. Bagging is a typical ensemble method that aggregates
multiple training results. For aggregating, voting was used,as this is commonly used in bagging for classifier. Let L(x)be an aggregated learner, Ls(x) be a multiple weak learnerwhere s = 1, 2, … , r; and c = 1, 2, … , C be class label. A
robust learner L(x) is defined by following expression.

where i = 0, 1, 2 … , n; j = 0, 1, 2 … , m; k = 0, 1, 2 … , K;
f is the activation function, and z0 and x0 are bias neurons.
Bias neuron always output 1.0 to next layer’s neurons. For
activation function, sigmoid function was used.
MLP modifies each weight value using backpropagation (BP) training [3]. Let be xp ={x1p,x2p,…xnp}, p =1, 2, … , N is pth input signal, and tp = {t1p,t2p,…tKp} is pth
teaching signal. The teaching signal is true output signalthat correspond to pth input signal xp where projection
function can be defined as follow© ISAROB 2013(4)

where λ is a learning rate, and µ is a momentum rate. Eachweight is commonly initialized by random value. As aresult of training, MLP learns a function g(x) by modifying
weight values.
In this research, MLP was used as 2 class classifier suchthat classifies positive or negative (1 or 0) for paddy fieldclass. However MLP output is continuous value, so that it isnecessary to decide positive or negative from thecontinuous output value. In this experiment, pth final output
was defined by following function.

Output Layer

wjn

xi

2
1 N p
t − yp .
∑N p =1At the training step in BP training, the weight modification∆wji(s) and ∆wkj(s) are defined as follows:

wkm

zj

(3)

L( x) = arg max {s; Ls ( x) = c} .

(8)cEach learner is trained by using bootstrap samples [5]. Let
T be training data set, training data subset Ts∈T for sth
learner is constructed by using random sample.

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MLP learned the patterns of paddy annual cycle for thetarget prefecture that derived by the administrative division.

2.3 MLP Paddy Classifier
For the input signals of samples, 1-month composite
MODIS sensor data on 1 pixel was used. The input signals
consist of red, infra-red and short wave infra-red of January
to September per 1-month (36 inputs) in order to learn
temporal pattern of paddy area. The band data of eachmonth was derived from 1-month composite MODIS sensor
data of 500m resolution. 1-month composite data is thetechnique that composites multiple satellite image between
1 month into a single satellite image in order to reduce thenoise and distortion such as the cloud and the sensor angle.
1-month composite data is a commonly used for the
analysis on the remote sensing.
For teaching signal, 1km meshed land use map provided
by the Japanese Ministry of Land, Infrastructure, Transport
and Tourism (JMLIT) were used. The land use map isconverted into the raster image of same pixel size by theliner interpolation because the meshes of land use map are
not matched to the pixels of satellite image.

2.5 Visualization System of Paddy Classification
In this research we develop a paddy map visualization
system as an application framework of SIDAS in order topublish the latest paddy map by the latest satellite images.
Figure 2 shows an overview of paddy map visualization
system.
The data set provider has a data divider that divides the
samples of MODIS sensor data and land use data for the
MLP paddy classifier regard to the administrative division.
The divided data is used to train the MLP paddy classifiers
and classify the samples by corresponding learned paddy
classifiers. The MLP paddy classifier is separately trainedby the given data for an area of administrative division.
After the training, the samples can be classified into paddy
or non-paddy by the learned MLP paddy classifier for eachadministrative division. Finally, the classification result isvisualized as RGB image by the map visualizer for web
publishing.

2.4 Training Data Segmentation
From the experiments of our previous research,
proposed paddy field classifier yielded a good result in theclassification accuracy. However, the classification
accuracy is depends on the set geographical area for the
training data samples. The paddy annual cycle patterns isdiversified when the training data area is increased in size,
therefore the large number of hidden layer neurons and thelong training iterations are necessity to learn the annual
cycle patterns.
Considering that the difference of paddy annual cycle isoccurred by the culture, the political decision and thegeographical feature, we investigated an administrative
division based data segmentation method that the data set isdivided by the administrative division. From the viewpointof the geomorphology, it is expected that the administrative
division based segmentation is effective to separate thepattern of paddy annual cycle because the boarder ofadministrative division is generally decided on thegeographical feature such as the river and the mountain.
The samples that consist of the input signal values and ateaching signal value are divided into the groups by theadministrative division. For the data of administrative
division, the administrative division map provided by
JMLIT was used. The maps provided by JMLIT wasconverted into the raster image because the maps
constructed by vector image. The MLP classifier isseparately trained for each divided data sets. Therefore the© ISAROB 2013

MODIS Sensor Data

Land Use Data
Data Set Provider

Data Divider
Administrative Division Data

Data Set 1

Data Set 2

Data Set 3

MLP Classifier 1

MLP Classifier 2

MLP Classifier 3

Result 1

Result 2

Result 3

for Prefecture 1

for Prefecture 2

for Prefecture 3

Map Visualizer
Map ImageFig.2. Overview of paddy map visualization system
SIDAS has a database of MODIS sensor data as theraster image format. The land use data and administrative
division data also stored in SIDAS database as raster image.
The data divider is implemented by using the composite
function in SIDAS middleware. SIDAS has a middlewarefor satellite image analysis such as the image I/O, image
composition, image masking, image visualization, and etc.
The data division function was implemented as the image
masking of MODIS sensor data by the administrative
division data. Because the mask image should be consistedby {0, 1} value in SIDAS middleware, the administrative

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difficult to classify due to the mixture of different land use
in pixel.

division data was converted into the raster image with {0,1} value.
The divided data set is provided as the multi-channel
RAW image format similar to MODIS sensor data. In thissystem, MLP classifier is implemented to handle the multichannel RAW image for input signals. The input channels
consist of red, infra-red, short wave infra-red, and land use
of January to September per 1-month. The result ofclassification is also provided as the multi-channel RAW
image. The output channel consists of the label ofclassification.
For the visualization of paddy map, the multi-channel
RAW image to RGB image convert functions on SIDAS
was used. The SIDAS image convert functions is developed
to visualize the maps of visual light, vegetation index andsea surface temperature, and land surface temperature. This
convert functions can convert the multi and single channel
RAW image into the RGB color image. The RGB color
image is used for the web publishing of satellite image.

4 CONCLUSION
In this paper, we proposed an administrative division
based data segmentation method in order to divide the data
set into different groups to separate the different pattern of
paddy annual cycle by administrative division. Moreover,
we develop the SIDAS application framework andimplement the proposed method on this framework.
From the result of paddy map visualization, the paddy
map generated by proposed paddy classifier is yielded goodresult in the classification accuracy. On the other hand, it
was clarified a problem of the mixture of different land use
in pixel for improving accuracy.
For future works, we plan to generate the maps forwhole prefecture in Japan. Moreover, we evaluate theaccuracy and investigate the effectiveness for the practicaluse of paddy field monitoring.

3 EXPERIMENTACKNOWLEDGEMENT

In this experiment, we evaluated classification accuracy
by using proposed paddy classifier. For evaluatingclassification accuracy, MODIS data was divided into 2
disjoint sub set, training data set and test data set, by using
random sampling. The number of test data set was 10% of
the number of training data set.

This research was supported by the Research project ofTokyo University of Information Sciences for thesustainable development of economic and social structure
dependent on the environment in eastern Asia

REFERENCES[1] T. Le Toan, F. Ribbes, L. Wang, et al (1997), Ricecrop mapping and monitoring using ERS-1 data based on
experiment and modeling results. IEEE Transactions on
Geoscience and Remote Sensing, 1, 41-56[2] X. Xiao, S. Boles, S. Frolking, C. Li, et al (2006),
Mapping paddy rice agriculture in south and southeast Asia
using multi-temporal MODIS images. Remote Sensing ofEnvironment, 100, 95-113[3] D. E. Rumelhart, G. E. Hinton, R. J. Williams(1986) Learning representations by back-propagating Errors.
Nature, vol. 323, pp. 533-536[4] L. Breiman, (1996) Bagging Predictors. Machine
Learning, 24(2), 123-140[5] B. Efron, R. Tibshirani (1993), “An Introduction to
the Bootstrap”. Chapman and Hall.
[6] Takashi Yamaguchi, Eiji Nunohiro, Jong Geol Park(2009) Application of Artificial Neural Network for Land
Cover Classification using Spatiotemporal Information, 5thInternational Conference on Information,[7] W.Takeuchi, Y. Yasuoka (2004), Development of
normalized vegetation, soil and water indicesderived from satellite remote sensing data, Journal of theJapan Society of Photogrammetry and Remote Sensing, Vol.
43, No. 6, pp. 7-19 (in Japanese).

Fig.3. Result of Paddy Map Visualization
From the computer simulation, the proposed paddy
classifier yielded 0.851 classification rate in the data subset
of Chiba prefecture. Figure 3 is a result of paddy map
visualization. Green colored area is correctly classified area
as paddy field. Red colored area is incorrectly classified as
non-paddy for paddy field and Yellow colored area isincorrectly classified as non-paddy for paddy field. This
result shows that the larger area of paddy field can becorrectly classified. On the other hand, the small paddy
field area and the neighbor of the boarder of paddy area are© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Analysis of seasonal variability of methane over global land area
Jonggeol Park1, Sooyoung Park1, Youngjoo Kwak2, Eiji Nunohiro 1, and Takashi Yamaguchi 1
1

2

Tokyo University of Information Sciences, Japan
International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Japan(Tel: 81-43-236-4644, Fax: 81-43-236-4644)
1

amon@rsch.tuis.ac.jp

Abstract: In this study, we determine the global emission concentration of methane using SCIAMACHY data. We analyzed
land and sea area to investigate the nine-year changes in methane concentrations from 2003 to 2011. Moreover, by subtracting
the concentration of methane from land and sea, we can found the methane emission concentration of land. As a result, it iscleared that a big amount of CH4 emission concentration was found not only in the Northern Hemisphere paddy fields but also
in the Southern Hemisphere broadleaf evergreen areas (Central Africa and South America). And we also found that the global
land CH4 growth rate is 3-5ppb/year during 9 years.
Keywords: CH4, emission concentration, Greenhouse gases, SCIMACHY, Sources, NDVI.
Anthropogenic sources account for the other two-thirds ofthe total CH4 emission budget, with the most important
being energy consumption (coal mining and combustion,oil- and gas-related emissions), domestic ruminants andwaste treatment, rice paddies, biomass burning, landfills
and waste water. A study from Nisbet et al. (2009) suggeststhat the emission of methane from plants under normal
conditions is due to the transport of water containing
dissolved methane from the soil to the atmosphere throughtranspiration. Transpiration could help to explain thesatellite-observed methane enhancement over the tropicalregions of South America and some of the publishedground-based measurements (Carmo 2005, Miller 2007).
Recent studies have proposed that there is an additionalsignificant CH4 source that could radically impact thecurrent CH4 budget estimates. Keppler et al. (2006)
suggested that living plants with CH4 emissions (aerobic)
10–40% of the total annual source, may be a major CH4
source on a global scale. Wang et al (2008) have confirmedthat some plant species emit methane under aerobic
condition in the Inner Mongolia steppe. After a decade ofnear stable concentrations, the growth rate of atmosphericmethane has started to increase again.
Recently SCIAMACHY (launch 2002) and GOSAT(launch 2009) enabled precise measurements ofatmospheric CH4 from space. Unfortunately the global
source strength of CH4 remains uncertain. In this study, we
will focus on CH4 sources of global area using thesesatellite data, vegetation map and MODIS NDVI toinvestigate the characteristics of this CH4 sources

1 INTRODUCTION
A greenhouse gas is a gas in atmosphere that absorbs
and emits radiation within the thermal infrared range. This
process is the original cause of the greenhouse effect. The
primary greenhouse gases in the Earth's atmosphere arewater vapor, carbon dioxide (CO2), methane (CH4), nitrous
oxide (N2O), and ozone (O3). Which from four of theprincipal greenhouses gases are human activities results(CO2, CH4, N2O, and the halocarbons). These gases
increase concentrations of the long-lived greenhouse gases(LLGHGs). Greenhouse gases affect the temperature of the
Earth. Since the beginning of the Industrial Revolution, theburning of fossil fuels have contributed to the increase incarbon dioxide in the atmosphere from 280ppm to 390ppm,
despite the uptake of a large portion of the emissions
through various natural "sinks" involved in the carbon cycle.
In the past few years, many workers have noted that the
combined effect on climate of the increase in theconcentrations of a large number of trace gases could rivalor even exceed the increasing concentration of carbon
dioxide. Atmospheric CH4 is the second most important
anthropogenic greenhouse gas after CO2. CH4’s mixing
ratio has increased by a factor of 2.5 compared topreindustrial levels and reached almost 1,800 ppb today.
The direct radiative forcing of anthropogenic CH4 is
0.48W/m2 that is almost one third that of anthropogenic
CO2 (1.66W/m2) [1][2]. CH4 emissions are separated
into anthropogenic and natural sources. According to
Lelieveld et al. (1998) and IPCC (2001), natural sources
amount is only one-third of the total CH4 emission budget,which the most important natural source is wetland.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013valid because of the use of Thermal And Near infrared
Sensor for carbon Observation-Cloud and Aerosol Imager(TANSO-CAI). GOSAT can only provide us four years ofdata, which is not sufficient, so we use it as SCIAMACHY
data supplement (Fig. 1).

2 DATA
The World Data Centre for Greenhouse Gases(WDCGG) is one of the WDCs under the Global
Atmosphere Watch (GAW) program. It serves to gather,archive and provide data on greenhouse gases (CO2, CH4,
CFCs, N2O, surface ozone, etc.) and related gases (CO,NOx, SO2, VOC, etc.) in the atmosphere and ocean, as
observed under GAW and other programs. Vegetation Index(VI) have trended to estimate a large number of vegetation
properties such as LAI, biomass, chlorophyll concentrationin leaves. The enhanced vegetation index(EVI) is an`optimized` index designed to enhance the vegetation signal
with improved sensitivity in high biomass region andimproved vegetation monitoring and reduction inatmosphere influences. Global Land Cover by National
Mapping Organization (GLCNMO) has twenty land coverclasses using 16-day composite MODIS data of 2003.The
SCanning Imaging Absorption spectroMeter for
Atmospheric CHartographY (SCIAMACHY) instrument(Burrows 1995) is a part of the atmospheric chemistry
payload of the European Space Agencies (ESA)
environmental satellite ENVISAT, launched in March 2002.
SCIAMACHY is the first satellite instrument that measuresnear-infrared spectra in the 1–2.4μm spectral range fromspace at high spectral resolution. The SCIAMACHY nearinfrared spectra contain atmospherically interesting
molecules such as CH4, CO2, CO, and H2O, of which thebulk resides in the troposphere. Retrieval algorithms
applied to this wavelength range include the Iterative
Maximum A Posteriori-DOAS (IMAP-DOAS) algorithm(Frankerberg 2005). The Greenhouse Gases Observing
Satellite (GOSAT) instrument was launched in January
2009 with the aim of measuring the column amounts of
CO2 and CH4. GOSAT has a CO2 target to achieve 4 ppm
accuracy for a three month regional average using a
combination of short-wavelength infrared (SWIR), andinfrared (IR) channels (Kuze 2009).

a)b) Interpolated image
Fig. 1. Cloud Free and Interpolation CH4 concentration
SCIMACHY
3.2 Change in methane concentration on land
Figure 2 shows the concentration of CH4 by terrestrial
latitude band for nine years. CH4 concentration in the
Northern Hemisphere mid-latitude regions is higher thanother regions. It increased approximately by 20ppb from2003 (1750ppb) until 2011(1780ppb).Mainly in September,the high concentration areas are distributed on the paddy
fields of Asia. It shows that in the rice harvest period, CH4concentration raised up to 1780ppb max. CH4 concentrationin the high latitudes of the north hemisphere is 1680ppb,
near the equator is 1740ppb and in the south hemisphere islower than 1670ppb. After 2007 every year, from January to
February, it becomes clear that the CH4 concentration was

3 ANALYSIS AND RESULT
3.1 Data ProcessFrankenberg's method was used to reduce cloud effectfrom SCIAMACHY data. By using a cloud free pixel data,
a 15-day composite data was made to understand the CH4
source and its circulation. In addition, this data wasspatially and temporally interpolated. As a result, the data
was made up in 24 scenes per year during a period from2003 to 2011. After removing the clouds, GOSAT data wasdistributed as a monthly data. Cloud free GOSAT data is© ISAROB 2013

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increasing near the equator. There were no concentration
changes of CH4 from 2003 to 2006 but from 2007 to 2011
we understand that it was increasing.

a)CH4 concentration

Fig. 2. Land methane concentration in time series from2003 to 2011
3.3 CH4 emission concentration
CH4 emission concentration is defined as the emissionof land CH4 concentration. Time series changes in the CH4
concentration of land can be relatively compared. it isdifficult to compare the CH4 emission concentration
because of the different background concentration of CH4.
Therefore, we can assume that a very small amount of CH4
is emitted from the sea surface, that CH4 concentration of
the sea is used as a same latitude background. The
difference of CH4 concentration between land and sea isequal to the CH4 emission concentration of land at thesame latitude (Fig. 3). As a result, it is cleared that a big
emission concentration of CH4 was found not only in the
Northern Hemisphere paddy fields but also in the Southern
Hemisphere broadleaf evergreen areas (Central Africa andSouth America)(Fig. 4).

b) CH4 emission concentration
Fig. 4. CH4 concentration (a) and CH4 emission
concentration (b) in January 2004
3.4 Relation between CH4 emission concentration area and Landcover type
We understood that CH4 emission concentration
increased in the Southern Hemisphere from 2007. Figure 6
shows CH4 average emission in 2009 and the vegetation
map that was used to determine the geographical features of
the CH4 emission concentration areas. From that weunderstood that there are a lot of broadleaf evergreen areas
near the equator of the southern hemisphere, which emits
50-80ppb/year of methane (b). CH4 emission concentration
areas of the northern hemisphere are consistent with the
distribution of paddy fields which emits 80ppb/year (c).
And because of the clouds in Northern hemisphere highlatitude areas, we don’t dispose of enough data to extract
information about CH4 emission concentration on wetland
areas.

Fig. 3. CH4 emission concentration© ISAROB 2013

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4 CONCLUSIONa)

In this study, we examined separately the changes inmethane concentration in land and sea areas using a timeseries SCIMACHY. Paddy field in Southeast Asia showed
the highest among the CH4 concentration areas by 1780ppb.
According to the MODIS NDVI comparison, the CH4
concentration increases by the paddy growth. And in theharvest time, the CH4 concentration raises its maximum.
Methane concentration in the sea becomes higher graduallyduring 9 years. It reaches its maximum at the mid-latitudeof the northern hemisphere, but becomes lower as it goes tothe polar regions. Assuming that CH4 does not emit in thesea, the increase in CH4 concentration of sea areas iscaused by the flowing CH4 emitted in land. The differenceof land and sea methane concentration is the emission ofland CH4 concentration. According to the land CH4
emission concentration, the high CH4 emission
concentration areas are not only in paddy fields(80ppb/year) but also broadleaf evergreen areas in SouthAmerica and Central Africa(50- 80ppb/year).
Finally, we removed the seasonal variation of CH4
concentration from land during 9 years to investigate theCH4 growth rate. As a result, in most of land areas, thegrowth rate of CH4 concentration is 3-5ppb/year, but insome areas in Brazil, Indonesia and China is more than
6ppb/year. Next, it is necessary to search the cause of thisgrowth rate increasing in these 3 areas.

CH4 average emission concentrationb）Broadleaf Evergreenc) Paddy field
Fig. 5. The comparison of the CH4 emission concentrationand Land Cover Type by MODIS
3.5 The growth rate of CH4 concentration
We investigated the CH4 concentration’s growth rateduring 9 years of its data that reduces a seasonal change
effect. As a result, in almost of land, there was a trend of 35 ppb/year, but especially, in Brazil, China and Indonesia
we noticed that it increased to more than 6ppb/year (Figure7). According to the WMO-GAW, the average growth rateis 2.2ppb/year during 10 years until 2009. From 2003 to2006 there was no CH4 growth, but announced that from2006 it has been increasing by about 6ppb/year. This meansthat there is no big difference from our results.

ACKNOWLEDGEMENT
This research was supported by JSPS KAKENHI Grantin-Aid for Scientific Research (C) and the Research projectof Tokyo University of Information Sciences for thesustainable development of economic and social structure
dependent on the environment in eastern Asia.

REFERENCES[1] IPCC, 2007, Climate Change 2007: The PhysicalScience Basis. Contribution of Working Group I to the
Fourth Assessment Report of the Intergovernmental Panelon Climate Change,S. Solomon, D. Qin, M. Manning, Z.
Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller(Eds) (Cambridge, UK: Cambridge University Press)[2] IPCC, 2001, Climate Change 2001: The Scientific
Basis. Contribution of Working Group I to the Third
Assessment Report of the Intergovernmental Panel on
Climate Change, J.T.Houghton, Y. Ding, D.J. Griggs, M.
Noguer, P.J. van der Linden, X. Dai, K. Maskell and C.A.
Johnson (Eds) (Cambridge, UK: Cambridge University
Press)

Fig. 6. CH4 growth rate during 9years© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Estimating a floodwater from MODIS time series and SRTM DEM data
Youngjoo Kwak1, Jonggeol Park2, and Kazuhiko Fukami1
1

International Center for Water Hazard and Risk Management, Tsukuba 305-8516, Japan
2
Tokyo University of Information Sciences, Chiba 265-8501, Japan(Tel: 81-29-879-6779, Fax: 81-29-879-6709)
1

kwak55@pwri.go.jp

Abstract: Extreme climate event, such as heavy rainfall and Typhoon, is anticipated to escalate extreme floods. In fact, many
flood plains in the Asian-Pacific region have already experienced a rising number of flood disasters. In this circumstance, realtime flood mapping with automatic detection technique is increasingly important in emergency response efforts. However,current mapping technology is still limited in accurately expressing information in flood areas such as inundation depth andextent. For this reason, the authors attempt to improve a floodwater detection method with a simple algorithm for a better
discrimination capacity to discern flood areas from turbid floodwater, mixed vegetation areas, snow, and cloud. In this research,pixel classification was performed on the Moderate Resolution Imaging Spectroradiometer (MODIS) time series images (8-daycomposites, MOD09A1, 500-m resolution) for floodwater detection. The purpose of this image classification was to estimate aflood area based on the spatial distribution of a nation-wide flood from near real-time MODIS images coupled with a digital
elevation model (DEM). Moreover, the authors improved the accuracy of the water extent boundary using a 8-direction
tracking algorithm to find the same level between flood-prone area and non-flood area. The results showed the superiority ofthe developed method in providing instant and accurate flood mapping by using three algorithms, which indicates decision tree,
modified land surface water index (MLSWI) and 8-direction tracking based on DEM data.
Keywords: Flood mapping, floodwater, MODIS, DEM.
ρ1.24 are the reflectance at 0.85 μm and 1.24 μm wavelength,
respectively. NDWI is used to derive water fraction and aflood map from MODIS data. Xiao et al. [14, 15] used themulti-temporal MODIS images (8-day composites,
MOD09A1, 500-m resolution) to extend the approach tolarger regions, south China, South and Southeast Asia. They
used a relaxed set of criteria, LSWI+0.05 > EVI or
LSWI+0.05 > NDVI, to identify flooded paddy rice fields.
The land surface water index (LSWI) is calculated using thespectral signals in shortwave infrared (SWIR) and in NIR
ranges to detect water at the soil surface [4].
The purpose of this study was to detect flood areas innear real-time accurately and rapidly, based on the spatial
distribution of nationwide flooding by using an improved
version of MODIS indices so that mapping the spatial andtemporal dynamics of flooding is possible. To determine thewater extent boundary more accurately, the authors improvean extraction method of surface water with a simplified
decision tree method using reflectance of MODIS bands 6(CH6) and 7 (CH7) acquired from a regional flooding. The
improved method was then applied to the Indus River basin
in Pakistan, which was selected as the prime research focus
area. The selected area suffered from a huge, severe floodcaused by abnormally heavy rainfall from late July to earlyAugust 2010.

1 INTRODUCTION
Over the last decades, the number of fatalities and thescale of economic damage caused by river floods haveconsiderably increased worldwide. Moreover, extreme
rainfall and typhoon are projected to increase the frequency
and magnitude of river floods in the future, which are likely
to cause a further increase in flood risk leading to morehuman and economic damage given the current population
growth and the ongoing accumulation of value assets inriver deltas. The Asia-Pacific region is particularly
vulnerable to the impacts of natural disasters [13]. Satellite
remote sensing is a useful tool for interpretation andanalyses of water bodies, including floodwater with costeffective, accurate monitoring at frequent time steps overlarge areas. Various remote sensing methods have been
introduced to detect surface water or estimate floodwater
during flooding while considering floodplain topography [4,7, 9, 12]. Flood detection is one of the classical themes ofsatellite-based remote sensing. Despite its usefulness,satellite-based remote sensing has also limitations,particularly, in spatial coverage and detection of mixed
floodwater. Gao [3] developed the Normalized Difference
Water Index (NDWI), a satellite-derived index from nearinfrared (NIR, band 2) and short wave infrared (SWIR,
band 6) bands: NDWI=(ρ0.85―ρ1.24)/(ρ0.85+ρ1.24), where ρ
represents the radiance in reflectance units. Both ρ0.85 and© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013index, Modified LSWI (MLSWI). Third step is a 8direction tracking algorithm based on DEM, which uses theflow direction of the same elevation pixels resulting fromthe previous two steps.

2 DATA
2.1 MODIS MYD09A1
The Moderated Resolution Imaging Spectroradiometer(MODIS) is an Earth Observing System (EOS) instrument
onboard the Terra and Aqua platforms, launched in
December 1999 and May 2002, respectively. The sensor
scans ±55° from nadir in 36 spectral bands. During eachscan, 10 along-track detectors per spectral band
simultaneously sample the earth. From its polar orbit,
MODIS provides day-time and night-time global coverage
every 1 to 2 days [8]. MODIS Surface Reflectance products
are the estimates of surface spectral reflectance as if they
were measured at the ground level in the absence of
atmospheric scattering or absorption. The MODIS L3 8-daycomposite surface reflectance product (MYD09A1)
provides reflectance values for bands 1-7 from the MODIS
Level 1B land bands, which are centered at 0.648 μm, 0.858
μm, 0.470 μm, 0.555 μm, 1.24 μm, 1.64 μm, and 2.13 μm at
500-meter resolution in a gridded level-3 product in thesinusoidal projection [10].

3.1 Decision tree method
Illustrations Decision tree classification is employed on
500 m MODIS reflectance images, in which regions ofinterest as samples are collected from each flood case fortrial, to generate the map by using a post-classificationwater detection process while comparing with SRTM Water
Body Data (SWBD) product. Decision tree classification
can be divided into two categories: water surface and dry
land surface. Water surface areas are considered to bepermanent water bodies such as rivers, lakes, and ponds
while dry land areas are considered to be non-water areasthat are never flooded. A water surface needs to satisfy thefollowing three conditions to be extracted as water areas:
band 7 (CH7) < 10%, band 1 (CH1) +10% > band 2 (CH2)and band 3 (CH3) < 20% (e.g., forest = 0.14, clean water =0.1, Fig. 1.A). The reflectance of bands 6 and 7 are lower
than the other bands in the case of surface water such as
clean water, muddy water, and floodwater, which reflect thetemporal-spatial pattern of water content (Fig. 1.B).

2.2 SRTM DEM
The Shuttle Radar Topography Mission (SRTM)
obtained elevation data on a near-global scale to generate
the most complete high-resolution digital topographic
database model of the Earth. SRTM consisted of a speciallymodified radar system that flew onboard the Space Shuttle
Endeavour during an 11-day mission in February of 2000. It
used dual radar antennas to acquire interferometric radar
data, processed to digital topographic data at 1 arc-sec
resolution. SRTM is an international project spearheaded by
the National Geospatial-Intelligence Agency (NGA) and the
National Aeronautics and Space Administration (NASA)[2]. The elevation layers, DEM, are based on a combinationof the original SRTM-3 and DTED-1 elevation models of
SRTM. The DEM is provided in geographic projection(latitude/longitude) referenced to the WGS84 horizontal
datum and EGM96 vertical datum. The DEM were acquired
from Hydrological data and maps based on the SHuttle
Elevation Derivatives at multiple Scale (HydroSHEDS) [6,11]. In this study, DEM is used to calculate potential flood
areas at 15 arc-second resolution (approximately 500
meters at the equator).

Fig. 1. Spectral reflectance of the land cover classification(A) and the characteristic of the turbid water in flooding (B)
3.2. Modified LSWI
After examining the water and land pixels using thedecision tree classification, the flood map is produced basedon the modified LSWI (MLSWI) by comparing it withNDVI, NDWI and LSWI, which are the most frequently
used and has been proved effective in detecting soil
moisture, vegetation and water-related objects, though theirdetecting ability is somewhat different depending on theircharacteristics [1, 5]. These indices are calculated asfollows:

R RED － R NIR
R RED  R NIR
R－ R SWIRNDWI  RED
R RED  R SWIRNDVI 

3 METHODOLOGY
The proposed three-step process reduces errors in the
direct extraction of water bodies including floodwater. First
step is a decision tree method. Second step is the new flood© ISAROB 2013

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(1)
(2)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

LSWI 

R NIR － R SWIRR NIR  R SWIR

hand, NDVI, NDWI and LSWI, which usually detect water
bodies at a critical reflectance ratio of over 0.5, fail todistinguish water from dry land in some cases, especiallywhen the area contains snow or cloud cover. The process of
integrated floodwater detection was made possible toestimate the water extent boundary using a 8-direction
tracking algorithm to find the same level between
floodwater area and non-flood area, because we foundparticularly an underestimation of floodwater pixels
obtained from MLSWI. Fig. 3 showed the superiority of thedeveloped method in providing instant and accurate
mapping of not only water extent but also indirect flood
detection for a 2010 extreme event. Flood map was
generated with 500 m resolution product in Fig. 3.b. On theother hand, the permanent water body was marked in Fig.
3.d. Moreover, the satellite-derived product uncertainties in
the Indus river basin were verified through ground gauge
stations by examining actual high water marks and ALOSPALSAR images.

(3)

where, RRED, RNIR, and RSWIRD are reflectance values of
MODIS bands 1, 2 and 7.
Compared with the extraction of the floodwater,
MLSWI was modified and proposed as a new algorithm for
identifying flood areas by using the combination of NIR(841-875 nm, band 2) with SWIR (1 628-1 652 nm, band 7).
Equation (4) for MLSWI used in this study is as follows:
1 - R NIR - R SWIR
(4)MLSWI Flood 
1 - R NIR  R SWIR
3.3. Tracking algorithm
After determining floodwater from MLSWI, tracking
algorithm for calculating the floodwater boundary isapplied to each pixel as the height difference between
floodwater area and non-flood area. The floodwater flows
into the next pixel with the lower DEM among 8-direction
pixels (Fig. 2.). The relative height of floodwater pixel(Floodwater_DEM) indicates the potential flood inundation
depth of a given target pixel with the accuracy of 1 m. In
the case of Floodwater_DEM ≧ Non-flood_DEM, Fig. 2
shows that floodwater area is calculated because it meansthat flooding is expected to occur over such pixels.

Fig. 2. Flood map from the MLSWI and DEM
algorithm in the Indus river flood case

4 RESULTThis study focused on the characterization of floodindex for the flood mapping by using a comprehensive
approach within a whole basin, solely based on remotelysensed data sources via open internet. The pixel-based
water detection for flood identification using training data,
and the application in flood area was obtained according to
the simple index model process with spectral influences
between land cover types water and land. As a result, the
MLSWI threshold, the critical value of the reflectance ratio
was found at 0.5 for detecting water bodies, such as cleanwater, muddy water, and turbid floodwater. On the other© ISAROB 2013

Fig. 3. Flood map from the MLSWI and DEM algorithm in
the Indus river flood case

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Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, M.
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water index for remote Sensing of Vegetation Liquid waterfrom space, Remote Sensing of Environment 58:257-266[4] Islam A.S., Bala. S.K. and Haque. M.A. (2010),
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basin, Thailand from MODIS images coupled with flood
Inundation level", IEEE-IGARSS2012, pp887-890[6] Lehner B., K, Verdin, Jarvis A. (2006), HydroSHEDSTechnical Documentation, Version 1.0, World Wildlife
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[7] Ordoyne C., Friedl M.A. (2008), Using MODIS data
to characterize seasonal inundation patterns in the Florida
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5 CONCLUSION
To conclude, the authors suggested to consider the
spatial resolution in horizontal and vertical directions for
detecting floodwater. We improved the accuracy ofdetecting flood areas in the Indus River basin from nearreal-time MODIS images of 500 m resolution by using a
MLSWI and DEM tracking algorithm. Particularly MODIS
images have a higher practicability to detect water body
because of moderate-resolution optical sensor (250-500 m)
and high temporal resolution (daily). It is clear that MLSWI
is detecting directly floodwater during the period offlooding to reduce the ambiguity of floodwater. And
DEM surface has the effect of the buffer zone at theboundary of the flood extent which is typically 1 metervertical resolution in turbid floodwater and mixed
vegetation areas. Ultimately, this new approach fornationwide flood map in this study is expected to play animportant role to support emergency relief efforts in highrisk flood areas not only in a national level but also AsiaPacific region.
However, this approach has a limited evaluation of areal flood differed from a hydrological inundation
simulation. In addition, MODIS image is easily affected byweather conditions, especially by cloud cover. Also a pixelof 500 meter is too coarse to estimate the temporal
dynamics of flooding at different pixels, the authors areplanning to improve the spatial resolution in horizontal andvertical directions by using the integrated high resolution
images and self-organizing map (SOM) neural network fordetection of floodwater area on the land surface. Moreover,the combination of SAR and ASTER image should befurther considered to improve the accuracy of flood
detection.

ACKNOWLEDGEMENTS
This research was supported by the Ministry of
Education, Culture, Sports, Science, and Technology(MEXT)/Japan Society for the Promotion of Science (JSPS)
KAKENHI Grant-in-Aid for Young Scientists (B:24710211).

REFERENCES[1] Ceccato, P., Flasse, S., Tarantola, S., Jacquemond, S.,
and Gregoire, J.M. (2001), Detecting vegetation watercontent using reflectance in the optical domain. Remote
Sensing of Environment 77:22–33[2] Farr T., P. Rosen, E. Caro, R. Crippen, R. Duren, S.
Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, D.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Adaptive learning neural networks for system identificationof a magneto-rheological damper
Chia-Nan Ko1, Guan-Yu Liu2, Yu-Yi Fu3, and Pi-Yun Chen4
1,2,3

Nan-Kai University of Technology, Taiwan
National Chin-Yi University of Technology, Taiwan

41

t105@nkut.edu.tw

Abstract: In this article, adaptive learning neural networks (ALNNs) are proposed to identify nonlinear systems. In the
proposed NNs, integrating support vector regression (SVR) and adaptive learning algorithm is adopted to optimize thestructure of neural networks. In the evolutionary procedure, first, SVR is adopted to determine the number of hidden layer
nodes and the initial structure of the NNs. After initialization, adaptive learning algorithm (ALA) with nonlinear time-varying
learning rate is then applied to train NNs. In ALA, a computationally efficient optimization method, particle swarm
optimization (PSO) method, is adopted to simultaneously find optimal learning rates. Due to the advantages of SVR andadaptive learning algorithm, the proposed NNs (SVR-ALNNs) have good performance for identifying a magneto-rheological(MR) damper system. Simulation results are illustrated the feasibility and superiority of the proposed SVR-ALNNs.
Keywords: adaptive learning neural networks, support vector regression, magneto-rheological damper system.

1 INTRODUCTION

2 SVR-BASED INITIAL NNs

Due to the characteristic of structure simplicity, lowpower requirement, large force capacity, and high dynamic
range, a magneto-rheological (MR) damper attracts attentions
as developed semi-active control devices for structuralcontrol applications recently. In order to describe theperformance of an MR damper, Spencer et al. in 1997
proposed a phenomenological model based on a Bouc-Wenmodel [1], Chang and Chang and Zhou [2] developed aneural network model, Zhou and Chang [3] proposed anadaptive fuzzy control, and Du et al. designed a model of an
MR damper by evolving radial basis function networks [4]. A
nonlinear black-box model was proposed to identify an MR
damper system to design a force-sensorless control method[5].
In this article, adaptive learning neural networks (ALNNs)
are proposed to identify an MR damper system. In the
proposed NNs, integrating support vector regression (SVR)
and adaptive learning algorithm is adopted to optimize thestructure of neural networks. In the evolutionary procedure,
first, SVR [6, 7] is adopted to determine the number ofhidden layer nodes and the initial structure of NNs. Afterinitialization, ALA with nonlinear time-varying learning rateis then applied to train NNs. In the adaptive learningalgorithm, a computationally efficient optimization method,
particle swarm optimization (PSO) method, is adopted tosimultaneously find optimal learning rates. Due to theadvantages of SVR and adaptive learning algorithm, theproposed NNs (SVR-ALNNs) have good performance for
identifying the MR damper system. Simulation results areillustrated the feasibility and superiority of the proposed
SVR-ALNNs.

Neural networks can be used to estimate the input–output
relation of a nonlinear system. In this paper, radial basis
function neural networks (RBFNNs) are adopted because
they have a simple structure. When the Gaussian function ischosen as the radial basis function, RBFNNs can be
expressed in the form

© ISAROB 2013

214

L

⎛

xˆ − m i

i =1

⎝

2σ i

yˆ j (t + 1) = ∑ wij exp ⎜ −xˆ (t ) = [ xˆ1 (t )

where

[

yˆ (t ) = yˆ1 (t )"2xˆ m (t ) ]T"yˆ p (t )⎞⎟ for j = 1, 2, " , p (1)⎠2

is the input vector;] is the output vector; w is theTijsynaptic weight; m i and σ i are the center and width ofGaussian functions respectively; and L is the number ofGaussian functions, which is also equal to the number ofhidden layer nodes.
It is very difficult, if not impossible, to solve the above
problem directly. Usually, the initial values of L, wij , m i ,and σ i are chosen first. Then a training algorithm is applied
to the NNs to search for the optimal combination of thesevalues in an iterative manner.
In support vector machine (SVM), an SVR method canapproximate an unknown function. An output of the
RBFNNs and its corresponding training pairs will be used for
simulations. Meanwhile, assume that a set of basis functions,g l ( x), l = 1, 2, " , M , is given. Then the problem of function approximation is transformed into one of finding theparameters of the basis linear expansion.
Mf ( x, θ ) = ∑ θ l g l ( x ) + b
l =1

(2)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where θ is a parameter vector to be identified and b is aconstant to be determined.
From [8], one sees that the solution is to find f ( x, θ )that minimizes
1 N(3)
R (θ ) = ∑ Lε ( yi − f ( x i , θ ))N i =1subject to the constraint

θ

2

<C

algorithm (ARLA) is adopted in the training procedure of thepresented NNs by Chung et al. [7]. A cost function for theannealing learning is defined here as
1 N
(k )
(11)
J j (t ) = ∑ ρ ⎡⎣ e j (t ); β (t ) ⎤⎦ for j = 1, 2, " , p
N k =1where⎛ x ( k ) − mi 2 ⎞e j (t ) = y j − ∑ wij exp ⎜ −
⎟
2
⎜
⎟
2σ i
i =1
⎝
⎠
L

(k )

(4)

where Lε (⋅) is the ε-insensitive loss function defined as⎧ 0

for e ≤ ε⎩e −εotherwiseLε (e) = ⎨

(k )

(12)

(k )

t is the epoch number, e j (t ) is the error between the kth(k )desired output y j(5)of the annealing learning NNs at epoch

for some non-negative numbers ε .
By using the Lagrange multiplier method, it has beenshown in [6] that the minimization of (3) leads to a dual
optimization problem. A set of basis functions g l ( x r ) ist for the jth input–output training data in an identification
system, β (t ) is a deterministic annealing schedule acting as
the cut-off point, and ρ (⋅) is a logistic loss function definedasreplaced via the kernel function.

ρ [e j ; β ] =(k )MK ( x r , x s ) = ∑ g l ( x r )g l ( x s )(6)Then the dual optimization problem can be expressed asfollows:
Minimize

RMSE =N∑ (α + α ) −∑ y (α − α )Q (α , α ) = ε
*

*

r

*

r

r

r =1

1

r

r =1

∑

N

*

*

*

r

r =1

r

r

∑ (e )
N
(k )

∂J j
∂wij
∂J j

Δm i = −η m

∂m i

M

*

*

2

j

r =1

and the solution is in the form of the following linear
expansion of kernel functions [6]f ( x, α , α ) = ∑ (α l − α l )K ( x, x l ) + b

β

N

1

Δwij = −η w

(8)

r

2

2

⎥ for j = 1, 2, " , p⎦(9)

Δσ i = −ησl =1Note that some of the (α l − α l ) values are not zeros and the*for j = 1, 2, " , p

∂J j
∂σ i

=−

ηw

ϕ j (e j ; β ) =(k )In this study, since the Gaussian function is used as thekernel function, (9) can be rewritten as∑ ϕ (e ; β ) ∂w
j

N k =1

=−
=−

(k )

(k )

ηm

N

ij

p

(k )

j =1 k =1

p

(k )

N

∑ ∑ ϕ j (e j ; β )∂ρ (e j ; β )∂e jN∑ ∑ ϕ j (e j ; β )

N

ησ

(15)

j

(k )

j=1

k =1

∂e j

(k )

∂m i

∂e j

(16)

(k )

∂σ i

(17)

(k )

=

ej

1 + (e j )(k )2β (t )(18)

where η w , η m , and ησ are the learning rates for the# SV

synaptic weights wij , the centers m i , and the widths σ i ,(10)respectively, and ϕ (⋅) is usually called the influencefunction. The adaptive annealing schedule has the following
properties [7]:(A) β initial , β (t ) for the first epoch, has large values.

where l is the number of SVs, λl = (α l − α l ) ≠ 0 , and x l*denotes SVs. Comparing (10) with (1), # SV l , λl , andx l in (10) can be regarded as L, i , wij , and m i in (1),(B) β (t ) → 0

respectively.

+

for t → ∞ .

In the ALA, a nonlinear time-varying evolution concept is
adopted over iterations, in which the learning rates η w , η m ,and ησ with a high value ηmax and nonlinearly decreases to

3 ADAPTIVE LEARNING NNs
To overcome the existing problems in robust backpropagation learning algorithms, the annealing robust learning© ISAROB 2013

(14)

∂e j

N

(k )

corresponding vectors x l are called support vectors (SVs).

⎛ x − xl 2 ⎞f ( x, λ ) = ∑ λl exp ⎜ −⎟+b2
2σ l ⎠
l =1
⎝

(13)

of Gaussian functions are updated, respectively, asN∑ α =∑ α 0 < α , α < C

⎣

(k )

Based on a gradient descent type of learning algorithm,
the synaptic weights wij , the centers m i , and the widths σ i(7)(α r − α r )(α s − α s ) K ( x r , x s )2 r , s =1
subject to the constraint+*(e j ) ⎤

k =1

r

N

⎡

ln ⎢1 +

To evaluate the performance of training NNs, the root meansquare error (RMSE) of the training data is adopted anddefined as

l =1

N

β

215

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013ηmin at the maximal number of epochs, respectively. Thismeans that the mathematical expressions are given as shownasz = −γ x − y z z(21)

where epochmax is the maximal number of epochs and h is thecurrent number of epochs. In the updated procedure,appropriate functions for the learning rate and η w , η m , andησ can promote the performance of ALNNs. However,simultaneously determining the optimal combination of pw,
pm, and pσ is a time-consuming work. An efficient
evolutionary algorithm, PSO, will be adopted to determine
the optimal solution ( pw, pm, pσ ).

(c0 + c1 )© ISAROB 2013

[α z + c0 x + k 0 ( x − y )],(27)

stiffness at large velocities; x0 is the initial displacement ofspring k1 associated with the nominal damper force due tothe accumulator; γ , ζ , A are hysteresis parameters for theyield element, and α is the evolutionary coefficient. A setof parameters which is obtained by Spencer to characterize
one MR damper using experimental data and a constrained
nonlinear optimization algorithm is listed in Table 1.
The data is generated using a sinusoidal displacement
function with an amplitude of ±1 cm and a frequency of
3Hz and a sinusoidal voltage function with mean value of
1.6 V, an amplitude of 0.5V and a frequency of 0.5 Hz. The
time duration for this validation data is 4s and the timeincrement is 0.002s which amounts to a total of 2000 training
data sets.

The identification scheme of a nonlinear system is
depicted in Fig. 1, training input-output data are obtained byfeeding a signal x(k) to the system and measure its
corresponding output y(k+1) Then subject to the same input
signal, the objective of identification is to construct an SVRALNNs using PSO method, which produces an outputyˆ ( k + 1) to approximate y(k+1) as closely as possible.
In this section, a magneto-rheological (MR) damper
system adopted to verify the feasibility of the proposed SVRALNNs. When applying the proposed algorithm, thepopulation size, the maximal iteration number, and the
maximal epoch number are chosen to be 30, 100, and 1000,
respectively. The variables pw, pm, and pσ in learning
rate functions (19) to (21) are all chosen as real numbers in
the range [0.1, 0.5] . Meanwhile, the values of η max
and η min are set as 4.0 and 1.0, respectively.
Example:A phenomenological model has been proposed by
Spencer [1] to portray the behaviour of a prototype MR
damper. This phenomenological model is based on a Bouc–
Wen model, the model as shown in Fig. 2, and is governed by
the following seven simultaneous equations:
F = c1 y + k1 ( x − x0 ),(22)1c0 = c0 + c0 u ,viscous damping coefficients observed at large and low
velocities, respectively; k 0 is the gain to control the

4 SIMULATION RESULTS

y =

(26)

b

(28)
where F is the force generated by the MR damper; x is thedisplacement of the damper; y is an internal pseudodisplacement of the MR damper; u is the output of a firstorder filter; v is the command voltage sent to the current
driver; k1 is the accumulator stiffness; c0 and c1 are the

(20)

pσ

h
⎛
⎞
ησ = η min + ⎜ 1 −⎟ (η max − η min ) ,⎝ epochmax ⎠c1 = c1 + c1 u ,bu = − λ (u − v ),

pm

h
⎛
⎞
η m = η min + ⎜ 1 −⎟ (η max − η min ) ,⎝ epochmax ⎠

(24)
(25)

a

(19)

n

− ζ ( x − y ) z + A( x − y ),α = α a + α bu,
a

pw

h
⎛
⎞
η w = η min + ⎜ 1 −⎟ (ηmax − ηmin ) ,⎝ epochmax ⎠n −1y(k +1)e(k +1)x(k )yˆ (k +1)

Fig. 1. The proposed PSO-based SVR-ALNNs scheme for
identification of a nonlinear system.
y Bouc-Wen

c1

x

k0

c0
k1
F

(23)

Fig. 2. Phenomenological model of MR damper.

216

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013sets are obtained as (pw, pm, pσ)=(4.3658, 0.01, 1.3637) and(pw, pm, pσ) =(2.4125, 0.1035, 3.0082) for ε = 0.2 andε = 0.1 respectively. Meanwhile, the final values of RMSE
with ALNNs are found to be 0.0070, and 0.0038 shown in
Table 2. To show the superiority of the proposed SVRALNNs, the comparison of the proposed NNs with SVRARLA-NNs has been shown in Table 2. From Table 2, theproposed SVR-ALNNs have obtained promising results after
only 1000 training epochs.

Table 1. Parameters for an MR damper
Parameter Value
Parameter Valuea21.0 Ns / cm

αa

140 N / cmbc03.50 Ns / cm V

αb

695 N / cm V

k0

46.9 N / cmγ

363 cm−2a

283 Ns / cm

ζ

363 cm−2

c1

b2.95 Ns / cm V

n

2

k1

5.00 N / cm

λ

190 s

x0

14.3 cm

A

301

c0

c1

−1

5 CONCLUSIONSThis paper presents the integrating SVR and ALA to train
NNs for identification of an MR damper system. With theinitial structure of the NNs using SVR method, the nonlinear
time varying learning rates are simultaneously determined by
PSO method to perform the ALA. Then the optimal NNs are
obtained to identify the MR damper system. From thesimulation results, one can conclude that the proposed SVRALNNs using PSO method have good performance for
identification of the MR damper system using only few
training epochs. Meanwhile, the superiority of the proposed
Problem 2:
SVR-ALNNs with nonlinear learning rates over SVR-ARLAWith the nonlinear learning rates, the ALA is adopted to train
NNs with fixed learning rates for identification has been
NNs, in which the optimal learning rates are determined by verified. The proposed SVR-ALNNs can be further extended
linear time-varying evolution PSO method [9]. The optimal to identify more complex systems.

Problem 1:
In this investigation, when applying SVR, various parametersof ε = 0.2 and ε = 0.1 in (7) for C = 100 in (8) are
adopted to determine the initial structure of NNs, respectively.
In the ARLA, various learning rates, 1.0 ≤ η ≤ 4.0 , are used
to train the NNs. After 1000 training epochs, the RMSE values
for various learning rates are obtained, respectively. The
details of the simulation results are shown in Table 2.

Table 2. The values of RMSE (14) for an MR damper system after 1000 training epochs, in which ARLA with various
learning rates and ALA with time-varying learning rates are applied to train NNs.
ARLA (learning rate η )
ε
ALA
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.2
0.00920.00900.01140.00830.01010.01520.00700.00810.1
0.00560.00580.00620.00800.00830.01110.00380.0049ACKNOWLEDGMENT
This work was supported in part by the National Science Cou
ncil, Taiwan, R.O.C., under grants NSC 99-2221-E-252-017.

REFERENCES[1] Spencer Jr BF, Dyke SJ, Sain MK, Carlson JD (1997),
Phenomenological model for magnetorheological dampers.
Journal of Engineering Mechanics 123(3):230-238[2] Chang CC, Zhou L (2002), Neural network emulationof inverse dynamics for a magnetorheological damper. Journalof Structural Engineering 128(2):231-239[3] Zhou L, Chang CC (2003), Adaptive fuzzy control for
nonlinear building-magnetorheological dramper system.
Journal of Structural Engineering 129(7):905-913[4] Du H, Lam J, Zhang N (2006), Modelling of a magnetorheological damper by evolving radial basis function
networks. Engineering Applications of Artificial Intelligence© ISAROB 2013

19:869-881[5] Truong DQ, Ahn KK (2011), Nonlinear black-box
models and force-sensorless damping control for damping
systems using magneto-rheological fluid dampers. Sensors &Actuators: A. Physical 167(2):556-573[6] Vapnik VN (1995), The Nature of Statistical Learning
Theory. Springer, New York
[7] Chuang CC, Jeng JT, Lin PT (2004), Annealing robustradial basis function networks for function approximation
with outliers. Neurocomputing 56:123-139[8] Pei L, Liu JB, Guinness R, Chen YW, Kuusniemi H,
Chen RZ (2012), Using LS-SVM based motion recognition
for smartphone indoor wireless positioning. Sensors 12:.
6155-6175[9] Ratnaweera A, Halgamuge SK, Watson C (2004), Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficients. IEEE Transactions on
Evolutionary Computation 8(3):240-255

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Identification of RBFNs with SVR under censored data
Yu-Yi Fu1, Yu-Fen Fu2, Kuo-Lan Su3 , Chia-Nan Ko4 and Jhih-Cheng Chang5
1,4,5

Department of Automation Engineering, Nan Kai University of Technology
Tsaotun, Nantou 542, Taiwan
2
Department of Information Management, China University of Technology
Wunshan, Taipei 116, Taiwan
3
Department of Electrical Engineering,National Yunlin University of Science and Technology
Douliou, Yunlin 640, Taiwan(email:1t098@nkut.edu.tw;2yfen@cute.edu.tw;3sukl@yuntech.edu.tw;4t105@nkut.edu.tw;5cjcb@nkut.edu.tw)
Abstract: In this paper, we proposed RBFNs with SVR to identify a distribution under censored data. Radial basis function
networks (RBFNs) with one hidden layer and rapid convergence speed used to identify system generally. Support vector
regression (SVR) with optimal quadratic programming to determine the number of hidden nodes and the initial parameters ofkernel and the initial weights of RBFNs. By annealing robust learning algorithm to tune the parameters of kernel and theweights and to overcome the error measurement due to data censored. The simulation result of bivariate normal distribution
identification under censored data shows the feasibility of proposed method.
Keywords: Radial basis function networks, Support vector regression, censored data, bivariate normal distribution.
convergence speed and the ability coping with censoreddata can identify perfectly.

1. INTRODUCTION
Interval censored data analysis is important insurvival analysis and biomedical statistics. It has lower
limit and upper limit, the lower limit replaces the data
values when the data values are lower or equal to lower
limit, the upper limit replaces the data values when the data
values are greater or equal to upper limit. Therefore the data
we get are not correct data but censored data. Zheng et al [1]proposed Hybrid Monte Carlo Markov chain and
Hamiltonian method to estimate parameters of interval
censored data. Cheng and Mordeson [2] investigated thefuzzy estimation of the parameter of underlying probability
distribution for total failure time of censored data. In thisarticle we proposed RBFNs with SVR to identify a
distribution under the censored data.
Radial basis function networks (RBFNs) with one
hidden layer and rapid convergence speed used to identify
system and predict generally [3-7]. But the number ofhidden nodes, the initial parameters of the kernel and theinitial weights of the networks not decided mathematicallyyet. Besides, the data we obtained sometimes contain theerror measurement due to data censored.
Vapnik [8] proposed Support vector regression (SVR)
approach in 1995, by the ε insensitive loss function canmake use of a small subset of the training data, called the
support vectors (SVs), to approximate the desired outputs
within a tolerance band. That is, the SVR uses the quadratic
programming optimization to determine the initial structureof the traditional RBFNs.
In this article, the purpose is to identify a system
under censored data in one of the variables. First, an ε
SVR is used to determine the number of hidden nodes, theinitial parameters of the kernel, and the initial weights of
the RBFNs. Then the algorithm is applied to tune theparameters of radial basis functions and the synaptic
weights. It is expected that the proposed method has fast

© ISAROB 2013

2. RBFNs FOR THE IDENTIFICATION OF THENONLINEAR SYSTEM
Assume that the unknown nonlinear system isexpressed byy (t + 1) = f ( y (t ),⋅ ⋅ ⋅, y (t − n), u (t ),⋅ ⋅ ⋅, u (t − m)),(1)
where y (t ) is the output of the system, u (t ) is the inputof the system, f (⋅) is the unknown nonlinear function to be
estimated by a neural network, and n and m are thestructure orders of the system. Our purpose is to find a
identification modelyˆ (t + 1) = fˆ ( y (t ),⋅ ⋅ ⋅, y (t − n), u (t ),⋅ ⋅ ⋅, u (t − m))(2)
to approximate y (t + 1) as close as possible.
A structure of the RBFNs consists of an input layer, ahidden layer of radial basis functions and a linear output
layer. When the radial basis functions are chosen as
Gaussian functions, an RBFNs can be expressed in the formLx − mi

i =0

2σ i2

yˆ j (t + 1) = ∑ wij exp(−
where

ŷ j

is

the

2

)

(3)

jth

output,x = ( y (t ), y (t − 1),⋅ ⋅ ⋅, y (t − n), u (t ), u (t − 1),⋅ ⋅ ⋅, u (t − m))isthe input to the neural networks, wij ,0 ≤ i ≤ L,1 ≤ j ≤ p,
are the synaptic weights, m i ,0 ≤ i ≤ L, and σ i ,0 ≤ i ≤ L,
are the centers and the widths of Gaussian functions
respectively, and L is the number of the Gaussianfunctions, in which we can find that L also denotes thenumber of hidden nodes.

3. INITIAL STRUCTURE BY SVR APPROACH

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

An SVR approach is used to approximate an unknown
function from a set of (input, output) samples{( x i , y i ), i = 1, L, N }. Suppose that a set of basis functionsmrepresented ask =1(α k − α k ) ’s are not zeros and the corresponding vectors*{g ( x k )}kN=1 is given, there exists a family of functions thatx k ’s are called support vectors (SVs).
In this paper, the Gaussian function is used as thekernel function. And let f s ( x, λ ) = f ( x, λ ) − b, hence, (9)can be rewritten as

can be expressed as a linear expansion of the basis
functions. The theme is then be changed into finding theparameters of the following basis linear expansionNf ( x, θ ) = ∑ θ k g ( x k ) + b ,

(4)

k =1

<C,

σk

r =1

r =1

N

r =1

r =1

x k andin (13) can be regarded as the L, i, wij , m i and σ i

4. ANNEALING ROBUST LEARNING
ALGORITHM FOR UPDATING PARAMETERS
When utilizing the RBFNs for the identification ofsystems, the goal is to minimize
1 N
J N (h) = ∑ ρ[ek (h); β (h)] ,(14)
N i =1where
ek (h) = y (k ) − yˆ s ( x k ),
(15)

(8)

h is the epoch number, ek (h) is the error between the kth
desired output and the kth output of the RBFNs at epoch hand ρ (⋅) is a logistic loss function and defined asρ [e k ; β ] =∑ α r* = ∑ α r , 0 < α r* , α r < C for r = 1,2,⋅ ⋅ ⋅, N . (9)βln[1 +(10)

Hence the optimization of (8) is rewritten as
Minimize(A) β intial , β (h) for the first epoch, has large values;NN(B) β (h) → 0+ for h → ∞ ;

r =1

r =1

(C)

Q(α , α * ) = ε ∑ (α r + α r* ) − ∑ y r (α r* − α r )
(11)
1 N *
*
+ ∑ (α r − α r )(α s − α s ) K ( x r , x s ).
2 r ,s =1
It was shown in Vapnik [8] that the solution of the SVR
approach is in the form of the following linear expansion ofkernel function

for anyhepoch, where

c

is a

the width σ i of Gaussian function are updated as∆wij = −η

(12)

k =1

This means that the parameter θ k in equation (4) can be© ISAROB 2013

β ( h) = c hconstant.
Based on the gradient-descent kind of learningalgorithms, the synaptic weights wij , the centers m i andNf ( x, α , α * ) = ∑ (α k* − α k ) K ( x , x k ) + b.

(e k ) 2

],
(16)
2
β
where β (h) is a deterministic annealing schedule acting
like the cut-off points. In this paper, the annealing robust
learning algorithm (ARLA) is applied to train the proposed
RBFNs. In the ARLA, the properties of the annealingschedule β (h) have [10]:It proposed by Vapnik [8] and Smola et al. [9] and theinner product of basis function g ( x r ) ⋅ g ( x s ) is replaced
via the kernel function
K ( xr , xs ) = g( xr ) ⋅ g( xs ) ,(13)

the initial parameters m i and σ i , of the RBFNs are
determined via the SVR approach.

Q(α , α * ) = ε ∑ (α r + α r* ) − ∑ y r (α r* − α r )N),in (3), respectively. From the above derivation, we can findthat the number of hidden nodes L , the initial weights wij ,

(6)

1 N
+ ∑ (α r* − α r )(α s* − α s ) g ( x r ) ⋅ g ( x s ) .
2 r ,s =1
subject to the constraint

2σ k2

From equation (13) and (3), # SV , k , λ k ,0for e ≤ ε,(7)Lε (e) =  e − ε otherwise
for some previously chosen nonnegative number ε .
By using the Lagrange multiplier method, it was
shown in [8] that the minimization of (5) leads to thefollowing dual optimization problem.
Minimize
N

k =1

2

and x k are SVs.

where Lε (⋅) is the ε -insensitive loss function defined asNx − xkwhere # SV is the number of SVs, λ k = (α k* − α k ) ≠ 0

identified and b is a constant to be found.
Vapnik [4] firstly proposed the ε -SVR approach.
The solution for the theme is to find f ( x , θ ) thatminimizes
1 N
R(θ ) = ∑ Lε ( y i − f ( x i , θ )) ,(5)
N i =1subject to the constraint2# SV

f s ( x, λ) = ∑ λk exp(−

where θ = (θ1 ,θ 2 ,⋅ ⋅ ⋅, θ m ) is a parameter vector to beθ∑ (α k* − α k )g ( x k ) . Note that only some of

219

N
∂J N
∂e
= −η ∑ ϕ (ek ; β ) k
∂wij
∂
wij
k =1

(17)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

∆m i = −η

N
∂J N
∂e
= −η ∑ ϕ (ek ; β ) k
∂m i
∂m i
k =1

(18)

∆σ i = −η

N
∂e
∂J N
= −η ∑ ϕ (ek ; β ) k
∂
σi
∂σ i
k =1

(19)

∂ρ (ek ; β )
ek
=
,
∂ek
1 + (e k ) 2 / β ( h )(20)

ϕ (e k ; β ) =parameters of the kernel and the initial weights of the
proposed RBFNs. Then the ARLA is applied to tunes theparameters of the kernel and the weights of the bivariate
normal distribution. From the result indicated that the
proposed method can be used as a reliable technique for the
training of bivariate normal distribution.

ACKNOWLEDGMENTS

where η is a learning constant.

5. SIMULATION RESULTS

This work was supported in part by the National Science
Council,Taiwan,R.O.C.,
under
grants
NSC101-2221-E-252-009.

In this section, the root mean square error (RMSE) of
the training data is used to measure the performance of the
proposed networks. The RMSE is defined as

REFERENCES[1] Zheng G, Liu J, Zhang G (2010), A Novel Hybrid
MCMC Method for Interval-Censored Data, International
Conference on Computer Application and System
Modeling, Taiyuan, China, Oct 22-24, 2010, pp. V12-66 V12-69[2] Cheng SH, Mordeson JN (2008), Fuzzy Confidence
Estimation of the Parameter Involving in the Distribution of
the Total Time on Test for Censored Data, Fuzzy
Information Processing Society, Annual Meeting of theNorth American, May 19-22, 2008, pp. 1 - 5[3] Fu YY, Wu CJ, Ko CN, Jeng JT (2011), Radialbasis function networks with hybrid learning for system
identification with outliers, Applied Soft Computing, 11:3083–3092[4] Fu YY, Wu CJ, Ko CN, Jeng JT, Lai LC (2009),
ARRBFNs with SVR for prediction of chaotic time series
with outliers, Artif Life Robotics, 14:29–33[5] Liu J, Ling D, Wang S (2012), Ammunition Storage
Reliability Forecasting Based on Radial Basis Function
Neural Network, International Conference on DigitalObject Identifier, Chengdu, China, June 15-18, 2012, pp.
599 – 602
[6] Venkateswarlu RLK, Kumari RV, Jayasri GV (2011),
Speech recognition using Radial Basis Function neuralnetwork, International Conference on
Electronics
Computer Technology, Kanyakumari, India, April 8-10,
2011, pp. 441 – 445[7] Hao Y, Tiantian X, Paszczynski S, Wilamowski BM(2011), Advantages of Radial Basis Function Networks for
Dynamic System Design, IEEE Transactions on Industrial
Electronics, 58 (12): 5438 – 5450
[8] Vapnik V (1995), The Nature of Statistical Learning
Theory, Springer, Berlin[9] Smola AJ, Schölkopf B (1998), From regularization
operators to support vector kernels. Neural Inf. Process
System 10:343–349[10]Chuang CC, Su SF, and Hsiao CC (2000), Theannealing robust backpropagation (BP) learning algorithm.
IEEE Trans. Neural Networks 11(5): 1067–1077

N

RMSE =∑( y i − yˆ i ) 2

i =1

,
(21)
N
where y i is the desired output and ŷ i is the output ofthe proposed method.
Example:A bivariate normal distribution is defined as

2


1
 x − µ x f ( x, y ) =exp − 2(1 − ρ 2 )  σ x 
2πσ σ 1 − ρ 2
(22)

x y

1

2

 y − µy 
 x − µ x  y − µ y    ,
− 2 ρ + σy 
 σ x  σ y  

where µ x and µ y are the mean of x and y , σ x2 andσ y2 are the variance of x and y , and ρ is thecorrelation coefficient of x and y . We suppose that x
data have censored, and the upper limit is equal to 3, thelower limit is equal to -3. The diagram of exact data andcensored data are shown in Fig. 1 and Fig. 2. The proposed
networks are chosen asyˆ (t + 1) = fˆ ( y (t ), y (t − 1), x(t ), x(t − 1), x(t − 2)) . (23)50 simulation data points are generated from equation(19). The parameters in ε -SVR are set as C=10, theGaussian kernel function with ε = 0.95 , σ = 2 , with thehidden nodes (i.e. the number of SVs) is obtained as 15.
Based on the initial structure and the learning constant is
0.05, after 3000 epochs training, the final training output,
the error are shown in Fig. 3 and Fig. 4, and the final
RMSE is 0.0242. From the simulation results show that the
proposed method can overcome the error measurement due
to censored data and attain a good identification.

6. CONCLUSIONSIn this paper, an SVR based the RBFNs with ARLA
for the identification of bivariate normal distribution isdeveloped. We firstly utilize the SVR approach todetermine the number of hidden nodes, the initial© ISAROB 2013

220

5

5

4

4

3

3

2

2

1

1

0

y

y

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

0

-1

-1

-2

-2

-3

-3

-4

-4

-5
-5

-4

-3

-2

-1

0
x

1

2

3

4

-5
-5

5

Fig. 1. The diagram of example for exact data.

-4

-3

-2

-1

0
x

1

2

3

4

5

Fig. 3. The diagram of example (dot scatter) and the final
result of the identification example (circle scatter) undercensored data.

5
4

0.2

3
2

0.15

1
y

0.1
0
0.05
error

-1
-2
-3

0
-0.05

-4
-0.1
-5
-5

-4

-3

-2

-1

0
x

1

2

3

4

5
-0.15

Fig. 2. The diagram of example for censored data.

-0.2
-5

-4

-3

-2

-1

0

1

2

3

4

5

Fig. 4. The error of training under censored data.

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Drift Algorithm Second Order Sliding Mode Control for a Synchronous
Reluctance Motor
Wen-Bin Lin1,2, Huann-Keng Chiang3, and Yi-Chang Chang3
Graduate School of Engineering Science & Technology, National Yunlin University of Science & Technology, Yunlin
64002, Taiwan, R.O.C.
2
Department of Optoelectronic Engineering, Far East University, Tainan 74448, Taiwan, R.O.C.
3
Department of Electrical Engineering, National Yunlin University of Science & Technology, Yunlin 64002, Taiwan,
R.O.C.
(Tel: 886-5- 5342601 ext: 4247, Fax :+886-5-5312065)

1

3

chianghk@yuntech.edu.tw

Abstract: This paper shows the design of a drift algorithm second order sliding mode controller (SOSMC) for asynchronous reluctance motor. The second order sliding mode control is an effective tool for the control of uncertain
nonlinear systems since it conquers the main shortcomings of the classical sliding mode control, namely, the large
control effort and the chattering effect. Its theory implies simple control laws and assures an improvement of the sliding
accuracy with respect to conventional sliding mode control. This paper proposes a novel scheme that based on thetechnique of drift algorithm second order sliding mode control. First, the SOSMC is obtained by mathematics. Finally,
the presentation of the proposed method is verified by simulation. The proposed SOSMC shows the robustness for the
motor parameters variation and the elimination of chattering effect.
Keywords: Drift Algorithm, Second Order Sliding Modes, Synchronous Reluctance Motor, Chattering Effect.
SynRM modeling is discussed in Section 2. In Section 3,
integral variable structure sliding mode controller isintroduced. In Section 4, drift algorithm second-ordersliding mode controller is described. In Section 5,
simulation results show that the proposed algorithm
controller
provideshigh-performance
dynamic
characteristics and robustness under parameter variationand external load disturbances. Finally, conclusions arepresented in Section 6.

1 INTRODUCTION
The motor control system with the high robustness
is an important issue in research. Synchronous
reluctance motors (SynRMs) have a mechanicallysimple and robust structure. They can be used in highspeed and high temperature environments. The rotor
circuit of the SynRM is open circuit such that the flux
linkage of SynRM is directly proportional to the statorcurrents. The torque of SynRM can be controlled byadjusting the stator currents. Therefore, there has beenrenewed interest in SynRM [1-3].
Sliding mode control (SMC) has attracted increasing
attention in recent years because it is an effective androbust technology for parameter variation and external
disturbance rejection. It has been applied to robot andmotor control [3,4-6]. Sliding mode control (SMC) is arobust control for nonlinear systems. However, sliding
mode is a mode of motions on the discontinuity set of adiscontinuous dynamic system. Therefore, reducing thechattering is very important for SMC. The second-ordersliding mode technique has the same properties ofrobustness to uncertainties of model and external
disturbances. Second-order SMC (SOSMC) [7]
improves the chattering phenomenon. Due to few
literatures about SOSMC in SynRM control application,
therefore, it has valuable on investigation in SynRM
control application for SOSMC.
Distinct from the conventional first order SMC, the
SOSMC is belonging to the region of higher-ordersliding mode (HOSM). Levant [7] had discussed thetheory of HOSM. HOSM control has been applied to
motor, and automatic docking [8-10].
The rest of this paper is organized as follows.

© ISAROB 2013

2 MODELING OF THE SYNRM
The d-q equivalent voltage equations of ideal
SynRM model with a synchronously rotating rotor
reference frame are shown in Fig. 1:
ids

Rs− ωr Lqiqs
+
−

iqs

Ld

Vqs

Vds

Rs

ω r Ld ids

+

−

Lq

−

−

Fig.1. The d-q axis equivalent-circuit of SynRM
diVds = Rsids + Ld ds − ωr Lqiqs
(1)
dt
diqs
Vqs = Rsiqs + Lq
+ ωr Ld ids(2)
dt
The corresponding electromagnetic torque Te is:()

3
Pole Ld − Lq idsiqs(3)
4
The corresponding motor dynamic equation is:dω r
Te − TL = J m+ Bm ω r(4)dt
where Vds and Vqs are direct and quadrature axis
Te =

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

terminal

voltages,

respectively; i ds

and

i qs are,of (8) can be defined as equation (10):u (t ) = ueq (t ) + u n (t )(10)where ueq (t ) is used to control the overall behavior of
the system and u n (t ) is used to suppress parameter
uncertainties and to reject disturbances. By making
mathematical calculation, we get the overall control
u (t ) as equation (10) [3]:1 ∗
ηu (t ) =ω& r − a 0ω r + ce (t ) + ( K + ) sgn( S ) (11)b0borespectively, direct axis and quadrature axis terminal
currents or the torque producing current; Ld and Lq
are the direct and quadrature axis magnetizing
inductances, respectively; R s is the stator resistance;and ω r is the speed of the rotor. Pole , T L , J m , and

Bm are the poles, the torque load, the inertia momentof the rotor, and the viscous friction coefficient,
respectively. In this paper, the maximum torque control(MTC) strategy [3,4] is adopted. The torque current
commands are shown in equation (5) and (6) [3]:
Te

∗
ids
=

(

3
Pole Ld − Lq
8

)

π 
cos 
4Teπ 
sin  3
4
Pole Ld − Lq8∗iqs
= sgn (Te )

(

)

[

where f ≤ K and η > 0 .

4 DRIFT ALGORITHM SECOND-ORDER S
LIDING MODE CONTROLLER(5)

In conventional sliding mode control design, thecontrol target is let the system states move into sliding
surfaces S = 0 . But a second-order sliding mode
controller aims for S = S& = 0 . The system states
converge to zero intersection of S and S& in state
space. Drift algorithm mainly develops relative oneorder system for reducing chattering phenomenon [7].
The state trajectory of S and S& phase plane is shown in
Fig.2. The drift algorithm in the phase trajectories on
the 2-sliding plane are characterized by loops withconstant sign of the sliding variable y1 , furthermore it
is characterized by the use of sampled values of theavailable signal y1 with sampling period δ .

(6)

3 INTEGRAL VARIABLE STRUCTURESLIDING MODE CONTROLLER
We can rewrite the equation (4) asdωr  Bm 
1
ωr +
= −
(Te − TL )dt  J m Jm= aωr + b(Te − TL )(7)

= (a0 + ∆a)ωr + (b0 + ∆b)(Te − TL )= a0ωr + b0 (u(t) + f )whereBa ≡ − m = a0 + ∆a
Jm
b≡

]

S&

1
= b0 + ∆b
Jmu ≡ Tef ≡S

1(∆aωr + ∆bu (t ) − bTL )b0

J m ≡ J 0 + ∆J
Bm ≡ B0 + ∆B
The subscript index “ o ” indicates the nominal system
value; “ ∆ ” represents uncertainty, and f represents
the lumped uncertainties.
Define the velocity error as e(t ) = ω r∗ − ω r , where

Fig.2. The phase plane trajectory of drift algorithm
Now consider the following uncertain second ordersystem: y&1 (t ) = y2 (t ))(12) y& 2 (t ) = ϕ (x(t ), y (t ), t ) + γ (x(t ), y (t ), t )ν (t )in which ϕ (⋅ ⋅ ⋅) and γ (⋅ ⋅ ⋅) are uncertain functions
with the upper and lower bounds of equation (13).

ωr∗ is the velocity command. The velocity error
differential equation of SynRM can be expressed as
equation (8):de(t )= ω& r∗ − a0ωr − b0 [u(t ) + f ]dt(8) ϕ (x(t ), y (t ), t ) ≤ Φ0 < Γm ≤ γ (x(t ), y (t ), t ) ≤ ΓMLet

S = e(t ) + ct∫ e(t )dτ , c > 0−∞(9)

By the control rule of equation (14), we can define thiscontrol method [8,9]：The input control u (t ) (the electromagnetic torque Te )© ISAROB 2013

(13)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

− u,
if u > Uv(t ) = − Vm sgn( ∆y1i ), if y1∆y1i ≤ 0 ; u ≤ U
− V sgn( ∆y ), if y ∆y > 0 ; u ≤ U (14)
1i
1 1i
 M

controller speed control block diagram of the SynRM
servo drive are shown in Fig. 3.
Outer loop speed control

where Vm and VM are suitable positive constantsVsuch that Vm < VM and M sufficiently large, and
Vm

ωr∗ +
−

SOSMC

inner loop torque control

u = Te∗ids∗

limiter

Strategy +

MTC

∗
qs

i

∆y1i = y1 (t i ) − y1 (ti − δ ) , t ∈ [t i ,t i +1 ) . The corresponding

vds∗

+
−

−

PI Current
Controller

hardware driver circuitvα∗
2φ / 2φ

v

∗
qs

v

∗
β

V
S
V
gating
P
W

M

ids
iqs

sufficient conditions for the convergence to the sliding
manifold are rather cumbersome [8] and are omitted
here for the sake of simplicity. After substituting y2

ωrfor ∆y1i a first order sliding mode on y2 = 0 would beddt

ias ics
3φ / 2φ−∑θr

Fig.3. Drift algorithm SOSMC speed control
block diagram of SynRM servo drive

achieved. This implies y1 = const., but, since anartificial switching time delay appears, we ensure a real
sliding on y2 with most of time spent in the set

y1 y2 < 0, and therefore, y1 → 0. The accuracy of thereal sliding on y2 = 0 is proportional to the samplingtime interval δ ; hence the duration of the transient
process is proportional to δ −1 .This algorithm has noovershoot if parameters are chosen properly [8].
Equation (4) can be rewritten as equation (15):
(15)
dω r
1
(Te − TL − Bmω r )
=
dt

Jm

We define state variable as shown in equation (16):t x1 (t ) = −∞ x2 (τ )dτ(16)
 x2 (t ) = e(t ) = ω r∗ − ω r∫Fig.4. Simulation velocity response of the SMC due toωr∗ = 600 rev/min without machine load in the nominal
motor inertia and friction coefficient condition

We define sliding function y1 , and y2 as y1 = x2 + cx1
 y2 = y&1(17)Then, the system equation can be expressed as y&1 (t ) = y 2 (t )


 B

B
1 &
 y& (t ) = ω&&r∗ + m ω& r∗ +  − m + c  x& 2 +TL + v(t )
 J

 2
J
J
m
m
m




(18)

where


Bm ∗  Bm
1 &∗+ c  x& 2 +TLω& r +  −ϕ (⋅) = ω&&r +
Jm
Jm

 Jm

γ (⋅) = 11 &v(t ) = − J Te
m


(19)

Fig.5. Simulation velocity response of the drift
algorithm SOSMC due to ωr∗ = 600 rev/min withoutmachine load in the nominal motor inertia and friction
coefficient condition

According to (19) Te is calculated from theintegration of v(t) which is a switching signal defined in(14), so improving the chattering problem in SOSMC
control of SynRM.

5

The proposed controller was applied to a 0.37 kw
three-phase SynRM whose nominal parameters andproposed controller parameters are shown in Table 1. In
Fig.4, the simulation velocity response of the SMC due

SIMULATION RESULTS

A block diagram of the experimental SynRM drive
and the drift algorithm second-order sliding mode
© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

to ωr∗ = 600 rev/min without machine load in thenominal motor inertia and friction coefficient condition
is depicted. In Fig.5, the simulation velocity response of
the proposed SOSMC due to ωr∗ = 600 rev/min

acceptable in application.

ACKNOWLEDGMENT
This work is supported by the National Science
Council in Taiwan, Republic of China, through grant
NSC100-2221-E-224-002.

without machine load in the nominal motor inertia andfriction coefficient condition is depicted. The velocity
response of SOSMC is smoother than the convention
SMC.
In Fig.6, the simulation velocity response of the
SOSMC due to ωr∗ = 600 rev/min under an 0.3 Nt-m

REFERENCES[1] Bianchi N., Bolognani S., Bon D., and Pre M. D.
(2009), Rotor flux-barrier design for torque ripple
reduction in synchronous reluctance and PM-assisted
synchronous reluctance motors. IEEE Transactions on
Industry Applications 45(3):921-928[2] Kim W. H., Kim K. S., Kim S. J., Kang D. W.,
Go S. C., Chun Y. D., and Lee J. (2009), Optimal
PM design of PMA-SynRM for wide constantpower operation and torque ripple reduction.
IEEE Transactions on Magnetics 45(10):46604663[3] Lin W. B., Chen C. A., and Chiang H. K. (2011),
Design and implementation of a sliding mode controller
using a Gaussian radial basis function neural network
estimator for a synchronous reluctance motor speed
drive. Electric Power Components and Systems 39(6):548- 562.
[4] Shyu K. K., and Lai C. K. (2002), Incremental
motion control of synchronous reluctance motor via
multisegment sliding mode control method. IEEE
Transactions on Control Systems Technology 10(2):169-176[5] Huang Y. S., and Sung C. C. (2010),
Implementation of sliding mode controller for linear
synchronous motors based on direct thrust control
theory. IET Proceedings on Control Theory &Applications 4(3):326-338[6] Bartolini G., Ferrara A., Levant F., and Usai E.
(1999), Variable Structure Systems, Sliding Mode andNonlinear Control. Springer-Verlag[7] Levant A. (1993), Sliding order and sliding
accuracy in sliding mode control. International Journalof Control 58(6):1247-1263[8] Bartolini G., Ferrara A., Levant F., and Usai E.
(1999), On second order sliding mode controllers, in
Variable Structure Systems, Sliding Mode andNonlinear Control, Lecture Notes in Control andInformation Sciences K. D. Young and U. Ozguner,
Eds., Springer-Verlag, London Ltd: 329-350[9] Tournes C., and Shtessel Y. (2007), Automatic
docking using second order sliding mode control. 2007
American Control Conference, New York City, USA,
July 11-13, 2007 , pp.3455-3460[10] Mohamadian M., Pedram M. M., andAshrafzadeh F. (2004), Digital second order sliding
mode control for a synchronous reluctance motor.
Proceeding of 2004 IEEE Industry Applications
Conference, Seattle, U.S., Oct 3-7, 2004, pp.1899-1902

machine load at the beginning and an 0.9 Nt-m machineload at 3seconds is added for the 2 times nominal caseof the motor inertia and friction coefficient condition ispresented. Hence, the SOSMC is a robust controller andimprove the chattering phenomenon when the system
has external disturbances and parameter variations.

Fig.6. Simulation velocity response of the drift
algorithm SOSMC due to ωr∗ = 600 rev/min under an
0.3 Nt-m machine load at the beginning and an 0.9Nt-m
machine load at 3 seconds in the 2 times nominal caseof the motor inertia and friction coefficient condition
Table 1. Parameters of SynRM (0.37kW)
Rs = 4.2 Ω
Pole = 2

Lqs = 0.181 H

Lds = 0.328 H

J m = 0.00076 Kg-m

2

Bm = 0.00012 Nt-m/rad/sec

6 CONCLUSION
In this paper, a drift algorithm second-order sliding
mode speed control design for robust stabilization anddisturbance rejection of SynRM is presented. The
simulation results show good performance of SOSMC
under uncertain load subject to variaitons in inertia andsystem frcition. Also with SOSMC, there is no need foracceleration feedback. The proposed SOSMC law
shows the advantage of continuous control signal which
eliminates the chattering effect apparently and is more

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Development of the Multi-detection system using Multi-sensor Fusion
Algorithms
Ying-Yao Ting1,3 and Huan-Sheng Wang2
1

Institute of Engineering Science and Technology, National Kaohsiung First University of Science of Technology, Taiwan
Department of Computer and Communication Engineering, National Kaohsiung First University of Science of Technology,
Taiwan3
Department of Information Management, WuFeng Universityof Science of Technology, Taiwan
E-mail: u9615903@ccms.nkfust.edu.tw

2

Abstract: The paper develops the multi-detection system using multi-level surveillance structure. The system contains active
detection modules, passive detection modules, a supervised computer, an image system and an intelligent home. The passive
detection modules contain wire/wireless detection modules and appliance control modules, and decide the event to be true or
not using fusion algorithms, and transmit detection signals to the supervised computer. Mobile robots are active detection
modules and carry various sensors to search dangerous events. Each mobile robot transmits the real-time event signal to thesupervised computer and the other mobile robots via wireless RF interface. The image system detects fire source using Otsu
algorithm. The system integrates wire/wireless passive detection modules, mobile robots and image system to detect firesource using weighted average method. If the fire event occurrence, the supervised computer calculates the dangerous gradeusing logical filter method according to the signals of detection modules in the first step, and transmits the position of the fire
event to the other mobile robots. The assigned mobile robots move to the event location for double check autonomously, andtransmit the detection results to the supervised computer. The supervised computer gives the final decision according to the
feedback signals in the second step. Finally, we present some experimental scenarios using active detection modules, passive
detection modules and image recognition for the fire detection in the intelligent home.
Keywords: mobile robots, wireless RF interface, Otsu algorithm, weighted average method, logical filter method

I. INTRODUCTION

modules and passive detection modules to detect gas leakage inthe chamber [4].
In the fire detection, Ding proposed a hierarchical framework
for a more efficient fire detection using modified Hidden Markov
Model (HMM) [1]. Ha concentrated on the smoke detection
system in video for early fire alarming using block-based smoke
detection algorithm consist of three basic steps [5]. Liu usedtemperature, smoke concentration and CO concentration to detectfire source using multi-sensor fusion algorithm [6]. Yang used thetrained (Support Vector Machine) SVM with RBF kernel and
SMO algorithm to recognize image of the fire source [7].

Intelligent building and home can provides safety,
convenience and welfare for human living in the 21st century. The
most important issue of the intelligent building is fire detection.
The fire disaster hurts many people’s life and brings great losses tothe society in every year. Fire detection becomes more and more
importance in surveillance system [1]. In generally, the measured
values of the security system is redundant and complementally
information, and uses fusion algorithms to enhance system
reliability and certainty, and constructs the safety detection
network using multiple processing protections. The
multi-detection security system solves the negligence of the users.
The paper proposes the multi-detection system that contains
various detection modules and image system, and develops theintelligent home to implement the functions of the proposed
security system.
In the past literatures, many experts research the security
system of the intelligent home. Azegami [2] described asystematic approach to intelligent building design. Chung and Fu
expected to set up the standard of appliances and communication
protocols, and proposed a complete system architecture withintegrate control kernel to construct an intelligent building [3]. Su
designed multi0level security system using active detection© ISAROB 2013

II. SYSTEM ARCHITECTURE
The system architecture has four levels to be shown in Fig. 1.
There is passive detection level, active detection level, systemsupervised level and remote supervised level [8]. The active
detection modules are mobile robots. The robot carries various
sensors to detect the environment moving in various floor of theintelligent home. The passive detection modules contain detection
modules, position system and elevator system. The detection
modules include wire/wireless detection modules and image
system. Position system locates the position of each mobile robot
moving in the intelligent home. The elevator carries mobile robots
to search dangerous event in various floors. The system supervised

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of the dangerous event. The label “C” displays the information
between the supervised computer and detection modules. The
label “D” can sets the threshold of the passive detection modules,
control command of the elevator and mobile robots. The label “E”
displays the real-time status of the intelligent home, detection
signals, image signals and mobile robots, and displays the alarm
position of the home.

level receives detection signals from active detection modules andpassive detection modules via wire/wireless interface, and decides
the event to be true or not. The remote supervised computercommunicates with the supervised computer via Internet.
In the intelligent home system, there are many detection
modules and appliance control modules. These modules are
independent and autonomous, and work cooperation. Eachdetection module of intelligent home system can transmits themeasurement values, parameter values and decision results to thesupervised computer, and uses multi-level processing to enhance
the accuracy of the event detection, and speaks Chinese language
using the voice module for real-time event status.
In the active detection modules, there are more merits to use
multi-robot cooperation capabilities to such a large fleet of robots.
In general, the control structure of the large fleet mobile robots isclassified centralized control and decentralized control. A
centralized control requires robust and permanent communication
capabilities between all mobile robots and the supervised system.
A decentralized control only requires local communication
between robots and the supervised system. Each mobile robot of
the multiple robots’ system will communicates with the otherrobots [9]. We combine the centralized control and thedecentralized control in the intelligent home system.

III. DETECTION MODULES
The detection processing of intelligent home system is shownin Fig. 3, and contains active detection modules and passive
detection modules. We use dash line to represent the wirelessinterface. The detection processing of the system has two steps.
We explain the detection processing as following. In the first step,
the system uses wire detection modules, wireless detection
modules and image system as the passive detection modules todetect dangerous event, and uses mobile robots as the active
detection modules to detect the same event simultaneously. These
detection modules transmit the decision signals to the supervised
computer using fusion algorithms, respectively. The supervised
computer decides the event to be true or not using weighted
average method. In the weighted average method, we set the same
weight value for the detection signals of these modules. The
decision rule is according to equation (1) and (2). Then we set a
threshold value for the detection module. The average value x isover than threshold, and we can say the event to be true. Otherwise
we can say no event condition. The ith measurements value of nsensors is presented xi , and the weight must be satisfied
0 ≤ ω i ≤ 1 isn

x = ∑ ω i xi

(1)

i =1

Fig. 1. The system architecturen∑ω = 1
i =1

i

(2)

Fig. 2. User interface
Fig 3. The detection processing

The user interface of the multi-robot system is shown in Fig. 2,
and contains five parts. The label “A” can programs thecommunication protocol between the supervised computer and themobile robots, and sets the communication port between the
supervised computer and the wire detection modules. The label“B” displays relation information from mobile robots and thewire/wireless detection modules, and records the time and location© ISAROB 2013

The supervised computer decides the dangerous event to betrue, and controls other mobile robot moving to the event position
for the second step, and transmits the event signal to the remotesupervised computer, too. The main role of the active detection
modules is mobile robots. The mobile robot is equipped with amicrochip (HT46RU25) as controller, and calculates the227The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

movement distance from the pulse numbers of the encoder, anduses the compass module and the IR distance module to computethe orientation and the relation distance from the elevator and thewall.
The mobile robot has the shape of cylinder, and uses IR
receiver to locate the position of the elevator. The position system
uses IR sensors to decide the location of each mobile robot, andcommunicates with the mobile robot through the supervised
computer. the mobile robot knows the real-time location via
wireless RF interface. The mobile robot can communicate with theothers robot and the supervised computer via wireless RF interface,and knows the location of each mobile robot, too.
We use flame sensor (R2686) in the passive fire detection
module. Flame sensors look for characteristic emissions of either
infra-red or ultra-violet light from the flames. Its peak wavelengthis 220 um and sensing wavelength is 185~260 um. The R2868 has
wide angular sensitivity (directivity) and can reliably and quickly
detect weak ultraviolet radiations emitted from flame due to use of
the metal plate cathode. It can detect the flame of a cigarette lighter
at a distance of more than 5 m. The R2868 is well suited for use inflame detectors and fire alarms, and also in detection of invisible
discharge phenomena such as corona discharge of high-voltage
transmission lines. We use three flame sensors to detect fire source,
and use the same reliability value to decide event. The block
diagram of the fire detection module is shown in Fig. 4. We use
logical filter method to compute the reliability of the fire detection
module. The AND filter has been applied could then be expected
to correspond to significant aspects of the environment. In asimilar manner, an OR filter could be used to reliably segment animage because all of the sensory information would be available
for use in the segmentation process. In the logical filter method,
we can use AND or OR filter on the fire detection module. Then
we can calculate the system reliability of AND filter ( RS ) andOR filter ( RP )RS = Π Ri (t )(3)RP = 1 − Π [1 − Ri (t )]

(4)

n

i =1

n

i =1

IV. EXPERIMENTAL RESULTS
We use the fire detection processing to implement the security
system of the intelligent home. We use passive fire module, image
system and fire based mobile robots to detect fire sources in theintelligent home. We use the candle to represent the fire source.
The intelligent home contains some passive detection modules,
image system (outside), a supervised computer and two mobile
robots. One is moving in the first floor (No. 1); the other is moving
in the second floor (No. 2).

(a)

(b)

Ri (t ) is the reliability of each flame sensor of the fire(c)Fig. 5. The experimental result of fire detection for step one

detection module. We can use the modules to integrate the fusion
results of passive detection modules and active detection modules,
and get the high accuracy of the security detection.

Fig. 6. The robot moves into and leaves the elevator
The passive fire module detects the fire source in the second
floor, and displays the fire symbol in the right side of Fig. 5(a). The
image system recognizes the fire source using the Otsu method,and decides the fire event to be true, and transmits the fire signal toFig 4. Block diagram of the fire detection module© ISAROB 2013

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in the security system, and can transmit real-time event signals tothe supervised computer. The supervised computer decides the fire
event to be true using fusion algorithm, and orders command tothe other mobile robots. The assigned mobile robots can move tothe position of the fire event doing double check. The
experimental results are very nice to decide the fire event
occurrence. In the future, we want to integrate more and more
detection modules, and cooperate with the multi-robot system, anduse multi-sensor fusion algorithms to enhance the accuracy. The
intelligent security can be applied in many fields, such as hospital,transportation, building and factory. We can extend the results tobe applied in gas leakage detection and intruder detection, too.

the supervised computer. The user interface displays the firesymbol, too. The mobile robot detects the fire source in the second
floor. The supervised computer decides the fire event to be trueusing weighted average method according to the signals of thepassive fire module, the image system and the mobile robot, andorders command to the other mobile robot that is moving in thefirst floor. The detection processing is step one. We can see the firesymbol is plotted in the robot (No. 2). The robot (No. 1) moves in
the first floor, and has not searched the fire source. The userinterface displays no fire symbol to be shown in Fig. 5(b).

REFERENCES[1] Ding J and Ye M (2011), A Novel Way for Fire Detectionin the Video Using Hidden Markov Model, IEEE International
Conference on Electronic & Mechanical and Information
Technology, pp.4413-4416.
[2] Azegami M and Fujixoshi H (1993), A systematic
Approach to Intelligent Building Design, IEEE Communications
Magazine, pp.46-48.
[3] Chung WL, Fu LC (2001), A Flexible, Hierarchical andDistributed Control Kernel Architecture for Rapid Resource
Integration of Intelligent System, IEEE International Conferenceon Robotics Automation, pp.1981-1987.
[4] Su KL, Liao YL, Chen CH and Yang YC (2012),
Multisensor Fusion Algorithm Based Intelligent Home Using
Multi-Team Mobile Robots, The Innovative Computing,Information and Control – Express Letters (ICIC-EL),Vol. 6, No.
5, pp.1283-1289[5] Ha C, Jeon G and Jeong J (2011), Vision-based Smoke
Detection Algorithm for Early Fire Recognition in Digital Video
Recording System, International Conference on Signal Image
Technology & Internet-Based System, pp. 209-212.
[6] Liu W, Wang F, Hu X and Yang Y (2011), Detection ofFire Based on Multi-Sensor Fusion, International Conference on
Computer Science and Network Technology, pp.223-227.
[7] Yang X, Wang J and He S (2012), A SVM Approach for
Vessel Fire Detection Based on Image Processing, International
Conference on Modelling, Identification and Control, June, 24-26,
pp.150-153.
[8] Chia SH, Su KL (2010), Multi-level Multi-sensor Based
Security System for Intelligent Home, International Symposium
on Artificial Life and Robotics (AROB 15th), Beppu, Japan, Feb.
4-6, pp.379-382.
[9] Liu MT and Yu PT (2010), Robust candidate purring
approach based on the Dempster-Shafter evidence theory for fast
corner detection with noise tolerance in gray-level image,International journal of Innovative Computing, Information andControl, Vol. 6, No. 7, pp.3131-3143.

(a)

(b)
Fig. 7. The experimental result of fire detection for step two
The mobile robot of the first floor receives the command fromthe supervised computer, and moves to the front side of theelevator using the IR distance module autonomously. The
movement scenarios are shown in Fig. 5(c). The assigned robot
controls the elevator via wireless RF interface, and moves into theelevator going up to the second floor. The elevator moves to theassigned floor to stop, and opens the doors to trip the mobile robot
leaving the elevator via wireless RF interface. The assigned
mobile robot leaves the elevator, and turns right 900 moving to thefire source. The movement scenarios are shown in Fig. 6(c).
The assigned mobile robot moves approach to the location of
the robot (No.2), and detects the fire event using flame sensor, andtransmits the event signal to the supervised computer to be shownin Fig. 7(a). The user interface displays fire symbol for the two
robots. The supervised computer receives the event signal to givethe final decision. The detection result presents the fire event to betrue according the logical filter method. The detection processing
is step two to be shown in Fig. 7(b). .
V. CONCLUSION
We have presented a multi-level based intelligent security
system to be applied in intelligent home. The controller of thedetection modules is HOLTEK microchip. We use multiple fusion
algorithms to enhance the accuracy for the fire detection. Mobile
robots, image system and passive detection modules are integrated© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Design a Feasible Docking Station for Mobile Robots
Yi-Lin Liao1, Li-Chun Lai2, Kuo-Lan Su3, Jr-Hung Guo1
1

Graduate Electrical Engineering, National Yunlin University of Science & Technology
Douliou, Yunlin, Taiwan,
2.
Bachelor Program in Robotics, National Pingtung University of Education
Pingtung City, Pingtung County 900, Taiwan
3.
Department of Electrical Engineering, National Yunlin University of Science & Technology, Taiwan(Tel: +886-5-5342601, Fax: +886-5-5312065)
E-mail: sukl@yuntech.edu.tw, g9910813@yuntech.edu.tw, g9710801@yuntech.edu.tw
Abstract: The article develops a docking station that has feasible movement and rotation function for mobile robots. The
docking station contains a docking structure, a limit switch, a charger, a power detection module and a wireless RF module.
The docking structure is designed with one active degree of freedom and two passive degrees of freedom. It rotates in the
Z-axis, and uses compression spring to move for various docking condition. The weight of the docking station is almost 6 kg,
and its length, and height, and width are 70cm, 50cm and 80cm. The maximum rotational angle and horizontal offset are 30
degree and 2 cm respectively. The power detection module is controlled by HOLTEK microchip. We calculate the power
values using redundant management method, and isolate the error values using statistical signal prediction method, and executethe auto-recharging processing for mobile robots. The processing can enhances the successful rate to guide the mobile robot
moving to the feasible docking station from various directions. In the experimental results, the power of the mobile robot isunder the threshold value. The mobile robot searches the landmark of the docking station using laser range finder (SICK). The
laser range finder guides the mobile robot approach to the docking station, and uses the adequate docking angle to be approachto the station.
Keywords: mobile robots, wireless RF module, HOLTEK microchip, redundant management method, statistical signal
prediction method, laser range finder

I.

INTRODUCTION

capabilities using mobile wireless sensor network gateway forhome security [6]. Oliveira proposed a method based on the
Extended Kalman Filter (EKF) to estimate the batteries state ofcharger [7]. Keshmiri proposed an opportunistic control approachto address the multi-robot, multi-rendezvous recharging problem.
The control strategy adapted attempts to take advantages of both
centralized and distributed methodologies [8]. How to design ageneral charging structure is an important problem for mobile
robots. The paper develops the feasible docking station that has
widely docking angle.

Mobile robots have been widely applied in many fields suchas factory automation, dangerous environment detection, officeautomation, hospital, entertainment, space exploration, farm
automation, military, education and security system and so on.
The power of the mobile robot is under the threshold value forworking in long time. The mobile robot executes auto-recharging
processing; otherwise users can not control the mobile robot, andsome dangerous event may be happened.
We have designed a power detection and diagnosis module
applying in the mobile robot using HOLTEK microchip [1,2]. The
module can detect multiple powers simultaneously, and extend theinterface function to transmit the data to the main controller of themobile robot via series interface, and reduce detection error using
redundant management method and statistically method, andpredicts the residual power to work [3].
In the past literature, many researches have been proposed
power detection methods. Malaiy and Su use IDD testing andestimating the effects of increased integration on measurement
resolution [4]. Frenzel proposed the likelihood ration test method
applying on power-supply current diagnosis of VLSI circuits [5].
Song presented the development and characterization of asurveillance robot with automatic docking and recharging© ISAROB 2013

II. SYSTEM ARCHITECTURE
The auto-recharging system is shown in Fig. 1 to be classified
two parts: one is designed to supply and detect power for target
device. The other receives the power to be a mobile robot. In thedocking station, there are a connection structure, a limit switch, alandmark, a power detection module, a wireless RF module and acharger. The landmark can guides the mobile robot to find out thedocking station using laser range finder. The power detection
module can detects and controls the current output, and decides thestatus of the charging current, such as over loading (short), no
current (shutoff) or normal current. The limit switch detects themobile robot to touch the docking station or not. The wireless RF

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module can communicates with the mobile robot, and transmits
and receives the real-time signals of the auto-recharging
processing. In the mobile robot, there are a charging connection
structure, a laser range finder, a wireless RF module, an
auto-switch, a power detection module and power system.
The mobile robot has the shape of cylinder and its diameter,height, and weight are 40 cm, 60 cm, and 30 kg, respectively. The
robot is equipped with an IPC (Industry Personal Computer) as themain controller. The power detection module is embedded in thedocking station and the mobile robot. The controller of the module
is HOLTEK microchip, and computes the exact power using
redundant management method. In the mobile robot, the module
measures the exact power using four current sensors, and isolates
the faulty measurement values, and predicts the residual power ofthe power system.

backward, turn right, turn left and stop to be shown in the label“D”. The label “E” sets the baud-rate of the laser range finder. The
label “F” displays the direction of the docking station. The outlookof the detected environment is plotted in the label “G” by the laser
range finder. The relation information of the LRF is shown in thelabel “H”.
The user interface of the auto-recharging system contains fiveparts to be shown in Fig. 3. The label “A” displays the measured
values of current and voltage, and computes the estimation value
and average value of the power system. The label “B” displays the
charging processing of the auto-recharging system, and contains
alarm, start charging and finished. The label “C” displays thereal-time information in the processing. Users want to stop andrestart the charging processing to shown in the label “D”. The
label “E” plots the current curve and the voltage curve of thecharging processing.

Fig. 1 The auto-recharging system

Fig. 3 The user interface of auto-recharging system

III. DOCKING STATIONThe overview of the docking station is shown in Fig. 4. The
docking station provides two contact points to connect the
charging connection structure of the mobile robot. The docking
station is designed with two passive DOF (Degree Of Freedom)and one active DOF. In the two passive DOF, it rotates on the
Z-axis, and uses compression spring to move on the x axis forvarious docking condition. The active DOF rotates on z axis, andcontrols the connection pins moving forward to touch theconnection structure of the mobile robot. The connection structureof the mobile robot is mounted on the back side of the mobile
robot, and places on the same side of the laser range finder. The
prototype of the docking station is shown in Fig. 4.
The recharging adapters of the mobile robot are at the inside of
the holes. Each hole is shaped as a cone in order to help thedocking smoothly. Since both the connection pins and adapters are
rigid, the docking station is designed for providing compliance forreasonable robot docking angle and offset. When the mobile robot
is approach to the docking station with offset and docking angle,
the guiding stick mounted on the docking station functions toFig. 2 The user interface of motion planning
The user interface is the motion planning of the mobile robot
to be shown in Fig. 2, and contains eight parts. The label “A” canprograms the communication protocol of the laser range finder
and wireless RF interface, and sets the communication port is 3 for
wireless RF interface. The label “B” is control command for themobile robot, and sets the motion mode of the mobile robot. The
label “C” displays the information of the IR sensors and thecompass. Users control the mobile robot moving forward,© ISAROB 2013

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guide the recharging pins for inserting into the adapters. The
maximum rotational angle and horizontal offset are 300 and 2 cm
respectively.
In the fig. 5, the mobile robot moves to the docking station
with offset angle. The guiding stick can’t touch the center of theconnection structure, and rotates on the z axis to guide theconnection pins to insert into the adapters. In the other condition,
extension sprints are used to bring the recharging pins backing to
the center after the robot disengages. A little entry offset and angle
are allowed when the robot is in the docking process. The
experimental result is shown in Fig. 6.

Fig. 7 The landmine detection
We explain the docking processing for two cases. The firstcase, the mobile robot locates on the right side of the station, anddetects the landmine on the left side to be shown in Fig. 8(a). It
computes the relation distance using the equation:

x = R cos θ(1)
The parameter R is the distance far from the docking station.
The mobile robots turns left θ , and moves the distance x . Thenthe mobile robot turns right 900 moving approach to the station.
The experimental scenario is shown in Fig. 8(b). In the other case,the mobile robot locates on the right side of the docking station.
The mobile robot executes the similar processing moving to thestation. The docking station is detected by the mobile robot, andlocates on the right side. The mobile robot turns left θ moving
the assigned distance. Then the mobile robot turns left 900 moving
approach to the station to be shown in Fig. 9.

Fig. 4 The docking station

Fig. 5 The docking processing with offset angle

Fig. 6 The docking processing with offset distanceIV. EXPERIMENTAL RESULTS
The mobile robot moves autonomously in the free space, anduses the power detection module to detect power variety, andcalculates the residual power. Then it computes the residual time
to be under the threshold time. The user interface of the mobile
robot displays to execute the auto-recharging processing. The
mobile robot searches the docking station using laser range finderaccording to the landmark. The landmark is fixed on the top of thestation to be shown in the right side of Fig. 7. In general, thedocking station is fixed on the front side of the wall. The landmark
is more near to the mobile robot than the wall. The mobile robot
decides the position of the station according to the information of
the laser range finder to be shown in Fig. 7.

© ISAROB 2013

(a)

(b)
Fig. 8 The docking processing from right side

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013detection module measures the charging current to be about 2.5A.
The user interface of the mobile robot displays the charging
processing to be shown in Fig. 10.

V. CONCLUSION
We have developed a feasible docking station of theauto-recharging system for the mobile robot, and finished a series
auto-recharging processing. The docking station uses the landmark
to guide the mobile robot, and adjusts two passive axis to connect
the charging pins of the mobile robot. The mobile robot uses laser
range finder to search the docking station, and moves to thedocking station according to the landmark. We program a newauto-recharging processing to enhance the successful rate of thedocking behavior for the mobile robot. In the docking processing,
the system detects the charging current of the docking station andthe mobile robot. The docking station communicates with themobile robot using wireless RF interface.

(a)

ACKNOWLEDGMENT
This work was supported by the National Science Council of
Taiwan, (NSC 101- 2221- E-224-007).

REFERENCES(c)
Fig. 9 The docking processing from left side[1] Su KL (2006), Automatic Fire Detection System Using
Adaptive Fusion Algorithm for Fire Fighting Robot, IEEE
International Conference on System, Man and Cybernetics, Grand
Hotel, Taipei, Taiwan, October, pp966-971.
[2] Chien TL, Guo JH, Su KL and Shiau SV (2007), Develop a
Multiple Interface Based Fire Fighting Robot, IEEE International
Conference on Mechatronics, Kumamoto, Japan, May 8-10,
WA1-B-3.
[3] Su KL, Tzou JH and Liu CC (2007), Development of aMultisensor-Based Residual Power Prediction System for Mobile
Robots, IEEE Workshop on Advanced Robotics and its Social
Impacts, Hsinchu, Taiwan, December 9-11, pp.114-119.
[4] Malaiya YK (1984), Testing stuck-on faults in CMOS
integrated circuits, Proceedings of International Conference on
Computer-Aided Design, pp.248-250.
[5] Frenzel JF (1994), Power-Supply Current Diagnosis of
VLSI Circuits, IEEE Transaction on reliability Vol. 43, No.1,
pp.30-38.
[6] Song G, Wang H, Zhang J and Meng T (2011), Automatic
docking system for recharging home surveillance robots, IEEE
Trans. on Consumer Eletronics, Vol. 57, No. 2, pp. 428-435.
[7] Oliceira MM, Galdames JPM, Vivaldini KTand Magalhaes
DV (2011), Battery state estimation for applications in intelligent
warehouses, IEEE International Conference on Robotics andAutomation, Shanghai, China, May 9-13, pp.5511-5516.
[8] Keshmiri S (2011), Multi-robot, multi-rendezvous
recharging paradigm: an opportunistic control strategy, IEEE
International Symposium on robotic and sensors environments,
Montreal, Canada, Step. 17-18, pp.31-36.

Fig. 10. The charging processing
The mobile robot moves to the docking station, and touch theconnect pins and the limit switch to be shown in the left side of Fig,
9. The docking station transmits RF signal to the mobile robot.
The mobile robot must stops, and prepares to execute the charging
processing. Then the mobile robot turns on the auto-switch toreceive the charging current. The docking station supplies the
charging current to the mobile robot. The user interface displays
green label on “Charging” to present the docking station is
successful, and plots the current curve and voltage curve, andcomputes the charging time for the mobile robot. The charger ofthe station provides the power to the mobile robot. The
experimental result is shown in Fig. 8 (a) and (b). Then the power© ISAROB 2013

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A PSO method for time-optimal motion planning of a mobile robot
Li-Chun Lai1, Kuo-Lan Su 2, Chia-Nan Ko3, Sheau-Wen Lin4, and Chia-Hung Shih1
1

Bachelor Program in Robotics, National Pingtung University of Education
Pingtung City, Pingtung County 900, Taiwan
2
Department of Electrical Engineering, National Yunlin University of Science and Technology
Douliou, Yunlin 640, Taiwan
3
Department of Automation Engineering, Nan Kai University of Technology
Tasotun, Nantou 542, Taiwan
4
Graduate Institute of Mathematics and Science Education, National Pingtung University of Education
Pingtung City, Pingtung County 900, Taiwan(email:1lclai@mail.npue.edu.tw; 2sukl@yuntech.edu.tw; 3t105@nkut.edu.tw; 4linshewen @mail.npue.edu.tw;5hiroshi.seki@gmail.com)
Abstract: Based on a particle swarm optimization (PSO) algorithm for time-optimal control problem of a two-wheeledmobile robot is addressed in this paper. The PSO algorithm is that it is easier to implement and there are fewer
parameters to be adjusted. Different from usual cases, in which the Pontryagin’s Minimum Principle (PMP) is used, an
iterative procedure is proposed to transform the time-optimal problem into a nonlinear programming (NLP) one. Motion
planning of a mobile robot is a NLP problem. In the NLP problem, the count of control steps is fixed initially and thesampling period is treated as a variable in the optimization process. Because the NLP has a initial feasible solutions
problem that is not easier to find. In this paper, The PSO algorithm and fitness function to solve initial feasible solutions
problem and optimal problem. To show the feasibility of the proposed method, simulation results are included forillustration.
Keywords: Particle Swarm Optimization (PSO), Mobile Robot, Time-optimal Control.
to obtain the closed-form solution for the time-optimal
trajectory of any mobile robot. Because of thedisadvantage in applying the PMP, a NLP method that
does not utilize the PMP was developed by one of theauthors of this paper to solve the time-optimal control
problem of linear systems [6]. The basic idea of thismethod is that instead of considering a fixed sampling
period, the count of control steps is fixed initially andthe sampling period is treated as a variable in the
optimization process. Extending the concept in [6] tononlinear systems, this paper shows the generation oftime-optimal motion between two configurations for amobile robot with two independently driven individual
wheels. In the beginning of this study, a mobile robot is
introduced and its kinematic equations are derived.
Then an iterative procedure is proposed to transform thetime-optimal problem into a constrained NLP one.
However, since the NLP has a initial feasible solutions
problem that is not easier to find and two level timeoptimal process is very complex. In this paper, The PSO
algorithm and fitness function to solve initial feasible
solutions problem and time-optimal problem.

1. INTRODUCTION
Motion planning is an important issue in mobile
robotics. In recent years, many research works havebeen addressed the control problem associated withmobile robots [1,2]. Among these works, a typicalmission for a mobile robot can be described as to movefrom one configuration (position and orientation) toanother. This problem has been thoroughly studied byassuming that the motors control the wheel velocities[3]. On the other hand, one also can use the motor tocontrol the accelerations of the wheels [4]. For mobile
robots equipped with two independently driven wheels,
the time-optimal motion planning problem is that offinding the time-optimal motion in an unobstructedenvironment between two configurations. Usually, thiskind of time-optimal control problem leads to theutilization of the Pontryagin’s Maximum Principle(PMP) [5], in which the dynamical equations of themobile robot are used and one needs to solve a set ofdifferential equations. Since this set of equations isusually highly nonlinear and coupled, it is very difficult© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

By assuming that the wheels do not slip, thekinematics of the mobile robot as shown in (1).
ur (t )ul (t )
 r (t )    (t )  r (r (t )  l (t )) l  L
 (t )   (1)  r cos  (t )(r (t )  l (t )) 
 x (t )  2 y (t )  
 r sin  (t )(r (t )  l (t )) 2
where r (t ) and l (t ) represent the angular veloci
ties of the right and left wheels, respectively. ur (t )and ul (t ) are the control inputs.

Particle Swarm Optimization (PSO) algorithm
proposed by Kennedy and Eberhart [7] is another kindof evolutionary computation techniques. This method
was based on the simulation of simplified animal social
behaviors such fish schooling and bird flocking. In PSO
searching space, each single solution acts as a flying
bird, which we call it a particle. The PSO algorithm
works on a social behavior of particles in the swarm. It
finds the global best solution by simply adjusting the
trajectory of each particle its own best particle andtoward the best particle of the entire swarm at eachgeneration. The PSO algorithm has been used in solving
many optimization problems successfully. Comparedwith GA, the advantage of PSO is that it is easier toimplement and there are fewer parameters to be
adjusted. In this paper, The PSO algorithm is proposed
to determine velocities of two wheels. The path of amobile robot is time-optimal from a given initialconfiguration to a desired final configuration.
The remaining sections of this paper are organized
as follows. Section 2 shows kinematics equations of amobile robot. Section 3 presents time-optimal control
between two configurations. Section 4 presents timeoptimal motion planning with PSO algorithm.
Simulations are performed in Section 5 to confirm thefeasibility of the proposed algorithm. Section 6
concludes the paper.

3. TIME-OPTIMAL MOTION PLANNING
PROBLEM BETWEEN TWOCONFIGURATIONS
The time-optimal motion planning problem between
two configurations is to find the control inputs that will
move the initial configuration ( x0 , y0 ,  0 ) to the final
configuration ( x f , y f , f ) while minimizing thetraveling time. The control inputs ur (t ) and ul (t )are assumed to meet the following constraints in (2) and(3).
umin  ur (t )  umax
(2)
umin  ul (t )  umax
(3)
The mobile robot moves time [0, t f ] is to divide
into N equal time intervals as shown in (4).
tf
(4)
t 
N
With (1) through (3), it is turned into (5) through (7).

2. KINEMATICS EQUATIONS OF A

MOBILE ROBOTIn the section, the kinematics of a mobile robot with
two independent driven wheels will be described. The
configuration of the mobile robot will be denoted by( x, y,  ) as shown in Fig. 1. Where x and y are thecoordinates of the midpoint of the wheel axis, and  isthe heading angle of the robot. L is the distance
between two wheels and r is the radius of the wheel.

r ((i  1)  t )  ur (i  t )  t





((
i

1)


t
)

u
(
i


t
)


t
l
l
r (i  t )    (i  t )  r (r (i  t )  l (i  t ))
l







((
i
1)
t
)
t

 

L
  (i  t )    r cos  (i  t )(r (i  t )  l (i  t )) t 
 x(i  t )   x((i  1)  t ) 2
 y (i  t )   y ((i  1)  t )  r sin  (i  t )(r (i  t )  l (i  t ))  t 
2for i  1, 2, L , N

Yumin  ur (i  t )  umax
umin  ul (i  t )  umaxrWith (5) through (7), the time-optimal motion
planning can be formulated. Assume the following
conditions are given:( x(0), y (0),  (0))  ( x0 , y0 ,  0 )(8)
r (0)  0(9)l (0)  0(10)(x, y)L

M o b ilero b o t

X
Fig. 1. A diagram of a mobile robot© ISAROB 2013(5)for i  1, 2, , N (6)for i  1, 2, , N (7)235The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Step 4:

Determine the control inputs ur (t ) and ul (t ) for[0, t f ] to minimize as shown in (11).
J  N  t
(11)
Subject to(12)( x(t f ), y (t f ), (t f ))  ( x f , y f ,  f )r (t f )  0(13)l (t f )  0(14)

Determine the best personal position
visited so far by each particle.
Determine the best global position visited
so far by all the particles.
Update velocities using Eq. (15).
Update particles’ positions using Eq. (16).
Repeat the procedure in Step 3 throughStep 7 until all the particles have attained
their desired fitness.

Step 5:
Step 6:
Step 7:
Step 8:and the constraints in (6) and (7).

4. TIME-OPTIMAL MOTION PLANNING
PROBLEM WITH PSO ALGORITHM

5. SIMULATION RESULTS
Example 1:

The mathematic description of PSO is as the follow:
Suppose the dimension of the searching space is D ,thenumberofparticleisn .
Vector
x j  ( x j1 , x j 2 , , x jD ) represents the position of the

In this simulation example, the mobile robot will be
moved
from
( x , y ,  )  (0, 0, 0)
to 0 0 0( x f , y f ,  f )  (1m, 1m, ) . The initial time of t , the3
initial value of N, the radius of the wheel r , and the
distance between two wheels L are chosen to be
5 (sec.) , 10, 5 cm , and 30 cm , respectively.
Meanwhile, the bounds on the control inputs, umin andumax are chosen to be 0.5 rad 2 and 0.5 rad 2 .
ss
In applying PSO algorithm, the population size andmaximal generation number are chosen to be 1000 and
1000, respectively. The value c1 , c2 , the inertia weight,and  are chosen to be 2, 2, 0.8, and 10000,
respectively. The error e will be defined as in (18).
e  [x f y f  f rf (t f ) lf (t f )](18)

i-th particle and Pj  ( p j1 , p j 2 , , p jD ) is the best
position searched by now, and the whole particle
swarm's
best
positionisrepresentedas
Pg  ( p g1 , p g 2 , , p gD ) Vector v j  (v j1 , v j 2 ,  , v jD )is the position change rate of the i-th particle. Eachparticle updates its position according to the following
formulas:v jd (k  1)  w  v jd (k )  c1  rand ( )  ( p jd (k )  x jd (k ))(15) c2  rand ( )  ( pgd ( k )  x jd (k ))x jd (k  1)  x jd (k )  v jd (k )(16)

Where c1 and c2 are positive constant parameters
called acceleration coefficients. The rand ( ) is arandom function with the range [0,1]. w is called theinertia weight and is less than 1.
The fitness function of PSO algorithm is defined asshown in1
fitness (17)
 eeT +1
where e is a error between the final states of themobile robot and final configuration. The  is a
penalty value that the error e is convergence to zero
fastly. In order to reach final configuration in optimal
time, the paper is used PSO algorithm repeatedly. The
method is used the optimal time of the last to replace thenext initial time until the maximum iteration.
The details of the determination of the attractive
factor can be summarized as follows:
Algorithm:
Define the problem space.
Step 1:
Step 2:
Initialize an array of particles with random
positions and velocities.
Evaluate the desired fitness function of theStep 3:
particles using equation (17).
© ISAROB 2013

x f  x(t f )  x f(19)

y f  y (t f )  y f(20)

 f   (t f )   f(21)

rf  r (t f )  0(22)

lf  l (t f )  0(23)

Applying PSO algorithm, the optimal time of
t is found to be t  1.6073 (sec.) and the plots ofx(t ) , y (t ) ,  (t ) , r (t ) , l (t ) , ur (t ) ,and ul (t ) are shownin Fig. 2 through 8, respectively.
1.4

1.2

1

x (m)

0.8

0.6

0.4

0.2

0

0

2

4

6

8
10
Time (sec)12

14

Fig. 2. Plot of x(t )

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1.4
0.5

1.2

0.4
0.3

1

sec2

)

0.2

ul ( rad

y (m)

0.8

0.6

0.1
0
-0.1
-0.2
-0.3

0.4

-0.4

0.2

-0.5
0

0
0

2

4

6

8
10
Time (sec)

12

14

16

2

4

18

Fig. 3. Plot of y (t )6810Time (sec)

12

14

16

18

Fig. 8. Plot of ul (t )1.4

6. CONCLUSIONS

1.2

The paper uses PSO algorithm to solve time-optimal
motion planning problem of a mobile robot with two
independently driven wheels. The PSO algorithm andfitness function to solve initial feasible solutions
problem and optimal problem. Compared with GA, theadvantage of PSO is that it is easier to implement andthere are fewer parameters to be adjusted. The
simulation results can show the feasibility of the
proposed method.

theta (rad)

1

0.8

0.6

0.4

0.2

0
0

2

4

6

8
10
Time (sec)

12

14

16

18

Fig. 4. Plot of  (t )ACKNOWLEDGMENTS
r (rad sec)

This work was supported in part by the National
Pingtung University of Education, Taiwan, R.O.C.

REFERENCES[1]
Fig. 5. Plot of r (t )l (rad sec)

[2]

[3]

[4]
Fig. 6. Plot of l (t )ur ( rad

sec2

)

[5]

[6]

[7]
Fig. 7. Plot of ur (t )© ISAROB 2013

237

Wang SM, Lai LC, Wu CJ Shiue YL (2007),
Kinematic control of omni-directional robots fortime-optimal
movement
betweentwo
configurations. Journal of Intelligent and Robotic
Systems 49(4):397-410
Lai LC, Wu CJ, Shiue YL (2007), A potential
field method for robot motion planning in
unknown environments. Journal of the Chinese
Institute of Engineers 30(3):369-377
Soueres P, Laumond JP (1996), Shortest paths
synthesis for a car-like robot. IEEE trans. on
Automatic Control 41(5):672-688
Reister DB, Pin FG (1994), Time–optimal
trajectories for mobile robots with two
independently driven wheels. The International
Journal of Robotics Research 13(1): 38-54
Pontryagin LS, Boltyanskii VG, Gamkrelidze RV,
Mishchenko EF (1986), The Mathematical
Theory of Optimal Processes Gordon and Breach
Science Publishers. New York
Chung TS, Wu CJ (1992), A computationallyefficient numerical algorithm for the minimumtime control problem of continuous systems.
Automatica 28:841-847
Kennedy J, Eberhart R (1995), Particle swarm
optimization. IEEE International Conference on
Neural Network, pp. 1942-1948

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The implementation of driving anti-sleep safety warning system
Yi-Yu Lu, Kuo-Kung Chen, Po-Yu Chen, and Wen-Bin Lin
Far East University, Taiwan(Tel: 886-6-597-9566, Fax: 886-6-597-7570)
yiyu@cc.feu.edu.tw
Abstract: Sometimes a driver deviates from his natural or normal driving style due to inadequate attention or faces abnormal
situation caused by a number of psychological and physical factors. Such abnormalities often lead a driver to a mistake that
may cause an accident. This paper presents a novel approach called driver-adaptive assist system to avoid such abnormalities
in driving scenario as a preventive measure against occurrence of vehicle collisions, assuming that natural driving style ofindividual drivers is the safest style. This paper presents a driving anti-sleep safety warning system. To prevent from trafficcollision due to the driver fall asleep, the proposed system can supervise the driver by LED and buzzer. The system consists ofthree-axis accelerometer, LED, buzzer, and control circuit. When the system horizontal angle is bigger than setting ones, thesystem turns on the buzzer to alarm the driver. Finally, a driving anti-sleep safety warning system for vehicle is implemented
and to verify the theoretical analysis.
Keywords: alarm systems, automation, driver warning systems, human factors, safety.
vehicle gap reduces to less than a critical distance. The
critical distance for warning a driver is often determinedheuristically based on the skill of an average driver in eachof the systems [2-4]. Thus those warning systems provide
the same type and the same level of assistance to all drivers,
regardless of their driving behaviors, skills, ages andpreferences. But it is believed that in reality the types andthe levels of assistance desired by drivers vary widely
according to their driving behaviors, because driving
behavior of a beginner and an expert person, a young andan old person are completely different. So a typical assist
system that assists all drivers in the same way may not be
accepted widely due to mismatch with individual
preference. This system may seem appropriate to only somedrivers whereas it may seem noisy for providing unwanted
advice to some other and may seem insufficient assistanceto the others.
Since abnormality in driving leads a mistake that may
cause an accident, a system of notifying abnormality indriving rather than only notifying at emerging collision
probably produces a much more conducive outcome for
reducing rear-end collisions. Some researchers have beenworking on the development of safety systems using
different techniques based on physiological measures likebrain waves, heart rate, pulse rate, respiration, etc. [5-7].
There are different methods that can be used to detectfatigue, drowsiness and other psychological facts in driving.
But these techniques have the drawbacks since they requiresensing equipment (electrocardiogram, electromyogram,
respiration, skin conductance, wristband etc.) to be attachedto the drivers causing annoyance to them [7], [8]. The other

1 INTRODUCTION
Increasing trend in traffic collisions has become aserious social problem all over the world. A number offactors contribute to the risk of collision including; vehicle
design, speed of operation, road design, road environment,
driver skill and/or impairment and driver behavior. A 1985
study by K. Rumar, using British and American crash
reports as data, found that 57% of crashes were due solely
to driver factors, 27% to combined roadway and driverfactors, 6% to combined vehicle and driver factors, 3%
solely to roadway factors, 3% to combined roadway, driver,and vehicle factors, 2% solely to vehicle factors and 1% tocombined roadway and vehicle factors [1]. Human factors
in vehicle collisions include all factors related to drivers
and other road users that may contribute to a collision.
Examples include driver behavior, visual and auditory
acuity, decision-making ability, and reaction speed.
Worldwide motor vehicle collisions lead to death anddisability as well as financial costs to both society and theindividuals involved. Traffic collisions are often caused by
mistakes of human drivers. A large number of mistakes are
due to drivers with inadequate attention in driving led by
fatigue, tiredness, drowsiness, having drunk, etc. It is
obvious that a warning systems could be effective in mostrear-end crushes and other accidents.
Researchers have been working to develop collision
avoidance and steady following systems to avoid trafficcollisions by providing automatic warning to the driver or
braking the car in an emerged danger. Most of such systems
use a similar algorithm to warn a driver when the inter-© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

techniques monitor eyes and gaze movement using
complicated image analysis.
A driver’s state of attention can also be characterized bycomparing current driving behavior with his natural driving
behaviors like the lateral position, variation in headway
range, braking and acceleration characteristics, speed andrange fluctuations, steering wheel movements, and time-toline crossing. To cope with the actual inattentiveness indriving it is necessary to develop a non-intrusive system
based on observation of indirect driving behavior ofindividual driver. Although, these methods seem a bitcomplicated due to variation in vehicle type, driver
experience, road type, weather etc., it may be an efficientand widely accepted system, and contribute in reducing
traffic accident due to mistakes greatly [9].
This study develops the design and fabrication for a 3axis accelerometer and Programmable System-on-ChipController (PSoC) devices-based driving anti-sleep safety
warning system.

Fig. 2. ADXL330
The product measures acceleration with a minimum
full-scale range of ±3 g (g=9.8m/m2). It can measure thestatic acceleration of gravity in tilt-sensing applications, aswell as dynamic acceleration resulting from motion, shock,or vibration. The user selects the bandwidth of theaccelerometer using the CX, CY and CZ capacitors at the
XOUT, YOUT, and ZOUT pins. Bandwidths can be selected tosuit the application, with a range of 0.5 Hz to 1600 Hz for
X and Y axes, and a range of 0.5 Hz to 550 Hz for the Zaxis.

2 ELEMENT CONFIGURATION AND OPERA
TING PRINCIPLE
2.1 Three-axis accelerometer
A micromachined capacitive accelerometer is used forthe motion tracker. There are various types ofaccelerometers with piezoresistive, capacitive, tunneling,resonant, and thermal properties. Among them, thecapacitive type has high sensitivity, good DC response andnoise performance, low drift, low temperature sensitivity,
lower power dissipation, and simple structure. Shown in
Fig. 1 and 2, the ADXL330 [10] is a small, thin, low power,
complete 3-axis accelerometer with signal conditioned
voltage outputs, all on a single monolithic IC.

2.2 Programmable system-on-Chip (PSoC)
The PSoC family consists of many Programmable
System-on-Chip Controller devices. These devices aredesigned to replace multiple traditional MCU-based systemcomponents with one, low cost single-chip programmabledevice. PSoC devices include configurable blocks of analog
and digital logic, as well as programmable interconnects.
This architecture allows the user to create customized
peripheral configurations that match the requirements ofeach individual application. Additionally, a fast CPU, Flashprogram memory, SRAM data memory, and configurable
I/O are included in a range of convenient pinouts andpackages.
Shown in Fig. 3 and 4, the PSoC architecture, as
illustrated on the left, is comprised of four main areas:
PSoC Core, Digital System, Analog System, and System
Resources. Configurable global busing allows all the device
resources to be combined into a complete custom system.
The PSoC CY8C29x66 family can have up to five I/O portsthat connect to the global digital and analog interconnects,
providing access to 8 digital blocks and 12 analog blocks[11].

Fig. 1. ADXL330 functional block diagram© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 3. CY8C29466 functional logic block diagram
Fig. 5. The flow chart diagram of design

Fig. 4. CY8C29466 connection diagram

3 THE DESIGN STEPS
Fig. 5 is the flow chart diagram of design. From the
beginning, we found the related literature reviews andconfirmed basic model and material as we need. Secondly,
we use PSoC to edit the program to control output element
by ADXL330. And then, wiring these elements at solderlessbreadboard to pre-test. After several trial and errors, we canfind the angle sensitivity as we need. Third, we check eachfunction of the combination of pre-test solderlessbreadboard. Finally, we finish and validity a 3-axis
accelerometer and PSoC-based driving anti-sleep safety
warning system by the assembly of ADXL330, useful
output elements, and power.

© ISAROB 2013

Fig. 6 The flow chart diagram of PSoC system
programing

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Roads Magazine[2] Nagiri S, Amano Y, Fukui K and Doi S (2004), A
Study of a Personally Adaptive Driving Support System
Using a Driving Simulator,Driver Behavior and Active
Safety, Special Issue R&D Reviw of Toyota CRDL, 39(2)[3] Doi A, Butsuen T, Niibe T, Yakagi T, Yamamoto Y
and Seni H (1994), Development of a Rear-End Collision
Avoidance System with Automatic Braking Control, JSAE
Review, 15(4), pp.335-340[4] Fujita Y, Akuzawa K, and Sato M (1994), Radar
Brake System, in 1995 Annual Meeting of ITS America,
Washington D.C., vol. 1, pp.95-101[5] Bergasa LM, Nuevo J, Sotelo MA, Barea R and
Lopez ME, Real-Time System for Monitoring Driver
Vigilance (2006), IEEE Trans. on Intel. Trans. Syst.,
7(1):63-77[6] Kaneda M, Obara H and Owada M (1999), Study on
a Method of Using Image Processing Technology to Detect
Inattentive Driving, Proceedings. JSAE Annual Congress,
pp. 21-24[7] Healey J, and Picard R (2000), SmartCar: Detecting
driver stress, Proceedings. 15th International Conference on
Pattern Recognition, Barcelona, Spain, 2000, vol. 4,
pp.218-221[8] Kircher A, Uddman M and Sandin J (2002), Vehicle
control and drowsiness, Tech. Rep. VTI-922A, Swedish
National Road and Transport Research Institute, Linkoping,
Sweden, 2002
[9] Kamal MAS, Kawabe T, Murata J and Mukai M(2007), Driver-Adaptive Assist System for Avoiding
Abnormality in Driving, 16th IEEE International
Conference on Control Applications Part of IEEE Multiconference on Systems and Control Singapore, Oct. 1-3,
2007, pp. 1247-1252[10]http://www.alldatasheet.com/datasheetpdf/pdf/150893/AD/ADXL330.html[11]http://www.alldatasheet.com/view.jsp?Searchword=C
Y8C29466[12] http://www.ti.com/lit/ds/symlink/lm1084.pdf

4 PSoC SYSTEM PROGRAMING
In PSoC System program, we declare a variable of
ADXL330 inclination angle of human head as a virtual
switch in Fig. 6. When the angle is bigger than setting angle
1, it will trigger and turn on the LED. When the angle isbigger than setting angle 2, it will trigger and turn on thebuzzer to alarm the driver. Whatever the direction of left,right, front, and rear, they all have the same function. Angle
2 is bigger than angle 1, consequently.

5 HARDWARE F
ABRICATION AND CONCLUSION
Shown in Fig.7, we place the ADXL330 at control
module region due to sense balancing angle. The power
circuit includes 5V circuit and 3.3 V circuit. Using 9V
battery box, then converts 5Vpart by IC7805, and 3.3V partby IC LM1804 [12]. Because the voltage supply of
ADXL330 and CY8C29466 is different, so it needs twovoltage sources. And then, we write the program into
CY8C29466 by PSoC Designer 5.0 Service. It cooperates
with ADXL330 to control LED and buzzer. We wear thefinished product to test four-side inclination angles. Various
function testing has been completed to verify the accuracyof the hardware design.

Fig. 7 The hardwere schematincs

ACKNOWLEDGMENT
This work is supported by the National Science Council,
TAIWAN, ROC under the Research Grant NSC 97-2622-E269-013-CC3.

REFERENCES[1] Lum H and Reagan JA (1995), Interactive highway
safety design model: accident predictive module. Public© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Motion Planning of the Multi-robot Based Chess Game
Yung-Chin Lin1,3, Yi-Lin Liao1, Cheng-Yun Chung1, Kuo-Lan Su2
1

Graduate school Engineering Science and technology, National Yunlin University of Science & Technology
Douliou, Yunlin, Taiwan,
2
Department of Electrical Engineering, National Yunlin University of Science & Technology, Taiwan(Tel: +886-5-5342601, Fax: +886-5-5312065)
3
Department of Electrical Engineering, WuFeng University of Science of Technology, Taiwan
E-mail: yclin@wfu.edu.tw, sukl@yuntech.edu.tw, g9910801@yuntech.edu.tw

Abstract: The article uses mobile robots to present movement scenario of each chess for Chinese chess game, and programs theshortest motion path using enhance A* searching algorithm. We play the Chinese chess game using the mouse according to theevaluation algorithm on the user interface of the supervised computer. The supervised computer controls mobile robots
according to the programmed motion paths of the designed chesses moving on the platform via wireless RF interface. We
develop the user interface using Borland C++ Builder language. The user interface displays the positions of the mobile robot
based chesses, and plots the motion paths of the assigned chesses. The proposed algorithm can programs the shortest motion
trajectories of the chesses that move to target positions from start positions, and avoids the collision points of the motion
trajectories for the assigned chesses simultaneously, and can re-programs the new motion path to avoid collision. Finally, we
implement the movement scenario on the chessboard platform using mobile robots. Mobile robots move on the platform
according to the programmed motion paths, and uses IR sensor modules to avoid the obstacles (mobile robots) and detect thecross points of the platform, and calculate numbers of the cross point to decide the final locations.
Keywords: mobile robots, Chinese chess game, enhance A* searching algorithm, wireless RF module, Borland C++ Builder

I.

INTRODUCTION

computer Chinese chess game platform that created favorable
conditions for the development of discrete event dynamic systemtheory and provided a good research platform for solving dynamic
counter measure problems [6].
We use multiple mobile robots to present the movement
scenario of the chesses. In some condition, mobile robots must
program the shortest path and avoid the obstacles moving to thenext positions. The obstacles may be static or dynamic obstacles.
We used A* searching algorithm to program the shortest path on
the Chinese chess game. A* heuristic function is introduced to
improve local searching ability and to estimate the forgotten value.
Kong used adaptive harmony search algorithm to solve
optimization problems [7]. Flavio presented a multi-robot
exploration algorithm that aims at reducing the exploration time
and to minimize the overall traverse distance of the robots bycoordinating the movement of the robots performing the
exploration [8].

Chinese chess [1] game is one of the most popular games. A
two-player game with a complexity level is similar to Westernchess. The Chinese chess game has gradually attracted many
researchers’ attention, and many evolutionary algorithms to be
proposed in the recent. In the paper, we use thirty-two mobile
robots to present the movement scenario of each chess, and uses
enhance A* searching algorithm to program the shortest paths formobile robots moving to the target positions. In general, users
move chess (robot) to the target position according to the game
rules. In some conditions, user moves the chess to take the chess ofthe other side. There are two chesses (robots) moving in theplatform simultaneously. The assigned two robots may collide on
the programmed motion paths. We want to solve the collisioncondition to improve the shortest motion paths of the two robots
using enhance A* searching algorithm.
There are many algorithms to be proposed in Chinese chessgame. Darwen and Yao proposed the co-evolutionary algorithm tosolve problems where an object measure to guide the search
process is extremely difficult to device [2]. Wang used adaptive
genetic algorithm (AGA) to solve the problems of computer
Chinese chess [3]. Su developed smart mobile robot using voice
module, and programmed the motion trajectories for multiple
mobile robots based Chinese chess game [4]. Fu used the position
evaluation function to play an important role for building an
intelligent Chinese chess computer game [5]. Zhou constructed the© ISAROB 2013

II. SYSTEM ARCHITECTURE
The system architecture of the multiple robots based Chinesechess game system is shown in Fig. 1. The system contains asupervised computer, a chessboard platform, some wireless RF
modules and thirty-two mobile robots. The players of the chessgame are classified red side and black side. Each side includes
sixteen chesses. Each player only moves chess on one step, andwaits to play the next step until the other side moving chess on the242

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

the grid based platform. The communication protocol is listed inTable 2 from the mobile robot to the supervised computer. The
data bytes contain ID code, robot number, and position andorientation of robots and obstacles and finished command
feedback data.

user interface. These chesses stay at fixed positions to start thechess game. The movement rules of the chess game have twocases. Player moves chess to free position of the chessboard
platform in the case one. The user interface programs the shortest
path for the assigned robot. In the case two, player moves thechess to take the chess of the other side. The user interface
programs two motion paths for the assigned chesses, and avoids
collision points on the programmed motion paths. The two mobile
robots move in the chessboard platform using the programmed
motion paths.
Players move chesses using the mouse on the user interface ofthe supervised computer to be shown in upper side of Fig. 1. The
supervised computer transmits the ID code and motion commandto the assigned mobile robots, and receives the ID code andposition from the mobile robots via wireless RF interface. Users
can set game time until one player wins the game, and programs
the using time for player moving chess on one step. The mobile
robot has the shape of cylinder, and it’s equipped with a microchip(MCS-51) as the main controller [9].

Table 1. Control command
Byte
0
1
2
3
Definition Start
ID R_CH Work[1]5678Work[3] Work[4]R_D
GO_flag

4
Work[2]
9
Check

Table 2. Feedback command
Byte
0
1
2
3
Definition Start
ID R_CH Work[1]5678Work[3] Work[4]No useText

4Work[2]
9CheckIII. Motion Planning
We use A* searching algorithm to solve the shortest path
problem of multiple nodes travel system. The formula of A*
searching algorithm is following
f (n ) = g (n ) + h(n )(1)

The core part of an intelligent searching algorithm is thedefinition of a proper heuristic function f (n ) . g (n ) is the exact
cost at sample time n from start point to the target point. h(n ) is
the minimum cost. In this study, n is reschedules as n′ togenerate an approximate minimum cost schedule for the next point.
The equation (1) can be rewritten as follows:f (n ) = g (n ) + h(n′)(2)
The programmed motion path only computes the movement
distance, and can’t considers the total displacement using A*
searching algorithm. We use pulse numbers of the encoder todefine the total movement cost f (n ) . The pulse numbers are 355
to move one grid on the platform, and the pulse numbers are 92 torotate 900 for the mobile robot. The total displacement contains
movement distance and turning numbers. The paper uses enhance
A* searching algorithm to programs the shortest motion path, andconsiders the turning numbers. We can program seven steps tocompute the shortest motion path using the proposed algorithm tobe following:
Step 1: We construct two labels to be Open list and Close list.
Step 2: The “Close list” fills the start point and evaluation points,
and the neighbour points of the start point fill in the “Open list”.
Step 3: We construct label on the first searching result, andcalculate the values of f (n ), g(n ) and h(n ) function, andcompares the values of the function. Then we select the minimum
value of the function f (n ) to be stored in “Close list”.
Step 4: Repeat the processing from step 2 to step 4.
Step 5: We can find the target point on the final searching.
Step 6: We compare the motion paths on the neighbour points ofFig. 1. System architecture
We implement the Chinese chess game using mobile robots on
the grid based chessboard platform to be shown in the bottom of
Fig. 1. The arrangement of the chessboard is 11 grids on thehorizontal direction (X axis), and is 12 grids on the verticaldirection (Y axis). The game only uses 9 × 10 grids based platform
to arrange all chesses. The taken chesses move to the assigned
positions around the chessboard. The distance is 30cm between
the center of aisle on the X direction and Y direction of the chessboard, and the width of the corridor is 12cm. The mobile robot
uses the IR sensor modules to detect static and dynamic obstacles,
and stays at the cross points of the chessboard platform.
The communication protocol of the system is 10 bytes. There
are start byte (1 byte), data byte (8 bytes) and check byte (1 byte).
We set the communicate protocol from the supervised computer tothe mobile robot to be listed in Table 1. The start byte trigs thewireless RF module to receive the signals from the transmitter.
The data bytes contain ID code (one byte), robot number (onebyte), and movement direction and distance of the mobile robot.
The definition of the movement direction is listed in right side ofFig. 2 for mobile robots. There are four various directions to match© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Then we can find out new programming motion path to makefour landmarks “1” “E” “3” and “F”. We can select the new
motion path through three points “1” “E” and “3” to instead of theold motion path through three points “1” :F” and “3”. The newpath can reduces two turning numbers to be shown in Fig. 3(c).
Finally, we can search the shortest motion path to be shown in Fig.
3(d). The motion path turns left two times and turns right two
times.
We compare the pulse numbers of the two motion paths. The
total pulse numbers of the old shortest path f (n ) can be calculatedaseach turning points, and we can decide a shortest path to control
the mobile robot moving to the target point.
Step 7: The “Open list” can’t include the target point. We decide
the path does not exist.
We make an example to explain the motion planning using
enhance A* searching algorithm. Player moves the chess “redcannon” to take the chess “black horse”. The user interface
programs the motion path using A* searching algorithm to beshown in Fig. 2, and avoid the obstacles (chesses). The
programmed motion path turns right four times and turns left fourtimes. We can find out the four points to make the landmarks “B”“C” “D” and “4”, and select the new motion path from thelandmarks “4” “C” and “B” to reduce two turning numbers. The
experimental results are shown in Fig. 3(a) and (b).

f (n ) = 11× 355 + 8 × 92 = 4694

(3)

In the other condition, we rebuild enhance A* searchingalgorithm on the step 6. We can calculate the pulse numbers of the
new motion path moving to the target position. The total pulse
numbers f (n ) asf (n ) = 11× 355 + 4 × 92 = 4218

(4)

IV. EXPERIMENTAL RESULTS
Users move the chess to play Chinese chess game, and present
movement scenario of mobile robots using enhance A* searchingalgorithm. The experimental scenario is the chess “red cannon”
moving to take the chess “black horse” on the user interface. The
user interface programs two motion paths using A* searchingalgorithm for the assigned two robots. The first motion path is thechess “black horse” moving to the back side of the red side. The
programmed motion path is shown in Fig. 4(a). The motion paths
have eight times to turn right or left, and must spend more time
moving to the target position for the mobile robot. The userinterface programs the new motion path using enhance A*
searching algorithm. The new motion path reduces turning right
one time and turning left one time. The supervised computerorders the command to the mobile robot “black horse” via wirelessRF interface. The mobile robot moves one grid, and turns right 900
moving six grids. Then it turns right 900 moving four grids. The
chess moves to the final position to be shown in Fig. 4(b).
Then the user interface programs the motion path for the chess“red cannon” that moves to the position of the chess “black horse”.
The assigned motion path has the collision points with thepre-programmed motion path of the chess “black horse”. The userinterface displays the alarm signal to re-program the new motion
path. The experimental result is shown in Fig. 4(c). The
programming processing is shown in Fig. 3. We can find out the
final motion path of the chess “red cannon” to be shown in Fig.
4(d).
The user interface has been programmed the two motion paths
for two chesses “red cannon” and “black horse”. Then the
supervised computer orders command to the two mobile robots(red cannon and black horse) moving on the chessboard platform
according to the programmed motion paths. The two mobile
robots move on the platform simultaneously, and speak the

Fig. 2. Path planning

(a)

(b)

(c)
(d)
Fig. 3. Re-programmed motion path
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the game, and computes using time to move chess by player on
one step, and receive the status of mobile robots via wireless RFinterface. The user interface programs the shortest path using
evaluation algorithm and A* searching algorithm. Then the userinterface improves the motion paths to reduce the turning numbers,
and avoid static and dynamic obstacles (chesses) using enhance
A* searching algorithm. Users can move the chess using themouse on the supervised computer, and moves the chess in theassigned time.

movement status using Chinese language. The movement scenario
is shown in Fig. 5(a). We plot the motion trajectories of the two
mobile robots in Fig. 5. Finally, we can see the chess “black horse”
moving to the back side of the chess “red rook”, and see the robot“red cannon” moving to the target position.

ACKNOWLEDGMENT
This work was supported by the National Science Council of
Taiwan, (NSC 101- 2221- E-224-007).

REFERENCES
(a)

(b)

[1] Yen SJ, Chen JC, Yang TN and Hsu SC (2004),
Computer Chinese Chess, ICGA Journal, Vol.27, No. 1, pp.3-18.
[2] Darwen P and Yao X (2002), Coevolution in Iterated
Prisoner’s Dilemma with Intermediate Levels of Cooperation:
Application to Missile Defense, International Journal ofComputational Intelligence Applications, Vol. 2, No. 1,
pp.83-107.
[3] Wang J, Luo YH, Qiu DN and Xu XH (2005), Adaptive
Genetic Algorithm’s Implement on Evaluation Function in
Computer Chinese Chess, Proceeding of ISCIT, pp.1206-1209.
[4] Su KL, Shiau SV, Guo JH and Shiau CW (2009), Mobile
Robot Based Onlin Chinese Chess Game, The FourthInternational Conference on Innovative Computing, Information
and Control, pp.63.
[5] Fu T and Yin H (2012), Designing a hybrid position
evaluation function for Chinese-chess computer game,
International Conference on Software Engineering and Service
Science, Beijing, China, pp.75-78.
[6] Zhou W, Liu JC and Zhao YH (2011), The constructionof Chinese chess computer game platform, International
Conference on Computer Science & Education, SuperStar Virgo,
Singapore, pp.126-128,.
[7] Kong Z, Gao LQ, Wang LF and Ge YF, Onan adaptive
harmony search algorithm, International journal of Innovative
Computing, Information and Control, Vol. 5, No. 9, pp.2551-2560.
[8] Flavio CM and Xiao JH (2012), Aflood algorithm for
multirobot exploration, IEEE Transactions on Systems, Man, andCybernetics—Part B: Cybernetics, Vol. 42, No. 3, pp.850-863.
[9] Su KL, Chung CY, Liao YL and Guo JH (2011), A*
Searching Algorithm Based Path Planning of Mobile Robots, The
Innovative Computing, Information and Control – Express Letters,
Part B: Applications (ICIC-ELB),Vol. 2, No. 1, pp.273-278.

(c)(d)
Fig. 4. Motion paths of two chesses

(a)

(b)
Fig. 5. Movement scenario of two robots

V. CONCLUSION
We have presented a Chinese chess game system using
multiple mobile robots. Mobile robots execute the chess attributeusing two interfaces. One is wireless RF interface, and the other isvoice interface. The user interface displays the total time to play
© ISAROB 2013

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An Indoor Autonomous Surveillance Robot via Humanoid Vision System
Kuo-Hsien Hsia1, Shao-Fan Lien2, Juhng-Perng Su3 and Wei-Yi Ciou4
1

Department of Computer Science and Information Engineering, Far East University, Tainan, Taiwan, R.O.C.
Graduate School of Engineering Science & Technology, National Yunlin University of Science & Technology,
Yunlin, Taiwan, R.O.C.
3, 4
Department of Electrical Engineering, National Yunlin University of Science & Technology, Yunlin, Taiwan, R.O.C.
(1Tel: 886-6-597-9566#7599; 1Fax: 886-6-597-7595)
2

1

khhsia@cc.feu.edu.tw, 2g9510801@hotmail.com.tw, 3sujp@yuntech.edu.tw, 4g9912712@yuntech.edu.tw

Abstract: This paper is devoted to develop a positioning and tracking scheme for a surveillance robot aiming at suspicious
target and measuring the distance to the target. The vision of a general camera is too narrow for tracking fast moving targets. In
addition, the flexibility of tracking is limited by robot’s dynamics. We design a particular humanoid vision system with two
pan-tilt servo systems which can be operated collaboratively or independently. The humanoid vision system is fast and flexible
for tracking a moving target and measuring the distance. An embedded system is exploited for the humanoid vision system androbot servo control. An image recognition and tracking algorithm has been developed using LabVIEW for the system.
Camshift tracking algorithm can recognize and track suspicious targets efficiently. Different from the previous systems, thissystem is cheap and easy to control.
Keywords: Surveillance robot, Humanoid vision system, Image process, Image tracking.
pan-tilt servo systems. Different from [4] and [5], oursystem is very cheap and easy to control. These two pan-tilt
servo systems can be operated collaboratively or
independently. The humanoid vision system is fast andflexible for tracking the moving target and measuring the
distance to an intruder or a suspicious person. The robot
system is shown in Fig. 1.

1 INTRODUCTION
The efficient image positioning and tracking method for
an indoor autonomous surveillance robot for security
technology has been a very important issue. An excellent
surveillance robot must have the ability to recognize and to
track suspicious targets. Furthermore, the relative distance
between the target and robot is the significant information
for the remote observers.
Luo [1] and Chien [2] et al proposed the systemarchitecture of security robot with multiple sensors. Hsia
[3] proposed the mobile robot autonomous obstacle
avoidance in unknown indoor environment with a scanninglaser rangefinder. However, a scanning laser rangefinder
cannot identify the suspicious targets. Therefore, this paper
is devoted to develop a positioning and tracking scheme fora surveillance robot aiming at a suspicious target andmeasuring the distance to the target. From many references,
we know that the vision system is a good sensor for robots
to achieve the surveillance missions in complex indoor
environments. However, the vision of a general camera is
too narrow for tracking fast moving targets. In addition, thevision systems are usually fixed on robots. Thus theflexibility of tracking is limited by robot’s dynamics.
In this paper, we have accomplished three major
objectives. Firstly, in order to track and to measure thedistance between the robot and the moving target, we
designed a particular humanoid vision system with two© ISAROB 2013

Fig. 1. The surveillance robot system
An embedded system, called PSoC (Programmable
System-on-Chip), is exploited for the humanoid visionsystem and robot servo control. PSoCs integrate memory,
analog and digital peripheral functions, and a
microcontroller on a single chip. It has good performancefor servo control. An image recognition and tracking
algorithm has been developed using LabVIEW software for

246The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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the humanoid vision system. Camshift tracking algorithm
can recognize and track suspicious targets efficiently. Basedon the triangulation method, the position of the target in a
3D space can be measured by the humanoid vision system.
In addition, we have succeeded in designing humancomputer interface which makes the operation, data logging
and real-time image observation of the robot system
possible.
To investigate the performance of the proposed
humanoid vision system from a practical point of view, a
red target is used for demonstrating the image recognition
and tracking algorithm. The Camshift tracking algorithm
can not only recognize but also track the suspicious target
quickly. Moreover, it shows that the humanoid visionsystem computes the distance to the target successfully inthe experiments. The robot with a humanoid vision system
is able to track and recognize the target, and it also canmeasure the distance to the target in the indoor environment.
The practicality of the humanoid vision system has beenverified in this paper.

Z

Camera_1
Z-axisCamera_2

X-axisFig. 2. The geometric relationship of the humanoid visi
on system
Camera_1

Target

H

1

1
2

p
Camera_2

Z
D

D-p

S

2

3

Fig. 3. The target position estimation

3 IMAGE TRACKING METHOD
3.1 HSV Color Model
Color model transformation aims to simplify the image
and to segment the object preliminary. In here, HSV (HueSaturation-Value) color model is utilized in the image
tracking applications [7]. The transformation from RGB to
HSV is:(1)(2)
V  max( R, G, B), S V  minR, G, B  ,V
 G  B   60 / S , if V  RH  180  B  R   60 / S , if V  G .
240  R  G   60 / S , if V  B
(3)The Z and H can be obtained as© ISAROB 2013

1

TargetSubstituting (2) into (1), we haveD  tan 1Z 
tan  2 .
ZY-axisFrom (1), we havetan 1 Z  p.

(5)

where S  D  p 2  Z 2 2 . The θ1, θ2 and θ3 are provided by
the encoders of the pan-tilt servo systems.

The humanoid vision system is composed by two pantilt servo systems. Therefore the two pan-tilt servo systems
can drive the cameras to align the target at the same time.
The target image will be located in both cameras. The
geometric relationship of the humanoid system is shown as
Fig. 2. Based on the special architecture of the humanoid
vision system, the complex pair image matching problem
and 3D coordinate computing problem [6] can be simplified
significantly. For a real-time robot system, this is a goodsolution for fast distance measuring.
By analyzing the relationship shown in Fig. 2, we canobtain the depth, Z, and the height, H, of the object. From
Fig. 3, we havepD p, tan  2 
.
Z
Z

(4)

,

H  tan  3 S ,2 TARGET POSITION ESTIMATION VIA HUMANOID VISION SYSTEM

tan 1 Dtan 1  tan  2 

247

(6)

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Pan  V * cos .
 Tilt  V * sin

3.2 CamShift tracking algorithm
The CamShift [8] (Continuously Adaptive Mean Shift)
tracking algorithm is a very effective way to track the target.
The steps of CamShift tracking algorithm are:
Step 1 Calculate the HSV color histogram of target and set
it to be the template.
Step 2 Calculate the target's color histogram of next image
and start to search.
Step 3 Calculate the moving vector V. (If V is divergent,back to Step 1.)
Step 4 Calculate the Bhattacharyya coefficient for finding
the best matching block.
Step 5 Record the coordinate of the best matching blockfrom Step 4 as the initial searching position for the
next image.
CamShift algorithm has good results for moving target
tracking problem. For our application, CamShift algorithm
is very quickly and efficiently for tracking and locking thetarget.

4 EXPERIMENTAL RESULTS
4.1 Hardware and Image Display Interface
The humanoid vision system is composed by two pantilt servo systems. The pan-tilt camera system is shown as
Fig. 5. This system is very small and agile. More preferably,
this design can be used in pairs or individually. In ourapplication, the distance between the two pan-tilt servo
systems is 10cm.

3.3 Moving Vector of the Target
The moving vector of the target is computed by the
Camshift tracking algorithm. Fig. 4 shows the moving
vector analysis.

(a)

Tilt

Pan

( Xc ,Yc )(b)Fig. 5. Pan-tilt camera system

The controller of the pan-tilt servo systems is a PSoC 5,
as shown in Fig. 6. The PSoC 5 provides PWM control
signals to the servo system and captures the signals of
encoders. Moreover, the position of the target is computedby the PSoC 5. The human-computer interface is shown in
Fig. 7. The real-time image, image processing results andthe signals of encoders are displayed on it. In addition, thethreshold values of HSV can be adjusted by this interface.

( X f (t ) , Y f (t ) )θ(9)Fig. 4. The moving vector of target
From Fig. 4, we have  tan 1 (

Vy
Vx

)

(7)

where Vx  X f (t )  X c , V y  Y f (t )  Yc . DefineFig. 6. PSoC 5
Fig. 7. Image display interfaceV2 Vx  V y .
2

2

(8)
The tracking results are illustrated as Fig. 8. Figs. 8 (aL) - (d-L) and Figs. 8 (a-R) - (d-R) are the images capturedby the left and the right camera, respectively. The images oftarget are all located at the centers of captured images.

Therefore, the moving vectors along the pan and tilt
directions are© ISAROB 2013

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For further investigating the measurement ranges of thehumanoid vision system, we measure the target positions 10times in each interval. The measurement range is from 0.5m
to 6m and the interval is 0.5m. Fig. 9 shows the averageabsolute errors of Z (depth) and H (height), where Z and H
have been described in Fig. 3. The acceptable measurement
error for our robot is less than 0.2m. In other words, theeffective measurement range of the humanoid vision system
is 3.5m. This measurement range for indoor surveillancerobot is adequate.

(a-L)

5 CONCLUSION
In this paper, we proposed a particular humanoid visionsystem for indoor autonomous surveillance robot. The
humanoid vision system is composed by two pan-tilt servo
systems. Different from other humanoid vision system, oursystem is very cheap and easy to control.
In experimental results, the Camshift tracking algorithm
can not only recognize but also track the suspicious target
quickly. The images of target are all located at the center ofthe captured images. Moreover, it shows that the humanoid
vision system computes the distance to the target
successfully. The human-computer interface displays thereal-time image, image processing results and the signals of
encoders.
The robot with humanoid vision system is able tomeasure the distance of the target. It also can track andrecognize the target in the indoor environment. The
practicality of humanoid vision system has been verified inthis paper.

(a-R)

REFERENCES
(b-L)

(b-R)

(c-L)

(c-R)

[1] Luo RC, Hsu TY and Su KL (2005), The
development of a multisensor based intelligent security
robot: Chung Cheng #1. Proceedings of IEEE International
Conference on Mechatronics, Taipei, Taiwan, Jul 10-12,
2005, pp.970-975[2] Chien TL, Su KL and Guo JH (2005), The multiple
interface security robot – WFSR-II. Proceedings of IEEE
International Workshop on Safety, Security and Rescue
Robotics, Kobe, Japan, Jun 6-9, 2005, pp.47-52[3] Hsia KH, Su JP, Lien SF and Lin WC (2011), The
mobile robot autonomous obstacle avoidance in unknown
indoor environment. Proceedings of International
Conference on Automation Technology, Douliou, Yunlin,
Taiwan, Nov 18-20, 2011, Paper No. R052
[4] Dankers A, Barnes N, Bischof WF (2009), Humanoid
vision resembles primate archetype. Experimental Robotics,
54: 495-504[5] Rougeaux S, Kuniyoshi Y (1998), Robust tracking by
a humanoid vision system. Proceedings of IAPR the FirstInternational Workshop on Humanoid and Friendly
Robotics, Tsukuba, Japan, Oct 26-27, 1998
[6] Hsia KH, Lien SF, Su JP (2011), Camera position
estimation and feature extraction from an incomplete imageof a landmark. Artificial Life Robotics, 16:152–156[7] Gonzalez RC, Woods RE (2002), Digital image
processing, second edition. Prentice-Hall[8] Ju MY, Ouyang CS, Chang HS (2010) Mean shift
tracking using fuzzy color histogram. International
Conference on Machine Learning and Cybernetics(ICMLC), Jul 11-14, 2010, 6:2904-2908

(d-L)
(d-R)
Fig. 8. The image tracking results

Fig. 9. The target position estimation results© ISAROB 2013

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An ultra-high speed motor driver with hybrid modulations
Chung-Wen Hung
Department of Electrical Engineering
National Yunlin University of Science and Technology, Yunlin, R.O.C. Taiwan(Tel: 886-5-534-2601)
wenhung@yuntech.edu.tw
Abstract: An ultra-high speed motor driver with hybrid modulations is proposed in this paper. The speed range of a spindle
motor is more wider and trends to ultra-high, but the traditional driver which provides either sinusoidal pulse width modulationor six-step modulation could not support so wide speed range. Normally, the variable voltage variable frequency control isused to support different type motors, then, SPWM is chosen in low speed range drivers and six-step is picked in high speed
application. In this paper, the hybrid modulation driver with adjustable DC bus voltage is proposed, and the modulation could
be selected for different motor and speed by users. For lower switching power loss, the six-step modulation is better whenultra-high speed application. For low speed range, the trapezoidal PWM is used to replace the SPWM for the reduction ofswitching power loss. The experimental results show the performance of proposed driver.
Keywords: Ultra-high Speed, Trapezoidal modulation, Hybrid Modulation
strategy is workable. A hybrid modulation driver for ultrahigh speed spindle motors is proposed in this paper, thehybrid modulation strategy will detail, and the driver
hardware structure will be described. Finally, the THD of
motor current in the experimental results will show the
hardware and the modulation method of the proposed driver
feasible and valuable.

1 INTRODUCTION
In most applications, an inverter is the popular solution todriver motors, such as electric vehicles, appliances,machine tool and so on. As the applications increasing,
there are many researches studied the control method toenhance the performance of system, and got good
improvement [1-3]. But they didn’t discuss the ultra-highspeed case. As the speed variation is wider, the range of thefundamental frequency supported by inverter is more
extensive. These years, it is extended to 300,000 rpm, evento 500,000 rpm. The traditional modulation method,sinusoidal pulse width modulation (SPWM), is consideredto implement ultra-speed inverter [4-6], but the switchingfrequency of power switches are requested to support 20times higher than the frequency of the sinusoidal, then thematerial cost will rise and the switching power loss will
also up. Another solution is six-step modulation withvariable DC bus voltage [7], nevertheless, the current
includes big harmonic distortion, and which will cause
torque ripple. The other modulation strategies are also
discussed: Third harmonic injection modulation could
improve the modulation ratio, and it could get the same line
to line voltage of motor driven by the lower DC bus
voltage[8], but the switching power loss is still the same
like SPWM. With variable DC bus voltage, the trapezoidal
modulation is used to perform similar modulation ratio
effect, but to reduce about 66% of switching power loss [911]. A series of the current harmonic distortion are analysis
for different kinds of trapezoidal modulation in [11].
Author proposed the hybrid modulation strategy for suchkind of wide speed rage [12], with the variable DC bus
voltage, the trapezoidal modulation is applied for the low
speed range to reduce torque ripple, but the six-step
modulation is used to lower the switching power loss whenrunning in high speed range. The simulation results ofevery modulation method in different speed show the© ISAROB 2013

2 THE MODULATION STRATEGY
2.1 SPWM, third harmonic injection modulation, and Trapezoidal modulation
The SPWM is a standard and popular modulation
method, and the power switch turn-on/-off intervals of threephases are proportional to the voltage of a three phasesinusoidal waveform. If the switching frequency is fastenough, the line current of motor will be also sinusoidal,
and the motor rotates smoothly. However, the switching
power is proportional to switching frequency. On the other
hand, due to property of a power switch, the dead-time isnecessary when switching, and it will induce torque ripple.
The THD is increasing when the fundamental frequency ofsinusoidal waveform is getting higher. So, the fundamental
frequency supported by SPWM is limited.
To get higher phase voltage in the same DC bus voltage,
the third harmonic injection modulation is proposed [8]. It
means the same performance of motor could be driven
lower DC bus voltage, and the cost of power switch device
is also lower. In other words, the third harmonic injection
modulation could improve the modulation ratio. If theoriginal phase voltage is Vbus•sin(2πf), the third harmonicwith 1/6 amplitude is injected. Then the phase voltage isshown in (1), and VXN3 (X is U, V and W) denotes thephase voltage with third harmonic injection. The phase tophase voltage with third harmonic injection (ex: VUN) is assame as original sinusoidal waveform. As shown in Fig.1,
the dash line is the original sinusoid and the dot line is

250

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013sinusoid with third harmonic, the amplitude of the latter isabout 86.5% of the former, but it could perform the sameline to line voltage effect in three phase power source.
Vbussin(3  2f )6
V
VVN 3  Vbus  sin( 2f  2 / 3)  bus sin(3  (2f  2 / 3))
6Vbus
 Vbus  sin( 2f  2 / 3) sin(3  2f )6
V
VWN 3  Vbus  sin( 2f  2 / 3)  bus sin(3  (2f  2 / 3))
6Vbus
 Vbus  sin( 2f  2 / 3) sin(3  2f )6

To get high modulation ratio, the sinusoid could be
replaced with the sinusoid with third harmonic. And, toreduce the power switching loss, the sinusoid with third
harmonic could be replaced by trapezoid with variable DCbus. However, due to the dead-time request of a powerswitch, a trapezoid is not suitable for ultra high speed.

VUN 3  Vbus  sin( 2f ) (1)

Fig. 3. The six-step modulation and the line-to-line voltage
2.2 Six-step modulation
The Six-step modulation is applied to driver motor in
ultra high speed. For three phase driver, every phase isconnected to DC bus or ground for a half of a cycle, and thetime interval is 1/3 cycle, 2π/3. The phase voltage is shownin Fig. 3. If the fundamental frequency is very high, thephase current will be quasi-sinusoid due to the low passfilter effect of the resister and Inductor (RL) in static coil.
This method will introduce bigger harmonic distortion in
low speed, but switching power loss is lower than above
modulations due to less switching.

Fig. 1. The comparison of the sinusoid (60Hz), sinusoidwith third harmonic and two trapezoids, and the partial
zoon-in
From the Fig. 1, the sinusoid with third harmonic
approximates to the trapezoids, specifically around the peak
portion. Obviously, if the system hardware could provide avariable DC bus voltage and keep the output in a same
voltage, the trapezoid is a good choice to approach thesinusoid with third harmonic. The reason is that there is notswitching power loss when the output keeps in high or low
base voltage. Two trapezoids are shown in Fig. 1, the slopsof trapezoids are different. The fig. 2 shows the FFTanalysis results of the sinusoid with third harmonic andtrapezoids, ‘x’ denotes the frequency components of theformer, then ‘o’ and ‘*’ indicate the latter’s’. The frequency
analyses of three waves look similar.

2.3 The modulation strategy
To design an ultra-high speed motor driver and lower
switching power loss, a hybrid modulation driver is
proposed in [12]. The effect is verified by simulation, from
THD comparison of the different speed with variable
modulation methods. The driver is designed to supply the
adjustable voltage of DC Bus, and then a motor is driven by
the six-step modulation in high speed range and handled bytrapezoidal modulation in lower speed.

0.5

0.4

0.3

0.2

0.1

0

0

1

2

3

4

5

6 Hz

Fig. 2. The FFT analysis results of the sinusoid with th
ird harmonic and trapezoids (Note: To identify, the ‘o’and ‘*’ are right shifted)© ISAROB 2013

Fig. 4. The system block diagram of the experimental
hardware251

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a)Fig. 5. The photo of the experimental hardware(b)Fig. 7. The line-to-line voltage and phase current run in 10krpm for (a) the six-step modulation and (b) the trapezoidal
modulation

3 EXPERIMENT
3.1 The hardware system
The block diagram and photo of the hardware system
are shown respectively in Fig. 4 and Fig. 5, it includes threeblocks: the variable output DC bus voltage control block,the power switch block, and control block. To provide avariable voltage, a variable output power factor correction
structure is considered, however, only the buck function isused in the experiment. The power switch block is used toperform modulation, and the six switches belong to threelegs to supply three phase power. The other block is thecontrol system block, and the controller could be the TI
TMS320F28x35 DSP or Renesas RX600 MCU. Note, only
the VOPFC block and power switch block are shown in Fig.
5. To prevent shoot through current, the dead time isnecessary when switching, both of controllers support the
automatic dead time generation function in the build-in
PWM peripheral block. However, the dead time will cause
the distortion for motor voltage and current, this distortion
is worse when the switching frequency is getting higher.

3.2. Experimental results
In low speed, the phase voltage of three phases for thesix-step modulation and trapezoidal modulation are shownin Fig. 6 (a) and Fig. 6 (b) respectively. Every phase voltageof the six-step modulation is constructed from a 50% duty
square-wave, what changes between variable DC bus
voltage and ground. To simplify program, the original
trapezoid is the dash dot line shown in the Fig. 1, waveform
is formed with two straight line segments of 120 degree,
one segment is connected to the variable DC bus voltage,
and the other is turned to ground. The phase voltage
between two segments is the PWM base and the duty is
proportional to the slope. However, when motors run inultra-high speed, due to the dead-time limitation, only thesix-step modulation could be performed.
Next, the low speed (10k rpm) experimental results of
the line to line voltage and line current for the six-step
modulation and trapezoidal modulation are shown in Fig. 7(a) and Fig. 7 (b) respectively. The current waveform oflatter is more similar to sinusoid than formers. Then, theexperimental results in middle speed (130k rpm) are shownin Fig. 8, and the two current waveforms are not different.
And, for high speed (here is the 300k rpm), theexperimental current of the six-step modulation is shown in
Fig. 9, the current looks more like sinusoid than the current
in low speed. Although only the six-step modulation could
be applied in ultra-high speed range, it is more suitable forhigher speed.
To compare the effect of two modulation method, theTHDs of the motor current are measured and shown in Fig.
10. In low speed, the THD of the trapezoidal modulation islower than the six-step modulation’s, and it means former ismore suitable to implement. As the speed is increasing, theTHD is getting higher. However, the THD of the six-step
modulation will reduce when over 130k rpm, it shows thatthe six-step could be used to driver in ultra-high speed.
Note, due to instrument issue, the vertical-axle scale of Fig.
10 is skipped.

(a)

(b)
Fig. 6. The phase voltage of three phases for (a) the six-step
modulation and (b) the trapezoidal modulation© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

REFERENCES[1] Jianru Wan, Yingpei Liu, Hailiang Zhou and Runqing
Bai,” Direct Torque Controlled Permanent Magnet
Synchronous Motor Based on Active DisturbanceRejection Controller”, Advanced Science Letters.
Vol. 4, pp. 2087-2091, 2011[2] Guohai Liu and Yi Zhang, “LSSVM Inverse Controlof Two-Motor Variable Frequency Speed-Regulating
System”, Advanced Science Letters. Vol. 4, pp. 12081213, 2011[3] Chung-Wen Hung, Cheng-Tsung Lin, Chih-Wen Liu,and Jia-Yush Yen,” A Variable-Sampling Controller
for Brushless DC Motor Drives With Low-Resolution
Position Sensors”, IEEE Transactions on Industrial
Electronics, Vol. 54, pp. 2846- 2852, 2007
[4] Christof Zwyssig, Simon D. Round, and Johann W.
Kolar, “An Ultrahigh-Speed, Low Power Electrical
Drive System”, IEEE Transaction on industrial
electronics. Vol. 55, No. 2, pp. 577-585, 2008
[5] Christof Zwyssig, Simon D. Round, and Johann W.
Kolar, “An Ultra-High-Speed, 500000 rpm, 1 kW
Electrical Drive System”, Power Conversion
Conference , Nagoya, Japan, April 2-5, 2007,
PP.1577-1583[6] J. Oyama, T. Higuchi, T. Abe, K. Shigematm, X.
Yang, and E. Matsuo, ” A trial production of small
size ultra-high speed drive system”, Proceedings ofthe IEMDC 2003, Madison, Wisconsin, USA, June 14, 2003, PP. 31-36[7] A. Mishra, O. Ojo, “Analysis of an induction motor
fed from a six step voltage source inverter”, IEEE
Proceedings of Southeastcon, Williamsburg, Virginia ,USA, April 7-10, 1991, pp. 1001 – 1004
[8] John A. Houldsworth and Duncan A. Grant,” The
Use of Harmonic Distortion to Increase the Output
Voltage of a Three-Phase PWM Inverter”, IEEE
Transactions on Industry Applications, Vol. IA-20,
pp. 1224-1228, 1984
[9] Katsunori Taniguchi, Masakazu Inoue, Yoji Takeda,
and Shigeo Morimoto, ” A PWM strategy for
reducing torque-ripple in inverter-fed induction
motor”, IEEE Transactions on Industry Applications,Vol. 30, pp. 71-77, 1994
[10] Katsunori taniguchi and Hisaichi Irie, “Trapezoidal
Modulating Signal for Three-Phase PWM Inverter”,
IEEE Transactions on Industry Electronics, Vol. IE33, pp. 193-200 , 1986[11] Francisco Vargas-Merino, Mario J. Meco-Gutiérrez,
Juan R. Heredia-Larrubia, and Antonio RuizGonzalez, “Low Switching PWM Strategy Using a
Carrier Wave Regulated by the Slope of a
Trapezoidal Modulator Wave”, IEEE Transactions on
Industry Electronics, Vol. 56, pp. 2270-2274, 2009[12] Chung-Wen Hung, Tsun-Hsi Chang and Wen-Yu
Chu, “A Modulation Strategy for a Super Wide Speed
Range
Inverter”,
ICIC
Express
Letter-An
international Journal of Research and Surveys, Vol. 6,
No. 3, pp.825-831, 2012

(a)

(b)
Fig. 8. The line-to-line voltage and phase current run in
130k rpm driven by (a) the six-step modulation and (b) the
trapezoidal modulation

Fig. 9. The line-to-line voltage and phase current run in
300k rpm driven by the six-step modulation

4 CONCLUSION
An ultra-high speed motor driver with hybrid
modulations is proposed in this paper. Due to dead-time
limitation and reduction of switching power loss, the sixstep modulation is considered to driver motor in ultra-highspeed range. But in low speed range, the trapezoidal
modulation is implemented into the driver, for the similar
THD but lower DC bus voltage. Depended on the property
of a motor, for different speed, the modulation method
could be selected to provide better driver current and lower
torque ripper.

Fig. 10. The THD comparison between the six-step
modulation (solid) and the trapezoidal modulation© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A current balance strategy for multi channel interleaved power converters
Jhih-Han Chen1, Chung-Wen Hung2
Graduate School of Engineering Science and Technology1
Department of Electrical Engineering2
National Yunlin University of Science and Technology, Yunlin, R.O.C. Taiwan(Tel: 886-5-534-2601)
wenhung@yuntech.edu.tw

Abstract: The multi-channel scheme is a more and more popular solution in high power electronic system, due to the current
limitation of an inductor, the power switch in every channel, and the reduction of output voltage or current ripple. However,the current unbalance is an issue for the multiphase scheme, because the current control is independent in high number channel
scheme. To solve this issue, a novel current balance control method is introduced in this paper. Compared with the traditional
method which needs a current sensor in each cannel, the bill of material cost in the proposed method is much lower, because
there are only three sensors in the structure: one is used to sense the total current to control the totally current, and the othertwo sensors are built in the first phase and the last phase for the current balance. The simulation results will be illustrated inthis paper to prove this method is workable.
Keywords: multi-channel scheme, current balance, sequential trigger
digital control processor to control each phase.
In this paper, a current balance method is presented bydifferent beginning time and two current sensors. The
sequential triggers performed the different phase delay. By
the trigger sequence, the first and last phase which build thecurrent sensor could be assigned. Next, based on that
information from the first and last phase, the current
balance method can be performed. The simulation results
will be discussed in the section 4 and demonstrate theefficiency of the proposed method.

1 INTRODUCTION
In high power system, the multi-phase scheme is more andmore popular. This scheme could be used to solve thelimitation of the components issue, and reduce the output
voltage and current ripple. But, based on this multi-channel
scheme, the current unbalance will be the problem. There
are several papers[1] to [6] discussed how to solve theunbalance. [1] introduced a multiphase voltage-mode
hysteretic controlled point-of-load (POL) dc–dc converter.
This paper used a current sharing technique to implement
the voltage-mode hysteretic-controlled without current
reference, current compensation and loop compensation. [2]introduced a buck converter in the multiphase scheme. It isbased on the mismatch-error shaping technology toimplement it. And, the proposed technology is used theconcept of 1-bit Delta-Sigma DAC to implement it. [3]discussed an Automatic multi-phase digital pulse width(A)

modulator Automatic multi-phase digital pulse width modulator, it
implemented in the Field- Programmable Gate Arrays (FPGA).
And, it used a duty cycle distribution algorithm to divide theduty cycle in each phase, and it based on this algorithm toperform the current balance. [4] discussed an active
multiphaseconverterwithmixed-signal
current
programmed mode controller. This research used acapacitor charge balance algorithm to implement thecurrent balance. [5] introduced a multiphase buck converterwith single controller. This circuit only used one controller
with the output voltage to be the feedback. To sense thecurrent of each phase with a current sensor, and based on it
to change the ramp of carrier to modify the duty cycle of
PWM. [6] introduced a multiphase DC-DC converter, it
used a average current method to balance the current ineach phase. And, it used the PWM of build-in peripheral in© ISAROB 2013

(B)

(C)
Figure 1. Buck Circuit

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

channel scheme is called an interleave technique, which isused to turned on power switch of each phase in the same
duty, but the turn-on timing of each switch are different. To
perform the operation of the interleave structure above, asingle controller is to used to handle all phase circuit.

2 THE BASIC CONCEPT OF BUCK CIRCUIT
AND MULTIPHASE SCHEME
2.1 Buck Scheme
The single phase buck circuit will be discussed in thissubsection, then the operating concept of the multiphase
scheme will be more clear. The buck circuit is illustrated inFig. 1, (A). Depending on the switch state, this circuit could
be divided into two modes, and shown in Fig. 1 (B), andFig. 1, (C). And, the charged and discharged inductor
current in these two modes could be described as (1) and(2). The inductor current, inductance, output voltage, andinput voltage, duty and time are iL, L, VO, VDC, D, and T,
respectively. The (3) and (4) could be obtain from the (1)and (2). The (3) is a charged equation, in which, therelationship between the inductor current and charged time
could be clearly identified. And, the (4) is obviously
describing the discharged state for the inductor current.

diL iL iL VDC VO



dt
t
Dt
L
V
i L
iL

 O
t 
1 D T
L
V VO 
iL ( on )  DC
DT

 L

V 


i L ( off )  O 
1 D TL 

Based on this structure, there are some obviously
questions should be considered, like the current balanceissue. The traditional method would sense each phaseinductor current. For example, if this is a 6 phases scheme,
it will need 6 current sensors. For approaching the current
balance and reducing the cost, a low cost current balance
strategy which is proposed to overcome the balance
problem is proposed in this paper.

(1)
(2)
(3)
(4)

Based on the (1) to (4), the operating concept of inductor
current of buck circuit can be understood.
Figure 3. Diagram of System Block

Figure 4. The block diagram of Balancer

3 CURRENT BALANCE METHOD FOR LOW
COST SOLUTION
3.1 Current Balance Method
The proposed method for the current balance is a low
cost strategy. Compared with traditional method, thiscurrent balance method only costs three sensors for sensing
inductor current. This current balance method could be
divided into two parts: sequential trigger technique and abalancer.
The Sequential trigger technique. The purpose of
sequential trigger is used to perform the sequentially starttiming of current phases. And, this technique is used to
avoid the forward trigger issue of last phases. When the
beginning of the system, the duty cycle obtains from the PI

Figure 2. Multiphase Buck Converter
2.2 Multiphase Scheme of Buck circuit
The multiphase scheme is shown in fig. 2. This scheme is aparallel structure consists with more than two buck circuits.
However, the output capacitor useds the same component,
and input power is the same source. The Fig. 2 shows a 6
phases parallel scheme. The control method of this multi-© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

controller will be very large even close to 80% or muchmore. Because, the feedback signal of inductor current isvery small. This situation could cause the conduction of
first and last phases in the high number phase system. This
forward trigger problem will cause unexpected situation ofphase current which situation will be shown in sec. 4. The
sequential trigger is performed by adding phase delay to theduty cycle. The phase delay time is depended on thenumber of the phase.
The balancer. Due to the start timing of conduction of
every phase is different. The current is also non-uniform
when steady state. The inductor current of every phase isshould be the arithmetic progression, and the balancer uses
the average of the first and last phase to calculate theadjusted proportions of the duty cycle for every phase. Then,the duty cycle is multiplied by the adjusted proportion to
get a compensational duty cycle for every phase. Thebalance of phase current will be approached.

6 show the inductor current of the multi-channel schemewithout the sequential trigger technique, as discussed in
the section 3.1. The Fig. 7 to Fig. 10 are the inductor
current of each channel with the proposed current balanced
method, the two enlarge figures and the balanced result,respectively. To compare with the Fig. 5 to Fig. 8, theproposed method could be demonstrated the correction
impression is very well. And, the inductor current of eachphase can be asthmatic progress by the sequential trigger
technique. The effect of the current balanced method isvery good, and it only cost 0.8ms for balanced current. Thebalanced processing and result are shown in the Fig. 9 andFig. 10.

3.2 Simulation structure
The simulation scheme is illustrated in Fig. 3. This
schematic consists of main circuit, proportional integration(PI) controller, PWM, balancer block and the function
block that is used to generate adjusted duty cycle of PWM.
The main circuit is a multiphase buck converter that has
already discussed in section II. There is only single
controller to regulate each phase circuit in this study, andthe feedback signal is the total current. The PWM
modulation which duty cycle is from the PI controller isused to perform the conduction time of the switches.
However, the duty cycle in each phase will be delivered at
different timing with the sequential trigger. And, thesequential trigger is implemented by delay block Z-1, asshown in the box of dash line in the Fig. 3. Next, thebalancer block is the most important part in this research.
This block is used to generate the duty compensation by the
first and last phase current. The balancer block diagram isshown in the Fig, 4. This block produces a totalcompensation ratio which will be used to adjust the duty of
every phase in function block. Last, the function block isused to generate the adjusted duty cycle of PWM is shownin the dot-dash line box of the second PWM in the Fig. 3.
And, this function block could be described as (5). The DPI
is the duty cycle from the PI controller. The Rcompensation is
the total compensation ratio from the balancer. The DNewDuty
is a calculation result that is used to be a new adjusted duty
cycle of PWM. The Pi is a proportion that is depends on theordinal number of phase circuit.
DNewDuty=DPI*(Rcompensation*Pi+1)(5)

Figure 5. The inductor current of each phase without thesequential trigger

Figure 6. The enlarge figure of inductor current without thesequential trigger

4 SIMULATION RESULTS
Simulation results are discussed in this section. The
environment of the simulation is Matlab/Simulink. The
specification of the system is described as below:
Input voltage = 48V
Output voltage = 15V
Output current = 60A.
This simulation could clearly illustrate the circuit
function, as shown in the Fig. 5 to 10. The Fig. 5 and Fig.

© ISAROB 2013

Figure 7. Phase current of each phase by the proposedcurrent method

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the unexpected situation of the multiphase scheme. Thebalancer accomplishes the current balance goal by theaverage of the first and last phase. And, this current balancemethod also effectively reduces the current sensor numbers.
It only costs three current sensors for implementing thecurrent balanced. One is utilized to be the current feedback
sensor for sensing the total current. And, the other two
sensors are placed for sampling the first and the last phasecircuit for performing the current balance. Next, thesimulation results can show and demonstrate the current
balance method and this simulation scheme is workable.

Figure 8. Enlarge figure of the beginning timing of theinductor current.

REFERENCES
[1]

[2]

[3]

[4]

Figure 9. Enlarge figure of current balanced processing.
According to these simulation results, this multiphasebuck converter with the current balance method is proved
workable, and these methods are also demonstrated that
could be implemented in physical circuit and theperformance is well.

[5]

[6]

Figure 10. The result of the balanced current

5 CONCLUSION
A current balance method is proposed in this paper forthe multiphase buck converter. This proposed balancemethod is consisting with a sequential trigger technique anda balancer. The sequential trigger technique successfully
avoids the forward trigger issue of last phases and reduces© ISAROB 2013

257

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Guo Guoyong, Shi Bingxue, “Designofmulti-phase DCDC converter with averaged cur
r
e
nts
ha
r
i
ngc
o
nt
r
ol
”
, 5th
International Conference on ASIC, Vol. 1, Oct. 2003, pp.
522 - 525.

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Automatic identification of 3-D shape of head for wig manufacture
Yonghu Zhu1, Yoshiaki Adaniya, Tsujino Kazuhiro, and Cunwei Lu2
Fukuoka Institute of Technology, Japan(Tel: +81-92-606-3578, Fax: +81-92-606-0726)
1

mam12004@bene.fit.ac.jp
2
lu@fit.ac.jp

Abstract: When making an order-made wig, in order to reduce cost, production of wig is performed in overseas factory where
personnel expenses are usually cheap. In order to send the information of customer's head to the overseas factory, awrap-model is often used. There are two problems in this method, one is that the production cycle time is too long, and anotheris that when the wig is damaged it is inconvenient for re-production. The aim of our research is to propose a wig manufacture
system whose production cycle is short and re-production of the wrap-model is convenient. The key point is using
three-dimensional (3-D) information instead of the customer's wrap-model. We use the 3-D image measurement method basedon Optimal Intensity-Modulation Pattern (OIMP) projection technique to obtain each side of the wrap-model, and thensynthesize it to represent the entire wrap-model. Then we send the 3-D information of the wrap-model to overseas factory byusing the internet. In the overseas factory, according to the 3-D information of the wrap-model, we select one mold which isthe fittest to produce the wig. Now, we are developing the application of the proposed method.
Keywords: Three-dimension measurement, Optimal Intensity-Modulation Pattern, wig manufacture, wrap-model, mold.

time for the consumers to decide whether to put on a wig
for the first time. However, they hope to obtain the wig assoon as possible when decide the purchase once and ask it
for production. So, shortening the time is necessary during
the period of the production process. Secondly, when the
wig is damaged the re-production of the wrap-model isinconvenient. As for the use of the wig, the customers do
not want to be noticed by neighboring acquaintances or afriend. Therefore a lot of customers think that when thedamage of the wig occurs they cannot go to the company or
the school. Furthermore, the wig is used every day, so it
should be re-purchased every 2 or 3 years. It is better to use
the wrap-model which is the same as is produced several
years ago. However, the material of the wrap-model isplastic film, and it’s easy to become shrinkage andtransformation by a secular variation. The re-production of
the wrap-model is necessary and the customers have tocome to the store again.
The target of our research is to solve the two problems.
That is to shorten the time and to make the re-production
more convenient. And in this way, the satisfaction of thecustomer could be improved.
The key point is using three-dimensional (3-D)
information instead of the customer's wrap-model. There
are many methods in 3-D measurement field. Such as thestereo vision method, it uses two cameras, just as the eyesof human. With the triangulation principle, the 3-D
information was calculated. The system of stereo vision

1 INTRODUCTION
For young ladies and famous actors who always need tochange their fashion style in different places, the wear of wig
is high, which will make them more confident and beautiful.
When people get older, the hair will become less and less.
In addition, the cancer patients who are undergoing thechemical treatment will cause a certain degree of hair loss.
In these conditions, a wig is also necessary. The wig whichis commercially available in a supermarket is cheap, but it
is difficult to accord with the shape of the head accurately.
The order-made can make a wig according to the shape ofthe user’s head, but it is expensive and the production cycle
is long.
When making a wig, besides the information of the size,
we also need to know the property of the hair, the positionof the whorl, the flow direction of the hair and the hairshades. In order to make the wig cost cheaper, almost all
the production makers usually use the overseas factory
instead of the factory in Japan. In order to send theinformation of customer's head to the overseas factory, awrap-model is often used. In the overseas factory according
to the sent wrap-model to select the fittest mold to make thewig, and send the completed wig back to Japan and reachthe customer.
This method has two deficiencies. First of all, thisproduction method will have to spend 30 days to finish thecycle, so the time needs to be shortened. It will take a long

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

measurement method contains the camera and computer,and it has a wide range of measurement. However, when do
the measurement, because it is difficult to obtain thecharacter information, the reliability is very low. And it isalso very hard to be automated. The Kinect developed byMicrosoft could also get the color image and depth of theobjects. The core of it is regardless of ambient lighting
conditions, you can let Kinect COMS IR sensor of thesensible world. This sensor to sense the environment
through the black and white spectrum: pure black on behalfof infinity, white represents infinity. Grey area between theblack and white object that corresponds to the physicaldistance sensors. It collects every point within the vision,and form the environment around a picture that represents
the depth of field images. Sensor to generate depth of field
image flows at a rate of 30 frames per second, real-time
3-D rendering environment. However, the error of Kinect islarger than 1mm, it is cheap and well used in moving object.
But what we are measuring is the Wrap-model, which record
the human head's size and form, needs higher precision.
In this paper, we use the 3-D image measurement
method based on Optimal Intensity-Modulation Pattern(OIMP) projection technique to obtain each side of thewrap-model, and then synthesize it to represent the entire
wrap-model. Then we send the 3-D information of thewrap-model to overseas factory by using the internet. In theoverseas factory, according to the 3-D information of thewrap-model, we select one mold which is the fittest to
produce the wig.

make the wig (Step 2). According to the wrap-model, we
select one mold which is the fittest (Step 3). After that wewill make the wig by using the mold (Step 4). Finally wesend the completed wig back to the customer in Japan (Step
5).
Step 1 Make the wrap-model in Japan
Step 2 Transport the wrap-model to Indonesian(7-8 days)
Step 3 Select one mold
Step 4 Make the wig (two weeks)
Step 5 Send back the wig to Japan (7-8 days)
Fig. 1.

There are two problems in this method, one is that the
production cycle time is too long, and another is that when
the wig is damaged it is inconvenient for re-production. The
aim of our research is to propose a wig manufacture system
whose production cycle is short and re-production of thewrap-model is convenient. In order to achieve the goal of
our study, we propose our method that is shown in Fig. 2.
Instead of transport the wrap-model to overseas, we just takeseveral minutes to measure the 3-D information of thewrap-model in Japan, and then take several seconds to send
the 3-D information to Indonesian. Then we use the sent 3-D
information to select the mold. In this way, the time of 7-8
days lost in transportation of the wrap-model can be saved.
Therefore, the production cycle is shortened by 25%. In
addition, we can make a database of the wig production
information so that the re-production is convenient. This also

2 CONFIGURATION OF MANUFACTURE
SYSTEM
There are three parts of the configuration of the
manufacture system. First we use the 3-D measurement to
get the 3-D image of the wrap-model. Then take photos frommultiple aspects to synthesis the images, so that we get thewhole image of the wrap-model. Finally according to the
3-D image of the wrap-model, we will choose the fittest
mold by using the software.

Step 1 Make the wrap-model in Japan
Step 2 Measurement 3-D information of thewrap-model in Japan (several minutes)
S t e p 3 Send the 3-D information of thewrap-model to Indonesian (several seconds)
Step 4 Select one mold by 3-D information

2.1 Improvement of the production process

Step 5 Make the wig (two weeks)Now, the production of the wig is like this, which can beclear seen in Fig. 1. First we record the size of the wig andother information of the hair by using the wrap-model
which is made of plastic film (Step 1). Then we transport
the wrap-model to the Indonesian manufacturing facility to© ISAROB 2013

The production flow of the wig

Step 6 Send back to Japan (7-8 days)
Fig. 2.

259

The production flow of our method

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013is defined in cases like that when the projection lightintensity difference of the stripes strength distribution
combination I1 , I 2 ,  I N  is at the maximum, evaluation
function d I1 , I 2 ,  I N  is also at the maximum.

solves the second problem. As we can see from this method,
how to measure the 3-D information of the wrap-model isbecome quite important.
In this study, by using the 3-D image measurement, we
could find out the fittest mold to produce the wig for theelderly people and the cancer patients who are undergoingthe chemical treatment and the necessity for wear of wig ishigh. We measure 3-D information of shapes of user’s headin Japan and send the result to the overseas factory using
the Internet. At the overseas factory, according to the 3-D
information, we choose one mold which resembles the formof a user's head most, from the inside of tens of thousandsof mold, to produce the suitable wig.

NM

d ( I1 , I 2 ,..., I N )    k j I i  I i  j(1)

i  M 1 j 1Here, kj is a weight factor. M is width of the filter onwhich evaluation function has been set. Fig. 4. shows theexample of the projected pattern generated with formula
1[6].
255

2.2 3-D measurement system
When we say 3-D measurement, it means that wemeasure the shape or the position of the target object. Now,
in a lot of field the 3-D measurement is widely used, from
topographic survey, building surveying to products, thehuman body and semiconductor industry and so on. 3-D isvery practical. In addition, as the rapid development of
information process and image process, in the research ofmachine vision, the 3-D measurement method which has
high-speed, high precision and adaptability was needed.
In order to obtain the 3-D information of wrap-model, we
use the 3-D image measurement technique based on patternprojection method. The composition graph of measurement
system is shown in Fig. 3. We send the pattern by using the
projector, according to the projection pattern, we can make arecognition of the stripes, and correspond the pattern
between the original and the photo taken by the camera, afterthat we can get the 3-D image of the wrap-model [3].

0Fig. 4.

Optimized pattern

As we can see from Fig. 5, Fig. 5.(a) shows theoriginal image of the wrap-model taken by the camera. Fig.
5. (b) is the optimized pattern projected by the projector.
And Fig. 5. (c) is the initial image of observation patternimage. After doing the image processing, we get theextracted pattern shown in Fig. 5. (d). As we compare thestripes of the optimized pattern and the extracted patterncalculated by the software by using the triangulation
principle, we could get the 3-D image of the wrap-model[2].

Computer(a)Original image(b)Optimized pattern

Projector
Wrap-model
CameraFig. 3.

Measurement system(c)Observation pattern image
pattern

The optimal intensity-modulation pattern projection
method is a technique for improving the accuracy of the
stripes address detection by optimizing the combination of
stripes intensity of the projected pattern, and maximizing theintensity differences between adjacent stripes. The function© ISAROB 2013

Fig. 5.

(d)Extracted pattern

Measurement principle

If we find out the corresponding relationship, we canuse it to do the 3-D measurement with the triangulation
principle, as is shown in Fig. 6.

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Fig. 7.The relation of coordinates Xi in an independent
coordinate system and coordinates X’i in a common
coordinate system is like the following formula [4].

P(X,Y,Z)
Image plane1
Target object

P1(x1,y1)Y

fProjector

X 'i  RX i  t

Z

O

(7)

P2(x2,y2)

Here, R is a rotation matrix, and t is a translation vector.
The synthesis becomes possible by calculating R and t, anddoing the geometric transformation. Feature points between
two 3D images are necessary to calculate R and t by at least
three points. Among R and t, t can be easily calculated bytaking the differences of coordinates of the common featurepoints. In this research, we have calculated each coordinateof the center of gravity from the specified common feature

Image plane2

X
Lens center, distance=bf
CameraFig. 6.

Principle of calculation

1  tan 1( x1 / f )(2)

 2  tan 1( x2 / f )

(3)

Yw

Zw

While we find out viewing angle and observation plane
coordinate, the coordinate of point P can be calculated out
through triangulation principle.
bx1
x1  x2

(4)

by1
Y
x1  x2

(5)

X

Xw

Ow

World coordinate

Yc1

Yc 2
Z c1

Z

b
tan 1  tan  2

Zc2

Oc1

(6)

Because the relation of all stripes can be correctly
detected by calculating the intensity value of noteworthy
stripes in the observation image and adjacent stripes basedon intensity change rule, this measurement method can bemeasured by only one projection and capturing two pictures.
Therefore, it can be declared that it is a technique toachieve a high-speed measurement, and to expect theapplication of the measurement for the moving object
possibly.

X c1

Camera coordinate 2
Fig. 7. World coordinate
points, and have matched them.
Consideration of measurement error, R is calculated asfollows:n X '  RX
i

i 1

2
i

 min

(8)

According to formula 8, we can get the following whichis the same meaning, as can be calculated:

2.3 Synthesis of 3-D images

 X 'n

When performing 3-D measurement, since themeasurement of the area can only fit into the camera in one
measurement, it is not easy to get the entire circumference
shape information of the object. So we need to synthesize
the obtained measurement results from multiple directions.
Therefore we can represent 3-D shape of the entire
wrap-model.
Because 3D image of each aspect obtained is recordedin the independent coordinate system that depends on thecamera respectively, it is necessary to convert coordinates
into a common coordinate system. As can be seen from© ISAROB 2013

X c2

Oc 2

i 1

2

i

n

 2( X 'i , RX i )  RX i

2

n

n

i 1

i 1



  X 'i  2 ( RT X 'i , X i )   RX i
2

i 1
n

n

n

i 1

i 1

  X 'i  2Tr[ RT  X 'i X iT ]   X i
2

i 1
n

n

  X 'i  2Tr[ RT N ]   X i
2

i 1

2

2

(9)

2

i 1

Here, n is the number of common feature points, when
the correlation matrix is as follows:nN   X 'i X iT
i 1

261

(10)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The formula 8 is the same as solving the following
formula:Tr[ RT N ]  max

First of all, we find out the highest point of the mold, thenaccording to this point, draw 6 curves on the surface of themold whose distance to the highest point are respectively
2cm, 4cm, 6cm, 8cm, 10cm, 12cm. In the same way, byusing the 3-D image information on computer, we find out
the 6 curves on the surface of the wrap-model. By comparing
the length of the curves, we find out which mold have thesmallest error from the wrap-model. We will use this mold as
the fittest to produce the wig.

(11)

We have solved this by using method of the quaternion[1].
The quaternion can represent rotation with four
components, as a composite of a scalar and an ordinary
vector, or as a complex number with three different
imaginary parts.

3 EXPERIMENTSq  (q0 , q1 , q2 , q3 )  q0  q1i  q2 j  q3k(12)

In this study, the purpose of the experiment is to find out
the fittest mold by using the measured 3-D information of
the wrap-model. First of all, we use the 3-D shapemeasurement system to obtain the image of the wrap-model,
then synthesis the images to get the entire shape information.
According to the 3-D information of the wrap-model we
could select the fittest mold to make the wig.

The rotation matrix is represented by the quaternion as
follows:
 q0 2  q12  q2 2  q32 2(q1q2  q0 q3 )2(q1q3  q0 q2 ) R   2(q1q2  q0 q3 ) q0 2  q12  q2 2  q32 2(q2 q3  q0 q1 ) 
 2(q1q3  q0 q2 )2(q2 q3  q0 q1 ) q0 2  q12  q2 2  q32 (13)

3.1 synthesis of the wrap-model
In this experiment, we measured from the direction of
the front, rear, right and left and right above to obtain thethree-dimensional information of the entire wrap-model.
It can be seen from the results shown in Fig. 9 that the
wrap-model could be synthesized correctly and 3-D
information of the entire wrap-model could be obtained.

The rotation matrix R is obtained by the unit eigenvector
to the maximum eigenvalue of four-dimensional matrix
when Tr[ RT N ] is maximized [5].
N 32  N 23
N13  N 31
N 21  N12
 N11  N 22  N 33

 N NN11  N 22  N 33
N12  N 21
N 31  N13
32
23

N 

 N13  N 31
N12  N 21
 N11  N 22  N 33
N 23  N 32

N 31  N13
N 23  N 32
 N11  N 22  N 33 
 N 21  N12(14)

Thus, due to calculating rotation matrix, it is possible tosynthesize 3D image.

2.3 Choice of moldFig. 9.

Synthesis wrap-model

3.2 Choose the moldNow we get the 3-D image of the wrap-model, then we
will use the software to calculate the data of the image tofind out the fittest mold. The method is that we find out thehighest point of the wrap-model, and according to this point,
draw 6 curves on the surface of the wrap-model whose
distance to the highest point are respectively 2cm, 4cm,
6cm, 8cm, 10cm, 12cm. After that we compare the formand size between the 3-D image of the wrap-model and themold, to find out the fittest mold. We will use this mold as
the fittest to produce the wig. As can be seen from Fig. 10,
Fig. 10(a) is the flat figure and Fig. 10(b) shows the spacecurve length, the sixth curve is broken so it can’t be used.

Fig. 8. Choose the mold
When producing the wig in overseas factory, we choose
one mold which resembles the form of the user's head most,from the inside of tens of thousands of mold, to produce thesuitable wig. As can be seen from Fig. 8.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) Flat figure
Fig. 10.

wrap-model is convenient. The key point is using 3-D
information data instead of the customer's wrap-model. In
order to obtain the 3-D information of wrap-model, we use
the 3-D image measurement method based on Optimal
Intensity-Modulation Pattern (OIMP) projection technique.
When performing 3-D
measurement, since themeasurement of the area can only fit into the camera in one
measurement, it is not easy to get the entire circumference
shape information of the object. So we need to synthesize
the obtained measurement results from multiple directions.
Therefore we can represent 3-D shape of the entire
wrap-model. Then we send the 3-D information data of thewrap-model to overseas factory by using the internet. In theoverseas factory, according to the 3-D information of thewrap-model, we select one mold which is the fittest to makethe final wig.
But there are also problems that the mold and thewrap-model don’t fit quite well actually. Maybe we shouldchange the curves we select in the further study.

(b) Space curve length

Curves on the surface of the wrap-model

After that we compare the data between the wrap-model
detected by the software and the mold detected by hand, tofind out the fittest 3 mold from 100 mold models. The result
can be seen from Table 1.
Table 1. Wrap-model and the fittest three molds
Lap Wrap-model The first The second The third(mm)
fittest(mm) fittest(mm) fittest(mm)
1
118.9
123
123
122
2
240.7
239
241
239
3
339.5
338
339
340
4
417.4
418
420
419
5
473.5
473
472
471
6
/
502
509
501
error /
0.53%
0.56%
0.59%
As we can see from the result, the error is very small, but
actually we could not always find out the fittest mold,sometimes the mold and the wrap-model don’t fit quite well.
As we can see from Fig. 11, this mold fit quite good except
the back side of the mold.

REFERENCES[1] B. K. P. Horn: “Closed-Form Solution of Absolute O
rientation Using Unit Quaternion,” JOSA A, Vol. 4, No.
4, 1987.
[2] C. Lu and L. Xiang: “Optimal Intensity-Modulation
Projection Technique for Three-Dimensional Shape Meas
urement,” Applied Optics-IP, Vol. 42, No. 23, pp. 4649-4
657, 2003.
[3] C. Lu and G. Cho: “Projection pattern intensity control technique for 3-D optical measurement,” Optics Express, Vol. 13, No. 1, pp. 106-114, 2005.
[4] K. Kanatani: “Unbiased Estimation and Statistical An
alysis of 3-D Rigid Motion from Two Views,” IEEE Tra
ns. Patt. Anal. Machine Intell., vol. 15, No. 1, pp. 37-50,
1993.
[5] I. Shimizu and K. Deguchi: “A method to register multiple range images from unknown viewing directions,”
Proc. MVA’96, pp. 106-114, 1996.
[6]C. Lu, G. Cho and J. Zhao, “Practical 3-D Image
Measurement System using Monochrome-Projection Col
or-Analysis Technique,” Proc. of the 7th IASTED International Conference on computer grapics and imaging(CGIM 2004), pp.254-259, Kauai, Hawaii, USA, August, 2004.

Fig. 11. The result

4 CONCLUSION
In this study, we propose a new manufacture system
whose production cycle is short and re-production of the© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Retrieval method for augmented reality objects based on color impression
Kazunori Nakamura, Kaoru Sugita, and Masao Yokota
Fukuoka Institute of Technology, Japan(Tel: 81-92-606-4965, Fax: 81-92-606-4965)
mgm11010@bene.fit.ac.jp, sugita@fit.ac.jp, yokota@fit.ac.jp
Abstract: In this paper, we propose a retrieval method for Augmented Reality (AR) objects based on color impression. In thismethod, AR objects can be retrieved by Kansei words and superposed on a live video at the positions specified dynamically byusers in use of special markers. The retrieval method employs color information such as RGB value and ratio of the color in
the texture. We are implementing a virtual clothes fitting software as a smart-phone application.
Keywords: augmented reality, Kansei retrieval method, color impression(2) Provider Oriented AR (POAR) allows only
providers to exchange objects. Users can only
specify the places of AR objects.
(3) User Oriented AR (UOAR) allows users to
exchange objects at markers. Providers cannot any
operation.
(4) Dynamic AR (DAR) allows both users andproviders to exchange objects using retrieval
function at markers.

1 INTRODUCTION
Nowadays, Augmented Reality (AR) has become apopular technology with many applications on PCs, tabletsand smart phones. Also, there have been many studies
conducted on AR technologies to augment live videos withartificial objects [1-3]. However, these studies are to treat
objects registered in advance only and therefore they are
not dynamically replaceable to user’s order.
On the other hand, several methods have been proposed
to intuitively retrieve images from the viewpoint of Kansei
engineering [4-7]. Most of them are so-called Kansei
retrieval methods where images are retrieved by affective
words representing characteristics of objects such as shapeand color.
Considering these facts, we have developed a Kansei
retrieval method based on color impression. In this paper,
such human intuitive and subjective emotion is statisticallyquantized via affective adjective phrases such as elegant
and pretty [8]. The remaining sections are as follows.
Firstly, Section 2 digests our extension of AR technology
and Section 3 details the proposed method for dynamic AR
object retrieval based color impression. In Section4, theKansei retrieval method is designed for computer
implementation. The development environment and theapplications are presented in Section 5. Lastly, Section 6
discusses and concludes this paper.

3 AR Object Retrieval Method based on Color
Impression
In this method, an AR content is composed of a
retrieved object and a live video on AR space. The user
puts a marker on a certain real object to specify the display
position and selects one of Kansei words concerning color
only here as shown in Fig.1. The system retrieves its
corresponding objects from the database, consisting of thefollowing modules. Figure 2 shows their organization.
(M1) Kansei Retrieval Engine retrieves several objects
with textures matching with the Kansei word.
(M2) Database stores markers, objects and relations
between Kansei word and color information in RGB.
(M3) AR Space Compositor synthesizes the retrieved
objects and the live video according to the pairs ofmarker and Kansei word.

2 The Expansion of AR Technology

Table 1. Expansion of AR technologies

In the traditional AR technologies, neither content
providers nor users could exchange any desired object after
release of AR contents. The AR can be categorized as 4
types as shown in Table 1.
(1) Static AR (SAR) is the traditional AR only
supporting one object at one mark.

© ISAROB 2013

Non-exchangable by Provider Exchangable by Provider
NonProvider Oriented
exchangable Static Augmented Reality(Traditional Technology)
Augmented Reality
by User
Exchangable User Oriented Augmented
Dynamic Augmented
by User
Reality
Reality264

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

4 Kansei Retrieval Method

for Key, 16 adjective phrases were employed considering
their independency in color impression as shown in Fig.1.
On the other hand, O is an element belonging to the set ofobjects same in shape and different in texture color as
shown in Fig.3. Each O is translated into a vector
representing area ratios of all the colors involved inadvance to compute the correlation value with the Key asshown in Fig.4.
The value U(Key) is calculated based on statistics ofusers’ Knasei evaluation of retrieved objects. The value
Fo(Key, O) is given by (2).
(2)
Fo(Key, O)=Filt(Fc(Key),Fs(O),ko)

Conventionally, it is difficult for users to obtain desired
objects or data from databases by using keywords or indicesbecause these are prepared by the system designers andinfluenced by their subjectivities. Considering this fact, wehave employed the Kansei retrieval method based on color
impression. This method evaluates an object O by a Kansei
word Key formalized as Equation (1).
(1)
Fuo(Key, O)=U(Key) ◦Fo(Key, O)
where
Fuo(Key, O): Calculated value for the pair of Kansei
word Key and object O,

where

U(Key): Calculated value of subjectivity for Kansei

Fc (Key): Set of objects retrieved by Key,

word Key,

Fs(O): Set of objects retrieved by O.
Filt(Fc(Key),Fs(O),ko): Set of objects with area ratios
over k0 included in both Fc(Key) and Fs(O).

Fo(Key, O): Statistic value for the pair of Kansei word
Key and object O.
The color impression is quantized in terms of adjective
phrases associated with a certain statistics [8]. In this study,

Fig. 1. Conversion of Kansei words based on color impression© ISAROB 2013265

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

5 Implementation
Our method has been implemented as a virtual clothes
fitting system running on smart phones as shown in Fig.5.
This system enables a user to try clothes on virtually byputting a marker on him/her and to select one among thoseretrieved by his/her Kansei word.
The software supports video capturing, AR spacecomposition and Kansei retrieval configured by such
modules as shown in Fig.6. The video capturing function
and AR space composition are available on smart phoneclients while Kansei retrieval works on the PC server.
The system was developed in C++ using STL, open

Fig. 2. AR Object Retrieval Method based on Color
Impression

frameworks and Magic++ as shown in Fig.7.

Fig. 5. The user interface of the software

Fig. 3. Objects in the Database

Fig. 6. Module configuration

Fig. 4. Comparison in RGB between
Kansei words and objects
Fig. 7. Software development environment© ISAROB 2013

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6 CONCLUSION
In this paper, we proposed a Kansei retrieval method for
AR technology expanding from the static AR to the
dynamic AR and its application. Currently, we are
implementing a virtual clothes fitting software in order toadapt our method appropriate to the statistic color
impression and to much more objects both in quantity andkind. In future, we will evaluate and improve our Kansei
retrieval method enforced with user models.

REFERENCES[1]
Mark B, Hirokazu K, Ivan P (2001); The
MagicBook: a transitional AR interface. Computers &Graphics 25 (2001): 745-753[2]
Hirokazu K (2002); ARToolkit: Library for
Vision-based Augmented Reality (in Japanese). Technicalreport of IEICE PRMU 101(652): 79-86[3]
Hirokazu K, Mark B, Koichi A and Keihachiro T(1999); An Augmented Reality System and its Calibration
based on Marker Tracking (in Japanese). Journal of thevirtual reality society of Japan 4(4): 607-616[4]
Manabu F and Yoshitaka S (1996); Kansei
Retrieval Method Reflecting Shape Pattern of Design
Images (in Japanese). IPSJ DPSWS 96(1): 267-274[5]Takio K, Toshikazu K, Ikumi F and Ayumi S(1992); Sense Retrieve on a Image Database of Full Color
Paintings (in Japanese). IPSJ 33(11): 1373-1383[6]
Shoji H, Yukihiro I and Hiromasa N (1999); On
Constructing Shape Feature Space for Interpreting
Subjective Expressions (in Japanese). IPSJ 40(5): 23562366[7]
Kaoru S, Akihiro M and Yoshitaka S (2003);
Relation Between Kansei Words and the Room Space inDigital Traditional Japanese Crafting System. Proc. of theInternational Conference on Advanced Information
Networking and Applications (AINA): 159-162[8]
Shigenobu K (1990); Color Image Scale. Nippon
Color & Design Research Institute Inc., ISBN-4-77001564-x.

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Preparatory data analysis for customer servicing interfaceof Buddhism statue ordering system
Tzu-Hsuan Huang

Kaoru Sugita

Masao Yokota

Fukuoka Institute of Technology(Tel: 81-92-606-5897, Fax: 81-92-606-8923)
yokota@fit.ac.jp
Abstract: The authors have proposed a human mind model consisting of Stimulus, Knowledge, Emotion and Response
Processing Agents and simulated human-robot communication based on it. This paper describes artificial Kansei, namely,
Kansei for a robot as tight collaboration of Knowledge and Emotion Processing Agents of our mind model, and verbalizationof affective information so called Kansei expression by Response Processing Agent, focusing on basic analysis of Kansei
words for the customer servicing interface of a Buddhism statue ordering system under development.

Keywords: artificial Kansei, customer servicing interface, image retrieval

1 INTRODUCTION

2 SYSTEM OVERVIEW

In not so far a future, there will come true a symbiotic
world of people and robots where they interact as naturally
as people do without robots. For such a symbiosis, robots
will be required to understand, as exactly as possible, what
people do. Recently, there have been developed various
types of real or virtual robots as artificial partners. However,they are to play their roles according to programmed
reactions to stimuli and have not yet come to perform
human higher-order mental functions such as Kansei. The
multi-agent model of human mind was proposed by Minsky,
M. [1]. This mind model, however, was too theoretical andcomplicated to computerize. The authors have proposed ahuman mind model consisting of Stimulus, Knowledge,
Emotion and Response Processing Agents, much simpler
and practical than the Minsky’s model, and defined Kansei,
more exactly, artificial Kansei as tight collaboration of
Knowledge and Emotion Processing Agents [2].
Recently, they have been considering its application to
the customer servicing interface of a Buddhism statueordering system. This system is intended to help thecustomers specify their favorite Buddhism statues
intuitively by Kansei expression.
The remaining sections of this paper are as follows.
Firstly, the Buddhism statue ordering system is overviewed
in Section 2 and a brief sketch of the multi-agent mind
model and artificial Kansei is given in Section 3. Section
4 analyses several kinds of preparatory data of the systemfrom the viewpoint of Kansei (or affective) engineering.
Lastly, Section 5 discusses and concludes this paper.

Figure 1 shows the configuration of the Buddhism
statue ordering system intended here. The customers are
expected to input keywords and Kansei words and specify
their intended statues well enough. At the present stage,
such specifications are limited to statue categories (e.g.,
Dainichi-nyorai, Fudomyoo), materials (e.g., ebony,
sandalwood), and facial expressions (e.g., fearful, peaceful).

© ISAROB 2013

Fig.1. Buddhism statue ordering system

3 MIND MODEL

AND ARTIFICIAL KANSEIFigure 2 shows the multi-agent mind model proposedby the authors [2]. This is a functional model of humancentral nervous system consisting of the brain and the spine.
The basic performances of its agents are as follows.

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(1) Stimulus Processing Agent (St) receives stimuli
from the world (W) and encodes them into mental
images (i.e. encoded sensations) such as “I sensed
something cold.” (if verbalized in English.)(2) Knowledge Processing Agent (Kn) evaluates
mental images received from the other agents basedon its memory (e.g. knowledge), producing othermental images such as “It is false that the sun goesaround the earth.”(3) Emotion Processing Agent (Em) evaluates mental
images received from the other agents based on its
memory (e.g. instincts), producing other mental
images such as “I dislike the food.”(4) Response Processing Agent (Re) converts mental
images (i.e. encoded physical actions such as “I’llrun fast.”) received from the other agents into real
physical actions against W.
A performance P for a stimulus X with a result Y at eachagent can be formalized as a function by the expression (1).
Y=P(X)(1)where
P: a combination of atomic performances described later
X: a spatio-temporal distribution of stimuli from W to St
or a mental image for another agent
Y: 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 and combined
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 moststrongly with X is to be applied automatically.

It is well known that emotion in a human can beaffected by his/her world, namely, W in Fig.2. For example,
a person’s evaluation of live image of an object (i.e. image
output from St) expressed by such words as ‘favorite’,‘beautiful’, ‘tasty’, etc. can vary depending on his/heremotional bias such as ‘hunger’, ‘depression’, etc.
Kansei is one of mental functions with emotion
involved but has a more complicated phase than pureemotion originated from instincts or imprinting. For
example, sweet jam may be nice on toast but not on pizza
for certain people knowledgeable about these foods. For
another example, people can be affected on their evaluationof an art by its creator’s name, for example, ‘Picasso’.
These are good examples of Kansei processing as
emotional performance affected by knowledge in humans.
Therefore, Kansei can be defined as human emotion
toward an object affected by its information, so called,‘concept’, including his/her intellectual pursuits, traditions,cultures, religions, etc. concerning it. In this sense, Kansei
is assumed to be reasonable among the people sharing suchconcepts unlike pure emotion. These hypothetic
considerations are formalized as (2) and (3).
IP(x)= PE(S(x))(2)
IK(x) =PE(S(x)O(x)) = PE(S’(x))
(3)
where
PE(X): Performance of Em for mental image ‘X’,
IP(x): Mental image as pure emotion for object ‘x’,
IK(x): Mental image as Kansei value for object ‘x’,
S(x): Live image of object ‘x’ from St,
O(x): Concept of object ‘x’ from Kn,
S’(x): Unified image of live image and concept.

4 AFFECTIVE ANALYSIS OF STATUES
Many psychologists have claimed that certain emotions
are more basic than others [3]. We have assumed thathuman emotion consists of 5 primitives representing thedegrees of 1) Anger, 2) Disgust, 3) Anxiety, 4) Happiness,and 5) Superiority. For example, the degree of Anger is
intuitively measured by using such a word set as {terror,
fear, scare, annoyance, upset, irritation, displeasure, anger,
furry}, whose each element is possibly arranged on acoordinate axis and fuzzified with a certain characteristic
function. Therefore, we have assumed Kansei as a certain
function to evaluate totally the loci in the attribute spaces ofthese primitives [4]. Figure 3 shows our tentative
arrangement of the word meanings on the scales of theprimitive emotions (i.e., -9 through +9), where each wedge
points the representative value of the corresponding word
without characterization such as fuzzification.
Based on the 5 primitive emotional parameters, we haveanalyzed 18 Buddhism statues of 11 categories as shown in
Fig.4 in order to plot them in the attribute space of Kansei

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.
Fig.2. Multi-agent model of human mind

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[4]. At the present stage of our study, semantic
characterization of the involved words has not been
completed yet and therefore we have used a rough scalewith three ordered ranges such as ‘Anti-emotion degree
range (A)’, ‘Neutral degree range (N)’ and ‘High degree
range (H)’, whose intervals on each axis are [-9, -3], (-3, 3),
and [3, 9], respectively. Table 1 and Fig.5 show an example
of such analysis using this scale, where the values represent
the averaged subjective scores of the authors (3 persons)and, as a matter of course, the averaged image (AI) was put
at the origin (0,0,0,0,0) of the Kansei space [2, 4] (See Fig.6
in APPENDIX).

(a) Average(AI) (b) Dainichi(DN) (c) Fudomyoo(FM)(d) Daikoku(DT)(e) Ashura(AS)(f) Daishi(DS)(a) Anger(g) Shichimen(ST) (h) Juichimen(JK) (i) Koyasu(KK)(b) Disgust(j) Shofuku(SK)(k) Monten(MT)(l) Nio(NO)Fig.4. Samples of Buddhism statue images(c) Anxiety

Fig.5. Tentative arrangement of images on Happiness-axis(d) Happiness
Table 1 Affective analysis of Buddhism statues
Sample
AI
DN
FM
DT
AS
DS
SK
MT
NO

(e) Superiority
Fig.3. Scales of the primitive emotions© ISAROB 2013

270

anger
0
N
H
N
H
N
N
H
H

disgust
0
N
H
N
N
N
N
N
H

anxiety
0
N
H
N
H
N
N
H
H

happiness
0
N
A
H
A
N
H
A
A

superiority
0
N
N
N
N
H
H
H
H

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theories such as neural network, genetic algorithm, fuzzy
logic, etc. in near future.

5 DISCUSSION AND CONCLUSION
The affective analysis of Buddhism statue images andthe semantic analysis of Kansei words were described.
Quite distinguished from others [e.g., 5], the images and thewords are associated with each other indirectly via theirsemantic expressions, namely, vectors in the 5-dimensional
space of Kansei and therefore their semantic analyses wereconducted independently each other.
These results are to be associated with moresophisticated Kansei words for Buddhism statues such as
peaceful, divine, gentle, reverential, noble, valiant, etc. andfinally to be applied to the customer servicing interface of a
Buddhism statue ordering system. The meanings of suchwords are to be represented as certain complicated vectors
in the Kansei space as well as the images while each ofprimitive words such as happy, angry, etc. is represented
simply in terms of the unit vector for each corresponding
primitive emotion. The customers of the system are
expected to use these words together with primitive ones
for describing their own favorite statues at ordering and thesystem is bound to propose the users several candidates ofhigh correlation values.
The averaged image was generated through theprocesses shown in Fig.6 in APPENDIX. Firstly, the image
data were arranged for the noses to come to the center ofthe frame. Secondly, the faces were resized for theircontours to touch the rectangular border of the frame andfinally, they were averaged at each pixel. The computed
cross-correlation values show, for example, that the average
image is near to Dainichi-nyorai and far from Fudomyoo.
This fact corresponds well with our intuition shown in
Table 1. By the way, the variance image is not considered
for any use at present.
As defined by Equation (3), Kansei can be affected by
the knowledge of the person involved as well as his/herpure emotion. For example, the legend of Acala (Fudomyo)
says that he is to be a powerful deity who protects ‘All the
Living (Shujo)’ by burning away all impediments anddefilements, thus aiding them towards enlightenment [6].
The knowledge of this fact can affect persons in theiremotions so as to weaken the degrees of ‘Disgust’ and‘Anxiety’ in comparison with their pure emotions evokedby its appearance. However, our qualitative and quantitative
study of this point is still shallow and therefore its further
elaboration will be also included in our future work.
Our mind model is much simpler than Minsky’s but its
realization based on the attribute spaces can work forrepresenting and computing mental phenomena fairly well[2, 4]. The attribute space of Kansei is to play an importantrole in a certain robotic individuality of general use. For
realizing a plausible Kansei, it is most essential to find out
functional features of Em and to deduce from them suchlaws that rule PE. The most important problems to besolved are how to realize the attribute space of Kansei andhow to build its corresponding atomic performance. In
order to solve these problems, focusing on Buddhism
statues, we will consider the application of soft computing© ISAROB 2013

REFERENCES[1] Minsky, M: The society of mind, Simon and Schuster,
New York, 1986.
[2] Yokota,M., Shiraishi,M. and Capi,G.: “Human-robot
communication through a mind model based on the Mental
Image Directed Semantic Theory,” Proc. of the 10th
International Symposium on Artificial Life and Robotics(AROB ’05), pp.695-698, 2005.
[3] Ortony,A. and Turner,T.J.: “What’s basic about basic
emotions?”, Psychological Review, Vol.97, pp.315331,1990.
[4] Huang, T., Sugita, K. and Yokota, M.: “Toward
artificial Kansei based on Mental Image Directed Semantic
Theory”, Artificial Life and Robotics: Vol.17-2, pp. 186190, 2012.
[5] Sugita, K., Ishida, T., Miyakawa, A. and Shibata, Y.:“Kansei Retrieval Method using the Quantitative Feature of
Traditional Japanese Crafting Object,” Proc. of The 6thInternational Workshop on Multimedia Network Systems
and Applications (MNSA'2004), 112-117, 2004.
[6] ‘Acala’, Wikipedia.

APPENDIX
The 18 image data were averaged through the processes
shown in Fig.6 below. Additionally, the variance and thecross-correlation values among them were computed.

Fig.6. Process flow for image averaging

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Toward multimodal user interface for intuitive interaction
with Buddhism statue ordering system
Tzu-Hsuan Huang

Kaoru Sugita

Masao Yokota

Fukuoka Institute of Technology(Tel: 81-92-606-5897, Fax: 81-92-606-8923)
yokota@fit.ac.jp
Abstract: The authors have been developing a Buddhism statue ordering system which enables customers to use affective
words such as peaceful and divine, intending to specify the facial expressions of their favorite statues. Such affective words,
however, are bound to vague and ambiguous specification of their demands. This paper proposes an innovative two-staged
specification method allowing customers to elaborate their demands by graphical inputs following Kansei words.

Keywords: artificial Kansei, multimodal interface, product customization

1 INTRODUCTION

2 MULTIMODAL DEMAND SPECIFICATION

For ordinary people, natural language is the mostimportant among the various communication media
because it can convey the exact intention of the sender tothe receiver due to its syntax and semantics common to its
users [1]. This is not necessarily the case for anothermedium such as gesture and therefore natural language canas well play the most crucial role in intuitive human-robot
interaction [1]. Natural language, without any doubt, is anextremely powerful means for people to express their ideas
of various kinds in abstract, i.e., conceptual level. However,for example, it is not so powerful to describe naturalscenery in concrete as picture. This is also the case forcertain customer servicing systems allowing users tospecify their demands for the favorite products in natural
language. Recently, the authors have been developing a
Buddhism statue ordering system which enables customers
to use affective words such as peaceful and divine,
intending to specify the facial expressions of their favorite
statues. Such affective words, however, are bound to vague
and ambiguous specification of their demands. This paper
proposes an innovative two-staged specification method
allowing customers to elaborate their demands by graphicalinputs following affective words, so-called Kansei words.
The remaining sections of this paper are as follows.
Firstly, the multimodal interface of the Buddhism statueordering system is overviewed in Section 2. Secondly,
Section 3 describes the methodology of semantic definitionof Kansei words as membership functions. Thirdly,
Section 4 and 5 present linguistic and graphical expression
processing, and lastly, Section 6 concludes this paper.

The Buddhism statue ordering system underdevelopment is expected to customize prototypes according
to the user’s demands specified both linguistically andgraphically as shown in Fig.1. The interactions between theuser and the system are largely such as follows.
(Step 1) User asks the system for the favorite statue innatural language such as ‘Extremely peaceful’.
(Step 2) System retrieves candidate prototypes such as
Fig.2 (a)-(c) from Raw Image Database to display.
(Step 3) System retrieves line-drawn faces of thecandidates such as Fig.3 (a)-(c), respectively, from
Sketch Database to display through a certain linedrawing tool.
(Step 4) User retouches the sketches through the drawing
interface.
(Step 5) System reedits and customizes the prototype
image data according to the graphical demand
specification.
(Step 6) User continues the interaction by returning to
Step 1 or Step 4. Otherwise, User terminates theinteraction.

© ISAROB 2013

Fig.1. Multimodal intuitive interaction in Buddhism statueordering system

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Fig.2. Examples of raw images of statues
Fig.4. Fuzzification of Kansei word meanings
Table 1 Primitive Kansei words and MFIs†
Word
Fury
Anger
Displeasure
Irritation
Upset
Annoyance
Scare
Fear
TerrorNauseaDisgustDislike
Disfavor
Favor
LikingLove
Ardor
Despair
Discouragement
Anxiety
UneaseEase
Anticipation
Encouragement
Assurance
Ecstasy
Joy
Happiness
Gladness
Cheer
Contentment
Discontent
Gloom
Unhappiness
Sorrow
Distress
AnguishAwe
Respect
Admiration
Sympathy
Pity
Disregard
Disrespect
Misery(a) DN1S(b) DN2S(c) DN3S
Fig.3. Face sketches of the statues in Fig.2

3 SEMANTIC DEFINITION OF KANSEIWORDS AS MEMBERSHIP FUNCTIONS
The meaning of a Kansei word is characterized by a setof membership functions defined respectively at 5
dimensions of the Attribute space of Kansei, each of whichcorresponds with the 5 primitive emotions, namely, 1)
Anger, 2) Disgust, 3) Anxiety, 4) Happiness, and 5)
Superiority [2]. In this study, each of the membershipfunctions for a Kansei word W is given in such a way that it
is characterized by a trapezoid, symmetric at the verticalline passing (C, 0) on a primitive emotion axis, with theheight 1, the top length T, and the bottom length B as
shown in Fig.4. Therefore, the membership function fki(x)for i-th dimension of a word Wk is defined uniformly as (1),where x denotes some degree of a certain primitive emotion.
Hereafter, for the sake of simplicity, each trapezoid is
normalized as T=1 and B=4, which necessarily implies thatthe meaning of each Kansei word is representable simply
by the coordinate consisting of Cs for all the primitive
emotions. Each C ranges over [-9, +9] on its corresponding
axis [2] and is called MFI (Membership Function Identifier)
here.

(1)
According to this definition and its normalization, suchprimitive Kansei word concepts as fury, favor, and joy canbe defined as Table 1 (tentative) because they are
represented simply in terms of the unit vector for eachcorresponding primitive emotion. On the other hand, themeanings of such complex Kansei words as divine andpeaceful are defined as certain complicated vectors in the
Kansei space as shown in Table 2 (tentative). Figure 5
shows the membership functions assigned to several Kansei
words of ‘Happiness’, namely, the 4th dimension of the
Attribute space of Kansei.

© ISAROB 2013

†

1) Ag
+8.5
+7
+5
+3
+2
-2
-4
-6
-8.5

0

0

0

0

2) Dg

3) Ax

4) Hp

5) Sp

0

0

0

0

+8.5
+7
+5
+2
-2
-5
-7
-8.5

0

0

0

0

+8.5
+6
+4
+2
-2
-4
-6
-8.5

0

0

0

+8.5
+7
+5
+4
+3
+1
-1
-2
-5
-6
-7
-8.5

0

0

+8.5
+7
+5
+2
-2
-5
-7
-8.5

0

0

0

Ag=Anger, Dg=Disgust, Ax=Anxiety, Hp=Happiness, Sp=Superiority

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the adverbial D as shown in Table 3 (tentative) in order tomagnify or deduce each primitive emotion identified by Cki.

Table 2 Complex Kansei words and MFIs
Word
Peaceful
DivineReverential
Noble
Valiant
Jingoistic

1) Ag
0
-5
-1
0
+7
+5

2) Dg
0
0
-1
0
+5
+5

3) Ax
0
0
+1
0
+4
+2

4) Hp
+1
+1
-1
+1
-7
-5

5) Sp
+1
+8
+7
+5
-7
-6

Cki (deg(D1)  deg(D2) …deg(Dn))
Table 3 Adverbials and degrees
DDdeg(D)

deg(D)

Extremely
Very
Much
More

1/20
1/10
1/5
10/11

10
3/2
3/2
11/10

Scarcely
Slightly
A little
Less

That is, the modifiers D1D2…Dn work only to translate
the membership functions of Kansei word Wk on eachcorresponding axis. According to this formalization, forexample, the meaning of such a Kansei expression as ‘more
noble’ is given as (8) by consulting Table 2 as well.

Fig.5 Membership functions of ‘Happiness’

V1=V2=V3=(x,0)V4=(x,1.1)V5=(x,5.5)

4 LINGUISTIC EXPRESSION PROCESSING{KA | (K) | not K | K and K | K or K,
AW | DA,Wpeaceful | noble | …,D extremely | very | much | more | less| slightly | a little | scarcely | …}

(2)

peaceful, more noble, a little fearful(3)

not very valiant and (peaceful or awful)(4)very very very extremely peaceful

(5)

(8)

Thirdly, the logical expressions ‘not K’, ‘K1 and K2’,and ‘K1 or K2’ are translated into (9), (10), and (11),respectively, ‘~’ and ‘’ are the logical NOT and OR,
respectively.

The syntax of Kansei expression K is defined by (2) as aset of production rules of a context-free grammar.
According to this definition, for example, such aconsiderably complex Kansei expression as (4) can begenerated as well as rather simple ones such as (3).
Moreover, such an extreme expressions as (5) can be aswell generated as one of the terminal expressions for DnW(n≥0), the generalized form of the structure A.

~Sem(K)(9)

Sem(K1)Sem(K2)(10)

Sem(K1)Sem(K2)(11)

Meanwhile, semantic synthesis of membershipfunctions in the logical structures of K is in principle all thesame as in the conventional multivalued logic. That is, theequivalences listed in (12) hold for any single or paired
membership functions, where Min and Max are suchfunctions that they take the minimum and the maximum
value, respectively, of the two functions at each x.
{~(Vi=g(x))  (Vi=1-g(x)),(Vi=g(x)  Vi=h(x))  (Vi=Min(g(x),h(x))),(Vi=g(x)  Vi=h(x))  (Vi=Max(g(x),h(x)))}

Simultaneously, the semantics of Kansei expression is
given in terms of membership functions although, as easily
imagined, most of the expressions generated from the above
rules are semantically anomalous or of no significance.
Firstly, the semantic interpretation Sem(Wk) of Kansei
word Wk is formally represented as (6) while it isconventionally given as an ordered set of membershipfunctions as shown in Table X and Y. This formalization
reads that the value of the i-th primitive emotion at Wk, Vi isequal to fki(x) with Cki and that all the 5 equations are
combined with the logical AND denoted by .
Sem(Wk) (V1=(x,Ck1)V2=(x,Ck2)…V5=(x,Ck5)),
where (x,Cki) is fki(x) with Cki.
(6)

(12)

For example of negation, ‘not Wk’, one of the simplest
structures, is interpreted as (13) so that ‘not’ modifies (6)by replacing each (x,Cki) with 1-(x,Cki), and  with .
V1=1-(x,Ck1)V2=1-(x,Ck2)…V5=1-(x,Ck5)(13)

For stereotypical example of conjunction, the meaning of‘Wk and Wj’ is synthesized as (14).
V1=Min(fk1(x), fj1(x)) …V5=Min(fk5(x), fj5(x))(14)

For disjunction, exclusively in the case that the twowords in ‘Wk or Wj’ are both primitive for the same
emotional category like the pair of happy and joyful, such asemantic synthesis as (15) is possible, where the very same

Secondly, such a structure as D1D2…DnWk (n≥1) issemantically interpreted so that the combination ofadverbials D1D2…Dn modify (6) by replacing each Cki with
(7). Here, deg(D) is a function to assign a certain degree to© ISAROB 2013

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{FHDCDG, HBlRBr, CElPNPEr, GM,
Blbl1|bl2|…, Brbr1|br2|…, Elel1|el2|…,Erer1|er2|…, Nn1|n2|…, Mm1|m2|…,
Dd1|d2|…, Pp1|p2|…}

category is denoted by the suffix d and the 4 remainders, bya, b, c, and d, respectively.
Vs=Max(fks(x),fjs(x))Va=Vb=Vc=Vd=(x,0)(15)

This definition consists of non-terminal symbols such as
follows, respectively representing a set of real sketches offace parts or their placements denoted by terminal symbols(i.e., b, e, n, m, d, and p).
H, C, G: The upper, middle and lower part of the face,respectively,
Bl, Br: The eyebrow left and right from viewers,
respectively,
El, Er: The eye left and right from viewers, respectively,
N, M: The nose and the mouth, respectively,
D, P: Downward and rightward placement of thefollowing part, respectively.
The users of the system are allowed to rearrange what
are represented by the terminal symbols through a certain
graphical interface as shown in Fig.1. Figure 7 shows
several examples of such rearrangements.
On the other hand, the semantics of face expression canbe defined in terms of f(x) and MFI as well as Kansei
expression while, in general, nonlinguistic expression suchas picture cannot bear definite meaning comprehensible forevery one [1].

As easily understood, semantic synthesis in more
complicated structures has only to employ thecorresponding membership functions synthesized from acertain set of primitive ones, namely, {fki(x)} involved. At
conclusion, any Kansei expression can be translated intosuch a disjunctive normal form as (16), where each g(x)denotes a membership function resulted from someconjunctions or negations for each dimension.

j=1,n (k=1,5Vk=gjk(x))(16)

Lastly, the candidateship of a statue S for a demand D isevaluated by the function Fc (Candidateship Function)
defined by (17), employing a special function Hst to detect
the highest peak (i.e., maximum value) of the synthesized
function h(x). Each statue image is characterized by a pairof f(x) and MFI as well as a Kansei word, and therefore
0≤Fc(S|D)≤1. Table 4 (tentative) shows the MFIs of thestatues shown in Fig.6.
Fc(S|D)=Max(M1,M2,…,Mn),
where
Mj=Min(Hst(hj1(x)), Hst(hj2(x)), …, Hst(hj5(x)))
hjk(x)= Min(fk(x),gjk(x)),
Sem(S) (V1= f1(x)V2= f2(x)…V5= f5(x)),
Sem(D) j=1,n (k=1,5Vk=gjk(x)).

(17)
Fig.7. Faces with different eyebrows (Bl or Br)

Table 4 Statue images in Fig.6 and their MFIs
Statue
DT
DN
AS
FM

1) Ag
0
0
6
8

2) Dg
0
0
2
4

3) Ax
0
0
5
6

4) Hp
8
-2
-3
-5

(18)

6 CONCLUSION

5) Sp
1
2
-1
2

Here was presented an innovative specification
method based on semantic processing of both linguistic andgraphical demands in the Buddhism statue ordering system.
Our proposed method interactively allows Users togradually approximate System’s proposals to their real
intentions unlike others [e.g., 4] while it is still on the way
to practical implementation.

REFERENCES(a) DT(b) DN(c) AS
Fig.6. Examples of images of statues[1] Yokota, M.: “Subjective Knowledge Representation for
Intuitive Human-Robot Interaction Based on Mental Image
Directed Semantic Theory”, in (Ed. Thomas S. Clary)
Knowledge representation, Nova Publishers, 2012.
[2] Huang, T., Sugita, K. and Yokota, M.: “Toward artificialKansei based on Mental Image Directed Semantic Theory”,
Artificial Life and Robotics: Vol.17-2, pp. 186-190, 2012.
[3] Ekman, P. & Friesen, W.: The facial action coding
system: A technique for the measurement of facial
movement, Consulting Psychologists Press. 1978.
[4] Sugita, K., Ishida, T., Miyakawa, A. and Shibata, Y.: “Kansei
Retrieval Method using the Quantitative Feature of Traditional
Japanese Crafting Object,” Proc. of The 6th International
Workshop on Multimedia Network Systems and Applications(MNSA'2004), 112-117, 2004.

(d) FM

5 GRAPHICAL EXPRESSION PROCESSING
The graphical expressions to be processed in the systemhere are limited to sketches of the faces of statues. A sketch
here is to show only how the main parts (i.e., mouth, nose,
eyes, and eyebrows, here) of the face lie, for example, lying
gradient, direction, curvature, extent, etc. [3] each of whichis called ‘Attribute’ (of a matter such as face part) ingeneral [1]. The syntax of face sketch F is defined by theset of production rules (18).

© ISAROB 2013

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On a relation between feeling Impression and 3PACF of sound signal
Yusuke Kawakami1, Tetsuo Hattori1, Hiromichi Kawano2 and Tetsuya Izumi3
1

Graduate School of Engineering, Kagawa University
2217-20 Hayashi, Takamatsu City, Kagawa 761-0396, Japan(Tel: 81-87-864-2292, Fax: 81-87-864-2302)
2
NTT Advanced Technology
19-18 Nakamachi, Musashino-shi, Tokyo 180-0006, Japan
3
Micro Technica Co., Ltd.
3-12-2 Higashi-ikebukuro, Toshima-ku, Tokyo 170-0013, Japan1

s11d621@stmail.eng.kagawa-u.ac.jp, hattori@eng.kagawa-u.ac.jp

Abstract: This paper presents an experimental investigation correlation between human feeling impression and some physicalfeature quantity in sound signal. We focus on three kinds of values, that is, fluctuation value, intercept, and sum of squared
errors that are obtained when making a regression analysis of sound signal in Fourier domain, as for the feature quantity. In ourinvestigation using a questionnaire survey over 34 persons, we apply multiple regression model to the relation between thefeature quantity of signal and human evaluation about Kansei impression for each person. And, after making the constructionof the regression equation for each person, we show the strength between the quantity and impression. Moreover, we classify
the set of coefficients of the equation into three groups and discuss some tendency of human impression resulting from thequantity.
Keywords: feeling impression, fluctuation value, intercept, signal processing, sum of squared errors
vertical axis shows the logarithm of the PS. Where, thisregression line has an absolute-slope of a. In this paper, wecall the absolute-degree of the slope “Fluctuation value”.
Accompanying when the regression line is computed asshown in Fig. 1, three kinds of parameters are defined, i.e.,(1) Fluctuation value as the absolute value of slope a, (2)Intercept b, and (3) Residual of the line. We call the set ofthese parameters “ 3PACF” that stands for Three
Parameters Accompanying Calculation of Fluctuation [8].

1 INTRODUCTION
Recently, 1/f fluctuation in various fields of signal has
been actively researched, and it brings about an effect ofsuch healing as a human being psychologically feels at ease,
if there is a 1/f relation between the power spectrum of thesignal and the frequency f ([1]-[7]).
However, in the past research about 1/f fluctuation
focused attention on the value of fluctuation. And it has not
yet led to analyze when signals have the same 1/f
fluctuations those power spectrum distributions. Therefore,
we wonder that feeling impression strongly affects not only
value of fluctuation but also other factors.
In this paper, we introduce three kinds of parameterssuch as fluctuation value, intercept, and sum of squared
errors (or residual) as feature quantity in sound signal
obtained from the calculation of the signals’ fluctuation
degree, and we investigate the relation between feeling
impression and those parameters. We investigate sensitivityof the feeling impression by those parameters, especiallyfluctuation value and residual.

()= log 1 f a + b

log fFig. 1. Example of regression line
The residual of the regression line is defined by Eq. (1)as in the form of a sum of the squared error, where y and Y
show a target variable and an estimated regression value
respectively.

2 QUANTITIES ACCOMPANYINGCALCULATION OF FLUCTUATION
2.1 3PACF
Fig. 1 shows an example of the regression line. Its
horizontal axis shows the logarithm of the frequency and© ISAROB 2013

y = −a log f + bys = ∑ ei = ∑ ( yi − Yi )
2

i

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3 INVESTIGATION

2.2 Correlation between 3PACF
We have investigated as to whether or not there exists
correlation between 3PACFs of music. Table 1 shows usingmusic list of wave file. Their sampling rate and quantization
bit rate are 44.1 kHz and 16 bit. Fig. 2 (a)-(c) shows theplotted graph between two parameters of 3PACF of music.

3.1 Outline
We have used questionnaire survey in order toinvestigate the relation between 3PACF and feeling
impression of music. The examinees are 34 university
students in the age of early twenties. We have given somequestions about their feeling impressions for 10 pieces ofmusic. The list of music used in this survey is the same asshown in Table 1. For every piece of the music, we havetaken 20 seconds to play it. After that, we have asked thestudents to evaluate the 4 items as shown in Table 2, by
scoring from one to four. Also the students have judged thetotal evaluation for each music by scoring from 1 to 10.

Table 1. Music list of wave fileNo

Title (.wav)
1 Another_Sky
2 Londonderry_Air
3 Blieve_you
4 Drafting
5 Down_by_the_Riverside
6 Space_Odessey3_Revelation
7 TOMORROW
8 Old_French_Song
9 Freedom
10 Red_River_Valley (brass)

Genre
Easy Listening
Classic
Easy Listening
Easy Listening
Jazz
Easy Listening
Pops
Classic
Pops
Jazz

Table 2. Questions in the survey
Item 1
Item 2Item 3Item 4

0.65

12.0

Slow

1⇔4

Quick

Heavy
Natural
Negative

1⇔4

Light
Artificial
Positive

1⇔4
1⇔4

0.60

10.0
0.55

ResidualIntercept

8.0

6.0

3.2. Multiple regression analysis
We have conducted multiple regression analysis of theresults as shown in Eq. (2) in the four-item score (item1,
item2, item3, and item4) and the total evaluation score,
where, y is the score of the each examinee, x 1 and x 2 are
explanatory variables of “Fluctuation” and “Residual”,
respectively. And, error ε is independence variable andfollows the normal distribution N(0, σ2). We eliminate thethird variable for “Intercept”, because this quantity (orparameter) is substantially equal to the volume of sound [8].

0.50
0.45
0.40

4.0
0.35

2.0

0.30

0.0

0.5

1.0

1.5

2.0

2.5

2.0

Fluctuation value

4.0

6.0

8.0

10.0

12.0

Intercept(a)(b)
Another_Sky

0.65

Londonderry_Air

0.60

Blieve_you
0.55

Residual

Drafting
0.50(

Down_by_the_Riverside
0.45

y = α 0 + α1 x1 + α 2 x2 + ε , ε ~ N 0, σ 2Space_Odessey3_Revelation

0.40

TOMORROW

0.35

Also, we have noticed that the influence of four kinds offrequency domains, i.e. (i) All-frequency domain (AF;0~22050Hz), (ii) Low-frequency domain (LF; 0~300Hz),(iii) Middle-frequency domain (MF; 300~1000Hz), (iv)High-frequency domain (HF; 1000~22050Hz). So, we haveanalyzed the results of questionnaire for each person using
Eq. (2), for each item, and for each frequency domain.
Then we will obtain the concrete the coefficients andregression equation for Item1 in AF using the Least Squares
Method. However we have set the coefficient α 0 = 0,
because the multiple correlation coefficient tends to behigher as a result of analysis at this time, as a whole. In thisway, the regression equation for each item of {Item1, Item2,
Item3, Item4, Total Evaluation} and for each person will beavailable. So we will obtain five regression equations from

Old_French_Song

0.30
0.0

0.5

1.0

1.5

Fluctuation value

2.0

2.5

Freedom
Red_River_Valley (brass)(c)Fig. 2. Correlation of 3PACF (a) Fluctuation value andIntercept (b) Intercept and Residual (c) Fluctuation va
lue and Residual

The correlation coefficient in Fig. 2: (a) 0.99061
between Fluctuation value and Intercept. (b) 0.85238
between Intercept and Residual. (c) 0.78630 between
Fluctuation value and Residual. We consider that there exist
high correlation between Fluctuation value and Intercept,
between Intercept and Residual, or between Fluctuation
value and Residual.

© ISAROB 2013

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Table 3. Correlation coefficient and regression coefficient (a)AF, (b)LF, (c)MF, (d)HF(a)

feeling impression data of each examinee, and we carry out
this analysis for 34 examinees.
After that, we make a clustering analysis for them, using
Ward Method [9]. As the result, those equations are
generally classified into three groups. We will get
regression coefficient of Fluctuation (α 1 ) and regression
coefficient Residual (α 2 ). However we have to calculate
Normalized Fluctuation Coefficient (NFC) α 1 * andNormalized Residual coefficient (NRC) α 2 * using Eq. (3)and Eq. (4), in order to compare α 1 and α 2 .

xn =*xn − xn*, ∴ xn = xn σ n + xn ,
σn

(

) (

(n = 1, 2)

y = α 1 x1 + α 2 x2 = α 1 x1 σ 1 + x1 + α 2 x2 σ 2 + x2*(*

= (α 1σ 1 )x1 + (α 2σ 2 )x2 + α 1 x1 + α 2 x2**

= α 1 x1 + α 2 x2 + α 3
*

*

*

*

)

Evαluαtion
Item
Item 1
Item 2Item 3Item 4Totαl
Evαluαtion

Averαge of Regression CoefficientGroup 1
Group 2
Group 3

α1∗

α2∗

α1∗

α2∗

α1∗

α2∗

0.625
0.772
0.528
0.317

0.132
0.024
0.395
0.620

0.252
0.408
0.112
0.014

1.478 -0.186 0.150

0.831

0.991
0.980
0.980
0.990

1% -0.949
1% -0.941
1% -0.657
1% -0.812

1.082 -0.295
1.154 -0.404
0.968 -0.097
1.103 0.199

0.949

1% -0.939

1.925

(b)
Evαluαtion
Item

(3)

Item 1
Item 2Item 3Item 4Totαl
Evαluαtion)(4)Multiple Significαnce
correlαtionlevel ofcoefficient regression

Averαge of Regression CoefficientGroup 1
Group 2
Group 3

α1∗

α2∗

α1∗

α2∗

α1∗

α2∗

0.763
0.669
0.316
0.829

1% -0.182
1% 0.099
1% 0.085
1% -0.143

0.662
0.757
0.788
0.862

0.403
0.563
0.258
0.449

0.448
0.520
0.634
0.629

-

-

0.021

1% 0.904

1.684

1.177

1.228 0.830

0.317

(c)

*
Evαluαtion
Item

Where x n is average of explanatory variables ofFluctuation or Residual, and x n * is normalized explanatory
variables of Fluctuation or Residual. And σ n is standard
deviation.

Item 1
Item 2Item 3Item 4Totαl
Evαluαtion

Multiple Significαnce
level ofcorrelαtion
coefficient regression

Averαge of Regression CoefficientGroup 1
Group 2
Group 3

α1∗

α2∗

α1∗

α2∗

α1∗

α2∗

0.831
0.816
0.879
0.911

1% -0.671
1% -0.293
1% -0.444
1% -0.556

0.887 -0.174
0.880 0.112
0.983 -0.050
1.048 -0.218

0.670
0.629 0.699
0.778 0.303
0.842 0.289

0.349
0.422
0.583

0.613

1% -0.173

2.070

1.365 0.841

0.620

0.397

(d)

3.3. Results
3.3.1 AF (All-frequency domain)
Table 3 shows correlation coefficient and average of
normalized regression coefficient of AF. The average of
normalized regression coefficient is between Normalized
Fluctuation Coefficient (NFC) and Normalized Regression
Coefficient (NRC).
As shown in Table 3, in Group1, NRC α 2 * is higher
than NFC α 1 *. So we consider that the Residual has more
affected feeling impression than the Fluctuation. Thissituation is same in Group2 or Group3. Further more, wehave used the Wilks-Lambda Test for statistical tests to thegrouping in each evaluation item. Those groupings holdwithin 1% of significance whole frequency domains, i.e. AF,
LF, MF and HF. Fig. 3(a) shows the clustering result for
Total Evaluation in AF, as an example of classification.
3.3.2 LF (Low-frequency domain)
Table 4 shows correlation coefficient and average of
normalized regression coefficient of LF.
Where, we have to pay attention to Evaluation Item inTable 4. NRC α 2 * is higher than the NFC α 1 * same as AF in
Item1 through Item4. Then, Item3 and Total evaluation
have weak correlations, and NFC α 1 * is higher than NRC
α 2 * in Total Evaluation of Group2 and Group3.
From these results, Item1, Item2 and Item4 haveslightly strong correlations, but Item3 and Total Evaluation© ISAROB 2013

Multiple Significαnce
correlαtionlevel ofcoefficient regression

Evαluαtion
Item
Item 1
Item 2Item 3Item 4Totαl
Evαluαtion

Multiple Significαnce
correlαtionlevel ofcoefficient regression

Averαge of Regression CoefficientGroup 1
Group 3
Group 2

α1∗

α2∗

α2∗

α1∗

α2∗

α1∗

0.649 -0.031
0.280 0.240
0.367 0.178
0.709 0.061

0.984
0.973
0.947
0.984

1% -0.361
1% -0.846
1% -0.423
1% -0.859

0.502 0.359
0.840 -0.293
0.585 0.046
0.841 0.365

0.143
0.503
0.398
0.227

0.880

1% -0.620

1.299

0.661 1.053

0.260

0.193

have very poor correlations. Though Residual is thestronger factor than Fluctuation in this feeling impression
evaluation. Fig. 3(b) shows the clustering result for Total
Evaluation in LF, as an example of classification.
3.3.3 MF (Middle-frequency domain)
Table 5 shows correlation coefficient and average of
normalized regression coefficient of MF.
As shown in Table 5, NRC α 2 * is higher than NFC α 1 *in Group1 and Group2. So we thought that the feeling
impression of them has much more related NRC. Generally
speaking, Residual is the stronger factor than Fluctuation in
MF-feeling impression evaluation, too. Fig. 3(c) shows theclustering result for Total Evaluation in MF, as an example
of classification.
3.3.4 HF (High-frequency domain)
Table 6 shows correlation coefficient and average of
normalized regression coefficient of HF.

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As shown in Table 6, in Group2 and Group3, NFC α 1 *is higher than NRC α 2 * in Item1 and Item4. In the rest ofthem, NRC α 2 * is higher than NFC α 1 *. As for the feeling
impression of Group2 and Group3 in Item1 and Item4,
Fluctuation is stronger factor than Residual. And rest ofthem, Residual is stronger factor than Fluctuation.Fig. 3(d)shows the clustering result for Total Evaluation in AF, as an
example of classification.

impression in sound signal. As a way of research, we haveused a questionnaire survey concerning how the relation
between 3PACF and the feeling impression of music is.
Based on the constructed multiple linear regression
equations, we can also consider the statistical tendency as
to how the human impression will change if the explanatory
variable of Fluctuation (x 1 ) or Residual (x 2 ) change. Briefly
speaking, the effect of the Residual seems to be stronger
than that of Fluctuation. For further study, we need somesensitivity analysis for those parameters.
The questionnaire survey was acquired by limited
number of persons or age, music as investigation objects
was also limited, though we have understood the feeling
impression of music can be some classified using 3PACF of
the frequency domains.
For further study, we need some sensitivity analysis for
3PACF or we need to use principal component analysis.
Moreover, we have to investigate if we modify 3PACF ofthe each frequency domains independence, how affectfeeling impressions change.

3.4. Discussion
In section 3.3, we have found that each item of feeling
impressions can be expressed as Eq. (2), and the average ofregression coefficient as shown as Table 3 (AF), Table 4(LF), Table 5 (MF), and Table 6 (HF).
As shown in Table 3 trough Table 6, we can see thetendency that Normalized Residual Coefficient (NRC) α 2 *is higher than Normalized Fluctuation Coefficient (NFC)
α 1 * as investigation results, generally. And Group1 has afeature that the NFC is negative value for all items in whole
frequency domain. From these results, NRC α 2 * is muchhigher than NFC α 1 *, so normalized explanatory variable ofResidual x 2 * has more affective to feeling impression scorey than normalized explanatory variable of Fluctuation x 1 *.

REFERENCES
18.00035.000[1] T. Musha (1999), Reason why person feels pleasantness
and unpleasantness--- sound, image, and sense of touch-- that approaches to the mystery of “pleasantness” (inJapanese), Kawade Shobo Shinsya (KAWADE dream
new book).
[2] T. Musha (1980), The world of fluctuation – Mystery of
1/f fluctuations of the world – (in Japanese), Kodansha
Bluebacks, Kodansha.
[3] H. Akaike and T. Wada (1997), Fluctuation and rhythmof living body (in Japanese), Kodansha Scientific.
[4] S. Horiuchi (1997), Mystery of fluctuation (in Japanese),
Shinano Mainichi Newspapers Co.
[5] E. Teramoto, R. Hirota, T. Musha, and M. Yamaguchi(1985), Infinity, chaos, and fluctuation (in Japanese),
Bifukan.
[6] T. Musha(1998), Conception of fluctuation thatapproaches to the mystery of 1/f fluctuation (inJapanese), NHK Books.
[7] T. Musha (1991-1999), Science of fluctuation, <1>through <10> (in Japanese), Morikita Publication.
[8] Y. Kawakami, T. Hattori, T. Yamamatsu, T. Izumi, H.
Kawano (2011), Experimental Investigation of theRelation between Feeling Impression and Quantities
Accompanying Fluctuation Calculation in Sound Signal(in Japanese), Transactions of Japan Society of Kansei
Engineering, Vol.10, No.3, pp.365-374, 2011.
[9] B. S. Everitt (2002), The Cambridge Dictionary of
Statistics, Cambridge University Press 2nd edition

Group 130.00016.000Group 125.00014.00012.000Group 2

Residual coefficient

Residual coefficient

20.00015.00010.0005.000Group 310.0008.000Group 2

6.000

0.000
4.000

-5.000Group 32.000

-10.000

0.000-15.000-6.000-4.000-2.000

0.000

2.000

4.000

6.000

-1.000

8.000

0.000

1.000

2.000

3.000

4.000

5.000

Fluctuation coefficient

Fluctuation coefficient

(a)

(b)
35.00014.000Group 1

Group 130.00012.00025.0008.000Group 2
6.000

4.000

Residual coefficient

Residual coefficient10.00020.00015.000Group 210.0005.000Group 3

Group 3
2.000

0.000

-5.0000.000-4.000-3.000-2.000-1.000

0.000

1.000

Fluctuation coefficient

2.000

3.000

-3.000-2.000-1.000

0.000

1.000

2.000

3.000

4.000

Fluctuation coefficient(c)(d)
Fig. 3. Example of Regression analysis of feeling
impression (a)AF, (b)LF, (c)MF, (d)HF

4 CONCLUSION
In this paper, we have researched the relationshipbetween 3PACF of frequency domains and feeling© ISAROB 2013

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Color image arrangement by elastic transform
based on histogram matching
Daisuke Kutsuna1, Tetsuo Hattori1, Yusuke Kawakami1, Haruna Matsushita1,
Yoshiro Imai1, Tetsuya Izumi2
1

Kagawa University, Japan
Micro Technica Co., Ltd., Japan
Corespondence:hattori@eng.kagawa-u.ac.jp
2

Abstract: Aiming at automated affective color image arrangement, this paper proposes a new method using histogram based
Elastic Transform (ET) on some kinds of axis including Lightness axis. If we represent a pixel of input image as a vector in thethree-dimensional RGB color space, the input image corresponds to a set of the three-dimensional pixel vectors. As for axis
other than the Lightness axis, there are PC axes that can be obtained by Principal Component Analysis (PCA) from the set ofthree-dimensional vectors. In this paper, we present a principle of the ET on the Lightness axis and PC one. We also illustratethat HMGD (Histogram Matching based on Gaussian distribution) is regarded as one of the ET method. In addition, for theinvestigation of the performance, we show the experimental results by applying the transform (especially HMGD) to some
images.
Keywords: Color arrangement, elastic transform, histogram equalization, histogram matching.
Thus, if we know the y = φ (x ) and g ( y ) , then we
have the f (x) .

1 INTRODUCTION
Recently, automated image processing for enhancement
and/or arrangement is becoming more familiar to us
according to the spreading of Digital Camera, Smart Phone,DVD, etc. ([1]-[3]). However, we consider that the researchon the automated image arrangement method withsensibility effect is still on the way to practical use.
In this paper, we propose a method for image
arrangement by elastic transform utilizing histogram (e.g.
Histogram Equalization (HE), Histogram Matching (HM))
on some kinds of axis ([4]-[6]). As for the axis, there areLightness axis and principal component axis that can beobtained by Principal Component Analysis (PCA). Also inthis paper, we show the principle of the method and some
application results.

yy = φ (x)φ (L)g ( y)φ ( x0 + dx)yφ ( x0 )0x

2 ELASTIC TRANSFORM (ET)x0

2.1 Principle
We describe the principle of histogram based elastictransform in the following. Let f (x) and g (y ) be two
probabilistic density functions on real variables x and y,
respectively. The probabilistic density function (pdf) iscorresponding to histogram of gray level image. However,
the histogram is defined on discrete variable. In addition, let
y = φ (x ) be a continuous and monotonous increase function
between variables x and y as shown in Fig. 1. In addition,
let value of x be the range from 0 to L. Accordingly,
variable y ranges from 0 to φ (L) . Let P mean theprobability. From the above definitions and Fig.1, we haveEq.(1)-Eq.(3). From Eq.(3), we obtain Eq.(4)and Eq.(5).

xx0 + dxf (x)Fig.1. Continuous and monotonous increase function
y = φ (x ) and probabilistic density functions f (x ) andg( y) .
x=L

P (0 ≤ x ≤ L) = ∫ f ( x )dx = 1
x =0P(0 ≤ y ≤ φ ( L)) =

280

(1)

y =φ ( L )∫ g ( y )dy = 1

y =0© ISAROB 2013

L

(2)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

φ (x ) and the uniform distribution such as Eq.(8) (Fig.3).

f ( x )dx = P ( x0 ≤ x ≤ x0 + dx )= P (φ ( x0 ) ≤ y ≤ φ ( x0 + dx ))

y
φ (L)

= P ( y0 ≤ y ≤ y0 + dy )= g ( y )dy
where

(3)

y 0 = φ ( x0 ) , y 0 + dx = φ ( x0 + dx) .

g ( y)f ( x )dx = g ( y )dy = g ( y )f ' ( x )dx
f ( x ) = g ( y )f ' ( x )y = φ (x)φ ( x0 + dx)yφ ( x0 )(4)(5)
Using the above equations, we derive the principle ofHistogram Equalization (HE). Let φ (x ) be defined by thefollowing Eq.(6).

1/ L

0

x

x0

x

L

x

x0 + dx

φ (x)f ( x ) = L ∫ f ( x )dx(6)
Fig.3. Conceptual image of the transform from uniform
distribution (pdf) to the desired one (pdf).

0Since f ' ( x) = Lf ( x) , according to Eq.(5),f ( x ) = g ( y ) Lf ( x )(7)

The abovementioned theory means that, if we combine
the both transform, we can obtain the transformation froman original distribution (pdf) to a desired one. This meansthat an image with original histogram can be transformed
into another image with desired histogram. We consider that
it is the principle of the Histogram Matching (HM) [4](Fig.4).

Then we haveg ( y ) L = 1 and g ( y ) = 1 / L .

(8)Using the above equations, we derive the principle ofHistogram Equalization (HE). Let φ (x ) be defined by thefollowing Eq.(6).
Therefore, we understand that, if we take the transformfunction as Eq.(6), g ( y ) becomes uniform distribution asshown in Fig.2. It corresponds to the Histogram
Equalization (HE) processing, which means that function
defined by cumulative histogram transforms the original
histogram into the uniform one.
y
φ (L)

≒
Reference Histogram

y = φ (x)Input Histogram

Output Histogram

Fig.4. Histogram Matching (HM) [4].
g ( y)φ ( x0 + dx)

1/ L

2.2 Transform on axisyφ ( x0 )
0

x

x0

L

We can choose the abovementioned transform such as
HE and HM on arbitrary axis (for example, principal
component axis) in the color attribute space (RGB space) as
shown in Fig.5.
For example, if we choose HE processing on the lightaxis, it can bring about image enhancement by contrast
stretching. In addition, if we choose the HE on a principal
component axis in RGB space, we guess that the contrast
stretching will be done along to a certain tone of color.
Fig.6 shows examples of the HE on Lightness axis and PC(Principal Component) axis.
For example, if we choose HE (Histogram
Equalization) processing on the light axis, it can bringabout image enhancement by contrast stretching. And if wechoose the HE on a principal component axis in RGB space,
we guess that the contrast stretching will be done along to acertain tone of color as shown in Fig.6 that shows examples

xx0 + dxφ (x)Fig.2. Conceptual image of the relation between uniform
distribution and original distribution.
Inversely, if we define the transform function φ (x ) as
an integral of desired pdf f (x ) for example, Gaussian
distribution, we can obtain the desired pdf using the© ISAROB 2013

281

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of the HE on Lightness axis and PC (Principal Component)
axis.

e1

R

R

B
G

e1
(a)Original image.
(b)HE.
(c)HMGD.
Fig.7. Example of the results by HE and HMGD.

B
G

Fig.5. Conceptual image of histogram based elastictransform on arbitrary axis in the RGB space.

(a)Input image A(b) Output image A(a)Original image.
(b)Lightness axis.
(c)PC axis.
Fig.6. Examples of the HE on Lightness and PC axis.

3 HISTOGRAM MATCHING BASED ET
The abovementioned Histogram Matching (HM) can be
regarded as one of the Elastic Transform (ET) method.
Since there are many kinds of reference histogram and axis,
we will find various ET methods as much.
As one of those ETs, we focus on a histogram based ET
method in which the reference histogram is Gaussian
distribution (Fig.6) on Lightness axis. We call it “Histogram
Matching based on Gaussian Distribution (HMGD)”.
Fig.7 shows the results by two histogram based ET
methods such as HE and HMGD on Lightness axis. From
this, we find that HMGD results in moderate transformationfrom the original image, comparing with HE.

(c)Input image B(d) Output image B
Fig.8. Examples of the resultant HMGD.

(a)Image A.

(e)(a) Gaussian distribution(b)Cumulative histogram
Fig.6. Gaussian distribution histogram.

(b)HMGD.

(f)(c)Image B. (d)HMGD

(g)

(h)

4 INVESTIGATION OF HMGD
In this paper, we investigate the effects by the HMGD.
Fig.8 shows examples that HMGD works well from feeling
impression. From Fig.9, we can find out that the cumulative
histogram of the resultant image clearly approaches to the
cumulative Gaussian distribution.

© ISAROB 2013

(i)
(j)
(k)
(l)
Fig.9. Results of HMGD and the corresponding histogram((e)-(h): histogram of the above image, (i)-(l):cumulative
histogram of the above histogram).

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On the contrary, Fig.10 shows examples that it does not
so work well, judging from our feeling impression. From,
Fig.11 shows that the corresponding histograms. If wecompare with Fig.9, we can find out that there are double
peaks in the histogram of Image C and D (Fig.12), while
there is single peak in that of the Image A and B.
(a)Histogram of Image C. (b)Histogram of Image D.
Fig.12. Bimodality histogram on Lightness of Image C andD. ((a) and (b): the same as (e)and (g) in Fig.11,respectively.
There are double peaks in each histogram. )5(a)Input image C(b) Output image C(c)Input image D(d) Output image D
Fig.10. Examples of the resultant HMGD in the case where
it does not work well.

(a) Image C. (b)HMGD.

(c)Image D.

(d)HMGD.

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

REFERENCES[1] Rafael C. Gonzalez, Richard E. Woods (1993), Digital
Image
Processing,
Addison-Wesley
Publishing
Company.
[2] Bernd Jahne (1995), Digital Image Processing -Concepts, Algorithms, and Scientific Applications --,4th Edition, Springer.
[3] Scott E Umbaugh (1998), Computer Vision and Image
Processing: A Practical Approach Using CVIPtools,
Prentice Hall PTR.
[4] Wilhelm Burger, Mark J. Burge (2009), Principles ofDigital Image Processing: Fundamental Techniques,
Springer, 2009.
[5] Tetsuya
IZUMI，Tetsuo
HATTORI，Shunichi
SUGIMOTO, Toru TAKASHIMA (2009), "Color Image
Arrangement Using Elastic Transform on Principal
Component Axis", Journal of Japan Society of Kansei
Engineering, Vol.8, No.3, pp.667-674, Feb. 2009 (inJapanese).
[6] Tetsuya IZUMI, Tetsuo HATTORI, Emiko FUJITA,
Shunichi SUGIMOTO, Hiromichi KAWANO (2010),"Feeling Impression and Quantities Accompanying
Calculation of Fluctuation in Color Image", Journal ofJapan Society of Kansei Engineering, ISSN 1882-8930,
Vol.9, No.2, pp.243-250, Feb. 2010 (in Japanese).

Fig.11. Results of HMGD and the corresponding histogram((e)-(h): histogram of the above image, (i)-(l):cumulative
histogram of the above histogram).

© ISAROB 2013

CONCLUSION

Aiming at automated affective color image arrangement,
we have proposed a concept of Elastic Transformation (ET)method based on histogram, that varies on some kinds ofaxis such as Lightness axis and Principal Component one in
RGB space. We also have described the principle in thedetails. And we have explained that HMGD (Histogram
Matching based on Gaussian Distribution) is regarded as
one of the ET method. Moreover, for the investigation of
the performance, we have shown the experimental results.
From the experimentation, we have found that HMGD
works well for images if the histogram has single peak.
Then we consider that automated detection of the single
peak is promising for our aim.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Face identification insensitive to facial expression in the crowded people scene
Qingyu Shu1, Tetsuo Hattori1, Saki Masunari1, Tetsuya Izumi2,
Hiromichi Kawano3, Bahram Javidi4
1

Kagawa University, Japan
Micro Technica Co., Ltd., Japan
3
NTT Advanced Technology, Japan
4
University of Connecticut, USA
Corespondence:hattori@eng.kagawa-u.ac.jp
2

Abstract: Face identification in the crowded people with various facial expressions is one of the most difficult themes in thepattern recognition field. In addition, the recognition of facial expression after the identification is very important as anadvanced pattern recognition problem in Kansei Engineering field. Currently, there have been developed some methods at thelevel of practical use such as personal authentication. However, neither the theory nor the method has been established yet forthe partial recognition problem that means the identification of a specific person’s face from the crowd people scene. In thispaper, we propose a novel face identification method insensitive to facial expressions using the notion of weighted vector field
and weighted similarity. Also, we show the effectiveness by showing the experimental results.
Keywords: face identification，KL Expansion，region weighted vector field，spatial correlation, weighted similarity
facial expression. That is, the method is proposed not only
for the identification but also for the recognition of feelingafter the identification processing.
This paper also describes the effectiveness of this method,by showing the experimental results for people images thatare naturally acquired indoor and outdoor.

1 INTRODUCTION
Although research on identification of person by face has
being widely done ([1]-[6]), the recognition for arbitrary
plural people image under genaral background is still
difficult problem.
For such a problem, we have proposed a recognition
method using a vector field ([7]-[12],[15]), that judges
whether the face of acquaintance exists or not in thecrowded people image, and also detects and extracts the faceregion from the image. In the method, there is an assumptionthat the pattern to be recognized is perturbed by somevariation which is roughly equals to be an Affine transform(rotated, enlarged/reduced, and translated) from a registeredoriginal face pattern.
However, since the recognition method using the vector
field method is sensitive to the facial expression, when theexpression is big, there occurs a case where the method
does not work well.
Then in this paper, we propose a region weighted vector
field (RWVF) method insensitive to the facial expression.
The RWVF itself is obtained by emphasizing somecharacteristic parts in the vector field that is constructed bymaking a normalized gradient vector field (NGVF) of grey
level image ([13],[14]). This method is also aimed for therecognition of the kansei (feeling) information from the© ISAROB 2013

2

RECOGNITION METHODThis method is composed of 2 stages, one is Learning andAnalysis stage as a preprocessing one and another is
Recognition stage. The input images are monochrome
grayscale (or gray level) images with a size of 256x256
pixels. Color images are converted into grayscale images
before input to recognition processing.
Here, we use the following terminology.
(a)Original registered pattern: the original face image to berecognized, also called original pattern.
(b)Registered pattern: the variation of original patternobtained by Affine transform.
(c)Reference pattern: the eigenvector obtained from a set ofseveral registered patterns by Karhunen-Loeve (KL)expansion. It will be used for similarity computation with
input image.
This method automatically generates reference patterns at
the learning and analysis stage, and after that, it executes therecognition process as recognition stage.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

These reference patterns are used in the subsequent spatial
correlation and similarity computation.

2.1. Learning and analysis stage as preprocessing
The flow of this processing is as follows.
STEP1：Make plural registered patterns from an original
face pattern by Affine transform (enlarged or
reduced, rotated).
STEP2：Divide registered patterns into some groups, andconstruct the Normalized Gradient Vector Field(NGVF) expression. Then, apply the KL(Karhunen- Loeve) expansion to every group, andmake the reference pattern that has common
component.
2.2. Generation of registered patterns
Applying Affine transform to given original registeredpattern, our method automatically makes some registeredpatterns by enlargement/reduction and rotation (Fig.1).
As an experiment, this method totally generates 240
registered patterns, changing original registered pattern by
every 3% enlargement/reduction from 50% to 150%, and by
every 3 degrees rotation from -10 degrees to +10 degrees.
Then it can be divided them into 20 groups, where eachgroup contains 12 registered patterns.

Fig.2. (a) Example of NGVF. (b) Example of reference
pattern ([12],[15]).
2.5. Recognition stage

The flow of this recognition processing is as follows.
STEP1: Calculate the similarity which are based on spatial
correlation between input image and the reference
pattern (pattern matching), and find similarity
distribution with the reference pattern in each point
in an input image.
STEP2: Based on similarity distribution, detect a candidate
locations where the face pattern of interest may
exist in the input image, and roughly estimate thescaling and rotation parameters.
STEP3: Repeatedly calculating the similarity, perform theadjustment (fine estimation) of parameter values bymeans of correction formulas based on Taylor
expansion of the affine transforms.
STEP4: Recognize the biggest similarity using the correctedparameters, and extract the face patterns exhibiting
the maximum similarity.
2.6. Similarity computation
In order to detect the existence of a target face pattern,
our method computes the similarity between input image
and reference pattern both of which are represented as
NGVF, based on the spatial correlation as shown in Eq.(2).
The input image g ( g ( x, y ) ) and the reference pattern ev( ev ( x , y ) ) are defined as complex valued functions.
The similarity between reference pattern ev and input g is
given by Eq.(2).

Registered original face pattern

Enlarged

Reduced

Reduced and rotatedFig.1. Example of registered patterns ([12],[15]).
2.3. Normalized Vector Field（NGVF）

Gradient vector is obtained at each point (x，y ) bypartial differentiation of the gray level image as Eq.(1). To
obtain the NGVF, normalize the size (norm) of the gradientvector to 1.0 if the norm is greater than a threshold, else to 0.

Similarity = ∂f ∂f 
grad f (x, y ) =  ,  = grad f (x, y ) ⋅ (cos θ , sin θ ) (1) ∂x ∂y 
where θ is the phase of the vector.
2.4. Making reference patterns
By using Karhunen-Loeve(KL) expansion for eachgroup of registered patterns represented as NGVF, we canobtain the reference patterns (eigenvectors in KL expansion)
which has the common feature among patterns in each group.

© ISAROB 2013

| Re{ g ev } |ev ⋅ g s(2)

where g ev is the inner product between input image andreference pattern, and the inner product is defined asg ev = ∫ ∫ g ( x, y ) ⋅ ev ( x , y )dxdy . The notations Re and | |stand for real number of complex number and absolute value,
respectively. || || denotes norm of pattern, and it is defined astheev = 〈 ev | ev〉 . g s represents the norm ofsubpattern g s in input image g, which is defined on the285

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

overlapped region between g and ev. The spatial correlation
can be considered as inner product g ev at every pixel
when the reference pattern ev is shifted.

1.85GB. Programming Language: Borland C++ 5.02J.
3.2. Experimentation of comparison between the NGVF
method and RWVF one(i) Outline of the experiment
We compare the recognition rate and recognition time by
the two methods, NGVF method and RWVF one byexperimenting for 100 pieces of input images that are takenindoors and outdoors. There are 2-5 people in the images.
The weight in RWVF is 2.0, that is, the vector norm on theweighted region is 2.0, otherwise the same as NGVF.
(ii) Experimental results
In this time experimentation, recognition rate has been
almost same level. But the failed images are different (Fig. 5
and 6). In Fig.5 and Fig.6, the persons in the red circle andblue circle are the target to be recognized and misrecognizedperson, respectively. From this result, the recognition failure
by the usual NGVF method can be improved by RWVF
method. The results are shown as in the following.
[NGVF]
Accurate recognition rate: 99/100 (99%).
Average recognition time: 3.6 sec.
(Rough estimation: 2.6 sec. Fine estimation: 1 sec.)[RWVF (weight:2.0)]
Accurate recognition rate: 98/100 (98%).
Average recognition time: 3.8 sec.

(a) Original face. (b) Input image.(c) Recognition andextraction result.
Fig.3. Recognition processing using NGVF ([12],[15]).
2.7. Region Weighted Vector Field (RWVF)
In RWVF, the absolute of the vector is normalized using
some weights (e.g., 1.5, 2.0), depending on face regions. At
the pixel other than the weighted region, the norm becomes
1.0 (i.e. the same as the usual NGVF). The purpose of using
this RWVF is to emphasize the characteristic part of theface (i.e. eyes, nose) and to reduce the lighting influence.
In the similarity calculation, we use a spatial weightfunction w( x, y ) (real variable function). And so, the innerproduct between input image and reference pattern is shownas Eq.(3). Then, the similarity is defined as Eq.(4).

∫∫ w(x，y ) ⋅ g (x，y ) ⋅ ev (x，y )dxdy= ∫∫ g ( x，y ) ⋅ w( x，y )ev ( x，y )dxdy = g w ⋅ ev
Similarity ={Re g w ⋅ ev}

w ⋅ ev ⋅ g s(Rough estimation: 2.5 sec. Fine estimation: 1.3 sec.)
(3)

(4)

Fig.5. Recognition failed image by NGVF.

(a)
(b)
(c)
Fig.4. (a)NGVF, (b)Weighted region, (c)RWVF.

3 EXPERIMENTATION
Fig.6. Recognition failed images by RWVF method in thecase where the weight is 2.0.

3.1. Environment
Using the RWVF method, we perform some experiments.
The experimental environment is in the following.
OS: Microsoft Windows XP Professional. CPU: Intel(R)Core(TM)2. Drive frequency: 2.40GHz. Memory storage:© ISAROB 2013

3.3. Relation between weight and recognition rate by
RWVF method
Based on the result of the comparative experiment, we

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[6] M. Weber, W. Einhauser, M. Welling, P. Perona,“Viewpoint-Invariant Learning and Detection of Human
Heads,” Proc. of the International Conference on
Automatic Face and Gesture Recognition 4TH, pp.20-27,
2000.
[7] T. Hattori, W. Fukuda, K. Takeda, and T. Yamasaki,“Face Recognition among Others as an Affine
Transformed Subpattern,” Intelligent Autonomous
Systems, Y. Kakazu et al.(Eds.), IOS Press, pp.791-796,
ISBN:9051993986, June, 1998.
[8] Tetsuo Hattori, Seiu Shu, Waka Fukuda, Toshinori
Yamasaki, “New Face Recognition Method among
Others in Complex background,” Proc. of 2000
NOLTA2000, IEICE, pp.201-204, ISBN: 3933592844,
Sept., 2000.
[9] Tetsuo Hattori, Seiu Shu, Toshinori Yamasaki,Waka
Fukuda,"Face Pattern Recognition under Complex
Background", CD ROM Proceedings of IEEE
International Conference on SMC, pp.3890-3895,
ISBN:0-7803-6586-0, Oct. 2000.
[10] Tetsuo Hattori, Hiroyuki Kitajima, Toshinori Yamasaki,“Face Recognition in the Crowded People under General
Background,” Proc. of IEEE IECON2002, vol.2/4,
pp.1428-1433, ISBN: 0780374754, Nov., 2002.
[11] Tetsuya Izumi, Tetsuo Hattori, Shu Qing Yu, Hiroyuki
Kitajima, Toshinori Yamasaki, “Face Identification forPeople Image with General Background,” Proc. of
ISHS'06, pp.140-145, Busan, Korea, Oct., 2006.
[12]Tetsuya Izumi, Tetsuo Hattori, Shu Qing Yu, Hiroyuki
Kitajima, Toshinori Yamasaki, “Face Recognition for
Image of People under General Background Using
Approximate Parameter Estimation of Affine
Transform”, IEEJ Trans. EIS, Vol.127, No.4, pp.623-629,
April 2007 (in Japanese).
[13] Shu Qing Yu, Tetsuo Hattori, Tetsuya Izumi, Hiroyuki
Kitajima, and Toshinori Yamasaki: "Face Recognition
Method for People Image Using Vector Phase Field andRegion-weighted Similarity", Proc. of The 3rdInternational Symposium on Humanized Systems(ISHS'07), Japan Society of Kansei Engineering,
Chinese Association for Artificial Intelligence, The
Institute of Control, Automation, and System Engineers(Korea), pp.115-118, Muroran Japan, Sept.13-15, 2007.
[14] Qingyu Shu, Tetsuo Hattori, Tetsuya Izumi, Hiroyuki
Kitajima, Toshinori Yamasaki: "Face Identification forPeople Image with General Background Using Vector
Phase Field," CD Proc. of IEEE ICIT2008, WP1-D5,
pp.1-5, IEEE Catalog Number: CFP08CIT-CDR,
ISBN:978-1-4244-1706-3, Chengdu, China, 2008.
[15]Tetsuya Izumi, Tetsuo Hattori, Shu Qing yu, Hiroyuki
Kitajima, Toshinori Yamasaki: "Face recognition forimages of persons against a general background using
approximate parameter estimation of affine transform",
Journal of Electronics and Communications in Japan,
John Wiley & Sons, ISSN 1942-9541, Vol.92, No.11,
pp.1-8, Nov. 2009.

change the weight of the RWVF method and investigate therelation between the weight and the recognition rate for thesame 100 pieces of images. We have changed the weightfrom 1.0 to 2.0. Fig.7 shows that the recognition rate
becomes 100% when the weight lies in the range from 1.09
to 1.46. Then we consider that RWVF with suitable weightis effective to the facial expression variations.

Recognition rate[%]

100

99

98

97
1.00

1.20

1.40

1.60

1.80

2.00

Weight(norm in region weighted vector field)

Fig.7. Relationship between weight and recognition rate.

4 CONCLUSION
The aim of our study is not only the identification of theperson by face but also to read the Kansei (or feeling)
information from the facial expressions. However, because
our previous method is sensitive to the facial expression, it
does not work so well if the expression is big. This paper has
proposed RWVF method insensitive to the facial expression.
The experimental results for 100 images have shown that, if
the weight value is in the range from 1.09 to 1.46, the face
identification is very successful (100% recognition rate).

REFERENCES[1] M.A.Turk and A.P.Pentland, “Face recognition using
eigenfaces,” Proc. of IEEE conference on Computer
Vision and Pattern Recognition, pp.586-591, June, 1991.
[2] Murase and S.K.Nayar, “Learning object models fromappearance,” Proc. of AAAI-93, American Association
for Artificial Intelligence, pp.836-843, July, 1993.
[3] A.Pentland, B.Moghaddam, T.Starner, “View based andmodular eigenspace for face recognition,” Proc. of IEEE
conference on Computer Vision and Pattern Recognition,
pp.84-91, 1994.
[4] Hiroshi Murase and Shree K.Nayar, “Illumination
Planning for object recognition using parametriceigenspace,” IEEE Trans. on Pattern Analysis andMachine Intelligence, Vol.16, No.12, pp.1219-1227,
1994.
[5] H.Wechsler, P. Jonathon Phillips, V. Bruce, F.F. Soulie,
T.S. Huang, ed., Face Recognition --From Theory to
Applications--, Springer-Verlag, 1998.

© ISAROB 2013

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Kansei Engineering based Evaluation for Distance Learning on Distributed
Multiple Servers
Yoshio Moritoh1 , Yoshiro Imai2 , Tetsuo Hattori2
1

Department of Management Information, Kagawa Junior College, 1-10 Utazu-cho, Ayautagun 769-0201 Japan
2
Graduate School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396 Japan(Tel : +81-87-864-2244)
E-mail: 1 moritoh@kjc.ac.jp, 2 {imai, hattori}@eng.kagawa-u.ac.jp

Abstract: A distributed multiple server system is designed and implemented with Web-DB based services for Distance Learning
as well as Emergency Communication. The system has employed multiple servers located in a distributed campus network environment. Each server of the system has multi-core processors. With virtualized CPUs by server virtualization, some programs
are executed in parallel (on the virtual servers) so that our system can efficiently perform several functions. Based on our related
works, two major applications are realized as a Cloud services on the system. It can provide Distance Learning environment for
educational tool as well as Web-based surveillance functions with Emergency Contact. With Kansei Engineering approach, trial
evaluation of system has been performed in some classrooms of distributed campus.
Keywords: Visual Computer Simulator, Distance Learning, Distributed Multiple Server System

1. INTRODUCTION

DOS prompt of Windows or in the command interpreter ofLinux.
A necessary condition is to equip the Java VM prepared
to execute the simulator. So it is very useful because any
executable environment will do, just like Windows, Linux
and/or Macintosh. There are both sides of views for our visual simulator as are summarized below. The first one is atool to visualize an internal structure and behavior of computer. It is useful for teacher to show students how a computer works graphically and to explain step-by-step actionsof internal register and memory with VisuSim which is shownin Figure1. The second is an e-Learning tool to provide an
Assembly Programming environment. Such a case is suitableenough to support Assembly Programming Exercise duringlecture.

Computer Architecture education is very much useful inthe domain of Information Science education because it provides total image computer structure and behaviour and thenexplains how a computer works correctly. Serval educational
tools[1]have been developed and utilized in the practical education fields
Cloud approach is effective and efficient for realization ofsome e-Learning environment, especially, for developmentof Distance Learning and/or Collaborative Learning. Somepowerful LSI technologies can provide multi-Core CPUs andvirtualized servers with such CPUs. These are relatively useful to realize multiple server system for Cloud services.
This paper presents a visual computer simulation for
Computer Architecture education and its conventional evaluation in the next (second) section, realization of cloud service
with distributed multiple server system for Distance Learning environment and detail of cloud service with virtualized
CPUs in the third section, approach of Kansei Engineeringbased evaluation to relevant cloud service in the fourth section, and finally summarized conclusion in the last (fifth) section.

2. VISUAL COMPUTER SIMULATOR
2.1 Summaries of a visual computer simulator
First of all, a simulator called VisuSim has been implemented as pure Java program to provide two kinds of entries
for both Java applet code and Java application one[2]. Eachcan be selected automatically to invoke the suitable mode ofJava program. Namely, for example, VisuSim recognizes its
invoking environment and decide to execute as a Java appletin the environment of browser or to work as a Java application in the environment where the Java VM executes in the© ISAROB 2013

Fig. 1 Using VisuSim in Classroom Lecture of Computer
Architecture.
Visualization tool:
VisuSim has 8 sets of general-purpose registers, including
Stack Pointer, and 256/512 words of main memory. It can•

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simulate computer internal behavior in a register-transfer
level. For example, a teacher of Computer Architecture canutilize it to demonstrate graphical view about von Neumann
computer architecture by means of wall-hanging screen and
PC projector just like Figure1.
• e-Learning tool for Exercise environment:
VisuSim is utilized for writing essays and answering problems of Assembly Programming exercise. Users(Students)of VisuSim can understand computer behavior, program processing and well-defined algorithm through verification ofassembly program execution. So it is very applicable forusers to write correct programs and recognize how a computer processes their programs at the register-transfer level.

about basic vonNeumann computer architecture but also include Logic Circuits, Microprogramming, Pipeline Processing and its expansive Architecture, memory architecture,and
System Architecture.
Students who know only vonNeumann computer architecture cannot perfectly understand the above themes of “Computer Architecture” and cannot always make excellent scoresof semester-end Examination. So we would like to confirm
that understanding several themes of “Computer Architecture” is significantly affected by understanding vonNeumann
computer and such a case is also significantly affected by using our simulator, VisuSim .

2.2 Relation between Scores of Essay and Examination ofComputer Architecture
Secondly, it is investigated whether there is any relationof Scores between “Essay using VisuSim ” and semester-end
Examination or not. It has been not yet a perfect investigation and confirmation to analyze any relation between Essay
and Examination yet. We should not exactly mention abouta kind of relation between Essay and Examination. So wewould like to demonstrate some kind of good correlation of
Scores between Essay and Examination in 2009. Such a relation is shown in Figure 2.

3. CLOUD SERVICES ON VIRTUALIZEDCPUS OF MULTIPLE SERVER SYSTEM
3.1 Cloud Services on Virtualized CPUs
It is very efficient to realize parallel programs execution
and smart management of concurrent services. If some applications need more powerful CPU services, with virtualized
CPUs by server virtualization, some programs are executedin parallel (on the virtual servers) so that such a specific system can efficiently perform several functions. In the above
case, each information server and its hypervisor (mentined
below) can adjust its facilities to migrate (assign) virtualized
server and related resources to relevant applications according to dynamic demand changing and/or modification.
One of multi-core CPUs is assigned to execute most useful Linux-based Web-DB software fundamentals. This is abasic layer (i.e. platform) for usual and classical applications. Another is sometimes assigned to multimedia information processing modules, such as image understanding, video
transmission, voice generation and so on. Some of them arepotentially adjusted to carry out asynchronous information
sharing functions and support for emergency contact and/or
urgent situation changing.

Fig. 2 Relation of Scores between Essay and semester Examination.
This result of comparison is the same aptitude of previous ones, for example, in 2007 and 2008. So it can be confirmed that the simulator VisuSim helps students learn “Computer Architecture” in some way according to the above
comparison and/or correlation between Scores of Essay andsemester-end Examination. Of course, it is necessary to explain that contents of semester-end Examination are not only

© ISAROB 2013

Fig. 3 Live Migration in multi-core CPU architecture.
In figure 3, special-purpose controlling monitor called“hypervisor” can switch one task on some virtualized server
from one to another through “Live Migration” contextswitching mechanism.

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Fig. 4 Overview of Distributed Multiple Server System with VisuSim for Distance Learning Environment
multiple server system, we would like to take an opportunity
to employ Kansei Engineering approach and perform trial
evaluation in some classrooms of distributed campus. And
the relevant results are demonstrated in the next section.

3.2 Distributed Multiple Server System for Distance
Learning Environment
Figure 4 shows an overview of system configuration fora distributed multiple server system. The system has been
implemented in a distributed campus network environment.
And each sub-system includes rack-mounted server, UPS
and some kinds of clients, such as usual PC, PDA/mobile
phone and multimedia input/output devices. Sub-systems arelinked and interconnected with campus network by means ofGiga high speed and/or Wi-Fi wireless LAN.
Each information server also employs multi-core CPU architecture and virtualized server technology, namely Virtual
Machine Scheme, provided by Citrix Xen Server Virtualization1 . With such a powerful technology, the same system
configuration can support stationary normal routines as wellas emergent special routines without extra scheme nor irreglar provision.
Key idea for distributed multiple sever system is to utilize the above “Virtual Machine Scheme” in order to construct not only Distance Learning environment with VisuSim
as one of stationary routines but also Web-based surveillance
functions with Emergency Contact as one of other routines.
Merits of the system shown in Figure 4 are as follows;• The system has been utilized for daily business routine ofDistance Learning environment. With VisuSim , Assembly
Programming exercises can be carried out through “Computer Architecture” education as one of Distance Learning
practices.
• The system can provide Web-based surveillance service
and could be useful with Emergency Contact in the case of
extraordinary condition like fire, earthquake, and so on.
For the sake of suitable evaluation about our distributed

4. EVALUATION WITH KAISEI ENGINEERING
4.1 Qualitative Evaluation of Simulator on Cloud
There are some comments and recommendation describedin Essays from the students, who learned “Computer Architecture” education. They includes relatively subjective expressions, but, at the same time, they can be also qualitativeevaluation about VisuSim and the relevant distributed multiple server system from the students who were real users ofthem. Some of positive comments are as follows:• It is easy to verify and point out mistakes through step-bystep excution and display the result by VisuSim .
• I can check change for contents of registers and memory
provided by VisuSim .
• As I use sample programs through VisuSim , it is goodenough for me to write new similar ones.
At the same time, some of negative comments are as follows:• It is difficult for me to operate VisuSim , so I must help myfriends teach me to operate it. Finally I do write my essay
only just until deadline.
• When I modify my program, I have found mistake at the
point where program jump from another routine. So I must
adjust and shift the according routine.
These are not convenient so that it will be necessary to
be improved. These comments must be considered to beprecious recommendations for improvement of VisuSim andteaching methods. We should have a plan to modify our visual simulator from these feedbacks.

1 http://www.xen.org/© ISAROB 2013

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4.2 Using Condition under Distance Learning
It has been introduced in the previous papers that VisuSim is applied to Distance Learning. And it is also reported
that using effect of VisuSim has been evaluated quantitatively
through statistics analysis. In our case, it is confirmed that
effectiveness of VisuSim is well evaluated in a domain forsmall users’ space only.
Our assertion can be expansible to the following assumption; If some e-Learning tools have original communication
facilities and/or information sharing ones, they can provide
some kind of effect for Distance Learning under using condition of small member’s space.
Based on our experience, we will introduce and discuss acategory of member’s size for Distance Learning. The category seems to be three scale of member’s size, namely, asmall scale for no more than 10 members, a medium one forno more than 30 members, and finally a large one for morethan 30 members.
Table1 gives a category for Distance Learning environment. Methods for achievement are e-mail as uni-cast communication and bulletin board as broadcast one in Table1.
These services are easily available in our distributed multiple server system for Distance Learning environment.

Especially, it is confirmed that Communicabiliy, Virtuality,
Reproductability and Understanding are good for students
through real experience.

Table 1 Effect of Usage for Distance Learning Environmentby means of Communication Facilities

This paper reports summaries of Distance Learning toolfor Computer Architecture (visual computer simulator VisuSim ), construction of distributed multiple server systemwith virtualized CPUs by server virtualization, and someevaluation for such server system from the aspect of users
by means of Kansei Engineering approach.
It is concluded as follows;• VisuSim is effective for students to learn “Computer Architecture” through a practical education.
• Distributed multiple sever system with “Virtual Machine
Scheme” can construct Distance Learning environment with
VisuSim as one of stationary routines as well as Web-based
surveillance functions with Emergency Contact as one ofother routines.
• Some kinds of evaluations have been performed throughqualitative analysis and Kansei Engineering approach.

small
scale
medium
scale
large
scale

UnicastConfirmationof Effect
It is necessary
to be confirmed
Only unicast method would be
difficult from our experienceFig. 5 Radar Chart of Evaluation for System by Kansei
Engineering Approach.

5. CONCLUSION

Broadcast
Not Confirmed(But expectable)
Same asin the left
It is considered
to be useful

4.3 Evaluation based on Comments with Kansei Engineering approach
In the above case, multi-cast communication includes
broadcasting one so that we will denote explicitly “multi(broad)cast” in the later explanation. In uni-cast communication, e-mail needs knowledge about the participants ina small scale of cooperative education environment. Thereis a trend that a user who want to ask his/her question do
communicate to others who seems to reply such a question.
Probability and expectation are very important in order toachieve communication for information sharing in Distance
Learning environment. Some comments in student’s essay
may include useful information and knowledge[3].
These comments has been analyzed and categorized in
the six criteria, namely, “operate easily”, “communicate efficiently”, “response quickly”, “virtualize smoothly”, “reproduct(= repeat) flexibly” and “understand effectively”. Evaluation has been performed in some classrooms of distributed
campus through their students’ essay. And analyzed results
are averaged and expressed in Radar Chart shown in Figure5.

© ISAROB 2013

REFERENCES
[1]

[2]

[3]

291

Null, L. and Lobur, J.: MarieSim: The MARIE
Computer Simulator. ACM Journal of Educational Resources in Computing. Vol. 3, No. 2, Article #1, 29
pages (June 2003)
Imai,Y. and Tomita,S.: A Web-based Education Tool
for Collaborative Learning of Assembly Programming.
In: 2nd IADIS International Conference on WWW /Internet 2003(Algarve), Vol.1, pp. 703–711.
Scott, K., Benlamri, R. : Context-Aware Services
for Smart Learning Spaces. IEEE Transactions on
Learning Technologies, Vol.3, No.3, pp.214–227, JulySeptember 2010.

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An Application of Kansei Engineering to Community-base Collaboration
Masatoshi Imai1 , Yoshiro Imai2 , Tetsuo Hattori2
1

Department of Management Information, Kagawa Junior College, 1-10 Utazu-cho, Ayautagun 769-0201 Japan
2
Graduate School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396 Japan(Tel : +81-87-864-2244)
E-mail: 1 imai@kjc.ac.jp, 2 {imai, hattori}@eng.kagawa-u.ac.jp

Abstract: This paper presents a proposal to reconstruct some specific towns which were attacked and destroyed by Huge
Tsunami, for example, in Tohoku region of Japan on the 11th of March, 2011. A collaborative approach have been employed
to provide some trial proposal to reconstruct such damaged towns by means of Internet Community as follows. (1) Providing a
proposal and pictures to reconstruct, especially offering visual design concept of living space for slope topography through Social
Network System as an example of Internet Community, (2) Consulting and discussing the above proposal and pictures among
specific Internet Community with analysis by Kansei Engineering, and (3) Improving an original proposal into more efficient andsuitable one. The paper also explains detail of the senario to brush up the proposal by means of the above collaborative approachand some problems to be resolved in order to make it more fruitful.
Keywords: Collaborative Design, Information Sharing through Social Network, Evaluation by Kansei Engineering Approach

1. INTRODUCTION

firm that information and/or ideas obtained from it are suitable and constructive for them at the glance. Therefore theusers want to have not only access to Internet but also specific connectivity of Internet with trust and reliability. Social
Network Systems in Internet, which is abbreviated as only
SNS, can provide a suitable environment for the members to
be safe and confortable in order to perform suitable collaboration for work, evaluation and discussion[1].
One user of SNS can work together with his/her othermembers in order to communicate one another, exchange
each information, and discuss about common problems asynchronously (i.e. not in realtime) but directly (almost without misreading). With connectivity of SNS, users can show
their concept/solution, obtain efficient criticism about themfrom others, evaluate them among their community, and rebuild/modify original concept/solution into improved ones.

Collaborative Design is one of the suitable and reasonable approaches to reach the temporal goal with help from
related members and/or community in the relatively short period. Our research focuses on procedure for arranging or improving of residence through collaborative design methodology. One of key ideas is to utilize Social Network in orderto achieve collaborative design. And other is to apply Kansei Engineering methodology to evaluation of procedure anddecision making of results such a design has produced.
This paper includes concept for collaborative design in thenext section, prototyping and improving of collaboration in
the third section, evaluation and improvement by Kansei engineering in the fourth section, and summarized conclusionin the final section.

2. COLLABORATIVE DESIGN CONCEPT

3. PROTOTYPING OF COLLABORATION

It is difficult to decide the most efficient and/or effective
element(s) for the relevant users in a short period. Especially,
only one or very few persons cannot resolve several problems
which one positively evaluate but at the same time anothernegatively does. Certain size of group can provide a lot of
valuable ideas which can determine and adjust such problems into more acceptable solutions for wider range of human requests. Community-based approach will realize suchvaluable ideas and good design methodology.
Some people say Internet looks like one of huge communities. Of course, Internet itself provides loosely coupled human connectivity so that Internet users always want to haveseamless access to LAN, WAN and/or global network in order to obtain several kinds of information, benefits, hints,
idea and so on. But unfortunately Internet also includes a
lot of evil-mind and ill-intention for a third party.
Whenever connected with Internet, its users have to con-

© ISAROB 2013

3.1 A Draft Senario of Collaboration
First of all, we illustrates a draft senario of collaborationand implementation with SNS and then demonstrates an example of Collaborative Design[2] of living space for slope
topography through “Facebook” as an example of SNS. Our
attempt has been carried out with utilization of Facebook asa efficient SNS of Internet Community.
Reasons to employ Facebook connectivity as SNS are as
follows;• In Japan, there are some famous SNS’s for several kindsof users as Internet Community. But almost all are not sofamous for foreign countries and also not so convenient forforeign users of Internet Community. Currently, Facebook
has a huge numbers of members who belong to several countries and it seems to bes one of most powerful and influential
SNS’s of the world.

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Even in the case of our personal experiences, Facebook ismore affective and available for us to communicate with ourfriends and colleagues and exchange several kinds of information and ideas by means of multimedia than others. Usersof Facebook must enjoy easy handling of Information, multimedia and computer programming.
Figure1 shows a scheme for data distribution by meansof Facebook. It shows that it is convenient for users to distribute original design and idea into their friends and colleagues through a function of Facebook asynchronously anddirectly. This user wants to proposes his idea and detail ofdesign, transfer the relevant set of idea/design to his colleagues and realize information exchange with utilization ofengineering drawing and photos of prototype.

5. Finally, the user can rebuild his original idea and reconstruct design based on the more useful concept and recognition.
With some benefits of Internet Community, we can prepare collaborative environment for efficient design methodology and discussion. We can expect direct communication
with foreign colleagues although they are away from us andliving in other countries. As the results of communicationand discussion, we can obtain professional idea and criticism
about our current problems asynchronously but smoothly. In
the next subsection, we will demonstrate practical exampleof Collaborative Design for living space for slope topography through human connectivity of Facebook.

•

3.2 Example of Collaborative Design
We have been wanting to propose some specific idea toreconstruct living space and/or home for slope topography
especially near the seashore for the sake of avoidance of
Tsunami’s damage. One of authors, namely Masatoshi Imai,
has a lot of idea, design concept, and images of prototyping
about living space and/or building for slope topography. He
is in domain of design field and not so familiar with practicalarchitecture for building and construction.
So he has been expecting that colleagues discuss his design concept, check and evaluate it and finally improve it into
more fruitful new one together with himself. Because it isvery nice for not only him but also people, who want to live insuch a living space for slope topography without a fear about
Tsunami, to propose more suitable design concept of living
space/building for slope topography, he want to discuss his
idea and perform collaborative design through Facebook as acreative environment of Collaborative Design.
We will introduce a practical design concept to build living space for slope topography on window of Facebook
shown in Figure2. The relevant design concept displayed in
Figure2 can be realized and explained by means of several
multimedia information such as engineering drawing, someviews of prototyping and physical images for beginners andexperts(professional engineers).

Fig. 1 Collaborated Degin among distributed Colleagues
with utilization of benefit by Social Networks
With utilization of SNS, we will be able to expect someeffects of Collaborative Design which realize a typical story
as follows in Figure1;
1. One of the relevant user’s colleagues obtained an originalidea through Facebook as Internet Community.
2. He/She thought about such idea and had some different
idea and relevant comments for it.
3. He/She sent such different idea and his/her comments to
the relevant user for convenience and improvement.
4. The user obtained such useful idea and comments for thesake of his convenient and improvement directly and on-line.

© ISAROB 2013

Fig. 2 Design Concept to Build Living Space for Slope
Topography on Facebook.

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If someone has a glance at such URL, he/she can lookat each information as precisely as he/she wants. In orderto watch in detail, he/she can focus specific information,
namely engineering drawing and/or three dimensional viewof relevant building/living space. Figure3 is an example ofthree dimensional image which demonstrates other view as
design concept.

strategy to accommodate the above comments and criticism
for more suitable and allowable design, product, image andso on. Sometimes, comments and criticism are expressedin not quantitative but qualitative notation, so that we needsome efficient mechanism to translate qualitative expression
into qualitative one smoothly and suitably. Kansei Engineering is one of the most excellent methodologies to obtain
quantitative (i.e. numerical and computer-oriented) parameters from psychological (emotional) expression or feeling.
• Kansei Engineering is one of the well-established methodologies founded in Japan and one of the most applicable procedures to analyze and classify multiple-parameterized input
data, which sometimes differ each other for the unified targets. In the world, Kansei Engineering is utilized from car
manufacturing to fashion design and available in many kindsof domains which include not only engineering but also several decision making.
For example, in the case of discussion about window sizeof living space for slope topography, we want to decide
whether height and width of window size is suitable for relevant height and width of according wall or not and to obtain
information about parameter to enlarge or reduce the height
and width of window against the fixed size of wall. For thesake of realization of Collaborative Design, the problem is
how to get and find such parameterized request in comments
and criticism from colleagues of Internet Community.

Fig. 3 Sample Three-Dimensional View as a Design Concept on FaceBook.
As described before, we are not professional engineers forarchitecture, so a chart of engineering drawing is not perfectly suitable to discuss our idea among professional engineers, but three dimensional image will be able to provide
certain viewpoints even for nonprofessional persons. We
want to obtain some comments and criticism for our draft
proposal of design concept of living space for slope topography.
We think that beginners of architecture/landscape needsome practical images in order to understand whether living
space for slope topography is suitable or not even they wantto avoid potential damage of disaster from Tsunami. So wehave provide some three dimensional views of living spacefor slope topography and let the relevant people imagine as
if they were living there more closely. Of course, we thinkthat virtual reality and other visualization techniques are necessary for people who want to have more information about
our idea. That must be another problem to be resolved byourselves in future.

4.2 Improvement procedure by Kansei Engineering
We have employed Kansei Engineering approach and its
powerful procedure of attribute rating. In order to determine
whether size of window should be remained, enlarged or reduced and obtain scale of parameterization for enlargement
or reduction of window size, we will apply the following
steps;
1. preparing some questionary investigation with 5 stepwise
graduations, namely, “enlarge more”, “enlarge a little”, “remain (or keep this size)”, “reduce a little”, and “reducemore”.
2. defining the maximal amplitude for enlargement and reduction.
3. comparing the result rating from the answer against questionary investigation with 5 stepwise graduations statistically.
4. calculating quantitative result with compared rating into
the value from “enlarge more” to “reduce more”.
5. mapping the above value into parameterized scale and get
scaled amplitude to enlarge or reduce.
For example, we can define mapping function to translate emotional expression into rating of Kansei Engineering
shown in “param.”-column of Table1. Parameter ‘f ’ in thistable means a factor to specify how to enlarge windows or reduce ones for the adjustment according to request from colleagues from Internet Community. Therefore, when the valueof ‘f ’ is plus, we decide to choose an operation for enlarging
of window. In contrast, when it is minus, we do for reducing

4. EVALUATION AND IMPROVEMENT
4.1 Evaluation procedure by Kansei Engineering
As is known, Kansei Engineering[3] is one of efficientapproaches to “translate users’ psychological (emotional)
feeling about a product/image into perceptual (machineunderstandable) design element/methodology” for the sakeof efficient realization[4]. Our key idea to utilize Kansei Engineering for Collaborative Design are summarized as follows;• One proposal may obtain a few comments and criticism
which differ from one another. In such a case, we need smart© ISAROB 2013

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5. CONCLUSION

of window. And absolute value of ‘f ’ specifies amplitude offactor for level of enlarging/reducing.

This paper describes an example approach of Collaborative Design through Internet Community, which is implemented to provide an idea to reconstruct some living spaceand environment for people who had been attacked by disaster such as Earthquake and especially Huge Tsunami. We
want to provide a visual design concept of living space forslope topography, which is normally seemed not to be suitable for ordinary living but to be of advantage for avoidanceof disaster near from sea shore especially.
With our approach, we can conclude as follows;
1. It is necessary to discuss more suitable design concept
and/or idea for people who want to have temporary or permanent living environment because they have been damaged
by disaster. We decide to employ efficient and effective approach which can be faster and more flexible than usual.
2. It is suitable and convenient for people to provide theiridea for some domain by means of Internet Community suchas Social Network System. Utilization of “Facebook” as SNS
is to obtain several comments and/or messages from Community.
3. Kansei Engineering is good enough to evaluate proposals and analyze their comments, notices and criticism from
Community, because of being qualitative ways and data.
As one of our future works, we will develop new information server system to provide an efficient environment for
Collaborative Design and analyze in-bound comments andcriticism for specific idea and design concept by means ofKansei engineering methodology. We want to communicate
with people who consider to live where they are really afraidof potential damage of Tsunami and discuss about our original plan/design concept of living space for slope topography
through SNS and/or Internet Community in near future.

Table 1 Calculation of Parameterized Attribute with Emotional Expression from Internet Community
emotional ex.
enlarge moreenlarge a little
remainreduce a little
reduce moreparam.
1.0f
0.5f
0.0
−0.5f
−1.0f

P1
√
-

P2
√

P3
√

-

-

P4
√
-

P5
√
-

1.0f
1.0f
0.0
−0.5f
0.0f

If there are 5 numbers of emotional expressions as comments or criticism from Internet Community,namely from P1
to P5 , we can try to calculate a parameterized attribute basedon some criteria with such expression, which are demonstrated in the right side of Table1 and below numeral expression. And then we can obtain the precise factor to enlarge therelevant window by below calculation.
(1.0f + 2 ∗ 0.5f + 0.0f + (−0.5f ) + 0.0f )/5 = 0.3f
For example, assuming that default value of scaling factor
is 10%, we can draw from the above result into enlargement
by only 30% ×f in this example. Of course, ‘f ’ can play arole for characteristic constant for original designers to adjust their final decision to operate. If they want to keep their
original idea, they should specify ‘f ’ as small as possible.
Just like the above procedure, we can obtain other parameterized requests for Window’s size as well as Window’s position (namely, XY-position means Width and Height) andsome suitable information for Collaborative Design among
Internet Community. With Kansei Engineering methodology, we can determine which sensory attributes express particular subjective requests and draw conclusion about whichperceptual elements are responsible to enhance emotional decision, for example in Figure4. It is convenient for us torealize Collaborative Design based on Internet Community
precisely and sufficiently.

REFERENCES[1]

Wang, Y., Yang, J.: Relation Based Service Networks
for Reliable Service Selection. In: Proc. of IEEE Conference on Commerce and Enterprise Computing (CEC2009). pp.209–214, 2009.
[2] Steimle, J.,Brdiczka, O., Mühlhäuser, M. : CoScribe:
Integrating Paper and Digital Documents for Collaborative Knowledge Work. IEEE Transactions on
Learning Technologies, Vol.2, No.3, pp.174–188, JulySeptember 2009.
[3] Nagamachi, M.: Kansei Engineering: A new ergonomic consumer-oriented technology for productdevelopment. International Journal of Industrial Ergonomics, Vol.15, No.1, pp.3–11, January 1995.
[4] Bouchard, C., Lim, D., Aoussat, A.: Development of a
KANSEI ENGINEERING SYSTEM for Industrial design: Identification of input data for KES.

http://citeseerx.ist.psu.edu/viewdoc
/versions?doi=10.1.1.100.7761

Fig. 4 Radar Chart of Parameters’ Value for Improvement.

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Bottom-Up Pyramid Cellular Acceptors with n-dimensional Layers
Makoto Sakamoto1 , Tatsuma Kurogi1 , Makoto Nagatomo1 , Takao Ito2 , Yasuo Uchida2 ,Tsunehiro Yoshinaga3 , Satoshi Ikeda1 ,Masahiro Yokomichi1 ,and Hiroshi Furutani1
1
University of Miyazaki, Miyazaki, Miyazaki 889-2192, Japan
Ube National College of Technology, Ube, Yamaguchi 755-8555, Japan3
Tokuyama College of Technology, Shunan, Yamaguchi 745-8585,Japan

2

Abstract

dimensional tape. In [2], C.R.Dyer and A.Rosenfeld
introduced an acceptor on a two-dimensional pattern(or tape), called the pyramid cellular acceptor, anddemonstrated that many useful recognition tasks are
executed by the pyramid cellular acceptors in time
proportional to logarithm of the diameter of the input. They also introduced a bottom-up pyramid cellular acceptor, which is a restricted version of the pyramid cellular acceptor, and proposed some interesting
open problems about it. On the other hand, the question of whether processing n-dimensional digital patterns is much difficult than (n-1)-dimensional ones is ofgreat interest from the theoretical and practical standpoints. Thus, the study of n-dimensional automata
as the computasional model of n-dimensional patternprocessing has been meaningful. From this point ofview, we are interested in n-dimensional automata.
In this paper, we study about bottom-up pyramid
cellular acceptors with n-dimensional layers, and deal
with the following problems (which is one of the open
problems) : Does the class of sets accepted by deterministic bottom-up pyramid cellular acceptors withn-dimensional layers include the class of sets acceptedby deterministic n-dimensional finite automata [3-7]?
This paper shows that the class of sets accepted byn-dimensional finite automata is incomparable withthe class of sets accepted by deterministic bottom-up
pyramid cellular acceptors which operate in time oforder lower than the diameter of the input.

In theoretical computer science,the Turing machine
was introduced as a simple mathematical model ofcomputers in 1936, and has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing. After that, the developmentof the processing of pictorial information by computer was rapid in those days.Therefore, the problem
of computational complexity was also arisen in thetwo-dimensional information processing. M.Blum and
C.Hewitt first proposed two-dimensional automata as
a computaional model of two-dimensional pattern processing in 1967[1]. Since then, many researchers in thisfield have been investigating many properties of twoor three-dimensional automata. In 1997, C.R.Dyer
and A.Rosenfeld introduced an acceptor on a twodimensional pattern (or tape), called the pyramid cellular acceptor, and demonstrated that many usefulrecognition tasks are executed by pyramid cellular acceptors in time proportional to the logarithm of the diameter of the input. They also introduced a bottom-up
pyramid cellular acceptor which is a restricted versionof the pyramid cellular acceptor, and proposed someinteresting open problems about bottom-up pyramid
cellular acceptors. On the other hand, we think thatthe study of n-dimensional automata has been meaningful as the computational model of n-dimensional
infomation processing[9]. In this paper,we investigate about bottom-up pyramid cellular accptors withn-dimensional layers, and show their some accepting
powers.

2

Key Words : cellular automaton, diameter, finite automaton, n-dimension, pattern recognition.

1

Let Σ be a finite set of symbols. An n-dimensional
tape over Σ is an n-dimensional array of elements of
Σ. The set of all the n-dimensional tapes over Σ isdenoted by Σ(n) . Given a tape x ∈ Σ(n) , for each j (1≤ j ≤ n), we let lj (x) be the length of x along the jthaxis. The set of all x ∈ Σ(n) with l1 (x) = n1 , l2 (x) =n2 , . . ., ln (x) = nn is denoted by Σ(n1 ,n2 ,...,nn ) . When1 ≤ ij ≤ lj (x) for each j (1 ≤ j ≤ n), let x (i1 , i2 ,. . ., in ), denote the symbol in x with coordinates (i1 ,
i2 , . . ., in ). Furthermore, we define x [(i1 , i2 , . . ., in ),(i1 ’, i2 ’, . . ., in ’)], when i ≤ ij ≤ ij ’ ≤ lj (x) for each
integer j (1 ≤ j ≤ n), as the n-dimensional input tape

Introduction

In 1967, M.Blum and C.Hewitt first proposed twodimensional automata as a computational model of
two-dimensional pattern processing, and investigatedtheir pattern recognition abilities [1]. Since then,many researchers in this field have been investigating
a lot of properties about automata on a two- or three-© ISAROB 2013

Definition

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y satisfying the following (i) and (ii) : (i) for each j(1 ≤ j ≤ n), lj (y) = ij ’ - ij + 1; (ii) for each r1 , r2 ,. . . ,rn (1 ≤ r1 ≤ l1 (y), 1 ≤ r2 ≤ l2 (y), . . ., 1 ≤ rn ≤ln (y)), y (r1 , r2 , . . ., rn ) = x (r1 + i1 - 1, r2 + i2 - 1,
. . ., rn + in - 1).

henceforth called the base, in such a way that x (i1 , i2 ,
i3 , i4 ) is stored at the cell of the i1 th row and the i2 thcolumn on the i3 th plane of the i4 th n-dimensional
rectangular array, and the other cells are initialized toa quiescent state qs (∈QN −QT −A). As usual, we let
δ (qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs , qs ,qs ) = qs . The input is accepted if and only if the root
cell ever enters an accepting state. This 4-U P CA iscalled deterministic. A nondeterministic bottom-up
pyramid cellular acceptor is defined as a 4-U P CA usQN
ing δ : Q17
instead of the state transition funcN →2tion of the deterministic 4-U P CA. Below, we denotea deterministic n-U P CA by n-DU P CA, and a nondeterministic n-U P CA by n-N U P CA. An n-DU P CA
(or n-N U P CA) operates in time T (n) if for every ndimensional tape of size 2t × 2t × 2t × 2t (t ≥ 0) it accepts the n-dimensional tape, then there is an accepting computation which uses no more than time T (t).
By n-DU P CA (T (t)) [n-N U P CA(T (t))] we denotea T (t) time-bounded n-DU P CA [n-N U P CA] which
operates in time T (t).

We next give some basic concepts about bottom-up
pyramid cellular acceptors with n-dimensional layers[7]. A bottom-up pyramid cellular acceptor with ndimensional layers (n-U P CA) is a pyramidal stackof n-dimensional arrays of cells in which the bottom
n-dimensional layer has size 2t × 2t × · · · × 2t (t ≥0), the next lowest 2t−1 × 2t−1 × · · · × 2t−1 , and soforth, the (t + 1)st n-dimensional layer consisting ofa single cell, called the root. Each cell is defined as an
identical finite-state machine, M = (QN , QT , δ, A),
where QN is a nonempty, finite set of states, QT ⊆QN is a finite set of input states,A ⊆ QN is the setnof accepting states, and δ : Q2N +1 → QN is the statetransition f unction, mapping the current states of M
and its 2n son cells in a 2 × 2 × · · · × 2 block on the
n-dimensional layer below into M ’s next state. For
example, the definition in the case of four-dimensionalcase as follows. Let c be some cell on the (i + 1)st ndimensional layer, and let c (U N W P ), c (U N W F ),c (U SW P ), c (U SW F ), c (U SEP ), c (U SEF ), c
(U N EP ), c (U N EF ), c (DN W P ), c (DN W F ), c
(DSW P ), c (DSW F ), c (DSEP ), c (DSEF ), c
(DN EP ), and c (DN EF ) be sixteen son cells (on theith n-dimensional layer) of c, where c(U N W F ) is c’s
upper northwest son in the most future direction, c(DN W P ) is c’s lower northwest son in the most past
direction, etc. For example, if the coordinates of c onthe (i + 1)st layer is (1, 1, 1, 1) ((2t , 2t , 2t , 2t )), thecoordinates of sixteen son cells of c on the ith layerc (U N W P ), c (U N W F ) ,c (U SW P ), c (U SW F ),c (U SEP ), c (U SEF ), c (U N EP ), c (U N EF ), c
(DN W P ), c (DN W F ), c (DSW P ), c (DSW F ), c
(DSEP ), c (DSEF ), c (DN EP ), and c (DN EF ) are(1, 1, 1, 1), (1, 1, 1, 2), (1, 2, 1, 1), (1, 2, 1, 2), (1, 2, 2,
1), (1, 2, 2, 2), (1, 1, 2, 1), (1, 1, 2, 2), (2, 1, 1, 1), (2,1, 1, 2), (2, 2, 1, 1), (2, 2, 1, 2), (2, 2, 2, 1), (2, 2, 2, 2),(2, 1, 2, 1), (2, 1, 2, 2), ((2t − 1, 2t − 1, 2t − 1, 2t −1), (2t − 1, 2t − 1, 2t − 1, 2t ), (2t − 1, 2t , 2t − 1, 2t− 1), (2t − 1, 2t , 2t − 1, 2t ), (2t − 1, 2t , 2t , 2t − 1),(2t − 1, 2t , 2t , 2t ), (2t − 1, 2t − 1, 2t , 2t − 1), (2t − 1,2t − 1, 2t , 2t ), (2t , 2t − 1, 2t − 1, 2t − 1), (2t , 2t − 1,2t − 1, 2t ), (2t , 2t , 2t − 1, 2t − 1), (2t , 2t , 2t − 1, 2t ),(2t , 2t , 2t , 2t − 1), (2t , 2t , 2t , 2t ), (2t , 2t − 1, 2t , 2t
− 1), (2t , 2t − 1, 2t , 2t )),respectively. Then qc (t + 1)
= δ (qc (t), qc(U N W P ) (t), qc(U N W F ) (t), qc(U SW P ) (t),qc(U SW F ) (t), qc(U SEP ) (t), qc(U SEF ) (t), qc(U N EP ) (t),qc(U N EF ) (t), qc(DN W P ) (t), qc(DN W F ) (t), qc(DSW P )(t), qc(DSW F ) (t), qc(DSEP ) (t), qc(DSEF ) (t), qc(DN EP )(t)), qc(DN EF ) (t)), where for example qc (t) means thestate of c at time t. At time t = 0, the input tape x ∈(4)
QT [l1 (x) = l2 (x) = l3 (x) = l4 (x) = 2t , t ≥ 0] is stored
as the initial states of the bottom n-dimensional layer,© ISAROB 2013

We next introduce an n-dimensional finite automaton [8]. An n-dimensional f inite automaton(n-F A) is an n-dimensional Turing machine withno workspace. An n-F A M has a read-only ndimensional tape with boundary symbols #’s, finite
control, and an input head. For example, we explain
the definition in the case of four-dimensional case as
follows. The input head can move in eight direction −east, west, south, north, up, down, future, or past −unless it falls off the input tape. Formally, M is definedby the 5-tuple M = (K, Σ ∪ {#}, δ, q0 , F ), where
K is a finite set of states, Σ is a finite set of input
symbols, # is the boundary symbol (not in Σ), δ : K× (Σ ∪ {#}) → 2K×{E,W,S,N,U,D,F,P,H} is the statetransition f unction, where E, W , S, N , U , D, F , P ,and H represent the move directions of the input head− east, west, south, north, up, down, future, past, andno move, respectively, q0 ∈ K is the intial state, andF ⊆ K is the set of accepting states. The action of
M is similar to that of the one-dimensional (or twodimensional) finite automaton [4], except that the input head of M can move in eight directions. That is,when an input tape x ∈ Σ(4) with boundary symbols ispresented to M , M starts in its initial state q0 with the
input head on x (1, 1, 1, 1), and determines the next
state of the finite control and the move direction of the
input head, depending on the present state of the finite
control and the symbol read by the input head. We
say that M accepts the tape x if it eventually entersan accepting state. We denote a deterministic n-F A[nondeterministic n-F A] by n-DF A [n-N F A].
We let each sidelength of each input tape of ndimensional automata, throughout this paper, beequivalent. We denote the set of all n-dimensional
tapes accepted by M by T (M ). Define £ [n-DU P CA]= {T | T (M ) is accepted by some n-DU P CA M }.

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ℓn (x) = 2t }, and£[n-N U P CA], £[n-DF A], etc. are defined similarly.
Finally, we give definition of diameter. For example, we explain the definition in the case of fourdimension. Given a subset S of a tape x ∈ Σ(4) , wecan define its extent in a given direction θ as the lengthof its projection on a plane in that direction. Here thelength of a projection is the distance between its farthest apart nonzero values. Thus the extent of S isthe distance between a pair of parallel planes perpendicular to θ that just bracket S. The diameter of S is
defined as its extent in any direction.

3

W ′ (n) = {x ∈ {0, 1}(n) |ℓ1 (x) = ℓ2 (x) = · · · = ℓn (x)= 2t−1& x[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 1)] ∈ {0, 1}(n)& x[(1, 1, . . . , 2), (2t−1 , 2t−1 , . . . , 2t−1 )] ∈ {0}(n) }.

We consider the cases when the tapes in W (t) arepresented to B. Let c be the cell which is situated at
the first row, the first column, the first plane, . . . ,and the first (n-1)-dimensional array in the tth layer(i.e., the layer just below the root cell). (Note that
there are 2n−1 cells in the tth layer.) For each x in
W (t) such that x[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )] ∈W ′ (t), and for each r ≥ 1, let qr (x) be the state of cat time r when x is presented to B. Then the following
proposition must hold.

Results

In this section, we show that the class of sets accepted by n-DFA’s is imcomparable with the class ofsets accepted by n-DUPCA’s which operate in timeof order lower than the diameter of the input. It hasoften been noticed that we can easily get several properties of n-dimensional automata by directly applying
the results of (n-1)-dimensional case, if each sidelengthof each n-dimensional input tape of these automata is
not equivalent. So we let each sidelength of each input
tape, throughout this paper, be equivalent in order toincrease the theoretical interest.

Proposition 3.1. Let x, y be two different tapes in
W (t) such that bothx[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]andy[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]are in W ′ (t) andx[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]6= y[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )].
Then, (q1 (x), q2 (x), . . . , qT (t) (x))= (q1 (y), q2 (y), . . . , qT (t) (y)).
[Proof : For suppose that (q1 (x), q2 (x), . . . , qT (t) (x))= (q1 (y), q2 (y), . . . , qT (t) (y)). We consider two tapes z,z ′ in W (t) such that

Lemma 3.1. Let T1 = { x ∈ { 0,1 }(n) | ∃ t ( t ≥ 1)[ℓ1 (x) = ℓ2 (x) = · · · = ℓn (x) = 2t ] and x (2t−1 , 2t−1 ,. . ., 2t−1 ) = 1 }. Then,(1) T1 (x) ∈/ £[n-DFA], and(2) T1 (x) ∈ £[n-DUPCA(t)].

(i) z [(1,1,. . . ,1), (2t−1 , 2t−1 , . . . , 2t−1 )]= x [(1,1,. . . ,1), (2t−1 , 2t−1 , . . . , 2t−1 )]andz ′ [(1,1,. . . ,1), (2t−1 , 2t−1 , . . . , 2t−1 )]= y [(1,1,. . . ,1), (2t−1 , 2t−1 , . . . , 2t−1 )],Proof : The Proof of (1) is similar to that of Theorem 3 in [7]. On the other hand, by using the same
technique as in the proof of Lemma 1 in [6], we can getPart (2) of the lemma.
(ii) the part of z except for z [(1, 1, . . . , 1), (2t−1 , 2t−1 ,. . ., 2t−1 )] is identical with the part z ′ except forz ′ [(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )],andLemma 3.2. Let T2 = {x ∈ {0, 1}(n) | ∃t (t ≥ 1)[ℓ1 (x) = ℓ2 (x) = · · · = ℓn (x) = 2t ] and x [(1, 1, . . . , 1),(2t , 2t , . . . , 1)] = x [(1, 1, . . . , 2t ), (2t , 2t , . . . , 2t )] }. LettT (t) be a time function such that limt→∞ [T (t)/22 ] =0. Then,(1) T2 ∈ £ [n-DFA],and(2) T2 ∈/ £ [n-DUPCA(T (t))].

(iii) z[(1, 1, . . . , 1), (2t , 2t , . . . , 1)]= z[(1, 1, . . . , 2t ), (2t , 2t , . . . , 2t )].
By assumption, the root cell of B enters the samestates until time T (t), for the tapes z and z ′ . Since B
operate in time T (t) and z is in T2 , it follows that z ′ isalso accepted by B. This contradicts the fact that z ′ isnot in T2 .
]

Proof : It is obvious that there is an n-DFA accepting T2 , and so (1) of the lemma holds. Below, we prove(2). Suppose that there is an n-DUPCA B which accepts T2 and operates in time T (t), and that each cellof B has k states. For each t ≥ 2, let
W (t) = {x ∈ {0, 1}(n) |ℓ1 (x) = ℓ2 (x) = · · · =© ISAROB 2013

Let s(t) be the number of different sequences ofstates which c enters until time T (t). Clearly, s(t) ≤k T (t) . On the other hand (for any set S, let |S| de(t−1)2

note the number of elements of S.), |W ′ (t)| = 22.
2t
Since limn→∞ T (t)/2 = 0 (by assumption of the298

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013lemma), it follows that |W ′ (t)| > s(t) for lange t.
Therefore, it follows that for large t there must exist
two different tapes x, y in W (t) such that[4] K.Inoue, I.Takanami, and A.Nakamura, A noteon two-dimensional finite automata, Information
Processing Letters, Vol.7, No.1, p.49, 1978.

(i) both x[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]and y[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]and in W ′ (t),[5] K.Inoue and I.Takanami, Remaks on twodimensional finite automata with multiplication
and bottom-up pyramid acceptors, Technical Report IECE of Japan, No.AL77-61, 1978.

(ii) x[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )]6= y[(1, 1, . . . , 1), (2t−1 , 2t−1 , . . . , 2t−1 )], and[6] K.Inoue and I.Takanami, A note on bottom-up
pyramid acceptors, Information Processing Letters Vol.8, No.1, pp.34-37, 1979.

(iii) (q1 (x), q2 (x), . . . , qT (t) (x))= (q1 (y), q2 (y), . . . , qT (t) (y)).

From Lemmas 3.1 and 3.2, we can get the following
theorem.

[7] M.Sakamoto, S.Nogami, K.Inoue, and M.Kono,
A relationships between the accepting powers of three-dimensional finite automata andtime-bounded bottom-up pyramid cellular acceptors with three-dimensional layers, Trans. of
SCI(Japan), Vol.17, No.10, pp.451-458, 2004.

Theorem 3.1. Let T (t) be a time function such thattlimn→∞ [T (t)/22 ] = 0 and T (t) ≥ t(t ≥ 1). Then £[n-DFA] is imcomparable with £ [n-DUPCA(T (t))].

[8] H.Taniguchi, K.Inoue, and I.Takanami, A noteon three-dimensional finite automata, Information Siences, Vol.26, pp.65-85, 1982.

Corollary 3.1. £[n-DFA] is incomparable with £ [nDUPCA(t)], which is the class of sets accepted by nDUPCA’s operating in real time.

[9] Y.Uchida,
T.Ito,
H.Okabe,M.Sakamoto,
H.Furutani, and M.Kono, four-dimensional
multi-inkdot finite automata, WSEAS Trans. on
Computers, Issue 9, Vol.7, pp.1437 - 1446, 2008.

This contradicts the above Proposition 3.1, andthus the Part (2) of the lemma holds.


Corollary 3.2. £ [n-DFA] is incomparable with £[nNUPCA(t)].

4Conclusion

In this paper, we dealt with the accepting powers of bottom-up pyramid cellular acceptors with ndimensional layers, and showed that the class of sets
accepted by n-DFA’s is incomparable with the classof sets accepted by n-DUPCA’s which operate in timeof order lower than the diameter of the input. It is
still inknown whether the class of sets accepted by
n-DUPCA’s includes the class of sets accepted by nDFA’s.

References[1] M.Blum and C.Hewitt, Automata on a twodimensional tape, in IEEE Symposium on Switching and Automata Theory, pp.155-160, 1967.
[2] C.R.Dyer and A.Rosenfeld, Cellular pyramids forimage analysis, Technical Report TR-544, Computer Science Center, University of Maryland,
1977.
[3] K.Inoue, and A.Nakamura, Some properties of
two-dimensional on-line tessellation acceptors,
Information Sciences, Vol.13, pp.95-121, 1977.

© ISAROB 2013

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Homogeneous Systolic Pyramid Automata with n-Dimensional Layers
Makoto Sakamoto1 , Takao Ito2 , Tatsuma Kurogi1 ,Makoto Nagatomo1 ,Yasuo Uchida2 ,Tsunehiro Yoshinaga3 ,Satoshi Ikeda1 ,Masahiro Yokomichi1 ,and Hiroshi Furutani1
1Dept. of Computer Science and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JAPAN
2
Dept. of Business Administration, Ube National College of Technology, Ube 755-8555, JAPAN
3
Dept. of Computer Science & Electronic Engineering, Tokuyama College of Technology, Shuman 745-8585, JAPAN

Abstract

ical and practical standpoints. Thus, the study of ndimensional automata as a computational model of ndimensional pattern processing has been meaningful[423]. Cellular automata were investigated not onlyin the viewpoint of formal language theory, but also
in the viewpoint of pattern recognition. Cellular automata can be classified into some types [2]. A systolicpyramid automaton is also one parallel model of various cellular automata. In this paper, we propose ahomogeneous systolic automaton with n-dimensional
layers (n-HSPA), and investigate some properties ofreal-time n-HSPA.
Let Σ be a finite set of symbols. An n-dimensional
tape over Σ is an (n − 1)-dimensional array of elements of Σ. The set of all n-dimensional tapes over
Σ is denoted by Σ(n) . Given a tape x∈Σ(n) , for eachj(1 ≤ j ≤ n), we let lj (x) be the length of x along thejth axis. When 1 ≤ ij ≤ lj (x) for each j(1 ≤ j ≤ n),
let x(i1 , i2 , . . . , in ) denote the symbol in x with coordinates (i1 , i2 , . . . , in ). We concentrate on the input
tape x with l1 (x) = l2 (x) = l3 (x) = · · · = ln (x).
A homogeneous systolic pyramid automaton withn-dimensional layers (n-HSPA) is a pyramidal stackof n-dimensional arrays of cells in which the bottom
n-dimensional layer (level 0) has size an (a ≥ 1), thenext lowest (a − 1)n , and so forth, the (a − 1)st ndimensional layer (level (a − 1)) consisting of a single cell, called the root. Each cell means an identicalfinite-state machine, M = (Q, Σ, δ, #, F ), where Q isa finite set of states, Σ ⊆ Q is a finite set of input
states, # ∈ Q − Σ is the quiescent state, F ⊆ Q isnthe set of accepting states, and δ : Q2 +1 → Q is thestate transition function, mapping the current statesof M and its 2n son cells in a 2 × 2 × · · · × 2 blockon the n-dimensional layer below into M ’s next state.
The input is accepted if and only if the root cell everenters an accepting state. An n-HSPA is said to bea real -time n-HSPA if for every n-dimensional tape ofsize an (a ≥ 1) it accepts the n-dimensional tape intime a − 1. By £R [n-HSP A] we denote the class ofthe sets of all the n-dimensional tapes accepted by areal-time n-HSPA[1].
A 1-way n-dimensional cellular automaton (1nCA) can be considered as a natural extension of the

Cellular automata were investigated not only in the
viewpoint of formal language theory, but also in the
viewpoint of pattern recognition. Cellular automata
can be classified into some types. A systolic pyramid
automata is also one parallel model of various cellular
automata. A homogeneous systolic pyramid automaton with n-dimensional layers (n-HSP A) is a pyramid stack of n-dimensional arrays of cells in which
the bottom n-dimensional layer (level 0) has size an(a≥1), the next lowest (a − 1)n , and so forth, the (a1)st n-dimensional layer (level (a-1)) consisting of asingle cell, called the root. Each cell means an identical finite-state machine. The input is accepted if andonly if the root cell ever enters an accepting state.
An n-HSP A is said to be a real-time n-HSP A if forevery n-dimensional tape of size an (a≥1) it accepts
the n-dimensional tape in time a-1. Moreover, a 1way n-dimensional cellular automaton (1-nCA) can
be considered as a natural extension of the 1-way twodimensional cellular automaton to n-dimension. The
initial configuration is accepted if the last special cellreaches a final state. A 1-nCA is said to be a realtime 1-nCA if when started with n-dimensional arrayof cells in nonquiescent state, the special cell reaches
a final state. In this paper, we propose a homogeneous systolic automaton with n-dimensional layers(n-HSP A), and investigate some properties of realtime n-HSP A. Specifically, we first investigate a relationship between the accepting powers of real-time
n-HSP A’s and real-time 1-nCA’s. We next show therecognizability of n-dimensional connected tapes byreal-time n-HSP A’s.
Key Words : cellular automaton, diameter, finite automaton, n-dimension, parallelism, pattern recognition, real time.

1

Introduction and Preliminaries

The question of whether processing n-dimentional
digital patterns is much more difficult than (n-1) dimentional ones is of great in terest from the theoret-© ISAROB 2013

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1-way two-dimensional cellular automaton to n dimensions [3]. The initial configuration of the cellular automaton is taken to be an l1 (x) × l2 (x) × · · · × ln (x)array of cells in the nonquiescent state. The initialconfiguration is accepted if the last special cell reaches
a final state. A 1-nCA is said to be a real -time 1-nCA
if when started with an l1 (x) × l2 (x) × · · · × ln (x) array of cells in the nonquiescent state, the special cellreaches a final state in time l1 (x)+l2 (x)+· · ·+ln (x)−1.
By £R [1-nCA] we denote the class of the sets of all then-dimensional tapes accepted by a real-time 1-nCA[3].

2

same as that of (i). Q.E.D.]
Propositon 2.2. F or any two tapes x, y ∈ W (t)whose 1st (1-3) planes are dif f erent, σ(x) 6= σ(y).
[Proof : Suppose to the contrary that σ(x) = σ(y).
We consider two tapes x′ ,y ′ ∈ W (t) satisfying the follwing :(i) x(1 − 3)1 and x(1 − 3)t , are equal to x(1 − 3)1 of x,
respectively(ii) y ′ (1 − 3)1 is equal to y(1 − 3)1 , and y ′ (1 − 3)t is
equal to x(1 − 3)1 .
As is easily seen, x′ ∈ V and so x′ is accepted by M .
On the other hand, from Proposition 2.1(ii), γ(x′ ) =γ(y ′ ). From Proposition 2.1(i), σ(x) = σ(x′ ), σ(y) =σ(y ′ ). It follows that y ′ must be also accepted by M .
This contradicts the fact that y ′ is not in V . Q.E.D]

Main Results

We mainly investigate a relationship between theaccepting powers of real-time n-HSP A’s and real-time
1-nCA’s. The following theorem implies that real-time
n-HSP A’s are less powerful than real-time 1-nCA’s.

Proof of Theorem 2.1 (continued) : Let p(t) be
the number of tapes in W (t) whose 1st (1-3) planes
are different, and let Q(t) = {σ(x)|x ∈ W (t)}, where
k is the number of states of each cell of M . Then,2p(t) = 2t , and Q(t) ≤ k 4 . It follows that p(n) > Q(t)for large t. Therefore, it follows that for large t, there
must be two tapes x,y in W (t) such that their 1st (1-3)
planes are different and σ(x) = σ(y). This contradicts
Proposition 2.2, so we can conclude that V 6∈ £R [3HSP A]. In the case of n-dimention, we can show thatV 6∈ £R [n-HSP A] by using the same technique. This
completes the proof of Theorem 2.1.
Q.E.D.

Theorem 2.1. £R [n-HSP A] ( £R [1-nCA].
Proof : Let V={x ∈ {0, 1}(n) |l1 (x) = l2 (x) = · · · =ln (x)&[∀i1 , ∀i2 , . . . , ∀in−1 (1 ≤ i1 ≤ l1 (x), 1 ≤ i2 ≤
l2 (x), . . . , 1 ≤ in−1 ≤ ln−1 (x))[x(i1 , i2 , . . . , in−1 , 1) =x(i1 , i2 , . . . , in−1 , ln (x))]]}.
It is easily shown that V1 ∈ £R [1-nCA]. Below,
we show that V 6∈ £R [n-HSP A]. Suppose that there
exists a real-time n-HSP A(n = 3) accepting V . For
each t ≥ 4, let
W (n) = {x ∈ {0, 1}(3) |l1 (x) = l2 (x) = · · · = ln (x)& [x (1, 2, 1), (t, t − 1, t)] ∈ 0(3) }.
Eight sons of the root cell A(t−1,1,1,1) of
M A(t−2,1,1,2) , A(t−2,1,2,2) , A(t−2,2,1,2) , A(t−2,2,2,2) ,A(t−2,1,1,3) , A(t−2,1,2,3) , A(t−2,2,1,3) , A(t−2,2,2,3) are denoted by CU N W , CU SW , CU SE ,CU N E , CDN W , CDSW ,CDSE , CDN E , respectively. For each x in W (n),x(U N W ), x(U SW ), x(U SE), x(U N E), x(DN W ),x(U SW ), x(U SE), x(U N E) are the states of CU N W ,CU SW , CU SE , CU N E , CDN W , CDSW , CDSE , CDN E ,at time t-2, respectively. Let σ (x) = (x(U N W ),x(U SW ), x(DN W ), x(DSW )), γ (x) = (x(U SE),x(U N E), x(DSE), x(DN E)). and ρ(x) = (x(U N W ),x(U SW ), x(DN W ), x(DSW ), x(U SE), x(U N E),
x(DSE), x(DN E)). Then, the following two propositions must hold:We next show the recognizability of n-dimensional
connected tapes by real-time n-HSP A’s by using thename technique of Ref.[3]. Let x in {0,1}(n) . A maximal subset P of N n satisfying the following conditons
is called a 1-component of x.
(i) For any (i1 ,i2 ,. . . ,in ∈ P , we have 1≤i1 ≤l1 (x),1≤i2 ≤l2 (x),. . .,1≤in ≤ln (x), and x(i1 , i2 , . . . , in ) = 1.
(ii) For any (i1 ,i2 ,. . .,in ), (i′1 ,i′2 ,. . .,i′n )∈P , there exists a sequence (i1,0 ,i2,0 ,. . .,in,0 ),(i1,1 ,i2,1 ,. . .,in,1 ),. . .,(i1,n ,i2,n ,. . .,in,n ) of elements in P such that
(i1,0 ,i2,0 ,. . .,in,0 ) = (i1 ,i2 ,. . .,in ), (i1,n ,i2,n ,. . .,in,n ) =(i′1 ,i′2 ,. . .,i′n ), and |i1,j -i1,j−1 | + |i2,j -i2,j−1 | + . . . +|in,j -in,j−1 |≤ 1(1 ≤ j ≤ n). A tape x ∈ {0, 1}(n)is called connected if there exists exactly one 1component of x.
Let Tc be the set of all the n-dimensional connected
tapes. Then, we have

Proposition 2.1. (i) F or any two tapes x, y ∈ W (n)whose 1st(1 − 3) planes are same, σ (x) = σ (y). (ii)
F or any two tapes x, y ∈ W (n) whose n − th(1 − 3)planes are same, γ (x) = γ (y).

Theorem 2.2. Tc 6∈ £R [n-HSP A].

3[Proof : From the mechanism of each cell, it is easily
seen that the states of CU N W , CU SW , CDN W , CDSW
are not influenced by the information of x(1 − 3)t ’s.
From this fact, we have (i). The proof of (ii) is the© ISAROB 2013

Conclusion

We investigated a relationship between the accepting powers of homogeneous systolic pyramid automaton with n-dimensional layers(n-HSP A) and one-way

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n-dimensional cellular automata (1-nCA) in real time,and showed that real-time n-HSP A’s are less powerfulthan real time 1-nCA’s.

[11] M.Sakamoto, N.Tomozoe, H.Furutani, M.Kono,
T.Ito, Y.Uchida, and H.Okabe, A survey of
automata on three-dimensional input tapes,
W SEAST T ransactions on Computers,Issue
10,Vol.7, pp.1638-1647,2008.

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Some accepting powers of three-dimensional parallel Turning machines, International Journalof Artif icial Lif e and Robotics,Vol.13,No.1,
pp.27-30,2008.

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[13] M.Sakamoto, M.Fukuda, S.Okatani, T.Ito,
H.Furutani, and M.Kono, Path-bouded Threedimensional finite automata, International
Journal of Artif icial lif e and Robotica,
Springer,Vol.13,No.1,pp.54-57,2008.

[2] K.Inoue and I.Takanami, A survey of twodimensional automata theory, Information
Sciences, 55:pp.99-121, 1991.
[3] C. Iwamoto, K. Inoue, and I. Takanami, Some
Properties of Homogeneous Pyramid Automata,
The IEICE T ransactions on Inf ormation and
Systems (Japanese Edition), J73-D-I(9) : 778780, 1990.

[14] M.Sakamoto, S.Okatani, M.Fukuda, T.Ito, and
H.Furutani, and M.Kono, A relationship between
Turning machines and finite automata on fourdimensional input tapes, International Journalof Artif icial lif e and Robotica, Springer,
Vol.13,No.1, pp.58-60,2008.

[4] K. Krithivasan and M. Mahajan, Systolic pyramid automata, cellular automata and array
languages, International Journal of P attern
Recognition and Artif icial Intelligence, 3(34):405-433, 1989.

[15] M.Sakamoto, S.Okatani, K.Kajisa, M.Fukuda,
T.Matsuawa, A.Taniue, T.Ito, H.Furutani, andM.Kono, Hierarchies based on the number ofcooperating systems of three-dimensional finite
automata,International Journal of Artificial Lifeand Robotics,Vo.4,No.3, pp.425-428.2009.

[5] M.Sakamoto, Three-Dimensional Alternating
Turing Machines, Ph.D. T hesis, Y amaguchi
U niversity, 1999.

[16] M.Sakamoto,
T.Matsukawa,
R.Katamune,
H.Furutani, M.Kono, S.Ikeda, T.Ito, Y.Uchida,
and T.Yoshinaga, Four-dimensional synchronizedalternating Turning machines,P roceedings ofthe 10th American Conf erence on Applied
M athmatics, Harvard U niversity, Cambridge,
U SA, pp.195-200,2010(CD-ROM).

[6] M.Sakamoto, H.Okabe, and K.Inoue, Some
properties of four-dimansional finite automata, in 2002 Chugoku − Section Joint
Convention Record of Insistes of Electricaland Inf ormation Engineerings,
Japan,
p.351,2002.
[7] M.Sakamoto, S.Nagami, K.Kono, A relationship between the accepting powers of threedimentional layers, T rans. of SCI(Japan),Vol.17, No.10, pp.451-458,2004.

[17] M.Sakamoto,
R.Katamune,
T.Matsukawa,
H.Furutani, M.Kono, S.Ikeda, T.Ito, Y.Uchida,
and T.Yoshinaga, Some result about hierarchy
and recognizability of four-dimensional sychronized alternating Turning machines, P roceedingsof the 10th American Conf erence on Applied
M athmatics, Harvard U niversity, Cambridge,
U SA, pp.201-205, 2010(CD-ROM).

[8] M.Sakamoto, H.Okabe, S.Nagami, S.Taniguchi,
T.Maki, Y.Nakama, M.Saito, M.Kono, andK.Inoue, A note on four-dimensional finite automata, W SEAS T ransactions on Computers,
Issue 5,Vol.3, pp.1651-1656,2004.
[9] M.Sakamoto, T.Ito, N.Tomozoe, and H.Furutani,
Asurvey of three-dimensional automata, T hepapers of T echnical M eeting on Inf ormation
Systems, IEE, Japan, IS-07-12, pp.11-17,2007.

[18] M.Sakamoto,
T.Matsukawa,
R.Katamune,
H.Furutani, M.Kono, S.Ikeda, T.Ito, Y.Uchida,
and T.Yoshinaga, Synchronized Alternating
Turning Machines on Four-Dimensional Input Tapes, W SES T RAN SACT ION S on
COM P U T ERS,Issue 4,Vol.9, pp.319-328,2010.

[10] M.Sakamoto, T.Ito, H.Furutani, and M.Kono,
Some accepting powers of three-dimensional
parallel Turning machines, in 1st European
W orkshop on Artif icial Lif e and Robotics,
V ienna, Austria, pp.73-79,2007.

[19] M.Sakamoto,
R.Katamune,
T.Matsukawa,
H.Furutani, M.Kono, S.Ikeda, T.Ito, Y.Uchida,
and T.Yoshinaga, Hardware Hierarchies andRecognizabilities of Four-Dimensional Synchronized Alternating Turning Machines, W SES© ISAROB 2013

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T RAN SACT ION S on COM P U T ERS,Issue 4.
pp.329-338,2010.
[20] M.Sakamoto, T.Ito, X.Qingquan, Y.Uchida,
T.Yochinaga,
M.Yokomichi,
S.Ikeda,and
H.Furutani, A Note on Three-dimensional Probabilistic Finite Automata, T he SeventeenthInternational Symposium on Artif icial Lif eand Robotics 2012, Oita, Japan, pp.492495,2012(CD-ROM).
[21] M.Sakamoto, Y.Uchida, T.Hamada, T.Ito,
T.Yoshinaga,
M.Yokomichi,
S.Ikeda,and
H.Furutani, A Relationship between the Accepting Powers of Nondeterministic Finite Automata
and Probabilistic Finite Automata on ThreeDimensional Input Tapes, in the 2012 IEICE
General Conf erence, Okayama U niversity,p.4,2012(CD-ROM).
[22] T. Toffoli and N. Margolus, Cellullar automaton
machines − new environment f or modeling,
MIT Press, 1987.
[23] J. Wiedermann, Parallel Turing machines,
Technical Report RUU-CS-84-11, Department ofComputer Science, U niversity of U trecht, theN etherlands, 1984.

© ISAROB 2013

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A proposition of addition and integration of q-values in Q-Learning
Kathy Thi Aung1, Takayasu Fuchida2
1,2

1,2

Kagoshima University, Kagoshima 890-0065, Kohrimoto 1-21-40, Japan
Graduate School of Science and Engineering, Department of System Information Science(Tel: 81-99-285-3408, Fax: 81-99-285-8464)
1kathythiaung@gmail.com , 2fuchida@ibe.kagoshima-u.ac.jp

Abstract: This article presents a method of addition and integration of q-values to reduce the number of states and memory
usage based on Q-learning algorithm in continuous state space using the concept of Voronoi space division. It also aims toshow the improvement of learning efficiency when it is compared to the existing method, such as lattice. We constructed anexperimental model to examine these scenarios. This model is based on continuous state and discrete action of feeder mouse.
The results indicate that the proposed method greatly improve than normal Q-learning. As a method of space division, we used
Voronoi diagram that is a general space division method. However, Voronoi diagram has a lot of flexibility therefore it
becomes a problem to decide the position of q-values. Therefore, we presented the addition method in order to realize theposition of q-values using LBG algorithm though there are many methods for adaptive vector grouping. In addition, we
integrate the q-values which have the same action selections using Delaunay tessellation technique to find the nearest q-values.
Keywords: Q-Learning, Voronoi Diagram, LBG algorithm, Delaunay Tessellation technique
is a policy for estimating the optimal state-action value, andone of the most fundamental in RL. Q-learning can also
apply in many practical applications based on the idea of
expected future reinforcements, and can be estimated by thefunction of each action in each state. However, it works
only for discrete state space, and difficult to handle incontinuous state space because of curse of dimensionality
problem therefore it needs to discretize the state space intoa lot of smaller discrete regions when we treat suchcontinuous cases.
Curse of dimensionality means if the number of state
and action variables increase, the size of the Q-Table used
to store Q-values grows exponentially and the learning
speed decrease suddenly.
In general Q-learning, every action and state pair havetheir own Q-value denoted by Q(st , at ). , and it stores in atable called Q-Table. It looks like a square lattice in 2dimesions. These Q-values are initialized to small random
numbers and gradually change toward the optimal values
through learning. The Q-value is updated in taking the
maximum Q-value of next state using this following
equation.

1 Introduction
There are several kinds of learning methods but
reinforcement learning (RL) is the most suitable method in
machine learning that deals with the decision to take anaction using an agent at discrete time steps and expectedthat would be useful anywhere in the future [1]. RL
methods attempt to improve the agent’s decision-making
policy over the time. The agent’s goal is to get as muchreward as it can over the long run.
In this paper, we present an experimental study incontinuous state space based on Q-learning algorithm withaddition and integration of q-values method using Delaunay
tessellation technique, addition method of q-values using
LBG algorithm, and normal lattice arrangement. It also
aims to show the improvement of learning efficiency using
the proposed method when it is compared to the existing
method, such as lattice.
We constructed a computational model to examine thesescenarios. In the model, an agent aims to get as muchreward as possible during a specific period, and observes
the angle (θ) and the distance (d) to reward-area. If theagent reaches to the reward-area, the agent gets a reward of+1 and the position of agent is randomly changed.

Q(st , at )  Q(st , at )   (r   max Q(st 1 , a)  Q(st , at ))a

2 Methods and procedures
In this equation, a state in time t is st , Q-value of action
at in the state st is Q(st , at ) ,  is the learning rate,  is thediscount rate, and r is a reward. Furthermore, max Q(st+1,a)shows the maximum Q-value of next state in time t+1.

2.1 Q-Learning algorithm
There are several ways to implement the learning
process. However, Q-learning algorithm due to Watkins [2]© ISAROB 2013

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Fig.3. An image of q-values addition

Fig.1. Voronoi diagram in a random set of points
2.2 Voronoi diagram
Voronoi diagram is used in our approach to partitions
space into cells or a number of regions, where each region
consists of all points that are closer to one site than to any
other. It can also be used to perform nearest-neighborsearches. As a simple illustration, show in figure 1. Voronoi
regions are bounded by line segments and Voronoi edge is abisector of two sites whose regions are adjacent.

Additionally, generate the new point at the place of each
representative vectors. Moreover, delete the temporary
points in which new points are added. A first-stage
formation of addition is illustrated in figure 2.
In the second stage of addition, we save STV again thatcome from temporary points to new points of the first stage
output. Nevertheless, we do not take STV that come fromnew points to new points. In addition, generate the new
points at the position of the representative vectors andrepeat the above process. In algorithm, a new point is puton the position of a suitable multiple at the direction ofrepresentative state transition vectors but we do not judge
yet whether that position is really appropriate location ornot. Figure 3 shows the image of addition of q-values.

2.3 Addition of q-values
In the first stage of addition process, we put thetemporary points into lattice structure on state space firstly.
When the learning process is started, we save the statetransition vector (STV) that enters from the position of theagent to the reward area on action space. If we get 1000
STV or more, we quantize or group those STV bycontinuously taking the same action using LBG algorithm(section 2.3.1). Furthermore, we seek representative vectors
and describe STV groups as representative vectors.

2.3.1 LBG algorithm
We used LBG (Linde-Buzo-Gray) algorithm for vector
quantization. It is like a clustering algorithm which takes a
set of input vectors as input and generates a representative
subset of vectors with a quantum vector. The modificationof adaptive vector quantization method was introduced

Fig.4. Initial state of vector quantization

Fig.2. First-stage of q-values addition© ISAROB 2013305

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.5. Illustration of the LBG vector quantization
enhanced LBG (Patane & Russo, 2001) [3], and adaptiveincremental LBG (Shen & Hasegawa, 2006) [4]. LBG
algorithm is as
1. Collect input vectors.
2. Initiate quantum vector at random.
3. Make group that input vectors belongs to the cluster ofnearest quantum vector that yields minimum distance.
4. Shift quantum vector to the gravity center of the groupdividing the own coordinates by the number of input
vectors.
5. Move quantum vector in random direction and repeat
the process in several times until the amount of
quantum vector is less than a threshold value of 0.1.
The convergence of LBG algorithm depends on theinitial quantum vector and the threshold in implementation.
Figure 4 describes an initial state of vector quantization
algorithm. The input vectors are marked with red andquantum vector are marked with blue. Figure 5 obtained byusing the above mentioned algorithm.

Fig.6. Type of actions on state space for integration
2.4.1 Delaunay tessellation algorithm
We applied a Delaunay tessellation (DT) technique tointegrate the closest q-values. Delaunay tessellation isanother fundamental computational geometry structure anddual tessellation of Voronoi diagram. DT is the straight-line
dual of the Voronoi diagram obtained by joining all pairs of
points belongs to the set. In our algorithm, we assume thatthe number of points are three or more but finite.
All triangles of the Delaunay triangulation are obtainedby connecting the adjoining points. Then, we draw a
hypercube rectangle containing all points that adjacent tonearest point and generate a random point inside ofinclusive rectangle. After that, it finds the connection ofnearest point and second-nearest point by measuring the
distance from that random point. Finally, two nearest points
with high mark are connected. An example of java applet
animation of Delaunay tessellation is shown in figure 7.

2.4 Integration of q-values
There are five conditions to integrate q-values.
(1) both two points must be new added points.
(2) Those two adjoining points must take the same optimal
action (Figure 6).
(3) Both two optimal actions have not changed over thenumber of 10,000 action times in 500,000 integrated
timing of new points.
(4) Both two new points are not already used for
integration.
(5) Start the integration process after the number of qvalues is added to 300 as a restriction of integration
theory and reduces to 169.
A new point is added to the center of adjoining point,
and those two adjoining points are deleted. The key point of
our integration method is to integrate same actions.

© ISAROB 2013

Fig.7.Java applet animation of Delaunay tessellation

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Fig.10. Experimental results of 3-methods
Fig.8. Bait View World experimental model

3.1 Experimental results

3 Experiments

We examined the performance of the following several
different methods in a stationary situation of reward-areathrough the computer simulations. These are 1) latticearrangement of normal Q-learning on discrete state space,2) implementation of addition method using LBG algorithm,
and 3) implementation of integration method using
Delaunay tessellation technique on continuous state space.
Figure 10 shows the result of these three experiments.

In the simulation, an agent is supposed to head toward
the reward-are. We call this model “bait view world”, also
called “non-coincidence of state space and action spacemodel”, shown in figure 8. An agent gets a reward when it
enters the reward-area and the agent’s position change
randomly on this bait view world. The objective of agent is
to get as much reward as possible during a specific period.
This model is based on continuous state and discrete actionof feeder mouse.
The agent observes the angle and distance to rewardarea as shown in figure 9 and these 2 input values construct
the 2-dimensional state space. The agent has 3-types ofcontrol actions selection; 1) go straight ahead, left rotation
and right rotation. Each action is displayed by color “red”,“green” and “blue” (Fig 6). The agent normally selects thenext action which has a maximum Q-value but sometimes
selects the next action at random.
We conducted the experiments with 100,000 continuous
learning times make one “episode” and executed 160
episodes in one “experiment”. We did 10 trials for each
episode by changing the random initial seed and took an
average. The learning rate α was set to 0.1 and discount rate
γ was set to 0.9.

4 Conclusions
We presented the addition and integration method of qvalues on continuous state space in order to realize theposition of Voronoi points and to reduce the number ofstates and to speed up the learning efficiency. Moreover, weinvestigated the performance and efficiency of our
proposed methods using Bait View World experimental
model. As a result of experiments, the number of state
decreases greatly and our proposed methods give goodresults in simulation. However, we occurs the overintegration problem though the number of states has
decreased and the learning speed is suddenly decreased at
the halfway. Therefore, we need to consider about thisproblem and it is need to able to change the integrated
timing adaptively. Furthermore, we applied Voronoi
diagram, LBG algorithm and Delaunay Tessellation
technique in this study.

References[1] Sutton RS, Barto AG (1998), Reinforcement learning an
introduction, MIT Press, Cambridge.
[2] Watkins CJCH, Dayan P. Technical notes: Q-learning.
Machine Learning 1992;8:279-292.
[3] G.Patane and M.Russo, The enhanced LBG algorithm,
In proceedings of Neural Networks, 2001, pp.1219-1237.
[4] F.Shen, O.Hasegawa, An adaptive incremental LBG forvector quantization, Neural Networks 19 (2006) 694-704.

Fig.9. 2-input values for experimental model© ISAROB 2013

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Recipe search engine inspired by ant colony’s foraging
Jinwook KIM1, Kazuyuki ITO1, and Katsunori TACHIBANA2
1

2

Hosei University, Tokyo 184-8584, JapanNEC Communication Systems, Ltd., Tokyo, Japan(Tel: 81-42-387-6093, Fax: 81-42-387-6381)

1

jinwook.kim.d5@stu.hosei.ac.jp, 1 ito@hosei.ac.jp

Abstract: These days, concern with link between consumer electrical appliances and information devices has been growing
such as the refrigerator which has a managing function of food ingredients. This environment is called ‘Ubiquitous computing’,‘Smart home appliance’ which has possibility of further development. In this environment, we consider the system whichsuggests users some well-balanced recipes that can be cooked by the available ingredients. Especially, we focus on recipe
search engine for limited state such as using only existing foods ingredients in the refrigerator. However, conventional
keyword search does not suit this system. In this paper, we focus on the ant colony’s foraging in order to make a new recipe
search engine. We confirm the effectiveness of the proposed search engine by conducting simulations.
Keywords: ant colony’s foraging, ubiquitous computing, smart home appliance, recipe search

1 Introduction

2 Goal of the proposal system

With progress of information network technology, ourlife style becomes remarkably convenient. Furthermore,according to link with mobile phone, consumer electricalappliances and many other information devices recently, we
can get a lot of high quality services more and more. This
environment is called ‘Ubiquitous computing’, ‘Smarthome appliance’ which has possibility of further
development [1].
Especially, the concern with link between consumer
electrical appliances and information devices has been
growing for these days. The refrigerator which has amanaging function of food ingredients and a recipe searchengine, also a built-in kitchen linked this are expectedleading example of application.
Now, we propose the system which suggests users somewell-balanced recipes that can be cooked by the available
food ingredients in the refrigerator. However, conventional
keyword search does not suit this system.
In this paper, we focus on the ant colony’s foraging inorder to make a new recipe search engine. Ant repeats
simple actions – ‘Lifting up’, ‘Carrying’, ‘Lifting down’and ant colony make food clusters. Ant colony makes foodclusters at random, so it is different size and placed position.
We consider ant’s food as food ingredients in refrigerator,and food clusters as recipes. Then we can make a recipe
search engine by represent ant colony’s foraging on
simulation. We confirm the effectiveness of the proposedsearch engine by conducting simulations.

We propose the system which suggests users some wellbalanced recipes that can be cooked by the available foodingredients in the refrigerator. Therefore, new recipe searchengine have to find various desirable combinations as
recipes from existing foods ingredients in refrigerator.
Furthermore, In order to improve usefulness, the system
makes general Japanese menu plans per serving.

© ISAROB 2013

3 Problem with conventional keyword recipe
search engine
In conventional keyword search, users input somekeywords such as name of dish or food ingredient to searchengine, then it give them information about relative to
keywords. Because of keyword search engine provide us
information that only based on input keyword, and foodingredients are not unlimited, many search results
containing a keyword are obtained.
On the other hand, we keep food ingredients in the
refrigerator, and we consider using these every day. There
are many kinds of combinations from existing foodingredients in the refrigerator. So we can make use of thosecombinations as recipes. In this case, conventional system
can not make useful combinations from limited state suchas only existing foods ingredients in refrigerator, because it
depend on only keyword and does not consider foods
ingredients in refrigerator.
Therefore, we need new concept for new recipe searchengine and we focus on the ant colony’s foraging. Using
this ant’s simple repeat action, the recipe search engine

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5 Proposal method

gives information to users even if user does not input
keywords.

Ant colony’s foraging is reproduced on virtualenvironment, and it uses for recipe search engine. Foodingredients in refrigerator correspond to ant’s food andcollected by ant colony’s foraging algorithm to make recipe.
When all required food ingredients are collected to makerecipe, it shows as a recipe which can cook.

4 Ant colony’s foraging
Ant colony does not have central controller, howeverants can collect foods on the basis of local information.
Using following Fig. 1, we explain this mechanism.
An ant moves at random on the environment where
objects are scattered such as Fig. 1 a). When it finds anobject, ant lifts up the object by a certain probability. An ant
which has the object moves to other place. When it finds
other objects, ant lifts down the object by a certain
probability, and then a small cluster is made by theseobjects.
The probability is determined by the size of that cluster
which placed somewhere. In certain place where are many
objects, ant tends to lift down the object. Conversely, incertain place where are few objects, ant tends to lift up theobject. Ant colony repeat simple actions – ‘Lifting up’,‘Carrying’, ‘Lifting down’ and they make many small
clusters. Smaller size cluster move to larger one by repeatof ant colony simple actions. As a result, clusters grow up
and it becomes a few larger clusters [2].

a) Initial statec) Growth of cluster

5.1 Environment of recipe search engine
As shown in Fig. 2, environment of recipe search engine
is one-dimensional space with several cells in the shape of astraight line.

Fig. 2. Environment of recipe search engine
Recipes are assigned on a several cells. To make Recipe
which is required a large number of food ingredients, takes
more times for carry food ingredients and it is hard to bemade up. Therefore, several recipe cells are proportional to
the number of food ingredients and we consider that agent
visits the recipe cell which is more required number of foodingredients. Food ingredients are arranged in the
refrigerator cell at first and carry on the recipe cell by theagent which is reproduced ant colony’s foraging. Agentmoves at random such as jumping each cell. The recipe
which all the required food ingredients are collected on an
own cell is recognized as a recipe which can be cooked.

b) Formation of cluster

5.2. Recipe search engine inspired by ant colony’s fo
raging
The flowchart of recipe search engine inspired by ant
colony’s foraging is shown in Fig.3.
First, agent moves at random on the each cell. When it
enters the cell which is existing food ingredient, agent lifts
up the food ingredient by a certain probability. The
probability of lifting up the food ingredient is shown inequation (1).

d) End stateFig. 1. Ant colony’s foraging
We apply this ant colony’s foraging algorithm to recipe
search engine, we consider ant’s object as food ingredientsin refrigerator, and clusters as recipes on simulation.

© ISAROB 2013

·Lifting up probabilitya Pi = 1 − 0 .999 ⋅  i  + 0.001
n
 i



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(1)

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6 Simulationi : Identification number of a recipeni : Total number of food ingredients required for the i-th
recipeai : Number of food ingredients which placed current recipecell

We simulate recipe search engine inspired by ant
colony’s foraging. The recipes used for the simulation are
selected 80 kinds of recipes as shown in the following Table.
1[3].

Start

Table 1. Recipes used for the simulation
Recipe No.
Name of Recipe1
Beef curry
2
Chicken cream stew
3
Japanese-style pancake
4
Hashed beef with rice5
Omelet rice
6
Spaghetti with meatballs
·
·
·
·
·
·
79
Hamburg steak
80
Pork cutlet

Move at random

N
There are food ingredientsY
Lift up an food ingredientby a certain probability

N

Y
Move at random

N

Lift down an food ingredientby a certain probabilityN

6.1 Simulation condition
To confirm the effectiveness of the proposed method,
we conduct simulations with 3 different conditions written
below.

Y
Require food ingredient fora recipe are collectedNY
The recipe add
menu plana) General search: In order to verify the usefulness of
the recipe search engine, we simulate it with the condition that a lot of food ingredients which is enoughto search various recipes are in the refrigerator.
b) Search with condition that a lot of meat: We simulate recipe search engine with the condition that a lotof meat are in the refrigerator.
c) Search with condition that a lot of vegetables: We s
imulate recipe search engine with the condition that a
lot of vegetables are in the refrigerator.

Y
Total caloric intake on the menu planis over 600 kcal

N

End

Fig. 3. Flowchart of ant colony’s foraging
The agent which has the food ingredients moves at
random on the each cell again. When it enters the cellwhich is existing food ingredient, agent lifts down the foodingredient by a certain probability. The probability thatagent lifts down the food ingredient is shown in equation(2).

6.2 Simulation results
Each simulation results are shown below.
1200

Search Count

1000·Lifting Down probabilitya 
Q i = 0 .999 ⋅  i  + 0 .001
 ni 

(2)

800
600
400
200

When all required food ingredient for certain recipe arecollected, it is recognized as a recipe which can be cooked,
and the recipe adds menu plan. We consider that caloric
intake set 600 kcal for serving, and when the total caloric
intake on menu plan is over that value, recipe search is end.

© ISAROB 20130
5

10 15 20 25 30 35 40 45 50 55 60 65 70 75
Recipe No.

Fig. 4. The result of general search

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Table 6. The result of condition c) – A lot of vegetables
Menu 1
Menu 2The vertical axis of Fig. 4 shows each recipe searchcount and the horizontal axis show recipe number in Table.
1. As a result, various recipes were searched by searchengine. Most searched 2 recipes are as below.
Table 2. Most searched 2 recipes
Recipe No.
Recipe Name
Food ingredient13
Boiled spinach
Spinach62
Salmon meuniere
Salmon25
53

Boiled spinach

Boiled spinach

Chop suey

Scallop cream croquette

Chinese dumpling

Potato croquette

Cabbage roll

Fishcake soup

Fishcake soup

Omelet rice

Omelet rice

© ISAROB 2013

Boiled spinach

REFERENCES[1] R. Kango, P.R. Moore, J. Pu (2002), Networked smart
home appliances - enabling real ubiquitous. Proceedings
5th IEEE international workshop on networked appliances,
pp.76-80[2] J. L. Deneubourg (1991), The dynamics of collective
sorting robot-like ants and ant-like robots. Proceedings ofthe First International Conference on Simulation of
Adaptive Behavior, pp.356-363[3] Tanaka O (2008), The useful standard recipe for every day. (Mainichi yakudatu teiban okazu, in Japanese), Shog
akukan Inc.

Table 5. The result of condition b) – A lot of meat
Menu 1
Menu 2
Hamburg steak(Okonomiyaki)

In this paper, we consider the recipe search engine
inspired by ant colony’s foraging for the refrigerator which
has a managing function of food ingredients. In order toverify the usefulness of the proposed system, the proposedsystem was mounted on the personal computer and we haveconducted several simulations by each condition.
As a result, various recipes were searched by new
search engine, proposal system was realized.
Our future work is to take the nutritional information
into consideration and to add learning mechanism to thesearch engine for adapting user's taste．

Potato gratin
Japanese hotchpotch

Japanese-style pancake

7 Conclusion

Table 4. The result of condition a) –various food ingredients
Menu 1
Menu 2

Rice with bamboo shootSpaghetti with tomato sauce

Each table shows that recipes were searched according
to food ingredients in the refrigerator. If users would like to
get other menu plans, they can get useful menu plan
information by re-search.

Compare Table 3 with Table 4, we can figure out thatrecipes which is required lower number of food ingredientsare well-searched than others. If users would like to getother recipes, they can get useful recipe information by research.
Table 4 - Table 6 show the examples of the searchedmenu plans for each condition.

in miso sauce

Boiled spinach

Potato gratin

Table 3. Most searched 2 recipes
Recipe Name
Food ingredientBeef, Mushroom
SukiyakiEgg, Welsh onion
Beef, Green pepper
Green pepper steak
Ginger, Welsh onion

Mackerel simmered

Rice with bamboo shootScallop cream croquette

Least searched 2 recipes are as below.

Recipe No.

Rice with bamboo shoot311

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Constructing Obstacle-Based Triangularized Roadmap
JeongHyeon Wang1 and JuJang Lee2, Fellow, IEEE
1

Korea Advanced Institute of Science and Technology, Daejeon, Korea
Korea Advanced Institute of Science and Technology, Daejeon, Korea

21

jh_wang@kaist.ac.kr
jjlee@ee.kaist.ac.kr

2

Abstract: This paper proposes an algorithm which makes roadmap quite fast using obstacle information. The proposed
algorithm purposes to expand graphs which consist of edge and vertex based on obstacles. After the algorithm detects corner
information, using Harris corner detection, keep all data and use it. The proposed algorithm consists of two parts, one isarranging initial nodes using space decomposition, and the other is constructing a graph from initial nodes. In this case, we
assume that all the obstacles are polygon. And then we can detect corners of obstacles. Using this, connecting each node tovisible corners, we can expand our graph. The proposed algorithm is quite simple and straightforward to understand and it has
advantage of constructing fully-covered roadmap. This property has been verified through several experiments.
Keywords: Central Voronoi Tessellation, PRM, Roadmap, Triangularize;in next step. The position of these initial points will beselected by the construction of Central Voronoi Tessellation(CVT) [8] in order to spread randomly chosen samples
evenly. After evaluating well-distributed initial nodes, we
use the Harris Corner Detector [9] to get information about
geometric configuration of the environment. All the
obstacles are approximated by polygon. So using Harris
Corner Detector, we can find lots of corners and with these
corners, we will explore uncovered region in the map.
This paper is organized as follows. Section 2 describes
the node distribution method using CVT. and using thisresult, Section 3 describes the proposed node expansion
algorithm. An implementation and simulation results arepresented in Section 4, and the discussion follows

1 INTRODUCTION
Path planning is becoming an important one in the manyof the fields such as indoor and outdoor mobile robot
planning, underwater path planning, motion planning andso on. Also it increases needs for searching optimal path inany environments.
Different from grid-based approach, the probabilistic
roadmap method (PRM) is one of the typical samplingbased approaches [1]. Contrary to Rapidly-exploring
Random Trees (RRT), it deals with constructing multiple
queries [2]. The PRM based planner searches for freespaces using two phases, a learning phase and a query
phase. But efficiency of PRM is not good because it
produces lots of redundant samples to maintain total
connectivity and it also needs lots of sample points to reachwhole connectivity. In order to solve this problem many of
the sampling-based roadmap constructing method is
proposed. In [3], [4] to increase connectivity of PRM,
various sampling strategies such as narrow passage
sampling, bridge test and so on are introduced. In [5], [6]creating useful cycles, It increases total connectivity andalso controls the essential number of vertices. In [7],adopting new node sampling method, efficiency ofprobabilistic roadmap planner’s performance increases.
From these papers, we can think about that how to constructsimple and straightforward roadmap without including
complex and burden tasks. That is the starting points of ouridea.
In this paper, we propose a new algorithm to construct
the roadmap. The proposed method has two phases. First, in
initializing phase, the algorithm randomly selects several
points in the free space. But when we select this randomly,
we cannot guarantee about well-distribution of this points.
It is important to our proposed algorithm because after
finishing initializing phase, we will use these points as startpoints and expand to free configuration space in the map.
The more evenly distributed initial points, the better results© ISAROB 2013

2 Node Initialization
The proposed algorithm distributes initial nodesconsidering geometric information, using CVT construction
process. Before explaining CVT, we introduce a voronoi
tessellation first. Figure 1 shows an example of the voronoi
tessellation. In this figure, there are two generators 𝑧1 and𝑧2 . In this case, we can divide two regions 𝑉1 and 𝑉2 .
These two regions are divided by opposite sides of
perpendicular bisector. This is voronoi tessellation. If there
are more than two generators in constrained region, voronoi
tessellation constructs regions for a number of generators inentire region. Furthermore we can define the center of mass.
For example, given a region V in ℝ𝑛 and a density
function 𝜌(𝑤) defined as 𝑤 ∈ 𝑉, the center of mass 𝑍 ∗ of
V is given as follows.

𝑧∗ =∫𝑉 𝑤𝜌(𝑤)𝑑𝑤∫𝑉 𝜌(𝑤)𝑑𝑤(1)

CVT is the special cases for the generators of voronoi sets
and centers of mass in voronoi region do actually coincide.
Figure 2 shows an example of the comparison about generalvoronoi tessellation and CVT. Left figure is general voronoi
tessellation and right is CVT. Contrary to general voronoi

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Figure 1 example of voronoi tessellation
Figure 3 positions of center of mass are updated using Lloyd's
algorithm

Algorithm 2 Lloyd’s Method
1. Start with some initial set of K points {𝑍𝑖 }𝐾
𝑖=1
2. Construct the voronoi tessellation {𝑉𝑖 }𝐾
𝑖=1 of omega
associated with the points {𝑍𝑖 }𝐾
𝑖=1
3. Construct the centers of mass of the voronoi regions{𝑍𝑖 }𝐾
𝑖=1 found in Step 1; these centroids are the new set ofpoints {𝑍𝑖 }𝐾
𝑖=1
4. If under some convergence criterion, quit otherwise gobase to step 2.
satisfy the properties that all the expansions are in the free
configuration spaces. So we use the Lloyd's algorithm once
again only in that region. Choose any two generators in that
region and iterate Lloyd's algorithm again. This step will be
end when all generators are on the free configuration space.

Figure 2 Comparison between random sampling and CVT

tessellation, CVT has almost well-distributed sample nodes.
That’s why CVT needs to distribute generators regularly in
our proposed algorithm.
In the real construction process of the CVT, positions ofnodes are updated iteratively until each position of thenodes is equivalent to each centroid of voronoi cells. The
McQueen's algorithm [10] and Lloyd's algorithm [11] arewell known process of constructing CVT. Algorithm 1
describes McQueen's Algorithm in detail. The McQueen's
algorithm doesn't require the construction of voronoi sets orcenters of mass. Despite this, the points produced by
McQueen's method converge to the generators of a CVT.
However the problem with McQueen's method is that it
samples only one point before it averages. So at worst, it
takes more than millions of steps to obtain a CVT set ofpoints from an initial set of points. The Lloyd's algorithm isalso called K-Means algorithm in computer science. It may
have better result than McQueen's algorithm under same
constraints. Algorithm 2 describes Lloyd's Algorithm indetail. Compared to McQueen's algorithm, it also has aheavy computational burden because the samples in thevoronoi region should be calculated with generators inevery iterations. But in my algorithm, it doesn't matter how
many times to iterate this algorithm to approximate exact
center of voronoi regions. Just we spread out our initial
generators appropriately. There is no need to step out manytimes.
When the iteration of Lloyd's algorithm ends, there is a
probability that center of voronoi region is on the obstacle
regions. In this case, we cannot use this initial position to
expand nodes and edges from this point. Because it doesn't
Algorithm 1 McQueen’s Method(Random sampling and Averaging).
1. Start with some initial set of K points {𝑍𝑖 }𝐾
𝑖=1
2. Sample another point w
3. Determine which of the 𝑧𝑖 ’s is closest to the w
4. Find the average of w and the 𝑧𝑖 closest to it.
5. Replace the 𝑧𝑖 used in the averaging by the average
point.

© ISAROB 2013

3 Node Expansion
The purpose of proposed algorithm is to constructroadmap covering entire map so that wherever the start andgoal position is, total feasibility of expected path will be
guaranteed. Before we start next step, we deal with some
useful and needed properties to construct roadmap
Assume all the obstacles are estimated by polygon.


Entire free space will be surrounded by triangles.

Expansion algorithm will construct triangles
iteratively keeping triangularity.
The reason why we estimate obstacle to polygon is todivide entire free space with triangles. The advantage oftriangular-based approach is as follows.


It represents detailed areas better and it doesn't
complicate open areas.

Triangulation has much fewer cells and is moreaccurate than grid-based approach.

It can deal with non-point objects quite easier thangrid-based approach.

Under these advantages, we can construct triangular-based
roadmap satisfying properties. Algorithm 3 describes mainalgorithm of construction step. Key idea of algorithm is asfollows. First find visible corners using Harris CornerDetectors and find convex-hull of corners. Convex-hull is
the smallest convex set that contains corners. If edge of
convex-hull is on the free space, not on the obstacle, add
new vertex in the middle of two points. If possible, connect
to other vertex which has already made before. The
proposed construction step will be classified by three cases.
One is just add vertex and edge, another is first case plus
connects toward other vertex made before and the other is

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Algorithm 3 Construct Roadmap(E, V, Xset, initialCentroids)
Begin
if firstIteration and ∃ Visibility(initialCentroids) thenremoveVisibleNodes(initialCentroids)
V ← V ∪ {initialCentroids}
Endif
Xset ← {initialCentroids}
do[CornerSet, numOfVisitingTime]← connectVisibleCorners (Xset(i), numOfVisitingTime)
makeConvexHull(CornerSet)
if numOfVisitingTime >= 2 thenif findAlreadyMadeVertex(CornerSet,
V, numOfVisitingTime) == TRUE thenaddEdge(E, V, Xset, CornerSet)
Else
addVertexAndEdge(E, V, Xset, CornerSet)
Endif
Else
addVertexAndEdge(E, V, Xset, CornerSet)
Endif
While isEmpty(Xset) == TRUE
End

Figure 4 Roadmap construction case: Add vertex and edge

first case plus check possibility of existing vertex before.
Add vertex and edge.
Figure 4 shows how to add vertex and edge in the firstcase. Before start construction roadmap, check
geographical condition of initial nodes. Although we spreadinitial nodes
regularly, there will be a problem when acouple of initial
vertices are visible each other. It will
break up whole triangularity when we expand from thesenodes. So we must ensure that each initial node is not
visible. In the first step, pre-expanded initial node will
invade the area where other initial node has to be covered.
First, detect visible corners from frontier vertex using
Harris Corner and update the number of visiting times foreach node. In this case, there are no corners that the numberof visiting time is more than two. That is, it is the first time
to visit whole corners. And check if adding new vertex in
the middle of the corners on the convex-hull is possible. In
figure 4, green stars are on the line of obstacle, so discard
them. But brown star is on the free space. So add new edge
from frontier vertex and add new edge.

Figure 5 Roadmap construction case: Add edge to the vertex madebeforeFigure 6 Roadmap construction case : Possibility of existing vertex

and right top, that the number of visiting time is more than
two in this figure. So there can be a probability of existingvertex in the middle of two points. Same as second case,check if edge exists in the middle of two points. But in thiscase, there is no vertex between two corners. So there is noneed to add new edge toward that. Other step is the same as
first case.

Add edge to the vertex made before.
Figure 5 shows how to add edge to the vertex madebefore. Other step is same as the first case. But in thisfigure, when checking the number of visiting time in all
visible corners, there are two corners that the number ofvisiting time is more than two. That is, other vertices had
already visited before. So there can be a probability ofexisting vertex in the middle of two points. If find them,
add edge to that vertex made before and other step is thesame as first case. If there is more than three corners which
satisfies this cases, we make a temporary vertex set withthat corners and check any vertex is exists in that set. Other
step is same.

4 Simulation Results and Discussion
All our experiments were run with MATLAB in
Windows 7 Service Pack1 on an Intel(R) Core(TM) i5
2.67GHz 2.66GHz with 4.00GB of internal memory. The
results are as follows.
Simulation Results
Figure 7 describes several results. In this figure, result (a)and (b) are the results based on the same obstacle. Bluelines are edges and blue points are vertices. Red line isconvex hull and in each iteration, we plotted to see whether

Possibility that vertex exists.
Figure 6 shows how to consider when there is apossibility of existing vertex. When detecting visible
corners from frontier vertex, there exist two corners, left top© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

it finds convex hull of corners or not. And small dot in the
background tells about a result of Lloyd’s algorithm. In thisfigure there are two colors so clustered in two parts. It also
represents that even though the same environment is given,
it is possible to construct different roadmap. Additionallyconstructing roadmap relies on the position of initial nodes.
That’s why the result (a) and (b) have different figure.
Result (c) and (d) are the examples of different number of
initial nodes.
Figure 8 also describes several results in more
complicated environment. It shows that whether thecomplexity of environment increases, we construct wellcovered roadmap.

[7] B. Park, W.K. Chung, “Adaptive Node Sampling
Method for Probabilistic Roadmap Planners,” in Proc.
of IEEE/RSJ International Conference on Intelligent
Robots and Systems, pp. 4399-4405, 2009[8] Q. Du, V. Faber and M. Gunzburger, “Central Voronoi
Tessellations: Applications and Algorithms,” in SIAM
Review, vol. 41, no. 4, pp.637-676, 1999
[9] C. Harris and M. Stephens, “A Conbined Corner andEdge Detector,” in proc. of The Fourth Alvey Vision
Conference, pp. 147-151, 1988
[10] J. McQueen, “Some methods for classification andanalysis of multivariate observations,” in Proceedings,
Fifth Berkeley Symposium on Mathematical Statistics
and Probability, Vol. 1, pp. 281-297, 1967.
[11] S. Lloyd, “Least square quantization in PCM,” in IEEE
Transactions on Information theory, Vol. 28, pp. 129137, 1982

Evaluation
But in these results, quite inefficient connections arefounded. In figure 7-(c), right top two vertices are notconnected. Because two vertices have same parents node. If
we use this algorithm to real path planning, for example, we
can always find a feasible path but not the shortest path in
this roadmap. That’s why we need to extra refinement step
to construct more realistic roadmap. For example, using
many search algorithms, we can find shorter path toevaluate each vertex whether extra connection will beneeded or not.
Furthermore, there is no appropriate definition of
number of initial nodes. For my experiment, the lesscomplexity is, the fewer initial nodes will be needed. In
other words, in the complex geometric environments,
arranging more initial nodes is better than arranging only
one or two nodes. But it is the conclusion about our several
simulation results. It seems to be needed to certain rule tocontrol the number of initial nodes.

(a)

REFERENCES(c)(d)
Figure 7 Four results of constructing roadmap under sameenvironment and different number of initial nodes[1] L. E. Kavraki, P. Svestka, J. Latombe and M. H.
Overmars, “Probabilistic Roadmaps for Path Planningin high-dimensional configuration spaces,” in IEEE
Transactions on Robotics and Automation, vol. 12, no.
4, pp. 566-580, 1996
[2] S. LaValle, “Rapidly-exploring random trees: A new
tool for path planning,” CS. Dept., Iowa State Univ.,
Ames, IA, Tech. Rep. TR 98-11, 1998
[3] Z. Sun, D. Hsu, T. Jiang, H. Kurniawati and John H.
Reif, “Narrow passage Sampling for Probabilistic
Roadmap Planning,” in IEEE Transactions on Robotics.
Vol. 21, No. 6, pp. 1105-1115, 2005
[4] M. Morales, S. Rodriguez, Nancy M. Amato,“Improving the Connectivity of PRM Roadmaps,” inInternational Conference on Robotics and Automation,
pp 4427-4432, 2003.
[5] D. Nieuwenhuisen and M. Overmars, “Useful cycles inprobabilistic roadmap graphs,” in IEEE International
Conference on Robotics and Automation, pp. 446-452,
2004[6] R. Geraerts and Mark H. Overmars, “Creating Highquality Roadmaps for Motion Planning in Virtual
Environments,” in IEEE/RSJ International Conferenceon Intelligence Robots and Systems, 2006.

© ISAROB 2013

(b)

(a)

(b)

(c)
(d)
Figure 8 Four results of constructing roadmap more complicatedenvironment and different number of initial nodes

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Non-uniform Cellular Automata based on Open-ended Rule Evolution
Noritsugu Sughimura, Reiji Suzuki and Takaya Arita
Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
E-mail: nxsughi@alife.cs.is.nagoya-u.ac.jp, {reiji, arita}@nagoya-u.jp
Abstract: Cellular automata (CAs) are mathematical models of spatially and temporally discrete mathematical systems. Nonuniform CAs are the cellular automata in which each cell may contain a different transition rule and change it with time, while allcells share the same transition rule in regular CAs. Little is still known about the dynamics of open-ended evolution of rules in
non-uniform CAs. The purpose of our study is to construct and investigate a model of non-uniform CAs capable of open-ended
rule evolution exhibiting a wide variety of behavior across all Wolfram’s classes. For this purpose, we construct 1-dimensional
2-state 3-neighborhood non-uniform CAs with evolving transition rules. In the model, we found an interesting dynamics that
Class II (periodical behavior) and III (chaotic behavior) patterns emerged alternately, between which Class IV patterns sometimes
emerged.
Keywords: Complexity, Elementary cellular automata, Non-uniform cellular automata, Open-ended, Rule evolution
1 INTRODUCTION

evolution of rules in non-uniform CAs.
The purpose of our study is to construct and investigate
a model of non-uniform CAs capable of open-ended rule
evolution exhibiting a wide variety of behavior across all
Wolfram’s classes. We also aim at applying it to visual artwork based on a never-ending autonomous flow of information. For this purpose, we construct 1-dimensional 2-state
3-neighborhood non-uniform CAs with evolving transition
rules. Each cell has not only its state but also its transition
rules. The transition rules of cells with high fitness that is
calculated using entropy tend to propagate to neighboring
cells. Specifically, we use the fitness of each transition ruledefined as Shannon entropy for the state distribution of the
3-step history of 5-neighborhood cells. In addition, mutation
may change the transition rule of each cell into another rule,
which brings a novelty into the system.

Cellular automata (CAs) are mathematical models of spatially and temporally discrete mathematical systems. They
consist of a large number of relatively simple units (“cells”).
Each cell is a simple finite automaton that repeatedly updates
its own state depending on the cell’s current state and those of
its immediate neighbors. They have been commonly used todescribe and explore complex systems. One of the pioneering work is the classification of the CA behavior into fourclasses (Class I, II, III and IV) by Wolfram [1]. In a typical Class IV CA, initial patterns evolve into structures thatinteract in complex ways based on a mixture of order and randomness (“edge of chaos”). The connection between such acomplexity and the (origin of) life have been frequently discussed in the artificial life community. Additionally, Langton[2] has been successful in assessing Wolfram’s each classes
quantitatively. He indicated that Class III show the highest
entropy and Class IV show the highest mutual information.
Moreover, Mori et al. [3] have proposed a rule-changing CA
model based on internal dynamics. They indicated that thisCA model may show higher mutual information than normal
CA.

2 MODEL
2.1 Algorithm
We use 1-dimensional 2-state 3-neighborhood cellular
automata (CAs) and their states of the cells. Each cellalso has a transition rule, represented as a binary sequence(x7 x6 x5 x4 x3 x2 x1 x0 ). This rule maps 3-neigborhood states
to a decimal number xi ∈ (0, 1): [000] to x0 , [001] to x1 ,· · ·, and [111] to x7 .
Each cell is updated by the the procdures as follows: (i) N
cells are arranged on a 1-dimensional array. Each cell has astate that is set 0 or 1 arbitrarily, and a rule that is chosen arbitrarily from 1-dimensional 2-state 3-neighborhood CAs (elementary CA, ECA) rules. (ii) Each cell determines its next
state on the basis of its rule and the states of 3-neighborhood
cells including itself. (iii) Each cell calculates its own fitness

Non-uniform CAs are the cellular automata in which eachcell may contain a different transition rule and change it withtime, while all cells share the same transition rule in regularCAs. In the previous studies, non-uniform CAs often havebeen evolved to perform computational tasks including density classification and synchronization. Before then, Packard[4] and Mitchell [5] used a genetic algorithm to evolve CA
rules to perform a specific task. Sipper [6] demonstrated that
non-uniform CAs is partially superior to uniform CAs. However, little is still known about the dynamics of open-ended© ISAROB 2013

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Fig. 1. The state distribution A.

Fig. 2. The typical state patterns of the 7 classes in
ECA.

(Section 2.2). (iv) Each cell replaces its own rule by the ruleof the cell with the highest fitness in 3-neighborhood. (v)Each bit xi in the rule representation (x7 x6 x5 x4 x3 x2 x1 x0 )is flipped with a mutation rate µ as mutation. (vi) Update
each cell’s state and time step. (vii) Return to (ii) ((ii) - (vi)are repeated G times with (iii) and (iv) being skipped during
the first Lh repetitions).

Fig. 3. The artwork.

Table 1. Classifications of ECA rules. Our classification is
as follows (The rules inside the parenthesis are equivalent to
the representative rule). Class I: 0 (255), 8 (64, 239, 253), 32, (251), 40 (96, 235, 249), 128 (254),
136 (192, 238, 252), 160 (250), 168 (224, 234, 248), Class II-F: The rest of ECA rules, Class II-P: 1 (127), 3, 5 (95), 6(20, 159,215), 7 (21, 31, 87), 9 (65, 111, 125), 11 (47, 81, 117), 14 (84, 143, 213), 15 (85), 19 (55), 23, 25 (61, 67, 103),
27 (39, 53, 83), 28 (70, 157, 199), 29 (71), 33 (123), 35 (49, 59, 115), 37 (91), 38 (52, 155, 211), 41 (97, 107, 121), 43(113), 50 (179), 51, 74 (88, 173, 229), 108 (201), 131 (62, 145, 118), 133 (94), 134 (148, 158, 214), 142 (212), 156 (198),
178, Class III-LC: 26 (82, 167, 181), 73 (109), 154 (166, 180, 210), Class III-C: 18 (183), 22 (151), 30 (86, 135, 149), 45

2.2 Fitness
The fitness of each cell is defined as Shannon entropy as
follows. Here, K is set to 2 as the number of possible states(0 or 1), and pi is the appearance ratio of each state in thespatial (Lw-neighborhood cells) and temporal (Lh-step history) domains (A in Fig. 1). Therefore, when the numbersof 0 and 1 in the domain are the same, it will be the highest
and, in contrast, when the number of 0 or 1 is 0, it will be thelowest.
K∑
F itness = H(A) = −pi log pi(75, 89, 101), 60 (102, 153, 195), 90 (165), 105, 106 (120, 169, 225), 129 (126), 146 (182), 150, 161 (122), Class IV-54:
54 (147), Class IV-110: 137 (110, 124, 193)

Fig. 2
(a)
(b)
(c)
(d)
(e)
(f)
(g)

WolframClass IClass IIClass IIClass IIIClass IIIClass IVClass IV

Li&Packard
null
fixed-point
periodic
locally chaotic
chaotic
chaotic
chaotic

This study
Class IClass II-F
Class II-P
Class III-LC
Class III-C
Class IV-54
Class IV-110

i=1

that in Phase II, the average fitness is stable and high, and thenumber of the different rules is high (10-30), and in contrast,in Phase III, the average fitness is rather unstable and lower,
and the number of the different rules is lower (-10). Especially, typically in Phase III, the average fitness sometimes
has a sudden decrease to around 0.8, that is accompanied bya sudden increase to around 40 in the number of the different
rules.
Concerning the Class IV, which is the most interesting class, Class IV-54 sometimes dominates the population
mainly in Phase II and Class IV-110 occasionally emerges alittle (0-10 cells) almost instantaneously typically in Phase
III. This tendency might due to the resemblance of the patterns generated by Class IV-54 to Phase II patterns or the resemblance of the patterns generated by Class IV-110 to Phase
III patterns.
Next, we focus on the process which realize the transition
from Phase II to Phase III, or vice versa.

3 EXPERIMENT
We investigate the rule evolution over the long time steps
in Section 3.1 and focus on two characteristic patterns of rule
evolution in Section 3.2. To do so, we classify the 256 ECA
rules into seven groups (Class I, Class II-F, Class II-P, Class
III-LC, Class III-C, Class IV-54 and Class IV-110). It covers
four classifications by Wolfram [1] and five classifications by
Li and Packard [7] as shown in Table 1.
We used the following parameter values: N = 200 (number of cells), G = 2.0 × 106 (number of steps), Lw =5, Lh = 3 (parameters for the fitness), µ = 1.0 × 10−4 (mutation rate). All cells began with the state 0 and the rule 0.
3.1 Rule evolution over the long time steps
Fig. 4 shows the distribution of classes to which the existing rules belong (upper), and the number of the different existing rules and the average fitness (lower), across 2 million
time steps. We see a general tendency that Class II-F (fixed
point) and Class III-C (chaotic) dominate the population alternatively (Phase II and Phase III). There is also a tendency© ISAROB 2013

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Fig. 5. The number of the existing rules and the average fitness (lower); the distribution of that the 7 classes, the 256 rules andthe 2 states (middle); the images of the distinctive patterns (upper) from Phase II to Phase III.
is high. After that, Class III emerges and spreads gradually.
It is also shown from Fig. 6 that a transition from Phase III
to II takes place through the three stages similar to the transition from Phase II to III: Stage (1) as trigger creation, Stage(2) as rule diversification, and Stage (3) as strategy stabilization. The trigger in Stage (1) shifts the state distribution from
Stage (1) to (2) introducing many various transition rules.
Specifically, (D) consists of Class II-F, III-C and IV-54, and(E) consists of Class II-F, II-P and IV-110. In Stage (2), theaverage fitness is also relatively low, and the number of existing transition rules is high. After that, Class II emerges andspreads gradually.
A closer observation of Fig. 5 reveals that there is a difference in dominant rules and state distribution between Stages(1) and (2), while Class II-F rules dominate the population
throughout Stages (1) and (2). We see actually a clear difference between the enlarged patterns (A) and (C) that are thedistinct patterns in Stage (1) and (2), respectively. The distinct pattern in Stage (3) is (D), which is a typical pattern ofClass III-C behavior.
On the other hand, we see from Fig. 6 that while ClassII-F dominates the population throughout Stages (2) and (3),there is also a difference in dominant rules and state distribution between Stages (2) and (3). Similarly, we see from theenlarged patterns that the distinct patterns in Stage (2) and(3) are (C) and (F), respectively, and that in Stage (1) is (A)which is a typical pattern of Class III-C behavior.
It is noticeable fact that the transitions from Phase II to III
and from Phase III to II share some common dynamics. Accumulation of mutations suddenly but, in a sense, inevitably
induces a state distribution in which several rules work together as a trigger to invoke Stage (2) in both transitions. In

Fig. 4. The number of the existing rules and the averagefitness (lower); the distribution of the 7 classes (upper) over2 million time steps.
3.2 Rule evolution from Phase II (III) to Phase III (II)Fig. 5 and Fig. 6 show the transitions of the number of thedifferent existing rules, and the average fitness (lower), thestate transition between 2 states, rule transition among 256
rules, and class transition among 7 classes (middle), and theenlarged parts of the distinctive patterns in the rule evolution(upper), across 20 thousand time steps, from Phase II to III,and Phase III to II, respectively.
We see from Fig. 5 that a transition from Phase II to III
takes place through the three stages: Stage (1) as trigger creation, Stage (2) as rule diversification, and Stage (3) as strategy stabilization. The trigger in Stage (1) shifts the state
distribution from Stage (1) to (2) introducing many varioustransition rules. In Stage (2), the average fitness of the cells
is relatively low, and the number of existing transition rules© ISAROB 2013

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Fig. 6. The number of the existing rules and the average fitness (lower); the distribution of that the 7 classes, the 256 rules andthe 2 states (middle); the images of the distinctive patterns (upper) from Phase III to Phase II.
this sense, (B) in both figures can be seen as a part of thetriggers.

REFERENCES

4 CONCLUSION[2] C. G. Langton. Computation at the edge of chaos: Phasetransitions and emergent computation. Physica D: Nonlinear Phenomena, 42(1):12–37, 1990.

[1] S. Wolfram. A new kind of science. Wolfram Media Inc,
2002.

We examined the space-time patterns generated by the
proposed non-uniform CAs. We found an interesting dynamics that Class II (periodical behavior) and III (chaotic behavior) patterns emerged alternately, between which Class IV
patterns sometimes emerged. It was observed continuously
over 2 million time steps and therefore, we regard this dynamics as underlying and inherent in the model.

[3] T. Mori, K. Kudo, Y. Namagawa, R. Nakamura, O. Yamakawa, H. Suzuki, and T. Uesugi. Edge of chaos inrule-changing cellular automata. Physica D: Nonlinear
Phenomena, 116(3):275–282, 1998.
[4] N. H. Packard. Adaptation toward the edge of chaos. In
A. J. Mandell J. A. S. Kelso and M. F. Schlesinger, editors, Dynamic Patterns in Complex Systems, pages 293–
301. World Scientific, 1988.

More detailed analysis showed that during the alternation,
the average fitness of the cells was relatively low and at thesame time, the number of existing transition rules was high.
Our interpretation of this is that some decrease in averagefitness could start the alternation and this decrease induced
various transition rules to emerge. We thus believe that decrease in average fitness and the resulting emergence of various transition rules are the key to the complex dynamics observed in the model.

[5] M. Mitchell, P. T. Hraber, and J. P. Crutchfield. Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems, 7:89–130, 1993.
[6] M. Sipper. Co-evolving non-uniform cellular automata
to perform computations. Physica D: Nonlinear Phenomena, 92(3):193–208, 1996.

The art exhibition “ALart 2012” was held at the Nagoya
University Project Gallery “clas” in February 2012 featuring the artwork “One-Dimensional Cells”. It was based ona never-ending autonomous flow of information created by
the proposed model (Fig. 3). The artwork was also used tocreate the posters of the exhibition [8], which were put up
in or around the campus including on the wall in the nearby
subway station.

[7] W. Li and N. Packard. The structure of the elementary cellular automata rule space. Complex Systems,
4(3):281–297, 1990.
[8] Artificial Life Laboratory at Nagoya University. The
poster of “Alart 2012”. http://www.vision.ss.is.nagoyau.ac.jp/clas/programs/docs/2012 alart2012.pdf,(inJapanese), 2012.

Future work includes investigating analyzing the level ofopen-ended rule evolution and the complexity of the global
state distribution quantitatively.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A Procedure for Constructing Social Network Using Web Search Engines:
The Case for Japanese Automotive Industry
Yuichi Kubota1 , Reiji Suzuki1 and Takaya Arita1
1

Graduate School of Information Science, Nagoya University, Japankubota@alife.cs.is.nagoya-u.ac.jp, {reiji, arita}@nagoya-u.jp

Abstract: Recently, search engines have enabled us to access immense quantities of useful information in an instant. In thispaper, we propose a procedure for analyzing the social relationship and structure using Web search engines, which includes novel
ways to create a search query and to use the number of hits. This allows us to construct various networks that reflect directed
and undirected relationships among actors under arbitrary contexts. As a case study for evaluations of the proposed procedure,
we focus on 50 companies belonging to automotive industry in Japan. We constructed several directed and undirected networks
under different temporal and geographical contexts. We show that we can obtain more general knowledge about this industrial
community from the analyses of these created networks and their centrality measures.
Keywords: Social Network, Web Mining, Visualization
1 INTRODUCTION

that we can observe various aspects of a social relationshipby constructing networks using different ways to use search
queries and the search results, although each previous study
mainly focused on a specific topic in detail. We believe thatsuch a multifaceted approach gives us valuable insights into
understanding of essential properties of social networks.
From this viewpoint, in this paper, we propose a simple
procedure for analyzing social relationships and structuresusing Web search engines, which includes novel ways to create a search query and to use the number of hits. This allows us to construct various networks that reflect directed andundirected relationships among actors under arbitrary contexts. As a case study for evaluations of this procedure, we
focus on 50 companies belonging to automotive industry inJapan. We constructed and analyzed several networks using
indices for measuring directed and undirected relatedness under different temporal and geographical contexts.

Characteristics of social networks are a valuable piece of
information for understanding social activities and communications. However, it had been very costly, or even impossible
for non-specialists, to obtain large-scale data for measuring
the strength of the relationship among many actors (e.g., persons, companies) to create their social network, which had
made such an approach only applicable to limited and wellorganized data.
On the other hand, the progress in the field of Web technologies have enabled us to access immense quantities ofuseful information for creating social networks in an instant.
Especially, various methodologies for extracting social networks using search engines have been proposed [1].
Lee et al. proposed a method for social network analyses
based on the statistical physics using the number of Google
search hits in order to estimate the relatedness between two
actors [2]. They introduced a general framework for measuring the disparity or heterogeneity of weights a node bears.
They constructed a social network of the 109th US Senate
members, and successfully showed the division or community structure, reasonably consistent with the senators’ political parties, and so on. Recently, Akaishi et al. proposed amethod to analyze the temporal change in a social network
every year by adding the enumeration of years to a query [3].
They observed the temporal dynamics of the social network
composed of the 93 people who have played major roles in
the US economy, showing its relational changes surrounding
the economic crisis in 2008. They also analyzed the interdisciplinarity of researchers and research topics by constructing a bipartite graph using queries composed of topics andresearchers, using a new quantity named visibility boost forthe calculation of relatedness [4]. These studies clearly show

© ISAROB 2013

2 METHOD
Generally, the number of hits obtained by a search
query consisting two keywords reflects the co-occurrence frequency of these words in the Web. We can approximate thestrength of the relationship between actors (e.g. companies,
researchers) by using this property of search engines.
If X and Y are the sets of the web pages obtained by search
queries for “x y”, respectively, we can regard statistical metrics for the relationship between these two sets as the strengthof the relatedness between the actors x and y. As for undirected relationships between actors, we use a standard Jac)card coefficient: h(X∩Y
h(X∪Y ) , where h(X ∩ Y ) and h(X ∪ Y )are the number of hits obtained by the search query for “x y”
and “x OR y” (using the Google search engine), respectively.
In order to measure the asymmetric relationship between two

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) The undirected network based on the Jaccard coefficient.

(b) The directed network based on the directed Simpson
coefficient.
Fig. 1. The social networks of automotive industry in Japan, constructed by using (a) the Jaccard coefficient and (b) the Simpson
coefficient.

Table 1. The 50 companies of automotive industry in Japan.
Toyota, Honda, Nissan, Matsuda Motors, Suzuki Motor Corporation, Daihatsu Motors, Mitsubishi Motors, Fuji Heavy Industries, Isuzu Motors, Hino Motors, Denso, Aisin Seiki, Toyota Auto Body, Toyota Industries, Toyota Boshoku, JTEKT,
Calsonic Kansei, NSK Ltd., Toyoda Gosei, NTN Corporation, Bridgestone, Sumitomo Rubber, Yokohama Rubber, Toyo Tireand Rubber, Tokai Rubber, Bando Chemical, Okamoto, Kinugawa Rubber, Nishikawa Rubber, Mitsuboshi Belting, Nippon
Steel, JFE Holdings, Kobe Steel, Sumitomo Metal, Nisshin Steel, Hitachi Metals, Daido Steel, The Japan Steel Works, Topy
Industries, Aichi Steel, Mitsubishi Chemical HD, Sumitomo Chemical, Mitsui Chemical, Shinetsu Chemical, DIC Corporation, Tosoh, Asahikasei, Nitto Denko, Ube Industries, Hitachi Chemical© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) Closeness(c) In-degree(b) Betweenness(d) Out-degree

Fig. 2. Network centralities of the top 20 companies. (a) and (b) are the closeness and betweenness in the undirected network.
(c) and (d) are in-degree and out-degree in the directed network.

actors, we introduce a new index named directed Simpson co)efficient: h(X∩Y
h(X) , where h(X) is the number of hits obtainedby the search query for “x”, which reflects the relatedness of
the actor y for the actor x. Thus, this coefficient allows us
to measure the asymmetric relationship between two actors.
As far as we know, the directed Simpson coefficient has notbeen used in the previous research despite of its simplicity.

3 MULTIFACETED ANALYSESIn addition, we can calculate the relatedness under an arbitrary context if we measure these indices by adding a specificword related to the context to the all queries for the calculation of relatedness. For example, we use the query “x y z”
instead of “x y” in order to obtain h(X ∩ Y ) under the context of “z”. In this paper, we use the name of places in orderto analyze the geographical variation of the networks as wellas the years to analyze the temporal variation, which has also
not been used in the previous research for this purpose, as far
as we know.

Fig. 1 (a) and (b) show the constructed networks using
the Jaccard coefficient and the directed Simpson coefficient,
respectively. In the undirected network (Fig. 1 (a)), we see
the two major clusters composed of material companies (including steel, chemical and rubber companies) and parts &assembling companies, respectively. The Jaccard coefficienttends to become large when h(X) and h(Y) are close if h(XOR Y) is large. Because the number of hits obtained by asearch query for a company is expected to reflect the scale ofthe company’s business, we can expect that mutually relatedcompanies with the more similar scale of business tended to
be clustered in the network in Fig. (a). When we used the
directed Simpson coefficient (Fig. 2 (b)), we observed the“flows” of the relationships from the former cluster to thelatter, implying the significance of the latter companies. We
also see that the assembling makers (e.g. Toyota, Nissan)
have more input links than output links. This implies that it
is likely that the assembling maker is socially depended onby other suppliers in automotive industry.

As a case study for evaluations of the proposed procedure,
we constructed social networks of 50 companies, whosenames are listed in Table 1, belonging to automotive industry(assembling, material and part makers) in Japan. We used
Google Search API for web searches. We also used the setting T = 0.1(Jaccard) and 0.4(directed Simpson).

We construct a network that reflects their social relationship using these indices. Each actor is represented as a node,and the strength of relationship between two actors is represented as the width of the link between their corresponding
nodes. Note that two nodes are connected only when thestrength between them is equal to or higher than a threshold
T . We visualized a network using Cytoscape1 and adopted aspring model in which the higher relatedness between nodes
pull them together, in order to arrange the nodes in a twodimensional plane. We can examine the topological properties of the network by measuring various metrics used incomplex network studies such as the closeness (the averagepath length from the focal node to the other nodes), the betweenness (the proportion of shortest paths from all nodes toall others that pass through the focal node), and the degree(the number of edges which a node has).

We calculated the several centrality measures of each nodein these networks, and listed the top nodes, as shown in Fig.
2. In this paper, we focused on the closeness and the betweenness in the undirected network (Fig. 2 (a) and (b)). It
should be noticed that both centrality measures of a major
chemical company (Mitsubishi chemical holdings) tended tobe higher than others’. This seems to reflect that this company has wide relationship with other companies in this field.
As for the directed network, we focused on the in-degree and

1 Cytoscape: http://www.cytoscape.org/© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(b) The temporal variation(a) The geographical variation

Fig. 3. The betweenness of each node in the directed networks under the different (a) geographical contexts and (b) temporalcontexts.

texts. The analyses clarified not only the overall topological properties of the social network, but also the existenceof its geographical and temporal variations. We believe thatthe proposed procedure enables many people including nonspecialists to understand social relationships and structure
between individuals, companies or countries through constructing and analyzing the networks by making use of searchengines.

out-degree (Fig. 2 (c) and (d)). We found that major car manufacturers (e.g. Toyota, Nissan) tended to have both high indegree and high out-degree, which is expected to be due totheir high importance in this industry.
We also constructed different directed networks using thedifferent temporal (from 2000 to 2012) and geographical(from Okinawa to Hokkaido) contexts. Note that these years
and names of places themselves were used for additional keywords for a query. The keywords except the focal context
are also used to limit search results to a particular context(e.g., “Toyota Nissan Tokyo -Osaka -Aichi, -· · ·” for calculating the relatedness between Toyota and Nissan under thegeographical context of Tokyo). Then, we calculated the betweenness of each node in the all networks as shown in Fig.
3. We have chosen some major companies in order to observe the general tendency of the relational variation underthese contexts.
We see from Fig 3 (a) that there were significant differences in the betweenness of each company among the networks in different geographical regions. This might to be dueto the large difference in the industrial structure or the size ofthe market in these regions. Fig 3 (b) also shows the temporal changes in the betweenness of each company through theyears, implying that some major companies played importantroles in turns through this period.

ACKNOWLEDGEMENTS
The authors thank Dr. Jin Akaishi for technical advices
for web searches using the Google search engine.

REFERENCES[1] Matsuo, Y., Mori, J., Hamasaki, M., Nishimura, T.,
Takeda H., Hashida, K. and Ishizuka, M.: POLYPHONET: An advanced social network extraction system from the Web, Web Semantics, 5: 262-278 (2007).
[2] Lee, S. H., Kim P. J., Ahn Y. Y. and Jeong, H., Googlingsocial interactions: Web search engine based social network construction, PLoS ONE, 5(7): e11233.
[3] Akaishi, J., Sayama, H., Dionne, S. D., Chen, X.,
Gupta, A., Hao, C., Serban, A., Bush, B. J., Head, H. J.
and Yammarino, F. J.: Reconstructing history of social
network evolution using web search engines, Proceedings of BIONETICS 2010, 155-162 (2010).
[4] Sayama, H. and Akaishi, J.: Characterizing interdisciplinarity of researchers and research topics using web
search engines, PLoS ONE, 7(6): e38747 (2012).

4 CONCLUSION
We have proposed a simple procedure for analyzing social relationships and structures using Web search engines,
which includes novel ways to create a search query and touse the number of hits, in order to understand them from amultifaceted view. As a case study, we constructed several
networks of companies in automotive industry in Japan, using different indices for measuring directed and undirected
relatedness under different temporal and geographical con-

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Statistical Analysis for Price Changes of Carbon European Union
Allowances
zeyu zheng1, Kazuko yamasaki2, Jun Yang3

3

1

Tokyo University of information sciences, Japan

2

Tokyo University of information sciences, Japan

Economics and Business Administration, Chongqing University, China(Tel: 81-43-236-4652, Fax: 81-43-236-4652)
1

zeyuzheng8@gmail.com

Abstract: Recent years, several national and regional emission markets have been established,
the carbon emission related assets already become an main investment goods. We analyzed theprice changes time series of European Union allowances (EUA) futures in European Climate
Exchange (ECX) market, which is the world's largest single market for CO2 emission
allowances. We showed probability density function of price changes time series. We find that
there are long-range correlations in the absolute of price changes (volatility). Further, detrended
fluctuation analysis (DFA) approach is assessed with focus on long-range correlations and Hurst
exponent. We find long-range power-law auto-correlations in the absolute of price changes(volatility) that quantify the size of risk, and find that they decay much more slowly than theauto-correlation of return time series. We also investigate the multi-fractal status of volatility of
EUA time series. All results show that the EUA price change time series have very similar
statistic properties as stocks price changes.

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A Real-Coded Genetic Algorithm
Taking Account of the Weighted Mean of the Population
Naotoshi Nakashima1, Yuichi Nagata1, and Isao Ono1
1

Tokyo Institute of Technology, Japan(Tel: +81-45-924-5545, Fax: +81-45-924-5442)
1

naoto@ic.dis.titech.ac.jp

Abstract: Continuous function optimization is an important problem in science and engineering. The real-coded genetic
algorithm (RCGA) has shown good performance in continuous function optimization. AREX/JGG is one of the mostpromising RCGAs. However, we believe that AREX/JGG has two problems in terms of search efficiency. In this paper, we
propose a new RCGA that overcomes the problems of AREX/JGG. In order to examine the effectiveness of the proposed
RCGA, we compared the performance of the proposed RCGA with that of AREX/JGG on several benchmark problems inwhich initial populations do not cover the optimal points. As the result, we confirmed that the proposed RCGA succeeded infinding the optimal points faster than AREX/JGG.
Keywords: function optimization, genetic algorithms, real-coded GAs, AREX/JGG
the performance of the proposed RCGA with that of
AREX/JGG on several benchmark problems withmultimodality, strong epistasis and ill-scaleness in which
initial regions do not cover the optimal points.

1 Introduction
Continuous function optimization is an important problem that we often face in various domains such as scienceand engineering. In recent years, several real-coded Genetic
Algorithms (RCGAs) for function optimization have beenstudied actively [1-4]. It has been reported that RCGAs are
effective in optimizing high-dimensional functions withmultimodality, epistasis and ill-scaleness. However, mostconventional RCGAs have a serious problem that theperformance deteriorates when the population does notcover the optimal point.
In order to remedy the above problem of the conventional RCGAs in the case where the population does notcover the optimum, Akimoto et al. has proposed
AREX/JGG and reported that AREX/JGG shows better
performance than the conventional RCGAs in benchmark
problems in which initial regions do not cover the optimal
points [3, 4]. However, we believe that AREX/JGG has thefollowing two problems in terms of search efficiency: 1) If
the population distribution does not cover the optimal point,
AREX/JGG does not always generate offspring and eliminate individuals in the population so that the population
distribution efficiently moves towards the optimal point. 2)
If the population distribution covers the optimal point,
AREX/JGG does not always generate offspring and eliminate individuals in the population so that the population
efficiently converges towards the optimal point.
In this paper, we propose a new RCGA that overcomesthe above two problems of AREX/JGG. In order to examine the effectiveness of the proposed method, we compare© ISAROB 2013

2 AREX/JGG and Its Problems
2.1 Just Generation Gap (JGG)
JGG has been designed as a generation alternation model
for multi-parent crossover operators [3, 4]. The algorithmof JGG is as follow.
(1) Initialization:
Randomly generate an initial population {pk | k=1,…,τ}within a specified region. Evaluate the object function
values of all the individuals in the population. Initialize
the generation number g with 1.
(2) Mating Selection: Randomly choose parents{yj|j=1,…,n+1} from the population {pk |k=1,…,τ},
where n is the dimension of the problem.
(3) Generation of offspring: Generate offspring{xi|i=1,…,τ} by applying AREX to parents{yj|j=1,…,n+1}. Evaluate the object function values ofthe offspring {xi|i=1,…,τ}.
(4) Survival Selection: Select the best n+1 offspringamong the offspring {xi|i=1,…,τ} and remove the parents {yj|j=1,…,n+1} form the population {pk |k=1,…,τ}.
(5) If termination conditions are satisfied, terminate thealgorithm. Otherwise, g ← g+1 and go to step2.
2.2 AREX (Adaptive Real-coded Ensemble Crossover)
AREX has been proposed to remedy a serious problem
of conventional real-coded GAs that premature convergence often occurs when the population does not cover the
optimal point [3, 4].
AREX generates offspring by

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013⋯( )distribution toward the optimal point.
[Problem 2] As shown Fig.2, if the population distribution
covers the optimal point, AREX/JGG does not always
generate offspring near around the optimal point andeliminate individuals in the outer area of the population
distribution. Furthermore, if the offspring distribution is not
generated so as to cover the optimal point, the expansion
rate in Eq.(4) increases, which means that the search
becomes inefficient. Therefore, we believe that AREX/JGG
cannot efficiently shrink the population distribution to the
optimal point.

where m is the center of crossover and α is the expansion
rate to be adapted.
The center of crossover m is calculated by⋯ (2)where k;μ denotes the best k individual out of μ parents. wk(k=1,…,μ) denote weights where ∑and arecalculated by Eq.(3).
⋯ (3)Using Eq.(2) as the center of crossover, AREX aims at
prompting the center of mass of the population to movetowards the promising region.
The expansion rate α is adapted so that offspring are
generated wider than their parents in order to keep thediversity of the population when the population moves. The
expansion rate α is adapted as follows:

Offspring distribution
Parents yj

Mean of parents<y>Population distribution
Center of mass of the population m
Optimal point⋯ (4)Fig.1. Offspring distribution generated by AREX/JGG
when the population does not cover the optimal point.

where the g and cα are the generation number and the learning rate, respectively. Lcdp is the Mahalanobis distance between the center of crossover m and the mean of the best μoffspring 〈 〉μ. Lcdp is defined by Eq.(5) and is calculated by
Eq.(6).

Parents yj
Offspring distribution
Center of mass population m
Population distribution⋯ (5)Mean of parents <y>⋯ (6)Optimal point
Mean of the best n+1 parents

Fig.2. Offspring distribution generated by AREX/JGG
when the population covers the optimal point.

C is the covariance matrix of the offspring distribution andis the Moore-Penrose inverse matrix of C. The i:λdenotes the index of the best i-th individual among the λoffspring. Lavg denotes the expected value of the squared
distance under random selection and is calculated by Eq.(7).

3 The Proposed RCGA
3.1 Basic ideas
In this section, we describe our basic ideas to overcome
the problems of AREX/JGG pointed out in section 2.3. If
the population does not cover the optimal point, we
generate offspring in the outer area of the population near
the optimal point and eliminate individuals far from theoptimal point from the population. This is expected to resultin the population quickly moving towards the optimal point.
On the other hand, if the population distribution covers the
optimal point, we generate offspring near around the
optimal point and eliminate individuals in the outer area ofthe population. By doing this, we expect that the population
distribution converges to the optimal point quickly.
Based on the above basic ideas, we propose a new RCGA
that consists of the following mating selection method,crossover operator and survival selection method.
Mating selection: The proposed RCGA chooses the n+1
worst individuals in the population as parents. As the result,as shown in Fig.3, if the population distribution does not⋯ (7)2.3 Problems of AREX/JGG
AREX/JGG generates an offspring distribution neararound n+1 parents randomly chosen from the population
and replaces the n+1 parent with the n+1 best offspring.
Therefore, assuming big-valley functions, we believe that
AREX/JGG has two problems in terms of search efficiency.
[Problem 1] As shown Fig.1, if the population distribution
does not cover the optimal point in the search space,
AREX/JGG does not always generate offspring in the outerarea of the population distribution near the optimal point,
eliminate individuals in the outer area of the population
distribution far from the optimal point and extend the
population distribution. Therefore, we believe that
AREX/JGG cannot effectively move the population© ISAROB 2013

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Weighted mean of the population m
Population distribution

cover the optimal point, the proposed mating selection
method is expected to choose individuals in the outer areaof the population distribution far from the optimal point. On
the other hand, if the population distribution covers the
optimal point, as shown in Fig.4, the proposed mating
selection method is expected to choose individuals in theouter area of the population distribution.
Crossover: As shown in Fig.3 and Fig.4, the proposed
RCGA generates an offspring distribution by crossover
whose center is a weighted mean of the all individuals in
the population. As the result, as shown in Fig. 3, if the
population distribution does not cover the optimal point, theproposed RCGA is expected to generate offspring also in
the outer area of the population near the optimal point. On
the other hands, if the population distribution covers the
optimal point, as shown in Fig.4, the proposed RCGA is
expected to generate many offspring near around the
optimal point. Furthermore, the proposed RCGA also
adapts the expansion rate according to the Mahalanobis
distance between the center of crossover and the mean of
the best n+1 offspring as AREX does. While the expansion
rate in AREX/JGG is only for expanding the population
distribution in order to avoid the premature convergence,the expansion rate in the proposed RCGA is for not only
expanding the population distribution but also shrinking it.
As the result, if the population distribution covers the
optimal point, the proposed RCGA is expected to be able toshrink the population distribution in the optimal point
quickly.
Survival selection: The proposed RCGA replaces the n+1
worst parents in the population with the n+1 best offspring.
As the result, if the population distribution does not cover
the optimal point, as shown in the Fig.3, the proposed
RCGA is expected to eliminate individuals far from theoptimal point in the population distribution. On the other
hand, if the population distribution covers the optimal point,
as shown in the Fig.4, the proposed RCGA is expected toremove the individuals in the outer area of the population
distribution.

Parents yj

Optimal point
Offspring distribution

Fig.4. Offspring distribution generated by the proposed
RCGA when the population covers the optimal point.
3.2 Algorithm
The algorithm of the proposed RCGA is as follows.
(1) Initialization: Generate an initial population{pk|k=1,…,τ} randomly within a specified region.
Evaluate the objective function values of all theindividuals in the population. Initialize the generation
number g with 1.
(2) Mating Selection: Choose the n+1 worst individuals
in the population {pk|k=1,…,τ} as parents{yj|j=1,…,n+1}, where n is the dimension of the problem.
(3) Generation of offspring: Generate offspring{xi|i=1,…,τ} with the parents {yj|j=1,…,n+1} according to Eq.(8) and evaluate the objective function values of the offspring {xi|i=1,…,τ}.
⋯ (8)m’ is a weighted mean vector calculated by Eq.(9).
⋯ (9)k;τ denotes the index of the best k individual in the
population whose size is τ. wk is a linear weight where∑. wk is calculated by using the best τα
individuals in the population {pk|k=1,…,τ} according
to Eq.(10).
⋯ ( 0)
α in Eq.(8) is the expansion rate and is given by
Eq.(11).
⋯()⋯ ( 2)⋯ ( 3)∑
where 〈 〉and ε N(0， ). i:λ is
the index of the best i individual of the λ offspring.
Note that the proposed RCGA allows the expansion
rate to become smaller than 1.0 while that in
AREX/JGG is always larger than or equal to 1.0.

Fig.3. Offspring distribution generated by the proposed
RCGA when the population does not cover the optimal
point.

(4) Survival Selection: Sort the offspring {xi|i=1,…,τ}by the objective function values. Replace the parents© ISAROB 2013

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Table 1. : Benchmark functions.

{yj|j=1,…,n+1} in the population with the n+1 best
offspring.
(5) Termination Condition: If termination conditions
are satisfied, terminate the algorithm. Otherwise, g ←g+1 and go to step 2.

3 Experiments
In order to examine the effectiveness of the proposed
RCGA, we compared the performance of the proposed
RCGA with that of AREX/JGG. The system parametersof AREX/JGG are set to the recommended values in theliterature [4].
We use the 20-dimensional unimodal benchmark
functions (Sphere, Rosenbrock-Star, Rosenbrock-Chain,
Ellipsoid, k-Tablet) and the 20-dimensional multimodel
benchmark functions (Ackley, Bohachevsky, Schaffer,
Rastrigin) as shown in Table 1. The optimal points of the
Rosenbrock-Star function and the Rosenbrock-Chain] and the optimal points of thefunction are xopt=[
other functions are xopt=[00] . The function value ofthe optimal point of each function f(xopt) is zero. The initialregions are set to [ 5] for the Sphere, Ellipsoid, ktablet and Rastrigin functions, [ 2 2]
for theRosenbrock-Star and Rosenbrock-Chain functions,[ 30]
for the Ackley function, [ 5]for theBohachevsky function and [ 00]for the Schaffer
function.
We made preliminary experiments to determine thepopulation size and the number of offspring per crossoverfor the combination of each algorithm and each benchmark
problem. They were determined so that the average numberof evaluations becomes the smallest under the constraintthat the algorithm succeeded in finding the optimum on thebenchmark problem in all the ten trials. We use the average
number of evaluations as a performance index.
Table 2 shows the experimental result. The proposed
RCGA succeeded in finding the optimal points faster than
AREX/JGG in all the benchmark functions. This result
suggests that the proposed RCGA is more effective than
AREX/JGG.

Table 2. : The average numbers of evaluations of
AREX/JGG and the proposed RCGA. The numbers in theparentheses are the population size and the number ofoffspring per crossover, respectively, where n (=20) is thedimension of the problem. Ratio is the number of
evaluations of the proposed RCGA divided by that of
AREX/JGG multiplied by 100. If the ratio is smaller than
100%, the proposed RCGA succeeded in finding the
optimum with fewer evaluations than AREX/JGG.

References

4. Conclusions[1] Herrera, F., Lozano, M. and Sanchez, A.M. : A taxonomy forthe crossover operator for real-coded genetic algorithms: An
experimental study, International Journal of Intelligent
Systems, 18:309–338 (2003).
[2] Sinha, A., Srinivasan, A. and Deb, K. : A population-based,
parent centric procedure for constrained real-parameter
optimization. Proc. 2006 IEEE Congress on Evolutionary
Computation (CEC2006), pp. 239–245 (2006).
[3] Akimoto, Y., Sakuma, J., Ono, I. and Kobayashi, S.:
Adaptation of expansion rate for real-coded crossovers, Proc.
Genetic and Evolutionary Computation Conference 2009
(GECCO2009), pp.739-746 (2009).
[4] Akimoto, Y., Nagata, Y., Sakuma, J., Ono, I. and Kobayashi,
S. : Proposal and evaluation of adaptive real-coded crossover
AREX, Transactions of the Japanese Society for Artificial
Intelligence, Vol. 24, No. 6, pp. 446-458 (2009) (in Japanese).

In this paper, we proposed a new RCGA in order toovercome the problems of AREX/JGG. The proposed
RCGA consists of the mating selection that chooses the n+1
worst individuals in the population as parents, the crossoverthat employs a weighted mean of the population as thecenter of the crossover and the survival selection that
replaces the n+1 worst parents in the population with then+1 best offspring. We confirmed that the proposed RCGA
outperformed AREX/JGG on various benchmark functions
through numerical experiments.
We have a plan to evaluate the performance of the
proposed RCGA in higher-dimensional problems and, if
necessary, we have to improve the proposed RCGA. We
also will apply the proposed RCGA to difficult real-world
applications such as the lens design problem in order toinvestigate the effectiveness of the proposed RCGA.

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Diesel Engine Fuel Injection Control with DDVC Hydraulic System
Xing Jin1, Feifei Zhang1, Masanori Ito1,Kyoko Narukawa1
1

Tokyo University of Marine Science and Technology Japan(Tel: 81-03-5463-0400, Fax: 81-97-594-0181)
1

salanghieyou@yahoo.co.jp

Abstract: Internal-combustion engine has been used on ship, automobile and so on. However increase of internal-combustion engine caused to the environmental and energy problem in those days. Then we must improve the perform
ance of it from the level of combustion. From this point of view, we put the target on a diesel engine which is usually used on ship as the main propulsion engine. Its performance is depending on a fuel injection system and it iswell known that controlling injection optimally reduce the fuel consumption and amount of toxic substance. In thisstudy, we proposed a new fuel injection system using Direct Drive Volume Control (DDVC) hydraulic system, and developed basic control method for fuel injection. The new fuel injection system is constructed with conventional inje
ction valve and new fuel pump using DDVC hydraulic system. We have already established the method to control fuel injection timing, duration, pressure, rate and pattern.
Keywords: Environmental and energy problem, Fuel injection system, DDVC hydraulic system.
low-pressure cylinder. The motion of cylinder piston iscontrolled with revolution of servo-motor and we can get
the high pressure which is proportional to the ratio of crosssection of both cylinders when it pushes the piston of springback type booster cylinder.
This high pressure fuel is send to injection valve andinjection will start when the pressure overcome than springforce in the valve. The fuel injection valve is cheaper andlow failure rate because it is not special type.

1 INTRODUCTION
In these days, electric fuel injection system is used to
improve the combustion efficiency and reduce amount of
toxic substance for diesel engine. Common rail type electricfuel injection system is one of the most popular for it. It is
keeping high-pressure oil in common duct and inject it into
the cylinder with the on-off of electro-magnetic valve
equipped on the fuel injection valve. However, it isexpensive because it should use high cost material for highpressure common duct and specialized injection valve.
Another electric fuel injection system is also expensivewith use of complex pressurizing system and specialized
injection valve. Taking into account those situations, we
proposed new electric fuel injection system which isconstructed with DDVC hydraulic System and using
conventional (mechanical) fuel injection valve.
In the conventional electric fuel injection system,
effective fuel injection method for improving combustion
efficiency and reduction of toxic substance is established.
Here we confirmed that the new injection system isavailable to realize effective fuel injection with control of
DDVC hydraulic system.

Fig. 1. DDVC type fuel injection system

3 MATHEMATICAL MODEL OF DDVC FUE
L INJECTION SYSTEM
3.1 Mathematical model of the DDVC actuator
As shown in Fig.1 ., discharged oil from gear pump is
supplied to cylinder and pushes piston, and the oil in
another side of cylinder is lead to pump suction.
When the piston can move freely, the motion of piston
is written with following equation.

2 DDVC TYPE FUEL INJECTION SYSTEM
DDVC type fuel injection system is including DDVC
actuator, booster unit and fuel injection valve. System
configuration is shown in Fig. 1.
DDVC actuator is constructed with AC servo-motor
driven reversible gear pump and hydraulic cylinder called© ISAROB 2013

m 1 ̇ẋ1 + c ẋ 1 + kx 1 = p 1 A1

329

(1)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(

Here, m1 is the mass of piston, c is friction
coefficient, k is spring constant, p1 is the pressure ofhead piston A1 is cross sectional space cylinder, and x1
is displacement of piston.
When the piston is attached to the booster, both piston
moves together and kinematic equation become as follows.

(m 1 + m 3 )̇ẋ1 + c ẋ1 + kx 1 = p 1 A1 − p 2 A 2 − p 3 A3ṗ 3 = (k V 3 ) − a

)

(10)

Then, the oil pressure in the booster is Eq.(11)p3 = − k V3 ∫ a

2 p 3 ρ dt + p 33

(11)

3.3 Mathematical model of a fuel injection valve
Fuel injection valve is very important part in fuelinjection system. When fuel pressure overcome to springforce, the needle is lifted up and injection starts. Here we
make a mathematical model for injection valve as therelation between fuel pressure, motion of needle and flowrate of injection. Unfortunately the flow rate should be
estimate because it is difficult to measure. The model offuel injection valve are illustrated in Fig. 2.

(2)Here, m3 is the mass of booster piston, p 2 is theppressure of booster piston, 3 is the pressure of booster,
A2 is cross sectional of booster piston, A3 is crosssectional of booster.
3.2 Mathematical model of booster
If the fuel injection does not start, change rate of highpressure pipe oil pressure shown in the Eq.(3)ṗ 3 = (k (V 4 − A 3 x 3 )) A 3 ẋ 3

2 p3 ρ

(3)

Here, V4 is the volume of booster unit,
displacement of booster piston.

x3

V 4 − A3 x 3 ≈ V 3

is

(4)

After two pistons are contacting,Fig. 2. Fuel injection valve

x 3 = x1

(5)
Kinematic equation of needle valve is shown in Eq.(12)Therefore, change rate of oil pressure in booster is Eq.(6)ṗ 3 = (k V 3 ) A 3 ẋ 1

m 5 ̇ẋ5 = p 6 ( A5 − A50 ) + p 6 A50 − p b Ab + f b + f 2 (12)(6)

Here, f b , f 2 shows spring force, damping force
respectively. Those are shown as following equations.

With integrating Eq.(6), we can get oil pressure in the
boosterf b = − k b x5
p 3 = (k V 3 ) A 3 x 1 + p 31

(7)

f 2 = − µπ d 5 ( x b + x 5 )ẋ 5 h 5After injection started, oil pressure in the booster iswritten with following equation.

(ṗ 3 = (k V 3 ) A 3 ẋ 1 − a

2 p3 ρ

)We assume there exist no time delay on a pressure offuel. Then,the injection valve has four (4) injection nozzles.

(8)

2

p3 +

And oil pressure in the booster becomes as follows.

()

p 3 = k V 3 A 3 x 1 − ∫ Q i dt + p 32

2⎛
ρ ⎛ Q ⎞
ρ Q ⎞⎜⎜⎟⎟ = 4 ⎜ p a + ⎛⎜ ⎞⎟ ⎟⎜2 ⎝ A6 ⎠2 ⎝ a ⎠ ⎟⎠⎝(15)

In accordance with it,
(9)

Q =

When fuel injection is continuing, the change rate of oilpressure will become as Eq.(10)© ISAROB 2013

(13)
(14)

a
4

1
2

1 − (a 4 A 6 )2(p3 − 4 pa )ρ(16)When the fuel is injected to air, the flow rate will beEq.(18)330

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Qi =

a
4

1
2

1 − (a 4 A 4 )

2 p3
ρ

(17)

As

a 4 A4 ≈ 0 ,(18)
Fig. 4. Results at -2V of input voltage

The flow rate becomes as follows finally.

Qi =

a
2

p3
ρ

(19)

4. INJECTION CONTROL
Fig. 5. Results at -4V of input voltage

4.1 Fuel pressure at booster
Oil pressure at the outlet of booster is determined withdisplacement and speed of piston, and injection flow rate atfuel injection valve. Since fuel injection valve ismechanical self balance type, we control fuel pressure not
at injection valve but booster with displacement and speedof high-pressure cylinder piston.
In DDVC hydraulic actuator, input voltage to the driverof servo-motor has proportional characteristics to the piston
speed of low-pressure cylinder. Then we use input voltage
for basic estimation of piston speed and add the differenceof actual position and estimated position to correct thevalue. The result of estimation is shown in Fig. 3.

Fig. 6. Results at -8V of input voltage
Those results show that the fuel pressure is rising withthe piston speed from contact of both pistons to start of
injection. After fuel injection start, the fuel pressure
decrease because initial flow rate of injection is so largethat piston speed cannot follow to it. However piston speed
is rising and it can follow to injection and fuel pressure
increase again.
This shows that fuel pressure can be controlled with the
speed of low-pressure cylinder.
Control method and control results of fuel-pressure isshown in Fig. 7. and Fig. 8.

Fig. 3. Piston speed of low-pressure cylinder
In booster unit, volume change of high pressure linewith the displacement of high-pressure cylinder is muchsmaller to total volume of high-pressure pipe.
With this reason, we ignored the influence of highpressure cylinder piston displacement to the pressure in our
experiment and to examine the relationship of low-pressure
cylinder piston speed and oil pressure of high-pressure
pipe, .we checked the fuel pressure when the input voltage
is-2v,-4v,-8v.

Fig. 7. Control method of fuel pressure© ISAROB 2013

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Fig. 8. Control results of fuel pressure
Fig. 11. Control method of injection flow and rate

4.2 Fuel injection flow and rate
If initial fuel pressure is same as the pressure of after
injection, we can think that the quantity of injected fuel isequal to compressed volume. Considering this, we
examined the relationship between injected quantity, piston
displacement of low-pressure cylinder and fuel pressure. It
is shown in Fig. 9.

Fig. 12. Control result of piston displacement

Fig. 9. Relationship between quantity of injected fueland piston displacement of low-pressure cylinder
Actual flow of fuel injection and estimated flow,
estimated rate are shown in Fig. 10.

Fig. 13. Control result of injection flow and rate

5 CONCLUSION
In this research, we have constructed the control methodof diesel engine fuel injection with DDVC hydraulic system.
We can control fuel pressure with the motion of piston in
low pressure cylinder, and injected fuel quantity, injection
timing or injection rate with using simulation.
Now we are expanding it to total system including the
combustion in cylinder and confirming the reduction of fuelconsumption, particle emission, and toxic substance such asoxide frugal gas with control to the new fuel injection
system.

Fig. 10. Actual flow and estimated flow, rate offuel injection
This result shows that for precise control of fuelinjection, piston displacement of low-pressure cylinder
should be controlled more precisely.
When conventional P-control is applied for the piston
displacement control, offset will remain and when we add Icontrol for it, it becomes cause to overshoot .Here, we
propose a new method to protect offset and overshooteffectively. This method is shown in Fig. 11.
11.Control resultfor piston displacement of Low-pressure cylinder and fuelinjection flow with this method is shown in Fig. 12. & Fig.
13.

© ISAROB 2013

REFERENCES[1] Feifei Zhang et. al, New Concept of Diesel Engine
Fuel Injection System with DDVC Hydraulic Actuator,
Journal of JIME Vol.44,No.1,2009,(pp.127-132)[2] Naohiro Yamaguchi et. al, A Study on application of
DDVC to an electronic fuel injection device for marine
engine, of Proc. of 11th MOVIC of JASME(2009),(pp.456460)

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The Complex Network Study of Money and CO2 Emission Flows between
Industrial Sectors in Asian Countries using Input-Output Table
X. Gao2; T. Fujiwara2; K. Yoshizawa3; S. Miyake3; Z. Zheng2; N. Sakurai2; K. Yamasaki1;
1. (Tokyo University of Information Sciences): department of environmental information, faculty of informatics, Chiba, Japan
2. (Tokyo University of Information Sciences): department of environmental information, faculty of informatics, Chiba, Japan
3. (Tokyo University of Information Sciences): department of business information, faculty of informatics, Chiba, Japan(Tel/Fax: 81-43-236-4653)
1

yamasaki@rsch.tuis.ac.jp

Abstract: In 2011, earthquakes in Japan and flood in Thailand revealed the serious risks that local break downs of Motorvehicle sector and Electronic computing sector cause the cascade breakdown of world economy. To estimate such risks, it
seems that the connections between international industrial sectors are important and recently developed complex networks
method is suitable for this study. We use the data of the Input-Output table of Asian countries and USA made by JETRO in
2000[1]. We regard the table as network matrix and make various directed weighted networks. In these networks, the nodes
correspond to each industry sector in a country and the weight of the link corresponds to the amount of the money which uses
the product of an industry sector as material to produce the product of another industry sector. We think cascade failure of theindustry sector has deep connection with not only quantitative weights of the links but also network structure. So we calculate
betweenness centrality of nodes using Dijkstra algorithm [2,3,4], which describes basically the number of paths through thenode. We found the betweenness centrality is high in Motor vehicle sector, Electronic computing sector and Semiconductors
and integrated circuits sector. Next we use CO2 emission data made by IEA [5] and make environmental extension of InputOutput table. Our results warn that CO2 emission of developing countries is also important problem..
Keywords: Complex Networks, CO2 Emission, Asian Countries, Input-Output Table.
interest in complex networks study, but only a few complex
networks studies have been done using Input-Output
table[12-19]. Because Input-Output table corresponds to thecomplete network with directed weighted links including
self loops, the analysis of such networks is not so simple as
networks which mainly have been studied in the past. We
think the recent development of the various analyticalmethods enables us to extract new results from different
point of view.

1 INTRODUCTION
The connections among various economic factors or
economic units have become complicated more and moreaccording to developing economy and society in eachcountry. We have to pay attention to the risk that a small
negative effect is amplified by these complicatedconnections. In other words, economic factors or economic
units with various connections make complex networks andthe risk of big economic failure lies behind the structure ofthese networks. In wide variety of research fields including
economy, the complex networks[6,7,8] have been studied
earnestly in these 10 years. A lot of analytical methods havebeen developed and applied to various data. They havecontributed to solve the various problems in real world.

2 DATA
We use "Input-Output table for East Asia" in 2000. They
are made by JETRO (Japan External Trade Organization)by combining 10 countries tables, 9 Asian countries(China, Taiwan, Indonesia, Japan, Korea, Malaysia,
Philippines, Singapore, Thailand) and USA, and include
76 industrial sectors per a country. One of the aims of thispaper is to track CO2 emission from the production of anindustry sector in a country until the final consumption of
the products of other industry sector in other country.
IEA(International Energy Agency) issues the data about
CO2 emission of each industry sector in each country
except Taiwan, so we remove Taiwan's data from thetable and analyze 9 countries data. So the inter-industry
sectors part of the table is a square matrix of the size of684(2000). The classification of industry sectors isdifferent between "Input-Output table for East Asia" and"IEA CO2 emission data". So we redistributed CO2

Industry-industry connections are described by InputOutput table[9], which has been widely applied in theeconomic analysis for the past 50 years. In many countries,
governments and local states or prefectures make InputOutput tables for analysis of domestic economy in every
year. And the environmental extension of the Input-Output
table has been used energetically in various environmental
problems in these 20 years[10]. This analytic method is oneof the most systematic and practical tool for environmental
problems. Though Input-Output table includes lots of
information, it does not seem that traditional analysis isenough to extract such information and show us
understandable way. Connections between nodes are main

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emission data to sectors in Input-Output table afterreferring to these papers[].

In this paper, we analyze production ratio not producti
on. That is, we analyze matrix L and EL which are described by

3 METHOD
3.1 Network Matrix
In this paper, we study the connections between the
production in an industry sector in a country and the final
consumption of the products in an industry sector in acountry. To get network matrix for this study, we start fromthe traditional induced analysis.
In case of NonCompetitive Import Type Input-Output table, induced

(1)

1

 s ,t (v )s  v ,t  v  s ,t

(2)

(7)

Here,  s ,t (v) is the number of shortest paths from node s to node t going through node v and  s ,t is thenumber of shortest paths from node s to node t. The
extended definition of Betweenness centrality for direct
ed weighted network is counting shortest cost paths wi
th link direction instead of shortest paths. In our netw
ork, the costs are the inverses of the network matrix elements. This centrality can be calculated using Dijkstra algorithm for shortest cost paths.

To get the information about which sector’s final cons
umption induces which sector’s production, we use diagonal final consumption matrix C whose diagonal elements are the elements of final consumption vectorC.
(3)Y  [I  A]1 C

4 RESULT(4)

Table 1 shows top ten sectors (nodes) of total produ
ction (total weight of links) in production ratio base n
etwork L In total outward weight, USA and Japan "Ot
her services sector" and "Wholesale and retail trade sector" have heavy weight. In total inward weight, indust
ry sectors do not have so much different weights. They have the same weight on the average.

The induced production matrix Y represents which secto
r’s final consumption induces which sector’s production
and the induced CO2 emission matrix EI represents
which sector’s final consumption induces which sector’s CO2 emission. We consider the matrix Y and EI
as network matrixes. So the network created by networ
k matrix Y is constructed by nodes which represent e
ach industry section and constructed by links which represent product flows from productions to consumptions. And the network created by network matrix EI isconstructed by nodes which represent each industry sec
tion and constructed by links which represent CO2 emi
ssion flows with products. In previous paper[20], we analyzed networks corresponding to these network matri
xes Y and EI . These results are strongly dominatedby big countries (Japan, USA and China) and big ind
ustry sections, so we can not see the structure well. S
ometimes small industry becomes a key of the econom
ic failure.

© ISAROB 2013

(6)cB (v)  

Here, λ is the diagonal matrix whose diagonal elements are direct CO2 emission in each industry sector.

EI  λ[I  A]1 C

EL  λ[I  A]1

Considering a cascade failure of the economy resulting
from a breakdown of small industry, we think the ne
ck of the paths in the network relates to the cascade.
Betweenness centrality describes the importance of thenode under the meaning of path going through it. The
definition of Betweenness centrality of node v in simple (undirected unweighted) network is,Here, [I  A] is the Leontief inverse and C is final
consumption vector. Induced CO2 emission EI is described by,

EI  λ[I  A]1 C(5)

3.2. Betweenness centrality

production vector Y is described byY  [I  A]1 C

L  [I  A]1

Table 2 shows top ten sectors (nodes) of betweenness centrality in production ratio base network L . Motorvehicles sector in Japan has high betweenness centrali
ty. This means if breakdown of this sector happens, a
lot of paths of the products are destroyed and affects
world economy. And it is not proportion to its amountof money. This is really the fact that happened after
earthquake in local area of Japan in 2011. Electronic c
omputing equipment sector in Singapore, Semiconductors sector in Malaysia and Philippines have high betweenness centrality either. They are not Thailand but we can guess the situations are similar in Thailand.

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Table 1. top ten sectors (nodes) of total production ratio(total weight of links)
Inward Weight (production ratio base network L )Total
Industry section
weight1
3.18 Japan
Cement and cement products
2
2.67 Korea
Cement and cement products
3
2.10 Japan
Non-metallic ore and quarrying
4
2.09 Japan
Motor cycles
52.04 China
Electronic products
6
1.97 Korea
Slaughtering, meat products
7
1.91 USA
Slaughtering, meat products
8
1.90 China
Lighting fixtures
9
1.89 China
Wooden furniture10
1.87 China
Metal products

Table 3. top ten sectors (nodes) of total production (total
weight of links)
Inward Weight (CO2 emission base network EL )Total
Industry section
weight1
1.21 China
Water supply
2
1.14 China
Unclassified
3
1.05 China
Metal products
4
1.04 China
Iron ore
51.04 China
Cement and cement products
60.93 China
Chemical fertilizers and pesticides
7
0.93 China
Basic industrial chemicals
80.93 China
Non-ferrous metal
90.92 China
Other metallic ore
100.84 China
Other transport equipment

Outward Weight (production ratio base network L )Total
Industry section
weight15.23 USA
Other services
12.75 Japan
Wholesale and retail trade
11.80 Japan
Other services
9.58 USA
Wholesale and retail trade
9.56 Japan
Iron and steel
8.71 Japan
Basic industrial chemicals
8.59 Singapore
Wholesale and retail trade8.25 China
Electricity and gas
7.44 China
Wholesale and retail trade
7.39 Thailand
Wholesale and retail trade

Outward Weight (CO2 emission base network EL )Total
Industry section
weight25.66 China
Electricity and gas
8.29 Malaysia
Electricity and gas
7.91 Thailand
Electricity and gas
7.03 Indonesia
Electricity and gas
6.91 Korea
Electricity and gas
6.10 Philippines
Electricity and gas
5.86 USAElectricity and gas
5.24 China
Iron and steel
4.70 Singapore
Electricity and gas
4.24 Japan
Electricity and gas

1
2
3
4
5
6
7
8
9
10

1
2
3
4
5
6
7
8
9
10

Table 1 shows top ten sectors (nodes) of total production (total weight of links) in production ratio base net
work L In total outward weight, USA and Japan "Other services sector" and "Wholesale and retail trade sector" have heavy weight.

Table 3 shows top ten sectors (nodes) of total production (total weight of links) in CO2 emission base netwo
rk EL . In total outward weight, "Electricity and gas sector" in rather developing countries are dominant.In t
otal inward weight, all top ten sectors are China.

Table 2. top ten sectors (nodes) of betweenness centrality

Table 4. top ten sectors (nodes) of betweenness centrality

Betweenness
betweenn
es

1
2
3
4
5
6
7
8
9
10

48945
38390
37084
36047
36036
35318
33162
31357
31312
29815

(Money base networkL)

Betweenness
betweennesIndustry section

1
2
3
4
5
6
7
8
9
10

Japan
Motor vehicles
Singapore Wholesale and retail trade
USA
Transportation
Singapore Electronic computing equi.
Malaysia Semiconductors
China
Wholesale and retail trade
Japan
Electricity and gas
Philippines Semiconductor
Thailand
Wholesale and retail trade
China
Other services

EL )Industry section

China
Iron and steel
China
Electricity and gas
Malaysia Crude petroleum & natural gas
Malaysia Electricity and gas
Singapore Refined petroleum
Japan
Electricity and gas
Malaysia Precision machines
Korea
Iron and steel
Malaysia Transportation
Japan
Iron and steel

Table 4 shows top ten sectors (nodes) of betweenness
centrality in CO2 emission base network EL "Iron andsteel" and "Refined petroleum" appears. They can be imported and exported between countries, and consequently carry heavy CO2 emission.

Table 2 shows top ten sectors (nodes) of betweenness
centrality in production ratio base network L .motor
vehicles sector in japan has high betweenness centrality.

© ISAROB 2013

85563
84824
82406
69520
59840
51189
50831
49816
44718
41394

(CO2 emission base network

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results warn that CO2 emission of developing countries isalso important problem..

Table 3 shows top ten sectors (nodes) of total produ
ction (total weight of links) in CO2 emission base net
work EL . In total outward weight, "Electricity and gas
sector" in rather developing countries are dominant.In
total inward weight, all top ten sectors are China. It
can be thought that the results are caused from the ine
fficient industrial machines or system in the case of ra
tio.
Table 4 shows top ten sectors (nodes) of betweenness centrality in CO2 emission base network EL "Iron and steel" and "Refined petroleum" appears. They can beimported and exported between countries, and consequ
ently carry heavy CO2 emission.

We think the complex networks analysis gives us the really useful tools and this paper will be a hint of theresearch from this direction.

ACKNOWLEDGEMENTS
We thank the Research project for a sustainable develo
pment of economic and social structure dependent on t
he environment of the eastern coast of Asia and Grants-in-Aid for Scientific Research (KAK-ENHI) for financial support. We also thank members of these research
projects for various discussions.

Figure 1 show the networks constructed by network
matrix L . To visualize, only 1000 links are left, after
cutting small links whose weights are under minimum
weight. The directions of links show product flow (counter direction of money flow). “Motor vehicles sector” and “Electronic computing equipment sector” are in
the middle part between countries. Coincide These res
ults coincide with the results of betweenness centrality.
The nodes connecting different country's industry sectors are neck of the paths and have hight betweenness ce
ntralities.These are considered also necks of the world
economy.

REFERENCESJETRO (Japan External Trade Organization)
http://www.ide.go.jp/Japanese/Publish/Books/Tokei/material.html[2] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A.
Vespignani, “The architecture of complex weighted
networks”, Proc. Natl. Acad. Sci., Vol. 101, No.
11,2004, pp.3747-3752[3] U. Brandes, “A faster algorithm for betweenness
centrality”, The Journal of Mathematical Sociology,
Vol.25, No.2, 2001, pp.163-177[4] E. W. Dijkstra, "A note on two problems in connexion
with graphs", Numerische Mathematik , Vol.1, 1959,
pp.269-271[5] IEA (International Energy Agency)
http://data.iea.org/ieastore/statslisting.asp[6] D. J.Watts, and S. H. Strogatz, “Collective dynamics
of 'small world' networks”, Nature, Vol. 393, 1998,
pp.440-442[7] A.-L.Barab´asi and R. Albert, “Emergence of scaling
in random networks”, Science, Vol. 286, 1999,
pp.509-512[8] S. H. Strogatz, "Exploring complex networks", Nature,
Vol. 410, 2001, pp.268-276[9] W.Leontief, “Input-Output Economics”, Oxford
University Press, 1966[10] S. Joshi, “Product Environmental Life-Cycle
Assessment Using Input-Output Techniques”, Journalof Industrial Ecology, Vol.3, No.2-3,1999, pp. 95-120[11] P. Changa,b, H. Shiha,” Comparing patterns ofintersectoral innovation diffusion in Taiwan and
China: A network analysis”, Technovation, Vol. 25,
2005, pp.155-169[12] A. M. Prieto, J. L. Zofı´o, “Network DEA efficiency
in input–output models:With an application to OECD
countries”, European Journal of Operational
Research , Vol.178, 2007,pp. 292-304[13] B. Meng, S.Inomata, “Production Networks andSpatial Economic Interdependence: An International
Input-Output Analysis of the Asia-Pacific Region”,
IDE Discussion Paper Series, No.185, 2009
[1]

Figure 1 Production ratio base network L

5 CONCLUSION
We show the possibility of the cascade failure of the world
economy caused by the breakdown of some local industry
sectors. The complex networks analysis of the data is
consistent with the facts in local breakdowns of Motorvehicle sector in Japan and of Electronic computing sector
in Thailand. We have to find the risks of the cascade failureof the world economy from various directions. And also our© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[14] F. Ai-li, G. Qi-sheng, Z. Si-ying,” Clustering andCorrelation Analysis of the Industry Networks”,
Systems Engineering , Vol.29, No.6,2009, pp. 178–
183
[15] Wang H. et al, Multi-Weighted Monetary Transaction
Network, Advances in Complex Systems, Vol. 14, No.
5, 2011, pp.691–710[16] K.Yoshizawa, T. Fujiwara, Z. Zheng , R. Shiba, N.
Sakurai, K. Yamasaki, “Network Analysis of Japanese
Ecological Footprint based on Input-Output Table forthe 47 Prefectures in Japan”, Proceedings of FOOTPRINT FORUN 2010[17] T.Fujiwara , K. Yoshizawa, N. Sakurai, K. Yamasaki ,“The Analysis of Ecological Footprint using Multiregional Input Output Table for 47 Prefectures inJapan”, Proceedings of 38th Annual Meeting ofEnvironmental Systems Research, 2010, pp.239-243.
(in Japanese)[18] K.Yamasaki, T. Fujiwara, N. Sakurai, K. Yoshizawa,
Z. Zheng , “Effect of Hot Summer against
Environment which was induced by Extra Economical
Demand via Japanese Ecological Footprint”,
Proceedings of 16th International Symposium on
Artificial Life and Robotics,2011[19] X.Gao, T. Fujiwara, N. Sakurai, K. Yoshizawa, S.
Miyake, Z. Zheng, K. Yamasaki ,”Network Analysis
of Ecologocal Footprint & CO2 Emission based on
Input-Output Table for East Asia”, Proceedings of
17th International Symposium on Artificial Life andRobotics,2012[20] K.Yamasaki, T.Fujiwara, K.Yoshizawa, S.Miyake,
Zeyu Zheng, Xiang Gao, N.Sakurai, “The Complex
Network Study of Money and CO2 Emission Flows
between Industrial Sectors in Asian Countries using
Input-Output Table”, Proceedings of International
Conference on Business Management & Information
System, 2012, pp.213-219© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

On a serendipity oriented recommender system based on folksonomy
Hisaki Yamaba1 , Michihito Tanoue1 , Kayoko Takatuka1 , Naonobu Okazaki1 and Sigeyuki Tomita1
1

University of Miyazaki, Miyazaki, Miyazaki 889-2192, Japan(Tel: 81-985-58-7425, Fax: 81-985-58-7425)(yamaba@cs.miyazaki-u.ac.jp)

Abstract: This paper proposes a recommendation method that focuses on not only predictive accuracy but also serendipity. On
many of the conventional recommendation methods, each item is categorized according to their attributes (a genre, an authors,etc.) by the recommender in advance, and recommendation is performed using the categorization. In this study, impressionsof user to items are adopted as a feature of the item, and each item is categorized according to the feature. The impressions
are prepared by using folksonomy. A recommender system based on the method was developed by java language, and theeffectiveness of the proposed method was verified through recommender experiments.
Keywords: recommender system, serendipity, folksonomy
1 INTRODUCTION
In these days, importance of recommender systems which
can present useful information to users is increasing because
numerous information comes to be broadcasted according to
the expansion of the Internet.
A lot of researches have been investigated about recommentation systems; many of them were realized based on thecollaborative filtering method or the contents based filtering
method [1]. The collaborative filtering method recommends
items such that users who have similar tastes with an active
user (a user who is given recommendation) like. On the other
hand, the content based filtering method recommends items
that are similar with items that the active user likes.
Recommender systems based on these conventional approaches focus on the predictive accuracy. However, development of recommender systems that consider measure that
go beyond the accuracy of its recommendation is attempted
in recent years [1].
This study proposes a method for serendipitous recommendation such that users feel surprised by recommended
books. A recommender system was developed by Java language and the validity of the proposed method was confirmed
through a series of recommendation experiments.

Horace Walpole based on the fairy tale titled “Three princesof Sarendip.” In general, this word means the ability to findsomething good or useful while not specifically searching forit. However, in the area of research in recommender systems,
this word means that recommended items are unforeseeable,
unexpected, or surprising for the user [1] . When a user
searches an item that suits to his tastes, the search will be
performed around the area that the item is expected. Therefore, it is supposed that the user cannot find such an item that
does not locates in the expected area. On the contrary, if theuser finds such a book by accident, it will be a serendipitous
discovery for the user. In this study, it is assumed that a user
feels serendipity when a recommended item suits the taste of
the user, and is unknown to him/her, and is not included in
the area that the user expected such items locate.
Under many of the conventional recommendation methods, items for recommendation (e.g. books) are categorizedaccording to their attributes (a genre, an authors, etc.) by arecommender in advance. Recommendation is performed using the categorization. For example, books that are classified
into mystery novels will be recommended to users who love
mystery novels. However, a book that is classified into lovestories will not be recommended to such users even if thebook has flavor of mystery novels (Fig.1). However, suchbooks might be serendipitous books for the kind of users(Fig.2).

2 PROPOSED METHOD
2.1 Serendipity oriented recommendation
The purpose of recommender systems is to recommend
items that are useful for users. However, items that are suitusers’ tastes are not useful in case that the users are familiar with the items. This means that recommender systems
are required to recommend items that not only suit tastes ofusers but also are novelty (they are unknown to the users)[1]. In these days, serendipity also comes to be required torecommender systems. The word serendipity is created by© ISAROB 2013

In this study, impressions of users on books are adopted
as one of attributes of the books. For example, if users feel aflavor of mystery novels from a book, which is not regarded
as a mystery novel but regarded as a love story, the impression will be added to the characteristics of the book. It is
expected that such books will be recommended to mysteryfans by using impressions of users in the process of recommendation. Serendipity in this study means such ability to

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013!"#$%&'()"#(&*+,"'$-&..$*/$'!")$1$#+2##*$34&.,#56782+&-6"#$#%&&'$0

97$2$+&&62:(88*&#+$'$-&..$*/$/
012$'3.,2#$',4"*5
䠖"+&&6

0'$-&..$*/$'
2$8$-#2+&&624'&.
#7$-"#$%&',&4
.,2#$',
䠖"+&&6#7"#(212$4184&'#7(212$'!"#$#%&&'$()**
%#+#,&--#./#/0

4&.,#5672;<&+)6#:&&'7

9$#+

Fig. 1. Conventional recommendation.
Fig. 3. Basic idea of the proposed method.
53."6$-*73.*$&283-"#$%%"&'"-

given to an item and a tag “weblog” is attached to another
item. Though they have tags that indicate same meanings,
the two items are not recognized to have same feature.
In this study, impressions of users themselves are used toclassify items. Since an impression that each user feels on abook is not known explicitly, a method to infer such impressions from tags data was developed based on an assumptionthat tags are selected according to impressions users feel onbooks. An inferred impression is called a “concept” in thisstudy (Fig.3).

!"#$%%"&'"'()*+,+"-.)*&/+,+"0,1
2$$/+3-"*&.)*+3-"34

Fig. 2. Serendipity on recommendation.

find out useful books beyond the conventional books classification. In order to realize the books classification mentioned
above, folksonomy was adopted.

2.4 Proposed method
In this section, books recommendation is used to explain
the proposed method (recommendation of books is also usedin the experiments mentioned in the next section).
In the proposed method, a concept is expressed in the formof a vector of degrees of relevance from the concept to tags
(see 2.4.3). At first, in a case that two tags are given to samebooks many times, it is assumed that the impression of theusers who gave the one of the two tags is same with theimpression of users who gave the other tag. Under this assumption, concepts are generated according to the steps asfollows. First, “degrees of similarity” between two tags were
calculated (see 2.4.1). Next, similar tags are gathered andclusters are generated (see 2.4.2). Such a cluster corresponds
to a concept.
Using concepts obtained above, “degrees of relevance”
from each book to concepts and “degrees of relevance” fromeach user to concepts are calculated (see 2.4.4 and 2.4.5).
Then, characteristics of each book or each user are represented by a vector of degrees of relevence. The recommender system recommends books that whose characteristics
are similar with the tastes of the user.

2.2 Folksonomy
Folksonomy is a bottom up style classification system
while conventional classification systems adopt a top downstyle. Under such conventional systems, items presented tousers are classified in advance based on categories definedby service providers. Folksonomy classifies items using tags
that are given by users. Tags are keywords that are generated
by users following characteristics, impressions and so on ofeach item. Users are allowed to select any words as tags. And
also, users are allowed to give more than two tags to a book.
Folksonomy has features shown below:• The classification reflects impressions or recognition ofusers.
• It is easy for users to give tags to items because what
users have to do is only to input keywords; some special
knowledge is not required.
• Classification results are flat, not stratified.

2.4.1 Degree of similarity between two tags
2.3 Introduction of the idea called ”concepts”
In cases that classification is performed using tags that are
attached to items, problems caused by synonym or polysemy
have to be resolved. For example, suppose a tag “blog” is© ISAROB 2013

It is assumed that two tags a and b are used reflecting asame concept in case that tag a and b are both attached tosame item in many cases. In this study, such a and b are
regarded to be similar. Books are classified into four types

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i. A degree of similarity between Clk and T agi
is calculated. Similarity of a tag T and a cluster C is the average of similarities between
T and all tags included in C. If the value isgreater than Vt , T agi is added to Clk .
ii. A degree of similarity between Clk and T agj
is calculated. If the value is greater than Vt ,T agj is added to Clk .

listed below with tag a and b:(I) both a and b are attached to the book,(II) neither a or b is not attached to the book,(III) only a is attached to the book and b is not.
(IV) only b is attached to the book and a is not.
In case that a sum of percentages of (I) and (II) is large,
the similarity between a and b is assumed to be high. On theother hand, the similarity between a and b is assumed to be
low when a sum of percentages of (III) and (IV) is large. In
this study, “a degree of similarity” between two tags is represented by AEMI (Augmented Expected Mutual Information)[2].

AEM I( , )=∑(d) Cset is updated. Concretely, clusters in CopySet
substitute corresponding clusters in Cset.
(e) If Cset does not include a cluster that includes
both T agi and T agj , a new cluster is created that
includes two tags (T agi and T agj ) and added toCset.
2.4.3 Creation of concepts
A concept (Coi ) is represented by a vector of degrees of
relevance from a cluster (Cli ) obtained above to all tags. A
degree of relevance from a cluster Cli to a tag T agi is calculated as follows:

M I(A, B)(A=α,B=β),(A=ᾱ,B=β̄)∑

M I(A, B)(A=ᾱ,B=β),(A=α,B=β̄)t(T agi , Cl1 )rel(T agi , Co1 ) = ∑j t(T agi , Clj )where is cases that tag a is attached to books and is casesthat tag b is attached to books. ¯ represents cases that tag a is
not attached to books. M I(A, B) is mutual information thatmeasures co-occurrence of A and B:
M I(A, B) = P (A, B) log

where t(T agi , Cl1 ) is an average of degrees of similarity between T agi and all tags in a cluster Cli .
2.4.4 Representation of characteristics of books
Characteristics of a book are represented by a vector of
degrees of relevance from the book to all concepts obtained
above. A degree of relevance from a book Book1 to a concept Co1 is represented as follows:∑rel(T agi , Co1 )rel(Book, Co1 ) =P (A, B)
P (A)P (B)where P (A) is the occurrence frequency of A, and P (A, B)
is the concurrence frequency of A and B.
2.4.2 Tag clustering
This section explains the process to generate clusters thatare composed of similar tags.

i2.4.5 Representation of characteristics of users
First, a degree of preference from a user (U ser) to a concept (Co1 ) is introduced as follows:∑pre(U ser, Co1 ) =rel(Booki , Co1 )1. An empty set Cset is prepared, which is used as the setof created clusters.
2. All tag pairs (T agi ,T agj ) (i 6= j) are created and sorted
in the order of their similarity. Then tag pairs that havehigher similarity than the threshold Vt introduced in advance are selected and stored in a list.

iwhere Booki is a book that U ser likes.
Characteristics of a user is represented by a vector of apre(U ser, Co1 ) for all concept obtained above.
There are several methods to find books that a user likes:
questionnaires, interviews, referring records of web viewing,
and so on. In the experiments of this paper, examinees indicate their favorite books in direct.

3. Tag pairs in the list are processed as follow in the higher
order of their similarities:(a) A copy of Cset is created (CopySet).
(b) Clusters that include both T agi and T agj are removed from CopySet.

2.4.6 Selection of books for recommendation
In this study, books whose characteristics vectors are similar with that of a target user are selected for recommendation. Concretely, an inner product of characteristics vectorsof a book and a user is adopted here.

(c) For each cluster (Clk ) in CopySet,• if T agi is not included in ClK , do step i,• if T agj is not included in ClK , do step ii0.

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

3 EXPERIMENTS
A book recommender system that is based on the proposed method was implemented by java language. A seriesof experiments was carried out using the system in order toconfirm the validity of the proposed method.

Table 1. The results of the recommendation experiment.
(1)
(2)
(3)
(4)
Q1 156 153 149
42
500
Q2 65
55
77
303 500
Q3 228 147 125 N/A 500

3.1 Acquisition of data for experiments
Data used in the experiments are collect from Booklog(http://booklog.jp). Booklog is a web service that provides
virtual book shelves. Over 500 thousands users are registeredat the site and over 33 million items (books, CDs, and so on)
are stored as of Jan. 2012. Web pages have been created forevery book. Each of the pages provides information about
the corresponding book. Each page also includes the links tothe pages of the relating books. Booklog adopt folksonomy
and its users can attach tags to books arranged on their bookshelves.
Books in the top list for 2011 and books linked from themwere selected. And 18,922 tags attached to the 6,717 books
were obtained.

!"#$!%#,%!&#$!'#
&+

,&

%((

*((

&()

%((

!&#

Fig. 4. The ratio of recommended books that were serendipitous.

3.2 Methods of experiments and evaluation
Concepts were generated and specific vectors were calculated from the obtained data by the proposed method. The
concepts and the vectors were embedded into the developed
recommender system. The system recommended 10 books
to each of 50 examinees.
A characteristics vector of each examinee was calculated
from favorite books which he/she listed up. Ten books wereselected for each examinee according to his/her characteristics vector. Next, the examinees answered the three inquiries
listed below about each of the recommended books:

such that almost all recommended books are surprising is
not adequate, it is considered that the obtained results, mostbooks are proper and the rest are serendipitous, was appropriate recommendation.
4 CONCLUSIONSIn this study, the serendipity oriented recommendation
method was proposed. Impressions that users felt from abook is extracted as a “concept” using tag data attached tobooks in the folksonomy style. In the proposed method, theconcepts are used for selection of books recommended tousers.
The recommender system was implemented and recommendation experiments were carried out using the system.
It was confirmed that the recommender system based on the
proposed method has enough recommendation accuracy andcan recommend serendipituous books to users.

Q1 Are you interested in the book?
(1) Yes. (2) A little. (3) Little. (4) No.
Q2 Do you know the book well?
(1) Yes, I have read it. (2) Yes, but unread.
(3) Only its title. (4) No.
Q3 Do you think the recommendation validates?
(1) Yes. (2) I don’t know. (3) No.

REFERENCES[1] Kamishima T (2007-2008), Algorithms for Recommender Systems (in Japanese), Journal of JSAI,
vol.22,no.6-vol.23,no.2

3.3 Results
The 500 answers (10 answers from each of the 50 examinees) for the three inquiries were obtained. The numbers ofeach of the answer are shown in Table 1. Positive answers((1) or (2)) were selected for 309 recommended books in Q1(61.8%). This result is as good as the result of the existing
research [3] . 200 books that the examinees did not know
well ((3) or (4) for Q2) were involved in the 309 books. 38
books that the examinees evaluated to be surprised ((3) forQ3) were involved in the 200 books. This means that about
12 % books were serendipitous books. Since the situation© ISAROB 2013

&()

(
,"

[2] Philip L. Cahn (1999), A non-invasive learning approach to building web user profiles, KDD.99 Workshop on Web Usage Analysis and User Profiling[3] Niwa S, Doi T, Honiden S (20066), Web Page Recommender System based on Folksonomy Mining (inJapanese), IPSJ Journal, 47(5), pp.1382-1392

341

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Self-Triggered Optimal Control
Based on Optimization with Prediction Horizon One
Koichi Kobayashi1 and Kunihiko Hiraishi1
1Japan Advanced Institute of Science and Technology, Japan(Tel: +81-761-51-1282; E-mail: k-kobaya@jaist.ac.jp)

Abstract: Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems, and is extensively studied in the field of networked control systems. In
this paper, a new approach for self-triggered control is proposed based on model predictive control. First, the optimal
control problem with horizon one, in which the first sampling period and the control input are found, is formulated. Bysolving this problem at each sampling interval, self-triggered model predictive control is realized. Next, an iterative solution method is proposed. In this solution method, a quadratic programming problem is repeatedly solved. Finally, theeffectiveness of the proposed approach is shown by a numerical example.
Keywords: optimal control, networked control systems, self-triggered control.

1. INTRODUCTION

that the optimal value of the cost function is improved, iscomputed under the constraint that other sampling periods are given as a constant. However, a nonlinear equation must be solved. Furthermore, input constraints cannot be considered in this method.
In this paper, a new approach for self-triggered control
is proposed based on model predictive control (MPC).
First, the optimal control problem with horizon one, inwhich the first sampling period and the control input arefound, is formulated. By solving this problem at eachsampling interval, self-triggered model predictive control
is realized. Next, an iterative solution method is proposed. In this solution method, quadratic programming(QP) problems are repeatedly solved. Then, we can impose input constraints for the system. Finally, the effectiveness of the proposed approach is shown by a numerical example. The proposed approach provides us a basic
result for self-triggered optimal control.
Notation: Let R denote the set of real numbers. Let
In , 0m×n denote the n×n identity matrix, the m×n zeromatrix, respectively. For simplicity, we sometimes use
the symbol 0 instead of 0m×n , and the symbol I insteadof In .

In recent years, analysis and synthesis of networked
control systems (NCSs) have been extensively studied [1,4]. An NCS is a control system in which plants, sensors,
controllers, and actuators are connected through communication networks. In distributed control systems, subsystems are frequently connected via communication networks, and it is important to consider analysis and synthesis of distributed control systems from the viewpoint of
NCSs. In design of NCSs, several technical issues such as
packet losses and transmission delays are included. However, it is difficult to consider these issues in a unified
way, and it is suitable to discuss an individual problem.
From this viewpoint, several results have been obtainedso far (see, e.g., [6-9]).
In this paper, the periodic paradigm is focused as oneof the technical issues in NCSs. The periodic paradigm isthat the controller is periodically executed at a given unitof time. The period is chosen based on CPU processing time, communication bandwidth, and so on. However, in NCSs, communication should occur, when there
exists important information, which must be transmitted
from the controller to the actuator and/or from the sensor
to the controller. In this sense, the periodic paradigm is
not necessarily suitable, and it is important to consider a
new approach for design of NCSs. As one of the methods to overcome this drawback of the periodic paradigm,
self-triggered control has been proposed so far (see, e.g.,[2, 3, 5, 10, 13]). In self-triggered control, the next sampling time at which the control input is recomputed iscomputed. That is, both the sampling period and the control input are computed simultaneously. In many existing
works, first, the continuous-time controller is obtained,
and after that, the sampling period such that stability ispreserved is computed. However, few results on optimal
control have been obtained so far. From the viewpoint of
optimal control, for example, a design method based onestep finite horizon boundary has been recently proposedin [12, 14]. In this method, the first sampling period such

© ISAROB 2013

2. PROBLEM FORMULATION
Consider the following continuous-time linear system:ẋ(t) = Ax(t) + Bu(t)(1)

where x ∈ Rn is the state, and u ∈ [umin , umax ] ⊆Rm is the control input. umin , umax ∈ Rm are given
constants, and the interval [umin , umax ] expresses input
constraints. By tk , k = 0, 1, . . . , denote the sampling
time, and by hk := tk+1 −tk , denote the sampling period.
Assume that the control input is piecewise constant, thatis, the control input is given asu(t) = u(tk ), t ∈ [tk , tk+1 ).
Hereafter, we denote u(tk ) as uk . In addition, assume
that a pair (A, B) is controllable.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

3. PROPOSED SOLUTION METHOD

Before the problem studied here is formulated, some
preparations are given. Suppose that h0 is a decision variable, and hi = h, i ≥ 1 is satisfied, where h ≥ 0 is agiven constant. We also suppose that the input constraint
is imposed in the time interval [t0 , h0 + h(N − 1)), whereN ≥ 1 is a given integer. Note here that the input constraint is not imposed in the time interval [h0 + h(N −1), ∞). Then, consider the following cost function
J = J1 + J 2 + J 3 ,
 h0
 T

x (t)Qx(t) + uT (t)Ru(t) dt,
J1 =First, for a fixed h0 , consider deriving Jh∗0 . The valueof Jh∗ can be derived by a similar method. The value ofJh∗0 is given as the optimal value of the following optimal
control problem.
Problem 2: Suppose that for the system (1), theinitial time t0 , the initial state x(0) = x0 , h0 and h1 =h2 = · · · = h, N ≥ 1 are given. Then find a control input
sequence u0 , u1 , . . . , uN −1 minimizing the cost function(2) under the input constraint (3)From the result on sampled-data control theory, Problem 2 can be equivalently rewritten as the following optimal control problem of discrete-time linear systems.
Problem 3: Suppose that the initial time t0 , the initial state x(0) = x0 , h0 and h1 = h2 = · · · = h,N ≥ 1 are given. Consider the following discrete-timelinear system
x1 = Ã(h0 )x0 + B̃(h0 )u0 ,xk+1 = Ã(h)xk + B̃(h)uk , k ≥ 1
where xk := x(tk ). Then find a control input sequence
u0 , u1 , . . . , uN −1 minimizing the cost function (2), i.e.,
T 



Q̃(h0 ) S̃(h0 )
x0
x0
J =
u0
u0
S̃ T (h0 ) R̃(h0 )





N
−1
T

xk
Q̃(h) S̃(h)
xk
+
uk
uk
S̃ T (h) R̃(h)

(2)

t0

J2 =
J3 =

 h0 +h(N −1)
h

0
 ∞

h0 +h(N −1) T
x (t)Qx(t) + uT (t)Ru(t) dt, T
x (t)Qx(t) + uT (t)Ru(t) dt= xT (h0 + h(N − 1))P (h)x(h0 + h(N − 1)).
In the above cost function, P (h) is a symmetric positive definite matrix, which is a solution of the following
discrete-time algebraic Riccati equationÃT (h)P (h)Ã(h) − P (h) − (ÃT (h)P (h)B̃(h) + S̃(h))×(B̃ T (h)P (h)B̃(h) + R̃(h))−1
×(B̃ T (h)P (h)Ã(h) + S̃ T (h)) + Q̃(h) = 0
whereÃ(h) := e
and



Ah

, B̃(h) :=Q̃(h) S̃(h)S̃ T (h) R̃(h)


:=

k=1

(5)
+xTN P (h)xN
under the input constraint (3).
Next, consider reducing Problem 3 to a QP problem.
DefineTx̄ := xT0 xT1 · · · xTN
,
 T T
T
.
ū := u0 u1 · · · xTN −1
Then we can obtain x̄ = Āx0 + B̄ ū where
⎡
⎤
I
⎢
⎥
Ã(h0 )⎢⎥⎢ Ã(h)Ã(h0 ) ⎥⎢⎥Ā = ⎢ Ã2 (h)Ã(h0 ) ⎥
⎢
⎥
⎢
⎥
..
⎣
⎦
.

At

e dtB

0

 h


F :=

 h

e

FTt

0

A B
0 0




Q
0

0
R



eF t dt,.

Since the input constraint is not imposed in the time interval [h0 + h(N − 1), ∞), the optimal value of J3 canbe explicitly characterized by x(h0 + h(N − 1)).
Under the above preparation, consider the following
problem.
Problem 1: Suppose that for the system (1), theinitial time t0 , the initial state x(0) = x0 , h1 = h2 =· · · = h, N ≥ 1, and γ ≥ 1 are given. Then find a control
input sequence u0 , u1 , . . . , uN −1 maximizing a sampling
period h0 under the following constraintsh ≤ h0 ≤ hmax ,(3)umin ≤ uk ≤ umax , k = 0, 1, . . . , N − 1,(4)
Jh∗0 ≤ γJh∗
where hmax ≥ 0 is a given constant, and Jh∗0 is the optimal value of the cost function of (2), Jh∗ is the optimalvalue of the cost function of (2) under h0 = h.
In Problem 1, control performance can be adjusted bysuitably giving γ. We remark that in this problem, h0
is maximized under some constraints. Furthermore, inthis problem, a control input sequence is computed, but
on sampling periods, only the first one (h0 ) is computed.
In this sense, this problem is regarded as a kind of the
optimal control problem with prediction horizon one.

© ISAROB 2013

ÃN −1 (h)Ã(h0 )
and

⎡

0

⎢
⎢
B̃(h0 )⎢⎢⎢ Ã(h)B̃(h0 )B̄ = ⎢⎢⎢ Ã2 (h)B̃(h0 )
⎢
⎢
..
⎣
.

0

···

0

···

B̃(h)

···

Ã(h)B̃(h)
..
.

···

0
..
.
..
.
..
.

···
0
ÃN −1 (h)B̃(h0 ) ÃN −2 (h)B̃(h) · · · B̃(h)

⎤
⎥
⎥
⎥
⎥
⎥
⎥.
⎥
⎥
⎥
⎥
⎦

In addition, we defineQ̄ := block-diag(Q̃(h0 ), Q̃(h), . . . , Q̃(h), P (h)),
block-diag(S̃(h0 ), S̃(h), . . . , S̃(h)),
S̄ :=0n×(N −1)mR̄ := block-diag(R̃(h0 ), R̃(h), . . . , R̃(h)).
343The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Then the cost function (5) can be rewritten as follows:

to Step 3.

J = x̄T Q̄x̄ + 2x̄T S̄ ū + ūT R̄ū
= ūT L2 ū + L1 ū + L0

Note here that in this procedure, the timing (i.e., thesampling time) to measure the state and to recompute thecontrol input is computed. In this sense, self-triggered
control is realized.

where
L2 = R̄ + B̄ T S̄ + S̄ T B̄ + B̄ T Q̄B̄,
L1 = 2xT0 ĀT (S̄ + Q̄B̄),
L0 = xT0 ĀT Q̄Āx0 .

5. NUMERICAL EXAMPLE
Consider the following system:




0
1
0
ẋ(t) =
x(t) +
u(t).
−5 −8
1

Finally, ūmin := [ uTmin uTmin · · · uTmin ]T and ūmax :=[ uTmax uTmax · · · uTmax ]T are also defined.
Under the above preparation, Problem 3 is equivalent
to the following QP problem:

The input constraint is given as u(t) ∈ [−10, +10]. Parameters in Problem 1 are given as follows: h = 0.5,
γ = 1.001, hmax = 5, Q = 103 In , and R = 1. In Algorithm 1, we set ε = 10−4 . Then P (h) can be derivedas
10615 593
P (h) =.
593
575

Problem A:
find u0 , u1 , . . . , uN −1 ,min ūT L2 ū + L1 ū + L0 ,subject to ūmin ≤ ū ≤ ūmax .
A QP problem can be solved by using a suitable solver
such as MATLAB and IBM ILOG CPLEX [15].
Finally, by using the obtained QP problem, we propose
an algorithm for solving Problem 1.

In addition, we consider two cases, i.e., the case of N = 1
and the case of N = 10.
We show the computation result on self-triggered
MPC using Problem 1. The initial state is given asx0 = [ 10 10 ]T , and the case of N = 10 is considered. Fig. 1 shows the obtained state and input trajectories. From this figure, we see that the sampling period is
non-uniform.
Next, compare two cases. In these cases, the obtained
state trajectories are almost the same. The difference between two cases is as follows. In Fig. 1, the value of thecontrol input at each time is shown as follows:

Algorithm 1:
Step 1: Derive Jh∗ by solving Problem A with h0 = h.
Step 2: Set a = h and b = hmax , and give a sufficiently
small positive real number ε.
Step 3: Set h0 = (a + b)/2.
Step 4: Derive Jh∗0 by solving Problem A.
Step 5: If Jh∗0 ≤ γJh∗ in Problem 1 is satisfied, then set
a = h0 , otherwise set b = h0 .

u(t) = −10.00, t ∈ [0, 1.83), h0 = 1.83,
u(t) = −1.90, t ∈ [1.83, 3.23), h0 = 1.41,
u(t) = −0.49, t ∈ [3.23, 4.49), h0 = 1.25,
u(t) = −0.14, t ∈ [4.49, 5.69), h0 = 1.21,
u(t) = −0.04, t ∈ [5.69, 6.89), h0 = 1.20,
u(t) = −0.01, t ∈ [6.89, 8.08), h0 = 1.19.

Step 6: If |a − b| < ε is satisfied, then the optimal h0 in
Problem 1 is derived as a, and the optimal control input
sequence is also derived. Otherwise go to Step 3.
In a numerical example (Section 5), we will discuss
the computation time of Algorithm 1.

On the other hand, in the case of N = 1, the value of thecontrol input at each time is derived as follows:

4. SELF-TRIGGERED MODEL
PREDICTIVE CONTROL

u(t) = −10.00, t ∈ [0, 0.62), h0 = 0.62,
u(t) = −10.00, t ∈ [0.62, 1.66), h0 = 1.03,
u(t) = −2.62, t ∈ [1.66, 2.88), h0 = 1.22,
u(t) = −0.75, t ∈ [2.88, 4.08), h0 = 1.20,
u(t) = −0.22, t ∈ [4.08, 5.27), h0 = 1.19,
u(t) = −0.06, t ∈ [5.27, 6.47), h0 = 1.19,
u(t) = −0.02, t ∈ [6.47, 7.66), h0 = 1.19,
u(t) = −0.01, t ∈ [7.66, 8.85), h0 = 1.19.

We show a procedure of MPC based on the proposedsolution method for Problem 1.
Procedure of Self-Triggered MPC:
Step 1: Set t0 = 0, and give the initial state x(0) = x0 .
Step 2: Solve Problem 1.
Step 3: Apply only u(t), t ∈ [t0 , t0 + h0 ) to the plant.
Step 4: Compute the predicated state x̂(t0 + h0 ) by usingx(t0 ), h0 and u0 .

From these results, we can obtain the following observation. In this example, input saturation is needed to
improve the transient behavior. However, in the case of
N = 1, the time interval of input saturation was not computed suitably. As a result, to derive the state trajectory

Step 5: Solve Problem 1 by using x̂(t0 + h0 ) as x0 .
Step 6: Wait until time t0 + h0 .
Step 7: Update t0 := t0 + h0 , and measure x(t0 ). Return© ISAROB 2013

(6)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

12

−1

8

Control input

State[1] Special section on complex networked control systems, IEEE Control Systems Magazine, Vol. 27, No.
4, 2007.
[2] A. Anta and P. Tabuada, Self-triggered stabilizationof homogeneous control systems, Proc. American
Control Conf., pp. 4129–4134, 2008.
[3] A. Anta and P. Tabuada, To sample or not to sample: Self-triggered control for nonlinear systems,
IEEE Trans. on Automatic Control, vol. 55, no. 9,
pp. 2030–2042, 2009.
[4] P. J. Antsaklis and J. Baillieul (Guest Editors), Special issue on the technology of networked control
systems, Proceedings of the IEEE, Vol. 95, No. 1,
2007.
[5] A. Camacho, P. Martı́, M. Velasco, C. Lozoya, R.
Villá, J. M. Fuertes, and E. Griful, Self-triggered
networked control systems: an experimental casestudy, Proc. IEEE Int’l Conf. on Industrial Technology, pp. 123–128, 2010.
[6] L.-S. Hu, T. Bai, P. Shi, and Z. Wu, Sampled-data
control of networked linear control systems, Automatica, Vol. 43, No. 5, pp. 903–911, 2007.
[7] H. Ishii, Stabilization under shared communication
with message losses and its limitations, Proc. 45th
IEEE Conf. on Decision and Control, pp. 4974–
4979, 2006.
[8] H. Ishii, H ∞ control with limited communicationand message losses, Systems & Control Letters, Vol.
57, No. 4, pp. 322–331, 2008.
[9] K. Kobayashi and K. Hiraishi, Optimal control of aclass of networked systems based on MLD framework, Proc. the 18th IFAC World Congress, pp. 66–
71, 2011.
[10] M. Mazo Jr. and P. Tabuada, On event-triggered
and self-triggered control over sensor-actuator networks, Proc. 47th IEEE Conf. on Decision and Control, pp. 435–440, 2008.
[11] T. Otsuka, A continuation/GMRESmethod for fastcomputation of nonlinear receding horizon control,
Automatica, vol. 40, pp. 563–574, 2004.
[12] M. Velasco, P. Martı́, J. Yépez, F. J. Ruiz, J. M.
Fuertes, and E. Bini, Qualitative analysis of a onestep finite horizon boundary for event-driven controllers, Proc. 50th IEEE Conf. on Decision andControl and European Control Conf., pp. 1662–
1667, 2011.
[13] X. Wang and M. D. Lemmon, Self-triggered feedback control systems with finite-gain L2 stability,
IEEE Trans. on Automatic Control, vol. 54, no. 3,
pp. 452–467, 2009.
[14] J. Yépez, M. Velasco, P. Martı́, E. X. Martı́n,
and J. M. Fuertes, One-step finite horizon boundary with varying control gain for event-driven networked control systems, Proc. 37th Annual Conf. of
the IEEE Industrial Electronics Society, pp. 2531–
2536, 2011.
[15] http://www.ilog.com/products/cplex/

−2

6

x1

4
2
0
−2

2

−4
−5
−6
−7
−9

−6
1

−3

−8

x2

−4

−8
0

REFERENCES

0

10

3

4

Time

5

6

7

8

−10
0

1

2

3

4

Time

5

6

7

8

Fig. 1 State and input trajectories (N = 10).
at time interval [0, 8), Problem 1 was solved eight times.
In the case of N = 10, Problem 1 was solved six times,
and the above problem is overcome. So it is important tochoose a suitable N . We remark that in this example, thecomputation result in the case of N = 20 is the same asthat in the case of N = 10. In this sense, N = 10 is oneof the suitable horizons.
In addition, we discuss the effect of changing γ in (4).
In the case of N = 10, consider the following cases:
γ = 1.001, 1.005, 1.010, 1.015, 1.020. For each case,the first h0 is obtained as follows:γ = 1.001 :γ = 1.005 :γ = 1.010 :γ = 1.015 :γ = 1.020 :h0 = 1.83,
h0 = 2.38,
h0 = 2.79,
h0 = 3.12,h0 = 3.44.

From these results, we see that h0 becomes longer by setting a larger γ. Since control performance decreases for alarger γ, it is important to consider the trade-off between
γ and h0 .
Finally, we discuss the computation time for solving
Problem 1. In the case of N = 10, Problem 1 with thedifferent initial state was solved six times. Then the mean
computation time for solving Problem 1 was 6.51 [sec],
where we used IBM ILOG CPLEX 11.0 [15] as the MIQP
solver on the computer with the Intel Core2 Duo 3.0GHz
processor and the 2GB memory. In the case of N = 1,
Problem 1 with the different initial state was solved eighttimes. Then the mean computation time was 6.22 [sec]. It
is one of the future works to consider several approaches
for reducing the computation time.

6. CONCLUSION
In this paper, we have proposed a self-triggered optimal control method based on model predictive control.
By focusing on only the first sampling period in the optimal control problem, we have proposed an iterative solution method (Algorithm 1). The effectiveness of the
proposed method has been shown by a numerical example. The proposed methods are useful as a new methodof self-triggered optimal control.
In the future works, it is important to develop a more
efficient method for solving Problem 1. Then continuation methods [11] may be useful. It is also significant toanalyze stability of the closed-loop system.
This work was partially supported by Grant-in-Aid forYoung Scientists (B) 23760387.

© ISAROB 2013

345

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

H∞ controller with graphical LMI region profile for Gantry Crane System
M. Z. Mohd Tumari, M. S. Saealal, M. R. Ghazali and Y. Abdul Wahab1
1Instrumentation and Control Engineering Research Group (ICE),
Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang,
Pekan, Pahang, Malaysia.
1zaidimt@ump.edu.my

Abstract: This paper presents investigations into the development of H∞ controller with pole clustering based on LMItechniques to control the payload positioning of INTECO 3D crane system with very minimal swing. The linear model of
INTECO 3D crane system is obtained using the system identification process. Using LMI approach, the regional poleplacement known as LMI region combined with design objective in H∞ controller guarantee a fast input tracking capability,
precise payload positioning and very minimal sway motion. A graphical profile of the transient response of crane system withrespect to pole placement is very useful in giving more flexibility to the researcher in choosing a specific LMI region. The
results of the response with the controllers are presented in time domains. The performances of control schemes are examinedin terms of level of input tracking capability, sway angle reduction and time response specification. Finally, the control
techniques is discussed and presented.
Keywords: INTECO 3D crane; sway control; H-infinity; LMI region.
trajectory planning. A few contribution of trajectory
planning scheme have been reported in [6]–[12].
In this project, H∞-synthesis with pole clustering basedon LMI techniques is used to control the positioning of
payload with very minimal swing. In order to design thecontroller, the linear model of INTECO 3D crane system asshown in Fig. 1 is obtained using the system identification
process. The reason for choosing H∞-synthesis is because ofits good performance in handling with various types ofcontrol objectives such as disturbance cancellation, robust
stabilization of uncertain systems, input tracking capabilityor shaping of the open-loop response. Nevertheless, theweakness of H∞ controller is in handling with transient
response behavior and closed-loop pole location instead of
frequency aspects [13]. As we all know, a good time
response specifications and closed-loop damping ofunderactuated crane system can be achieved by forcing theclosed-loop poles to the left-half plane. Moreover, many
literatures have proved that H∞ synthesis can be formulated
as a convex optimization problem involving linear matrix
inequalities (LMI) [14]-[16]. In this case, the normal
Riccati equation with inequality condition was used. Thisbehavior will give wide range of flexibility in combining
several constraints on the closed loop system. This flexible
nature of LMI schemes can be used to handle H∞ controller
with pole placement constraints. In this study, the poleplacement constraints will refer directly to regional poleplacement [17]. It is slightly difference with point-wise poleplacement, where poles are assigned to specific locations in
the complex plane based on specific desired time response

1 INTRODUCTION
The main purpose of controlling an underactuated cranesystem is transporting the payload in a precise location.
However, it is very difficult due to the fact that the payload
can exhibit a pendulum-like swinging motion. Various
attempts in controlling cranes system based on open loop
and closed-loop control system have been proposed. For
example, open loop time optimal strategies were applied tothe crane by many researchers [1,2]. Poor results wereobtained in these studies because open-loop strategy issensitive to the system parameters and could notcompensate for the effect of wind disturbance. In other
hand, feedback control which is well known to be lesssensitive to disturbances and parameter variations has also
been adopted for controlling the crane system. For example,
PD controllers has been proposed for both position andanti-swing controls [3]. However, the performance of thecontroller is not very effective in eliminating the steadystate error. In addition, fuzzy logic controller has also been
proposed for controlling the crane system by several
researchers [4]. However, the fuzzy logic designed stillneed to struggle in finding the satisfactory rules,
membership function, fuzzification and deffuzification
parameter heuristically. In addition, since crane system isan underactuated system, sliding mode control also has
been proposed by bringing the sliding surface into to thesystem [5]. Furthermore, the underactuated crane behavior
also gives a very challenging problem in achieving good

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

position, second order DC motor and pendulum sway
motion are described in (1), (2) and (3) respectively

specifications. In this case, the closed-loop poles of cranesystem are confined in a suitable region of the complex
plane. This region consists of wide variety of usefulclustering area such as half-planes, disks, sectors,
vertical/horizontal strips, and any intersection thereof [17].
Using LMI approach, the regional pole placement known as
LMI region combined with design objective in H∞
controller should guarantee a fast input tracking capability,
precise payload positioning and very minimal sway motion.
As an extension of previous work, this report presents agraphical profile of the transient response of crane systemwith respect to pole placement constraint variation. Thisgraphical analysis is very useful in giving more flexibility
to the researcher in choosing a specific LMI region.
The rest of this report is structured in the following
manner. The next section provides a description of thelinear model of underactuated crane system based on
system identification procedure. In section 3, the design of
H∞ controller with pole placement constraint is explained.
The graphical profile of crane performance with LMI
region variation also discussed in this section. Simulationand experimental validation are reported in Section 4.
Finally, concluding remarks are offered in the last section.

xl = xc + l sin θ

(1)

K
1
x& c + uTT(2)&x&c = −m&x&l = − D(x&l − x&c ) − mg sin θ(3)where the meaning of different variables is given in
Table 1.
Table 1: Variable description
Sym
Meaningbols
xlPayload position

xc

Cart position

l
θ
T
K
D

Length of the rope
Sway angle
Time constantMotor gain
Damping constant
Mass
of
the
payloadGravity effectDriving voltagemguUsing the assumption of cos θ ≅ 1 andand (3) can be expressed as follow

sin θ ≅ θ , (1)xl = x c + lθ(4)

m&x&l = − D( x&l − x& c ) − mgθ

Fig. 1.

INTECO 3D Crane.

Next, by substituting (4) in (5) and (2) respectively, thefinal equation of motion can be described as&x&l = − gθ −2 MODELLING OF INTECO 3D CRANE
This section provides a brief description on themodelling of the underactuated crane system, as a basis of asimulation environment for development and assessment of
the proposed control scheme. The system identification
approach is used to determine the parameter of DC motor
for cart position and pendulum behavior for sway motion
respectively. In this study, only two-dimensional cranesystem with payload is considered. In order to have aprecise end-point payload motion, the equation of payload© ISAROB 2013

(5)

θ&& =

lD &
θ
m

1
g
K
D 1
x& l − θ −  + θ& − u
lT
l
m
T
lT



(6)

(7)

Since the design of proposed controller must required
the state space representation of the system, equation (6)and (7) are arranged into state space form as shown in (8):x& = Ax + Bu

y = Cx + Du

347

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

0   A
0  =  − C
  

Where

0

0
A=
0

0


1

0

0

−g

0
1
lT

0
g
−
l

0

 0 
lD 


−
0 
m  B=

 0 
1
 K
D 1
− − 
− 
 lT m T

C = [1 0 0 0] D = [0][with state variable x = xl

(11)0 = r − Cx (∞ )By subtracting (10) to (11), the state space form isconverted to~~~x&e (t ) = A~xe (t ) + B2u~ (t )~~e (t ) = C ~x (t )]Tx&l θ θ& .

where
~  A
A=
− C

3 DESIGN OF H∞ CONTROLLER WITH LMI REGION

-

∫

G

+
+

u(t)

B

+
+

x& (t )u~ (t ) = F~x (t ) + G ~v ( t ) = K~
x e (t )∫~~~x&e (t ) = Acl ~xe (t ) + B1w~~~~
y (t ) = C ~x (t ) + D w + D u

C

1 e

Fig. 2. Block diagram of integral state feedback control.

function from w to y under state feedback control
u = Kx . Then, for a prescribed closed loop H∞ performance
γ > 0 , our constrained H∞ problem consists of finding astate feedback gain K that fulfil the following objectives:(1) The closed loop poles are required to lie in some LMI
stability region D contained in the left-half plane

The main objective of the proposed controller is to find thegain parameter matrix, F and G such that it fulfills thedesign requirement. From the block diagram of Figure 2,
the control input of the system is derived as follow(9)(2) Guarantees the H∞ performance G yw

∞

<γ

t

∫ e(τ )dτ and e(t ) = r − y (t )0[

Quote from the definition in [13], a subset D of the complex
plane is called an LMI region if there exist a symmetricmatrix α ∈ R m×m and a matrix β ∈ R m×m such that]

T

Using new state variable xe = x v and equation (9) the
representation of state space equation can be rewrite as x& (t )   A
 v&(t )  =  − C

 

T

D = {z ∈ C : f D ( z ) < 0}0  x (t )   B 0u (t ) +   r+
0  v(t )   0 
1 

(15)

where
(10)

f D ( z ) := α + zβ + z β Te(t ) = r − Cx (t )Then, pole location in a given LMI region can becharacterized in terms of the m × m block matrix

Next, at the steady state condition as t → ∞ , the state
space equation can be written in the following form

© ISAROB 2013

12

~
~ ~
~
~
Acl = ( A + B2 K ) , B1 = [0 0 0 0 − 1] , D11 = 1 ,~
D12 = 0and w is exogenous input disturbance or reference input
to the system. Let G yw (s ) denote the closed loop transfer

F

where v (t ) =

11

(14)

where

A

u (t ) = Fx (t ) + Gv (t )(13)

Finally, a closed loop state space equation with controller
gain, K can be obtained below

y (t )x (t )~

 x   x − x (∞ ) ~  B
0
xe =  ~  = , B2 =   , ~0 v   v − v (∞ ) 0~
C1 = [− C 0] , e~ (t ) = e − e(∞)

Then, the new control input function is described as follow

In this study, an integral state feedback control is used as aplatform to design the proposed controller. The block
diagram of integral state feedback control is shown in Fig.
2.
v(t)

(12)

1 e

Using simple empirical method in system identification
process, the parameters value of crane system are defined
as m = 0.732 kg, g = 9.8067 m/s2, T = 0.0999 s, K = 0.3175
m/s2V, D = 0.0168 and l = 0.4335 m.

r + e(t)0   x (∞ )   B 0+   u (∞ ) +   r0  v (∞ )   0 
1 

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

(

)

T
~
~
~
M D ( Acl , X D ) := α ⊗ X D + β ⊗ ( Acl X D ) + β T ⊗ Acl X D (16)frequency of oscillatory modes. As the transient response
profiles varied when the parameter of region is changed, it
is suitable to introduce the 3D graphical profiles of LMI
region performance. Consider λ is fixed, thisinvestigations only focused on the variation of parameterr and θ . Then, these parameters are chosen based ondesired underactuated crane performance from thegraphical profile.
Meanwhile, the H∞ constraint is equivalent to theexistence of a solution X ∞ > 0 to the LMI~
Quote from the theorem in [10], the matrix Acl is D-stable
if and only if there exists a symmetric matrix X such that~
M D ( Acl , X D ) < 0, X D > 0(17)

In this study, the region S (λ , r ,θ ) of complex numbers
x + jy such thatx < −λ < 0,x + jy < r ,tan θ x < − y

~
~ T
A
 cl X ∞~+ X ∞ Acl

C1 X ∞

~

B1T


(18)

as shown in Fig. 3 is considered.

~
X ∞ C1T
− γI
~ T
D11

~
B1 
~ 
D11  < 0
− γI 
(22)

Equation (22) is also known as the Bounded Real Lemma[18]. As mentioned before, the main objective of this study
is to minimize the H∞ norm of G yw (s ) over all stateθ

feedback gains K that enforce the pole constraints. However,this problem is not jointly convex in the variables X D1 ,r

X D2 , X D3 , X ∞ and K. The convexity can be enforced
by seeking a common solution
λX = X D1 = X D2 = X D3 = X ∞ > 0

to (19)-(22) and rewriting these equations using theauxiliary variable Y = KX . These changes of variables
lead to the suboptimal LMI approach to H∞ synthesis withpole assignment in LMI regions. As a result, the new
representations of (19)-(22) are shown in the following
equation.

Fig. 3. Region S (λ , r ,θ )The advantages of placing the closed loop poles to thisregion are the cart position response ensures a minimum
decay rate λ , a minimum damping ratio ζ = cos θ , and amaximum undamped natural frequency ωd = r sin θ [13].
Equation (19), (20) and (21) show the clustering regionused in this study which are λ –stability region, a disk andthe conic sector respectively.

~~~
M D1 ( Acl , X D1 ) := Acl X D1 + X D1 Acl T + 2λX D1 < 0 − rX D2~
M D2 ( Acl , X D2 ) := ~ T
X A D2 cl~Acl X D2 <0− rX D2 ~~
Herm[ A X + B 2 Y ] + 2λX < 0~~ − rX A
X + B2Y <0 ∗− rX 


(19)

(24)
(25)

~
~
~
~
 sinθ (Herm[ AX + B2Y ]) cosθ (Herm[ AX − B2Y ])
< 0 (26)~~∗sinθ (Herm[ AX + B2Y ]) 


(20)

~
~
~
 Herm[ A
X + B2Y ] XC1T


∗
− γI


∗
∗


~
MD3( Acl , X D3 ) :=
~
~
~
~
T
T 
 sinθ ( A(21)
cl X D3 + Acl X D3 ) cosθ ( Acl X D3 − Acl X D3 ) 

<0
~
~
~
~
 cosθ ( X A T − A X ) sinθ ( A X + A X T ) 
D3 cl
cl D3
cl D3
cl D3 


~
B1 
~
D11  < 0
− γI 


(27)

~
~
~
~
~
~
where Herm[ A X + B2Y ] = A X + B2Y + XAT + YB2T and ∗is an ellipsis for terms induced by symmetry [17]. In thisstudy, the entire LMI problem is solved using well known
LMI optimization software which is LMI Control Toolbox.
Next, to verify the effectiveness of graphical
LMI region profile, several specifications have been set up:• Settling time of 2.5 s with overshoot less than 1%
and zero steady state error for the cart movement;

where this region is the intersection of three elementary~
LMI regions ( M D1 ∩ D2 ∩ D3 ( Acl , X D ) ).
Motivated from the previous work, this study presents
an effective way to determine the parameter of region
S (λ , r , θ ) which in turn bounds the desired maximum
overshoot, rise time, settling time, control input and© ISAROB 2013

(23)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

• Sway oscillation is less than ±0.05 rad;• Control input does not exceed 1 V;
In other words, the designed state feedback gain K also
must fulfil the mentioned specifications. Computing (24)(27) using LMI Control Toolbox, the graphical profiles of
the underactuated crane system performance are depicted in
APPENDIX A. In this case, λ is fixed at -3 while theconic sector and disk are varies.

0.4

x(t)

0.3
Simulation
Experiment

0.2
0.1
0

0

1

2

3

4

5

6

7

8

9

10

0.05

0(t)

Simulation
Experiment

4 SIMULATION AND EXPERIMENTAL
VALIDATION

0

-0.05

Applying the LMI conditions in (24)-(27) with theadvantage of graphical profiles, the parameter of conic
sectors and disk that fulfil the design requirement is atr = 4 and θ = 28 o . Then, the state feedback gain, K is
obtained as followed:

0

1

2

3

4

5

6

7

8

9

10

Simulation
Experiment

0.8

u(t)

0.6
0.4
0.2
0
-0.2

0

1

2

3

4

5
Time (s)

6

7

8

9

10

K = [-11.2759 - 2.9956 13.6133 0.9993 8.42773]

Fig. 5. Response of the underactuated crane systemwith γ = 64.9073 . This state feedback gain also guarantees
the H∞ performance G yw

∞

<γ .

5 CONCLUSION

Fig. 4 shows that theThis study has introduced new graphical LMI region
profile which gives great flexibility in choosing specificparameter of pole placement constraint. The usefulness of
this profile has been demonstrated to underactuated cranesystem using H∞ synthesis with closed loop pole clustering
constraint. This LMI approach has been implemented in the
LMI Control Toolbox and validated using INTECO 3D
Crane experimental rig with satisfactory results.

location of poles has been confined in the selected LMI
region. The simulation and experimental validation
response of cart position, sway angle and control input are
depicted in Fig. 5. Close agreement between simulation andexperimental results has been successfully achieved. Note
that, the cart position response track the desired response of
0.3 m with zero steady state error and settling time of 2.5 swith minimal overshoot. The maximum oscillation of swayangle also has been reduced to ±0.05 rad. Finally, thecontrol input of less than 1 V, has been successfully
obtained.

REFERENCES[1]

LMI Region

[2]

4
3

[3]

Imaginary Axis

2

[4]

1
0

[5]

-1

[6]

-2
-3
-4
-4.5

[7]
-4

-3.5

-3

-2.5

Real axis

Fig. 4. Location of poles in selected LMI region

[8]

[9]

© ISAROB 2013

350

G.A. Manson, “Time-Optimal Control of and Overhead Crane
Model,” Optimal Control Applications & Methods, 3(2), 1992, pp.
115-120.
J. Auernig and H. Troger, “Time Optimal Control of Overhead
Cranes with Hoisting of the Load,” Automatica, 23(4), 1987, pp.
437-447.
H.M. Omar, Control of Gantry and Tower Cranes, M.S. Thesis,
Virginia Tech., 2003, Blacksburg, VA.
H.H. Lee and S.K. Cho, “A New fuzzy logic anti-swing control forindustrial three-dimensional overhead crane,” Proceedings of the2001 IEEE International Conference on Robotic and Automation,
2001, Seoul, pp. 2958-2961.
M. Orbisaglia, G. Orlando, and S. Longhi, “A comparative analysis
of sliding mode controllers for overhead cranes,” in Proc. 16th Med.
Conf. Control Autom., 2008, pp. 670–675.
M.A. Ahmad, A.N.K. Nasir and H. Ishak, “Techniques of anti-sway
and input tracking control of a gantry crane system,” IEEE
International Conference on Mechatronics and Automation, pp. 262267, 2009.
M.A. Ahmad, R.M.T. Raja Ismail, M.S. Ramli, “Hybrid input
shaping and non-collocated PID control of a gantry crane system:
Comparative Assessment,” in Proc. IEEE/ASME Conf. Adv. Intell.
Mechatron., 2009, pp. 1792-1797.
M.A. Ahmad, M.S. Ramli, R.M.T. Raja Ismail, A.N.K. Nasir and
M.A. Zawawi, “The investigations of PD-type fuzzy logic withdifferent polarities input shaping for anti-sway control of a gantry
crane system,” in Proc. of Conference Innovative Technologies inIntelligent Systems and Industrial Applications., 2009, pp. 452–457.
M.A. Ahmad, M.S. Saealal, R.M.T. Raja Ismail, M.A. Zawawi,
A.N.K. Nasir and M.S. Ramli, “Single input fuzzy controller withcommand shaping schemes for double-pendulum-type overhead

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

crane,” AIP conference proceedings, vol. 1337, no. 1, pp. 113–117,
2011.
[10] M.A. Ahmad, R.M.T. Raja Ismail, M.S. Ramli and N. Hambali,“Comparative assessment of feed-forward schemes with NCTF forsway and trajectory control of a DPTOC,” International Conferenceon Intelligent and Advanced Systems, 2010, pp. 1-6.
[11] M.A. Ahmad, R.M.T. Raja Ismail, M.S. Ramli and N. Hambali,“Investigations of NCTF with input shaping for sway control of adouble-pendulum-type overhead crane,” Proc. of 2nd IEEE
International Conference on Advanced Computer Control, 2010, pp.
456-461.
[12] M.A. Ahmad, R.M.T. Raja Ismail, M.S. Ramli and N. Hambali,“Analysis of IIR filter with NCTF-PI control for sway and trajectory
motion of a DPTOC System,” International Conference on
Electronics Devices, Systems and Applications, 2010, pp. 54-58.
[13] M. Chilali, P. Gahinet, “H∞ design with pole placement constraints:
an LMI approach”, IEEE Transactions on Automatic Control, Vol.
41(3), pp. 358-367, 1996.
[14] P. Gahinet and P. Apkarian, “A linear matrix inequality approach to
H∞ control,” Int. J. Robust Nonlinear Contr., vol. 4, pp. 421-448,
1994; also in Proc. CDC, 1993, pp. 656-661.
[15] T. Iwasaki and R. Skelton, “All controllers for the general H∞ control
problem: LMI existence conditions and state-space formulas,”Automatica, vol. 30, pp. 1307-1317, 1994.
[16] A. Packard, “Gain scheduling via linear fractional transformations,”
Syst.Contr. Lett., vol. 22, pp. 79-92, 1994.
[17] M. Chilali, P. Gahinet, P. Apkarian, “Robust pole placement in LMI
regions”, IEEE Transactions on Automatic Control, Vol. 44(12), pp.
2257-2270, 1999.
[18] S. Boyd, L. EL Ghaoui, E. Feron and V. Balakrishnan, “Linear
Matrix Inequalities in System Control Theory, Vol. 15 of Studies inApplied Mathematics, SIAM, Philadelphia, PA, June 1994.

2
1.8

Rise time (s)

1.6
1.4
1.2
1
0.8
0.6
0.4
3

25

35

5

45

7

55

9

11

Radius, r

13

75

65

Angle, 0Fig. 7 Rise time profile

10

Overshoot (%)

8

6
4
2

APPENDIX A
0
13
11

75
9

65

2.5

55

7

45

5

Radius, r

35
3Angle, 025

Settling time (s)2Fig. 8 Overshoot profile

1.5

1
0.5

0.5

0
3

5

7

9

Radius, r

11

13

75

65

55

45

35

Maximum sway (rad)

0.4

25

Angle, 0

0.3

0.2

0.1

Fig. 6 Settling time profile

0
13
11

75
9

65
55

7
Radius, r

45

5

35
3

25

Angle, 0Fig. 9 Maximum sway profile© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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Optimized Walking Control of a Biped Walking Robot
Considering Theory of a Pendulum
Hisashi Koike, Jae Hoon Lee1, and Shingo Okamoto
Ehime University, Japan(Tel: 81-089-927-9740, Fax: 81-089-927-9740)
1

jhlee@ehime-u.ac.jp

Abstract: A motion control based on the theory of passive walking has been investigated in this paper. It has been known thatgeneral passive walking robot can walk efficiently on the slope by using potential energy with no actuator. However, it cannot
walk on the horizontal ground. In order to utilize the merit of energy efficiency, two motors were installed at the hip joints ofthe passive-walking-type robot, which generate required torque for walking on the horizontal ground. The proportional control
algorithm was applied for successive walking. Computer simulations with its dynamic model were carried out to find out the
optimal condition for walking motion. Based on the simulation results, experimental robot was developed. As a result, thecapability of walking on the horizontal ground was confirmed through experimental works with the proposed method and thedeveloped robot.
Keywords: Biped walking robot, Passive walking, Walking control.
swing, the robot can walk successively. For generating
torque for walking motion, two motors are installed at the
hip joints. Where  1 and  2 denote the control torquebetween the stance-leg and the torso, and the torque theswing-leg and the torso, respectively.

1 INTRODUCTION
Recent biped robot shows advanced performance intheir motion, especially in walking. However, it has been
known that the energy efficiency of typical biped robots inwalking motion is worse than human [1]. Therefore,
walking efficiency has been considered as an important
issue for innovation of humanoid robot. Passive walking
robot has been investigated as a model to understand andsolve the problem [2]. The passive walking robot can walkefficiently on the slope by using their passive legs whichrotate like as pendulums. However, it can’t walk on thehorizontal ground because it has no energy source to moveits leg forward.
This research aims to develop the biped walking robotthat can walk on the horizontal ground. Besides, to improve
walking efficiency, the theory of passive walking is also
considered. For that, the minimum number of actuators, i.e.
two motors, were installed at the hip joints of the robot,
which generate the control torques for walking motion. In
the previous researches [3,4], the computer simulations
with dynamic model were carried out to investigate thewalking capability of the system. In this research, theexperimental system with embedded controller has beendeveloped. The system with the proposed method wasinvestigated through experimental works.

Fig. 1. Analysis model of the walking robot with motors [4]2.2 Equation of motion
The motion equation of the robot is given byM θ θ  C θ, θ  C θ  g θ   B τ  

2 COMPUTER SIMULATIONThe computer simulations with the dynamic model ofthe biped robot with a torso were performed [4].

2.3 Exchange between stance leg and swing leg
The condition of the touchdown of the swing-leg isgiven by(2)
1   2   / 2 .
The contact between the swing-leg and the ground when
the swing-leg passes through the stance-leg was ignored.

2.1 Analysis model
Figure 1 shows the analysis model of the walking robot.
The robot consists of a torso, a hip and two legs with feet.
By alternating the state of both legs between stance and© ISAROB 2013



(1)
c
d
T
where, θ  1  2  3  , τ   1  2  , M is theinertia matrix, C c is a Coriolis and centrifugal force
matrix, C d is a viscosity damping matrix, B is acoefficient matrix about control torque, and g is thegravity vector, respectively.

352

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Then the impact at the touchdown was assumed to beinelastic. It was also assumed that the stance-leg leaves
from the ground at the moment of touchdown.
The angular velocity of the joints after touchdown iscomputed as follows. The angular momentum is conserved
before and after touchdown for the whole robot about theleading contact point, the trailing leg about the hip and thetorso about the hip. Equation (3) is given by these
conservation laws of angular momentum:(3)Q  θ  θ   Q  θ  θ  .
The superscripts “-” and “+” denote the state before andafter touchdown, respectively. The relation of the angles
before and after touchdown is given by(4)θ   R θ   θ0 ,where
0 1 0 (5)R  1 0 0 ,0 0 1 

2.4 Motion control
The proportional (P) and proportional-differential (PD)control law have been employed. In the case of proportional
control, the control torques  1 and  2 are given by Eq.
(8) and (9).
(8)
  1   2  k3p  3d   3  ,(9)
 2  k 2p 12d  12  ,
where
(10)
12  1   2 ,(11)
12d   / 2 .
The k 2p and k 3p denotes the control gains. The control
torque  1 is given by(12) 1  k 2p 12d  12   k 3p  3d   3  .
The block diagram of active control for calculation isshown in Fig. 2, where the proportional control is used.

 

2.5 Result of computer simulation
The optimal control gains have been found out throughsimulations with various conditions. Figure 3 shows therelation between  3d and the number of successive
walking motion. An example of the calculated results isgiven in Fig. 4.

(6)
θ0    / 2  / 2 0 ,Resultantly, the angular velocity of all joints after
touchdown is calculated by1(7)θ   Q  θ  Q  θ  θ  .
T

 

 

Fig. 2. Block diagram of active control for calculations [4]Fig. 5. Experimental robot

3 DEVELOPMENT OF EXPERIMENTAL
ROBOT
3.1 Composition of the robot
Figure 5 shows the experimental robot which wasdeveloped based on the result of computer simulation. The
length of each leg and the torso are 310 [mm] and 140[mm], respectively. The total mass of whole system is 0.65[kg]. In order to constrain the walking motion in the verticalplanar space, each leg consists of two parallel links. So theinside legs and the outside legs are connected with eachother by additional frames, respectively. The control
torques are generated by the motors installed at the hip
joints. The encoders are installed at the motors to measure
the angle of each hip joint. The attitude angle of the torso is

Fig. 3. Relation between control gains andthe number of walking [4]Fig. 4. Walking motion using PD control [4]© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

measured by the attitude sensor (HiBot Co., Japan) attachedon the torso. Two micro controllers were employed formotion control and computation of sensor information.
Their photographs are given in Fig. 6. Two touch sensors
were attached on both, i.e. inside and outside, the feet todetect the contact between the feet and the ground. Figure 7
shows the touch sensor attached on the foot.

controller and the attitude sensor that measures theacceleration and angular velocity [5]. This angle is sent tomain controller and used for motion control. From theencoders of motors, relative angles between the torso andeach leg are measured. The absolute angle of each leg, 1
and  2 , are computed with the attitude angle of the torso
and two encoder values, which is used in the feedback
control for walking. While the robot moves, all sensor
information is sent to monitoring computer in real time.
The sampling time of the motor control is set to 1 [ms]. The
frequency of communication between main controller andthe monitoring computer is set to 100 [Hz].

Sub controller
Main controller

Attitude sensor

Fig. 6. Controller and attitude sensor

Inside leg

Outside leg(a)Inside leg is (b)Both legs are (c)Outside leg is (d)Both legs are
stance leg
stance leg
stance leg
stance leg(θ1>0)(θ1<0)

Fig. 9. The states of the experimental robot
Fig. 7. Touch sensor on the foot

4 WALKING EXPERIMENT
The developed robot system with the proposed method
has been confirmed through experiments of walking on thehorizontal ground.
4.1 Motion control for walking experiment
In simulation, the stance-leg was assumed to leave fromthe ground at the moment of the touchdown. However, in
the actual walking motion, there exists the period that bothlegs are in stance state. Therefore, motion control method
for the real robot needs to be modified as follows. Walking
motion of the real robot is divided into four states according
to the values of the touch sensors and the angle of the legs
as shown in Fig. 9. The states are as follows.
(a) The inside leg is stance leg.
(b) The outside leg is stance leg.
(c) The both legs are stance legs and 1 is positive.
(d) The both legs are stance legs and 1 is negative.
Therefore, different control methods are applied according
to the states as follows.
In the state (a) or (b), the control torques  1 and  2
are given as follows(13) 1  k 3p  3d  3  ,p(14)
 2  k 2  2d  2  .
Where k 2p and k 3p denote the control gains. The  2 dand  3d denote the desired angles.
In the state (c) or (d), the control torques  1 and  2
are given as follows(15)
 1  k 2p 1d  1  ,p(16)
 2  k 3  3d  3  .
The 1d denotes the desired angle.

Fig. 8. System configuration of the experimental robot
3.2 System configuration
Figure 6 shows the system configuration of theexperimental robot. The robot has two embedded
controllers. The main controller is for motor control, andthe other, sub controller, is for the attitude sensor. They areconnected by CAN communication channel with each other.
Besides, the information about motion and sensors istransferred from the main controller to a monitoring
computer outside the robot through serial communication
channel.
State of the robot is recognized by values of two touch
sensors. The angle of the torso,  3 , is achieved by the sub© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The control torques are generated at the motors by the
PWM (Pulse Width Modulation) signals from the main
controller.
4.2 Experimental conditions
The initial posture and velocity is required for the robot
when it starts its motion. In order to endow the robot withthe same condition through the experiments, a special framewith pushing device was prepared. Besides, the contact
between the swing-leg and the ground is occurred when theswing-leg passes through the angle of stance-leg because
the length of both legs is same. To solve the problem, theblocks for feet are set on the ground as shown in Fig. 10.
Fig. 11. The change of the joint angles and the values oftouch sensors

4.3 Experimental result
The appropriate control gains had been found throughexperiments with the manner of trial and error. Resultantly,
it has been observed that the robot can walk on thehorizontal ground with the proposed method. The
successive scene of the walking motion is shown in Fig. 10.
It was captured with a high speed camera (CASIO: EXFH25). The change of the angles 1 ,  2 ,  3 and thevalues of touch sensors are given in Fig. 11.

5 CONCLUSION
The motion control based on passive walking theory
had been investigated for biped walking on the horizontal
ground. Considering the simulation results, the real robot
system was developed. From experiments, it was found that
there exists considerable difference between the ideal
touchdown and that of real robot. Moreover, it adversely
affects proper interchange between legs during touchdown.
To cope with the problem, the walking motion was divided
into four states. And the appropriate control algorithm foreach state was applied in this paper. Resultantly, it was
confirmed that the robot can walk on the horizontal groundwith the proposed method.
The proposal of control algorithm for more stable
walking and that for biped robot with knee joint will be
carried out as future works.

Pushing device(a) t=0.0[s](b) t=1.50[s](c) t=1.60[s](d) t=1.69[s]REFERENCES(e) t=1.75[s](f) t=1.81[s](g) t=2.00[s][1] Ono K and Liu R (2002), Optimal Biped Walking
Locomotion Solved by Trajectory Planning Method.
Journal of Dynamic Systems 124:554–565[2] McGeer T (1990), Passive Dynamic Walking. The
International Journal of Robotics Research 9:2:62-82[3] Narukawa T, Takahashi M and Yoshida K (2010),
Efficient waling with optimization for a planar bipedwalker with a torso by hip actuators and springs. Robotica
29:641–648[4] Tani K, Okamoto S, Lee JH and Koike H (2012),
Simulations on Motion Control and Development of Biped
Walking Robot. Proceeding of the International
MultiConference of Engineers and Computer Scientists
2012 2:916-919[5] Ickowzcy E (2011), Design and Control of a ThreeWheeled Personal Vehicle with Active Suspension for
Rough-Terrain Applications.

(h) t=2.10[s](i) t=2.19[s] (j) t=2.25[s] (k) t=2.31[s] (l) t=2.56[s]( 1d  12[deg],  2d  78[deg],  3d  5[deg],
k 2p  0.20 k3p  0.30 )Fig. 10. Walking motion of experiment© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Sliding mode control of a quad rotor helicopter using
nonlinear sliding surface
Bambang Sumantri1,2, Naoki Uchiyama1, Shigenori Sano1, and Yuma Kawabata1
1

Toyohashi University of Technology, Japan
Electronic Engineering Polytechnic Institute of Surabaya, Indonesia
1bambang@eepis-its.edu

2

Abstract: In this paper, a sliding mode controller (SMC) based on a nonlinear sliding surface (NSS) is designed for controllinga quad rotor helicopter (quadrocopter). It is a well-known that a low overshoot can be achieved with a cost of longer settling
time, although a shorter settling time is needed for quick response in a quadrocopter system. In the conventional SMC, thesliding surface is designed as a linear surface that provides a constant damping ratio. The value of damping ratio should be
adjusted in order to obtain an optimal performance by making a tradeoff between the two criteria; overshoot and settling time.
In this paper, an NSS is designed so that the damping ratio of the control system can be varied from its initial low value to a
final high value in a finite time. A low value of damping ratio will cause a quick response, and the later high damping ratio will
avoid overshoot, and therefore the control performance can be optimized. First, a dynamics model of a quadrocopter ispresented. Next, an SMC with an NSS is designed for tracking control of a quadrocopter. The stability of the proposed control
system is proved based on the Lyapunov stability theory. The effectiveness of the proposed design is verified by simulation inwhich comparative results with the conventional linear sliding surface (LSS) is shown. The NSS is more effective compared tothe conventional LSS when the disturbances exist.
Keywords: quad rotor helicopter, sliding mode control, nonlinear sliding surface, tracking control.
[4][5]. A nested saturation approach was designed based ona priori input bounds of a quadrocopter [6]. This control
strategy was designed for stabilizing the attitude of aquadrocopter. Cruz et al. used a nested saturation strategy
for stabilizing the quadrocopter in a given trajectory [7].
In a real application, a control system of a quadrocopter
must be robust to uncertain disturbance such as wind
gusting. The SMC is well known as a robust control
strategy, and has been applied to a quadrocopter. Yokoyama
et al. designed a velocity tracking control by using an SMC
together with a backstepping method [8]. The robustness ofthe controller was proved by simulation under a gust ofwind. Problems of unmatched disturbance and chattering
have been concerned in developing an SMC. Vega et al.
designed a robust SMC by designing a disturbance
estimator [9]. They used a first-order differentiator called asuper-twisting algorithm for estimating aerodynamic forces.
As a high speed dynamic system in an uncertain
environment, a good control strategy of quadrocopter
should be designed to settle its body quickly without any
overshoot or oscillation under a disturbance. It is wellknown that a low overshoot can be achieved with a cost of
longer settling time, although a shorter settling time isneeded for quick response. Hence, the value of damping
ratio should be adjusted in order to obtain an optimal
performance by making a tradeoff between the two criteria;
overshoot and settling time. In this paper, an NSS isdesigned so that the damping ratio will be changed from its

1 INTRODUCTION
Quadrocopter is a class of vertical takeoff landing
unmanned aerial vehicle that uses two pairs of contrarotating rotors to provide lift and directional motion.
Quadrocopter has some advantages compared toconventional helicopter due to its compactness and simple
mechanical structure with high maneuverability. The
maneuver is provided by just varying the motors’ speed. Inorder to make the quadrocopter fully autonomous androbustly stable to disturbances are still challenging
problems because of its high nonlinearity.
Nonlinear control strategies of a quadrocopter havebeen concerned by many researchers. Mokhtari et al.
designed a feedback linearization controller combined withan observer to reduce sensors [1]. They applied feedback
linearization to control a partial dynamic system based on
rotational motion and an observer to construct translationalmotion information. Guisser et al. proposed an input-output
feedback linearization algorithm for stabilizing aquadrocopter and tracking a given trajectory in discrete
time [2]. Mian and W. Daobo employed feedback
linearization coupled with a PD controller to control
translation motion and a backstepping-based PID nonlinear
controller for rotational motion [3].
Saturation nonlinearity is also concerned by someresearchers. Kendoul et al. and Castilo et al. proposed anested saturation strategy for stabilizing a quadrocopter© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where
∈ ℝ3x3 ; ∈ ℝ3x3 ; ∑∈ ℝ3 and ∑∈ ℝ3
are the mass matrix, the inertia matrix, the vector of total
external forces, and the vector of total external torques,
respectively. Eq. (3) can be written as:

initial low value to a final high value at a finite time. ThisNSS is designed based on the algorithm presented in [10].
An SMC with an NSS is employed for stable tracking
control of a quadrocopter. The performance and robustnessof the proposed controller is proved by simulation. A
comparison with a conventional SMC is presented.

𝜙 𝜃 𝜓
𝜃 𝜓
𝑥̈
𝜃 𝜓]
] [𝑦̈ ] = [ 𝜙 𝜃 𝜓
𝜙 𝜃
𝑧̈

[

[

]

(5)

2 MODELING OF QUADCOPTER
where , and= 𝑓 𝑓2 𝑓3 𝑓4 are the total massof quadrocopter, the gravitational acceleration and theresulting thrust of the four rotors, 𝑓 , 𝑓2 , 𝑓3 and 𝑓4 ,respectively. Eq. (4) is the rotational dynamics of thequadrocopter. Substituting Eq. (2) into Eq. (4), we have( ̇ 𝛩̇

𝛩̈) =

𝛩̇

𝛩̇

∑

𝑇

[ 2,

(6)

Eq. (6) leads to𝑇[𝜙̈, 𝜃̈ , 𝜓̈] = [𝜃Fig. 1. Coordinate frame of quadrocopter=[

The position of a quadrocopter, 𝑋0 = [𝑥, 𝑦, 𝑧]𝑇 , is acoordinate position of the centre of gravity of thequadrocopter (“B”) with respect to the earth frame (“O”)
as shown in Fig. 1. Its attitude is represented by three
angles, 𝛩 = [𝜙, 𝜃, 𝜓]𝑇 , which are roll, pitch and yaw,
respectively. The coordinate position and attitude of thequadrocopter are described in the earth frame, although
they are measured in the body frame by using sensors
attached to the body of the quadrocopter. Denoting thelinear and angular velocities in the earth frame by𝑋̇0 ∈ ℝ3 and 𝛩̇ ∈ ℝ3 , respectively, and those in the body
frame by 𝑉 ∈ ℝ3 and 𝜔 ∈ ℝ3 , respectively, we have thefollowing relation:
𝑋̇0 = 𝑉

𝜃
=(
(
2 = (

(
3 = (

(

𝜙 𝜃 𝜓
𝜃 𝜓
𝜃 𝜓
=[ 𝜙 𝜃 𝜓
𝜙 𝜃
𝜃
𝜙 𝜃
𝜙 ]
=[
𝜃
𝜙 𝜃

𝜙 𝜓
𝜙 𝜓
𝜙

[

𝜙 𝜃 𝜓
𝜃 𝜓
𝜙 𝜃 𝜓
𝜃 𝜓 ];𝜙 𝜃© ISAROB 2013

𝜔

3,

𝜙 𝜃
𝜙 ];
𝜙 𝜃
)𝜙̇𝜃̇ 𝜃 ()𝜃̇ 𝜓̇ 𝜙 𝜃 (() 𝜃)𝜙̇𝜓̇ 𝜙2
̇)(𝜙𝜓̇ 2 2 𝜙) 𝜃 𝜃;̇)𝜙𝜃̇ 𝜃 ()𝜃̇ 𝜓̇ 𝜙 𝜃 (

4]

𝑇

(7)

)𝜙̇𝜓̇ 𝜙 𝜃)𝜓̇ 2 𝜙 𝜙 𝜃;)𝜙̇𝜓̇ 𝜙 𝜃)𝜓̇ 2 𝜙 𝜙 𝜃;

3x3

𝑥̈
𝑦̈
𝑧̈
3x3
] 𝜙̈
𝜃̈
[𝜓̈]

( 𝜙 𝜃 𝜓𝜃 𝜓)
( 𝜙 𝜃 𝜓
𝜃 𝜓)
𝜙
𝜃
=

(8)

2

[

2

3]

[

3
4

]

For notational simplification, Eq. (8) is written as𝑋̈

where and denote sinus and cosines, respectively.
The dynamics of the quadrocopter can be derived by the
Newton’s second law as follows:𝑋̈0 = ∑
𝜔̇ = 𝜔

3]2,

where ,
andare the moment of inertia of thequadrocopter about 𝑥, 𝑦 and 𝑧 axes, and 2 = (𝑓4 𝑓2 ) ,𝑓3 )and𝑓2 𝑓3 𝑓4 )are
3 = (𝑓
4 = ( 𝑓torques resulting from the rotors that cause roll, pitch andyaw motion, respectively.
is the distance of rotor to thecentre of gravity,
is the anti-torque coefficient. From
Eqs. (5) and (7), the dynamics model is written as

(1)
(2)

𝛩̇ = 𝜔

,

∑

=

(9)

3 NONLINEAR SLIDING MODE CONTROLTo design an NSS, the original dynamics model in Eq.
(9) is transformed into a regular form as follows:

(3)
(4)

357

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013𝑧̇ ( ) =𝑧̇2 ( ) =𝑧 ( )2 𝑧2 ( )2 𝑧 ( )

22 𝑧2 ( )

2

( )=

( )

𝑦

𝑧

𝑦̇

𝑧̇

𝜙
𝜙̇

𝜃
𝜃̇

𝜓]𝑇
𝑇
𝜓̇]

𝑉̇ =

(12)

where ( ) =() as a synthetic input. Withx
regard to the matrices in Eq. (10),= 2 = 22x
are null matrices, andare the identity
2 = 2 =
matrices.

̈

2]

𝑇

(13)

where
and 2 are tracking error vectors of 𝑧 and 𝑧2
to desired trajectories 𝑧
and 𝑧2 . The matrix=
diag( 𝑖 ), 𝑖 = , , . . , , consists of nonlinear functions asfollows:x ( ‖ ‖)𝑖 =x (‖ ‖)

,

𝑖 ∈

(14)

where 𝛽𝑖 > and 𝑘 > . Next, the control input thatconsiders the system stability will be designed based on the
Lyapunov stability theory.

𝑇

𝑇

(

) ̇

̇2 )

(

)

̇

ign( )

𝜙 = a an (𝑥̈in(𝜓 )
𝑧̈

(𝜓 )

𝑦̈

) ̇

( )

Designing the control input( ) as© ISAROB 2013

.

(17)
𝑥̈

(𝜓 )

𝑦̈

in(𝜓 )in(𝜓 )

𝑦̈(𝜓 ))The control parameters are as follows:

(

2

.

)

𝑉̇ =

̇

(21)

g. 2 ;= .

;𝜃 = a an(=3

= . 6x; = .

2

By differentiating Eq. (17) and substituting it into Eq. (16)together with dynamics in Eq. (12), the first derivative of
the Lyapunov function can be written as
𝑇(20)

The desired trajectories for [ 𝑥, 𝑦, 𝑧, 𝜓]𝑇 are given as[𝑥 , 𝑦 , 𝑧 , 𝜓 ]𝑇 , and from Eq. (5), desired angles [𝜙 , 𝜃 ]𝑇for [𝜙, 𝜃]𝑇 can be calculated as follows:

(16)

𝑧
𝑧2

ign( )) ̇

The tracking errors are=𝑧
2 = 𝑧2

(

= .
g;
=
2
= . 6x
g.

(15)

̇

(

𝑇

The parameters used in this simulation as follows arethose for the experimental system in our laboratory.

Differentiating Eq. (15) and considering Eq. (13), we have
𝑉̇ =

(19)

4 SIMULATION RESULTS

3.2 Stability analysis
Let us define a candidate of the Lyapunov function as
𝑉= .

𝑧̇2

Because∈ ℝ6x6 and∈ ℝ6x6 are positive definite, thedamping ratio of the closed loop dynamics will change
according to the magnitude of . If the error is relatively
large, then the damping ratio will be reduced, and therefore
the system will give faster response toward sliding surface.
If the system is close to the sliding surface then the error
will be reduced, and the damping ratio will be increased,
and therefore the response will be slower and overshoot canbe reduced. Next, the effectiveness of the SMC using the
NSS in Eq. (19) will be verified by simulation andcompared with a conventional linear sliding surface.

3.1. Nonlinear sliding surface design
The sliding surface is designed as a nonlinear function.
][

̇ign( )

If the minimum eigenvalue ofis greater than the normofand ∈ ℝ x is positive definite, then 𝑉̇ < , andhence the control input in Eq. (19) will stabilize the system
in Eq. (12).
By substituting the control input in Eq. (19) into Eq.
(12) and consideringas the unknown external
disturbance, the closed loop dynamics of the system is(11)𝑧̇ = 𝑧2𝑧̇2 = ( )

=[

) ̇

where
∈ ℝ x is a positive definite matrix, andsubstituting Eq. (19) into Eq. (18), we havewhere
= [𝑧 , 𝑧2 ]𝑇 = 𝑋 , 𝑧ℝ , 𝑧2 ℝ ,is amatrix transforming 𝑋 into , 2 ℝ x , is the numberof the inputs, andℝ is an estimated disturbance
vector. By introducing the disturbance term, Eq. (9) leads to𝑧 = [𝑥𝑧2 = [𝑥̇

(

(10)

𝑧̇2 )

√
( (𝑥̈

(18)

= diag 6,6,

358

,

,

,

;

= diag

2

(𝑧̈

,

,

)2 )

,

,

,

;

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

= diag . , . , , , , ; 𝛽𝑖 = [= diag . , . , . , . , . , .

,

,

,

,

,

]𝑇 ;

5 CONCLUSION; 𝑘= .

A sliding mode controller with a nonlinear sliding
surface is designed for robust tracking control of aquadrocopter. The control system stability is proved by the
Lyapunov stability theory. The effectiveness of the NSS isproved by simulation. Future work verifies the effectivenessof the NSS by experiment in which the practical
disturbance and signal noise exist.

Setting 𝛽𝑖 to zero of the control strategy in Eq. (19)leads to a conventional LSS. Both the NSS and the LSS
were applied to the tracking control to the same desired
trajectory. The result is shown in Fig. 2, in which both the
NSS and the LSS provides the similar performance.

REFERENCES[1] A. Mokhtari and A. Benallegue (2004), Dynamic
Feedback Controller of Euler Angles and Wind parameters
estimation for a Quadrotor Unmanned Aerial Vehicle.
Proceedings of the 2004 IEEE International Conference on
Robotics & Automation, New Orleans, LA, April 2004, pp.
2359-2366.
[2] M. Guisser, H. Medromi, J. Saadi and H. Ifassiouen(2006), Stabilization and Trajectory Tracking in Discrete-Timeof an Autonomous Four Rotor Mini-Rotorcraft. Proceedings ofSecond International Symposium on Communications, Control
and Signal Processing, Marrakech, Morocco, 2006.
[3] A. A. Mian and W. Daobo (2008), Modeling andBackstepping-based Nonlinear Control Strategy for a 6 DOF
Quadrotor Helicopter. Chinese Journal of Aeronautics, vol. 21,
pp. 261-268.
[4] F. Kendoul, D. Lara, I. Fantoni and R. Lozano (2006),
Nonlinear control for systems with bounded inputs: Real-timeembedded control applied to UAVs. Proceedings of the 45th
IEEE Conference on Decision & Control, San Diego, CA,
USA, December 13-15 2006, pp. 5888-5893.
[5] P. Castillo, A. Dzul and R. Lozano (2004), Real-Time
Stabilization and Tracking of a Four-Rotor Mini Rotorcraft.
IEEE Transactions on Control Systems Technology, July 2004,
pp. 510 - 516.
[6] J. Guerrero-Castellanos, N.Marchand, A.Hably, S.Lesecq
and J.Delamare (2011), Bounded Attitude Control of Rigid
Bodies: Real-time Experimentation to a Quadrotor MiniHelicopter. Journal of Control Engineering Practice, vol. 19, p.
790–797.
[7] S. Salazar-Cruz, A. Palomino and R. Lozano (2005),
Trajectory tracking for a four rotor mini-aircraft. Proceedingsof the 44th IEEE Conference on Decision and Control, and theEuropean Control Conference, Seville, Spain, December 12-15,
2005, pp. 2505-2510.
[8] M. Yokoyama and K. Fujimoto (2009), Velocity
Tracking Control of a Four-Rotor Mini Helicopter. Motion andVibration Control, Springer, Netherlands, 2009, pp. 335-344.
[9] L. Luque-Vegan, B.Castillo-Toledo and A. G.Loukianov(2012), Robust Block Second Order Sliding Mode Control fora Quadrotor. Journal of The Franklin Istitute, Elsevier, vol. 349,
pp. 719-739.
[10] B. Bandyopadhyay, F. Deepak and K.-S. Kim (2009),
Sliding Mode Control Using Novel Sliding Surfaces. Springer,
Chennai, India.

Fig. 2 Helicopter trajectory in NSS and LSS.
The comparison results after applying the step
disturbance to the control inputs [ ; 2 ; 3 ; 4 ] whose] , respectively, arevalues are [ . ; . ; . ; .
shown in Fig. 3.

Fig. 3 Helicopter trajectory in NSS and LSS under
disturbance
The NSS can track the trajectory with smaller error
compared to the LSS. The nonlinear function changes thecontroller gain according to the error magnitude, andprovides the robust performance. The control inputs in Fig.
3 are shown in Fig. 4. The control input magnitudes in the
NSS and the LSS show no significant difference.

Fig. 4 Control input in NSS and LSS under disturbance.

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Design of a user support system for event addition
based on route search using schedule information
Hiroki Imamura1 and Hiroyuki Nishiyama1
1 Tokyo University of Science，2641 Yamazaki，Noda-shi，Chiba-ken，Japan(Tel:+81-47122-1106; Fax:+81-47122-1106)(j7408022@ed.tus.ac.jp)
Abstract: In recent years, the ability to manage schedules using a mobile device equipped with a touch panel has increased.
However, when one wishes to add a new event to the schedule book, taking into account the transit time of an existing event,
it is necessary to switch to another application or website. In this paper, we design a support system for additional events toautomatically determine whether the new event can be added. If this is impossible, the system proposes a time range that canhave events added.
Keywords: mobile device, schedule, context, route search, event addition
1 INTRODUCTION

which automatically determines whether new events can be
added. The system is more accurate than an empirical determination and less troublesome than using a web route searchservice. In addition, if the event cannot be added, our system searches for times at which it can be added and proposes
candidate them.

The recent widespread use of smartphones has encouraged efforts to provide context-aware services (e.g., iconcier[1] and Google Now[2]). Here, ”context” encompasses various meanings: user context, physical context,
computing context, and time context. A context-aware service provides a user with the desired results, depending on
the changing context. To achieve this, you must know what,
where, and when to understand the current context, but the
spread of smartphones is resolving this issue. The smartphone is equipped with various sensor systems (e.g., a GPS
sensor, an acceleration sensor, and a touch sensor) and a camera function. In addition, we can easily develop applications that can acquire, collect, and disseminate information
obtained from each function.

2 MOTIVATION
Mobile phones are used in many different situations.
Therefore, research has focused on gathering informationthat can be observed using mobile phones with added sensors, and on the use of the collected information to suit theuser s situation. For example, Kamisaka et al. observed
actual daily mobile phone user operations with minimal interference for six months[3]. They then demonstrated thatmachine learning could be used to predict future operations
based on the collected information[4]. Additionally, they
proposed a system to recommend applications, according to
the situation, based on collected information and application
usage. In addition, Kozawa et al. proposed a method forextracting prior advice from the web[5]. The method first
identifies whether or not a given sentence provides advice. If
the sentence is identified as advice, the method then identifies whether or not the sentence constitutes prior advice.
Matsuura et al. created a design for a scheduling support
system[6]. Their method extracts Web information based on
position information and information provided by schedulemanagement. It is possible to provide relevant information
after asking the user for information, such as providing a mapof the vicinity of a specified destination and the travel routefrom the user s current position to that destination. The
user can obtain various information related to the schedule
without having to search a Web site. In addition, acquiring
position information provided by GPS enables providing in-

How to manage time for the individual is another important issue. We have complex schedules, and storing themis difficult. We have to manage events in the calendar andschedule book. With the recent development of an environment using networked computers, we can check and change aschedule anytime and anywhere by using the schedule management system in the smartphone. We believe that this kindof demand is increasing and will continue to increase in the
future. In addition, by using the existing scheduling information, we can understand what should be done where andwhen. When planning an additional appointment, it is necessary to consider the transit time. To determine the transit
time, we may use empirical data or use a web route searchservice. The former approach is less accurate, and the latter
is time consuming.
With this as background, the purpose of this study is todesign a system that supports the addition of an event whileconsidering existing events and that can quickly add events.
To achieve this, our system includes schedule management,© ISAROB 2013

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formation that reflects the current time and the user’s location.
In this paper, we collect the history of a user using the system as a life-log. We have achieved a decision-support system for individuals to use when adding events, which takes
into account the transit time for events on the schedule thathave already been registered.

by Google. For the local database we used SQLite, which isa standard feature on Android devices.
3.3 System configuration
Figure 2 illustrates the overall system configuration.
Input
Schedule Information

Mobile application system

3 SYSTEM DESIGN AND IMPLEMENTATIONThis section provides an overview of the system’s additional support events, the function of each module, and theinternal algorithms.

Event addition support unit

Candidate suggestion module

Suggesttime range candidates

GUI management
unit
Exchange of data

Schedule information

Setting

Fig. 2. System configuration
3.4 Schedule management module
The schedule management module manages event information including the title of the event, the location, the start
time, the end time, and the means of transportation.
3.5 Location management module
The module is implemented to enable the user to register and manage location information while looking at a map.
This module was created in order to reduce the need for typing. Thus, when entering position information into the schedule management module, it is unnecessary to describe thedetails of the address and buildings. Getting location information uses the location package (android.location) provided
with the Android SDK.

Determine whether adding
an event is possible

3.6 Route searching module
The route searching module displays the daily schedulealong with routing information based on the time affordedthat takes into account the time margin from the current position, the start time, the end time, the location, and the transportation. Route searching uses the web service provided by
Google. Analyzing the html source code of the route search
results provides the necessary time, the time required, the departure time, and the arrival time. The time margin has thesame meaning as in the Osako et al. study[7], and indicatesthat there is enough time to prepare the schedule. In addition, the starting point can be selected by the user from thefollowing three.

Yes

Schedule

Application system
in smartphone
Fig. 1. Use of the system

3.2 Implementation environment
This system is implemented on an HTC Desire SoftBank X06HT equipped with Android 2.2 using Java language. The development environments used Eclipse3.6 (Helios) with Eclipse plug-ins and the Android SDK distributed© ISAROB 2013

Local DB

Wi-Fi communication

Add event

User

Route search history
Location information

Event addition support

Select candidate
details of the event

Location management moduleex））propose candidateof time range

Route searchingNo

Schedule management module

Outputconsider other appointments.

Enter event information
details of the event

Route searching module

Determination module

3.1 System Overview
In this study, we design a mobile application system withthe function of schedule management, route searching, andsupport for adding events. For schedule management, wecreated a calendar that can be synchronized with Google
Calendar. For route searching, the system performs a route
search based on the event information that the user has registered and displays the information necessary to reach thedestination and to assist the action of the user. In addition,
it accumulates a life-log search history in a local databasestored in the high-performance mobile phone that is designed
to speed up the search. To support adding events, the systemdetermines whether a new event can be added. If this is notpossible, the system proposes a time when the event can be
added.
Figure 1 illustrates using the system.

I want to add an event, but I need to

Event data generating unit

1. Current position in the GPS information
2. Location that the user has previously registered
3. Position obtained from the schedule information

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The destination point uses position information obtained
from the schedule information. Transportation may include
car, bicycle, walking, or train. The arrival time is before thescheduled time margin.

SlotTime
Eventx

3.7 Module to determine whether adding an event is

Event
y

TimeMargin

Add

Transit
Time
x z

Event
z

⇔

possibleThis module checks to determine whether a temporal
overlap exists using the route searching module and event information obtained from the schedule management module.
If there is no overlap,it is possible to determine additional
events from event information stored in the local database. If
there is an overlap, the hours of the additional event are calculated using the overlap time and a candidate is proposed.

SlotTime’
SlotTime

NecessaryTime

NecessaryTime

Fig. 4. Maximum number of events that can be added
existing events. Equation 2 gives the maximum number of
events that can be added.

Case (a) is covered by Eq. (1), which determines the stagger time of the last or first event. We choose the larger of theoverlap time or time margin to determine the stagger time.
The system proposes the time the user can leave or arrivewith a time margin of TStagger .

Nmax (x, y, z)=

TNecessary (x, y, z)=

TSlot (x, y)⌋
TNecessary (x, y, z)TTransit (x, z) + TTransit (y, z)⌊+TEvent (z) + TMargin
TSlot (x, y)=

TStart (y) − TEnd (x) − TMargin(2)

To calculate the time range in which an event can be
added, we obtain the gap time between the candidates (SlotTime’ in Fig. 4). Correcting the value calculated by Eq. (2)so as not to exceed the number of candidates proposed, we
obtain the formula for the gap time between the candidates
in Eq. (3).

(TOverlap ≥ TMargin )(TOverlap < TMargin )(1)

Next, we describe Case (b). As an example, assume
that you are adding a new event (event z) to existing events(events x and y), as depicted in Fig. 3. The necessary time isdefined as the time that you need to consider when you wantto add event z. The necessary time is the total of the running
time of event z plus the time margin and the travel time between events x and z and the travel time between events z andy. In addition, the time slot between events x and y indicates
the time between the start time of event y and the end time of
event x.
The system proposes up to three candidate time ranges
when the event can be added. Thus, there is a possibility thatwhen the proposals between the same events, the system propose time range biased. To exclude this possibility, it is necessary to consider where new events can be inserted between© ISAROB 2013

NecessaryTime

MaxNumber(a) Pattern to be inserted at the beginning or end of an event
group(b) Pattern to be inserted between events

TStagger =⇔Fig. 3. Adding an event

accept an event
When the module determines that it is not possible to add
an event, this module proposes a candidate time. Here, acollection of one-day events is referred to as an event group.
Upon calculating the time range in which an event can be
added, we perform a two-way case analysis of the location
where the new event is to be added.

TOverlap
TMarginTransitTimez yNecessaryTime

3.8 Module to propose candidate for time range that can{

TimeMargin′
TSlot(x, y, z) =N(x, y, z) =TSlot (x, y) − TNecessary (x, y, z) × N(x, y, z)
N(x, y, z) + 1NCandidate(NCandidate < Nmax (x, y, z))N (x, y, z)
max







0





(NCandidate ≥ Nmax (x, y, z) > 0)(otherwise)(3)

Finally, Eq. 4 calculates the start time and end time for

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margin. Information Processing Society of Japan SIG
Technical Report, 1009-HCI-131(4):23-30

candidate w-th.
′
TStart(x, y, z, w)=′
TEnd (x) + wTSlot(x, y, z)+(w − 1)TNecessary (x, y, z)′
TEnd(x, y, z, w)=′
TEvent (z) + TStart(x, y, z, w)(4)
4 CONCLUSION
In this paper, we designed a user support system for additional events, taking into account the existing events. It
determines the additional events and proposes a time range
in which events can be added. This system enables users
to schedule new events more smoothly than by conventional
decision-making systems. In addition, problems related todetermining transit time (such as low precision, or being
time-consuming) were addressed in order to facilitate using
a life-log. Future work will demonstrate the effectiveness of
this system by evaluating the accuracy of the proposed candidates.

REFERENCES[1] i concier,
http://www.nttdocomo.co.jp/service/customize/iconcier/[2] Google Now, http://www.google.com/landing/now/[3] Daisuke Kamisaka, Shigeki Muramatsu and Hiroyuki
Yokoyama (2008), A Study on Situation-Adaptive
Operation-Assisting Method for Mobile Phone. Information Processing Society of Japan SIG Technical Report 2008-UBI-20(110):33-38[4] Daisuke Kamisaka, Takeshi Iwamoto, Shigeki Muramatsu and Hiroyuki Yokoyama (2009), Long-term Data
Analysis and Operation Prediction for Context-Aware
UI of Mobile Phones. FIT2009 8(4):23-29[5] Shunsuke Kozawa, Masayuki Okamoto, Shinichi
Nagano, Kenta Cho and Shigeki Matsubara (2011), Advice Extraction from Web for Providing Prior Information Concerning Outdoor Activities. Proceedings of the
4th International Conference on Intelligent Interactive
Multimedia Systems and Services (IIMSS 2011):251260[6] Hiroshi Matsuura, Tetsuya Tsukamoto and Hiroyuki
Nishiyama (2009), Schedule management support system using Web information. IPSJ National Convention
71st[7] Takashi Osako, Itaru Kuramoto, Yu Shibuya, Yoshihiro
Tsujino and Mitsuru Minakuchi (2009), Prior notification technique of tasks/schedules taking account of time

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The studyof walking control with plural cylinder leg robots

Jihao Cao1 , Feifei Zhang2 , Masanori Ito2 and Takumi Yaginuma3
1

Tokyo University of Marine Science and Technology(Tel: 81-3-5245-7422，Fax:81-3-5245-7422)
1

M114031@kaiyodai.ac.jp

Abstract: Our goal of this research is to develop a working robot which has a medium level of power and walks with four legs.
In our experiment, we used the robot which has two cylinder legs under the concept of multi-cylinder leg. It has very strong
structure and it can endure and keep motion under heavy load. Multi-cylinder leg robot can walk any direction and draw any
radius circle. In case of two cylinder leg, it can walk forward and backward on a straight or curved course. We are studying thecontrol scheme of walking for this robot, which is put mind especially on smooth and steady movement without rolling,
pitching, yawing or heaving motion and keeping the body horizontally. We confirmed the validity of control scheme withexperiments.
Keywords: Walking control.

Working robot.

Cylinder leg.
hydraulic cylinder for pivoting the main leg and electric
cylinder for rocking it. The specifications of the robot are
shown in Table.1and Table.2 and Table.3.

1 INTRODUCTION
Now, the study of working robot becomes active because
there exist very urgent request that robot should be applied,instead of human being to the work at the Fukusima atomic
energy generating station where has the high nuclear level
and complicated terrain. However, the current technology
of crawler-type or wheel-type robots cannot fully work insuch condition. Our goal of this research is to develop aworking robot which has a medium level of power andwalks with four legs. It is thought that the working robot
should equip special functions such as for maintenance,
management or setting of equipments with using tools.
We thought that it was desirable to give robots the functionchanging their form suitably for work environment andwork kinds to get high work performance and suggested themethod to environment and the work kinds. In this paper,
we refer to the moving mechanism and control of the robot.
As well known, robot faces to big reactive force when it
is working and it should move stably, carrying heavy
goods. We thought a quadruped walking robot which has
cylinder for the leg structure is adequate ,and confirmed thebasic performances keeping prescribed posture and smooth
movement by using the experiment. In the previous paper
by Yaginuma[1], He performed a study of geometry andexperimental confirmation about the type of the moving
cylinder leg for assumed constant speed walking withkeeping velocity contour. In this study, based on the resultsof the previous study we have carried out actual walking
with the large scale robot.

Fig.1 The experimental apparatus

Table1. The main leg cylinder specification
0.4
Motor output (kw)
6～75
Push(mm/sec)

2 ROBOT FOR EXPERIMENTS
Speed

We constructed each leg with two cylinders. The exper
imental apparatus is shown in Fig.1. We equipped thistype of leg for front leg of the robot and fixed leg withcaster for aft-leg. Although in the previous study, it was a
combination of the electric cylinder, we used DDVC© ISAROB 2013

Pull(mm/sec)
Propelling power(N)The diameter of a boa

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Table2. Cylinder specification for rocking
30
Motor output(W)300
Speed(mm/sec)
64
Propelling power(N)front direction with half pitch of walk. At the same time,
move the left leg backward to half pitch of walk.
3.1.2 Continuous walking
After, start motion, the front left leg and a front right leg
repeat full pitch motion mutually.
③Stopping
When it stops walking, the landing leg stop the motion at
the center of pitch and another leg also moves to center ofpitch.

Table3.The performance of the legs
72
Height legs(cm)12Stride (cm)28
Cycle(s)3.2 Walking locus
The human has various walking behavior according to aground state, when walking on the land. For example, abo
ut the motion of leg such as landing-backward movingfloating
forward moving, we set some basic patterns so that a walkof constant speed, horizontal posture and constant height can perform smoothly. The pattern is shown in Fig.3.

2.2 System Configuration

The command signal from a computer is changed
into an analog signal through DA board, and is outputted
to a driver. Through a driver, Actual speed/position of acylinder is measured with a sensor and feed-backed tothe computer through AD board.
Computer

D/A

DriverDriverElectric Cylinder

Sensor

DDVC hydraulic cylinder(speed)Sensor

Fig.3 Walking pattern(position)

4 CONTROL
A/D
4.1 Walking control
The legs structure of the robot proposed in this study as
described above are all the same and same control scheme
is applied to both legs. Operation of each leg is determinedaccording to the type of motion of the body.
In this walk control, a speed of operation and locus were
patterned from a rising to a start and landing, and the thingwhich makes a leg expand and contract in the shape of asine wave was considered. The signal shown in Fig.4 and 5
are the operation of the left leg. With the combination of thesignals, the left leg will be lifted in first two(2) seconds, andtwo cylinders will start motion and reach the peak value inthree(3) seconds. Right leg will start motion with the same
method at this timing. Continuous walk is performed withthis way.

Fig.2 System configuration

3 Walking
3.1walking process
In this study, we assumed constant speed walking withkeeping horizontal posture of body plane on a horizontal
flat ground as a basic walk.
However we cannot avoid yawing motion caused of thestructure.
As shown in previous study, the walking pattern has
three mode of walking start, continuous walking andstopping.
3.1.1 Walking start mode
①When it starts walking, lift up the left leg and , move it to
the traveling direction with half pitch of walk and lift downon the surface.
②. When left leg land, lift up the right leg and move it to© ISAROB 2013365

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Fig.8 Control result of the left leg
Fig.4 Motion of main cylinder (left leg)Fig.9 Control result of the right leg
Fig.5 Motion of support cylinder (left leg)

5 CONCLUSION
In this study, we confirmed the results of previous study
with actual walking robot. Although there is a little
difference on the structure, two cylinder leg worked
smoothly and made precise walking following to the pattern.
However this is a basic walk and we are thinking to try thewalking in actual situation such as
not leveled ground,step or place filled with obstacles.

6 REFERENCES

Fig.6 Motion of main cylinder (right leg)[1] Yaginuma T(2010),Quadruped walking with parallel
link legs. Artif Lift Robotics(2010)15:555-559 DOI
10.1007/s10015-010-0868-6

Fig.7 Motion of support cylinder (right leg)
4.2

Control method
When the robot is applied to a constant speed move witha horizontal posture，according to the purpose of the study,
the moving pattern of the four (4) legs should be designed
in different ways. As a result, the different methods ofcontrol are necessary.
In this experiment, PI control was adopted to confirm thewalking pattern such as constant velocity contour walk.
This control results are shown in Fig.8 and 9.

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The Ground Reaction Force generation algorithm for tracking the wearer’s
motionSukwoo Lee1, Jongwon Lee1, Hyogon Kim2, Jaeho Jang 2 and Sangdeok Park2
1

University of Science and Technology, Republic of Korea
Korea Institute of Industrial Technology, Republic of Korea(Tel: 81-31-8040-6348, Fax: 81-31-8040-6310)

2

1

sukwoo22@kitech.re.kr

Abstract: This paper explains the desired ground reaction force (GRF) generation algorithm for exoskeleton. Its key point is togenerate the desired torque of exoskeleton for tracking the wearer’s motion. We can obtain the torque by using the GRF whenexoskeleton foot is in contact with the ground. It is not necessary to measure exerted force at contact point. It requires only
simple calculation in contrast with inverse dynamics of full body. It can adapt to environment such as flat ground, stairs anduneven surfaces. We present simulation result using our two-leg model, which demonstrates the algorithm is applicable toexoskeleton.
Keywords: Exoskeleton, walking, center of pressure (CoP), zero moment point (ZMP), ground reaction force(GRF).

1 INTRODUCTION

2 GAIT OF EXOSKELETON

The external forces by the surrounding environment isexerted to the locomotive system During waking. The
ground reaction force (GRF) that is one of them has beenused for research on control of locomotive system. Satoshi
Ito and Haruhisa Kawasaki has been doing research onbalance control of the legged system by the method whichkeeps GRF measured by the force sensor at the footpositive [1]. Since elderly people and persons withhemiplegia may be difficult to maintain balance of theirbodies, a wearable lower extremity exoskeleton (LEE)
which has been developing at Nanyang Technological
University, Republic of Singapore maintain balance insteadof wearers by using the zero moment point (ZMP) [2]. The
ZMP can be obtained by the GRF measurement of
exoskeleton and wearer. The above mentioned control
algorithm require force sensor at the ground contact point.
Without them, the system cannot adapt to the GRF. But
although we install force sensors at all the ground contact
point, it is causing complicated calculations because of thenumber of contact Jacobians. To resolve this issue, Sang-Ho
Hyon proposed how to compute the commanded joint
torque for a desired contact force without force sensor [3].
As this method is applicable to the exoskeleton, we explainthat the method calculate desired ground reaction force fortracking wearer's motion.

© ISAROB 2013

Fig. 1. Gait phases of the exoskeleton. Adapted from [5].
A walking gait of the exoskeleton like figure 1 can bedivided into the single support phase and the double support
phase [4]. In the single support phase, the only one leg is incontact with the ground. But in the double support phase,both legs are in contact with the ground. The two support
phases take place alternately during walking.
Figure 2 shows the connection between ZMP, CoP and
GRF in the single support phase. The ZMP introduced in1968 for the first time is a concept related with dynamics
and control of locomotive system [6]. It is the point where
the sum of all moments of active forces is equal to zero. If
the ZMP trajectory remains within the support area when
the locomotive system is walking, gait of the system isstable. The support area is distinguished by the single
support phase and the double support phase. In the single

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Fig. 2. ZMP, CoP and GRF. Adapted from [6].

Fig. 3. Forces exerted to the exoskeleton.
R  m (g  a )support phase, support area is foot surface in contact withground. In the double support phase, support area is footsurface and the distance between the two feet. And The
center of pressure(CoP) is the point on the supporting
surface where moment of ground reaction force is zero [7].
If the ZMP is within the support area, the ZMP coincides
with CoP. In this paper, we use a concept of ZMP and CoP
for calculation of GRF.

3.2. Load Distribution in Double Support Phase
In the single support phase and the double support phase,
GRF is the same at the CoP of the exoskeleton. But in the
double support phase, we have to do load distribution of
GRF because both feet are in contact with the ground. Total
GRF is sum of GRF at the contact point of feet. If weknow GRF and CoP, we can derive (3)3 GRF GENERATION ALGORITHM
The object of our algorithm is to find desired groundreaction force of exoskeleton. Ahead of description of thealgorithm, we have to make several assumptions. First, atleast one of the feet of the exoskeleton is always in contact
with the ground. Second, the acceleration of theexoskeleton seems as that of wearer because theexoskeleton tracks the motion of wearer. Third, ZMP of theexoskeleton always coincides with CoP. Forth, we do notconsider swing phase.

x
  1
 1

x 
 R z1 x 2  F 1   
1   F 2 

(3)where x is distance between CoP and center of mass(CoM), R z is the vertical GRF, x1 and x2 are distance
between point of application of GRF
 at the contact point ofeach foot and CoM. And F1 and F2 are the vertical GRF
at the contact point of feet. CoP can be calculated as

3.1 Calculation of GRF
Figure 3 shows Forces exerted to the exoskeleton suchas inertia force, gravity force and GRF. Therefore, the sumof the force is as follows: F  m a(1)

mg  R  m a

n

x



i x i F ini F i(4)

But as we do not know the distributed force F i, we
could not calculate CoP. Instead, CoP can be replaced
by ZMP because ZMP remain within the support area.
ZMP can be calculated as
where m is mass of the exoskeleton, R is ground reactionforce, g is the acceleration of gravity and a is theacceleration of the exoskeleton. We can determine theacceleration of exoskeleton provided that we use an
accelerometer. In other words, it is possible to calculate
desired GRF with accelerometer by using equation (2).

© ISAROB 2013

(2)

x ZMP



m ( g  z ) x  m xzm ( g  z )(5)

We do not consider the angular momentum around CoM.
Finally, we can calculate the desired contact forces by using
a GRF and ZMP as (6). Figure 4 shows Distance Between
point of application of GRF and CoM.

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Fig. 4. Distance between point of application of GRF andCoM.

F 1 
x 1
    
 1
 F 2 

x 2 
1




1

x  
 R z1 (6)Fig. 6. Simulation results of GRF in the single support
phase.

4 SIMULATIONIn this section, the exoskeleton walking simulation on
flat ground is described. We simulated two cases: the single
support phase and the double support phase. Figure 5 shows
how to prove the effectiveness of our algorithm. We
compare GRF by the algorithm with Actual GRF. In thesimulation, we used a 6-DOF model and we gave a specificdesired trajectory to controller instead of human.
In the single support phase, we gave the exoskeleton
model sine trajectory. Amplitude of sine trajectory was 0.1
m. Figure 6 shows result of calculation of the desired GRF.
Errors of GRF by the algorithm and actual GRF was
measured within about ±0.2 N. This value represents ouralgorithm generates desired GRF for tracking wearer’s
motion.
In the double support phase, we compared GRF by theload distribution with actual GRF at the contact point.
Motion trajectory in the double support phase was the sameas that in the single support phase. In figure 7, we canFig. 7. Simulation results of GRF at the contact point ofeach foot in the double support phase.
confirm range of errors are ±30 N. As a margin of errors
is less than 10 percent, the algorithm for load distribution isapplicable to the exoskeleton. Errors tend to be increased
when the acceleration of exoskeleton is increased because

Fig. 5. Block diagram for proof of the algorithm© ISAROB 2013

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ZMP is affected by the acceleration. To raise the accuracyof the load distribution, more accurate ZMP model isneeded.

5 CONCLUSION
This paper described the desired GRF generation
algorithm for tracking wearer’s motion. We demonstrated
effectiveness of the algorithm with simulation experiments.
The algorithm has the following benefits. First, force sensor
is not required at the contact point of the exoskeleton.
Second, we can calculate ground contact force regardless ofnumber of contact point. Third, it is not necessary to solve acomplicated inverse dynamics of the exoskeleton by using
ZMP.
As future research, it is necessary to use accurate ZMP
model for more accurate load distribution. Next, we willtransform desired contact ground forces into joint torques
directly. We will apply the algorithm to the actual
exoskeleton.

REFERENCES[1] Satoshi Ito and Haruhisa Kawasaki (2000), A
standing posture control based on ground reaction force,
Proceedings of the 2000 IEEE/RSJ International
Conference on Intelligent Robots and Systems, Takamatsu,
Japan, Oct. 30 – Nov. 5, 2000, pp.1340 -1345 vol.2.
[2] K.H.Low, Xiaopeng Liu, C.H.Goh and Haoyong Yu(2006), Locomotive control of a wearable lower
exoskeleton for walking enhancement, Journal of Vibration
and Control, Dec 11, 2006, pp.1311-1336 vol.12[3] Sang-Ho Hyon, Joshua G. Hale, and Gordon
Cheng(2007), Full-Body Compliant Human-HumanoidInteraction: Balancing in the Presence of Unknown
External Forces, IEEE TRANSACTIONS ON ROBOTICS,
Saitama, Japan, Oct, 2007, pp884-898 vol.23[4] Whittle, M. W. (2007), Gait Analysis: An
Introduction, Butterworth-Heinemann, Oxford, UK. pp.54[5] J. Rose and J.G. Gamble(1994), Human Walking 2nded, Boltimore, MD: Williams and Wikins[6] Vukobratovic, M., Borovac, B. and Surdilovic, D.
(2001), Zero-moment point: Proper interpretation and new
applications, Proceeding of IEEE-RAS International
Conference on Humanoid Robots, Tokyo, Japan, Nov 22-24,
2001, pp.135-144[7] Phillippe Sardain and Guy Bessonnet (2004), Forces
acting on a biped robot. Center of pressureZero moment point, IEEE TRANSACTIONS ON SYSTEMS AND HUMANS, Sec, 2004, pp. 630-637 vol.34© ISAROB 2013

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A study on the communication system using Electrooculogram Signals for
persons with disabilities
Sou Go1, Mingmin Yan2, Hiroki Tamura1, and Koichi Tanno1
12Department of Electrical and Electronic Engineering

Interdisciplinary Graduate School of Agriculture and Engineering
University of Miyazaki, Japan(Tel: 81-985-58-7409, Fax: 81-985-58-7409)
1

tc11014@student.miyazaki-u.ac.jp

Abstract: The aim of this study is to present electrooculogram (abbr. EOG) signals that can be used for human computerinterface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand
movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or otherillnesses that prevent correct limb and facial muscular responses. Using EOG signals, it is possible to improve thecommunication abilities of those patients who can move their eyes. Investigating possibility of usage of the EOG for humancomputer interface, a relation between sight angle and EOG is determined. In other methodology, most famous approaches
involve the use of a camera to visually track of the eye. However, this method has problems that the eyes of user must always
be open. In this paper, we introduce the mouse cursor control system for Amyotrophic Lateral Sclerosis patients using EOG
and electroencephalograph (abbr. EEG). We proposed the algorithm using alternating current and direct current of EOG
corresponding to the drift. Therefore, our proposal EOG system did not have the problem of eye blinking artifacts, thedisplacement of electrode positions and the drift. In addition, we introduce the EEG that the brainwaves can assist us whether
the patient control their eye movements consciously. The EEG is not used to control. We are getting the EEG signals used todetermine the control state. In order to test the effectiveness of our proposal, we have tried the experiments of theQuestionnaire in NO or YES using our system and electroencephalograph device MYNDPLAY[7]. The three experimental
subjects are inexperienced persons about this system. In this experiment, we measured 30 states of EEG signals when EOG
was done and analyzed the EEG signals. According to the EEG signals changes and the answers of the questionnaire, 26 of the
30 states of the EEG signals were able to correctly determine the control state of EOG. From these results, the EEG signals canbe used to adjust the EOG system according to the condition of EOG signals. In addition, it is expected that can be applied todetermine whether patients are the way their want answers that can assist caregivers to know the brain handicapped patient’s
own thinking.
Keywords: Electrooculogram Signal; Mouse Cursor Control System; Electroencephalograph
EOG signals. To overcome this issue, previous system was
proposed using alternating current of EOG which reduces
baseline drift by segmentation of the signal [4].
In this study, we have developed the mouse cursor
control system and new idea of EOG Signal for
amyotrophic lateral sclerosis patients using EOG signals.
Our proposal EOG device did not have the problem of eye
blinking artifacts and the displacement of electrodepositions. But, the drift was problem. Then, we proposed
the algorithm using alternating current (abbr. AC) and direct
current (abbr. DC) of EOG corresponding to the drift. In
order to test the effectiveness of our proposal system, we
tried the experiments of the questionnaire using our
proposal system and EEG device MYNDPLAY. The
performance of the proposal system and our proposal arevery good, we measured 30states of EEG signals when
EOG was done and analyzed the EEG signals. According to
the EEG signals changes and the answers of thequestionnaire, 26 of the 30 states of the EEG signals wereable to correctly determine the control state of EOG.

1 INTRODUCTION
In the eye movements, a potential across the cornea andretina exists, and it is source of EOG. EOG can be modeledby a dipole [1], and these systems can be used in medical
systems. There are several EOG-based Human-Computer
Interface studies in literature. The eyes movement signals
and sensor signals are combined, and both direction andacceleration are controlled [2]. Using horizontal andvertical eyes movements and two and three blinking signala movable robot is controlled. Because the EOG signals areslightly different for the each subject, a dynamical thresholdalgorithm was developed [3].
In this approach, the initial threshold is compared withthe dynamic range; the thresh-old value is renewed after
each difference. According to this threshold the output
signal is made 1 or 0 and afterwards it is processed.
Investigating possibility of usage of the EOG for human
computer interface, a relation between sight angle and EOG
is determined. However, in-depth studies evoked that slow
changing baseline drift is difficult to estimate in continuous© ISAROB 2013

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frequency) and the change by the eyes movements. Whenthe channel1 or/and channel2 signals change without thechannel3 and 4 signal changing, this change is an influenceof the drift.

2 MOUSE CURSOR SYSTEM USING EOG SI
GNALS
2.1 The EOG Device
In this subsection, novel EOG measurement systemdesign has been proposed. Fig. 1 shows the formal scheme
for the acquisition and analysis of the EOG signal and how
to control organization flow of information through thesystem.
Horizontal and vertical eye movements are measured
with two passive electrodes. Five Ag/AgCl electrodes areused (two for CH1, two for CH2 and one is for ground).
Two channels of EOG signals can be used to recognize theeyes movement. EOG signals can be determined as high as
50Hz. However, the signals which are less than 10Hz aresafe and enough for our system. Therefore, we used the low
pass filter whose cutoff frequency is 10Hz to remove themore than 10Hz power line signals. In our proposal system,
channel 3 and 4that apply high pass filter to channel 1 and 2
are used in order to remove the DC level. The cutoff
frequency of high pass filter is 0.175Hz. After filtering andthe amplification (about 1000 times) stages, the EOG
signals are digitized and then transferred to the personal
computer. The sampling frequency of the measurement dateis 960Hz.

Fig. 2. The EOG signals recording samples in CH 1, 2 and
3 (Blink, Right and Left)

3 PROPOSED METHOD
3.1 Mouse Cursor Control Algorithm
In this section, we introduce our proposal algorithm forthe mouse cursor control. The initial thresholds of the eyes
movement class (Right, Left, and Voluntary Blink) are seton the user using the initialization screen. 3 eyes movementclass (Right, Left, and Blink) are command of the Right
movement, Left movement and click processing of themouse cursor. The values of Pright, Pleft, are defined as
the set using the initialization screen. Moreover, the centerparameters of the eye (centre) are set. When theexperimenter gazes on these points of the initialization
screen, the signals change of each channel will be recorded
as initial thresholds of the eyes movement class. For
example, when the experimenter gazes on the “Centre
point”, Horizontal centre parameter is set as Cx, andVertical centre parameter is set as Cy.
At first, in order to remove the more than 60Hz powerline noise, we use the moving average method for channel 1
and 2. The moving average method is the unweighted meanof the previous n data points. Because it is the sampling
frequency 960Hz, the value of n is 16 (= 960Hz/60Hz).
For the reduction of 60Hz power line noise.
Next, the difference between the centre parameter (Cx)and the input of Channel1 is calculated, and it is set to input
1. The input 2 is calculated similarly using difference
between the centre parameter (Cy) and the input ofChannel2. Our proposal system is judged whether eyes
moved by using the input1, 2, channel 3 and channel 4. Ifeyes do not move, center parameters are updated tocorrespond to the drift. In this process, Cx and Cy are
gradually updated to EOG baseline. I order to keep thesecurity of the system, centre parameters (Cx, Cy) are

Fig. 1. The mouse cursor control system using EOG
2.2 The EOG signals recording
As seen from the recordings channel 1, 2 and 3 in Fig. 2.
(the eyes movements: Right, Left, and intentional Blink),
after signal amplitude (about 1000 times) and considering
noise reduction measures in designing of the biopotential
data acquisition system, the EOG system performance isgood. Electronic noise reduction is also successful. Fig. 2
showed that 3 eyes movements are clearly different.
Moreover, channel 1 and 2 have the DC level signals, sothat the change of the EOG by the eyes movements
continues. So, the continuous control for mouse cursor
becomes possible. Channel 3 is the AC level signals of thehorizontal EOG. Therefore, channel 3 is strongly reacted
when eyes moved to right or left. Channel3 and 4signals
were removed the DC level by high pass filter. Therefore,channel 3 and 4 signals have the change, only when eyes
move. Channel 3 and 4 signals are information that knows
the difference between the change by the drift (low

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updated every interval by 30% of the drift.
If eyes move, the eyes movement class is identified
using each class thresholds using the rules of Table 1. Here,the value of Tright, Tleft, and Tblink are defined as thethreshold value of the eyes movement class. Since there isindividual difference, it is necessary to update the thresholdof Tright, Tleft using the other parameters of Pright and Pleft.
And, the threshold of the eyes movement class is updated
according to the dynamic threshold method. Therefore, ourproposal system can respond to online EOG characteristicchanges. The code of the dynamic threshold method of theright threshold is as follows:

tried the experiments of the Questionnaire in NO or YES
using our system and EEG device MYNDPLAY.

If 0.64 < input 1/(Pright-Cx) and input
1/(Pright-Cx) < 0.8 Then
Tright = Tright − 0.01∗ Tright
Else if input 1/(Pright-Cx) > 0.96 Then
Tright = Tright + 0.01∗ Tright
End if

4.1Experiments method
We have tried the experiments using our proposal
system and simple brainwave system MYNDPLAY. The
EEG signals of three subjects (S.G, KS.NN and KH.NT) aremeasured and analyzed 30 states. It is defined as:
1 9
A(t  i )(1)
H (t ) 
10 i0 B(t  i )  C
A(t) is the frequency from 7Hz to 9Hz of alpha wave of ttime power spectrum of Fast Fourier Transform(abb. FFT).
B(t) is the frequency from 14Hz to 16Hz of the Lowfrequency beta waves of t time power spectrum of FFT. C isconstant, the average of B(t). H(t) is the 10 pre moving
average.
If the average of H(t) is less than 0.5 then the EOG
system is uncontrollable of the patient's consciousness. If
the average of H(t) is greater than 0.5, take 50% of it as thethreshold and compared to the H(t) whether EOG iscontrollable. In addition, if the 10 pre moving average value
greater than threshold, then the patients according to theirown consciousness in using EOG system otherwise not.

4 EXPERIMENTS AND RESULTS
Three inexperience subjects have tried the experimentsof the Questionnaire what answer is Yes or No using our
EOG system and EEG. But, we cannot run the dynamic
threshold method and centre parameters updated method toincrease the control misses. The size of the monitor is 23
inches and the distance between the eyes of subject and themonitor is 50cm and use Bluetooth to send the EEG signals.

Other thresholds are similarly processed. In this process,the threshold is updated to low when the value of input islow, and the threshold is updated to higher when the valueof input is high. These processes are repeated. The mouse
cursor control interval is 0.2 seconds. The continuous
control for mouse cursor can be executed while
corresponding to the drift by this proposal algorithm andthe proposal EOG device.
Table 1. Pattern classification rules
Command

Right

Left

Input 1

Input 1>Tright

Input 1 <Tleft

And|input 2|<0.3|input 2|<0.3

input 2And

Blink
Input2 <Tblink

CH3>TCH3 rightCH3<TCH3left

CH3 >channel 3

TCH3 blink

And

CH4 >

channel 4

TCH4 blink

4.2Experiments results
Table 2 shows the correct rate of each subject. Fig. 3
shows the correct results for 10 pre moving average of EEG
power spectrum and Fig. 4 shows the error one. Fig. 5 andFig. 6 show the statistical significance less than 1% and theaverage value of subject KS.NN and S.G, error bar is thestandard deviation. The average value of H(t)-value of eachsubject when EOG is controlled by the subject’s own
thinking or cannot in controlling is calculated, thenassessing T-test of each trial. Fig. 7 shows the average ofsubject KH.NT, although it is no statistical significance, but
the correct recognition rate of 80% is a good result. By thisanalytical method, 30 states of data was measured (onesubject for 10 states), 26 states of it can be correct analyzed
and the offline correct rate is about 87% and the error is notserious than our previous study (using only EOG systemwith the dynamic threshold method and centre parameters
updated method) that the experimental result of using
mouse cursor system and camera is 97%. In addition, as
future work, we need online experiment.

3.2 ElectroencephalographIn this study, we also propose using EEG by
MYNDPLAY. EEG is the cording of electrical activity
along the scalp, measures voltage fluctuations resulting
from ionic current flows within the neurons of the brain.
Human brain waves can be divided into at least four states.
We determine the activity of the brain by using two
categories of low-frequency beta waves and alpha waves inthe EEG. Alpha is the frequency range from8 Hz to 13 Hz,
seen in the posterior regions of the head on both sides,
higher in amplitude on the dominant side. Alpha wave
increase during wakeful relaxation with closed eyes. Beta is
the frequency range from 13 Hz to about 30 Hz. It is seenusually on both sides in symmetrical distribution and ismost evident frontally. Low amplitude beta waves withmultiple and varying frequencies are often associated withactive, busy, or anxious thinking and active concentration.
We use EEG changes that can determine whether thepatients using our EOG system in their own meaning. Then,in order to test the effectiveness of our proposal, we have

© ISAROB 2013

Table 2 . Correct rate of each subject

Subjects
Correct rate

373

S.G
90%

KS.NN
90%

KH.NT
80%

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Fig. 3. Correct recognition rate of EEG power spectrum

Fig.6. T-test result of KS.NN

Fig. 4. Error recognition rate of EEG power spectrum*○ is EOG can be controlled, × is uncontrollable.

Fig.7. T-test result of KH.NT

REFERENCES[1] S. Venkataramanan, P. Prabhat, S. R. Choudhury, H.
B.Nemade and J. S. Sahambi, Biomedical
instrumentation based on electrooculogram (EOG)
signal processing and application to a hospital alarmsystem, Proc. of International Conference on Intelligent
Sensing and Information Processing, Chennai, India,
pp.535-540, 2005

Fig. 5. T-test result of S.G

5 CONCLUSION[2] R. Barae, L. Boquete and M. Mazo, System forassitedmobility using eye movements based on electrooculography, IEEE Transaction on Neural Systems
and Rehabilitation Engineering, vol.10, no.4, pp.209218, 2002

In this study, we have developed the mouse cursor
control system for amyotrophic lateral sclerosis, by which
the amyotrophic lateral sclerosis patients with their own
consciousness in control EOG when EOG system was done.
The performance of the proposed system was good.
According to the EEG signals changes and the answers of
the questionnaire, 26 of the 30 states of the EEG signals
were able to correctly determine that using Bluetooth tocommunicate the EOG system and control the EOG. The
correct recognition rate of three experimenters is 87%. Twoexperimenters have the statistical significance less than 1%.
From these results, the EEG signals can be used to adjust
the EOG system according to the condition of EOG signals.
In addition, it is expected that can be applied to determine
whether patients are the way their want answers that can
assist caregivers to know the brain handicapped patient’s
own thinking. Our proposed system has one problem thatthe average time of question increased by wave noisecaused by this kind of phenomenon, the whole proposedsystem should be considered with the properties ofelectrodes.

[3] Z. Lv, X. Wu, M. Li and C. Zhang, Implementation of
the EOG-based human computer interface system, Proc.
of the 2nd International Conferenceon
Bioinformatics
andBiomedical
Engineering(ICBBE’08), Shanghai, China, pp.2188-2191, 2008
[4] A. B. Usakli, S. Gurkan, F. Aloise, G. Vecchiato and F.
Babiloni, on the use of electrooculogram for efficienthuman
computerinterfaces,
Computational
Intelligence and Neuroscience, vol.2010, Article ID
135629, 5 pages, 2010[5] H.Tamura, M.Miyashita, K.Tanno and Y.Fuse, Mousecursor control system using electrooculogram signals,
Proc. of World Automation Congress 2010, IFMIP 239,
CD-ROM, pp.1-6, 2010[6] M. Yan, H. Tamura and K.Tanno, Development ofmouse cursor control system using electrooculogramsignals and its applications in revised hasegawa
dementia scale task, Proc. of World Automation
Congress 2012, CD-ROM, pp.1-6, 2012

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant
Number 23700668.

© ISAROB 2013[7] http://neuro-bridge.com/entertainment/myndplay.html

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A Study on the Electric Wheelchair Hands-Free Control Systemusing the Laser Range Scanner
Takayuki Murata1, Hiroki Tamura1.1, and Koichi Tanno1
1

University of Miyazaki, Miyazaki 889-2192, Japan(Tel: 81-985-58-7409, Fax: 81-985-58-7409)
htamura@cc.miyazaki-u.ac.jp

Abstract: This paper presents a semi-automatic control system for electric wheelchair using laser range scanner. The user ofthis system is supposed as severe disabilities paraplegic who cannot move the body under the neck. They can use the surface
electromyography (s-EMG) of the facial muscle for controlling the electric wheelchair. When they are controlling the electric
wheelchair with the s-EMG signals, to avoid the obstacle is difficult. Therefore, we developed the semi-automatic assist system
for avoiding the obstacle. The proposed system is composed by laser range scanner. The electric wheelchair could increasesafety by using our proposed assist system. We tried the experiments of driving in the hallway of the building. Our proposedsystem was confirmed that the electric wheelchair did not clash with the hallway of the building.
Keywords: Surface Electromyogram Signal, Electric Wheelchair, Laser range scanner, Motion Control Command Rule, SemiAutomatic Control
fore we developed the automatic control system for avoiding the obstacle.
Table 1. The way of controlling the electric wheelchair.

1 INTRODUCTION
An electric wheelchair is indispensable for disabled
people. In recent years, the joystick type of electric wheelchair is very popular. However, for persons with severe
disabilities who find it difficult to operate the joystick ofelectric wheelchair, in the other hand, the new types of devices for disabled people have appeared, for example thevoice, brain waves and surface electromyogram (abbr. sEMG) signals etc. Disabled people who have the problemof limbs can use these types of devices, but the well adjustment of the driving route by using these devices is difficult. In addition, the user of electric wheelchair must be
safe driving. Disabled people are hard to check the safety
around. Thus, it is necessary to check safety using sensors.
This paper presents a semi-automatic control system forthe electric wheelchair by using a laser range scanner [1].
The user of the proposed system is supposed as serious disabled people who cannot move the body under the neck.
They cannot control the electric wheelchair with hands orlegs, but they can use the Bio-signal in their face. For example, the Bio-signal system is the s-EMG system [2][3].
This system can be controlled by using the face s-EMG
signals. This system can recognize face motion (a left wink,a right wink and a bite) by using s-EMG sensors. The
method of controlling the electric wheelchair in this system
is shown in Table 1. This system is attached with a laser
range sensor for the safety, and the electric wheelchair
could be speed controlled, but this system cannot avoid the
obstacle automatically. Thus, the user has to control theelectric wheelchair to avoid the obstacle every time. There-© ISAROB 2013

Movementof faceStateof electric
wheelchair

Left wink

Right wink

Bite

During astop

Turn left

Turn right

FowardDuring a
forward

Forwardto the left

Forward to
the right

Stop

2 THE PROPOSED SYSTEM
2.1 Sensor Position
The proposed system is composed by a laser range
scanner (HOKUYO AUTOMATIC CO., LTD, sensor model
URG-LX04). The specification of this sensor is shown as
Figure 1. The measurable range of this sensor is maximum
4 meter in 120 degree left and right, and the sampling interval is 100 msec. The specification of this sensor is shown as
Figure 1.

Figure 1. The range of the laser range scanner.

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In the proposed system, the laser range scanner is attached on the brace by the 1.7m high from the ground (Figure 2). This position can measure the side of the electric
wheelchair. The laser range scanner can recognize the obstacle even as large as a wall.

Figure 3(b). Measured values.

Figure 2. The position of the laser range scanner.
2.2 Pretreatment
First step, get angle values and distance values between
the obstacle and the laser range scanner. Second step, convert the angle values and the distance values into Euclidean
distance, and plot these values on the picture (width: 160
pixel, height: 160 pixel) with dots. 1 pixel represents 50mm.
Thirds step, if the distance between two dots is less than thewidth of the electric wheelchair, these two dots will be connect with the line. The results of using these steps areshown in Figure 3. Figure 3(a) shows the actual scene. Figure 3(b) shows the measured values of the laser range scanner, and the center on the Figure 3(b) is the laser range
scanner position. Figure 3(c) shows reformed values. Figure3(c) is shown as the confirmation window on the personal
computer.

Figure 3(c). The confirmation window.
2.3 Speed Control
The speed of the electric wheelchair could be controlled
ac-cording the distance between the obstacle and the laser
range scanner. The area of controlling speed is separated
into three parts by the distance from the front of the electric
wheelchair to the obstacle (Figure 4).

Figure 4. The separated area.
(a)

Area AThe area A is less than 0.5 meter. If there is the obstacle,
the electric wheelchair will be stopped. The electric wheelchair cannot go forward, but it can also turn on the spot.
The electric wheelchair doesn’t move until a turn command.

Figure 3(a). Actual scene.

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3 EXPERIMENTS(b) Area B
The area B is less than 1.5 meter and more than 0.5 meter.
If the obstacle exists in the area B, the speed of the electric
wheelchair will be slowed down. In addition, for avoiding
the obstacle, the area LB and the area RB are checked (Figure 5). When both the area LB and the area RB does not exist
any obstacles, the electric wheelchair will turn to the left. If
there is the obstacle in one side, the electric wheelchair willturn to the opposite direction. When both the area LB andthe area RB exists the obstacles, the electric wheelchair will
be slowed down the speed only.

3.1 Structure
The overall structure of the electric wheelchair is shownas Figure 8. The laser range scanner is attached on the electric wheelchair. The subject is attached S-EMG sensors.
The data measured by the laser range scanner and s-EMG
sensors can be analyzed on the personal computer. The
flowchart of the control is shown as Figure 9. The user cancommand the electric wheelchair by using s-EMG sensors.
The laser range scanner is checking the state of the obstacle
around the electric wheelchair. The output command isjudged on the personal computer by both using s-EMG sensors and the laser range scanner.

Figure 5. The search area in area B.
(c)Area CIf the obstacle does not exist in the area A and the area
B, the proposed system will search the obstacle in the area
LC and the area RC (Figure 6). The speed of the electric
wheelchair is the normal speed. If the obstacle exists in thearea LC or the area RC, the electric wheelchair will be controlled by the fine adjustment. To check these areas is inorder to prevent approaching the wall.

Figure 8. The structure of the proposed system.
Figure 6. The search area in the area C.
(d) During a stop
When the user commands as the left or the right, theelectric wheelchair turns on the spot. By checking the obstacle in the area LD or the area RD (Figure 7), the electric
wheelchair turns slowly on the spot.

Figure 9. The flowchart of the control.
3.2 Experimental condition
We experimented with proposed system in the building
corridor. The width of the building corridor is 1.9 meter.
The subject is a healthy twenties male, and he agree to joinin the experiments himself. He controlled the electric
wheelchair by using s-EMG sensors. To check the proposed

Figure 7. The search area during the stop.

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5 CONCLUSION

system, he is supposed as severe disabilities paraplegic who
could use bite only.

We tried to control the electric wheelchair with the proposed system. The electric wheelchair did not clash the wall.
Using the laser range scanner, the user need not commandfor the fine adjustment. Therefore the electric wheelchaircan drive smoothly in the narrow space such as the hallwayof the building.
If the laser range scanner cannot measure the obstacle,
the user needs to command the stop with s-EMG sensors
himself. The proposed system provided the assist to the user.
While the obstacle is found of the laser range scanner, theuser doesn’t need any command, the proposed system avoid
the obstacle automatically. Now, we are trying the monitoring test for serious disabled people by using the proposedsystem.
But the wavelength of the laser range scanner is 785nm,
and this wavelength cannot measure such as the glass. So
the electric wheelchair needs another sensor such as theultrasonic wave sensor to deal with the obstacles such as
the glass.

4 RESULTS
We tried to drive the electric wheelchair with the proposed system. Actual scenes and driven routes are shown as
Figure 10. The interval between two points is 2 second. The
arrow in the figure shows the direction of driving the electric wheelchair.
The route of starting to the oblique angle against thewall is shown Figure 10(a). In the start position, the usercommanded to start the electric wheelchair. When the electric wheelchair turned to the right with the proposed system,
the user did not have commanded anything. In the goal, theelectric wheelchair was stopped by the user command. In
the case of this course, the electric wheelchair did not clash
with the wall.
The figure 10(b) is shown as driving routes in the corridor. The area X is the straight corridor. The width of thearea Y is larger than the width of the straight corridor. The
electric wheelchair did not clash the wall.

REFERENCES[1] A.Shimada, P.Kiddee, Y.Nishi "Obstacle AvoidanceControl on Omnidirectional Vehicle Robots Using
Range Sensor", IEEJ Transactions on Industry Applications, Vol.128, Issue 6, pp.849-856, 2008.
[2] H. Tamura, T. Manabe, K. Tanno, Y. Fuse“The electric
wheelchair
controlsystemusing
surfaceelectromygram of facial muscles”,Proc. of World Automation Congress 2010, IFMIP 240, CDROM, (pp.16), 2010.
[3] H.Tamura, T.Murata, Y.Yamashita, K.Tanno, Y.Fuse“Development of the electric wheelchair hands-freesemi-automatic control system using the surfaceelectromyogram of facial muscles”, Artificial Life andRobotics, Vol.17, Issue 2 , pp.300-305, 2012.

Figure 10(a). Starting to the oblique angle.

Figure 10(b). Driving routes in the corridor.

© ISAROB 2013

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On Driving Assistance using Forearm Vibrotactile Feedback for Wheelchair
Drivers
Aydin Tarik ZENGIN, Kazuki NABEKURA, Hiroshi OKAJIMA, and Nobutomo MATSUNAGA
Department of Computer Science and Electrical Engineering, Kumamoto University, Japan(Tel: 81-96-342-3639)(matunaga@cs.kumamoto-u.ac.jp)
Abstract: Welfare devices are getting more popular along with the growing population of elder and disable. The significanceof an easy to use wheelchair is rising recently. The authors have developed a new wheelchair STAVi which has a unique piggybacking design. Because of the growing importance of environmental safety, new driving assistance systems are required. A
superficial vibrotactile feedback mechanism is designed to transmit environmental information to the wheelchair driver unlike
the visual or audible signals that distract attention. Our goal is to develop a driving assistance system using vibrotactile feedback
to the driver and to guarantee the safety. As a first step we evaluate simple vibrotactile system for the newly designed electric
wheelchair. In this paper, we clarify the concept of vibration generation and propose a vibration feedback system. Also, we
analyze the cognitive characteristics of vibration in order to show the effectiveness of the obstacle localization using vibrotactile
feedback. It is expected that the drivers can easily estimate the location of environmental obstacles just by using the vibrotactile
feedback. The effectiveness of the proposed system is confirmed by experiments.
Keywords: Electric wheelchair, vibrotactile feedback, driving assistance, obstacle detection
1

INTRODUCTION

cle drivers, providing distance and turning direction using vibration motors attached gloves [3]. The connection between
the gloves and the control system is not comfortable for thedriver.

By the increasing demand on the welfare devices, it
has been a requirement to focus on the safety of those.
Wheelchair drivers need extra information about the environment around them, regardless of their disabilities. These may
be the obstacles around, or the road conditions such as the inclination and curves. In the case of electric wheelchairs, thedriver needs to spend less effort to move the vehicle than the
traditional manual steering methods. Wheelchair can easily
be driven on to an obstacle when the driver didn’t perceive it
especially when he/she has visual deficit or mobility impairment. Also, precise perception of the obstacles such as thestationary bodies or pedestrians is difficult for the disabled.
These difficulties make the situation dangerous, because thedriver won’t run into any physical difficulty to move the vehicle even in an unsuited environment when there is no visualor audible feedback.

Previous studies on vibrotactile feedback are generally focused on navigation systems providing basic directions. Audiovisual systems have been studied for driving assistancebut vibrotactile feedback systems have not been studied indetail. We have developed a new electric wheelchair “STAVi”[4] and focused on driving assistance using vibrotactile feedback [6] because the obstacle detection for the wheelchairusers is an important issue. As the STAVi has a piggybacking
design and several body supports such as the chest support
and arm rests, it will be effective to construct the vibrotactile
feedback system without annoying hard-wiring devices.
We propose a vibrotactile feedback system using arm rests
providing environmental information through vibration stimuli. The obstacle distance and direction detected by laser
range finder are transferred to the wheelchair user in order
to assist him/her. Proposed vibrotactile feedback system islocated onto the arm rests in order to provide convenienceto the driver. In this paper, we aimed to provide the abilityof environmental recognition to the wheelchair driver. Environmental obstacles’ location and distance will be attended
to the driver just by vibration stimuli. Experiments are conducted to show the accuracy of drivers’ obstacle position estimation by using prerecorded data instead of real-time sensor
data. The estimation of the obstacle’s position and moving
trajectory is evaluated by experiments.

Commercially available driving assistance systems use
predominantly visual or auditory channels to communicate
with the driver. Environmental factors such as the direct exposure to sunlight and background noise makes the driver’s
distraction increase. Yu et al. discussed and implemented
a driving assistant system for automobiles, containing ultrasonic sensors and various indicators including tactile vibrators placed on the steering wheel, a buzzer and an LED display [1]. A similar approach in navigation to provide distance to the destination is presented by Tsukada et al. bychanging the pulse interval of the vibration motor [2]. Bial et
al. proposed a vibrotactile feedback system for the motorcy-© ISAROB 2013

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Fig. 2: Experimental system and vibration motors with eccentric mass on the shaft
Fig. 1: Appearance of STAVi#2

2

CHARACTERISTICS

OF

transmit distance and direction as shown in Fig.3. Johansson
et al. gives the maximal cycle response of slowly adapting(SA II) units of skin as 0.5[Hz] even at the low stimulus amplitudes [5]. Therefore, by taking the SA II receptor’s sensitivity into consideration, the control period was taken as
1[s] which is the maximal cycle response time, in order todifferentiate the pause between vibration stimuli. The maximum vibration frequency was chosen below 100[Hz] in our
experiments. Stimulation on-time was varying according tothe obstacle’s direction detected by the sensor. Maximum
on-time interval was chosen as 1[s]. A time delay between
vibration signals was used to provide the direction. In lightof these information, we created the unique vibration patternshown in Fig.3.

WHEELCHAIR

STAVIIn this study, we developed a new electric wheelchair“STAVi”, shown in Fig.1 [4]. Users can get into STAVi by
piggybacking from a bed or wheelchair. While driving, user
weight is supported at three points: seat, chest pad, and arm
rests. This serves to distribute user’s weight and to reduce theload on any particular point. After the user is safely seated,
the seat escalates to the user’s standing position so that theireye line is at the same height as it would be in their standing
position. The elderly and disabled persons who took a testride stated that they do not feel inferior because they are at
the same height as their care personnel.
In this paper, a stationary experimental setup that has thesame frame structure with STAVi such as the seat, chest support and arm rests, was used. Fig.2 shows the experimental
system mimicking the STAVi’s frame.
3

3.2 Vibration generation method
Environmental information was encoded in two parameters: obstacle distance and direction. We adjust the vibration
magnitude in order to indicate the obstacle’s distance. Direction indication uses a similar method of interaural timedifference (ITD) model to mimic the perception of sound.
The difference on the arrival times of the sound waves

VIBROTACTILE FEEDBACK SYSTEM

3.1 Concept of vibrotactile feedback
Extra attention is needed by the drivers of STAVi because
the target users are the elder and disable. Proposed systemprovides this extra attention by scanning the environment forthe obstacles that may collide with STAVi. In order to warn
the driver, our system uses superficial stimuli applied to thedriver’s arms.
For superficial stimuli, Johansson clarifies the mechanoreceptors’ frequency response in his fundamental study that
implies a tactile stimulation at frequencies under 100[Hz]improves spatial resolution of vibrations perception [5]. In
our previous studies, we analyzed the frequency response ofhuman skin with multiple subjects and confirmed that the vibration frequencies over 100[Hz] are inefficient [6].
We encoded the environmental information in a pattern to© ISAROB 2013

Fig. 3: Vibration pattern

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Vibration generators are placed on the armrests because
the driver continuously contacts to the arms during the motion as shown in Fig.2. We didn’t attach the vibration motors
directly to the driver’s body because of the inconvenience ofthe permanently attached type stimulators such as arm bands
or bracelets which annoy and distract the driver.
Fig.2 shows the experimental system imitating the
STAVi’s frame. Vibrotactile feedback stimulators made by
DC motors (“28L28-416E” from Portescap) with eccentricmasses on the shaft are placed inside of the armrests. Vibration motors were driven by TD12770-48W05 servo controllers from Tokushu Denso and provides a maximum powerof 6.94[N]. Environmental information was taken by the laser
range finder (“UTM-30LX” from Hokuyo Automatic) andprocessed by the microcontroller (Atmel AVR 328). Vibration pattern was decided according to the obstacles’ positionand vibration signal was applied to the driver’s arms.

Fig. 4: Vibration time difference model inspired by ITD

is the key feature of the perception. According to this difference, sound source can be localized by the subject. We
mimicked this concept in our study and applied a delayed vibration stimuli to one of the arms according to the position of
the obstacle as shown in Fig.4. The time difference between
the right and left arm vibration signals was adjusted similar
to the arrival time of the sound waves to ears. Fig.4 represents the time difference model inspired by ITD. Using thismethod, subject could get the rough position of the obstacle
and protect himself from the collision.
Obstacle distance and direction were given by laser range
finder. A decision algorithm decides the position of the obstacle or the approaching object. According to the results of
the algorithm, the driver was warned by a unique vibration
stimuli that was created considering the obstacle’s position.
The warning signal is applied using the motors located on
the wheelchair’s armrests. The driver could have a chance to
avoid collision.

4 RESULTS OF PERCEPTION EXPERIMENTS
The effectiveness of the proposed system was measured
by experiments. Using the pattern in Fig.3, 5 participants
were subjected to a test of 4 moving object trajectories. In
this experiment, subjects were asked to guess where the obstacle around STAVi is moving according to the applied vibration signal. A training session was given to the subjects
before the actual experiment. Subjects used the same setup as
the training session and replied by using the joystick to give
their guess. Fig.5 shows the subject response for various trajectories. Solid line represents the actual obstacle trajectory.
Colored dashed lines represent the subject responses.
Table 1: Correlation coefficient of the subject response

3.3 Experimental setup
Subj.1
Subj.2
Subj.3
Subj.4
Subj.5
Average

Experimental setup was conducted with 5 able participants.
The average age was 23.16 (SD(Standard
Deviation)= 2.48), and the experiments lasted 30 minutes
for one participant. Fig.4 shows the schematic diagram oftime difference between the right and left arm vibration signals inspired by the ITD. dtk : k = {L, R} was varying
according to the obstacle’s direction. When the obstacle is in
front of the vehicle, dtk becomes zero. In our experiments,
dtk was varying between 0 and 1[s] as shown in Fig.3.
Each participant was subjected to a training of over 50 random vibration signals and simultaneously was shown the obstacle position on the screen. During the training session, thesubject gave response by moving the joystick to the directionof the obstacle and saw the correlation between his response
and the actual position on the screen. After the training session, the subject was asked to guess where the obstacle isaccording to the vibration signal applied to the arms without
seeing the actual obstacle position.

© ISAROB 2013

Traj. 1
0.987
0.951
0.992
0.988
0.977
0.979

Traj. 2
0.867
0.892
0.911
0.915
0.972
0.911

Traj. 3
0.896
0.869
0.975
0.977
0.965
0.936

Traj. 4
0.968
0.975
0.967
0.971
0.987
0.973

Table 2: Deviation for 2 subjects repating the test

Subj.1

Subj.2381

Trial 1
Trial 2
Std. Dev.
Trial 1
Trial 2
Std. Dev.

Traj.1
0.987
0.983
0.003
0.951
0.987
0.025

Traj.2
0.867
0.927
0.042
0.892
0.941
0.035

Traj.3
0.896
0.963
0.047
0.869
0.952
0.059

Traj.4
0.968
0.983
0.011
0.975
0.952
0.016

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Fig. 6: Repeatability test by 2 subjects

Correlation coefficient for the each subject and averagecorrelation coefficients to show the accuracy of the subject
responses are given in Table.1. According to the results, subject’s guesses are accurate and the average correlation coefficient for all the different trajectories is 0.949.
Analysis of the repeatability is done by experiments. Two
subjects took the same tests at different times and the subject
responses are recorded as shown in Fig.6. Table.2 shows thecorrelation coefficient of the subject response and the standard deviation of the repeated tests to verify the repeatability.
Results show that deviation between each trial is very
low and by using the proposed vibrotactile feedback system,
wheelchair drivers could estimate the outline of the obstacle location. The estimated accuracy is confirmed by the repeated tests.

Therefore the accuracy would be low when the subject is notcontacting the armrests completely. Support of vibrotactile
feedback via the seat and chest support pad is a future work.

5

REFERENCES[1] Yu, F. et al.: “A New Driving Assistant for Automobiles”, CCECE 2007, pp.1199–1202, 2007
[2] Tsukada, K. and Yasumura, M.: “Activebelt: Belttype wearable tactile display for directional navigation”, UbiComp 2004, pp.384–399, 2004
[3] Bial, D. et al.: “Enhancing outdoor navigation systems through vibrotactile feedback”, Proceedings of
CHI2011, pp.1273–1278, 2011[4] Maruno, Y. et al.: “Driving Experiment of Front Drive
Type Electric Wheelchair using Yaw-rate Control”,
SICE Annual Conference 2012, pp.1408–1413, 2012

CONCLUSION

In this study, we proposed a driving assistance system using superficial vibrotactile feedback for wheelchair drivers.
Primary goal in this paper is to show the accuracy and repeatability of the proposed system by multiple-subject experiments. Experimental results showed that subjects could estimate the obstacle’s position and the moving trajectory sufficiently. Repeated experiments verified the accuracy of the
proposed method and showed that the subjects could locate
the obstacle just by using the vibrotactile feedback.
In this setup, vibration motors were placed in the armrests.

© ISAROB 2013[5] Johansson, R.S. et al.: “Tactile sensibility in the human hand: receptive field characteristics of mechanoreceptive units in the glabrous skin area”, The journal ofphysiology, vol. 281-1, pp.101–125, 1978
[6] Zengin, A.T. et al.: “On vibration feedback method
for pain emulation and its feedback to human”, ICCAS
2011, pp.655–658, 2011382

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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Measurement and analysis of upper-limb essential tremor motion
for tremor suppression with an exoskeleton robot
Yosuke Ono1, Kazuo Kiguchi1, and Nobuhiro Okada1
1

Kyushu University, Japan(Tel: 81-92-802-3167)

1

2TE11304G@s.kyushu-u.ac.jp

Abstract: An essential tremor is one of the most common tremor disorders of the arm and it may occur during a voluntary
motion. Power-assist robots are useful for the physically weak persons to perform the daily motion. Although some powerassist robots are controlled based on electromyogram (EMG) signals, EMG signals are also influenced by the essential tremor.
Therefore, when the user who suffers from the tremor uses the EMG-based controlled power-assist robot, the robot might
magnify the vibration of the tremor. Although the tremor suppression control has been proposed for the EMG-based controlled
power-assist robot to suppress the tremor in the hand position, the other part of the upper-limb might still vibrate since actual
tremor movement of the upper-limb has not been analyzed precisely. Therefore, an upper-limb tremor sensing system is
proposed to measure and analyze the precise tremor motion in this paper.
Keywords: essential tremor, power-assist robot, electromyogram, tremor sensing system

1 INTRODUCTION
A human motion is classified into two groups. One is avoluntary motion and the other is an involuntary motion. In
a power-assist robot which is activated based onelectromyogram (EMG) signals, the EMG signals are used
to estimate the user’s motion intention in real-time. Since
the amount of EMG signal is related to the muscle activity
level, the EMG signal is affected not only a voluntary
motion but also an involuntary motion. Therefore, theinvoluntary motion might be misunderstood as the user’s
motion intention in the EMG-based controlled power-assist
robot.
A tremor is one of the involuntary motions. It issomewhat rhythmic motion that may occur in various bodyparts such as an arm, a leg and so on. An essential tremor is
one of the most common tremor disorders of the arm and it
may occur during a voluntary motion. If the essential
tremor occurs in the arm, the person may not be able toachieve the target task properly. To suppress a tremor, manymethods have been proposed [1,2]. For example, Kiguchi etal. proposed the tremor suppression control method thatextract the vibrational component of the estimated motion
using band pass filter, and suppress the tremor by adding
opposite phase force vector [3]. However, this method
focused on only the estimated motion of the hand and thetremor motion of the elbow or the shoulder is not taken into
consideration. Therefore, the other part of the upper-limb
might still vibrate since actual tremor movement of theupper-limb has not been analyzed precisely yet.

© ISAROB 2013

Fig. 1 7DOF upper-limb power-assist exoskeleton robot
Although there are a lot of researches of the essential
tremor [4-6], there are no research which investigated about
the relationship of the motion of hand, elbow, and shoulder.
Therefore, an upper-limb tremor sensing system, which
consists of three acceleration sensors, a gyroscope, and an
encoder, is proposed to measure and analyze the precise
tremor motion in this paper.

2 UPPER-LIMB POWER-ASSISTEXOSKELETON ROBOTThe 7DOF exoskeleton power-assist robot [3] for the upper
limb used in this paper is shown in Fig1. The robot consistsof seven DC motors. This robot is able to assist the most ofeach human upper-limb joint motion (shoulder vertical andhorizontal
flexion/extension
motion,shoulderinternal/external rotation motion, elbow flexion/extension
motion, forearm supination/pronation motion, wrist palm
flexion/extension motion and wrist palm radial/ulnar
deviation motion).

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3 EMG-BASED CONTROL METHOD

Fend  J T

To estimate the user’s motion and control the 7DOF
upper-limb power-assist exoskeleton robot, sixteen
channels of EMG signals are used as main input signals.

1(4) Fend (i)N f i 1
where Fend is the hand forece vector, J is the Jacobianmatrix, Favg is average of Fend in N f number ofFavg 

Since raw EMG signals are not suitable as input signals
to the controller, the feature of the raw signal must be
somehow extracted. In order to extract the feature of theraw EMG signal, the root mean square (RMS) of the EMG
signal is calculated and used as an input for the controller.
The RMS calculation is written as follows:

RMS 

1 N 2
 vi
N i 1

samples. Then, the hand acceleration vector can becalculated based on eq.(4).

  M 1F
X
d
avg

w12

 w115(1)

w22  w215

w62  w615
w72  w715

w116   ch1 
w216   ch2    w616  ch15 
w716  ch16 

robot. The desired hand velocity and position can becalculated based on eq.(5). In addition, the following
impedance control equation is used to calculate theresultant hand force vector F  B( X  X )  K ( X  X )F  MXddd(6)

where B is the viscous coefficient matrix and K is thespring coefficient matrix. The impedance parameters B
and K in eq.(6) depend on the upper-limb posture andactivity levels of activated upper-limb antagonist muscles.
Therefore, the impedance parameters have to be adjusted inreal time. So B and K are defined based on the upperlimb posture and activity levels of activated upper-limb
antagonist muscles.

(2)

where  i are the joint torques for i th joint motor, wij is
the weight value for j th EMG signal to estimate the torqueof motor-i, and ch j represents the RMS value of the EMG
signal measured in channel i . The weight matrix (i.e., themuscle-model matrix) in eq.(2) can be defined using theknowledge of human upper-limb anatomy or the results ofexperiments. Therefore, the joint torque vector generated bymuscle force can be calculated if every weight for the EMG
signals is properly defined. Furthermore, the posture of theupper-limb affects the relationship between the EMG
signals and the generated joint torques because ofanatomical reasons such as change of the moment arm. In
other words, the role of each muscle for a certain motion
varies in accordance with joint angles. Consequently, theeffect of the posture difference of the upper-limb must be
taken into account to estimate the correct upper-limb
motion for the power-assist. Therefore, a neuro-fuzzy
muscle-model matrix modifier [3] is applied to take into
account the effect of the upper-limb posture change of theuser in on-line manner. The neuro-fuzzy modifier is used toadjust the weight matrix in eq. (2) by multiplying thecoefficients in accordance with the upper-limb posture of
the user, so that the effect of upper-limb posture difference
can be compensated effectively.

Finally, the joint torque command vector
calculated as follows: motor  J T F

 motoris(7)

where  is the power-assist rate. When the user’s muscle
activation levels are low, force/torque sensor-based control
is used to control the robot.

4 EXPERIMENT.
The tremor is somewhat rhythmic motion that may
occur in various body parts. Especially, an essential tremor
is one of the most common tremor disorders of the upperlimb and it may occur during a voluntary motion. If theessential tremor occurs in the upper-limb movement, theperson may not be able to achieve target task properly.
There are various researches in regard to the tremor motionof upper-limb. Although the hand motion of tremor has
been analyzed up to the present, the detailed tremor
motions such as amplitude of vibration, angular velocity,
angular acceleration of the shoulder, the elbow, and theTo estimate user’s motion intention, hand force vector is
calculated based on the estimated joint torque vector.  istransferred to the hand force vector of the user as follows:© ISAROB 2013(5)

 is the desired hand acceleration vector, andwhere Xd
M is the weight matrix of the user’s upper-limb and thewhere N is the number of the segments (N=400) and v i isthe voltage at ith sampling. The sampling frequency is 1.5
kHz. Using sixteen RMS values, the joint torque vector iswritten as:
 1 
 
 2   w11
 3   w21     4    
 5   w61
  
 6   w71
 
 7

(3)

Nf

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Table.1 D-H parameter

wrist joint have not been analyzed. Therefore, in this paper,
the upper-limb tremor sensing system which consists ofthree acceleration sensors, a gyroscope, and an encoder is
proposed to measure the tremor motion precisely.
4.1 Experiment equipment
The upper-limb tremor sensing system developed inorder to measure tremor is shown as Fig.2. The
accelerometer was attached in the elbow, the wrist, and thehand. Moreover, the encoder was attached center of rotationof an elbow. Moreover, the gyroscope was attached in order
to measure the angle of 3 DOFs of the shoulder, and theacceleration of an elbow to the upper arm side of an elbow.
Since the frequency of the essential tremor 5 to 12 Hz, theband pass filter (5 to 12Hz) is applied to the output of theaccelerometers. In order not to restrict a patient's motion, it
was made by not using metaled frame as much as possible,
and sensors were attached on the human’s body directly.
However, the encoder which measures angle of rotation ofan elbow was attached on the aluminum frame. Moreover,since a wrist had the unstable surface, the accelerometers in
the wrist part were attached on the plastic plate. The initial
posture of elbow was set to be 90[deg] to avoid a singularconfiguration when the elbow was extended. The initial
postures of the other joints were determined to be 0[deg] inthe model of Fig.3. Moreover, I got the subject to take careso that an elbow may not be lengthened. Moreover, in order
to avoid a problem of the singular configuration due to theattached position of the singular configuration due to theattached position of an accelerometer, two accelerometers
are applied to compensate each other in the proposedsystem such as Fig.2 right side picture. Since the distal partfrom the wrist, angular information was calculated byintegrating the angular acceleration obtained by theaccelerometers of an elbow, a wrist, and a hand.

Fig.3 Model of upper-limb

4.2 The analysis method
The method that calculate the position, the velocity, theaccelerometer of an elbow, a wrist, a hand, and angle,
angular velocity, angular accelerometer of a shoulder, anelbow, a wrist is explained in this section. The model ofupper arm is shown as Fig.3. The coordinate frame 0 in
Fig. 3 is a coordinate frame fixed to the shoulder. The
coordinate frame fixed to the shoulder is the same as theinitial posture of the joint 1. Joint1-7 represent shoulder
abduction/adduction, shoulder flexion/extension, shoulderinternal/external rotation, elbow flexion/extension, forearm
supination/pronation, wrist palm flexion/extension, andwrist palm radial/ulnar deviation, respectively. The D-H
parameters were defined as shown in Table.1.
4.2.1 The calculation method of each angular information
The angles of joint1, joint2, joint3, and joint4 can becalculated based on the value acquired from the angle
sensor. The angular velocity of joint1, joint2, joint3, joint4
are calculated by differentiating the angle as follows:q1i~14  (q1i~14  q1i~ 4 ) / t(8)where i is the number of times of measurement and t issampling time. Sampling time is 0.01[sec]. Since the angle
sensor was not attached for the joint5-7 in order not to
restrict the patient’s motion, the value of the accelerometer
was changed into angular acceleration using the following
equation, and the angle and angular velocity are calculated
by integrating the angular acceleration using the eq.(10) anda eq.(11).

Fig.2 Experiment equipment (left: overall, right: wrist)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013ii  xh  i1 q q1  


q





 yhq 2  
 2





q
 q 3  3   xw 
 




4   J 1   yw   J q 4  
q


q
 q 5  5   zw 
 




 y

6 
q
 q 6   e q  ze  7 q 7  

(9)Fig.4 Elbow positionq5i ~17  q5i ~7  t  q5i

(10)1
q5i ~17   q5i ~ 7  (t ) 2  q5i ~ 7  t  q5i ~ 7
2

(11)

1

where Jis the inverse jacobian matrix, which is 7  7matirix, and J is the differentiated jacobian matrix. As
shown in eq.(9), it was calculated using the acceleration of
2 axes of a hand's x and y, the acceleration of 3 axes of awrist, and the acceleration of 2 axes of y of an elbow, and z.
In regard to the accelerometers of the wrist, accelerometer1
in Fig.2 becomes singular configuration when q5  0[deg] ,and accelerometer2 becomes singular configuration whenq5  90[deg]. Therefore when  80  q5  80[deg] , theaccelerometer2 was used, and when q5  80[deg] ,q5  80[deg] , the accelerometer1 was used.
4.2.2 The calculation method of other information
All of the position, the velocity, and the acceleration ofhand, wrist, and elbow were changed into the value in the
fixed coordinate frame of the shoulder. The homogeneoustransformation procession and the rotating matrix were
calculated with D-H parameters in Table1.

Fig.5 Wrist position

5 CONCLUSION.
In this paper, the upper-limb tremor sensing system
which consists of three acceleration sensors, a gyroscope,and an encoder is proposed to measure the tremor motion.
The effectiveness of the developed system was evaluated by
performing experiments with a healthy human subject. As aresult, the position of elbow and wrist is calculated
precisely, although q5-q7 could not be calculated precisely
since attachment of accelerometers were unstable. As afuture work, the attachment method of accelerometers must
be considered.

REFERENCES[1] F.Widjaja, C.Y.Shee, P.Poignet et al, “Filtering
Intended Motion for Real-time Tremor Compensation in
Human Upper Limb using Surface Electromyography,”31th Annual Inter. Conf. of the IEEEEMBS, pp.29962999,2009[2] K. Yano, E. Ohara, S. Horihata, T.Aoki et al,“Development of Tremor Suppression Control System
Using Adaptive Filter and Its Application to Meal-assist
Robot,” Trans. of the Society of Instrument and Control
Engineers, Vol. 45, No. 12, pp. 638-645, 2009.
[3] Kazuo Kiguchi, Yoshiaki Hayashi, Toyoko Asami,“An Upper-Limb Power-Assist Robot with Tremor Su
ppression Control,” Rehabilitation Robotics (ICORR), 2
011 IEEE International Conference.
[4] S. Pledgie, K. E. Barner, S. K. Agrawal, et al,“Observations on essential (heredofamilial) tremor, ” Brain, Vol. 72, No. 2, pp. 113-139, 1949.
[5] E. D. Louis, B. Ford, and L. F. Barners, “Clinic
al subtypes of essential tremor,” Arch Neurol, Vol. 57,
pp. 1194-1198, 2000.
[6] Juan-Manuel Belda-Lois, Helios de-Rosario, et al,“Can human movement analysis contribute to usability
understanding?”, Human Movement Science 29(2010)5
29-541

4.3 Experiment results
The effectiveness of the developed upper-limb tremor sensing system was confirmed with a healthy huma
n subject as a first step. Healthy person wore an exper
iment equipment, and moved arm in the various directi
ons at random. The calculated result of elbow positionis shown in Fig. 4. The calculated result of wrist pos
ition is also shown in Fig.5. As shown in Fig.4, andFig5, the position of elbow and wrist were calculated
precisely from q1-q4 which is taken by attached angle
sensor. However, q5-q7's angular information were notcalculated precisely. For the reason of this result, theinstability of the surface human's skin is considered tobe the cause. Because the noise caused by the accelerometers attached on the unstable surface of the subject
skin is accumulated. As a future work, the attachment
method of accelerometers must be considered so thatthe data of acceleration can be acquired correctly.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A describing method of latency tolerant hardware for a pure ANSI-C/C++
based high-level synthesis technology
Akira Yamawaki1 and Seiichi Serikawa1
1

Kyushu Institute of Technology, Japan(Tel: 81-93-884-3255, Fax: 81-93-884-3214)
1

yama@ecs.kyutech.ac.jp

Abstract: The image processing is important for the robotics and its hardware implementation is required in order to realize asmall and low-power device with the appropriate performance where the high performance computer cannot be used due to thecost, size and power limitation. To reduce the burden of such hardware development, the high-level synthesis (HLS)
technologies that automatically convert the algorithmic description to hardware have been proposed and developed. The
combination of the memory latency hiding and data process pipelining is very important to extract the hardware performancemaximally. However, nobody shows clearly how to describe the hardware behavior to generate such hardware. This paper
shows a generic describing method for HLS technology based on ANSI-C/C++ that can realize the combination of the memory
latency hiding and data process pipelining. The experimental results show that our method can be applied easily to the intuitive
C program. The logic simulation and an FPGA implementation reveal the effects to the performance improvement and the
hardware increase induced by our method.
Keywords: high-level synthesis, latency hiding, pipelining, hardware, image processing.
of the hardware explicitly, the HLS tools supporting ANSIC/C++ automatically extract them from the sequence ofstatements and loops in a pure C/C++ program.
Actually, the parallelism however cannot be extracted
enough. So, the developer must indicate parallelism bypragmas explicitly. Typical and simple examples showing
how to insert pragmas are shown by the venders of the HLS
tool. But, some describing methods which are expected togenerate a sophisticated hardware to improve the hardware
performance are not shown mostly. For example, it is tocombine the memory latency hiding and data process
pipelining. This combination is very important to pull theperformance of the hardware maximally.
Using an HLS tool (Vivado HLS of Xilinx [6])supporting pure ANSI-C/C++, this paper shows a genericdescribing method of ANSI-C/C++ program that can realize
the combination of the memory latency hiding and the dataprocess pipelining. In this method, the memory loading part,the data processing part and the memory storing part aredescribed isolated by FIFO passing the stream data. While
the memory loading part and the memory storing parttransfer the data to the memory, the data processing partprocesses the stream data via FIFO simultaneously. Thus,
the memory access is overlapped with the data processing
efficiently and naturally.

1 INTRODUCTION
The image processing, e.g. the object tracking, theobject recognition, and so on, is important for the robotics.
Hardware implementation of the image processing isrequired in order to realize a small and low-power device
with the appropriate performance where the highperformance computer cannot be used due to the cost, sizeand power limitation.
However, the hardware is developed by an expert using
hardware description language (HDL), spending asignificant burden. At last the large development load canrealize the image processing hardware optimized toperformance and power consumption.
To reduce such burden of hardware development, thehigh-level synthesis (HLS) technologies that automaticallyconvert the algorithmic description to HDL program havebeen proposed and developed [1-5]. Most HLS tools
employ the C language as the design entry. However, thedesign entries are C-like language, which is not pure ANSIC/C++. Also, some HSL tools provide their own grammar
suiting to represent the hardware nature. That is, thedevelopers have to learn the dedicated C-like language
although they need not to learn HDL.
In contrast, some HLS tools supporting ANSI-C/C++ asa design entry are provided recently [6], [7]. Instead of Clike language that describes parallelism and clock boundary© ISAROB 2013

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Fig. 1. Hardware model
In a case study to 3x3 image filter, we show that our
method can be applied easily to the intuitive C program. In
addition, through the logic simulation and implementation
to an FPGA, we confirm the effects to the performance
improvement and hardware increase induced by our method.
The rest of the paper is organized as follows. Section 2shows a hardware model to which we map C program.
Section 3 intuitively designs the hardware description in C
to 3x3 image filter. Section 4 extends the intuitive C
program mentioned above to combine the memory latency
hiding and data process pipelining by our method. Section 5
evaluates the hardware overhead and the performance
improvement induced by our method, compared with theintuitive C program. Finally, Section 6 concludes the paper.

Fig. 3. Intuitive C program
The memory loading part loads the data to be processedin a memory into the input FIFO. The data processing partgets the input data from the input FIFO, processes the input
data and pushes the processed data into the output FIFO.
The memory storing part pops the processed data from theoutput FIFO and stores them into the memory.
The data processing part processes the stream data
through its own pipelined data path, while the memory
loading and storing parts perform the memory accesses.
Thus, the memory latency is hidden by the pipelined dataprocessing.
This model is trivial but nobody has explained clearly
how to write to generate such hardware. That is, since a
generic describing method does not exist, the designer has
to write the hardware behavior in C carefully by its own
talent so that the hardware model as shown in Fig. 1 isgenerated.

2 HARDWARE MODEL
Fig. 1 shows a hardware model to which we map C
program. The organization of this model is commonly used.
Thus, any hardware designer can accept to employ thismodel.
This model is decomposed to the memory loading part,the data processing part and the memory storing part. These
parts are separated by FIFOs. They run completely inparallel, communicating via FIFO streamly each other.

3 INTUITIVE METHOD
To confirm that our method can be applied easily to theintuitive C program, we describe the case study of 3x3
image filter in this section. Fig. 2 depicts the dataprocessing image of 3x3 image filter and Fig. 3 describes
an intuitive C program of the hardware behavior.
For the input image, a column is loaded into the top of
the internal ring buffer along the image height as the linesof 11 to 12 in Fig. 3.
To the buffer holding columns, a 3x3 image filter isperformed as the lines of 14 to 18 in Fig. 3. 3 pixels in the 3

Fig. 2. Image of Data process© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013rows currently indexed in the buffer are pushed into the 3x3
window. Then, an image filter processes this 3x3 window.
The result of the image filter is stored into the memory as
the line of 19 in Fig. 3.
Once the 3x3 image filter finishes on the buffer, theindexes to banks of the buffer are updated to load a next
column to new top entry as the lines of 21 to 22 in Fig. 3.
Inserting pragma, the pipeline structure can be extracted
by an HLS tool, Vivado HLS 14.3 [6]. For example, theinner loops of the line 11 and the line 13 are converted tothe pipeline hardware. However, the parallel feature among
the memory access and data processing cannot be extracted
by any pragma. Although we cannot analyze the internal
processes of the Vivado HLS in detail, the data dependency
over some arrays may be a reason to obstruct theparallelization. That is, the C program must bereconstructed to extract the parallelism between thememory access and data process.

4 PROPOSED METHOD
Our proposed method is very simple and easy way toreconstruct the intuitive C program. Fig. 4 shows the C
program of which the intuitive C program shown in Fig. 3
is reconstructed by our method.
The program basically consists of 3 functions which are
the memory loading (mem_load), the data processing(data_proc) and the memory storing (mem_store). These
functions are connected by the FIFOs. This structure isequal to that of Fig. 1.
Each function runs individually from the top to thebottom of the image. To make them run in parallel, theDATAFLOW pragma [6] is assigned to the function Top inorder to extract the parallelism among the functions. In
addition, the data dependency among the functions has tobe cut. Thus, the streaming FIFOs, iFIFO and oFIFO which
are bridges among the functions are defined in the function
Top.
The mem_load pushes the pixel in the memory into theiFIFO until the iFIFO becomes full.
Simultaneously, the data_proc pops the iFIFO andstores the popped data into the top of the ring buffer while
the iFIFO is not empty. Once the top of the ring buffer
becomes full, the data_proc performs 3x3 image filter assame as Fig. 3. However, the processed data is pushed into
the oFIFO instead of the memory. The pipelining is also
performed to the inner loops of the same as Fig. 3 by the
PIPELINE pragma [6].
In addition, the mem_store pops the oFIFO and storesthe popped data into the memory in parallel.

© ISAROB 2013

Fig. 4. C program reconstructed by our me
thod
Using our method, the memory loading and storingparts are accessing the memory while the data processing
part is processing the data fast by the pipelining. That is,
our method can naturally express the hardware which hides
the memory latency to extract the performance of thepipelined hardware maximally.

5 EXPERIMENT AND DISCUSSION
5.1 Performance evaluation
To evaluate the performance of the hardware to which
Vivado HLS 14.3 converts the C program, we have389

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the number of lockup tables realizing the combinational
logic. The FF means the number of flip-flops. The BRAM
means the number of embedded memories in the FPGA.
The result shows that the investment of hardware
resources increased by our method is appropriate enoughfor the performance improvement of 1.5 times.

Table 1. Hardware cost

6 CONCLUSION
We have proposed a generic describing method of alatency tolerant hardware for a pure ANSI-C/C++ based
high-level synthesis technology. Through the case study
that uses 3x3 image filter, we have shown that our method
can be introduced easily into the intuitive C program. Also,
it has been confirmed that the performance improvement
can be achieved by the moderate hardware investment.
As future work, we will evaluate our method to more
application programs and apply it to more HLS tools.

performed the logic simulation on the Modelsim 10.1c ofMentor Graphics. We set the clock frequency to 100MHz.
Fig. 5 shows the experimental results. The ORG of thehorizontal axis means the intuitive version shown in Fig. 3.
Also, the PAR indicates the version that is modified by our
method shown in Fig. 4. The values on our method mean
the depth of the input and output FIFOs. The vertical axis
indicates the normalized execution time to the intuitive
version. The data are measured over several resolutions of
the input image. The filter was the average filter.
The result shows that our method can improve theperformance compared with the intuitive version by the
combination of the memory latency hiding and the dataprocess pipelining. The depth of FIFO affects theperformance improvement due to our method. The deeper,the better. However, the speedup is saturated by about 1.5
times. That is, the optimum depth exists to improve theperformance maximally, suppressing the hardware cost.

REFERENCES[1] Mitrionics (2008), Mitrion User’s Guide 1.5.0-001,
Mitrionics.
[2] Pellerin D and Thibault S (2005), Practical FPGA
Programming in C, Prentice Hall.
[3] Lau D, Pritchard O and Molson P (2006) AutomatedGeneration of Hardware Accelerators with Direct Memory
Access from ANSI/ISO Standard C Functions, IEEE Symp.
on Field Programmable Custom Computing Machines,
pp.45-56.
[4] Mentor Graphics (2012), Handel-C Synthesis
Methodology, http://www.mentor.com/products/fpga/handel-c/[5] Yamawaki A and Masahiko I (2011) High-level
Synthesis Method Using Semi-programmable Hardware for
C program with Memory Access, Engineering Letters,
Vol.19, Issue 1, pp.50-56.
[6] Xilinx (2012) Vivado Design Suite User Guide HighLevel Synthesis, Xilinx user guide, UG902 (v2012.2).
[7] Calypto (2012) Designing High Performance DSP
Hardware Using Catapult C Synthesis and the Altera
Accelerated Libraries, Calypto White Paper, WP-0004 052012, http://calypto.com.

5.2 Hardware Overhead
To improve the performance maximally while
suppressing the hardware cost, the optimum point of the
FIFO depth exists as mentioned above. Thus, we confirm
that the hardware cost at the boundary point of the FIFO
depth is compensated by the performance improvementcompared with the hardware size of the intuitive version. To
do this, we have implemented the hardware into an FPGA,
Spartan-6 of Xilinx. The used implementation tool is ISE
14.3. Table. 1 shows the result of this evaluation.
FIFO means the depth of the i/o FIFOs. The LUT means

Fig. 5. Performance evaluation© ISAROB 2013

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A Research on State Recognition in Wide Area by
Aerial Images Analysis
Ganwen Jiang1, Masayuki KASHIMA1, Kiminori SATO1
and Mutsumi WATANABE1
1

Kagoshima University, Japan(Tel: 81-80-5264-2806)
1

kyoukinbun@yahoo.co.jp

Abstract: This paper proposes an autonomous monitoring system to track the subject in real time by using aerial images, novel
image processing method, and helicopter control technique. In this study, flying robot named AR.Drone is used for solving the
problem of insufficient tracking capability in previous studies. In order to track the subject correctly and control the flying
robot in right direction, it is important to understand the information of the subject’s moving direction. For calculating themoving direction correctly from aerial images, new method by integrating various kinds of modules is proposed. With
experiments, the stability of human tracking capability and the effectiveness of the direction estimation by aerial images
analysis is verified.
Keywords: State recognition, Aerial images, Autonomous monitoring system, Moving direction, Automatic humantracking, AR.Drone autonomous control.
According to aerial photography and motion analysis, thesystem on state recognition in wide area is developed.
Fig.1 shows the functional diagram of the study. Firstly,
flying robot takes off, and hovers in air waiting humanentry. When subjects enter, it is to carry out head detection,
clothes detection, and human area determination. Then,judges whether human is in the vision. If human is in view,
track the subject and calculate the moving direction. If
human is out of the viewing range, it will move back tosearch the subject again by features of head and clothes
color learned. If the data of moving direction is received,
the flag of state control will become true, and transmit thedata to AR.Drone mode to control flying robot tracking the
subject. Otherwise, flying robot is hovering continuously.
The purpose of this study is to monitor the subject in realtime by utilizing flying robot.
The novelties of this study are as follows.
(1) To improve the effective range of vision andtracking capabilities, the aerial images taken by flying robot
is utilized.
(2) Comparing to conventional tracking method, newmethod by integrating various kinds of modules is devised.
(3) Control flying robot accurately with the propeller
control and predictive control.

1 INTRODUCTION
Recently, the study on state recognition has developed
rapidly [1]. For recording the state and behavior of thesubject, automatic monitoring system is necessary. In
previous studies, multiple fixed cameras are used to analyze
the subject state [2]. However, when it is difficult to ensure
field of view due to shielding, setting of cameras is
restricted. Also, when the subject leaves out of the camera
view, the subject would be lost. In other words, insufficient
tracking capability and limited field of vision as the main
problems exist in conventional technique.

Fig. 1. Functional diagram of study
To ensure effective field of view, and appropriately
recognize indoors and outdoors, the aerial images havebeen our attention in this research. By using flying robot
named AR.Drone, the aerial photography system is built.

© ISAROB 2013

2 PREVIOUS STUDIESFormerly, there are many studies on human behavior,action, and state recognition. In the present study, it is a

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4 PROPOSED SYSTEM and METHOD

new idea to monitor human action and behavior by using
flying robot and novel image processing method.
In the study of FUKUDA et al. [2], human detection bycharacteristics of hair color, head shape, clothes color, andposition have been done by multiple fixed cameras. Whenthe subject is out of camera view, the subject would be lost.
Also, when hair color is not black or clothes color is black,misdetection usually occurs. In the present study, for
ensuring effective field of view, flying robot is utilized.
From aerial images, the subject can be monitored in any
time.
Also, there are many tracking methods such as Mean
Shift, Particle Filter, and Active Contour Model (ACM)
previously, but there are some problems in each method, so
it will affect the tracking result. For example, in studies of
YUSUKE et al. [3], NAKAGAWA et al. [4], and AOKI etal. [5], when the number of particle is too much, processing
time will increase. On the other hand, if the number ofparticle is few, accuracy will degrade. On Mean Shift [6],the HSV or BGR information is used to track the subject.
However, if the subject is not in the search area at the
beginning, the average vector cannot be calculated, so thesubject cannot be tracked. About ACM method [7], it isdifficult to set the position of initial closed curve exactly.
This problem will affect the contour extraction result. In
addition, if there are some image noises, it is difficult to
extract the subject contour correctly.
From the above, it is necessary to build a novel system
to track the subject accurately, and then control flying robot
by moving direction of the subject.

4.1 Proposed system
Fig.2 shows the proposed system of the study. The
proposed system is composed of two threads. One is image
processing thread, and another is flying robot control thread.
In image processing thread, by carrying out humandetection, area determination, and tracking, the humanmoving direction vector can be obtained by time series
analysis. At the same time, flying robot control thread start,
and moving direction data is fed back to control thread.
After that, flying robot is controlled to track the subject byhuman direction vector.

Fig. 2. Proposed system
4.2 Proposed method
In image processing part, this paper proposes the new
method by integrating various kinds of modules to detecthuman moving direction.

3 AERIAL PHOTOGRAPHY SYSTEM
Generally, there are two ways of active flying robot andpassive balloon robot.
The advantages of using balloon robot are small power
consumption, and high stability, while the disadvantages arepoor tracking performance, low degree of freedom, andsusceptible to outside influence. On the other hand, theadvantages of flying robot are high tracking performance,
possible to automatic control, and high stability, but it also
has disadvantages of high power consumption.
Depending on the purpose of this research, the hightracking performance is essential, so flying robot is chosen.
In this study, AR.Drone manufactured by the Parrot
Company is utilized. About AR.Drone, there are twocameras attached on the front and bottom. Also, there aresensors installed on it, such as the Gyro Sensor, theUltrasonic Sensor, and the Acceleration Sensor.

© ISAROB 2013

Fig. 3. Flowchart of research
From the top view, characteristics of human are few,
such as head shape, head and clothes color, body contour,
and moving direction. In order to control flying robot
exactly, it is important to detect moving direction accurately.
Fig.3 shows the flowchart of our research. From aerial
images, head is important feature of people, so head

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4.2.3 Human tracking
Human area has been determined in previous paragraph.
In human area, according to the BGR or HSV histogram ofclothes, human motion vector can be decided by Mean Shiftalgorithm. However, when multiple people are wearing thesame color clothes, it is difficult to judge who the particularpeople is. Therefore, stability of moving direction with timeseries analysis is used to decide who the particular person is(as shown in Fig.5). It is important to achieve multiple
people tracking with aerial images.

detection is carried out firstly. To calculate moving
direction exactly, precise body contour is essential. Thence,
through calculating the human area and human tracking,accurate initial contour can be determined for ACM
algorithm. Therefore, accurate body contour can be got byinitial contour extracting. It is the different from theconventional ACM method.
In flying robot control part, through controlling thepropellers speed and rotation direction, flight direction of
AR.Drone can be controlled. In addition, by predictive
control, AR.Drone will be controlled to track the subject
correctly.
4.2.1 Head detection
Beforehand, the standard HSV histogram (Hist1) is
calculated by analyzing standard head image. Then, headcandidates are detected from aerial images of each frame by
the Hough Transform method. The edge image processedby the Sobel Operator is used for detecting head candidates.
In addition, HSV histogram (Hist2) of each head candidate
is calculated. Furthermore, head can be determined bycomparing Hist1 to Hist2.
4.2.2 Human area decision
From aerial images, clothes part is in the surrounding ofhead. Therefore, by the algorithm of repeatedly outward
diffusing based the head, the rough outline around head canbe obtained.

Fig. 5. Human tracking
4.2.4 Body contour extraction
In order to get human body contour, the Active Contour
Model (ACM) is utilized [8]. In conventional ACM method,
if image background is complex or object contour isirregular, it is difficult to set the initial contour andparameters. In this study, since human area has been
determined accurately, the inscribed circle of human area
can be as the initial contour. Then, body contour can beextracted exactly by initial converging.
4.2.5 Moving direction detection
From aerial images, moving direction is perpendicular
to the shoulder. Therefore, it is necessary to know thedirection of shoulder. The connecting line between thefarthest points of body contour is as the direction ofshoulder in this study. However, wrong results are received
sometimes. Therefore, the time series analysis is utilized to
except wrong results and predict moving direction nexttime.
4.2.6 Flying robot control part
The movement of AR.Drone is controlled by controlling
speed and turning direction of four propellers. When
moving direction is obtained in image processing thread, it
is fed back to AR.Drone. At the same time, flying robot
control thread starts, and then AR.Drone flies along themoving direction of human. However, when the moving
direction of human changes, AR.Drone cannot
automatically put on the brake rapidly in air, so predictive
control is also important. By using time series analysis, theprobability of moving direction in next time can be

Fig. 4. Human area determination
Within this range, clothes color can be detected. After
that, the color of clothes and head are learned. Then, many
particles are distributed randomly on the image.
Furthermore, the maximum likelihood of particles can becalculated due to BGR or HSV of head and clothes. Then,
particles distribution could be calculated shown in Fig .4. In
addition, human area will be determined according toparticles distribution. Because color of clothes and head arelearned, when particular people is out of view, subject canbe detected again by Particle Filter algorithm. Otherwise,
system must return to head detection again. Therefore, thisprocess not only improves the accuracy of detection, butalso reduces the processing time of whole system.

© ISAROB 2013

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6 DISCUSSION

predicted. By chance, if a particular person is out of view,
AR.Drone will go up to detect particular people again.

Due to evaluation experiment, the effectiveness ofproposed system is proved. However, there are
inadequacies also. To control AR.Drone accurately, thesuccess rate of moving direction detection will be improved
as much as possible. Currently, the time series analysis islimited in 10 frames. In future, it will be carried out insmaller interval. In addition, because there are many
influences such as airflow, inertia, and the accuracy of
sensors, it is difficult to control AR.Drone accurately. In
future, this matter will be considered in detail.

5 EVALUATION EXPERIMENT
5.1 Experiment environment
The experiment is carried out in gymnasium. The third
person view image and AR.Drone camera image are shownin Fig.6. This experiment is carried out when AR.Drone ishovering in air.

7 CONCLUSION
In this study, aerial images system is utilized to solve
the problems of insufficient tracking capability and limited
field of view. With evaluation experiment, the high tracking
performance, the high stability, and the effectiveness ofproposed system are verified. Also, the moving direction ofhuman can be detected in high accuracy. From now on,
flying robot control part will be carried out. High efficiency
aerial images system will be expected.

Fig. 6. Experiment environment
5.2 Experimental result
There are three cases in the evaluation experiment. First
case is only one person is in the scene. Second case is there
are two persons wearing clothes with different color. Third
case is there are two persons wearing clothes with the samecolor. Sample images of each case are shown in Fig.7.

REFERENCES[1] Takahiro KOIZUMI, Yuichi NAKAMURA, and Shin‘chi
SATOH, “Video Processing and Retrieval for Finding Lost Itemfrom Personal Eye-View Video”, Technical report of IEICE.
PRMU 109 (470), 395-399 (2010).

[2] Toshio FUKUDA, Tatsuya SUZUKI, Yasuhisa
HASEGAWA, Fumihito ARAI and Masaru NEGI, “Visual
Surveillance System with Multiple Cameras in Wide Area
Environment”, Transactions of the Japan Society of MechanicalEngineers. C, Vol.69, No.680,
pp.1011-1018 (2003).
[3] Yoshizawa YUSUKE, Koichi SHINODA and Sadaoki
FURUI, “Surveillance Event Detection Using Local and Global
Features”, the Technical Report of IEICE, PRMU, 110 (27),pp.163-168 (2010).
[4] Hidehito NAKAGAWA, Hitoshi HABE and Masatsugu
KIDODE, “Efficient prior acquisition of human existence byusing past human trajectories and color of image”, the Technical
Report of IEICE, PRMU, 108 (484), pp.305-312 (2009).
[5] Yasuhiro AOKI, Yoshio IWAI and Masahiko YACHIDA,
Detecting Suspicious Actions Using Particle Filtering”, theTechnical Report of IEICE, IE, 107 (538), pp.95-102 (2008).
[6] R. T. Collins, “Mean-shift blob tracking through scale
space'', Proc. IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, pp.234-240 (2003).
[7] Masahiro HORIE, Masayuki KASHIMA, Kiminori SATO,
Mutsumi WATANABE, Kouzi KUMA and Koichi TOYONAGA,“Automatic Endocardium Contour Tracing Method Using
Standard Left Ventricles Shape Model”, the Technical Report of

Fig. 7. Sample images of experiment results
With experiment, the success rate of human tracking isover 99%. When other people wearing the same color
clothes enter into scene, the particular people also can betracked exactly by using stability of moving direction.
Compare to conventional ACM algorithm, success rate ofcontour extraction can be dramatically improved from 66%
to 98% by setting initial contour accurately. In addition,
success rate of moving direction is 95% by utilizing timeseries analysis. In future, the study on particular people istracked automatically by AR.Drone will be carried out.

© ISAROB 2013

IEICE, PRMU, 108 (94), pp.35-40 (2008).

[8] Ryohei TANAKA, Hitoshi YAMAUCHI and Akihiro
KANAGAWA, “Object Tracking Using Color Distribution Basedon Active Contour Model'', Forum on Information Technology,
7 (3), pp.293-294 (2008).

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Semantic Segmentation in Manhattan-like Environments from 2.5D data
Sven Olufs1 and Markus Vincze1
1 Vienna University of Technology, Automation and Control Institute,Gusshausstrasse 25-29 / E376, A-1040 Vienna, Austria(olufs@ieee.org, vincze@acin.tuwien.ac.at)
Abstract: In this paper we propose a novel approach for the robust segmentation of room structure using Manhattan world
assumption. First, we estimate the Manhattan-like structure by using an MSAC variant that estimates such a Manhattan system
directly from the data. Once the orientation is estimated we extract hypotheses of the room structure by exploiting 2D histograms
using mean shift clustering techniques as rough estimate for a pre-segmentation of voxels i.e. their membership to planes of acertain position and orientation. Additionally we use the concept of vanishing points to extract 2D cues from the 2.5D data to
improve the segmentation. We apply superpixel over segmentation on the colour input to achieve a dense segmentation. The oversegmentation and pre-segmented voxels are combined using graph-cuts for a not a-priori known number of ﬁnal plane segmentswith a label minimizing graph cut variant proposed by Delong et al. with polynomial runtime.
Keywords: Segmentation, Manhattan-System, Computer Vision
1

INTRODUCTION

The estimation of semantic room structure, e.g. corridors,
doorways or walls, is a vital task for mapping or navigation.
With domestic robotics we face the problem of clutter
and visually weakly structured environments. The use of
2.5D sensors has become quite popular in the last decade,for instance the use of tilting 3D laser scanners or the
Swissranger SR-3000. With the recent release of Microsoft’s
Kinect structured light sensor, the popularity of 2.5D sensors
gained a boost. The Kinect sensor is suitable for the task for
two reasons: The sensors are cheaper than laser scanners andthey oﬀer an 2D colour image, which can be used for moresophisticated feature extraction.
The challenge with data from 2.5D data is to cope withnoise and uncertainty due to the nature of the sensors. For
instance, the quality of 2.5D data from the Kinect depends
on the reﬂection properties of the observed surface or theangle of incidence. Since the sensor uses structured lightin the infrared spectrum, the sensor is sensitive to sunlight.
Within the domestic robotics domain, the environments canbe single-coloured walls or furniture without texture, so
it can result in few certain and many uncertain estimates.
Another issue is that the sensor’s depth resolution does notscale linearly with the Kinec.
Many approaches for room structure estimation use theconcept of occupancy grids [3] or extensions to 3D, e.g. [4]:The grid contains information on a primitive level if a gridcell corresponds to oder belongs to a wall or ground. At thislevel, there is no information if certain parts of grid cells withthe label ”wall” are aligned to other ”walls” or if the ground© ISAROB 2013

is parallel to other structures, e.g. a table top. This kind of
constraints is referred to in the computer vision literature as
the so-called Manhattan world assumption; The frequently
observed dominance of three mutually orthogonal vanishingdirections in man-made environments [5, 6, 7, 8]. Many
indoor environments can be considered as Manhattan-likesince most walls of a room are aligned orthogonally to theground or quasi Manhattan-like if the walls are not aligned
orthogonally to each other. In many cases, furniture is alsoaligned Manhattan-like to its environment, e.g. a couch or
cupboard can be aligned to a wall. Here we emphasize that
it is not necessary that the furniture is aligned to all threemajor axes i.e. even if a table is not aligned to a wall its tablesurface is usually parallel to the ground.
The novelty of the paper is the use of 2D and 3D features ina uniﬁed framework at almost the same runtime as the previous approach in polynomial runtime unlike than common
NP hard solutions. The extraction of 2D cues is done in linear
runtime and improves the overall precision of the segmentation and also improves the robustness to false matches.
2

OUR APPROACH

The main idea of our approach is to use histograms to extract
room structure hypotheses for MRF segmentation rather thanusing the depth data as voxels. One advantage of using histograms is that it is relatively easy to estimate the Manhattanlike structure within the data if the camera origination is
known. One disadvantage is that we lose spatial information
about the voxels i.e. post-processing is needed to generate
hypotheses on the room structure on all three axes i.e. planesaligned to the X, Y and Z axes. The hypotheses are ﬁnally
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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

evaluated using the 2.5D data in the fashion of RANSAC andpre-segmentation using over segmentation techniques in thesource 2D image. In an additional step we extract 2D cues
from the 2.5D data using the concept of vanishing points. We
use the 2D cues in two ways: Depending on the structure in
the image it can be sometime easier to describe which part ofan image does not belong to a speciﬁc Manhattan like structure than sometimes the opposite (at two diﬀerent stages inthe segmentation). We The ﬁnal segmentation is achieved
using an MRF multi-label technique which provides a robust
and precise framework for 2D and 3D fusion.
2.1 Estimating Manhattan GeometryFirst we estimate the Manhattan System form the 2.5d
data with a method that has been proposed by us in [9]. It
is based on the idea to RANSAC a valid Manhattan systemon the base of normal vectors. The method estimates therelative roll, pitch and yaw to the scene. The method also
provides a plane segmentation of Manhattan-like structure,
based on a 1D âĂŸâĂŹConnected Components” RANSAC
that pre-segments all planes that are orientated to the Manhattan systems i.e. orientated to the X, Y or Z axis. For thesake of simpliﬁcation, we assume only one system per scene.
2.2 Extracting 2D cues
The basic idea of our approach is to estimate per pixel the
probability of the alignment (orientation of the pixel) to theone of the three vanishing points. Instead of using line segments we use straightforward gradients (i.e. orientation andmagnitude). In the fashion of the Canny edge detector, weﬁrst blur the image using a Gaussian 3x3 kernel and apply
a 3x3 Sobel ﬁlter on the image to obtain a gradient image.
In a next step we calculate the reference orientation of eachpixel to its three vanishing points as shown in ﬁgure 1 for two
pixels. One can see that reference orientation of two vanishing points can be quite similar to each other, e.g. the sample
point on the right bottom. In order to avoid artefacts in theestimation step, we calculate the similarity reference angles(a) 2D Image with projected vanish- (b) Colour coded alignments ofing points
gradients/pixels to vanishing points

Figure 1: Basic concept of vanishing points in 2D images.
The vanishing points are colour coded for better visibility i.e.
red=x, green=y and blue=z© ISAROB 2013in a 3x3 matrix and use it as a additional gain in the estimation step. The estimation per gradient pixel to a vanishingpoint is a simple winner-takes-it-all method based (ﬁg. 1(b))
on the smallest angle to a reference angle (of the vanishingpoint) i.e. we assume that every pixel is aligned only to one
vanishing point. Finally we convert the value of the smallest
angle into a probability by using a Gaussian weighting in a
way that 5 degree diﬀerence will result in 3 σ.
2.3 Pre-Segmentation
Next, we use the plane hypothesis to pre-segment theindividual voxels i.e. assign the voxels to planes and theirorientation. This segmentation is done straightforward by
re-projecting all hypotheses back into the 3D state space ofthe voxels. A voxel is assigned to a plane hypothesis if it intersects the plane hypothesis within a certain threshold. Eachvoxel is assigned only to one (or none, i.e. ”undecided”)”best ﬁtting” plane using the distance of the plane/voxel
intersection to the mean of the voxels as the metric formatching. We count the number of inliers per plane similar
to RANSAC. Planes with almost no support count < 0.05%
are then removed from the set and corresponding voxels are
freed.
In order to achieve a dense segmentation of the entire image
we over segment the colour image using superpixels. The
use of superpixels for over segmentation is quite popular in
the computer vision literature within the last decade. The
main objective is to locally merge pixels into ”superpixels”
i.e. pixels with similar e.g. colour, texture, appearance or
shading. In general we assume that true object boundaries aremostly (but not necessarily) represented by boundaries of thesuperpixels if the objects size is large enough (e.g. > 5 pixel).
In this paper, we use the fast Minimum Spanning Tree based
method by Felzenszwalb [10], giving us (by appropriate setting of parameters) 300-500 regions on average. However,any other over-segmentation method can be used.
2.4 MRF based multi-labeling
To label the pixels on a global level, i.e. to take into accountprior information about possible plane orientations, 2D
geometry and relations between neighbouring superpixels
simultaneously, we formulate the problem in a fully probabilistic framework; as searching for a maximum posterior(MAP) conﬁguration of the Markov Random Field [11] for
multi-labeling [12]: In a labeling problem we are given a setof observations P (e.g. voxel and superpixel) and a set oflabels L (e.g plane/orientation hypotheses). The goal is toassign each observation p ∈ P a label f p ∈ L such that thejoint labeling f minimizes the objective label function E( f ).
We assume a graph G =P, E consisting of a discrete set P
of objects and a set E ⊆ |P|
2 of pairs of those objects.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

An instance of the Max-sum problem is denoted by the tuple(G, L), where the elements D p ( f p ), V pq ( f p , fq ) and hL˙δL ( f )of g are of alignment costs or qualities. The quality of alabeling f is deﬁned
data costsmooth cost

pq∈N

L⊆L

where hl is the non-negative label cost of label l, and δL ( f )is the corresponding indicator function⎧⎪⎪⎨1, if ∃p : f p = l
δL ( f ) = ⎪⎪⎩0, else
A common approach in the computer vision literature is to
use three labels [5] for every major axis instead of using multiple labels per major axis. One reason to do so is that thegeneral labeling for more than 3 labels leads often to NP-hardsolutions [11, 12]. In this paper we use an MRF multi-label
approach proposed by Delong et al. [12] which solves themulti-label problem within polynomial runtime for arbitrary
number of labels. The polynomial runtime is achieved by using a diﬀerent strategy for the multi-labeling than common
approaches e.g. using a ﬁxed number of labels: The mainidea is to use the MRF to reduce the number of labels by
merging them using the E( f ) function. The strategy includes
starting with a reasonable number of labels e.g. all plane hypotheses and use the smoothness term as metric.
2.4.1 Graph entities
We build the graph G on the over-segmented image i.e. on
the superpixels. The use of superpixels signiﬁcantly reduces
the number of objects in the graph compared to building thegraph directly on the pixel grid. The superpixels represent
objects (the set P in the graph and edges). The set E, is established between every two neighbouring superpixels. The
number of nodes (labels) K is set to the number of observed
planes and four ”undecided” labels to mark ambiguous label assignments. The ”undecided” allow the solver to markthe places where there is not enough, information to decide
which plane the superpixel belongs to. The individual ”undecided” labels are ”not xy ”, ”not xz ”, ”notyz ” and ”undecided”.
The main idea with ”not” labels is that it is sometime easier to estimate where a plane orientation does not belong tothan the opposite. The problem is that we have only sparseinformation at the scale of superpixel labels to determine theactual orientation of a label with a high certainty, like thetypical scale space problem in computer vision. One can seethat the estimation of the opposite is easier if we use a ”higher
scale”. The ”not” labels are estimated using the 2D cues inthe ”low scale” version and with the ”label cost” as the ”highscale” i.e. estimating the orientation of a set of labels.
We use an additional parameter to represent weights of edges© ISAROB 2013

ρ[p, q] =label cost



E( f ) =Dp( fp) +V pq ( f p , fq ) +hL˙δL ( f )p∈Pthat connect the superpixels. The calculation of superpixel
likelihood is done using simple colour (RGB) histograms andusing the Bhattacharyya [13] metric:
m

√

pu qu

u=1
m
with mu=1 pu = 1 and
u=1 qu = 1 for u histogram bins of
the superpixels p and q. Note that this implementation differs from many other MRF based label methods where thesmoothness term is used to describe the likelihood of theneighbouring superpixels instead of using weights in thegraph. With Delong et al. [12] approach the smoothness
term describes the similarity between individual labels.

2.4.2 Smoothness term
The term V pq ( f p , fq ) describes the smoothness between twolabels p, q i.e. the cost to assign q to p. The function itself
must be injective i.e. V pq ( f p , fq )  Vqp ( fq , f p ) which is necessary for our MRF variant by Delong et al. [12]. In our
implementation we set cost function V pq that a label q from aplane hypothesis is set to p of an another hypothesis, if bothplanes are assigned to the same orientation and they have almost the same position |pq| (e.g. if the plane is y aligned, its
the height) and if p has less support by voxels of the plane
hypothesis, i.e. inliers, than q:⎧⎪0,
if p = q
⎪
⎪
⎪
⎪
⎪
⎪
⎪
1,
if porientation  qorientation
⎪
⎪
⎪
⎪
⎨
V pq ( f p , fq ) = ⎪cost pq · Δ pq · qinliers , if porientation = qorientation
⎪
⎪
⎪
⎪
⎪
⎪
costknown ,if p = ”undecided”
⎪
⎪
⎪
⎪
⎪
⎩cost
,
if q = ”undecided”
unknownwith cost pq  costunknown > costknown  1. We use the distance of the plane position of the corresponding labels p, q as
weight metric i.e.
⎧
|pq|
⎪
⎪
if |pq| ≤ c⎨2.0 − c
Δ pq = ⎪⎪⎩0otherwise
for a threshold c. We use costknown  1 since we want to allow the MRF to remove false positives from the graph, i.e.
false labeled x-axis oriented planes surrounded by z-axis oriented planes. The condition porientation = qorientation can combine labels with the same properties while the graph based
representation ensures that this is only applied if the source
superpixels are ”close” to each other in the source image.
2.4.3 Data term
The data term D p ( f p ) encodes the quality of assigning a labelf from the set L to an object/superpixel p in the graph. The
quality measures how the superpixel is oriented to a speciﬁcplane. We use the pre-segmented labels of the voxels/pixel,
i.e. the source pixels within the superpixels p. The data term
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EXPERIMENTAL RESULTS

for the ”not” labels is given by the 2D cues. For each f p
we sum up the assigned (to vanishing points) gradient values (see ﬁgure 1(b)) to the corresponding labels i.e. ”not xy ”,”not xz ” and ”notyz ”. The idea is simple, if a gradient pixel isassigned to the z axis, it votes with its gradient value for the”not xy ” label. If the gradient is assigned to the x axis it votes
for the ”notyz ” label and y axis votes for the ”not xz ” label.
The overall assumption is that if a segment is assigned to acertain orientation, then it does to contain gradient pixels thatare assigned to that axis, see ﬁgure 1. We normalize ”not xy ”,”not xz ” and ”notyz ” with the total sum of the raw unassigned
gradients within f p . This avoids that false or few matches
outvote the ”assignment labels” e.g. within textured areas.
The data term for the ”assignment labels” is given as follows:
For each f p we count the number of voxels/pixels with thesame label as f from the pre-segmentation. Note that the labels f corresponds to the plane hypothesis. Next the numberof pixels pn is normalized to pm and set as cost to D p ( f p )⎧⎪⎪⎨W(pm ) · costdata , if W(pm ) ≥ λ ∧ W(pn ) ≥ γ
Dp( fp) = ⎪⎪⎩0,
otherwise

3

With costdata  cost pq and⎧⎪⎪⎨ p · costdata ,W(p) = ⎪⎪⎩ p,

Our code runs on 2.4 GHz QuadCore PC, while the code is
not optimized and uses only one CPU (except for MSAC
Manhattan Geometry estimation). The average runtime for
one frame is 408ms the clear bottleneck is the superpixel
segmentation with 280ms. The next expensive function isthe calculation of the Manhattan Geometry (80ms) due to theusage of histograms. Using smaller histograms will result ina lower constant runtime, but will also inﬂuence the accuracy
negatively. The extraction of 2D cues the thrid third bottleneck with a constant runtime of 20ms followed by the mean
shift clustering with 10ms.

if f p is ”undecided”
otherwise

where λ and γ are thresholds and costdata is a normalizing
constant that can prevent false positives if costdata > 1. In our
implementation we use λ = 0.1 and γ = 10 since we set the”minimum superpixel size” Felzenszwalbs [10] superpixels
segmentation to 100 pixels. In our experiments costdata = 1.5
which produces fewer, but more certain labels.
2.4.4 Label cost
The label cost term hL˙δL ( f ) is used to penalize each uniquelabel that appears in f within E( f ). We use the cost hL toexpress the certainty of the label f i.e. a lower cost reﬂects
a higher certainty. Since we use the MRF to minimise thenumber of labels by fusing labels (=adding the costs of the
fused labels), a solution will be used with minimal overallcost. We use the 2D cues to obtain hL in a similar way weused it for the ”not” labels, but using ratios instead of sums.
For instance if a label is assigned to the x axis hL is given as
sum x
hL =
sumy + sumz + μ
where sum is a function that sums all gradient pixels that areassigned to the corresponding axis and μ the smallest notzero value that is assumable. The ratio for labels with y orz orientation calculates analog. Please note that we do not
use the δL ( f ) as it was meant to be used in the ﬁrst place as
we distinguish only between three orientations of a plane and”unkown” labels with our 2D cues instead of the individuallabels.
© ISAROB 2013

We choose a typical home environment (see ﬁg. 2) fordata acquisition using the Kinect. The data of all sensors isrecorded at 25 frames per second. We recorded a representative set of six tours through our lab with a total length ofapproximately 320 meters. Three tours have a Manhattan
like environment while the other ones represent a quasi
Manhattan like environment.
Figure 2 shows sample images for all three tours and theirsegmentation in comparison with state of the art techniquesfrom Saxena et al. [1] and Saxena et al. [1]. One can seethat the combination of MRF and superpixels produces
quite precise segmentation if stereo data is available and thesuperpixels do not contain glossy spots or other overexposed
areas. In some case false-matches can appear if a glossy
spot on the ground and the wall are to close too each other.
The images also show that our parameterization of thesegmentation is quite conservative since we want to produce
only few false positives.

4

CONCULSION

In this paper we presented a novel robust method for room
structure segmentation in a Manhattan like environmentfor 2.5D data using 3D and 2D cues. Once the camera(a) our approach(b) Saxena et al. [1](c) Hoiem et al. [2]Figure 2: Segmented sample pictures from our test data: Thecolour indicates the alignment of a structure to an speciﬁc
axis. The segmentation is executed on the identical superpixels on all three methods. Please note that the diﬀerent approaches are using diﬀerent color coding for the axis, yellow
indicates uncertian planes in [1, 2].
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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013and motion planning. Journal of Field Robotics, Special Issue: Three-Dimensional Mapping, 26(10):841–
862, 2009.

orientation is estimated using MSAC, we calculate theassignment of every voxel to the 3 major axes and we extract
plane candidates using histogram voting that are used aspriors for a MRF based segmentation. We extract 2D cues
from the 2.5D data using the concept of vanishing points and2D image geometry. We also showed that the segemntion of
the MRF can be improved using label cost metric and 2D
cues.

[5] B. Micusik and J. Kosecka. Piecewise planar city modeling from street view panoramic sequences. In IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
[6] D. Gallup, J. Frahm, P. Mordohai, Q. Yang, and M.
Pollefeys. Real-time plane-sweeping stereo with multiple sweeping directions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007.

The main drawback of our approach is that we depend on
Manhattan-like structures which is common with many
indoor environments. The extraction of 2D cues depends
on the proper estimation of the camera orientation, however
this could be overcome by estimating the vanishing points
separately like in pure 2D computer vision approaches.
Another drawback is that we depend on the output of thesuperpixel segmentation. Experiments have shown that theuse of smaller superpixels is more robust than the usage oflarge ones, i.e. otherwise the superpixel segmentation tends
to group the ground and white walls to one segment. At
this stage we do not use multiscale oversegmentation, which
would improve the performance of the 2D cues. Right now
we kind of abuse the MRF as quasi multiscale by using the”not” labels in a small scale and using it with the label costs
in a large scale.
Our next step is to extend the approach with multi-scale oversegmentation and to incorporate the 2D cues in the smoothness term of the MRF.

ACKNOWLEDGMENT
The research leading to these results has received funding
from the European Community’s Seventh Framework Program (FP7/2011-2014) under grant agreement no FP7-ICT2011-7 288146 (HOBBIT)

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M. Agrawal, J. Latombe, and M. Beetz. Leaving ﬂatland: Eﬃcient real-time three-dimensional perception© ISAROB 2013

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Modeling and robust decoupling control for hypersonic scramjet vehicle
Xiaofeng Su1 , Yuefei Jiang1 , Yingmin Jia1,2
1 The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA),

Beijing 100191, China (e-mail: xfsu@smss.buaa.edu.cn; jyf1214@126.com).
2 Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), SMSS, Beihang University (BUAA),

Beijing 100191, China (e-mail: ymjia@buaa.edu.cn).
Abstract: In this paper, the modeling and the robust decoupling control for a generic hypersonic scramjet vehicle are studied.
Firstly, the dynamics of the hypersonic vehicle are modeled by applying the Lagrangian approach, which captures the mostprimary characteristics such as elastic deformation, aerodynamics, aero-heating, variable mass, effect of spherical rotating earth
and their inherent interactions. Then, a robust output decoupling controller is designed by using nonlinear dynamic inversion plus
the desired proportional integral dynamics, and natural time-scale separation theorem between fast and slow variables. Finally,
the nonlinear simulations demonstrate that the controller can eliminate the interaction among the output channels and satisfy thehandling quality requirements when the vehicle has parameter uncertainty.
Keywords: HSV modeling, robust output decoupling, dynamic inversion, multitime-scale approach
1 INTRODUCTION

dynamics were exactly known. Moreover, the stability andperformance robustness within the dynamic inversion framework were addressed in [8, 9, 10].
In this work, because of the high computational requirements when applying the precise sub-models of the HSV,
some dominating elements and their primary interactions are
extracted in the process of modeling. And then, by utilizing the Lagrangian approach, a control-oriented model ofthe HSV is obtained, in which the effects of inertial dynamics, elastic deformation, aerodynamics, aero-heating, variable mass, and spherical rotating earth are captured. Basedon this model, a robust output decoupling controller is designed to enhance the maneuverability of the HSV. Specifically, the dynamics of the vehicle are separated into two timescales according to the natural time-scale separation theorem.
In each time-scale loop, an appropriate proportional integral
control law is designed in conjunction with the dynamic inversion controller, such that the outputs of the closed-loopsystem can be robustly decoupled at the steady state with respect to the parameter uncertainty.

With the historic scramjet-powered Mach 7 and 10 flightsof the X-43A in 2004, the research of hypersonic scramjet
vehicle (HSV) has seen a resurgence within the aerospacecommunity. This is attributed to the fact that the HSV isviewed as the next critical flying vehicle, which has the ability to easily penetrate and survive against enemy air defenses,
promptly reach critical targets at long ranges (i.e., realize
rapid global response), and access to space in a manner similar to commercial air travel. Therefore, on one hand, this typeof vehicle has great potential as a high-speed, time-saving,and low-cost transport for both commercial and military applications. On the other hand, the modeling and control of the
HSV are challenging, due to its severe aero-heating, unconventional propulsion system, much broader flight envelope,and tight interaction among airframe, propulsion, and structure [1, 2].
The modern HSV like NASA X-43 or X-51 has special
slender airframe-propulsion lifting-body configuration andoperates over a high speed ranging from Mach 5 to Mach 15
within large flight envelope, so that the traditional design approaches are invalid when there exist the manifold interaction
among sub-models of the HSV and the complex flight environment. For the modeling issue, several literature [3, 4, 5]
have discussed the determinants of the modeling and the interaction of the sub-models. In [3, 4], the inertial dynamics of the model were obtained under the consideration of
rigid-body motion, elastic deformation and spherical rotating
earth. And McNamara et al [5] focused on the coupling effects among the sub-models of aerodynamics, aero-elasticity
and aero-heating based on high fidelity codes. For the decoupling control issue, nonlinear dynamic inversion control
method was used in [6, 7] under the condition that the system© ISAROB 2013

2 VEHICLE MODEL
According to [11], the dynamical equations of the HSV
are directly presented by using the Lagrangian approach.
mV̇ − S̃ω̇ +ZZv̇dm =mm

ṽdm · ω + mg + F + ΨF

(1)

 Z

S̃V̇ + J ω̇ + r̃v̇dm = 2 r̃ṽdm − ω̃J · ωmZmṽdm + ω̃ S̃ ·V + M̄ + ΨM̄
−
Z

m

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[Λ]V̇ + [Λ]r̃T ω̇ + [Λ]v̇ = [Λ]Ṽ ω − [Λ]ω̃ ω̃rZ

−φi2 ω̄i2 ηi dm + Qe + Ψe .

and latitude lines, respectively. h is the altitude of the vehicle. ωx , ωy , ωz are rotational rates of the body axes relative
to the inertial axes. u, v, w are the velocities along the bodyaxes related to the earth axes. In practice, because of the parameter uncertainties, the real system is presented asm(3)
The expressions of external forces, moments, and thrust in(1), (2), (3) are extremely complicated, which are determined
not only by the velocity and the attitude, but also by the shapeand elastic deformation of the HSV. In summary, there are
12 + 3n (n is the number of elastic modes) equations suchas: 3 force equations, 3 moment equations, 3n elastic deformation equations, 3 trajectory equations, and 3 Euler angle
equations. Moreover, the kinematic equations of the HSV
are similar as the ones of the regular vehicle. For a vehicle
flying over a Mach number ranging from 5 to 15 and a height
ranging from 20 to 60km, the maximum and minimum magnitudes of the Coriolis force mωE,I × Vr are approximately
7.02% and 2.48% of the vehicle weight, and the magnitudeof the transport force mωE,I × (ωE,I × R) is about 0.35% of
the vehicle weight. Finally, the scalar form of the HSV model
which is used here are presented in Appendix based on three
regular assumptions: i) neglecting the effect of elastic deformation , ii) neglecting the transport item in expression of V̇ ,iii) the vehicle is symmetrical about the oxB zB plane.

ẋout = F̃(xout ) + G̃(xout ) · xin

where F̃(xout ) = F̃ 0 (xout ) + ∆F̃(xout ), G̃(xout ) = G̃0 (xout ) +∆G̃(xout ). And the specific expressions of F̃(xout ), F̃ 0 (xout ),G̃(xout ), and G̃0 (xout ) can be obtained from the Euler angle
equations and the trajectory equations. Moreover, the parameter uncertainties in these expressions have the forms as
ωE = ωE0 + ∆ωE , RE = R0E + ∆RE , and λ0 = λ00 + ∆λ0 .
Since the total relative degree is equal to the number ofsystem states, this time scale has no internal dynamics to beconsidered. Then, the outer-loop control law is given by (6),where the desired dynamics is chosen to have the proportional integral form.

0−1 −[F̃ 0 (x )] + v

out
des
 xin = [G̃ (xout )](6)vdes = kout 1 · (vout − xout ) + kout 2 · ΩΩ̇ = vout − xout
where Ω = [Ωφ Ωθ Ωψ Ωx Ωy Ωh ]T .
kout i =[kout φ i kout θ i kout ψi kout xi kout yi kout hi ]T , i = 1, 2 are the desired controller parameters which will be determined. vout =[vφ vθ vψ vx vy vh ]T is the virtual input vector. By calculating the determinant of the control matrix det[G̃0 (xout )],it is concluded that [G̃0 (xout )]−1 exists while the inequality
1/ cos θ 6= 0 holds, and it is obviously satisfied.
Substituting the control law (6) into the real system (5),the outer closed-loop system is obtained. Then, the objective is to find the appropriate controller parameters kout 1
and kout 2 , so that the equation yout = [ I ]vout (i.e., xout =[ I ]vout , where [ I ] is the identity matrix) is satisfied at thesteady state while the system contains parameter uncertainties. Next, setting ẋout = Ω̇ = 0 yields the equilibrium solutions of this system: xoute = vout , Ωe = [−∆ f˜1 /kout φ 2 −∆ f˜2 /kout θ 2 − ∆ f˜3 /kout ψ2 0 0 0]T . Linearizing about xout =xoute and Ω = Ωe yields the following linear systems: ∆ẋout =[Ã]6×6 · ∆xout + [B̃]6×6 · ∆Ω, ∆Ω̇ = [C̃]6×6 · ∆xout + [D̃]6×6 ·∆Ω. Then, the characteristic equation of such a linear systemis:s12 + ñ11 s11 + ñ10 s10 + · · · + ñ1 s1 + ñ0 = 0

3 ROBUST DECOUPLING CONTROLLER
The structure of resulting control law is shown in Fig. 1.,
in which the nonlinear dynamic inversion theory is appliedin two time scales. Because the integral feedback has theability of eliminating steady-state errors, a desired dynamic(i.e., proportional plus integral feedback dynamic) is applied
to solve the robustness problem of the dynamic inversion.

Fig. 1. Two-time scale feedback loop

3.1

Outer-loop Controller design

In the outer loop, the nominal dynamics of slow time scale
are placed in the form of (4). The objective of the dynamic
inversion in the slow time scale is to realize the decoupling
control of the attitude angles and flight trajectory using therotational rate and flight velocity. And the outputs are chosen
to be the system states.
ẋout = F̃ 0 (xout ) + G̃0 (xout ) · xin

where ñi = ñi (vout , kout 1 , kout 2 , ∆), i = 0, · · · , 11. Hence, thestability of that linear system is guaranteed, if the eigenvalues of the characteristic equation are less than zero for all
allowable ∆F̃ and ∆G̃. Moreover, desirable stability margin
can be obtained for better dynamic performance by substituting a new variable s1 = s + a into the original characteristic
equation. Therefore, the linear system is stable and the eigenvalues are on the left side of the axis s = −a.

(4)where xout = [φ θ ψ x y − h]T = yout , xin =[ωx ωy ωz u v w]T . Roll angel φ , pitch angle θ , yaw angle
ψ are the attitude angels of the body axes relative to the earth
axes. x, y are flight displacement along the longitude lines© ISAROB 2013

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Remark2: The information of vout is required in theprocessing of determining decoupling controller parameters
kout (vout ). And this information can be easily obtained fromthe commands.
Remark3: The linearized model of the nonlinear system
has been discussed here, which means that the states convergency can only be achieved in a neighborhood of the equilibrium. Fortunately, this problem can be solved by limiting
the change rate of the commands or enhancing the response
speed of the systems.

3.2 Inner-loop Controller design
In the inner loop, the control surfaces and vectored thrust
are commanded to realize prompt and precise tracking of theflight velocity and rotational angular velocity generated in
the outer loop. The most important point is that the slow
time scale states in the outer loop are treated as constants inthis fast time scale, since they evolve more slowly when compared with fast time scale ones. The real system dynamics offast time scale are written asẋin = F(x) + G(x) · u(7)

4 SIMULATION RESULTS
A typical nonlinear simulation of the maneuver is shown
to examine the effectiveness of the robust output decoupling
controller. The data of aerodynamic characteristics and thrust
performance in [12] is adopted here. The simulation resultsof nonlinear system responses to the change of roll angel,velocity and altitude are depicted in Fig. 2 − 4, in which
the solid line is the response to v1 and the dash line is the
response to v2. It is observed that the change of one output
value has no effect on the steady-state values of the others.

and F(x) = F 0 (x) + ∆F(x), G(x) = G0 (x) + ∆G(x), xin =[u v w ωx ωy ωz ]T , u = [Φx Φy Φz δe δa δr ]T . The system states xin are the same as ones in the outer loop, andx = [xTin , xTout ]. The three control surfaces deflection δe , δa ,
δr are the effective elevator, aileron and rudder deflection.
Throttle setting values Φx , Φy , Φz are used to generate vectored thrust. Besides, according to the force equations andmoment equations, the expressions of F(x), F 0 (x), G(x), andG0 (x) can be obtained. The parameter uncertainties exist not
only in ωE , RE , λ0 , but also in vehicle mass, moment of inertia, product of inertia, aerodynamic derivatives, and thrust
derivatives. Similarly, the desired dynamics of the fast timescale also have proportional integral form, thus inner-loop
control law is written as(u = [G0 (x)]−1 −[F 0 (x)] + kin 1 · (xinr − xin ) + kin 2 · ΩΩ̇ = xinr − xin(8)where Ω = [Ωu Ωv Ωw Ωωx Ωωy Ωωz
kin i =
[kin ui kin vi kin wi kin ωx i kin ωy i kin ωz i ]T , i = 1, 2 are the controller parameters to be designed. Besides, matrix [G0 (x)]−1
exists if the following inequalities holds.
 Ψ0Fx Φx (x) · Ψ0Fy Φy (x) · Ψ0Fz Φz (x) 6= 0hi M̄ 0 (x) M̄ 0 (x) · M̄ 0 (x) − M̄ 0 (x) · M̄ 0 (x) 6= 0
y δe
x δr
z δa
x δa
z δr
]T .

Fig. 2. Responses to the change of roll angle

Consequently, substituting the controller (8) into the realsystem (7) yields the inner closed-loop system. Next, get theequilibrium solutions of this closed-loop system by settingẋin = Ω̇ = 0. Then linearizing about xin = xine = xinr andΩ = Ωe could yield corresponding linear systems: ∆ẋin =[A]6×6 · ∆xin + [B]6×6 · ∆Ω, ∆Ω̇ = [C]6×6 · ∆xin + [D]6×6 · ∆Ω.
For the linear system above with uncertainties in ∆F and ∆G,the robust stability of that system is analogously achievablethrough finding suitable controller parameters kin i (xinr ), i =1, 2. Furthermore, the desirable stability margin can also be
obtained for better dynamic performance.
Remark1: The nonlinear dynamic inversion method is astraight forward approach for designing decoupled feedback
systems of nonlinear systems. For the system with parameter
uncertainty, the robustness of output decoupling controller
can be addressed by using integral terms within the loops.

© ISAROB 2013

Fig. 3. Responses to the change of velocity

Fig. 4. Responses to the change of altitude

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5 CONCLUSION
In this paper, a multivariable nonlinear strong-coupled
HSV model has been developed, which captures the mostprimary characteristics of this special vehicle. Based on
this model, a robust decoupling controller design method,which incorporates a desired proportional integral dynamic
with nonlinear dynamic inversion controller, has been proposed. This controller design method mainly addresses two
fundamental problems: decoupling of nonlinear system anddecoupling robustness. The nonlinear simulation results confirm that the vehicle model equipped with the robust output
decoupling controller can provide both steady-state decoupling of the outputs and robustness with respect to parameter
uncertainty.

7 ACKNOWLEDGMENTS
This work was supported by the National Basic Research Program of China (973 Program, 2012CB821200,
2012CB821201) and the NSFC (61134005, 60921001,
90916024, 91116016).

REFERENCES[1] Kelkar AG, Vogel JM, Whitmer CE, and et al (2011), Design Tool for Control-Centric Modeling, Analysis, and Trade
Studies for Hypersonic Vehicles. Proceedings of the 17th
AIAA International Space Planes and Hypersonic Systems
and Technologies Conference.
[2] Rodriguez AA, Dickeson JJ, Sridharan S, and et al(2009), Control-Relevant Modeling ,Analysis, and Design
for Scramjet-Powered Hy-personic Vehicles. Proceedings ofthe16th AIAA/DLR/DGLR International Space Planes andHypersonic Systems and Technologies Conference.

6 APPENDIX
Force equations:[3] Bilimoria KD and Schmidt DK (1995), Integrated Development of the Equations of Motion for Elastic Hypersonic Flight
Vehicles. Journal of Guidance, Control, and Dynamics, 18(1):73–81.

u̇ = [ωz + ωE (T31 cλ − T33 sλ )]v−[ωy + ωE (T21 cλ − T23 sλ ]w + gT13 + (Fx + ΨFx )/mv̇ = [ωx + ωE (T11 cλ − T13 sλ )]w−[ωz + ωE (T31 cλ − T33 sλ )]u + gT23 + (Fy + ΨFy )/m[4] Meirovitch L and Tuzcu I (2003), Integrated Approach to
the Dynamics and Control of Maneuvering Flexible Aircraft.
NASA/CR–2003–211748.

ẇ = [ωy + ωE (T21 cλ − T23 sλ )]u−[ωx + ωE (T11 cλ − T13 sλ )]v + gT33 + (Fz + ΨFz )/mMoment equations:[5] McNamara J and Friedmann P (2011), Aeroelastic andAerothermoelastic Analysis in Hypersonic Flow: Past,
Present, and Future. AIAA Journal, 49(6): 1089–1122.

Jxx ω̇x − Jxz ω̇z = Jxz ωx ωy + (Jyy − Jzz )ωy ωz + M̄x + ΨM̄x
Jyy ω̇y = (Jzz − Jxx )ωx ωz + Jxz (ωz2 − ωx2 ) + M̄y + ΨM̄y[6] Snell SA (1998), Decoupling Control Design with Applications to Flight. Journal of Guidance, Control, and Dynamics,
21(4): 647–655.

Jzz ω̇z − Jxz ω̇x = −Jxz ωy ωz + (Jxx − Jyy )ωx ωy + M̄z + ΨM̄z
Euler angle equations:[7] Bajodah AH (2009), Generalised dynamic inversion spacecraft control design methodologies. IET Control Theory andApplicaions, 3(2): 239–251.

φ̇ = ωx + (ωz cφ + ωy sφ )tθ − (cθ cφ + sθ cφtθ )(v/R)+(cθ sφ + sθ sφtθ )(w/R) − ωE (cψsθtθ + cψcθ )cλθ̇ = ωy cφ − ωz sφ + cθ (u/R) + sθ sφ (v/R) + sθ cφ (w/R)+ωE sψcλ[8] Reiner J, Balas GJ, and Garrard WL (1996), Flight Control
Design Using Robust Dynamic Inversion and Time-scale Separation. Automatica, 32(11): 1493–1504.

ψ̇ = (1/cθ )(ωz cφ + ωy sφ ) − ωE (cψtθ cλ − sλ )+sψcθtλ (u/R) + [(sψsθ sφ + cψcφ )tλ − cφtθ ](v/R)+[(sψsθ cφ − cψsφ )tλ + sφtθ ](w/R)[9] Ito D and Valasek J (2001), Robust Dynamic Inversion Controller Design and Analysis for the X-38. AIAA Guidance,
Navigation and Control Conference.

Trajectory equations:ẋ = T11 u + T21 v + T31 w
ẏ = T12 u + T22 v + T32 w−ḣ = T13 u + T23 v + T33 w[10] Georgie J and Valasek J (2003), Evaluation of Longitudinal
Desired Dynamics for Dynamic-Inversion Controlled Generic
Reentry Vehicles. Journal of Guidance, Control, and Dynamics, 26(5): 811–819.

where R = RE + h, λ = λ0 + x/RE , s = sin, c = cos, t = tan,cψcθ
[Ti j ] =  cψsθ sφ − sψcφcψsθ cφ + sψsφsψcθsψsθ sφ + cψcφsψsθ cφ − cψsφ−sθ
cθ sφ  .
cθ cφ

And u, v, w, ωx , ωy , ωz , φ , θ , ψ, x, y, h are the system states.
Input control variables are Φx , Φy , Φz , δe , δa , δr , which areincluded in the expressions of the force, moment and thrust.

© ISAROB 2013[11] Su XF, Jia YM, Du JP, and et al (2012), Integrated Approach
to Hypersonic Vehicle Modeling. Proceedings of the 31th
Chinese Control Conference, pp. 1752–1758.



[12] Li HF, Lin P, and Xu DJ (2011), Control-oriented Modelingfor Air-breathing Hypersonic Vehicle Using Parameterized
Configuration Approach. Chinese Journal of Aeronautics, 24:
81–89.
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Flocking control of multi-agent systems
in a limited space
Sharu Jiang, Yingmin Jia, Shichen LongThe Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191,
China (e-mail: jsrshark@126.com, ymjia@buaa.edu.cn, inuyashalsc@hotmail.com).
Abstract: In this paper, flocking control in a limited space is considered. First, a new conception of safety-value is proposedto evaluate the safety between agents and obstacles in a limited space. Then, a new distributed flocking control protocol called
the limited space flocking (LSF) algorithm is designed so as to extend the Olfati-Saber’s control protocol to the case of a limited
space. The algorithm utilizes control protocols corresponding to the safety-value, so the multi-agent system can automaticallychange its velocity and structure to pass the limited space both quickly and safely. Finally, simulation results show that the
proposed algorithm can greatly improve the average velocity of systems and enhance the safety-value in a limited space.
Keywords: flocking, limited space, LSF algorithm, multi-agent, safety-value
1 INTRODUCTION
Recently, multi-agent systems have drawn increasing attention and many good results on the control protocol design
have been obtained in a series of works [1]-[6]. An important
issue in the control of multi-agent systems is to design a control protocol in order to achieve flocking behavior of systems.
A model introduced by Reynolds in [4] plays an importantrole in the study of flocking. Reynolds’ three heuristic rules(flock centering, collision avoidance and velocity matching[7]) led to the first computer animation of flocking. Since
then, there emerge lots of works dealing with the flocking
problems [8]-[13]. Particularly, among these works the algorithm proposed by Olfati, Saber [8] had a long-term influenceon the later development of the flocking control protocol design because they provided a theoretical and computationalframework for design and analysis of scalable flocking algorithms.
Flocking algorithms have wide applications such as selfassembly of connected mobile networks; massive distributed
sensing using mobile sensor networks; performing military
missions and so on. In some practical cases, for example
the control of the air traffic, the space where a system forms
flocking is limited. In these situations, by using most algorithms [8]-[13] which may work well in free space, system usually couldn’t achieve effective obstacle avoidance andwould greatly slow down the average speed. Under the consideration that both the obstacle avoidance and the speed ofa system are important standards, in this paper a safety-value
is raised to evaluate the safety between agents and obstacles
in a limited space and a LSF (Limited Space Flocking) algorithm, which is developed from Olfati-Saber’s algorithm[8], is proposed to make the system to pass through a limited
space both safely and quickly.

ration for us to introduce the LSF algorithm which will befurther discussed in the following section.
Consider the following ”boids” model of N agents:{q̇i = pii = 1, 2, . . . , N,(1)ṗi = ui
where qi is the displacement of agent i, pi is its velocity, ui
is the behavior rule of agent i
Definition 1 (detection shell [19]). The detection shell Ωiof agent i is a region, within which the agent can sense therelative location of neighboring agents.
we assume that all the Ωi have the same radius R. Denotethe set of agents contained in Ωi by Ni (t), which can be givenasNi (t) = { j : ∥qi − q j ∥ ≤ R},(2)
where ∥qi − q j ∥, i, j = 1, 2, . . . , N is the relative position
vectors.
Definition 2 (neighboring graph [19]). The neighboringgraph, G = (V, E), is an undirected graph consisting of:
1. A set of vertices (nodes), V = {1, 2, . . . , n}, indexed by
the agents in the group, and
2. A set of edges, E = {(i, j) ∈ V ×V |i ∈ N j (t) or j ∈ Ni (t)}Definition 3 (adjacency matrix). The Adjacency matrix is amatrix A = [ai j ] with nonzero elements satisfying the property
ai j ̸= 0 ⇔ (i, j) ∈ E, with its elements:
ai j = ρk (∥q j − qi ∥/R) ∈ [0, 1],(3)where ρk (z) is a scalar function which smoothly varies
between 0 and 1 [20], and h ∈ (0, 1) is a constant parameter,
the fomula of ρk (z) is shown below:
1, z ∈ [0, h)( (z−h) )1
ρk (z) =(4)
2 [1 + cos π (1−h) ] , z ∈ [h, 1]0, otherwise

2 PRELIMINARIESIn this section, we provide some basic concepts in graphtheory [14]-[16], algebraic graph theory [17], spatially induced graphs (or proximity nets) [18] and make the prepa-© ISAROB 2013j ̸= i.

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distance between agent i and its β -agent.
{x x<1
fthr (x) =1 x ≥ 1.

The use of an indicator bump function leads to an adjacency
matrix with 0-1 position-dependent elements.
Definition 4 (α -lattice [1]). An α -lattice is a configuration
satisfying the set of constraints in∥q j − qi ∥ = d,

∀ j ∈ Ni (t).

soki = 1 means that agent i is safe with obstacle k. soki =0 means that agent i crashes the obstacle k. Analogously,
we can also define the Agent safety to quantify the safety
situation among agents:(5)

In reference [8], the conception of virtual agents and aclassification were first proposed. Corresponding controls
can be given according to different kinds of agents. The
physical agent with dynamics q̈i = ui is called an α -agent.
The primary objective of α -agent in a flock is to form α lattice with its neighboring α -agents. In nature, α -agentscorrespond to birds, bees, fish, and ants. Later, virtual agents
β -agents and γ -agents are also introduced, which model theeffect of ”obstacles” and ”collective objective” of a group,
respectively. γ -agent can be also called the virtual leader ofthe system.
The distributed control given by Olfati-Saber [8] is denoted as uOS
i and shown in (6):
β

γ

α
uOS
i = ui + ui + ui
βDefinition 6 (agent safety). The agent safety Sα = {sαi j } ∈RN×N is an array with each element varying between 0 and
1, sαi j denote the agent safety between agent i and agent j andcan be calculated as follows:
Sα = {sαi j },(6)γ

In this section we present the LSF control protocol in order to enhance the safety-value and the average speed of systems in limited space. First of all, an index is needed to quantitively describe the safety status of the system. Therefore a
new conception called the safety-value should be proposed toevaluate the saftey of agents and obstacles in a limited space.
3.1 Safety-value of system in limited space
When passing through a limited space, a multi-agent system will get greatly compressed by the repulsive force fromthe obstacles. To get efficient control, we introduce two kindsof safety-values to quantify the safety status of an agent as
below.

1. soki < 1 and (soki )′ > 0
2. There does not exist any j s.t. sok j ≥ soki , ai j > λmin and(p j · vγ )/∥vγ ∥ > (pi · vγ )/∥vγ ∥where (soki )′ is the derivative of soki with respect to time.
Definition 8 (δk -agent). δk -agent is a virtual agent and for
obstacle k, agent i of the multi-agent system is a δk -agent
when it satisfies:(7)

1. soki < 1
2. exists j s.t. sok j ≥ soki , ai j > λmin andpi · vγ /∥vγ ∥where the q̂i,k is the location of the β -agent, ∥q̂i,k − qi ∥ is thereal distance between agent i and its β -agent, Dβ is the set© ISAROB 2013

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Definition 7 (εk -agent). δk -agent is a virtual agent, and for
obstacle k, agent i of the multi-agent system is a ε -agent when
it satisfies:

Definition 5 (obstacle safety). The Obstacle safety Sko =[sok1 , sok2 , . . . , sokN ] ∈ RN is a vector with each element varying
between 0 and 1, where N is the number of the agents andsoki < 1 denote the Obstacle safety between agent i and obstacle k, where i, k are the indices of the agent and the obstacle,
respectively. The element soki can be calculated as follows:∥q̂i,k − qi ∥),Dβ( ∥q j − qi ∥ ).
Dα

3.2 LSF algorithm in a limited space
Based on the Olfati-Saber model, we propose an algorithm to improve safety-value and the average speed of amulti-agent system in limited space. Noticed that when amulti-agent system meets a limited space, its structure justcan be slightly changed by Olfati-Saber algorithm, accordingly, the key point of the LSF algorithm is to add corresponding control in order to automatically change the formation to greatly enhance the safety-value and speed up thesystem at the same time. When an agent i get dangerous withobstacle k, namely soki < 1, we can introduce two kinds ofspecial α -agents to get more precise control.

LSF CONTROL PROTOCOL

soki = fthr (sαi j = fthr

Here we have to mention that because of the cohesive
force from the γ -agent (virtual leader), even in a free space,
the system would not form a strict α -lattice, which means
the distance between agents may shorter than Dα even whena stable flocking behavior of the system is formed. So there
is an agent safety diminishment caused by the cohesive force
from the virtual leader (γ -agent) and the Sko will stabilize in amatrix with elements slightly smaller than 1.
By using the defined and , we can design the LSF control
protocol of system in a limited space.

where the uαi , ui and ui are the controls related to α agents, β -agents and γ -agents respectively. The stability of
the Olfati-Saber algorithm is proved in [8]. Extended fromits theoretical frame, a LSF control protocol is designed todeal with the flocking in a limited space.
3

(8)

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p j · vγ /∥vγ ∥ >The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

After adding up the control with respect to the obstacles,
the result can be expressed in the formulation below:

6WDUW

uδi = ∑ uδki ,i = 0, Layer = 1, ukid = 0uεi = ∑ uεki .

kThe algorithm 1 works well in obstacle avoidance, however, the improvement of the obstacle safety is gained on alittle sacrifices of the agent safety, namely the agent safety
may drop a little. In order to reduce such sacrifices and makea complement to algorithm 1, we use a complementary algorithm to make some amendment.
Algorithm 2:
When the system is passing through a limited place, theuθi can be added to the Algorithm and be gained as follows:i = i + 1, i = i , calculate the skioNskio < 1YNukid = ukid + cd (1 - skio )(qi¢ - qi ),i = i¢, Layer = Layer + 1

Layer £ 2Yu kie = u kie + c e skio

pi
pi
Y

Find i¢Î N kd (i ), s.t for "j Î N kd (i ),p j · vr pi¢ · vr£
vr
vr

Agent i is a e k -agent?

uθi = cθ ∑NYsαi, j∥q j − qi ∥

,

(13)

where ∆sαi, j = sαi, j (t) − sαi, j (t − ∆t) is the change of sαi, j fromt − ∆t to t, t is the temporary moment.
The function of uθi is like a buffer in the system, when reduction of the ,namely the ∆si, j is huge and the sαi, j is low, theuθi is fairly considerable. So by adding uθi , the reduction of
the agent safety can be greatly decreased and then the Algorithm 2 can make complement to the Algorithm 1. In conclusion, the LSF distributed control protocol can be given by thefollowing formula (14):Agent i is a d k -agent?
NNi >= NY(QG

Fig. 1. Flow chart of LSF algorithm 1

ε
θ
uLSF
= uOS
i
i + ui + ui

where λmin ∈ R, 0 < λmin ≤ 1, vγ is the velocity of the γ -agent(virtual leader).

(14)

where the uOS
i is the control of Olfati-Saber algorithm.

The collection of j here is denoted by Nkδ (i). Notice thatan agent can’t be εk -agent and δk -agent at the same time. We

4 SIMULATION RESULTIn this section, we use some simulations to demonstrate
the effectiveness of our design method - LSF algorithm.
Without loss of generality, we can specify the limited space
into a channel, the number of the system N is 25, the widthof the channel is 8, and length is 20. The parameters for eachkind of agent are set as follows:present an algorithm to give control to these two kinds ofagents respectively. The flow chart of LSF algorithm 1 isdisplayed in Fig. 1.
Algorithm 1:
It can be inferred from the definition of the εk -agent thatthe εk -agents are on the forth front of the system and areheading toward a safer place with an increasing obstacle
safety, which makes the effective control to simply be thepropulsion along with the same direction. The control uεki is
obtained in (10):pi
uεki = cε soki
(10)
∥pi ∥

cα1 = 1γ
c1 = 1βc1 = 4.5cδ = 6.55,,,,

cα2 = 1,γ
c2 = 1,βc2 = 5.8,cθ = 7.7.

and cε = 100.215. To contrast the control performances between the LSF algorithm and the Olfati-Saver algorithm ofmulti-agent systems in a limited space, three main qualitiesof the process of proceeding though the channel are calculated to get further analysis of the two algorithms. so =mink,i (soki ) and sα = mini, j (sαi j ) can show the safety-value ofthe system in a limited space, Vaverage is the average velocityof all the agents of the system.
In figure 2 , the blue solid line represents the results ofOlfati-Saber algorithm, and the red dotted line represent theresults of LSF algorithm. Looking at the Fig. 2, we can see,the LSF algorithm can accelerate the average speed, greatly
enhance the so and remain the sα .

where the cε is a constant coefficient for uεki . As to the δk agent, compared to the εk -agent, it functions like the follower
to the pioneer in the system. δk -agent always has agents withhigher obstacle safety before it. In order to maximum theeffect of changing the structure, enhance the obstacle safety,
and securely speed up the velocity, we propel the δk -agent toa forefront safer neighboring agent. The specific process is tofind i′ ∈ Nkδ (i), s.t for ∀ j ∈ Nkδ (i), p j · vγ /∥vγ ∥ ≤ pi′ · vγ ∥vγ ∥,then the corresponding control uδki is given in (11)(11)

where the cδ is a constant coefficient for uδki .
© ISAROB 2013a(i, j)∆sαi, j (q j − qi )juδki = cδ (1 − soki )(qi′ − qi )

(12)

k

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

[3] Ren W, Beard RW (2005), Consensus Seeking in Multi-agent
Systems Under Dynamically Changing Interaction Topologies. IEEE Trans. Automa. Control 50(5):655-661

1So

Olfati−Saber algorithm
LSF algorithm
0.5

0

0

2

4

6

8

10

12

14

[4] Reynolds CW (1987), Flocks,Herds,and Schools: A Distributed Behavioral Model. Computer Graphics 21(4): 25-34

16t[5] Shi H, Wang L, Chu TG, et al (2007), Flocking of MultiAgent Systems with a Virtual Leader. In: Proceedings of the2007 IEEE Symposium on Artificial Life, pp. 287-294

Vaverage12
Olfati−Saber algorithm
LSF algorithm

1086[6] Carpin S, Parker LE (2002), Cooperative Leader Following ina Distributed Multi- Robot System. In: Proceedings of IEEE
International Conference on Robotics Automation, pp. 29943001

4
0

2

4

6

8

10

12

14

16

t
1

Sa

Olfati−Saber algorithm
LSF algorithm[7] Tanner HG (2004), Flocking with Obstacle Avoidance in
Switching Networks of Interconnected Vehicles. In: Conference of Robotics and Automation, pp. 3006-3011

0.5

0

0

2

4

6

8

10

12

14

16

t

[8] Olfati-Saber R (2006), Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Trans. Automat. Control
51(3): 401-420

Fig. 2. Contrast figure between two algorithms about
so , sα ,Vaverage[9] Hong Y, Gao L, Cheng D, et al (2007), Lyapunov-based
approach to multi-agent systems with switching jointlyconnected interconnection. IEEE Trans. Autom. Control
52(5):943-948

It has been shown that, under the LSF flocking control
protocols, the stable flocking behaviors can be achieved andthe average speed of the system in a limited space is improved. What’s more, the obstacle safety is greatly enhanced
and the agent safety basically stays the same. Study of simulation reveals that the LSF control algorithm can speed up
the system and improve its safety as the same time.

[10] Gazi V, Passino KM (2004), A class of attraction/repulsion
functions for stable swarm aggregations. Int. J. Control
77(18): 1567-1579[11] Su H, Wang X, Lin Z (2009), Flocking of multi-agents with avirtual leader. IEEE Trans. Autom. Control 54(2): 293-306

5 CONCLUSION
In this paper, we study the flocking control for multiagent systems in a limited space. A new coefficient called
the safety-value is introduced to describe the safety between
agents and obstacles, On the basis of the safety-value andOlfati-Saber algorithm, a distributed flocking control protocol - LSF algorithm is designed to control the multi-agent
system in a limited space. By using local information ofeach agent, the system can automatically and flexibly change
its velocity and structure in order to pass the limited space
both quickly and safely. Simulation reveals that the proposed
LSF control protocol can greatly improve the velocity of themulti-agent system with even enhanced safety-value.

[12] Rimon E, Koditschek DE (1990), Robot navigation functions
on manifolds with boundary. Adv. Appl. Math 11(4): 412-442[13] Gazi V, Passino K (2004), Stability analysis of social foraging
swarms. IEEE Trans. Syst., Man, Cybern. B: Cybern. 34(1):539-557[14] Bollob s B (1998), Modern Graph Theory. Vol. 184 of Graduate Texts in Mathematics. New York: Springer-Verlag[15] Diestel R (2000), Graph Theory. Vol. 173 of Graduate Texts
in Mathematics. New York: Springer-Verlag[16] Horn RA, Johnson CR (1987), Matrix Analysis. Cambridge
Univ. Press, Cambridge[17] Godsil C, Royle G (2001), Algebraic Graph Theory, Vol.
207 of Graduate Texts in Mathematics. New York: SpringerVerlag

6 ACKNOWLEDGMENTS
This work was supported by the National 973 Program (2012CB821200, 2012CB821201) and the NSFC
(61134005, 60921001, 90916024, 91116016).

[18] Olfati-Saber R (2003), Flocking with obstacle avoidance. California Inst. Technol., Control Dyna. Syst., Pasadena, CA,
Tech. Rep. 2003-006, Feb

REFERENCES[19] Tanner HG (2004), Flocking with obstacle avoidance inswitching networks of interconnected vehicles. In: Conference Robotics and Automation, pp. 3006-3011.

[1] Li Q, Jiang ZP (2009), Global Analysis of Multi-Agent Systems Based on Vicsek’s Model. IEEE Trans. Automa. Control, 54(12): 2876-2881.

[20] Saber RO, Murray RM (2003), Flocking with obstacle avoidance: cooperation with limited communication in mobile networks. In: Proceedings 42nd IEEE Conf. Decision and Control. Dec, pp. 2022-2028.

[2] Lin ZY, Broucke M, Francis B (2004), Local Control Strategies for Groups of Mobile Autonomous Agents. IEEE Trans.
Automa. Control 49(4):622-629© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Inverse kinematics in Hyper-redundant robot using Adaptive Neural Network
Chatklaw Jareanpon
Polar Lab, Department of Computer Science, Faculty of Informatics
Mahasarakham University , Thailand(Tel: 66-86-225-5574, Fax: 66-43-754-359)
chatklaw.j@msu.ac.th
Abstract: The hyper-redundant robot has more degrees-of-freedom. The most difficulty of the hyper-redundant is to finding
the inverse kinematic problem. Most of usually used method is Neural Network. However, it is difficult to find the suitablestructure and number of node. This paper shows the novel algorithm that can find the suitable structure and number of nodedepends on the problem. The performance of this algorithm will demonstrated in the computer simulation and compare with
the Back-propagation with same structure. The algorithm shows the good performance to adapt the number of node with lesserror to solve the 8-20 serial link chain hyper-redundant robots.
Keywords: Inverse kinematics, Hyper-redundant robot, Neural Network.
caging method as it, allows caging to perform in a varietyof configuration. However, this arm must always maintain acertain shape around an object.
HDOF has been used by several researchers for solving
control problems such as kinematic modeling [1], [2] path
planning [3], [4], inverse kinematics [5], [6], [7], [8],locomotive gait design [9], [10], obstacle avoidance [11],[12], serpentine locomotion control [13], [14] andsidewinding locomotion control [15] problems.
In our work, we study the shape control of a highly
kinematic structure, called a HDOF arm manipulator. The
HDOF is composed of serial chain links li , i  1,....,N ,1 INTRODUCTION
The hyper-redundant robot is the robot that has morethan the minimum numbers of degrees-of-freedom aretermed “many kinematically redundant”. The hyperredundant are used in operation to snakes, elephant trunks,
and tentacles. There are a number of very important
applications such as obstacle avoidance, manipulated task.
However, the most difficulty in Hyper-redundant robot is
controlling the inverse kinematics of its.
The different techniques used for solving inverse
kinematics can be classified as algebraic that do notguarantee closed form solutions, geometric that usuallyused the curve and constraint, and iterative. The iterative
methods converge to only a single solution. The mostlearning method that are usually using Neural networks.
The Neural Netwoks usually used to solve the problem of
inverse kinematics are Back-propagation and Kohonen
network. However, the most question of using neuralnetwork is “How many are the best number of the nodes? ”
In this paper, I proposed a new sequential learningalgorithm, which is able to adapt the structure of the
network. Using this algorithm, it is possible to find thesuitable number of hidden node.

connected to other with revolute joints ji , i  0,...,N  1 . Eachlink is a straight rigid part of length L . The link l1 and linklN

are called the base and the tail, respectively. The angle

 1 is defined as the angle between link l1 and x-axis. The
set of angle defines the manipulator configuration as shownin Figure 1.

2 HYPER-REDUNDANT ROBOT
Highly redundant manipulators or hyper degree of
freedom (HDOF) has more degrees of freedom (DOF). A
HDOF manipulator can perform manipulation tasks, suchas moving in non-convenient environments, and pushingand caging a various sizes and shapes of objects. Due to allin-one arms, a HDOF manipulator significant enhances the© ISAROB 2013

Fig. 1. A hyper degree of freedom (HDOF) structure.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

to a high degree polynomial in the tangent of the hal
f-angle of joint variable. However, it is complicate in t
he nonlinear system.

3 INVERSE-KINEMATICS PROBLEMA HDOF manipulator has ability to move in highly
constrained environment or grasp various sized and shaped
objects. HDOF manipulators are categorized into 3 types ofmechanisms which are serial rigid, parallel rigid andtentacle-like. A serial rigid robot arm is consisting of links
and joints in chain structure. In forward kinematics or direct
kinematics, the joint displacement and link parameters are
given in order to find the end-effector position. Conversely,
the inverse kinematics is to solve for the joint displacement
when the end-effector position is given.
The transformation matrix relating ith coordinate
system (coordinate of end of link i) to the (i-1)th(coordinate of end of link i-1) coordinate system is i 1iT
given byc o si c o si s i ni

s i n i s i ni

c o si s i ni

 s i n i s i ni

0

 0

s i n ic o si001Zˆ i 1measured alongi 1
 s i ni
iT  

where parameters areai = the distance fromẐ i to i = the angle from Ẑ i to Zˆ i 1di= the distance fromX̂ i

 i c o si 
 i c o si ditoXˆ i 1

4 RADIAL BASIS FUNCTION (RBF) NETWORKThe Radial Basis Function (RBF) Networks is a single
hidden layer feed forward neural network as shown in
Figure 2. Each node of the hidden layer has a parameter
vector called the center. This center is used to compare with
the network input vector to produce a radial symmetricalresponse. The response of the hidden layer are scaled by theconnection weights of the output layer and then combined
to produce the network output. The response of thehidden node to input data vector , dimensionality M, isgiven by (4).

(1)





measured along

The numerical methods that are widely used in solv
ing for inverse kinematics is the Newton-Raphson iteration method. Other optimization techniques can also be
used. The concept of inverse kinematics problem is si
milar to minimization problem where the error between
the current position and desired position is minimized.
Therefore, nonlinear optimization techniques such as neural network [16] and genetic algorithm [17] can beapplied to this problem.

X̂ i

X̂ i

measured along

Ẑ i

2

i

= the angle from

X̂ i

to

Xˆ i 1measured alongij  e x p( xi  x j )

Ẑ i

(4)

Ẑ i = Zˆ i axis of frame {i}whereis an M-dimensional center andis a constantwhich determines the spread factor of the symmetricresponse of the hidden node. The network output is definedasX̂ i = Xˆ i axis of frame {i}The transformation matrix is divided into two parts
which are rotational part and translation part.

( i i1R)( i i1P)
i 1
iT  



0
1
1 
0

k

ˆi   ij h j

(2)

where
are the network's second layer connection
weights and k is the number of hidden nodes.

Transformation matrix form the base frame {0} to linkn is described by
0
0 1 2
n 1
nT  1T 2T 3T .... nT

The widely used RBF network, may use otherfunctions e.g. piecewise linear, cubic approximation, thethin plate spline, the multiquadratic, and the inverse
multiquadratic function in place of the Gaussian.

(3)

Since a HDOF manipulator has large number of degrees of freedom (DOF), the inverse kinematics solutio
n is not unique. Moreover, the solution of inverse kine
matics of the robot arm is difficult to find. The algebraic and numerical methods are usually employed to solve the inverse kinematics problem. The concept of alg
ebraic method is to transform the kinematics equations© ISAROB 2013

(5)

j 1

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013is calculated from the distance between end-effector andtarget position. Our experiment is tested on the Core-I7
3.4GHz and 16 GB of RAM.
yFig. 2. Architecture of an RBF neural network
The performance of the networks is measured by a MeanSquared-Error (MSE). The main objective of the training
procedure is to approximate the underlying function of thesystem.

x
Fig. 3. Comparison between Back-Propagation Neural (Dotline) and our proposed method (solid line) of 9 links serial
link chain

5 THE NEW RBF NETWORK WITHPROPOSED ADAPTIVE STRCUTURE
To determine an appropriate structure for the RBF
network, a modified RBF network with adaptive structure is
proposed. Initially, in the proposed structure, the radialbasis layer has one hidden neuron. Afterward, the network
iteratively appends one RBF node to the hidden layer at
each training epoch until the error falls beneath an error
goal or the maximum number of neurons has been reached.
Unlike a traditional network with a fixed structure, theproposed network gradually searches for a minimum
number of hidden nodes needed to meet the performancegoal. The overall algorithm is given as the following:








Table 1. The comparison between Back-Propagation
and Our proposed method
Back-PropagationLink

method
Node

err

time

Node

err

(s)

Step 1. Initialize the network using the Structure
having a single neuron in the hidden layer.
Step 2. For each training epoch, feed all input
vectors to the network and train the network
according to the RBF training algorithm.
Step 3. Find the input vector in which the network
output yields the greatest error.
Step 4. Add one neuron to the hidden layer with its
weight vector equals to the vector obtained from
Step 3.
Step 5. Repeat Step 2 until the performance goal ismet or the maximum number of hidden nodes has
been reached.

time
(s)

8

18

1.250

258

18

1.020

260

9

24

3.041

301

24

2.570

284

12

41

3.225

440

41

3.000

365

14

52

4.655

665

52

4.080

556

16

51

4.787

896

51

4.220

803

18

52

5.200

994

52

5.050

925

20

80

6.250

1226

80

6.220

1198

From Table 1, when the number of the link increased, theerror and time will increase. Especially, the time is verymuch consuming.

6 DISCUSSION
Our proposed method is show the performance offinding the suitable number of the node. The proposed
method can solve the problem of inverse kinematic.
Moreover, we can able to find the suitable number of node
for other neural network such as Back-propagation.
In the future, I will try to reduce the time consuming,
because the time is abundantly grown when add the numberof link.

5 EXPERIMENTAL AND RESULTIn this section, experimental results are presented inTable 1 and Figure 3. I try to solve the inverse-kinematics
in vary the number of links from the 8 and 20 serial linkchains. I will measurement the algorithm in term of theerror, time and number of nodes when compares with backpropagation neural network with same structure. The error© ISAROB 2013

Our proposed

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REFERENCES[11] H. Xie, “Real-time collision avoidance for aredundant manipulator in an unstructured environment,”
Proc. of the 1998 IEEE international conference on
intelligent robotics and system., pp. 1925-1930, 1998.
[12] S. Ma and M. konno, “An obstacle avoidance
scheme for hyper-redundant manipulators – Global motion
planning in posture space –,” Proc. of the 1997 IEEE
international conference on robotics and automation., pp.
161-166, 1997.
[13] J. Ostrowski and J. Burdick, “Gait kinematics for aserpentine robot,” Proc. of the 1996 IEEE international
conference on robotics and automation., pp. 1294-1299,
1996.
[14] J. Conradt and P. Varshavskaya, “Distributed central
pattern generator control for a serpentine robot,” Proc. of
the joint international conference on artificial neuralnetwork., 2003.
[15] J. Burdick, J. Radford, and S. Chirikjian,“Sidewinding” locomotion gait for hyper-redundantrobots,”, Proc. of the 1993 IEEE international conference
on robotics and automation., pp. 101-106, 1993[16] K. Mathaia and R. Saeks, “Inverse Kinematics via
Linear Dynamic Networks”, Proceeding on World Congress
on Neural Networks, pp. 47-52, 1994.
[17] Y. Li and A. Hong Lenog, “Kinematics control ofredundant manipulators using CMAC neural network
combined with a genetic algorithm”, Robotica, pp. 611-621,
2004.

[1] S. Chirikjain and J. Burdisk, “The kinematics ofHyper-Redundant robot locomotion,” IEEE Transactions on
robotics and automation., vol. 11, no. 6, Dec. 1995.
[2] F. Matsuno and K. Mogi, “Redundancy controllable
system and control of snake robots based on kinematic
model,” Proc. of the 39th IEEE conference on decisionand control., pp. 4791-4796, 2000.
[3] H. Poor, An Introduction to Signal Detection andEstimation.
New York: Springer-Verlag, 1985, ch. 4.
[4] B. Smith, “An approach to graphs of linear forms(Unpublished work style),” unpublished.
[5] H. Chang, “A closed-form solution for inversekinematic of robot manipulators with redundant,” IEEE
journal of robotics and automation., vol. ra-3, no. 5, Oct.
1987.
[6] Y. Li and H. Leong, “Kinematics control of redundant
manipulators using a CMAC neural network combined withgenetic algorithm,” Robotica., vol. 22, pp. 611-621, 2004.
[7] K. Mathia and R. Saeks, “Inverse kinematics via
linear dynamic network,” World congress on neuralnetworks, 1994.
[8] Y. Zhau, T. Huang, and Z. Yang, “A new numericalalgorithm for the inverse position analysis of all serial
manipulators,” Robotica., vol. 00, pp. 1-4, 2005.
[9] G. Kulali and et al, “Intelligent gait synthesizer forserpentine robots,” Proc. of the 2002 IEEE international
conference on robotics and automation., pp. 1513-1518,
2002.
[10] A. Crespi and et al, “An amphibious robot capableof snake and lamprey-like locomotion,” Proc. of the 2005
IEEE international conference on robotics and automation.,pp. 3035-3039, 2005.

© ISAROB 2013

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Reduction of Impact when a Humanoid Robot Lands on the Ground
Homare Fujieda, Shingo Okamoto1, Jae Hoon Lee
Ehime University, Japan(Tel: 81-89-927-9740, Fax: 81-89-927-9740)
1

okamoto.shingo.mh@ehime-u.ac.jp

Abstract: The purpose of this research is to reduce the impact force when robot land on the floor by landing motion. First,
landing postures of a human were analyzed measuring impacts when the human land on the floor. Through the experimental
result on relations between landing postures and the impact forces, it was hypothesized that a human may reduce the impact
force by motion control of gravity center of the human, for example to lengthen the time in landing motion. Then a landing
robot was developed and the experiment to measure the impact forces was conducted, in order to prove the hypothesis.
Keywords: Humanoid robot, Impact reduction, Passive landing, Active landing, Numerical calculation.
0.935[s], respectively. The period of time of IR landing was
twice as long as that of usual landing. Then, the impact
forces of the usual and IR landings were 2.48×103[N] and1.50×103[N], respectively, and that of IR landing decreased
by 40 [%] to that of the usual one. From these results, it was
hypothesized that a human may reduce the impact by
motion control his gravity center, for example, to lengthen
the period of time in landing motion.

1 INTRODUCTION
Recently, researches about humanoid robots have beenwell-advanced. In consequence of the well-advance, thehumanoid robots became able to practice various motions,such as walking, jumping, and low-speed running. Then, as
the development of the humanoid robots is advanced more,
the humanoid robots will be needed to behave more
intensely for example, like high-speed running. Then, theimpact that robots receive in the high-speed running will
become a big problem hereafter. This impact may cause
various troubles like falling and damage of parts.
The purpose of this research is to reduce the impact whena robot land on the floor by landing motion. Here, we
focused on the shifting gravity center of the human inlanding on the floor. Then, the experiment to measure theimpact was conducted in order to prove that the impact canbe reduced by motion control of gravity center of thehumanoid robot during landing motion.

(1) t = 0.00s (2) t = 0.12s(3) t = 0.21s(a) Usual landing(4) t = 0.39s

2 LANDING OF A HUMAN
2.1 Taking moving images
We took moving images of human’s landing by ahigh-speed camera (CASIO: EX-FH25). Then landing
postures when a human lands on the floor was analyzed.
Two types of landing, a usual landing and a IR(Impact
Reduction)landing, were analyzed. The result of moving
images is shown in Figure 1. Through the comparison of
two results, it was found that (1) there is no difference
between the usual and the IR landing in a touchdown
motion and, the human bends his knees more in considering
impact reduction than the usual landing. It means that ahuman shifts slowly down his gravity center when he needs
to reduce the impact.

(1) t = 0.00s (2) t = 0.12s (3) t = 0.21s(4) t = 0.43s(b) IR Landing
Fig. 1. Moving images of human’s landing

2.2 Measurement of impact
Figure 2 shows the impact force for each landing posture.
The period of time from beginning to end of landing
motions of the usual and IR landings were 0.480[s] and© ISAROB 2013

Fig. 2. Impact forces to variable landing postures

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3 CONPUTATIONAL CALCULATION

Eq.(20). These parameters were decided based on theexperiment. Then the coefficient of velocity cf was given as
Eq.(3).
𝑘𝑓 = 267,459(𝑢1 − ℎ1 − 0.08)2 − 1,790.1(𝑢1 − ℎ1 − 0.08),(20)
𝑐𝑓 = 𝛽2 𝑘𝑓 ,(21)
The values of β1 and β2 were set at 5.15×10-2 and 7.50×10-1,
respectively. The initial height of the landing robot was set
at 0.3[m].

3.1 Analysis model
An analysis model of a landing robot was designed andthe numerical calculation was performed in order todevelop the landing robot. Figure 3 shows the analysis
model of a landing robot. The analysis model is composedof a foot, an upper body, a shin and a thigh.
The landing robot falls along a rail with a slope in order todiminish the speed in shifting gravity center of the robot sothat a motor can control its speed. Then the physicalparameters of the lengths, the masses, the inertia moments,
the coefficient of viscosity and the spring constant used in
the calculation are shown in Table 1.
3.2. Equation of motion
The equation of motion is given by(1)
𝑴𝑸̈ + 𝑪𝑸̇ + 𝑲𝑸 = 𝝉 − 𝑮𝑇𝑇
where 𝑸 = {𝑢1 𝜃} , 𝝉 = {0 𝜏1 } , M is a inertia matrix, C

is a damping matrix, K is a stiffness matrix and G is a
gravity vector. The components of matrixes and vector in
Eq.(1) are represented as𝑀
𝑴 = [ 11
𝑀21

𝑀12
]
𝑀21 ,

(2)

𝑀11 = 𝑚1 + 𝑚2 + 𝑀1 + 𝑀2 ,(3)

𝑀12 = −{(𝑚1 + 𝑚2 )(𝐿1 + 𝐿2 ) + 𝑀1 𝑙1 − 𝑀2 𝑙2 } 𝑠𝑖𝑛 𝜃,(4)

𝑀21 = −{(𝑚1 + 𝑚2 )(𝐿1 + 𝐿2 ) + 𝑀1 𝑙1 − 𝑀2 𝑙2 } 𝑠𝑖𝑛 𝜃 ,

(5)

where

𝑀22 = {(𝑚2 + 𝑀2 )(𝐿1 + 𝐿2 )2 − 2𝑀2 𝑙2 (𝐿1 + 𝐿2 )}𝑠𝑖𝑛2 𝜃+𝑀1 𝑙1 2 + 𝑀2 𝑙2 2 + 𝐽1 + 𝐽2 ,𝐶11 𝐶12𝑪= []𝐶21 𝐶22 ,

(6)
(7)

where
𝐶11 = 𝐶𝑓 ,(8)

𝐶12 = −{(𝑚1 + 𝑚2 )(𝐿1 + 𝐿2 ) + 𝑀1 𝑙1 − 𝑀2 𝑙2 }𝜃̇ 𝑐𝑜𝑠 𝜃,

(9)
(10)

𝐶21 = 0,𝐶22 = 0.5{(𝑚2 + 𝑀2 )(𝐿1 + 𝐿2 )2 − 2𝑀2 𝑙2 (𝐿1 + 𝐿2 )}𝜃̇ 𝑠𝑖𝑛2𝜃 + 4𝑐𝑗 , (11)𝐾𝐾12𝑲 = [ 11],(12)
𝐾21 𝐾22

Fig. 3. Analysis model of the landing robot

where
𝐾11 = 𝑘𝑓 ,

(13)
(14)
(15)

𝐾12 = 0,𝐾21 = 0,𝐾22 = 4𝑘𝑗 ,Table 1. Physical parameters of analysis model
m1 [kg]2.40
L1 [m]
2.10×10-1
m2 [kg]
1.56
L2 [m]
2.10×10-1
M1 [kg]3.10×10-1
l1 [m]
1.05×10-1-1
M2 [kg]3.10×10l2 [m]
1.05×10-1
H1 [m]
1.70×10-1
J1 [kg•m2]
2.11×10-3
H2 [m]
8.00×10-2
J2 [kg•m2]
2.11×10-3-2
h1 [m]
8.50×10
cj [kg/m•s]
β1kj-2h2 [m]
4.00×10
kj [kg•m²/s²•rad]
3.20

(16)
𝑇

𝑮 = {𝐺1 𝐺2 } ,

(17)

where
𝐺1 = (𝑚1 + 𝑚2 + 𝑀1 + 𝑀2 )𝑔 𝑠𝑖𝑛 𝛼,
𝐺2 = −{(𝑚1 + 𝑚2 )(𝐿1 + 𝐿2 ) + 𝑀1 𝑙1 − 𝑀2 𝑙2 }𝑔 𝑠𝑖𝑛 𝜃 𝑠𝑖𝑛 𝛼(18)+(𝑀1 𝑙1 + 𝑀2 𝑙2 )𝑔 𝑐𝑜𝑠 𝜃 𝑐𝑜𝑠 𝛼 − 2𝑘𝑗 (𝜋 − θ0 ),(19)

3.3. Passive landing
3.3.1 Calculation method
A computer simulation using the Newmark-β method was
performed in a numerical calculation, and Scilab was used
as a computer language. Then control torque τ1 was set atzero in this calculation. The spring constant kf was given as© ISAROB 2013

3.3.2 Calculated results
Figure 4 shows the time histories of u1 and u2. The
experiment of the landing robot was conducted based on
this result.

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kd = 0 : P control, kd ≠ 0 : PD control
Fig. 5. Block diagram of an active control for calculation

Fig. 4. The calculated time histories of u1 and u2
in passive landing
3.4 Active landing
3.4.2 Calculation method
Two types of control method, the proportional control andthe proportional-differential control were used. Then thespring constant of the joint 3 kl and coefficient of viscosityof the joint 3 cl were set at zero in this calculation.

Fig. 6. Comparison of the displacement u2 ofthe upper body between the proportional andthe proportional-differential controls

3.4.3 P control (Proportional control)
The block diagram of an active control for calculation isshown in Figure 5. The control torque τ1 loaded to the upper
body was given by Eq.(22) when the proportional control
was used.
𝜏1 = 𝑘𝑝 (𝜃𝑑 − 𝜃)(22)

The calculated result of the proportional control is shownin Fig. 6 when setting at the control gain kp = 200[N•m/rad],the desired angle of the shin(or thigh) θd = 87[deg], and theangle of the shin(or thigh) θ = 87[deg] corresponding to thedisplacement of the upper body u2 = 0.2[m]. Then control
gain kd was set at zero in this calculation.

Fig. 7. Landing robot

3.4.4 PD control (Proportional-differential control)
The block diagram of an active control for calculation isshown in Figure 5. The control torque τ1 loaded to the upper
body was given by Eq.(23) when the PD control is used.
The kp and kd denote the control gain.
𝜏1 = 𝑘𝑝 (𝜃𝑑 − 𝜃) + 𝑘𝑑 (𝜃̇𝑑 − 𝜃̇)Fig. 8. Guide flame with a slope(23)

The calculated result of the PD control is shown in Fig. 6
when setting at the control gain kp = 200[N•m/rad], thedesired angle of the shin(or thigh) θd = 87[deg], the desired
angular velocity of the shin(or thigh)= 0[m/s].

Fig. 9. Force sensor

3.3.2 Calculated results
Figure 6 shows the comparison of the displacement u2 ofthe upper body between the proportional and the PD
controls. It was found that the displacement u2 wasstabilized by using the PD control.

knee joint to connect the shin and the thigh, in order togive a spring effect that thigh muscles of a human generate.
4.2. Guide flame with a slope
Figure 8 shows a guide flame with a slope. This slope
angle can be changed to desired angle. A rail that thelanding robot falls along the slope is attached on the guide
flame.

4 DESIGN AND DEVELOPMENT OF
EXPERIMENTAL SET-UP
4.1. Landing robot
Figure 7 shows a landing robot. The landing robot iscomposed of a foot, an upper body, the shin and the thigh.
Two rotary encoders are installed to measure the rotation
angles. The displacement of the foot and the upper body arecalculated by them. The torsion springs are attached to the© ISAROB 2013

4.3. Force sensor
A force sensor to measure the impact which the landing
robot is subjected to was developed. Figure 9 shows theforce sensor. The force sensor is composed of a polyvinyl
chloride pipe, parts of processed brass and strain gages.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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5 EXPERIMENT OF THE LANDING ROBOT
5.1. Passive landing
5.1.1 Experimental method
The initial angle of the shin(or the thigh) was set at θ =45[deg], and initial height of the landing robot at 0.3[m].
The spring constant of the torsional spring was set at
4.62[kg•m²/s²•rad]. The experiment was conducted in two
types of landing condition, (a)a landing with shifting
gravity center of the robot and (b)a landing with fixing the
distance between the foot and the upper body. Then boththe impact forces when the robot lands on the ground andthe positions of the moving body were measured to eachcondition.
5.1.2 Experimental results
Figure 10 shows the impact force on each condition.
Figure 11 shows the comparison of u1 and u2 between
calculation and experimental result. The period of timefrom beginning to end of landing motions of the (a) and (b)were 0.095[s] and 0.073[s], respectively. The period of timeof (a) was 1.3 times as long as that of (b). Then the impact
forces of (a) and (b) were 2.84×102[N] and 5.81×102[N],respectively, and that of (a) decreased by 50 [%] to that of(b). It was found that the impact force when the robot landson the ground can be reduced by shifting gravity center ofthe robot. Then the displacement of the foot u1 and theupper body u2 in the experiment follow the displacement incalculation. As this result, a validity of the landing robot
was confirmed.

Fig. 10. Impact forces to each condition

Fig. 11. Comparison of u1 and u2 between calculation andexperimental result in the passive landing

5.2. Active landing
5.2.1 Installation of a motor
A motor was installed at the upper body to reproduce
effect of the torsion spring. The landing robot installed the
motor as shown in Figure 12.
5.2.2 Control method of the motor
The PD control was used for control of the motor. The
block diagram of an active control for landing robot isshown in Figure 5. The desired angle 𝜃𝑑 of the shin(orthigh) and the desired angular velocity 𝜃̇𝑑 of the shin(orthigh) was set at 87[deg] and zero, respectively.
5.2.3 Experimental method
The motor is controlled using the conditions gotten in theexperiment. Then both the impact forces when the robot
lands on the ground and the positions of the moving upper
body are measured, and the control gains so that the motor
can reproduce the effect of torsion spring are examined.
5.2.4 Experimental results
The experiment has been being conducted currently.

6

Fig. 12. The landing robot installed the motor(2) The analysis model of the landing robot was designed
and the control gains kp and kd were found in thecomputational calculation.
(3) The landing robot was designed and developed
considering the calculated results.
(4) The experiment of the landing robot with passive
landing was conducted. Then the effect of shifting
gravity center of the robot in landing motion was
confirmed.
The experiment of the landing robot with active landing has
been being conducted. For the future, it will be proved thatthe impact when the robot lands on the ground is reducedby motion control of the robot through the experimental
results.

CONCLUSIONREFERENCES

The way to reduce the impact when the robot lands on the
ground had been studied. The summaries of the results are:(1) It was hypothesized that a human may reduce theimpact by motion control of his gravity center, forexample, to lengthen the period of time in landing
motion.

© ISAROB 2013

[1]

415

Tazaki T, Tomita M, Natori T, Shibata M(2002)： L
anding impact force reduction on redundant leg bipedrobot by center of gravity control, vol. IIC-02, no. 15,
pp. 1-6

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Cell-production Parts Layout by Virtual Factory System using
Reinforcement Learning and MTM
Hidehiko YAMAMOTO 1, Tsuyoshi SUGIMOTO 2, Takayoshi YAMADA 3 ,and Masahiro NAKAMURA 4
1, 3

Department of Human and Information Systems, Gifu University, Japan
2
Graduate School of Engineering, Gifu University, Japan,
4
Lexer Research Inc., Japan1

yam-h@gifu-u.ac.jp,q3128013@edu.gifu-u.ac.jp
3
yamat@gifu-u.ac.jp

2

Abstract: For a cell production system to use a part rack for production environment, the quality of efficiency of the
production time varies according to the part placement of the part rack. To solve this problem ，we developed Virtual
Assembly Cell-production System (VACS) which evaluates the movement distance of operators and decides partplacement．In this study, we suggest a new VACS which evaluates not only movement distance of the operators but also upand-down motion to get parts on the rack．In this VACS，by part placement decision, I adopt reinforcement learning andadopt the MTM as a work evaluation of the up-and-down motion of the rack in this reinforcement learning .
Keywords: Cell-Production , Virtual Factory, Reinforcement Learning, Method-Time Measurement

1 INTRODUCTION
The parts picking up operation which uses a rack in a cell
production system has the problem with different
production efficiency by the part locations of the rack. To
solve this problem，we developed Virtual Assembly Cellproduction System(VACS) which optimizes part placement，and visualizes the work process to an operator．[1]
VACS is the system which integrates a virtual production
system with a part placement decision system．In the partplacement decision system, we decide the most suitablecombination of rack and parts．In the virtual production
system , we make a shop floor of the cell production by
3DCG，and we visualize the installation process of the
production parts based on the combination of the mostsuitable rack - parts which were decided by a partplacement decision system. Thus, we realize the visualized
work production, but, VACS decided part placement isbased on movement distance．We should add the degree ofdifficulty of the movement of the operator to evaluate.
Therefore, this paper proposes new VACS withreinforcement learning. And we apply this VACS to theassembly cell product line of the PC and inspect theeffectiveness.

2 VACS
2.1 VACSVACS consists of two systems of a part placement
decision system and the virtual production system as shown© ISAROB 2013

416

in Fig.1. First, VACS makes the shop floor by thereinforcement learning in a virtual production
system．Next, VACS decides the most suitable placementof a part and the rack in a part placement decisionsystem，Finally VACS makes a virtual shop floor byacquiring the most suitable placement by a virtual
production system again.
Parts layout decision system

Optimization
Virtual production system

Visualize
Production

CreateWorking Place
Fig.1 VACS

2.2 Part placement decision system
The part placement decision system uses reinforcement
learning based on the workshop which is received fromvirtual production system，and decides part placement of
the cell production. The decision corresponds to the
movement distance of the operator and the evaluation of thedegree of difficulty of the up and down movement．
In this study, we use Profit Sharing as reinforcement
learning，In order to update the value of Profit
Sharing，the walk movement that an operator moves
between racks，and the work degree of difficulty of the up
and down movement when an operator takes a part are

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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converted to time value by MTM，and VACS uses thistime．We call this time an operating time. The time isdecided by conditions such as the weight and height and thepart of the operator.
The part placement decision system consists of two
modules, namely, the condition making module and thelearning module．The condition making module performs
reading with various information files，and compiles thedata to enable it to learn in a learning module． The
learning module performs the pre-decided number oflearning times and decides the most suitable part placement
by Profit Sharing．

The rectangular of the depth and the width that are readin STEP3-1 becomes rectangular. After reading theworkshop information, the size of rack parts, carts, andcoordinates are set. Fig.2. shows the example of theworkshop using this information.
(0,0)(0,50)(0,100)2.3 Condition making module
The condition making module sets a production condition
and a learning initial condition of Profit Sharing throughthe following steps.
Step3-1: Reading of the configuration file
By using the initial conditions, the conditions about
production are decided. Reading setting information anddecide a condition about the production．The initialconditions are as follows．
Width of the workshop
Depth of the workshop
The size of cart and rack
The number of the steps of the rack
Highest step height of the rack
The lowest step height of the rack
The learning times number
Learning rate
Height of the operator

A

I

B

H

C

G

(50,100)
DE

F(250,250)

Fig.2 coordinate data
Step3-4: Read order data．
The order data describes the kind of products, outputs,
necessary parts information, and the number of the parts.
We show an example of order data in Table.1. Table.1
indicates the kind of PC to produce, needed parts and,number of products. For example, in PC 1, one case,one
power supply , one motherboard, one unit of CPU,and twomemory. Furthermore, a person of order wants to produce
PC 1 20.
Table.1 example of order data
Product name
Case
Power
Mother board
CPU
Memory

PC1
1
1
1
1
2

PC2
1
1
1
1
4

20

17

…

…

Step3-2: Read the parts information of the part．
Parts information necessary for product is acquired. The
part information to read in this STEP are as follows.
Part nameSize of the part
Ease of grasping the part
Weight of the partThe size of the part data gives 0 or 1，The part which
can be held with one hand is 0.The part which is held withboth hands is 1.Ease of grasping the part is given as
1～3,which are used as analysis of the up and down
movement in movement analysis. As the numbers increase,the degree of difficulty of the grasp rises, and operatingtimes increase．The weight is in kg unit and is used as theanalysis of the up-and-down motion.

Number of products

Step3-5: Give the pair of all available placement
possibility place - parts an initial value.
This step gives all placement places and pair of the part
initial values. The values are the standards which judge thesuperiority of the state that the part is put is in the partplacement. The values are used in later roulette choice.
First, make a table of the values. In the part placement
place, the number of L1 - Ln, parts is referred to as partplacement possibility place m point, part n unit X1 - Xm,and the table of the value expresses with value ω(Li,Xj).
ω(Li,Xj) expresses it how superior it is to place part Xj inthe part placement possibility place Li. The table ofvalues of (a number of the parts) × (part placement

Step3-3: Setting of the condition of the workshop© ISAROB 2013

Work
space

(50,100)

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sequentially at each placement place.
Step4-3: Using the MTM method and analyzing it.
The step analyzes the picking up operations by the MTM
method. The step divides the operations into two operations
top and bottom movement and walk movement to move
between racks. Fig.3 shows that an operator moves to thefront of the rack for the up and down operations. STEP43, calculates each up and down time which are needed for
produce all products.

possibility place) is shown in Table.2.
Table.2 Values and racks relations
A

B

C

…

2

ω(A,1)
ω(A,2)

ω(B,1)
ω(B,2)

ω(C,1)
ω(C,2)

…
…

…

…

…

…

…

1

A,B…

Rack names

1,2… parts

Step3-6: Calculate the distance of work table and the
distance between two points between each placement
place.
The step calculates the distance between each necessary
part placement for analysis of the walk movement. The
distance between two points are caluclated using a
Pythagorean formula. For example, (XA,YA) is thecoordinate of place A, and B (XB ,YB) is the coordinate ofplace B. The distance between two points is calculated withthe following formula.
z =Step4-4:Compare the working hour with theconventional smallest working hour.
This step compares the working time taken with thesmallest working time of the work simulation. Replace thesmallest working time and memorize part placement as themost suitable part placement.
Step4-5: Update the value of each placement．
The step, updates the value of each placement possibility
place , by using the following equation.

( x B − x A )2 + ( y B − y A ) 2(1)
The distance between the two points for all partplacement possibility places can be calculated using theabove method. In the case where the part placement
possibility place to Li - Ln, the distance between pLi-Lj(0i,j <= n) is expressed as D(Li,Lj). The distance between
each part placement possibility place that is calculated
above is used in the analysis of the walk movement.

ω ( s , a ) ← ω ( s , a ) + γ (T − t + 1) r
s: placement place a:parts
γ: discount rater: Reward T: The number of the total parts
t: Part place decision order

Step4-6: Repeat Step4-2 - Step4-5.
As shown in Fig.3 , Step4-2 ～ Step4-5 are repeated.
The part placement whose working time is the smallest
become the solutions.

2.4 Learning module
The learning module is carried by using Profit Sharing in
the following Step.
Step4-1: Make the initial value of the standard value.
For an acquired operating time, the initial value of thestandard value to determine whether VACS gives a reward
or not is set. As the initial value, the average time value
acquired after random parts arrangements is adopted.
Step4-2: Performing part placement based on value(using roulette choice).
The step puts a part using the roulette selection based on thefollowing equation.

ω ( s, a )p (a | s ) =∑ ω ( s, b)

Making first standard time
Layout parts
Analyze the movement.
Is it shorter than the standard time?
YESNO

Is it shoter than the smallest time?
YES

NO

Replace layout and smallest time.

(2)

S: placement place a: The part which is chosenb: The number of total parts

Give a reward and update

The roulette choice is a method to choose an action
according to the ratio of the value. By this method, we
calculate the chosen probability of the part in the partplacement possibility place and decide the placement© ISAROB 2013(3)

Fig.3 flow of Optimization system

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2.5 Virtual production system
The virtual production system makes the shop floor andrealizes the visualization of the working process by
3DCG．The virtual production system makes the shop
floor including racks and the working table top to use in apart placement decision system. Based on placement
decided by a part placement decision system, part on therack of the shop floor is put．Finally, we input a work
process and express a work process with an animation.

3 Application example of VACS
We performed the most suitable placement of the PC partand in the workshop such as Fig.4 using VACS. The
simulation performs unevenness by learning ten times toconsider it and does the thing which we bought with a little
time with the most suitable placement. In addition, we tried
it before learning at random to confirm the effect by thelearning. As a result, the shortest result, as in Table.3 isadopted as the most suitable placement because there were
the fewest the fifth learning results. It was fewer than
141,804, and an effective result was provided. In addition amethod of VACS using the reinforcement learning was able
to confirm its effectiveness, VACS at minimum time whena value located it before 112,912, learning at random intime for part placement decision using VACS. In addition,
we set up a part in a virtual production system and made avirtual shop floor such as in Fig.5 based on the mostsuitable placement and realized to see the work.

Fig.5

4 CONCLUSIONVACS, which we suggested reduced the level in the timethat the part acquisition took in comparison with random
placement from a result for simulation, provided goodresults，It was shown to be effective for optimization of
the part placement of the cell production. In addition, wewere able to incorporate the evaluation of the movement
analysis using the MTM method in learning by Profit
Sharing．
We were able to do part placement of part placementlayout decision systems and confirming parts layout by
virtual production system by visualizing. In this way, wewere able to build VACS where a part placement layout
decision system and a virtual production system are linked
to each other.

Work
place

A

J

B

I

C

H
D

E

F

G

Fig.4 Rack locations

REFERENCES[1]Hidehiko YAMAMOTO, Takayoshi YAMADA and
Masahiro NAKAMURA, Parts Layout Decision for
Assembly Cell-Production by GA and Virtual Factory
System，Japan Society of Mechanical Engineers , Vol.76,
No.764, 994-999(2010-4).

Table .3 Simulation results
Before learning average
Before learning minimum

181753141804First standard time
Last standard time
Result time
Update number

1st18183112496711322738955

2nd18302111921311472652072

3rd
18256812125811555942708

4th18185612151711491733335

5th
18231111750811291260450

First standard time
Last standard time
Result time
Update number

6th18231911962011461323407

7th18221011929211406220029

8th1824801178631141431053769th
181168117554115401
10008910th
18163311891911320370012© ISAROB 2013

Result of VACS

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Gait motion of a six legged real robot employing associatron
Masashi Sakai1, Masato Hashimoto1, Tomo Ishikawa1,
Koji Makino1, Jin-Hua She1, Yasuhiro Ohyama1
1

Tokyo University of Technology, Japan(Tel: 81-42-637-2111 ex.2823, Fax: 81-42-637-2112)
1

g21110260e@st.teu.ac.jp

Abstract: In this study, we have proposed gait motion algorithm of a six legged robot in order to walk an unpredictableirregular field. The algorithm is adapted to the Associatron. The algorithm has a property that enables the robot to recall anentire pattern from partial information, sequentially. Therefore, the robot can walk in unknown field. First, we verified the
proposed algorithm by simulations using ODE (Open Dynamics Engine). It is clear that memorized pattern is recalled fromunknown pattern. Secondly, an experiment is performed using the developed real robot. The experiment result proved that the
robot is able to select suitable gait motion at the existence of an obstacle.
Keywords: gait motion, real legged robot, Associatron.
(include batteries), respectively.. The robot is similar to an
insect. To simplify the discussion, the each leg unit has two
actuators. They enable the each leg swing to roll (up-down)
and yaw (front-back) direction, respectively. The eachtouch sensor is installed on each leg in order to detectground. The PSD sensor is put on the front of the robot tomeasure the distance between own position and an obstacle.
The model numbers of the PSD and the actuator are
GPY0A21YK and KRS-784 ICS, respectively.

1 INTRODUCTION
Legged robots are focused on, because they are able tomove in irregular or unpredicted field flexibly [1]. However,the control of the robot is difficult. A lot of researchers
study various methods that enable legged robots to walk
unpredictable irregular field such as the stricken area. For
example, there are mimics insect[2], using neuron[3],subsystems[4], and CPG which is modeled in non-linear
trembler system[5]. The other method to walk of theunpredictable irregular field is known as a tire-type, acrawler-type and etc. The one of the advantage of thelegged robot is that it can move a center of gravity, and thatit can stabilize posture [6].
We have proposed gait motion algorithm of a six legged
robot using the property of the Associatron[7][8]. One ofthe properties of the Associatron is that it can recall anentire pattern from partial information. A mechanism of thememory of the robot is similar to one of an animal. We
focus on the Associatron that is associative memory model.
The aim of this paper is to implement the gait motion byusing the Associatron to the robot. Moreover, the proposed
gait motion is applied to a real robot. The validity andpossibility of the algorithm are confirmed by simulations
and an experiment.

Fig. 1 A six legged robot

3 ASSOCIATIVE MEMORYFirst, abstract of the Associatron is introduced. Secondly,
proposed algorithm is described. Finally, the motion that we
expect is shown.

2 SYSTEM

3.1 Associatron
We adopt the Associatron[9] to the robot. The
Associatron is a model for associative memory with aneural network structure. Then, it is a kind of memoryalgorithm that is similar to animal’s brain．The property

Assume that the robot has six legs, six touch sensors
and a position sensing detector (PSD). We deal with static
gait motion. The robot is shown in Fig. 1. The size andweight of the robot are 359 x 233 x 313 mm and 2.011 kg© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of the Associatron that we utilized is that it can recall anentire pattern from partial information, especially.
Moreover, the orthogonal method of the Associatron[10] is adopted in order to prevent interference.

incomplete. The region of the PSD sensor becomes thevalues of the obstacles, however the other region remains
the pattern of moving forward. Then, we predict following
steps:
1.Iincomplete information is recalled (F’-3)
2. Complete information is recalled (T1)
The selecting motion is confirmed by simulations (Sec.
4), and by an experiment (Sec 5).

3.2 Algorithm
We describe the method that the Associatron is applies
to gait algorithm. It is possible to recall next angles of allleg from the current angles, touch sensor information andmeasured distance. The reason is that the Associatronenables to recall the entire pattern from partial pattern.
In order to employ it to the robot, the memorized patternis divided into present state and next state. The divided
pattern is shown in Fig. 2. The memory size is 24 x 24.
Gray region means current information (leg’s angle, touch
sensor state, and distance from obstacle). White one is next
angles of the legs. We investigate whether the robot canrecall the memorize pattern from unknown or the partial
pattern in Sec 4.1.

Fig. 3 Selection of the pattern

4 SIMULATION
We verify the proposed algorithm by ODE (OpenDynamics Engine) simulator. ODE is an open source
physical simulator[11]. The parameters (weight, size, torqueof the actuator and characteristic of the PSD) are same as
the real robot.
4.1 Recall the memorized pattern
We confirm whether the robot can recall the memorized
pattern from unknown ones. The robot memorizes the eightpatterns that are shown in Fig. 3. In the simulations, one
hundred different initial poses of the robot is tried. Two
initial poses of the robot are illustrated in Fig. 4. Fig. 5
shows the results. Horizontal axis is step. "Step" depicts thenumber of the recall. Vertical axis means the similarity ofthe memorized pattern. "100 %" means that the recalled
pattern agrees with memorized pattern. The robot can recall
the memorized pattern within 10 steps from all performed
cases.
It is confirmed that memorized pattern is recalled fromunknown pattern.

Fig. 2 Structure of the memorized pattern
3.3. Multiple gait motions
The robot must learn multiple gait motion
corresponding to various irregular fields. And, the robot has
to select the appropriate pattern of the movement,
automatically. We consider the case that the Associatronmemorized two type motions. The patterns are shown in Fig.
3. One is forward gait motion, when there is no obstacle infront of the robot (black blocks). First, the robot recall theF1.Secondly, it recall F2. Next, F3 and F4 are recalled insequence. And, the F1 is recalled again. The other is turning
gait motion, when there is obstacle (white blocks). Eachpattern is repeated, if the value of the PSD sensor that ismounted in the front of the robot is not changed. It is
expected that the movement of the robot change anothergait motion by existence of obstacle.
Suppose that the robot faces the obstacle, after walking
in no obstacle area. In this time, the pattern becomes© ISAROB 2013

Fig. 4 Initial figures

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4.3 Error of the touch sensor
In the real robot, errors of touch sensor that is set at
each leg often occur. The robot that implements the
Associatron can walk though the cases occur.
First, the values of some touch sensors are reversed atevery step. Fig. 7 shows the trajectories of the robot, in thecase that the number of the reversed value of the sensor ischanged. When the number of it increases, the movement of
the robot is more different from basic motion.

Fig. 5 Recalling the memorized patternfrom known patterns
4.2 Multiple gait motion
An obstacle is set in the field. We investigate that the
robot can select the motion, corresponding to the obstacles.
The initial position and direction are (0,0) and minus
direction. The size of the obstacle and the position of thecenter is 0.95 x 0.22 m and (-1.1, 0.2). The result is shownin Fig. 6. The trajectory of the robot is black line and graybox means the obstacle. Cross symbol shows the position
that turning gait motion is selected. The robot moves tostraight, during it does not recognize the obstacle. Facing
the obstacle, it selects the turning gait motion.
As a consequence, it is clear that the robot is able toautonomously switch to gait motion by existence of an
obstacle. This result is called as “basic motion”, in order tocompare to the other cases.

Fig. 7 Influence of reversing some touch sensors
Secondly, the interval that the error occurs is changed(every, 2, 3, 5, 10, and 20 step, respectively). The numberof it is two in all simulations. When the interval is longer,
the trajectory is more similar to basic motion.

Fig. 6 Trajectory of the robot (basic motion)

Fig. 8 Influence of the interval© ISAROB 2013

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5 EXPERIMENT

6 CONCLUSION

We investigate the validity of the algorithm by anexperiment.

In this paper, we proposed gait algorithm of a six legged
robot using the Associatron. Simulations and an experimentof the obstacle avoidance were performed. As a result, therobot was able to select suitable gait motion, automatically.
We confirmed that the algorithm was effective to gait
motion.
In the future work, we will have the robot learn the
motion automatically.

5.1 Structure of the experiment
In the system of the experiment, the Arduino Mega is
adopted in order to control the robot (controlling of RC
servo motor, and sensing touch sensor and PSD).
Calculation of the Associatron is executed by the PC. The
connection with the Arduino and PC is used by XBee that is
a radio communication unit.

REFERENCES

5.2 Experiment using a real robot
The experiment is performed at same setting of thesimulation of basic motion. The snapshots are illustrated inFig. 9. The trajectories are shown Fig. 10. Red line isexperimental result. It is clear that the trajectory is similar
to trajectory of the simulation of the basic motion. As aresult, we confirm that the Associatron can be implemented
with a real robot.

[1] Maki K. Habib (2007) Bioinspiration and Robotics
Walking and Climbing Robots.
[2] Huang Q, Nonami K (2003), Humanitarian Mine
Detecting Six-Legged Walking Robot and Hybrid Neuro
Walking Control with Position/Force Control, Special Issue
on Computational Intelligence in Mechatronic Systems,
Mechatronics, Vol.13, No.8-9, pp.773-790[3] Umar Asif, Javaid Iqbal1 (2011), An Approach to
Stable Walking over Uneven Terrain Using a Reflex-Based
Adaptive Gait, Journal of Control Science and Engineering,Vol. 2011, pp. 1-12
[4] Hirose, S, Arikawa, K (2000), Coupled andDecoupled Actuation of Robotic Mechanisms, Proc.
International Conference on Advanced Robots ICRA’2000,
San Francisco, pp.33-39.
[5] Qingjiu Huang, Kenzo Nonami (2002) Neuro-Based
Position and Force Hybrid Control of Six-Legged Walking
Robot, The Japan Society of Mechanical Engineers pp160167[6] Kimura H, Fukuoka Y, Hada Y, Takase K (2002).
Three-dimensional adaptive dynamic walking of aquadruped - rolling motion feedback to CPGs controlling
pitching motion, IEEE International Conference on
Robotics and Automation, ICRA 2002., pp. 2228–2233[7] Sakai M，Ishikawa T，Hashimoto M，Makino K，
She J，Ohyama Y，(2012), Study on Autonomous Gait
Motion Using Associative Memory (in Japanese), The 13th
SICE System Integration Division Annual Conference (SI2012), pp. 2758-2759[8] Sakai M，Sato S，Hirose Y，Makino M，She J，
Ohyama Y，(2012), Study on Gait Motion of Six-Legged
Robot Using Associative Memory (in Japanese), Robotics
and Mechatronics Conference 2012 (ROBOMEC2012) pp.
2A2-V02(1)- 2A2-V02(2)[9] Nakano K, (1972), Associatron-A Model of
Associative Memory, IEEE Trans. Syst. Man & Cyb.,
SMC-2, No.3, pp. 380 - 388[10] Iwasaki M, Hashiyama T, Okuma S (2000), SelfOrganizing Feature Extraction Using Associative Memory,
IEEJ Transactions on Electronics, Information and Systems,
Vol. 120, No. 10, pp. 1467-1474

Fig. 9 Snapshots of the robot during the walking

Fig. 10 Trajectories of a simulation
and an experiment© ISAROB 2013

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Reinforcement learning for dynamic environment:a classiﬁcation of dynamic environments
and a detection method of environmental changes
Masato Nagayoshi1, Hajime Murao2 , and Hisashi Tamaki3
1

Niigata College of Nursing, 240, Shinnan, Joetsu 943-0147, Japan(nagayosi@niigata-cn.ac.jp)
2
Faculty of Cross-Cultural Studies, Kobe Univ. 1-2-1, Tsurukabuto, Nada-ku, Kobe 657-8501, Japan(murao@i.cla.kobe-u.ac.jp)
3
Graduate School of Engineering, Kobe University, Rokko-dai, Nada-ku, Kobe 657-8501, Japan(tamaki@al.cs.kobe-u.ac.jp)
Abstract: Engineers and researchers are paying more attention to reinforcement learning (RL) as a key technique for realizing
computational intelligence such as adaptive and autonomous decentralized systems. In general, it is not easy to put RL into
practical use. In prior research our approach mainly dealt with the problem of designing state and action spaces and we haveproposed an adaptive co-construction method of state and action spaces. However, it is more difﬁcult to design state and action
spaces in dynamic environments than in static ones. Therefore, it is even more effective to use an adaptive co-construction methodof state and action spaces in dynamic environments. In this paper, our approach mainly deals with a problem of adaptation in
dynamic environments. First, we classify tasks of dynamic environments and propose a detection method of environmental
changes to adapt to dynamic environments. Next, we conducted computational experiments using a so-called “path planningproblem” with a slowly changing environment where the aging of the system is assumed. The performances of a conventional
RL method and the proposed detection method were conﬁrmed.
Keywords: reinforcement learning, dynamic environment, slowly changing environment, detection of environmental changes,
entropy
1

INTRODUCTION

In recent years, artiﬁcial systems have become more complicated and large-scaled. The conventional way, in which
systems are controlled in a top-down manner mainly by humans, is facing up to the difﬁculties of not only optimality
but also adaptability and ﬂexibility. One of the solutions
to this issue is to develop an autonomously adaptive system. Engineers and researchers are paying more attention
to Reinforcement Learning(RL)[1] as a key technique of realizing autonomous systems. In general, however, it is noteasy to put RL into practical use. Such issues as satisfying the requirement of learning speed, resolving the perceptual aliasing problem, designing reasonable state and action
spaces of an agent and adapting dynamic environments must
be resolved. In prior research, our approach mainly dealt
with the problem of designing state and action spaces and wehave proposed a co-construction method of state and action
spaces[2]. However, it is more difﬁcult to design state andaction spaces in dynamic environments than in static ones.
Thus, it may be even more effective to use an adaptive coconstruction method of state and action spaces in dynamic
environments.
In this paper, our approach deals with problems of adaptation in dynamic environments. Previously, many methods© ISAROB 2013

for dynamic environments have been proposed. However the
researchers have only referred to the dynamic environment,
without focusing on the speciﬁc problems that are inherent
in a dynamic environment. We make a classiﬁcation of dynamic environments and propose a detection method of environmental changes to adapt a dynamic environment. In addition, computational experiments are conducted by using aso-called “path planning problem” with a slowly changing
environment where the aging of the system is assumed. The
performances of a conventional RL method and the proposed
detection method are conﬁrmed.

2 Q-LEARNING
Q-learning works by calculating the quality of a stateaction combination, namely the Q-value, that gives the expected utility of performing a given action in a given state.
By performing an action a ∈ AQ , where AQ ⊂ A is the setof available actions in Q-learning and A is the action spaceof the agent. The agent can move from state to state. Eachstate provides the agent with a reward r. The goal of theagent is to maximize its total reward.
The Q-value is updated according to the following for-424

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mula, when the agent is provided with the reward:

is the ‘share sate’ of type1, and if not, the state is the ‘sharestate’ of type2.

Q(s(t-1), a(t-1)) ← Q(s(t-1), a(t-1))+αQ {r(t-1) + γ max Q(s(t), b) − Q(s(t-1), a(t-1))} (1)b∈AQwhere Q(s(t-1), a(t-1)) is the Q-value for the state and theaction at the time step t-1, αQ ∈ [0, 1] is the learning rate of
Q-learning, γ ∈ [0, 1] is the discount factor.
The agent selects an action according to the stochastic
policy π(a|s), which is based on the Q-value. π(a|s) speciﬁes the probabilities of taking each action a in each state s.
Boltzmann selection, which is one of the typical action selection methods, is used in this research. Therefore, the policy
π(a|s) is calculated asexp(Q(s, a)/τ )(2)π(a|s) = 
exp(Q(s, b)/τ )b∈A
where τ is a positive parameter labeled temperature.
3

DYNAMIC ENVIRONMENTS

Dynamic environments are time-varying environments,
i.e. when the state transition probability T (s(t), a(t), s(t +1)), which is the probability of transition from s(t) to s(t+1)
under a(t), changes, or the return function R(s, a), which is
the reward at s under a, changes, an “environmental change”
has occurred and the environment is a dynamic environment.
Hereafter on the premise of time-variance, the state transition probability and the reward function including time t are
shown Tt (s(t), a(t), s(t + 1)) and Rt (s, a) respectively.
The learning becomes difﬁcult when environmental
changes occur as in the following formula:arg max Rt (s(t), a) = arg max Rt+1 (s(t), a)a∈Aa∈Aarg max Ri (s, a) = arg max Rj (s, a)a∈Aa∈A

Also, they indicate that determining adaptability to dynamic
environments is dependent on the abilities of the “detectionof the states of type 1 and type 2” and “reusing and restudying of learning results”.
In this section, previous works are organized by classifying this tasks of dynamic environments more ﬁnely.
For the sake of ease, we limit to episodic tasks such as acquiring a series of actions from start states to goal states. In
our classiﬁcation of dynamic environments we use the Boltzmann selection method(Eq.(2)), that is, the agent selects anaction with a larger value based on a higher probability.
Here we assume that each increasing value of the entropyof the action selection probability is given and is caused byenvironmental changes, as seen in ḢC (T, M, tE ) where tE isthe episode, M is the method used, and T is the task.
1. The a) presence or b) absence of the inﬂuence of environmental changes:
If the following formula is satisﬁed, then the agent canadapt to environmental changes by the learning performance of the method M even if environmental changes
occurred.
∀tE (ḢC (T, M, tE ) ≤ 0)
(6)
2. The process of environmental changes:
In almost all previous works, it is assumed that the casesof1) ∃1 tE (ḢC (T, M, tE ) > 0) and ḢC (T, M, tE ) is a repetition of a shape similar to an impulse function. In particular, those tasks in which the characteristic changes
momentarily or stages are switched are assumed. On
the other hand, 2) ḢC (T, M, tE ) is a continuously small
positive value, if the aging of the system or a change ofhuman characteristics is assumed.

(3)

where the best action changes over time in the states where
the agent transits. In particular, in the course of making
the action selection probability of the best action larger, environmental changes occur. After such an occurrence, theagent ﬁrst needs to make the action selection probability of
the action smaller, for example if the learning module is Qlearning, in the course of making the entropy of action selection probability H(s):p(a|s) log p(a|s),(4)
H(s) = −(1/ log |AQ |)a∈AQ

3. The timing of the appearance of environmental changes
in relation to learning progress:
It is known that the entropy of the action selection
probability approaches 0 in tasks without environmental changes[4]. Here, if the task has no environmental changes, it could be divided into 3 phases basedon the entropy of the action selection probability: aprophase tEB , a metaphase tEM , and a anaphase oflearning tEL . The task can be classiﬁed according towhen the appearance of environmental changes tEC =min{tE |ḢC (T, M, tE ) > 0} occurs: i)tEB ii)tEMor iii)tEL . Normally, in the case of the task having

the agent needs a process for ﬁnding the best action again.
This process is the reason for learning difﬁculties in dynamic
environments.
4

CLASSIFICATION OF DYNAMIC ENVIRONMENTS

Until now, Simada et.al.[3] divide ‘share states’ into 2
types: if the following equation is satisﬁed, then the state© ISAROB 2013

(5)

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tEC ∈ tEB , it is easy to adapt to environmental changes
since the entropy of the action selection probability islarge. In the case of the task having tEC ∈ tEL , inﬂuences of environmental changes become large since theentropy is small.
Except in b) the absence of inﬂuences of environmental
changes, it is necessary for researchers to take the remaining 2 × 3 = 6 types into consideration. In this paper, we
focus on the above 6 types.
In viewing of previous works organized in considerationof the above classiﬁcation of tasks of dynamic environments,
Takahashi et.al.[5] deal with 3 types: i), ii), and iii) in the caseof a)1) above by introducing an evaluation index of learning
progress in a detection method of environmental changes.
Other works deal with the types a)1)ii) and a)1)iii)[3] asabove. Thus, it is necessary for researchers to consider tasksof dynamic environments regarding the types other than theseabove.
5

Table 1. Usual parameters of MACD
Parameter
θEMAS
θMACD

The entropy of the action selection probability H(s),shown in Eq.4, becomes smaller in tasks without environmental changes. In contrast, when an environmental change
occurs, the entropy becomes larger from the time of the occurrence. Hence, when the entropy H(s) begins to increase,
the agent is able to detect the environmental change.
However, Preliminary computational experiments indicate that the entropy of the action selection probability inthe state s (Eq.4) shows a range of ﬂuctuations even if ina static environment. In order to decrease false-detections ofenvironmental changes by the inﬂuence of the ﬂuctuations,the agent detects the time of the occurrence of the reversalof a downward trend using MACD (Moving Average Convergence / Divergence), which is one of the most popular+(s),tools in technical analysis trading.
The entropy HD
after updating the Q-value, is used to reﬁne the detection only
when the agent selects an action with the maximal Q-value,
if the learning module is Q-learning. Then, a short-term(n = θEMAS ) EMA (Exponential Moving Average) value
and a long-term (n = θEMAL ) EMA value of the entropy
are calculated according to the following equation at a rate of+(s).
once every θt update of HD+
EMAn (s(t)) = (1 − α) × EMAnold (s(t)) + α × HD
(s(t))(7)
where EMAnold (s(t)) is the latest known value of EMA ins(t), α = 2/(n + 1) and n are constant numbers expressing the smoothing constant and the average amount of time
respectively.

© ISAROB 2013

Parameter
θEMAL

Value
26

(0,0)
start

start

450
goal

goal

450 (500,500)

Fig. 1. Dynamic path planning problem.
A MACD (Moving Average Convergence / Divergence)value is calculated according to the following equation, after
updating the short-term and the long-term EMA values.

DETECTION METHOD OF ENVIRONMENTAL CHANGES

Value
12
9

MACD(s) = EMAθEMAS (s) − EMAθEMAL (s)(8)

In addition, a ‘signal’ value is a moving average for thelatest series of θMACD values of MACD(s). In particular,when MACD(s) becomes larger than the signal value andMACD(s) < 0 and the following formula is satisﬁed fromthe condition of MACD(s) being smaller than the signal
value, the agent detects the environmental change.
MACD(s) − MACDold (s) > 0.01

(9)where, MACDold (s) is the latest known value of MACD(s).
Here, the above formula has been added in order to detectenvironmental changes only when a rapidly increasing trend
occurs.
Usual parameters of MACD shown in Table 1 to detect environmental changes are used in the following experiments.
When the environmental change is detected, all Q-values
in the detected state are set to the average value of the Qvalues in the detected state.
6 COMPUTATIONAL EXAMPLES
Q-learning (hereafter called “QL”) and the proposed detection method (hereafter called “PD”) are applied to a socalled “path planning problem” with a slowly changing environment where the aging of the system is assumed in a continuous state and action spaces, as shown in Fig. 1. Here,the agent has a circular shape (diameter 50 [mm]), and thecontinuous space is 500 [mm]×500 [mm] bounded by theexternal wall, with internal walls as shown in black. One ofinternal walls is slowly extended to the right from an episode

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10000

10000
1500
500

500

50

1500

1000

average steps

average steps

50

100

10

1000

100

10
0

500

1000

1500

2000

2500

3000

0

500

1000

episodesFig. 2. Required steps of 3 occurrence times of the environmental change by Q-learning.

1500

2000

2500

3000

episodesFig. 3. Required steps of 3 occurrence times of the environmental change by the proposed method.
3

The positive reinforcement signal rt = 10 (reward) is
given to the agent only when the center of the agent arrives
in the goal area, and the reinforcement signal is rt = 0 at allother steps. The period from when the agent is located at thestarting point to when the agent is given a reward, labeled as
1 episode, is repeated.
After dividing the state space evenly into 20 × 20 spaces,
and the action space evenly into 8 spaces, QL and PD basedon QL are compared with 3 occurrence times of the environmental change: tEB = 50, tEM = 500, and tEL = 1500
[episode]. The internal wall is extended to x = 500 during 175 [episodes] from the occurrence of the environmental
change.
Computer experiments have been carried out with the parameters of Q-learning: αQ = 0.1, τ = 0.1, and γ = 0.9. In
addition, the rating number θt of the detection method is set
at 5. All initial Q-values are set at 5.0 as the optimistic initialvalues[1].
The average number of steps required to accomplish thetask was observed during learning over 20 simulations with
QL and PD, as described in Fig. 2 and Fig. 3 respectively.
The average number of detections was observed during learning over 20 simulations with PD, as described in Fig. 4.

average number of detections

tES to an episode tEE as shown in the continuous space pictured on the right of Fig. 1. The agent can observe the center
position of itself (xA , yA ) as the input, and decide the direction θA as the output. The agent moves 25 [mm] in a direction
deﬁned by θA to which gaussian noise has been added.

1500

2

1.5
50

1

500
0.5

0
0

500

1000

1500
episodes

2000

2500

3000

Fig. 4. Number of detections of 3 occurrence times of theenvironmental change
7 CONCLUSION
We have classiﬁed tasks of dynamic environments andproposed the detection method of environmental changes toadapt to dynamic environments. Then, with computational
experiments we conﬁrmed that the proposed method has better performances than Q-learning with regard to the inﬂuences of the environmental change, and as the learning progresses, detection of the environmental changes improves.
Our future projects include 1) to upgrade the detection
method to consider 3 occurrence times of the environmental
change, and 2) to apply the adaptive co-construction methodof state and action spaces in dynamic environments.

REFERENCES[1] R.S. Sutton and A.G. Barto (1998), Reinforcement Learning, A Bradford Book, MIT Press.
[2] M. Nagayoshi, H. Murao and H. Tamaki (2012), Developing Reinforcement Learning for Adaptive Co-construction of Continuous High-dimensional State and Action Spaces, Artiﬁcial Life andRobotics,17(2), 204-210.

It can be seen from Fig. 2 and Fig. 3 that, 1) when the environmental change occurs later, that is, as the learning progresses, the inﬂuence of the environmental change becomeslarger, 2) PD has better performances than QL with regard to
the inﬂuences of the environmental change.

[3] S. Shimada and Y. Anzai: “Improving Adaptability of Reinforcement Learning System to Dynamic Environment by Decomposing andReusing Macro-Operators”, J.IEICE, J84-D-I(7), 1076/1088 (20017)(in Japanese)[4] M. Nagayoshi, H. Murao, and H. Tamaki (2006), A State Space Filter
for Reinforcement Learning, Proc. AROB 11th’06, 615-618(GS1-3).

It can be seen from Fig. 4 that, 3) PD has better performances to detect the environmental change as the learning
progresses, 4) but PD has few false-detections.

© ISAROB 2013

2.5

[5] T. Takahashi and M. Adachi: “Evaluating Progress of Reinforcement Learning Using Recurrence Plots”, IEICE Technical Reports,
NLP2005-155, 25/30 (2006)(in Japanese)

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Development of Outdoor Mobile Robot for Human Following Navigation
Jae Hoon LEE1, Kei OKAMOTO2, Masumi OUE2, and Shingo OKAMOTO1
1

Graduate School of Science and Engineering, Ehime University, Japan
2
Faculty of Engineering, Ehime University, Japan(Tel: 81-89-927-9709, Fax: 81-97-927-9709)
1

jhlee@ehime-u.ac.jp

Abstract: This paper deals with the development of a mobile robot for human following navigation in outdoor urban
environment. Laser scanner and omni-directional camera were installed on the mobile platform for detecting people including
a target person. The scan and image data from both sensors was fused into the information for tracking multiple moving
objects, i.e. people. System configuration has been designed for effective collaboration between components operating in realtime. In order to follow the target, a motion control method based on the relative distance and velocity between the target andthe robot itself. The proposed method was demonstrated through experiments of human following navigation with thedeveloped robot in the outdoor environment of university campus.
Keywords: Human Following, Motion Control, Outdoor Mobile Robot.
the secondary sensor to detect people around the robot,
respectively. The information of both sensors and theodometer data of the robot were fused in real time by thefiltering algorithm installed in the control system. The
system and the proposed method were confirmed throughexperiments of human following navigation.

1 INTRODUCTION
Over the classical contribution of robots to industry,
recent technology is advancing for the future role in humandaily life. Also in the case of mobile robot, over the last
several decades AGV (Automatic Guided Vehicle) has been
its typical application for object transportation in factory
automation. However, researches to find applications of theadvanced technology recently have been carried out for the
innovation of mobile system in human daily life. It has been
known that the capability to recognize daily environment
and interact with human is fundamentally required for suchrobot to exist with people and perform service tasks.
So the researches related to human recognition havebeen actively performed. Most of them use camera sensor
to detect human feature [1]. It can provide abundant data in
its image but requires complicated algorithms andcomputing power for processing to extract important
information in real time. Besides, stereo camera system isneeded for the case to use range information. By using
Omni-directional camera with a hyperbolic mirror, a special
image of 360-degree field of view in the horizontal plane
can be provided. Laser scanner is another alternative sensor
to detect the state around the robot, which provides range
data of horizontal plane with the time-of-flight informationof the laser ray reflected by the rotating mirror. It has beenutilized in various applications to mobile navigation such as
human detection, map building, positioning, and so on [2].
The objective of this paper is to develop a mobile robotthat can follow a target person moving in outdoor
environment. For that, a laser scanner and an omnidirectional camera have been employed as the primary and© ISAROB 2013

Fig. 1. Mobile robot system

2 DEVELOPMENT OF OUTDOOR MOBILE
ROBOT
2.1 Mobile robot system
The mobile robot developed in this research is shown in
Fig. 1. Its two active rear wheels rotate independently by
two motors and a passive caster was used for the front
wheel. An embedded controller is used for the motion
control of the robot. The maximum velocity of the robot
was designed as 4 [m/s] with the combination of reduction
gear and wheel size. The encoders of rear wheels provide
the position information with the manner of odometer.

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Fig. 3. Omni-directional image with scan data

Fig. 2. System block diagram of the mobile robot
2.2 System configuration
The block diagram to show the configuration of the
hardware system is given in Fig. 2, which mainly consistsof three parts, mobile platform, sensors for target
recognition, and laptop computer. The embedded controller
in mobile platform is connected to the robot manager of
laptop computer. Thus the motion command and odometerdata are exchanged with each other. Two sensors, omni
directional camera and laser scanner (Laser Range Finder),are installed on the top of the robot with the height of about
1.35 [m] and 1 [m], respectively. Their image and scan datais transferred to the main program in the computer. The
capturing frequencies are 30 [fps] for image and 40 [Hz] forscan data, respectively. The sampling time of the odometerdata is set to 5 [ms] in the robot. For the management of thelarge amount of high speed data, sensor manager with ringbuffer is employed in the computer. It transfers scan andodometer data as synchronized information. The image datais transferred directly to the user program because of its
huge amount. All of them are synchronized and fused withfiltering algorithm in the computer.

Fig. 4. Data association with position and color information
The association between the predicted state x!i (k) andmeasurement zi (k) was performed based on Mahalanobis
distance considering both position and color as shown in
Fig. 4. The state of each person near the robot is estimatedwith Kalman filtering method [5]. Resultantly the target
was selected from the objects recognized by the method.

2.3 Target recognition
In previous research [3, 4], a method to track people
with laser scanner and omni directional image. Figure 3
shows the resultant omni directional image in which thelaser range data is displayed with red points. This research
modified it to recognize the target person. The state ofpeople detected by the robot at time k is defined asxi (k) = ( xi (k) yi (k) x!i (k) y!i (k) hi (k) )T , (1)where the subscript i denotes the number of people.
hi (k) denotes the color state of i-th human, which is
extracted from the part of the image.

© ISAROB 2013

Fig. 5. Schematic representation of motion control for amobile robot to follow a target person

3 MOTION CONTROL ALGORITHM
3.1 Velocity control for human following
Figure 5 shows the schematic representation of motion
control for the robot to follow a target person. For target
following navigation, the velocity of the robot should becontrolled based on the position relationship between the
robot and the target. Besides, the velocity of the target is429

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The target was set to move with the linear velocity of 2[m/s] in the simulation. The relative distance between robot
and target was set to 3 [m]. The left figures of Fig. 6 (a) and(b) show the motion trajectories of both the robot and thetarget, and the right figures show the change of robot
velocity, respectively. It is observed that the robot canfollow the target and the distance between them can becontrolled in both cases.

also considered at the same time. The desired velocity isrealized by controlling both motors of wheels resultantly.
The desired velocity for the robot is computed asfollows. The desired linear velocity of the robot is decided
based on the velocity of the target person and the relative
distance from the robot to target as following.
vD = vT + K D (dT ! dD )(2)
where, vT denotes the velocity of the target person.
The distance from target person to robot, dT is given as(3)
dT = (xT ! x R )2 + (yT ! yR )2 ,and its desired distance is defined as dD . K D denotes the
feedback gain related to control the distance.
The desired angular velocity of the robot is decided
based on the moving direction of the target person and the
direction from the robot to the target as follows.
! D = !K" 1 (" R ! # ) + K" 2 ("T ! # )(4)
where !T denotes the moving direction of the target withrespect to the fixed global coordinate. ! is the direction
from the robot to the target on the global coordinate. K! 1
and K! 2 are the feedback gains related to control themoving direction.

4 EXPERIMENTIn order to test both algorithms for people recognitionand human following control, experiments with thedeveloped robot system had been carried out.
4.1 People recognition
Experiment of people tracking was performed with themobile robot in passive running by manual operation.
Figure 7 shows an example of tracking three people, inwhich three people running in front of the robot wererecognized robustly.

3.2 Computer simulation
Computer simulation with the above mentioned
algorithm has been carried out. Two results of following atarget in linear and sinusoidal motion are given as follows.

Fig. 7. Experiment of recognizing running people with therobot system in motion(a) Change of robot’s position (left) and distance (right)
from the target in linear motion(a) Velocity(b) Change of robot’s position (left) and distance (right)
from the target in sinusoidal motion
Fig. 6. Simulation results of target following control© ISAROB 2013(b) Distance between the robot and the target
Fig. 8. Experimental results of target following control

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4.2 Human following
Human following experiment was demonstrated with thedeveloped robot system with the proposed method. The
robot could follow the target person without failure during
the navigation whose total distance was about 500 [m].
Overall velocity of the robot was changed in the range
around 2 [m/s]. It was found that the robot’s velocity wascontrolled according to the target’s in spite of its noisy
situation as shown in Fig. 8 (a). Besides, the desired
distance between the robot and the target was controlled asshown in Fig. 8 (b), though fine tune is also required formore effective motion of fast response. Two representative
scenes of the experiment, captured by the omnidirectional
camera on the robot and the operator’s, are displayed in Fig.
9 and 10. The target person was recognized stably andmarked as ‘0’ in the omni directional images. The resultant
trajectory measured by odometry is depicted on the map asdisplayed in Fig. 11, where it shows distortion owing to its
natural characteristics.

(a) Image captured by operator’s camera(b) Omni-directional image captured by the robot
Fig. 9. Example 1 of human following experiment

5 CONCLUSION
Based on people tracking method with both laser
scanner and omni directional camera, the robot system thatcan follow a runner was developed in this research.
Through computer simulations and experiments in urban
outdoor environment, it was confirmed that the robot withthe proposed control could perform human following
navigation robustly. As future work, intelligent motion
control considering moving and static obstacles will be
investigated.

(a) Image captured by operator’s camera

REFERENCES[1] Sonoura T, Yoshimi T, Nishiyama M, Nakamoto H,
Tokura S, Matsuhira N, (2008), Person following robot
with vision based and sensor fusion tracking algorithm.
Computer Vision, InTech, pp. 519-538[2] Durrant-Whyte H, Bailey T, (2006) Simultaneous
localization and mapping: Part I, IEEE Robotics andAutomation Magazine, 13:2, pp. 99-110[3] Jung EJ, Lee JH, Yi BJ, Yuta S, Noh ST, Marathoner tracking algorithms for a high speed mobile rob
ot, Proceedings of International Conference on Intellige
nt Robots and Systems (IROS), pp. 3593-3600[4] Ueda H, Lee JH, Okamoto S, Yi BJ, Yuta S, (2011),
People tracking method for a mobile robot with laser
scanner and omni directional camera, Proceedings ofInternational Conference on Ubiquitous Robots andAmbient Intelligence (URAI), Incheon, Korea, pp. 503-507[5] Bar-Shalom Y, Li XR, (1995) Multitarget-Multise
nsor Tracking: Principles and Techniques. Vol. 3.

(b) Omni-directional image captured by the robot
Fig. 10. Example 2 of human following experimentFig. 11. Motion trajectory of the robot in human following
experiment© ISAROB 2013

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Parameter Estimation Experiment and Developmentof Decentralized Caster Modules
Kazuyoshi Ohara, Jae Hoon Lee1, and Shingo Okamoto
Ehime University, Japan(Tel: 81-89-927-9740, Fax: 81-89-927-9740)
1

jhlee@eng.ehime-u.ac.jp

Abstract: Parameter estimation method for distributed active caster has been investigated in this paper. By installing
multiple active casters to a target object directly, it becomes a mobile system for transportation and can be moved
to the goal position automatically. When the system is used to move an object of irregular shape, the position ofeach caster module should be estimated because it is basic parameter for control. Therefore, a parameter estimation
method based on the kinematic model of each caster mod ule and Kalman filter was proposed. The proposed
algorithm has been demonstrated by computer simulation and experimental work.
Keywords: Active Caster, Parameter Estimation, Distribute Modular System, Mobile Robot.

2 KINEMATIC MODEL

1 INTRODUCTION

Double wheel typed caster was adopted for the caster

Object transportation is a well-known application ofmobile robot technology. The representative example,
AGV(Automatic Guided Vehicles), has been utilized forfactory automation in industrial site. It has been used invarious factories because of its capability to move a heavy
load and autonomous operation. However, a typical AGV
has limitation on load capacity owing to its platform size,and sometimes requires special modification fortransporting objects with irregular shape. To cope with thisproblem, a novel device ‘active caster system’ has been
proposed in previous researches [1, 2]. It is a system
consists of multiple mobile caster modules that are
independent but collaborate with each other. After installingthem directly to a target object to move, and operating themby wireless communication, we can move it to the goal
position automatically.
While using the active caster system, the actual problem
is that the parameters in the kinematic model of the wholesystem changes according to the shape of the target object
and the attachment of multiple casters. It is required toknow the attached position of caster modules forcollaboration between them. Therefore, the purpose of thisresearch is to propose a method to estimate the attachment
position of each caster module for real application. For that,parameter estimation algorithm based on the kinematic
model of each caster module and external sensor
information was proposed and demonstrated throughcomputer simulation and experiment. Besides, the methodof augmented Kalman filter [3] was also applied. The
kinematic model of the mobile system with multiple casters
is based on the previous researches [2, 4].

© ISAROB 2013

module as shown in Fig. 1. It has two active wheels thatrotate independently, which are connected to both ends ofan axle that is also connected to the passive joint by a link.
So the system configuration with multiple caster modules
can be represented as Fig. 1.
Kinematic model between the velocity of the target
object and the angular velocities of three joints, a passive
joint and two active wheels, are given as follows. Let T be the velocity vector of target object.
y X  xAnd   iLi

iR i Tdenotes the angular velocity

vector of joints in the caster module.

Fig.1. Kinematic model of mobile system with multiple
double-wheel-typed casters

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The kinematic relationship between X and  is giveniasi  J i1 X

.

Then, Kalman filter was adopted for parameter estimation
as follows[3].
P(k １| k )  Fk P(k | k ) FkT  Qk
(8)

(1)

K k １  P(k １| k ) H kT１[ H k １P(k １| k ) H kT１  Rk １] １1iWhere J
denotes the inverse Jacobian matrix of i-th
caster module, and it can be defined as [2] lCi  lSi  ai lSi   bi lCi 


1 
1
 lC  lS  ai lS   bi lC 
 . (2)Ji iii
lrC  i rS
 rCi  lrC  bi rSi  ai rCi  iWhere ai and bi denote the x and y position of eachcaster with respect to the moving coordinate on the object.
From Eq. (2), it is found that the kinematic relationship isdependent to the attachment position of each caster module.

(9)xˆ(k  1 | k  1)  xˆ(k  1 | k ) K k 1[ zk 1  H k 1 xˆ(k  1 | k )] (10)P(k  １| k  １)  [ I  K k １H k １]P(k  １| k ) (11)Eq. (8) is state prediction covariance. Kalman gain
shown in Eq. (9) is utilized for revising the error by using
difference between observation value and prediction value[5]. Then, the state value and the covariance are updated by
Eq. (10) and Eq. (11) with Kalman gain, respectively.
Now, the new linearized system matrix, Faug and

3 PARAMETER ESTIMATION ALGORITHM

Gaussian process noise with covariance matrix Qaug takes

In this section, a parameter estimation algorithm basedon the kinematic model is described. The basic idea is toestimate the kinematic parameters based on the difference
between the position information from the external sensor
and odometry. The error of odometry is mainly depend on
the parameters included in the kinematic relationship. So,
they can be estimated with the comparison of motion
trajectories measured by external sensor. Here, it isassumed that the dimension of each caster module issufficiently accurate, and the slippage between the wheel
and the ground is small to be ignored.
The state vector including the posture of the object attime k is defined asform Eq. (12). The matrix A is the transition matrix
linking the augmented states to the location estimate. The
matrix S is a fictitious noise injection on the augmented
states to ensure convergence.

F
Faug  0 X k  a YA k  a  k  a(3).
The state value at time k+1 is computed with themanner of odometry as
xk 1  xk  V k t
(4).
Where V k , the velocity of object, is calculated by Eq.
(1) with the angular velocity measured by encoders, which
is based on the kinematic relationship of each caster asshown in Eq. (2). The actual position of caster module isdefined as ai and bi . And its initial position is also**
represented as ai and bi , respectively as follows.
,(12)X k 
 b 
 s11
X k  , S  0 b 
X k 
 b 

0 .
s 22 

(13)

4 COMPUTER SIMULATION
Computer simulation has been performed to verify theperformance of the proposed method to optimize the target
parameters. Fig. 2 shows motion trajectory of the object
adopted in the simulation. It was assumed that while thesystem, i.e. object with the caster module, is moving along
trajectory of dotted line, its position can be observed byexternal sensor such as a camera installed at the ceiling. At
the same time, the position is also computed by odometry
with the measured angular velocity of joint and wheels.
Because of the difference of parameters, there exists
position error between them. Finally, this position error ismodified by Kalman filter. Fig. 3 shows the change of theestimated parameters. According to the movement of the(5)bi   bi bi

(6).
By changing the parameter 𝛿𝑎 and 𝛿𝑏 , the estimationof real position of each caster module is performed. For
parameter estimation, the state vector including 𝛿𝑎 and𝛿𝑏 is defined as Eq. (7).
(7)
xˆ (k | k )  ( x(k ) y(k ) (k )  a  b )T

© ISAROB 2013

0
.
S 

Where,

xk  ( x(k ) y(k ) (k ) )Tai   ai aiQA, Qaug  I0system, the parameters ai* and bi* were changed andapproached 𝛿𝑎 = 0.3 and 𝛿𝑏 = 0.4, resultantly.

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5.2 Configuration of experimental system
The configuration of the experimental system and its
photograph are shown in Fig. 5 and Fig. 6, respectively. A
microcomputer and encoders were used to measure theangular position of passive joint and the angular velocity ofboth wheels. They can be embodied in a caster module withmotors and wireless unit in actual system. In thisexperiment, the information of each encoder is transferredto the laptop computer for computing odometry. Laser
scanner (LRF) is employed as an external sensor to achieve
the system position, which is connected to laptop computer
through Local Area Network (LAN) communication. In
order to achieve the position information by external sensor,
scan data from LRF is computed by using Iterative Closest
Point (ICP) algorithm. The parameter estimation is carriedout in the laptop computer by the algorithm of the previous
section with the measured data from LRF and odometry.

Fig.2. Motion trajectory of the system in simulation

Fig.3. Estimated results of parameters 𝛿𝑎 and 𝛿𝑏

5 EXPERIMENTS
Fig.5. Configuration of the experimental system

5.1 Development of experimental caster module
Experimental works were carried out to check the
proposed algorithm with real system. For that, a castermodule has been manufactured as shown in Fig. 4, which
has no actuator at each joint but three encoders to measure
angular velocity of each joints. So the system moves
manually by human operation.

Fig. 6. Photograph of the experimental system

Fig.4. Caster module developed for experiment© ISAROB 2013

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5.3 Experimental result
In the experiment, the position of active caster was
estimated while the system is moved by human manual
operation. The initial value of parameter and the true value
are defined as imaginary caster position and real caster
position as shown in Fig. 7, respectively. The motion
trajectory of object center in Fig. 7 is measured by LRF in
this experiment. As shown in Fig. 8, the motion trajectoryof object center was a semi-circular route in this experiment.
Fig. 9 shows the resultant change of parameters which wereobtained by the proposed algorithm with the measured data.
It was observed that the parameters approach to the realvalue in company with the motion of system resultantly.

In order to check the influence of the parameters, both
odometry data for the same motion with the estimated value
and un-estimated were compared. Fig. 10 shows odometry
data of motion trajectories with un-estimated parameters.
Fig. 11 shows odometry with estimated parameters,
respectively. It is observed that the odometry with estimatedparameters shows smaller error that the other. Therefore,
the usefulness of the proposed method was confirmed
through the experiment.

Fig. 10.Motion trajectory
Fig. 11.Motion trajectoryby odometry withby odometry with
un-estimated parameters
estimated parameters

Fig.7. Parameter setting for experiment

6 CONCLUSION
For real utilization of active caster system, theintelligent method to estimate parameters during its random
motion was investigated through computer simulation andexperimental works. For real application, redesign of thesystem with actuators and development of the controller
based on wireless communication are now in progress.
Fig.8. Motion trajectory of the object in experiment

REFERENCES

Fig.9. Experimental result on parameters 𝛿𝑎 and 𝛿𝑏[1] Tomokuni N, Kim BK, Tanikawa T, Ohba K, and Hirai
S (2006), Design of the Active Caster for the Actuation
Devices of Ubiquitous Robots, 2006 Int. Symposium on
Flexible Automation Osaka, Japan, pp.952-955[2] Lee JH, Kim BK, Tanikawa T, Ohba K (2008),
Parameter Identification for an Omni-directional Mobile
System with Distributed Mobile Actuator Module, IEEE Int.
Conf. on Multisensor Fusion and Integration for Intelligent
Systems Seoul, pp.232-234[3] Larsen TD, Bak M, Andersen NA, Ravn O (1998),
Location Estimation for an Autonomously Guided Vehicle
using an Augmented Kalman Filter to Autocalibrate theOdometry, Int. Conf. FUSION, pp.245-250[4] Batlle JA, Barjau A (2009), Holonomy in mobile robots,
Robotics and Autonomous Systems 57, pp.433-440[5] Maeyama S, Ohya A, Yuta S (1997), Retroactive
Positioning for a Mobile Robot, J. of Robotics Society ofJapan, October 1997, Vol.15 No7, pp.115-121© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Parameter optimization for decoupling controllers of 4WS vehicles
Mingxing Li, Haijing Xu, Yingmin Jia
The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191,
China (e-mail: lmx196@126.com, xuhaijing 2005@yahoo.com.cn, ymjia@buaa.edu.cn).
Abstract: In this paper, the performance analysis and parameter optimization for two typical decoupling controllers of the 4WS
vehicles are considered. Firstly, a new relationship between velocity and acceleration in effect on the nonlinear performance isestablished. Then, for the decoupling controller of a quasi-linearized system, the optimized region of parameters is obtained, inwhich the damping is bigger than one and eigenvalues are smaller than any given negative number. For the decoupling controller
of a linearized system, the necessary and sufficient condition that the overshoot and irritating are avoided is deduced by using a
new index. And the region, the measurement-error disturbance is attenuated to under any given positive number, is obtained forany expected eigenvalues. Simulation results show that optimized controllers can improve the safety and comfort obviously.
Keywords: 4WS vehicle, controller optimization, decoupling control, nonlinear performance analysis)Z\U

1 INTRODUCTION
Decoupling control can improve comfort and safety forthe vehicles. Thus its application has received a lot of attention in recent years [1]-[13]. Taking yaw rate and lateral
speed as states, the decoupling control with velocity-varying
and robust performances of the quasi-linearized system is advocated in [1] and [12]-[13]. Based on linearized systems,
the diagonal decoupling is achieved in [3]. Taking yaw rateand lateral velocity as states, the diagonal decoupling in [2]and triangular decoupling in [5]-[7] are obtained.
There are high prices to be payed for using the decoupling control laws, e.g., damping and natural frequency arechanged, and the desired disturbance attenuation is hard to
be ensured. These prices may reduce the safety and comfort.
Hence, the following issues are considered in this paper

Y\

aU d

)Z[U

Y
U

)Z\I

aI

b

Y[

)Z[I
dI

U

OU

OI

Fig. 1. Single-track model of 4WS vehicles
neglected, then the dynamics can be rewritten as [1]∑ Fxi − fa(1)

∑ Fyi + Fy

(2)

= l f Fy f − lr Fyr + Tz

(3)m(v̇x − rvy ) =i=r,lm(v̇y + rvx ) =

i=r,l

J ṙ

Two typical simplified models can be obtained by linear andquasi-linear methods, which are,
 
[ ] [
][ ] [] δfv̇yaa12 vybb12 b13  
δr
(4)
= 11
+ 11
ṙ
a21 a22 rb21 b22 b23
Fl
 
[ ] [
][ ] [] δfãã12 vyb̃
b̃12 b̃13  
β̇
δr
= 11
+ 11
(5)
ã21 ã22 rb̃21 b̃22 b̃23ṙFl

1. Are the system performance is affected by the same factors with different states?
2. Are there some special decoupling controllers which
can ensure the safety and comfort?
The first issue is answered by employing the linear system theory and joint-point locus approach, and the second issolved by optimizing some controller parameters. Models aredescribed in section 2. System performances are analyzed todifferent states and the relationship to velocity and acceleration is obtained in section 3. Optimization of two decoupling
controllers are discussed in section 4, one is used to control
quasi-linear model and the other is linear. Simulations areshown in section 5 and conclusions are given in section 6.

where, Fl is the longitudinal acceleration/braking force. Details of ai. j and ãi, j please refer to [1].
Remark 1. Parameters b11 , b12 , b21 , b22 are taken as,
cf
cf lf
cr
c r lr, b12 = , b21 =, b22 = −mm
JJ
while system (4) are linearized, and taken as
[
]
b11 =

(1−γ )Fl0 +c fm(1−γ )Fl0 +c flf
Jγ Fl0 +crm
lr γ Fl0J+cr

2 VEHICLE MODEL

while simplified by quasi-linearizing.

As mentioned in [7], the essential features of 4WS vehicle
steering dynamics can be described by the single-track model
as shown in Fig.1. If roll, pitch, and vertical dynamics are

Remark 2. bi3 , b̃i3 are almost zeros, and b̃i j , bi j satisfy© ISAROB 2013b̃1 j = vb1 j , b̃2 j = b2 j , j = 1, 2

436

(6)

(7)

(8)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

If λ0 , µ f (1 − ε f )/µ , and κ , (µr (1 − εr ))/µ f (1 − ε f ), then
λ0 corresponds to various road conditions which don’t effectthe nonlinear performance, whereas κ corresponds to load
distributions , and (4) , (5) may have both stable and unstable equilibrium points, and the saddle-node bifurcations withκ ≤ 1, while both systems have only globally stable equilibrium points with κ > 1 [8]. I.e. κ > 1 can ensure good
nonlinear performance. By simply deducing, we know

3 PERFORMANCE ANALYSIS
Linear and nonlinear performances of systems (4) and (5)are analyzed in this section.
3.1 Linear performance analysis
Stability, frequency and damping performances of systems (4) and (5) around equilibria pairs (v∗y , r∗ )(to system (4))
or (β ∗ , r∗ )(to system (5)), are determined by[J(v∗y , r∗ ) =c∗ +c∗r− fmv

c∗ l f −c∗r lr− f Jvc∗f +c∗r−J(β ∗ , r∗ ) =  c∗ l mv∗
f −cr lr
− f J

c∗ l f −c∗r lr ]−v − f mv

c∗ l 2 +c∗r lr2− f f Jv
c∗f l f −c∗r lr

(9)

mv2
c∗f l 2f +c∗r lr2
− Jv

(10)

−1 −



κ >1⇔

(11)

c∗r c∗f l 2 + mv2 (cr lr − c f l f )
ω02 =
mJv2

(12)

2ω0 D0 =(c∗f + c∗r )J + m(c∗f l 2f + c∗r lr2 )mJv

4 DECOUPLING RESULTS ANALYSIS AND

OPTIMIZATION(13)

In this section, decoupling results of quasi-linearizationand linearization systems are analyzed and optimized in order to improve vehicle safety and comfort further. As shownin previous section, (4) and (5) have the same location performances, thus we just talk about the system (4).

Thus (4) and (5) have the same linear performances as shownin [6]. Furthermore, if use ar to replace lr and a f replacel f , while v > 0 and the rear tyre isn’t working in decaying
region, i.e. rear tyres is driving wheels, then the stability
factor K = c f a f −cr ar , and the vehicle is understeering while
K > 0, neutralsteering while K = 0 and oversteering while
K < 0 [9]. And if denote a f as aN with K = 0, then

4.1 Quasi-linearization system
System (4) can be decoupled by decoupling laws [1]u = F ∗ (x) + G∗ (x)η , η = [η1 η2 η3 ]T

aN c f − (l − aN )cr = 0

[ ] [
ẋi
0
=
ẍi
ai1

(15)

N f = mglr (1 − ε f )/l, Nr = mgl f (1 − εr )/l

(16)

(21)

4.2 Linearization System
In [3], the decoupling controller is
[
[ ]
δc
b11
=
δr
b21

Then we can get© ISAROB 2013(20)

From Fig.2 we know, the feasible region of (a1i , a2i ) issmaller, while k10 becomes bigger. And linear ai1 − k10 ai2 −2 = 0 and quadratic curve a2 + 4a = 0 have only one ink10
i1
i2
2 , −2k ), i.e. for any k > 0,
tersection point, which is (−k101010points (a1i , a2i ) which satisfy inequalities (21), always exist.

3.2 Nonlinear performance analysis
By considering the load distributions and friction coefficient factors in the equilibria, we have

Fyw f = µ f N f f f (α f ), Fywr = µr Nr fr (αr )][ ] [ ]xi0η , i = 1, 2, 3
+
ẋi
1 i

2
ai1 − k10 ai2 − k10< 0, and a2i2 + 4ai1 > 0

Remark 4. The handling characteristics may be better whilea f is smaller than aN in a suitable range.

(14)

1
ai2

To get good smooth performance and retain the convergence
rate, point (a1i , a2i ) must be in the following region with any
constant k10 > 0, which is shown in Fig.2,

Remark 3. The damping and frequency of systems (4) and(5) mainly depend on vx . If vx is so big that D0 < 1, thenoscillation occurs and vehicle performance becomes badly.

Fyw f + Fywr = mrvx , Fyw f l f − Fywr lr = 0(19)Then the quasi-linearization system is decoupled into

Thus the vehicle is understeering while aN > l f and oversteering while aN < a f . And if the center of mass trends torear axle, then the vehicle brings about oversteering, which
is a dangerous situation with high velocity. From above analysis, we know

µ f (1 − ε f )µr (1 − εr )vx r
f f (α f ) =fr (αr ) =
µ
µ
µg

(18)

Remark 5. From formula (18) we know, the velocity and acceleration in the longitudinal direction are very importantto handling characteristics. And varying velocity model ismore suitable to reflect the actual physical performances ofthe steering vehicles.

Both above matrices have characteristic polynomial asp(s) = ω02 + 2D0 ω0 s + s2

lr l f (µ f − µr )(ha − h)ca 2
vx + v̇x >gmhlr µ r + l f µ f

(17)

437

]−1 [
]
−a12 r − b11 δ p−k p r − x1

(22)

ẋ1

= a21 (aCGm −rv − q1 δ p + k1 r + x2 )

(23)

ẋ2

= k0 (r − G(V, δ p )δ p ), x1 (0) = x2 (0) = 0 (24)

b12
b22

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

−2

−2

−4

−4

−6

−6

−8

−8

−10

−10

−12

−10

−5

−10

−5

0

0
−2

−4

−4

−6

−6

−8

−8

−10

−10

−12

−12

0

(32)

((kP − a22 )k1 − 9k0 ) − 4((kP − a22 ) − 3k1 )2

02(k12 − 3(kP − a22 )k0 ) < 0

while k10=2.0, points (a1i,a2i)

−2

−5

2
3
kP − a22 > k20 , k1 > 3k20
, k0 > k20−120

while k10=1.5, points (a1i,a2i)−10

The performance of system (29) is decided by κ0 . By employing theorem 1, all eigenvalues of subsystem (26) not only
negative but also smaller than −k20 equal towhile k10=1.0, points (a1i,a2i)
0(33)

And by complexly deducing, we know a sufficient condition
for the norm bound ∥Td (s)∥∞ ≤ γ is

−10

−5

0

Fig. 2. Regions of (a1i , a2i ), with constraints (21)(kP − a22 )2 − 2k1 > 0, k12 − 2k0 (kP − a22 ) − γ −2 > 0(34)(t1t2 − 9k02 )2 − 4(t12 − 3t2 )(t22 − 3k02t1 ) > 0(35)

The above desired region of kP , k0 and k1 is shown as Fig.3,
where P0 = (kP − a22 , k0 , k1 ). From Fig.3 we know, thegamma=0.2, k20=1

the controlled linearization system is decoupled into
6

6

4

5

k1

k1

(25)
2
3
2
k0

4
1 3

2
k0 1.5 3.6

gamma=0.008, k20=1.0

(26)

3.8
kP−a22

4

gamma=0.008, k20=1.2

5.5

6

5
4.5

The transfer function from δ p (s) to r(s) is

4
2.5

b21 s2 + q1 s + k0 G(v)Tδ p (s) = 3
s + (kP − a22 )s2 + k1 s + k0

4
2.5

3.5
kP−a22k1v̇y = a11 vy
 

 
ṙ
−(kP − a22 ) 1 0rż1  = −k1
0 1 z1 ż2−k0
0 0 z2
 


0
b21
+  q1  δ p (t) + a21  d(t)0k0 G(v)

gamma=0.2, k20=1.2

k1

while k10=0.5, points (a1i,a2i)
0

4

2
k0

(27)

1.5

3.6

5

4
2.5

3.8

2

kP−a22k0

4
1.5 3.6

3.8
kP−a22

Fig. 3. Regions of P0 with constraints (32)-(35)The characteristic polynomial isP(s) = s3 + (kP − a22 )s2 + k1 s + k0

region becomes smaller with k20 increasing and γ tending tozeros. Specifically, there aren’t k20 bigger enough or γ nearto zeros closely from over zeros direction, so that the region
doesn’t exist any longer. Such as, k0 /k1 is taken as smaller
than 1 and (kP − a22 )/k1 as a constant, then by choosing alarge enough kP − a22 , inequalities (32)-(35) can be held.

(28)

To eigenvalues λi , there exist k1,i ∈ R, i = 1, 2, 3, so thatTδ p (s) =

k1,1
k1,2
k1,3
+
+
s − λ1 s − λ2 s − λ3(29)Hence, the relationship between r(t) and δ p (t) isr(t) = k1 e

λ1 t

λ3 t

δP (t) + k2 e

λ3 t

δP (t) + k3 e

δP (t)

5 SIMULATIONIn this section, effects of optimized controllers are examined. The vehicle model BMW 735i is used [1]. The desired track and lateral acceleration are shown in Fig.4. Fig.5
is the simulation of the decoupled system of [1]. Fig.6 andFig.7 are the decoupled system of [2]-[3], where Fig.6 is theresult without disturbance while Fig.7 is the attenuation result of measurement error disturbance of lateral accelerationas d(t) = 0.1 sin(26π t). The output and control signals may
be both oscillation, however the output signals may be overshoot, while decoupling controllers are without optimizing,
especially when the disturbance exists. But the overshootand oscillation are avoided, and the output and control signals become more smooth, as the controllers are optimized.
Furthermore, arbitrary attenuation of measurement error disturbance is arrived by optimizing k1 . So as Shown in simulation results, the safety and comfort are improved further by
optimizing the parameters.

(30)

Thus, if there were complex λi , yaw rate arises overshootand oscillation. Then the safety and comfort will be worse.
So overshoot and oscillation must be avoided.
Theorem 1. For the cubic equation
ax3 + bx2 + cx + d = 0(31)

Roots x1 , x2 , x3 are real and negative, if and only if κ0 ≤0, ab > 0, ac > 0, and da > 0, where κ0 = B2 − 4AC, A =b2 − 3ac, B = bc − 9ad and C = c2 − 3bd.
Proof. From Cardano’s formula, the equivalent condition
that equation (31) only has real roots is κ0 ≤ 0. The roots
are multiple while κ0 = 0, and distinct while κ0 < 0. And
roots are negative while ba > 0, ac > 0, and da > 0 [15].
© ISAROB 2013

438

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

motion track4

lateral acceleration

1

2012CB821201) and the NSFC (61134005, 60921001,
90916024, 91116016).

0.9

3.5

0.8
3
0.7
0.6

ay[g]

y[m]

2.5
2

0.5

REFERENCES

0.4

1.5

0.3
1

[1] Y.M. Jia(2000), Robust Control with Decoupling Performance for Steering and Traction of 4WS Vehicles under
Velocity-Varying Motion, IEEE Transanction on Control System Technology, 8(3): 554-569.

0.2
0.5
0

0.1
0

50

100

150

0

200

0

1

2

x[m]

3

4

t[s]

Fig. 4. Desired track and lateral acceleration
lateral speed

yaw rate

0.1

0.06

0

[2] R. Marino and S. Scalzi(2010), Asymptotic Sideslip Angle
and Yaw Rate Decoupling Control in Four-Steering Vehicles,
Vehicle System Dynamics 48(9): 999-1019.

0.04

r[rad/s]−0.1

vy[m/s]Uncontrolled
Desired
Unoptimized
Optimized

0.05

UnControlled
Cotrolled

−0.2
−0.3

0.03

0.02
−0.4

−0.6

[3] R. Marino and F. Cinili(2009), Input-Output Decoupling Control by Measurement Feedback in Four-Wheel-Steering Vehicles, IEEE Transanction on Control System Technology,
17(5): 1163-1172.

0.01

−0.5

0

1

2

3

0

4

0

1

2

t[s]

3

4

t[s]

Fig. 5. Steering maneuver of the decoupled system (20)driver input

deltar deltaf [deg]deltap[deg]

20

0.015
0.01
0.005
0

UnControlled
Cotrolled012control signals

−3

0.02

3

10
5
0

Unoptimized
Optimized
0

1

2

t[s]

lateral speed

yaw rate
0.06

−0.2

r[rad/s]

UnControlled
Cotrolled

4[5] J. Ackermann and T. Bünte(1997), Yaw Disturbance Attenuation by Robust Decoupling of Car Steering, Control Engineering Practice, 5(8): 1131-1136.

Uncontrolled
Desired
Unoptimized
Optimized

0.05
0vy[m/s]

3

t[s]
0.2

[4] M.G. Skarpetis, F.N. Koumboulis, F.S. Barmpokas, and G.E.
Chamilothoris(2006), Decoupling Control Algorithms for
4WS Vehicles, IEEE 3rd International Conference on Mechatronics, Budapest, pp. 499-504.

15

−5

4

x 10

0.04

[6] J. Ackermann(1994), Robust Decoupling of Car Steering Dynamics with Arbitrary Mass Distribution, American Control
Conference, Baltimore, 2: 1964-1968.

0.03
0.02

−0.4
0.01
−0.6

0

1

2

3

0

4

0

1

t[s]

2

3

4

t[s]

[7] J. Ackermann, and W.F. Sienel(1993), Robust Yaw Dampingof Cars with Front and Rear Wheel Steering, IEEE Transanction on Control System Technology, 1(1): 15-20.

Fig. 6. Steering maneuver of (25)-(26) without disturbing
control signals
0.035

yaw rate
0.03

Unoptimized
Optimized

0.03

Desired
Unoptimized
Optimized

0.025[8] S.W. Shen, J. Wang, P. Shi, and G.L. Premier(2007), Nonlinear Dynamics and Stability Analysis of Vehicle Plane Motions, Vehicle System Dynamics, 45(1): 15-35.

0.02

0.02
0.015

r[rad/s]deltar deltaf [deg]

0.025

0.01
0.005
0

0.015
0.01
0.005

−0.005[9] N.J. Reza(2007), Vehicle Dynamics: Theory and Applications, Springer.

0−0.01−0.015

0

1

2

t[s]

3

4

−0.005

0

1

2

3

4

t[s]

Fig. 7. Steering maneuver of (25)-(26) with disturbing[10] B. Catino , S. Santini and M. Bernardo(2003), MCS adaptive control of vehicle dynamics: an application of bifurcation techniques to control system design, 42nd Conference on
Decision and Control, pp.2252-2257.

6 CONCLUSION
The 4WS vehicle performances have been analyzed and a
new relationship for nonlinear performance with varying vx
and v̇x has been given. Specifically, the optimization regionof a quasi-linearized system is obtained, in which eigenvalues can be assigned in any desired position and overshoot andoscillation are avoided. Moreover, the necessary and sufficient condition preventing overshoot and irritating of system(25)-(26) is proposed and the optimization region is got, inwhich any desired H∞ index γ and specified eigenvalues areachieved with avoiding overshoot and oscillation. And thesimulation results show that the comfort and safety for thevehicle driving are improved significantly.

[11] E. Ono, S. Hosoe, H.D. Tuan and S. Doi(1998), Bifurcationin vehicle dynamics and robust front wheel steering control,
IEEE Transanction on Control System Technology, 6(3): 412420.
[12] C.F. Chen, Y.M. Jia(2012), Nonlinear Decoupling Control of
Four-Wheel-Steering Vehicles with an Observer, International
Journal of Control, Automation, and Systems, pp.697-702.
[13] C.F. Chen, Y.M. Jia(2012), Nonlinear Decoupling Control of
Vehicle Plane Motion, IET, Acceptted.
[14] H. True(1999), On the theory of nonlinear dynamics and its
application in vehicle system dynamics, Vehicle System Dynamics, 31(1): 393-421.

7 ACKNOWLEDGMENTS
This work was supported by the National Basic Research Program of China (973 Program, 2012CB821200,© ISAROB 2013[15] R.S. Irving(2004), Integers, polynomials, and rings: a course
in algebra, Springer.

439

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A method of sharing for common coordinate system
by using relative position among the swarm
Makino Koji
Tokyo University of Technology, Japan(Tel: 81-42-637-2111 ex.2823, Fax: 81-42-637-2112)
rmakino@stf.teu.ac.jp
Abstract: I have proposed a method that the many robots share the common coordinate system using dead-reckoning andobservation to other robots without communication and without making the maps, like migration animals. To share thecommon coordinate system, both position and direction is necessary to be considered. I analyzed the effectiveness of thedirectional modification method, and confirmed it by simulations. In this paper, the modification of the position is focused on.
First, the effectiveness of the method is confirmed by simulations. Secondly, the influence of the coefficient parameter of thepositional modification is investigated, when the number of the robots and modification interval are changed. As a result, it isfound out that the modification method of the position has optimal values.
Keywords: swarm, migration, common coordinate system
grassland, the river and the sea). They do not communicate
position and direction with the others. However, they adjust
own velocity to correspond with the others.
I have proposed a method that the many robots share thecommon coordinate system using dead-reckoning andobservation to other robots without communication andwithout making the maps, like migration animals[7][8][9].
In this paper, the relation between the parameter of thepositional modification and optimal value is reported.

1 INTRODUCTION
A multi robot system that consists of many autonomous
robots is drawing attention, because it has various
advantages of fault tolerance, scalability and flexibility [1].
In many case, it is desirable that each robots recognizes
own position and direction on the same coordinate system.
For example, if all the robots recognize the flag position on
the common coordinate system, they reach the position
exactly (Fig. 1-a). On the other hand, assume that they can’t
recognize it on the common coordinate system (Fig. 1-b).
However, it is difficult to share the common coordinate
system. It is called as “localization problem”. To solve theproblem, GPS, SLAM[2][3], triangulation[4] andprobabilistic methods[5][6] have been proposed. GPS is notavailable in the indoor, in the water, or outside the earth.
The other method is necessary to communicate with otherrobots. Communication conflict occurs, if the large numberof the robots exists.

2 ROBOT SYSTEM
In the proposed modification method, the robots mustmove to the same direction and position. In this paper, thecontrol method of the robot swarm that has been proposed
is applied [10]. The control method consists of many robots
and a supervisor that broadcasts the same command to them(Fig. 1).

Fig. 2. The manipulation system
The robot has following ability:
 moving in 2-dimensional flat field localizing by dead-reckoning
 receiving the broadcast command from the supervisor
 measuring distance and relative velocity of otherrobotsa. on the common
b. on different
coordinate systemcoordinate systems
Fig. 1. Recognition the objective position
I pay attention to the animal of natural world. Migrantanimals (bird, animal, fish and insect) often move very long
distance. And they can reach their destinations (the lake, the© ISAROB 2013

440

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where, the each robot does not communicate with eachother. And, a general robot can be implemented with theseabilities easily.
The robot receives three kinds of force. The force issimilar to the force of the ‘BOID’ [11].
fri : attractive force to migratethough it tries to move to straight forward. Therefore, therobot estimates a different coordinate system to an initialcoordinate system.

f ri  K  pr  pi 

fai : attractive or repulsive force to gather
Fig. 4. localization error
Fig. 5 shows the trajectories of the robots. They movethe different direction by the dead-reckoning error.

 A p  p  A p  p 
 1 i
j
2
i
j 
f ai   

m1
m2 

jJ Ai  p  ppi  p j
j

 i

1000

And it decides the velocity by the sum of the forcesp i  f ri  f ai
initial position

In order to migrate, the reference position is changed
according to[unit]

p r  vr t
The variable and constants are shown in Table 1.
Table 1. Definition of variables and constants
notation
descriptionTabsolute position of agent ip  x , y iiip r   xr , y r Tvr  v xr , v yr Tn
K , A1 , A2 , B,

-1000

absolute position ofreference position
absolute velocity ofreference position
number of agents
constant value[unit]

initial position001000 [unit]

3.2 Modification of the direction
The modification method of the direction is described.
If all the robots recognize own position on the same
coordinate system, they can move to same direction. Eachrobot modifies adjust own direction to fit direction of otherrobot, if they move to different direction by the error. The
relationship between other and own velocity is illustrated inFig. 6. The robot can observe relative velocity and canobtain own velocity. Therefore, it is able to calculate  i .
And, it modifies own direction according to following
equation:Fig. 3 shows the simulation result that the robots moveby using basic setting without localization error. The basic
setting is described in section 4. All the robots migrate
together.

trajectory

trajectory
final position

Fig. 5. Trajectories with localization error

m1, m2, m3

300
200
100
0
-100
-200

0

i   i  dir i

final position
1000 [unit]

where, superscript “+” and “–“ mean after and before
modification.

Fig. 3. Trajectories without localization error

3 MODIFICATION METHOD
3.1 Dead-Reckoning error
Dead-reckoning is very simple method to measure theown position and direction. However, the localization error
occurs by dead-reckoning error. For example, the robot
moves along dash trajectory for localization error in figure,© ISAROB 2013

Fig. 6. Modification method of the position

441

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013the dir is employed to 0.5. The optimal value of dir isanalyzed theoretically and confirmed by the simulation [9].

[unit]

Fig. 7 shows the trajectories with modification of thedirection. All the robots migrate together in spite of thedead-reckoning error. This paper does not focus on the
different between average trajectory and target trajectory,
because it considers the sharing coordinate system.
300
200
100
0
-100
-200

4.2. Effectiveness of modification of position
The time variant of the direction and position is
illustrated in order to confirm the effectiveness of themodification method of the position.
First, standard deviation of the direction is shown in Fig.
10. The result using modification of the position (pos =0.2) is little worse. However, the value is not divergent.

initial position
trajectory
0

final position
1000 [unit]

0.2

standard deviationof angle [rad]Fig. 7. Trajectories with directional modification
without positional one
3.3 Modification of the position
The modification method of the position is described.
The robots are not able to gather, if the each robot
recognizes different reference position (Fig. 6). The own
position is modified by fai and weight coefficient pos as
following equation:

wpos = 0
wpos = 0.2
0.1

0
0

2000

4000 6000
time [step]

8000

10000

Fig. 10. Standard deviation of the direction
Secondly, Fig. 11 shows standard deviation of theposition. The value without modification of the position(pos=0) is divergent. On the other hand, the value withpositional modification ( pos =0.2) is converge.

pi  pi   pos f ai

standard deviationof position [unit]

20

300
200
100
0
-100
-200

10

0
0

initial position
trajectory
04000 6000
time [step]

8000

10000

Fig. 11. Standard deviation of the position
Finally, Fig. 12 depicts final positions of the robots withand without modification, respectively. The position
without modification is spread. However, the position isgathered using modification.

final position
1000 [unit]

Fig. 9. Trajectories with directional and positional
modification-3600029000

4 SIMULATION[unit]

4.1. Setting
Basic setting is shown. The reference velocity is given
as (0.5, 0)T [unit/step]. The dead-reckoning error is set to2%, and the number of the robot is 20. The interval time
between modifications is 20 [step]. The simulations are
executed 10 times using different initial random value at thesame parameter. The constant parameters K, A1, A2, m1 andm2 are 0.05, 1, 1, 1 and 2, respectively. The coefficient of© ISAROB 2013

2000

[unit][unit]

Fig. 8. Modification method of the position
The trajectories with modification of the direction andposition are shown in Fig. 8. The trajectories are similar
with ones in Fig. 7. The difference whether modification of
the position is adopted or not is discussed in section 4.

wpos = 0
wpos = 0.2-37000

28000

27000
84000

85000
[unit]

86000-38000125000126000[unit]

127000a. without positionalb. with positional
modification
modification
Fig. 12. Position of the 20 robots after long migration

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The effectiveness of the modification method of theposition is confirmed.

depend on the amount of the error, but does not depend on
the number of robots.
In the future work, the effectiveness of the positional
method is analyzed.

4.3. Influence of the parameters
In order to investigate the influence on the error of theposition, the coefficient parameter of the modification of
the position is changed from 0 to 1 step 0.01.

REFERENCES

average standard deviationof position [unit]

40
30

frequency ofmodification
1/50
1/20
1/10

20
10
0
0

0.2
0.4
0.6
0.8
weight coefficient (w pos)1

Fig. 13. Standard deviation of the direction changing posFig. 13 shows the results using three kinds of theinterval times between modifications. Localization error
increases, when interval time is long. Larger coefficientparameter is necessary in the case of large error. The robot
might have to estimate scale of own error in order to use
optimal value.

average standard deviationof position [unit]

40
30

the numberof robots
10
20
50

20
10
0
0

0.2
0.4
0.6
0.8
weight coefficient (w pos)1

Fig. 14. Standard deviation of the direction various for pos
The results using three kinds of the number of therobots (n = 10, 20, 50) is depicted in Fig. 15. The optimalvalue is the almost same, though the number of the robots isdifferent.

5 CONCLUSION
This paper described the modification method of thedirection and position in order to establish the common
coordinate system. Both modification of position anddirection are effectiveness to sharing direction and position,
respectively. Especially, the influence of the coefficientparameter of the modification of the position on theperformances is confirmed. I find that optimal parameter© ISAROB 2013443

[1] Y. U. CAO, et al. (1997), Cooperative Mobile
Robotics: Antecedentsand Directions, Autonomous Robots,
Vol. 4, No. 1, pp. 7-27.
[2] M.W.M.G. Dissanayake, S. Clark P.Newman, H.F.
Durrant-Whyte, and M. Csorba (2001), A Solution to thesimultaneous localization and map building problem, IEEE
Transactions on Robotics and Automation, Vol. 17, No. 3,
pp. 229-241.
[3] M. D. Marco, A. Garulli, A. Giannitrapani, andAntonio Vicino (2003), Simultaneous localization and map
building for a team of cooperating robots: A set
membership approach, IEEE Transactions on Robotics andAutomation, Vol. 19, No. 2, pp, 238-249.
[4] KURAZUME, R., NAGATA, S and Hirose, S. (1994),
Cooperative positioning with multiple robots, IEEE/RSJ
International Conference on Intelligent Robots and Systems(IROS), Vol. 8, pp. 325-344.
[5] Fox, D. ,BURGARD, W, KRUPPA, H and THRUN,
S. (2000), A probabilistic approach to collaborative multirobot localization, Autonomous Robots, Vol. 2, pp. 12501257.
[6] IITSUKA, T, MAKINO, K, SHE, J, H and
OHYAMA, Y (2010), Cooperative Localization ofAutonomous Mobile Robots applied MCL method (inJapanese), 22th SICE Symposium on Decentralized
Autonomous Systems, pp. 285-290.
[7] MAKINO, K. and MATSUO, Y. (2007), Method of
Coordinate System Merging for Collective Control ofAutonomous Mobile Robots (in Japanese), JRSJ, Vol. 25,
No. 8, pp. 70-81.
[8] MAKINO, K. SHE, J. H. and OHYAMA, Y. (2010),
Establishment of Common Coordinate System by Using
Migration of Autonomous Robot Herd, SICE Annual
Conference 2010, pp. 404-407.
[9] MAKINO, K. SHE, J. H. and OHYAMA, Y. (2011),
Analysis of a Method of Sharing for Common Coordinate
System by Migration, SICE Annual Conference 2011, pp.
2484-2487.
[10] MAKINO, K. and MATSUO, Y. (2004), Collective
Behavior Control of Autonomous Mobile Robot Herds by
Applying Simple Virtual Forces to Individual Robots (inJapanese), JRSJ, Vol. 22, No. 8, pp. 79-90.
[11] Craig W. Reynolds (1987), Flocks, herds andschools: A distributed behavioral model, SIGGRAPH '87
Proceedings of the 14th annual conference on Computer
graphics and interactive techniques, pp. 25-34.

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Evaluation of Efficiency of the Symmetry Biasin Grammar Acquisition
R. Matoba*1, H. Sudo*1, S. Hagiwara*1, and S.Tojo*2*1 Toyama National College of Technology
1-2, Neriya, Ebie, Imizu, Toyama, 933-0293, Japan(tel:+81-766-86-5283; Fax:+81-766-86-5110)
mail:{rmatoba ,i08323, hagiwara}@nc-toyama.ac.jp*2 Japan Advanced Institute of Science andTechnology
1-1, Asahidai, Nomi, Ishikawa, 923-1292, Japan(Tel : +81-0761-51-1372; Fax :+ 81-0761-51-1149)
mail: tojo@jaist.ac.jp

Abstract: The aim of this study is to evaluate the efficacy of cognitive biases in grammar acquisition. So far, wehave constructed Iterated Learning Model (ILM) in generation by generation, where a pair of a parent agent andan infant agent resides in a generation, and the infant becomes the parent of the next generation. Then, we haveadded the effect of such cognitive biases as symmetry bias and mutual exclusivity bias. Thus far, in evaluating
results of acquired grammar, the expressivity and the number of grammar rules have been focused on. When we
consider the efficacy of cognitive biases, however, we should observe how the infant agent could learn the parent
agent's intentions faithfully. Therefore, in this paper, we suggest assessing the difference of linguisticknowledge between a parent agent and an infant agenet, using Levenshtein distance. As a current result, we
could observe that the distance becomes further in generations.
Keywords: Cognitive Bias, Levenshtein distance, Grammar Acquisition, Iterated Learning Model.

by these two criteria, so far. However, when we pay
attention to the efficacy of cognitive biases, we shouldobserve how the infant agent could acquire the parent
agent's intentions correctly. Therefore, we need to assessthe difference of linguistic knowledge, consisting ofpairs of meanings and utterances, between the parent
agent and the infant agent. More precisely, we employeda notion of Levenshtein distance to evaluate twodifferent linguistic knowledge. Applying our suggested
distance, we have gotten plausible results of Kirby's
ILM to measure the efficacy of cognitive biases.

1. Introduction
It is well known that infants over 18 months old
can acquire new words very rapidly, e.g., 7 to 15 words
a day [1]. To enable this phenomenal learning, cognitive
biases which are constraint to limit possibilities ofwords-meaning mapping in a situation have suggested[2, 3]. This mapping is considered to be generally
difficult as is well known as `gavagai problem' [4],though infants achieve this operation.
So far, we have already reported efficacy ofcognitive biases in grammar acquisition using computer
simulation [5, 6]. Our model is based on Simon Kirby’s
Iterated Learning Model [7] where a pair of a parent
agent as and an infant agent resides in a generation. In
this model, the parent agent is a speaker and the infant
agent is a listener. A number of utterances would form acompositional grammar rules in the infant agent's mind,
being substrings are chunked. This process is iterated
generation by generation, and finally, a certain
generation would acquire a compact, limited number ofgrammar rules. We include cognitive biases into thisprocess. We implement agents with the bias in a virtualworld, and make them learn a grammar by computer
simulation.
In general, the more grammar is compositional, thehigher expressivity and the less number of rules.
Therefore, we have evaluated the progress of evolution© ISAROB 2013

2. Distance between Two Different
Linguistic Knowledge
2.1. Agent’s Linguistic Knowledge
According to Kirby's ILM, the parent agent
gives the infant agent a pair of a string of symbols as
an utterance, and a predicate-argument structure(PAS) as its meaning. The agent's knowledge is a setof a pair of a meaning and a string of symbols, as
follows.
S/love(john, mary)  lovejohnmary
Where a speaker's intention is a PAS love(john, mary)and its utterance becomes `lovejohnmary'; thesymbol `S' stands for the category Sentence. Afterthe listener receives a pair of a PAS and an utterance,
she/he tries to guess his/her parent's grammar rules,

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4. Carry out 1 to 4 for all grammar rules of Kp.
Calculate the sum of all the distances and regard
the average of them as the distance of two
linguistic knowledge. Thus, in this case, thedistance between Kc and Kp is calculated as
below.

as utterances are always paired with their meanings,
which are intrinsically compositional. This guessing
process consists of the following two operations;`chunk' is to find a common substring, and to
substitute it for a new category.
S/read(john, book)  johnreadsbook
S/read(mary, book)  maryreadsbook𝑖1
𝑑𝑖
𝐷𝐷𝐷𝐷𝐾𝑝 𝑡𝑡𝐾𝑐 = ���𝑖|𝑢𝑐𝑐 | + �𝑢𝑝𝑝 �↓
S/read(x, book)  N/x readsbook
while `merge' is to unify the identical category names
as:N/mary  maryN/john  john
B/john  john

The image of this measuring procedure is show
in Fig1.

↓
N/john john
As a result, an infant comes to acquire a set ofcontext-free grammar rules.
2.2. Distance between Two Knowedge
For evaluating the distance of two linguisticknowledge, i.e., the distance between the parent
knowledge and the infant one, we employed the edit
distance, aka Levenshtein distance, which is a metricfor measuring the difference between two sequencesof symbols; we count the number of
insertion/elimination operations to change one word
into the other. For example, the distance between`abc' and `bcd' becomes 2 (erase `a' and insert `d').
All the compositional grammar rules aredeveloped to a set of holistic rules, where one PAS isconnected directly to a superficial string excludingintermediate categories, beforehand.
Now thecomparison between a parent agent and an infant
agent takes the following procedure.
1. Pick up a grammar rule (gp) which is constructedby a pair of a PAS(pp) and an utterance (up) fromthe parent's knowledge (Kp).
Choose agrammar rule (gc) which PAS (pc) is most similar
to pp from a child's knowledge (Kc), in terms ofLevenstein distance. If there are multiple
candidates, all of them are kept for the next
process.
2. Focus on an utterance (uc) of gc and up, andmeasure a distance (d1) between up and uc using
Levenshtein distance. If there are some, choose
smallest one.
3. Normalize d1 from 0 to 1.
© ISAROB 2013

Fig1. Image of measuring procedure
Here, we give a concrete example. Assume
that a parent agent has the linguistic knowledge
including two rules as below.
rule1: S/love(pete, mary)  acd
rule2: S/hate(mary, john)  bd
Also, a child agent has
rule3: love(john, mary)  abc
rule4: hate(mary, pete)  bcd
rule5: admire(mary, john)  fcg
Now, pick up rule1 of parent's grammar rule,and search rules which have most similar PAS fromthe child's knowledge. In this case, rule3 is selected(procedure 1). Considering `acd' and `abc', theelimination of `d' and the succeeding insertion of `b'equate them, i.e., Levenshtein distance of them is 2
(procedure 2). Since the maximum distance is 6,
the result of normalization becomes 2/6 (procedure 3).
In the case of rule2, rule4 and rule5 are selected inprocedure1. In terms of Levenshtein distance, rule4
is selected, and its distance is 1. After thenormalization, we obtain 1/5. Therefore, thedistance between the knowledge of the child agent
and that of the parent agent becomes1

1

1

�2 × �3 + 5�� ≈ 0.27.
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3. Experiment and Result

3.2 Application of Distance to Kirby’s ILM
In the original experiment, the accomplishmentof the learning is evaluated by the number of rules inlinguistic knowledge and by the expressivity which is
the ratio of the utterable meanings to the wholemeaning space. Instead of these two criteria, we
employ our own, i.e., the distance between two
linguistic knowledge.
Here, we need to distinguish the distance from aparent to his/her offsprings, and that from an infant tohis/her ancestors, each of which is shown in Fig. 3
and Fig. 4, respectively.
In Fig. 3, each upward line shows a distance of
linguistic knowledge owned by a parent agent tohis/her offsprings; the topmost line, for example,
shows the distance from the parent agent in the first
generation to his/her offsprings in the second,
third, ..., and 100th generations. Because the distancebecomes larger as the generations go further, the line
becomes upward.
In this situation, for example, distance between
knowledge of the 2nd generation to the 3rd
generation is about 0.39. Also, distance between
the 2nd generation to converged generation is about
0.85.

In this section, we show the result of theapplication of our suggested distance as mentioned in
Section 2.2 to Kirby’s ILM to testify its plausibility.
3.1 Briefing Experiment of Kirby’s ILM
Kirby's ILM employs the following five two-placepredicates and five object words:
Predicates: admire, detest, hate, love, like
Objects: gavin, heather, john, mary, pete
where two identical arguments in a predicate likelove(john, john) is prohibited. This implies that there
are 100 distinct meanings (5 predicates ×five firstarguments × 4 second arguments).
Since the number of utterances is limited to 50 in his
experiment, the child argent cannot learn the wholemeaning space of 100; thus, the child agent comes
across the learning bottleneck problem. To obtain thewhole possible meanings, the child agent has togeneralize his/her own linguistic knowledge by somelearning process.
We have carried out this experiment until 100th
generation.

Fig2. Result of Kirby’s ILM
The result of this experiment was that thelanguage has low expressivity and a large number ofrules in the early stages; however, throughgenerations, the language acquires higher
expressivity and the number of rules decreases. We
can observe that the more the agent's linguisticknowledge becomes compositional the smaller thenumber of rules while expressivity persists or
improves.

© ISAROB 2013

Fig3. distance from ancestor to offspring

In the later stages, linguistic knowledge of theparent agent becomes enough compositional toexpress the whole meaning world with few grammar
rules, then the distance becomes smaller. For
example, the distance between the 82nd generation to
the 83rd generation is about 0.04. Also, the distance
between the 82nd generation to the converged
generation is about 0.14. The bottom line in Fig. 3
shows the distance between a generation and its
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acquisition speed of compositional language, theycannot evaluate the correctness of meaning; infants ineach generation may still misunderstand the mapping
between meanings and utterances in such rapid
leanings. Therefore, we suggested a criterion tomeasure the distance between two linguisticknowledge using Levenshtein distance. As a result,the distances between two linguistic knowledge inthe early stages are larger because their grammar are
not compositional.
On the other hand, after
grammar becomes enough compositional, thedistances become smaller. Our future works include
to employ this measuring procedure to our grammar
acquisition model and to verify the function of thecognitive biases.

immediate follwing generation. We can observe that
gradient of early generations is steep though that of
the latter generations is flat.
In the early stages, the parent agent does not
own compositional linguistic knowledge, i.e.,
expressivity of the knowledge is low, so the parent
agent cannot make utterances with his/her own
grammar rules, viz., the parent agent has no choice tomake utterances randomly. On the other hand, inthe latter stages, the parent acquires enoughcompositional linguistic knowledge, so the childagent can receive inputs in a regular pattern from theparent agent. Thus, the child agent can acquire
similar linguistic knowledge to the parent agent, i.e.,the distance of their knowledge is small.
The same tendency can be observed in Fig. 4.
The topmost line in Fig. 4 shows the distance fromthe infant in the 100th generation to his/her ancestors.
The further his/her ancestor is the larger the distancebecomes, so that each line decreases from left to right.
In the similar way, the bottom line in Fig.4 shows the
distance between two consecutive generations; thisline shows the exactly same tendency in Fig. 3.

Refferences[1] G. Schafer and K. Plunkett. Rapid word learningby 15-month-olds under tightly controlled conditions.
Child Development, Vol. 69, pp. 309{320, 1998.
[2] M. Imai and D. Gentner. Children's theory ofword meanings: The role of shape similarity in earlyacquisition. Cognitive Development, Vol. 9, No. 1, pp.
45~75, 1994.

5. Conclusion and Future Work
The cognitive biases are known to work to
reduce the number of possible interpretations for each
word, for first language acquisition. The authors thus
far have verified that the function of the cognitive
biases work not only in the lexical acquisition but
also in the grammar acquisition. For this mission,
we have revised Kirby's ILM, and built the cognitive
biases into our model.
To evaluate the efficacy of the cognitive biases,
however, we reconsidered the two traditional criteria;
the expressivity and the number of rules. Althoughthese two criteria are advantageous in evaluating the[3] E. M. Markman. Categorization and naming inchildren: Problems of induction, MIT Press,
Cambridge, 1989.
[4] W.V.O.Quine, Word and Object, MIT Press,
Cambridge, MA, 1960.
[5] R.Matoba, M.Nakamura, and S.Tojo, Efficiencyof the Symmerty Bias in Grammar Acquisition,
Information and Computation, Vol209, Issue 3,
Elsevier, pp536-547, 2010.
[6] R.Matoba, M.Nakamura, S.Hagiwara, and S.Tojo,
Efficiency of the Cognitive Bias in Grammara
Acquisition, CogSci, 2012.
[7] S.Kirby, Learning Bottlenecks and the Evolutionof Recursive Syntax, in: T.Briscoe(Ed.), Linguistic
Evolution
through
Language
Acquisition,
CambridgeUniversity Press, 2002.

Fig4. distance from offspring to ancestor© ISAROB 2013

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The Evolution of Pre-play Communication in the Interactive Minority Game
Keita Nishimoto1, Ivan Tanev2, Katsunori Shimohara2, Reiji Suzuki1 and Takaya Arita1
1

Graduate School of Information Science, Nagoya University, Japan
2
Faculty of Science and Engineering, Doshisha University, Japan1nishimoto@alife.cs.is.nagoya-u.ac.jp

Abstract: Minority Game (MG) is an N-person game which represents the collective behavior of agents in an idealized
situation in which they have to compete for some finite resource. MG has been studied actively in various fields, but moststudies have not focused on the communication among agents. To study the evolution of communication in the MG, we
extended the standard MG to a new game named Interactive Minority Game (IMG) by incorporating the two aspects: acontinuous strategy space and a pre-play communication stage. In order to understand basic behaviors of agents in the IMG, we
prepare three agents each of which is equipped with a recurrent neural network (RNN) to adjust the tentative strategy value inthe pre-play communication stage and evolved the connection weights of each RNN based on the payoff of IMG. As a result,
we saw the emergence of various communications such as the adaptive adjustment behavior and oscillation of strategy valuesof each agent. Moreover, we found the strategy differentiation among agents where two agents adopt "high-risk high-payoff"
strategy and the rest one adopts "low-risk low-payoff".
Keywords: Minority Game, Evolution of communication, Multi-agent simulation
they discovered that the scale-free imitation network
emerged on the random network.
In their model, the information which agents exchange
is only a binary decision and one-shot, and thus it cannot be
regarded as a dynamic communication. We incorporate preplay communication stage in which agents can modify theirintentions continuously and dynamically observing others’
intentions before their final decision making.
To incorporate the dynamic communication, we need tochange the agents’ intentions and payoff from binary to realvalues. In our model, each agent expresses its intention bystrategy value a ( ∈ [-1, 1]) and receives the payoff
depending on the value.
As a first step of our study [4], we dealt with theevolutionary dynamics of social sensitivity of agents androle switching. This paper focuses on the evolved pre-playcommunication among agents. We prepare three agents,
each of which is equipped with a recurrent neural network(RNN) to adjust the tentative strategy value in the pre-playcommunication stage. We evolved the connection weightsof each RNN with an evolution strategy (ES) based on the
payoff of IMG played among these agents.

1 INTRODUCTION
Minority Game (MG) is an N-person game which
represents the collective behavior of agents in an idealized
situation where they have to compete for some finite
resource. Each of N agents chooses one out of twoalternatives independently, and those who have chosen theminority choice among them win and are awarded a point[1]. Minority Game has been studied actively in various
fields because of its simplicity and emergent characteristics(e.g., the emergence of cooperation among agents and thephase transition [2], [3]).
However, most studies have not focused on thecommunication among agents in the MG. In the real world,
people in competitive situations do not make decisions
based only on the past record, but also based oncommunication among them.
We focus on the communication among agents andpropose a new model named Interactive Minority Game
(IMG) [4], where agents make decisions based on dynamic
interactions between them instead of the past record. There
are several studies to incorporate communication among
agents into the MG [5], [6]. Anghel et al. proposed a
network-based Minority Game where agents are connectedwith a random network [5]. Agents employ a two-stepdecision making procedure and exchange the decisionmaking information with others through the links. First,
each agent predicts what the minority choice will be basedon its own strategy table. Then, it selects the agent which
has made the most accurate predictions so far from amongits neighboring agents including itself, and adopts its
prediction as the final choice. As a result of simulations,© ISAROB 2013

2 Interactive Minority Game (IMG)
In the MG with N (odd) agents proposed by Challet andZhang [1], the payoff of an agent i choosing alternative Ai is
calculated as follows: N 
payoff   Ai sgn   Ai  , k 1  Ai 1,1 ,payoff 1,1 ,

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1
sgn ( x )   0
- 1


if x  0,
if x  0,

also on past inputs: RNNs can use their internal memory toprocess arbitrary sequences of inputs. Each RNN has threelayers (5 input units, 6 hidden units, 4 output units), and theunits use a sigmoid activation function ( f (x) = 1 / (1 + e-x )).
For simplification of the model, RNNs do not have bias
units. Two output units in the output layer are recurrently
connected to two input units in the input layer.
Every time step, the agent’s RNN receives five input
values: its own current strategy value, the distance from thestrategy values of the other two agents to its own, and thevalues from the two output units from the previous step.
Two units in the output layer generate the values au and ad,which determine ε( t + 1) =ε(t) + (au – ad ) / 100. The
remaining two output units are connected one-to-one to twoof input units.

otherwise .

We propose the Interactive Minority Game by extending
the MG on the following two points. First, we adopt a
continuous strategy space instead of a binary one. The
payoff of agent i with strategy value ai is calculated asfollows:

 N
payoff  ai sgn   sgn  ai   , k 1ai 1,1 ,payoff  1,1 .

(2)

ai(t) 2) Pre-play communication stage
1.5

This equation represents the situation as follows: The
possible signs of the strategy value (positive or negative)correspond to the alternatives in the standard MG. Agents
win the game if the sign of their strategy is the minority
sign in the group. Furthermore, the strategy’s absolute value
defines its “intensity”. Higher intensity values lead to both
higher risk and higher reward. The winning (losing) agentsobtain a positive (negative) payoff equal to the absolutevalue of their strategies.
Secondly, we add a pre-play communication stage
before the agents confirm their strategy. During this stage,agents can continuously adjust their strategy. The tentative
strategy of agent i at time step t (= 0, 1, .., T - 1) isrepresented as ai (t) (ai (0) = 0). Each agent can adjust ai (t)gradually by ε (t), after observing others’ tentative
strategies in the previous step. The final decision of agent i :ai is defined as ai (T), and used for calculation of payoffs.

Start Pointai  ai (T ),ai (t  1)  ai (t )   (t ),(t  0 ,1, ,T  1).

Winner
The sign (plus or minus) of ai⇒the alternative of the agent

0.51) Continuous strategy space00

Boundaryline 400
100
200
300

500

600

700

Minority Side

800

900

-0.5

Agent 1-1

-1.5

Agent 2Agent 3

The absolute value of ai⇒ the intensity of the strategytai(T) = -0.35
payoff = -0.35

Majority Sideai(T) = -1
payoff = -1
During time steps t = 0 ～ T, each agent can adjust theirstrategy ai (t) gradually with ε(t) observing other’s strategy.
ai(T): the final decision of agent i.

Fig. 1. A trial of IMG (N = 3, T = 1000).
3.2 Evolutionary algorithm
The full set of 54 connection weights in each RNN is
encoded in the genotype of each agent and evolved using asimple type of Evolution Strategy (ES). The connection
weights do not change during a trial. We assume three
independent gene pools, each of which provides one agent
in each trial, so the agents that interact in a game trial comefrom independently evolved gene pools. Each gene pool has
np individuals.

(3)

1.

Note that if ai (t) +ε(t) > 1(< -1), then ai (t + 1) = 1 (-1).
In this study, we focus on the case of N = 3, the minimum
number of agents for the MG. Fig. 1 shows an example
game. The x-coordinate corresponds to the time step t andthe y-coordinate represents ai (t) for each agent.

2.

3 MODEL
3.1 Decision making mechanisms
Every agent is equipped with a recurrent neural network(RNN) to decideε(t) at each step. The reason why wechoose to use RNNs is to enable agents to make decisions
appropriately depending not only on the current inputs, but© ISAROB 2013

ai(T) =1
payoff = 1

1

3.

449

Generating the initial population
np genes are generated in each gene pool using
uniform random number U as an initial population.
Evaluating individuals’ fitness
We assemble np groups of three individuals each ofwhich randomly selected from each gene pool without
duplications. Three individuals in each group play
IMG and receive the payoff. Individuals picked out
from pools are restored to their original pools after thetrial. This procedure of group assembly and game trial
is repeated R times. The fitness of each individual isdefined as an accumulation of fitness over R trials.
Creating the next generation

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The population in the next generation of each genepool is composed as follows: First, we select ne bestindividuals from np individuals (the elites), andpreserve them to the next generation. Then, each ofelites contributes two copies of themselves to the next
generation, and small random values from a normal
distribution NR with a fixed standard deviation areadded to each connection weight in the offspring.
Finally, np-3ne individuals with randomly generated
genotypes are added to the population. These
evolutionary operations for selection and reproduction
are performed on each gene pool independently.

the situation in which the final strategies fall in the same
area of the strategy space (yielding payoff far below 0) is
avoided. In Fig. 2 we see an increase in average fitness
from the initial generation to the 20th generation, likely due
to the emergence of the strategy differentiation among
agents.
15

105Fitness

0
-5
-10
-15
gene pool 1gene pool 2gene pool 3
average-20

4 RESULTS-25

We evolved the population for 10000 generations. We
used the following parameter settings: T = 1000, np = 40, ne= 12, R = 40. Initial connection weights are drawn
randomly from a uniform distribution U over [-1, 1], andmutation adds a random number from the normal
distribution NR (0, 0.22).
First, we focus on how pre-play communication among
agents develops during the early stages of the evolution
process. Fig. 2 represents the average fitness of each genepool and the average fitness of all individuals from the 0th
generation to the 99th generation. We see a rapid increase infitness in all gene pools. The average fitness reachedapproximately -5 at the 99th generation.
Fig. 3 shows an example communication at the 50th
generation. We see that the strategy value of one agent
reached the upper limit and that of another agent reachedthe lower limit, while the remaining agent’s value remainednear the boundary line between two signs (ai (t ) = 0). We
focus on the agent whose strategy value remained near theboundary line. In the situation shown in Fig. 3, thestrategies of the other two agents are on the upper and thelower area respectively, and they did not change theirstrategy values. Thus, the focal agent could not avoid
ending up on the majority side, and so its payoff falls below
0 regardless of which side it picks. The optimal behavior
thus is to choose a strategy value as close to the boundaryline as it can, and receive payoff of near 0. It was often
observed in the simulations that the final strategies of thethree agents settled on these three positions in the strategy
space: the upper limit, the lower limit (high risk and highreturn strategy) and around the boundary line (low risk andlow return strategy). This can be regarded as strategy
differentiation among agents.
The average payoff of each agent over R trials becomesnear 0, if 1) the final strategy of the agent nearby theboundary line stays very close to it, and 2) its sign splits
fifty-fifty between positive and negative. In this scenario,© ISAROB 2013

-30
-35

0

10

20

30

40

50

60

70

80

90

generation

Fig. 2. Evolution of fitness.
Fig. 4 shows a typical communication from the 70th
generation in which Agent 1 and Agent 3 can be seen to
respond each other’s behavior. In Fig. 3, all agents initiallylower their strategy values. Agent 3 (at around step 40), andAgent 1 (at around step 80) can be seen to switch theirdirections and start increasing their strategy values, in order
to avoid the situation in which the strategy values of allagents remain negative, in other words, all lose. Once thestrategy value of Agent 1 surpasses 0 (at around step 150),
Agent 3 switches its direction again correspondingly. The
most likely explanation for these behaviors of Agent 1 andAgent 3 is that they changed the increase or decrease in
their strategy values in response to the strategy values ofthe others.
Fig. 5 shows the communication among agents at the80th generation. We can observe that agents interacted witheach other more actively than was the case in the earlier
generations. This sort of oscillation was often observed in
the simulation.
Next, we classified the types of the pre-playcommunication among agents into the following three
types: (a) Oscillation type (All agents cross the boundaryline (ai (t) = 0) more than one time in the communication
stage.) (b) Fork type (All agents do not satisfy the conditionof (a), and each strategy value is as follows: one's strategy
value is more than 0.8, another's strategy value is more than-0.1 and less than 0.1, and the other's strategy value is lessthan -0.8), (c) Others. Fig. 6 shows the occurrence rate ofeach communication type from the 0th generation to the99th generation. From this figure, we see that the Fork typeoccurs first, followed by the Oscillation type. We conducted

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ten trials and confirmed this tendency in nine trials among
them. This increase in communicative complexity should
be worth noting as in the field of animal signaling it has
been believed that complex communication cannot be
evolved in conflicting situations [7].

simulations in which each agent’s RNN evolves based on
the payoff of 3-person IMG, we discovered that the strategy
differentiation among agents where two agents adopt "highrisk high-payoff" strategy and the rest one adopts "low-risk
low-payoff". We also saw the emergence of various
communications such as the adaptive adjustment behavior
and oscillation of strategy values of agents.

ai(t)
1.5

100%
Occurrence rate of each communication type

1
0.5
t

0
0

100

200

300

400

500

600

700

800

900

-0.5
-1
Agent1

Agent2Agent3-1.5

Fig. 3. Communication among agents at the 50
ion.

th

generat90%
80%

Others

70%
60%
50%
40%
30%

Oscillation

20%

Fork

10%
0%

generation
0

ai(t)

10

20

30

40

50

60

70

80

90

1.5

Fig. 6. Occurrence rate of each communication type ineach generation.

1
0.5
t

0
0

100

200

300

400

500

600

700

800

REFERENCES

900[1] Challet D and Zhang YC (1997), Emergence ofcooperation and organization in an evolutionary game,Physica A 246: 407-418.
[2] Savit R, Manuca R and Riolo R (1999), AdaptiveCompetition, Market Efficiency, and Phase Transitions,Phys. Rev. Lett 82(10): 2203–2206.
[3] Challet D and Marsil M (1999), Phase transition andsymmetry breaking in the minority game, Phys. Rev. E
60: R6271-R6274.
[4] Nishimoto K et al., Coevolutionary dynamics between
roles and social sensitivity in an extended minority
game, Proceedings of ALIFE13, Michigan, USA, July
19-22, 2012: 226-233.
[5] Anghel M, Toroczkai Z, Bassler KE and Korniss G(2004), Competition in social networks: emergence of ascale-free leadership structure and collective efficiency,
Phys. Rev. Lett. 92: 058701.
[6] Remondino M and Cappellini A (2005), Minority gamewith communication of statements and memory
analysis: a multi agent based model, International
Journal of Simulation: Systems, Science & Technology
6(5): 42-53.
[7] Maynard-Smith J and Harper D (2003), Animal signals,
Oxford University Press, Oxford, U.K.

-0.5
-1
Agent1

Agent2Agent3-1.5

Fig. 4. Communication among agents at the 70th generat
ion.
ai(t)

1.5
1

0.5
t

0
0

100

200

300

400

500

600

700

800

900

-0.5
-1
Agent1

Agent2Agent3-1.5

Fig. 5. Communication among agents at the 80th generation.

4 CONCLUSIONS
We extended the standard MG to a new game named
Interactive Minority Game (IMG) by incorporating the two
aspects: a continuous strategy space and a pre-playcommunication stage. As a result of evolutionary© ISAROB 2013

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A New Decision-Making System of an Agent
Based on Emotional Models in Multi-Agent System
ShogoWatada1, Masanao Obayashi1, Takashi Kuremoto1, Kunikazu Kobayashi2 and Shingo Mabu1
12

Graduate School of Science and Engineering, Yamaguchi University
School of Information Science and Technology, Aichi Prefectural Universitym.obayas@yamaguchi-u.ac.jp

Abstract: In this paper, a new behavioral decision method of the robot based on a Markovian emotional model is proposed and
it is applied to environmental identification problem. Noting the role of emotion in communication, behaviors of multi-robot
implementing the emotional transition model are optimized. The autonomous decentralized robot group with the proposed
method is applied to identify an unknown environment. Specially, each robot in this autonomous decentralized system cancommunicate with other robots located within a certain distance. While searching and exploring the environment, each robot
independently generates local topological maps, and uses the map for planning of actions. Finally, the effectiveness of the
proposed method is verified using own simulator through evaluating the exploration time and the number of double-visited
nodes for different complex environments.
Keywords: decision making system, Markovian emotional model, multi-agent system
genetic algorithm (GA), one of evolutionary optimization
method, the action along objective of the task is selected
automatically.
The proposed method is evaluated through an unknown
environmental identification problem. The simulation
results showed the effectiveness of the proposed method.
The paper is organized as follows. Section 2 describes
the proposed Markovian emotional model. In Section 3, theproposed behavior selection system with the Markovian
emotional model is described. In Section 4, the simulation
results of the proposed method are presented. Finally,
Section 5 summarizes the effectiveness of the proposed
method and future works.

1 INTRODUCTION
There exists much literature on the study which focuseson the concept of emotion that animals have, for developing
autonomous systems in various fields. Emotion of animals
are more attractive from the points of view that it flexibly
represents influence from the environment and internal
states of them, and that it makes communication with others
facilitate. Emotion has been considered as a significant
impact on decision making of autonomous robots. There
have been many studies of artificial emotion1 - 4.
The purpose of this study is to optimize the decision
making of a robot by introducing emotion to a multi-robot
control system. The autonomous decentralized robot groupin which each robot has the proposed system is applied to
identify an unknown environment. This task is to explore
all areas in unknown environment and to generate an
accurate map. Getting the map leads to effective
determining of behaviors of the robot because it can beused in order to path planning.
Assuming that each robot in the robot group has signal
emitting and receiving functions, the robot cancommunicate with other robots located within a certain
distance. While searching and exploring the environment,each robot independently generates local topological maps,
and uses the map for planning of actions.
The proposed behavior selection system incorporates
with a Markovian emotional model which is represented asa finite state machine, and the emotional state is determinedby the internal state and used for behavior selection.
In addition, by optimizing the parameters related tobehavior selection and generation of emotional value by a

© ISAROB 2013

2 The Markovian emotional model
The Markovian emotional model referenced in this paper
is a probabilistic model5 that consists of finite statemachine. It consists of four basic emotions: joy, anger, fearand sadness (Fig.1).

Fig. 1. Topology of Markovian Model

Emotional state transition is given by Eq.1

Yk 1  CYk(1)with emotional state points:  {Joy, Anger , Fear , Sadness} ,

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where Yk represents the current emotional state, and C is theemotional state transition matrix, which can be expressed asfollows:

recorded in the database module. Then, the cognition
module determines emotion-inducing factors (  ,  ,  ,  ).
For example, these factors are determined based onremaining battery capacity, the number of frontiers and theemotional state of other robots in communicable area. The
emotion module generates and updates the emotional state
Y by Eq.1, changing C by emotion-inducing factors
determined by the cognition module, and transfers it to the
behavior selection module. The behavior selection module
determines the behavior selection probability vector X by
Eq.5:

Pjoy / anger
Pjoy / fear
Pjoy / sadness  Pjoy / joyPPanger/ anger Panger/ fear Panger/ sadness  , (3)anger/ joyC Pfear / joy
Pfear / anger
Pfear / fear
Pfear / sadness 


P
P
P
P
 sadness/ joy sadness/ anger sadness/ fear sadness/ sadness
where PA/B is the probability of transition from state B tostate A. Each value P/  is initially set as basal values q/  ,elements of matrix named Q which gives the basal statetransition matrix (See Eq. 4). These values P/  can bechanged online by the influence of emotion-inducing
factors:  ,  ,  , and  for joy, anger, fear, and sadness,
respectively. These factors vary depending on the internal
state and information given by certain other robots and havethe role to stimulate each emotion. For example, theprobability of state transition from joy to other state
expressed by the following equations:X k 1  AYk 1  Vk 1 ,where Xk+1 consists of probabilities of next actions. A
expresses the probability transition matrix and is predefined.
The parameter vector V that affects instinctive action
selection is generated by the cognition module. The reason
for adding V is if use of only a simple emotional transition
model, it is difficult to realize behavior selections inaccordance with the rules. For example, while charging of
the battery, the selection probability of the particularbehavior increases due to the rise in the transition
probability of the joy. However, it will be more efficiency,
waiting until the completion of the charging. A behavior isselected stochastically according to the selection probabilityof each behavior. The behavioral system module generates
a control input u taking into account the posture of the robot
received from the cognition module to perform the actionthat has already been decided.
In this model, the policy of behavior selection depends
on transition matrices of Q and A. By adjusting theparameters being included in these Matrices, it becomes
possible to make various methods for behavior selection.

Panger/ joy  qanger/ joy  (   )qanger/ joy
Pfear / joy  q fear / joy  (   )q fear / joy
Psadness/ joy  qsadness/ joy  (   )qsadness/ joy

(5)

(4)

Pjoy / joy  1.0  ( Panger/ joy  Pfear / joy  Psadness/ joy )The emotional state Y is used to determine the behavior.

3 A Behavior Selection System
The proposed behavior selection system is achieved byincorporating the Markovian emotional model described in
section 2 into a general action selection system. The overall
structure of the system can be shown in Fig.2. The system
consists of five modules, cognition, database, emotion,
behavior selection, and behavioral system. The cognition
module performs recognition and mapping of theenvironment. The map created by the cognition module is

4 Simulation
The simulation was performed with a simulator which
we developed independently. The purpose of the simulation
is to identify an unknown environment by multi-agent. In
the simulation, a mapping method was simplified under theassumption that the robot is able to exact mapping. So, theenvironment was represented by a set of cells, and the robot
transitioned between cells (Fig.3). First, transition matrices
whose value was manually set was used and the proposedsystem evaluated by the result. Second, GA was introduced
to parameter setting, to automatically optimize the behavior
selection.
4.1 Common term
The environment was constituted by “Passage” where
the robot can reach and “Wall” where the robot cannot

Fig. 2. A behavior selection system proposed here

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 3. Environments used for exploration

reach (Fig. 3). The following terms are assumed about
robots: i) The robot gets the local coordinate information;ii) The robot can move to four neighboring cells adjacent to
the location in the action of one step. However, if thedestination is "Wall", it does not change the coordinates; iii)
The robot can perceive eight neighboring cells adjacent to
the location as the information from the sensor; iv) During
the search, the robot creates a local map of its own, andshares the map with other robots exist in the predetermined
distance; v) There is a base point in the environment, therobot starts from the base point, and then returns to it; vi)
The robot consumes battery by moving, it cannot do any
behavior if there is no amount of battery. When all robots
return to the base point and all passages in the environment
are visited simulation will be terminated.
Each robot selects one of the following four behaviors“search”, “confirmation”, “wait” and “return”, anddetermines the destination based on concepts of the
behavior. The “search” is an action which sets the mostvaluable frontier area to destination by referring to the map.
At this action, the robot does not select the area which isalready mapped by other robots. The worth exploring of thearea is determined depending on the distance and thenumber of unmapped areas around it. The “confirmation”
sets the area that the robot has not done mapping in its own
to destination even if the area has already been mapped by
the other robots. The “return” means to return to the basepoint by the shortest route which is derived from the map.
The robot learns the knowledge about the environment by
creating a local map. By doing this, it is possible tocalculate the shortest route to the target by using a graphsearch algorithm.

(a)(b)Behavioral state vs step

Fig. 4. Relationship between emotional state and behavioral state

described. In these simulations, Q and A were set manuallyby trial and error. The emotional state transition of one
robot out of three in simulation for narrow environment isshown in Fig. 4(a), and the behavioral state transition isshown in Fig. 4(b). By comparison of state transitions of
the emotion and the behavior, it is apparent that theemotional state was reflected to the behavior selection. For
example, in the vicinity of step 50, the behavioral statechanged from “search” to “confirmation” with increasing
the probability of the "Fear". The reason for this is because
the robot had finished mapping of all areas in the vicinity ofhim, and changed the target to the area that was allocated tothe other robots.
Next, the simulation was performed for the wider
environment. In this case, the robot needs returning to thebase point during one trial, for charging battery. At this time,
the number of visited areas by each robot was 33, 34, 33,
respectively, and this result shows that three robots sharedtarget areas equally.
These results showed that the Markov emotional model
worked properly.

4.2 The identification simulation of the unknown
environment by the proposed method
The simulation which deals with the task for
identification of unknown environment by three robots
using the proposed method was performed. First the resultof simulation for the narrow environment (left in Fig. 3),
and then for the wider environment (right in Fig. 3) are© ISAROB 2013

Emotional Intensity vs step

4.3 Parameter setting by GA
Next, the transition matrix A and Q were optimized inproposed system by GA. 80 individuals were used. Fiveenvironments with different sizes and structures were

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prepared, the four was for learning and the other was fortest. Fitness was determined by degree of achievement, thetime required for attainment, and the consumption of thebattery and such. So the higher the fitness became, the lessthe energy consumption and the amount of time spent by
the end of the task were.
The result performed under five environments using
excellent individual parameters in the last generation wascompared with the result of the parameters set manually.
Fig. 5 shows the average value of the number of steps
required to finish the task in 100 trials each. As a result, it
was confirmed that the methods for determining the
behavior using the parameters optimized by GA was better
than those of manual parameter setting.
Then the change in the number of areas which werealready visited was observed in the environment 1. As amethod for comparison of the proposed method, the searchalgorithm (named “conventional”) was prepared, that
determines behavior sequentially from the number of areas
not visited yet. Fig. 6 shows the changes in the average
number of visited area of 100 trials, respectively, for two
types of the proposed methods (settings of parameters by
GA or manually) or the conventional method. Stagnation of
the change in the graph results from the interruption of thetask due to the charging of the battery. Comparing steps
required to map all the 149 passages, it is clear from thegraph that the proposed method using GA is the smallest.
Observing the behavior of robots during the simulation in
the conventional method, we found there were inefficient
behaviors, for example, multiple agents charged batteries atthe same time or set the unreachable area as a destination.
On the other hand, in the proposed methods, the robots
charged batteries in a timely manner and shared the roles
efficiently.

Fig. 5. Average step

Fig. 6. Average number of areas visited vs stepsophisticated methods of robot’s behavioral plan thansetting the parameters manually.
In this study, the basis of evaluation was such factor as
the search time and so on, however there are various
optimal solutions depending on the purpose of the task. By
setting the fitness function in line with objectives of thosetasks, it is possible to automatically design the methods fordetermining the behavior of agents in the proposed system.
The basal parameters optimized by genetic algorithm weretreated as the parameters that were common to all agents.
As future works, we think it is possible to find a new
optimal solution by giving the different personality todifferent agent which may be realized by performing
different parameter settings for each agent.

5 CONCLUSION
In this paper, we proposed a method of a robot’s
behavioral plan using a Markovian emotional model for
identifying an unknown environment by multi-agent. The
simulation was performed with the simulator which wehave developed independently. The results showed thatvalid action decisions were made by the proposed system.
The policy of robot’s behavioral decision depends on theparameters being included in basal parameter matrix Q and
A. By adjusting these parameters, it is possible to makevarious plans for determining the robot’s behavior. The
optimization of the parameters was performed through theuse of genetic algorithm. From the results, it became clearthat the optimization of the parameters by GA led more

© ISAROB 2013

REFERENCES[1] J.Moren, C.Balkenius (2001), A computational Model of Emotional
Learning in the Amygdla, Cybernetics and System32(6), pp.611-636[2] Even Daglarli, Hakan Tameltas, Murat Yesiloglu (2009), Behavioral
task processing for cognitive robots using artificial emotions,
Neurocomputing 72, pp.2835-2844[3] Christopher P. Lee-Johnson and Dale A. Carnegie, Senior (2010), :"Mobile Robot Navigation Modulated by Artificial Emotions",
CYBERNETICS, VOL. 40, NO. 2, pp.469-480[4] Francois Michaud (2002), EMIB-Computational Architecture Basedon Emotion and Motivation for Intentional Selection and Configuration of
Behavior-Producing Modules”, Cognitive Science Quarterly[5] Sajal Chandra Banik, Keigo Watanabe, Kiyotaka Izumi (2008),
Improvement of group performance of job distributed mobile robots by anemotionally biased control system”, Artificial Life Robotics(12), pp.245249

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Design of a user-support system for finding the other person talking
on smart phones
Hiroyuki Nishiyama1 and Fumio Mizoguchi1
1

Faculty of Sci. and Tech. Tokyo University of Science
2641 Yamazaki, Noda-shi, CHIBA, 278-8510, Japan(Tel: +81-47122-1106; Fax: +81-47122-1106)(hiroyuki@rs.noda.tus.ac.jp, mizo@wisdomtex.com)

Abstract: In this paper, we have designed a user-support system for finding the other person using smart phones. When usersof this system talk over smart phones, it displays a map on the screen showing locations of both talkers as they share position
information (GPS information) via a support server. Using this system, the user can check where to wait for other persons. It alsoenables the user to search for a lost child or to search for an illiterate person who wandered off and can’t return. In our system,
the position of searchers is also displayed on the terminal of the person being searched for, so the child or illiterate person cancheck the searcher’s position on the screen of the smart phone in real time while talking.
Keywords: User Support System, Smart Phone, GPS
1

INTRODUCTION

on the screen of the smart phone in real time while talking.
In this research, we designed a function to manage the position information using the smart phone’s GPS, a function
to manage a telephone call, and a support server that enables
information sharing between smart phones. The function to
manage a telephone call records the telephone number of thecaller and the called persons. The support server recognizes
the connection as a key for the telephone number informationand determines the positions by sharing GPS information. A
map showing the caller’s position and that of the called person is then displayed on the screens of the smart phones during the telephone call.

Recently, many multifunctional cellular phone terminals,
such as smart phones (e.g., Androids and iPhones), havebeen developed as a result of the evolution of the computer,
network infrastructure, and lightweight battery technology.
Thus, the number of users is rapidly increasing [4]. The
smart phone is equipped with various sensor systems (e.g.,
GPS, an acceleration sensor and an infrared sensor). Furthermore, easily programmed applications acquire input fromeach function. Various research activities and services areprovided by a smart phone using such features [1, 2, 3, 6].
Examples are the study of service applications using the GPSfunction such as car navigation systems and store searching
services, so various location-based services have been developed [6]. In addition, one service can locate the person being talked to on a map through advanced mutual registration
among users. These services are realized by making maximum use of the GPS function in a smart phone, but the telephone call function is not utilized. A user checks where theother person on the phone or a telephone call location is after
completing the telephone call.

2

In our study, we use HTC Desire SoftBank X06HT as asmart phone (Android phone). The smart phone is similar toa small laptop personal computer. However, a smart phone
has a telephone call function, and it is easy to carry because
it is small and light. Its display is rather small, and it does nothave a keyboard. Moreover, the development of an Android
smart phone is comparatively easy because the development
language basically conforms to Java in the Android application. In our research, we regard a smart phone as a general
portable computer terminal that has a telephone call function,and enable cooperation with the various personal computer
terminals that the user uses.

We have therefore designed a user-support system forfinding the other person using smart phones. When usersof this system talk over smart phones, it displays a map on
the screen showing locations of both talkers as they shareposition information (GPS information) via a support server.
Using this system, the user can check where to wait for otherpersons. It also enables the user to search for a lost child or
to search for an illiterate person who wandered off and can’t
return. In our system, the position of searchers is also displayed on the terminal of the person being searched for, so
the child or illiterate person can check the searcher’s position© ISAROB 2013

SMART PHONE

2.1 Communication Facility of the Smart Phone
Some kinds of communication facilities can be used withthe smart phone. The wireless LAN is a wireless communications function that can communicated at a maximum speedof 54Mbps, which the computer terminal uses. Its use requires a connection to a wireless LAN router. 3G high speed

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013is basically a communication facility intended for the cellular phone, and the wireless communications function cancommunicate at a speed of 7.2Mbps or less. Its use requires
a contract with the communication enterprise, and the costcorresponds to the wire traffic. Bluetooth is a wireless communications function that connects to a computer terminalby a PtoP connection and can communicate at a maximum
speed of 2.1Mbps. Connection requires paired setting with aconnected terminal (only once).
2.2

Internet

Support Server
・Coincidence ofthe number
・Mutual demand

Start Service

API for Telephone Use

By using API [5], the Android application can call anotherphone and recognize the use state of its own phone. Additionally, API (android.telephony.TelephonyManager) dynamically identifies calling and receiving, and the following
information can be collected.

Fig. 1. Illustration of using our systemuse android API (com.google.android.maps.*), which displays its location information on a map easily in combination
with a Google map.

• Ringing State (CALL STATE RINGING):

3

The ringing state is the state by which a telephone callis received. The calling person’s telephone number canbe identified.

When the telephone receiver is raised in response to areceived call, the ringing state changes to the off-hookstate. It can be assumed that the smart phone is calling
another phone when the idle state changes to the offhook state.
• Idle State (CALL STATE IDLE)：
A change from the ringing state to the idle state of thestandby mode means no response to a call. A change
from the off-hook state to the idle state means the telephone call ended.
The API is a function basically intended for receiving;when sending, not much information can be obtained. The
obtained information involves calling and ending only, andthe telephone number and whether it was possible to talk over
the telephone cannot be confirmed. Therefore, it is necessary
to acquire information about the calling telephone number
and the calling person’s response after the end of telephone
call by using the API (android.provider.CallLog.Calls) to acquire the vita information prepared for the Android phone.

• The Telephone Monitor Module monitors the transmission and reception of a telephone. This monitor informs
the User Support Module when a telephone call is transmitted or received; it also informs the User Support
Module when the call is terminated.

GPS Function and API of the Android Phone

The Android phone has a GPS function and a variety
of other sensor systems (e.g., acceleration sensor, brightness sensor, and temperature sensor). GPS and each sensor can confirm a change of sensor value by using thesensor manager API (android.location.LocationManager, android.hardware.SensorManager, etc.). It is also possible to© ISAROB 2013

SYSTEM ARCHITECTURE

In our research, we designed a user-support system forfinding the other party using smart phones. When users of
this system talk over smart phones, the system displays a mapon the screen showing locations of both talkers as they shareposition information (GPS information) via a support server(such as Fig. 1). Using this system, the user can determine
where to wait for other persons. In our system, the positionof searchers is also displayed on the terminal of the person
being searched for, so the user and other person can checkeach other’s position on the smart phone screen in real time
while talking.
In this research, we designed a function to manage the position information using the smart phone’s GPS, a function
to manage a telephone call, and a support server that enables
information sharing between smart phones. The function to
manage a telephone call records the telephone number of thecaller and the called persons. The support server recognizes
the connection as a key for the telephone number informationand determines the positions by sharing GPS information. A
map showing the caller’s position and that of the called person is then displayed on the screens of the smart phones during the telephone call.
Figure 2 depicts the structure of the user-support system
for finding the other party using smart phones. The modules
in this system have the following roles.

• Off-hook State (CALL STATE OFFHOOK):

2.3

・Telephone number
・Location information• The User Support Module transmits a request for mapsharing service to the Support Server by using the457

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User Support
ModuleGPS Monitor
ModuleCommunication
ModuleTelephone Monitor
ModuleMap Viewer
ModuleInternet

4

Communication
ModuleIn order to achieve the system depicted in Fig. 2, we implemented each module on the smart phone (Android phone)
and a server machine. Each module in the Android phone canbe implemented using Android SDK [5] via a computer terminal. Since the programming language is basically similar
to Java, we used Java language for implementing all modules.

Number Check
ModuleShare Location
Module

4.1
Smart Phone

User support tool on the Android Phone

The telephone monitor module dynamically monitors thetelephone call using the Android telephone API described in
section 2.2. When the situation changes, this module sends
information to the user support module with additional information (e.g., telephone number).
The GPS monitor module dynamically monitors the location of the smart phone using the Android GPS API describedin section 2.3. This module checks location information (latitude and longitude) for every second.
Based on the caller’s and called party’s location information, the map viewer module creates a map and displays it on
the smart phone screen. Map information is created and displayed using the Android GPS API described in section 2.3.
The map scale is automatically adjusted so that both parties
in the telephone conversation can be indicated on the map.
The user support module consists of the state change rule
for starting map sharing service automatically according to
the use situation of a user’s telephone. By recognizing telephone call transmission or reception, this module requests the
support server to initiate the map-sharing service through thecommunication module. In addition, in the present configuration, the user support module judges whether the service isused for the caller her/himself. After service starts, this module shares location information with the other party throughthe communication module and the support server.
The communication module exchanges messages with thesupport server using socket communication (a smart phone
becomes the client side of socket communication). This communication can use either Wi-fi or 3G communications.

Support Server

Fig. 2. Structure of the user-support system for finding theother party talking over smart phones

caller’s and the called person’s telephone number as a
key when transmission or reception information is received from the Telephone Monitor Module. Duringmap-sharing service, the partner’s GPS information isreceived from the Support Server, which it then transmits to the Map Viewer Module. This module receives
the caller’s GPS information from the GPS Monitor
Module and transmits it to the Support Server. This
module terminates map-sharing service when the telephone call is terminated.
• The GPS Monitor Module continues monitoring own
GPS location information during map-sharing service.
When location information changes, this module reports
the change to the Map Viewer Module and the User Support Module.
• The Map Viewer Module displays a circumference map
during map sharing service, and displays the location of
the caller and the called party on the map. This module
also displays the distance brtween the parties.
• The Communication module functions as an agent between the Smart Phone and Support Server through theInternet.

4.2

Support Server on a Server Machine

The number check module compares the telephone numbers of the caller and the called parties and uses the telephone
numbers to specify the intended user. This is possible because the telephone monitor module can identify both thecaller’s and the called party’s telephone number.
The share location module exchanges position information over the smart phones identified by the number check
module. This function terminates when the telephone callends.
The communication module exchanges messages withthe support server using socket communication (the Support
Server becomes the server side of socket communication).

• The Number Check Module receives the request fromthe User Support Module of the Smart Phone in the
Support Server. This module determines which Smart
Phone made the telephone call by detecting the combination of the caller’s called party’s telephone numbers
and starts the map-sharing service.
• The Share Location Module exchanges location information between the Smart Phones engaged in the telephone call during map-sharing service.

© ISAROB 2013

SYSTEM IMPLEMENTATION

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Other
personUser

Display distance(a) Start(b) moving(c) Finding and meeting

Fig. 3. Flow of our system. The users can check the location of the other party on the map while talking and moving, until theyfind each other and meet.
4.3

Flow of User Support via Map Sharing

caller and the called persons. The support server recognizes
the connection as a key for the telephone number informationand determines the positions by sharing GPS information. A
map showing the caller’s position and that of the called person is then displayed on the screens of the smart phones during the telephone call. It also enables the user to search for alost child or to search for an illiterate person who wandered
off and can’t return. In our system, the position of searchers
is also displayed on the terminal of the person being searched
for, so the child or a mentally incapacitated person can checkthe searcher’s position on the screen of the smart phone inreal time while talking.

By using our user support system, the called party can access the map sharing service during the telephone call as infigure 3. Figure 3 depicts using our user support system tomeet a new enrollee at the main front gate of our university.
At this time, the new enrollee has arrived at another gate instead of the main gate (Fig3(a)). The user talks to the enrollee
over the telephone and moves around the campus (Fig. 3(b))
until finding and meeting the enrollee (Fig. 3(c)).
Figure 3 is displayed on both caller’s and the called party’s
smart phone, so the called party can confirm that the user
her/himself approaches talking over the telephone. Moreover, the separation distance can also be displayed, so it ispossible to estimate the time of meeting. In general, suchservice is useful and also enables the user to search for a lostchild or for a mentally incapacitated person who has wandered off and can’t return by themselves.
5

REFERENCES[1] H. Nishiyama and F. Mizoguchi: Design of a User-Support
System for Vision Information Using Smart Phone, International Journal of Artificial Life and Robotics, 2012 (to appear).
[2] H. Nishiyama and F. Mizoguchi: Design of a Cognitive UserSupport System for Skin Progress Analysis Using a Smart
Phone, The 11th IEEE International Conference on COGNITIVE INFORMATICS & COGNITIVE COMPUTING:ICCI*CC 2012, pp.203-210, 2012.

CONCLUSION

In this paper, we have designed a user-support system forfinding the other person using smart phones. When users of
this system talk over smart phones, it displays a map on thescreen showing locations of both talkers as they share position information (GPS information) via a support server.
Using this system, the user can check where to wait for otherpersons. In our system, we designed a function to manage theposition information using the smart phone’s GPS, a function
to manage a telephone call, and a support server that enables
information sharing between smart phones. The function to
manage a telephone call records the telephone number of the© ISAROB 2013[3] H. Nishiyama and F. Mizoguchi: The Design of Personal
Support System in Telephone Correspondence using Smart
Phone, 16th International Symposium on Artificial Life andRobotics 2011, pp.745-748, 2011.
[4] Mobile Content Forum: Smart Phone White Paper 2012, Impress R&D, 2012. [in Japanese][5] Android SDK Guide， http://www.android.com/[6] Sekai Camera: http://sekaicamera.com/

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Epilepsy Diagnosis Using PSO based ANN
Nesibe Yalçın1, Cihan Karakuzu1, Gülay Tezel2
1

Bilecik Şeyh Edebali University, Turkey
2
Selçuk University, Turkey(Tel: 90-228-214-1412)
1

nesibe.yalcin@bilecik.edu.tr

Abstract: Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very usefulclinical tool in classification of epileptic attacks and epilepsy diagnosis. In this paper, epilepsy diagnosis by evaluation of EEG
records is presented. Artificial Neural Networks (ANN) is used as a classification technique. Particle Swarm Optimization(PSO) method, which doesn't require gradient calculation, derivative information and any solution of differential equations ispreferred for ANN training. This training method is compared with back propagation algorithm, which is one of the traditional
methods, and the results are interpreted. In case of using the PSO algorithm, the training and test classification accuracies
are %99.67 and %100, respectively. PSO based neural network model (PSONN) has a better classification accuracy than backpropagation neural network model (BPNN) for epilepsy diagnosis.
Keywords: Artificial neural networks, back propagation algorithm, EEG, epilepsy diagnosis, particle swarm optimization.
In recent years, there have been lots of studies using
artificial intelligence techniques to recognize EEG signals.
ANN as an artificial intelligence technique is usedfrequently in the classification of EEG signals. In addition
to these techniques, heuristic optimization methods are used
to increase the success, performance and / or the speed ofthese techniques. PSO which is one of the heuristic
methods is successfully applied to the training of ANN.
Because number of PSO parameters is small, PSO studies
with real numbers, its realization and implementation areeasy [4].
In this work, we aimed to able to diagnose epilepsy
whether evaluate automatically EEG records with PSO
based ANN. For this purpose, EEG signals taken fromepileptic and healthy volunteers are normalized and thenclassified with ANN. PSO algorithm is used for training of
ANN (PSONN) and performance of PSONN is comparedwith traditional ANN trained using back propagation
algorithm (BPNN).
Following this introduction section, the rest of the paper
is organized as follows: in the next section, materials andmethods used in this study have been mentioned. In thethird section, the ANN's trained with back propagation
algorithm and the PSO method are emphasized. The fourth
section provides information on the applications. The
obtained results are presented with tables and thecomparisons. The final section summarizes and concludes
the results.

1 INTRODUCTION
Epilepsy disease is a major brain disorder. Particularly,
waveforms contained in EEG recorded during theoccurrence of epileptic discharges can show similarity withwaveforms of some other diseases occurring in the brain.
So epilepsy disease cannot be detected easily [1]. EEG
signals as shown in Fig. 1. and Fig. 2. are not periodic; theirfrequency, phase and amplitude continuously change. Thechanging forms of EEG signals are very complex anddifficult to define and interpret [2], [3].
1000
500

EEG signal

0

-500
-1000

-1500

-2000

0

100

200

300
Sample

400

500

600

Fig. 1. Example of EEG signal for an epileptic person
100

EEG signal

50

0

-50

-100

0

100

200

300
Sample

400

500

600

Fig. 2. Example of EEG signal for a healthy person© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2 MATERIAL AND METHODS
2.1 EEG dataset
In this study, we used publicly available EEG data
which is described in [5]. The complete data consists of five
sets: Set A and Set B include data that has been taken
healthy volunteers with eyes open and closed respectively.
Set C and Set D taken from people, having epilepsy disease
contain activities which has been measured in the interval
has no attack. Set E includes only epileptic attack activities[5, 6].
Set A and Set E have been used in this work. Dataset
consists of 1600 segments (800 segments for each class(epileptic and healthy)) and 512 samples for each segment.
Dataset has been pre-processed using statistical features
which are minimum, maximum, mean and standard
deviation of each sample and thus number of samples ineach segment has been reduced to 4. Then the new dataset
has been normalized into the interval of [0, 1].

Fig. 4. Backward computing schematic structure (transpose
network)
In Fig. 3., X1 and X2 are inputs; C1, C2 and C3 are
output vectors of the layers. In Fig. 3. and Fig. 4., W1 andW2 are weight matrixes; W3, θ1, θ2 and θ3 are bias
vectors; values of E1, E2 and E3 biases are chosen as 1.
NET1, NET2 and NET3 are net input vectors for the related
layer. sigmoid activation function φ is preferred for all
neurons. φ' is derivative of the activation function. δ1, δ2and δ3 are local gradient vectors.

2.2 Neural network learned by back propagation (BPNN)
Back propagation [7], is one of error correction
algorithms, is used in multi layer and feed forward ANN's
training. A multi-layer back propagation network includes
an input layer, at least one hidden layer and an output layer.
Back propagation algorithm is a supervised learning
method and aims to optimize weights and biases between
input layer and the output layer depending on output errorof the network. Input vector is given to input layer, and thenreaches to the final output layer after passing throughhidden layers. Each neuron in the network transmits theresult to the all neurons of the next layer after receiving thearithmetical addition of the weighted signal from theprevious layer's neurons, depending on the activation
function.
ANN's training by back propagation is implemented
consistently forward computing seen in Fig. 3. andbackward computing given in Fig. 4.

2.3 Neural network learned by PSO (PSONN)
Particle swarm optimization (PSO) which is a heuristic
method based on social behavior of bird flocking wasdeveloped by Eberhart and Kennedy in 1995 [8].
PSO algorithm starts with a set of random particles(candidate solutions for the problem) and then searches foran optimal solution updating its individuals by generations.
In each generation, each particle is updated based on two
special particles. The first one called "pbest" is the personal
best particle for each particle found so far. The other one
called "gbest" is a global best particle found so far by any
particle in the swarm (population). Fig. 5. illustrates
position and velocity updating of a particle.

Fig. 5. The velocity and position updating of a particle
Fig. 3. Forward computing schematic structure© ISAROB 2013

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In Fig. 5., vij and xij variables are respectively the jth(j=1, 2, ..., D) velocity component and the jth position
component of ith (i=1, 2, 3, …, N) particle at generation k.
N is the number of particles in the swarm. D is dimension
size of the search space. In an improved PSO version, thevelocity updating and the position updating are determinedby (1) and (2), respectively. In these equations, r 1 and r2 are
two random numbers uniformly distributed in the range of(0, 1). c1 and c2 are acceleration constants. w is inertia
weight updated using (3) by generations. α variable in (3) isdecrease factor and used to decrease linearly inertia weight.

fitness value of gbest=min(MSE of all particles)
if stop criteria is not provided thenUpdate pbest and gbest particles
end ifwhile stop criteria (maximum generation number or target
fitness value of gbest) is provided
Adjust weights according to gbest particle
Present testing dataset to ANN
Calculate output

3 EXPERIMENTAL STUDIESIn this work, we used EEG dataset belonging toepileptic and healthy volunteers. Then dataset divided totwo subsets: training dataset which was formed by 1200
segments (600 epileptic) and test dataset which was
prepared as 400 segments (200 epileptic).
PSONN and BPNN models have been developed via
Matlab® Software Package without Neural Network
Toolbox. Training dataset has been used to train these
network models. Each network has been prepared as threelayers: an input layer, a hidden layer and an output layer.
The different number of particles, generations and neurons
in the hidden layer have been used to determine optimum
network model. Optimum numbers of these parameters has
been experimentally obtained as 30, 200 and 3,
respectively.
In order to determine training and test accuracies, theclassification threshold value has been selected as 0.4. If
output value is lower than this value, the classification
result is 0 or healthy else 1 or epileptic.
Initialization values of α and w in (3) are chosen 0.975
and 0.9, respectively [9]. Acceleration constants are
c1= c2=2.1. Limitations are Vmin= -0.1 and Vmax= 0.1. These
values provided fast convergence to the target and increased
the performance of network models.
Sensitivity, specificity and accuracy statistics are usedfrequently to determine the success of a classifier.
Sensitivity is the proportion of data belonging to epileptic
patients; specificity is the proportion of data belonging tohealthy people; accuracy is true classification proportion[10]. These statistical measures are calculated by (7), (8)and (9).

(1)
(2)
(3)
Limitations (Vmin and Vmax) defined in (4) determine theminimum and maximum velocity change of a particle
during one generation. They are used to supply detailed
searching, to allow the particles to escape local minima andto prevent the particles to leave the space research.

(4)

In this study, we used PSO for training of ANN toobtain the best classifier model. During the training phase,
we computed fitness value of each particle using Mean
Squared Error (MSE) by (5) where, S is the number ofsegments in the training dataset, e is the difference between
expected and obtained output after presenting ith segment(data) to the network. Structure of a particle (Pi) of PSO to
train ANN is used as shown by (6).
(5)
(6)
Algorithm's pseudo code is as following.
Initialize values for PSO and ANN
Initialize all weights with random valuesDo(7)for each particle do
Adjust weights according to the particle(8)

Present training dataset to ANN
Calculate MSE of the particle(9)

end for© ISAROB 2013

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best PSONN are too high (approximately 99.67% and100%, respectively). The computed results of sensitivity
analysis of PSONN are 1 in both the training and test
dataset. Thus, it can be said PSONN is more successful than
BPNN. PSONN can be adapted for different medical
diagnosis problems and other PSO versions can be used to
improve the success of the PSO as training algorithm.
This study shows any heuristic optimization method canbe used as a learning algorithm to increase ANN's
classification success.

In the above equations, TP (True Positive) is the total
number of epileptic patients which are correctly diagnosed,
TN (True Negative) is the total number of healthy people
which are correctly diagnosed, FP (False Positive) is thetotal number of epileptic patients which are incorrectly
diagnosed, FN (False Negative) is the total number ofhealthy people which are incorrectly diagnosed.
PSONN is run 30 times for the training set. The
classification accuracies and computed results of sensitivity
and specificity analysis in the training and test dataset for
BPNN and PSONN are given in Table1. The results about
PSONNs are based on the best and worst run among 30
runs. The best, worst and average fitness values changing
by generations are shown in Fig. 6.

REFERENCES[1] Barışçı N, Müldür S (2003), Classification of
epileptic EEG signals using neuro - fuzzy system. Journalof Polytechnic, vol. 6, No:2, pp. 445–449[2] Toprak IB (2007), Analysis of EEG signals using the
wavelet transform and artificial neural network. Süleyman
Demirel University Graduate School of Applied andNatural Sciences, M.Sc. Thesis, Turkey[3] Yazgan E, Korurek M (1996), Medikal Elektronik.
ITU, Istanbul, No:15741, pp. 386[4] Cavuslu MA, Karakuzu C, Karakaya F (2012),
Neural identification of dynamic systems on FPGA withimproved PSO learning. Applied Soft Computing, vol. 12,
pp. 2707–2718[5] Andrzejak RG, Lehnertz K, Mormann F, Rieke C,
David P, Elger CE (2001), Indications of nonlinear
deterministic and finite dimensional structures in timeseries of brain electrical activity: Dependence on recording
region and brain state. Physical Review E, 64[6] Tezel G, Özbay Y (2009), A new approach for
epileptic seizure detection using adaptive neural network.
Expert Systems with Applications, 36 (2009), pp. 172–180.
[7] Rumelhart D, McClelland J (1986), Parallel
Distributed Processing. MIT Press, Cambridge, Mass
[8] Kennedy J, Eberhart RC (1995), Particle swarm
optimization. Proc. IEEE int'l conf. on neural networks,
IEEE service center, Piscataway, NJ, vol. 4, pp. 1942–1948[9] Şevkli M, Yenisey MM (2006), A particle swarm
optimization for the job shop scheduling problems.
itüdergisi/d mühendislik, vol. 5, No: 2(1), pp. 58–68[10] Özbeyaz A (2010), LS-SVM hyper parameterselection Based on PSO in EMG and EEG signals
classification. University of Kahramanmaraş Sütçü Imam,Institute of Natural and Applied Sciences, Msc Thesis,
Turkey.

Table 1. Performance of Comparison Networks
Performance Measures
Best
PSONN

Network Type
Worst
BPNN
PSONN

Fitness value of gbest / MSE
Training classification acc.(%)
Test classification acc. (%)
Training sensitivity
Training specificity

0.004199.6667100
10.22895050
0.5

0.000999.8333
90.7500
0.9967

0.99340

1

Testing sensitivity
Testing specificity

1

0.5

0.84391

01

Training
0.35
the best PSONN of 30 runs
the worst PSONN of 30 runs
avg of 30 runs
BPNN

0.3

MSE/Fitness Value

0.25

0.2

0.15

0.1

0.05

0

0

20

40

60

80

100
120
Generation

140

160

180

200

Fig. 6. Training graphics of PSONN and BPNN

4 CONCLUSION
In this work, PSO and back propagation (traditional)
algorithms are used to train ANN for epilepsy diagnosis.
Comparative results of PSONN and BPNN (traditional
ANN) are presented in Table 1. According to table, training
performance of BPNN is 99.83% and test performance of
BPNN is 90.75%. Training and test classification results of© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The Effect of the Internal Parameters on Association Performance of
A Chaotic Neural Network
Shun Watanabe1, Takashi Kuremoto1, Kunikazu Kobayashi2, and Masanao Obayashi1
1

Yamaguchi University, Japan
Aichi Prefectural University, Japan(Tel: +81-(0)836-85-9005, Fax: +81-(0)836-85-9016)
1{s005we, wu, m.obayas}@yamaguchi-u.ac.jp
2
kobayashi@ist.aichi-pu-u.ac.jp
2

Abstract: The chaotic neural network (CNN) proposed by Aihara et al. is able recollect stored patterns dynamically. But there
are difficult cases such as a long time processing of association, and difficult to recall a specific stored pattern during the
dynamical associations. We have proposed to find the optimal parameters using meta-heuristics methods to improveassociation performance, for example, the shorter recalling time and higher recollection rates of stored patterns in our previous
works. However, the relationship between the different values of parameters of chaotic neurons and the association
performance of CNN was not investigated clearly. In this paper, we analyze how the change of values of internal parameters ofchaotic neurons affects the characteristics of chaotic neurons when multiple patterns are stored in a CNN. Q-Q plot, leastsquare approximation (LSM), hierarchical clustering (HC), and Hilbert transform (HT) are used to investigate the similarity ofinternal states of chaotic neurons, and to classify the neurons. Simulation results showed that the different values of the internalparameter yielded different behaviors of chaotic neurons and it suggests the optimal parameter which generates higher
association performance may concern with the stored patterns of the CNN.
Keywords: chaotic neural network, Q-Q plot, least squares approximation, hierarchical clustering, Hilbert transform.

1 INTRODUCTION

2 CHAOTIC NEURAL NETWORKThe chaotic neural network (CNN) proposed by Aihara
et al. is well-known as a recurrent neural network which isable to dynamically recollect stored patterns [1]. And CNN
is applied to optimization, parallel distribution processing,robotics, and so on. In our previous works, we haveproposed some meta-heuristic methods (e.g. genetic
algorithm, particle swarm optimization) to determine theoptimal parameters of chaotic neurons to realize higher
performance of association memory of CNN [2] [3].
However, the causality between the determined parameters
and the association ability was not investigated. In thispaper, we intend to analyze how the varied internal states ofchaotic neurons are yielded by the different parameters ofneuron dynamics. The spatiotemporal change of the internal
state of each chaotic neuron are observed, and thecomparison of these time series data is given by kinds ofmethods such as Q-Q plot, least squares approximation(LSA), hierarchical clustering (HC), and Hilbert transform(HT). Q-Q plot, LSA, and HC is used to show the similarity
between the dynamical changes of the internal states ofchaotic neurons of CNN. HT is used to show thesynchronization of neurons during association process.
Associative simulation showed the change of the internalparameter affected the characteristics of neurons and the
optimal parameter may concern with the stored patterns.

Aihara et al. ‘s chaotic neural network (CNN) is a kindof interconnected recurrent artificial neural network which
neurons perform chaotic output. The dynamics of a chaotic
neuron i of CNN is defined as follows:© ISAROB 2013

i (t  1)  k f i (t )  i0 wij x j (t )(1)

 i (t  1)  k r  i (t )  xi (t )  a(2)yi (t  1)  i (t  1)   i (t  1)

(3)

N 1

xi (t  1) 

11 e yi ( t 1) (4)N is number of chaotic neurons in network, i (t ) and  i (t ) is internal state for the feedback inputs
and refractoriness, k f and k r is the decay parameter for
the feedback input and the refractoriness, wij is synaptic

Whereweights from jth neuron to ith neuron,  is refractory
scaling parameter, a is threshold, y i (t ) is internal state,xi (t ) is chaotic neuron output,  is steepness parameter.
In this paper, the internal states of the chaotic neurons arelimited to yi (t ), i  0,1,2,...,N  1 and the internal parameterk r is investigated in detail.
Hebb learning rule is used to store patterns in CNN, thatis, the synaptic weights between two arbitrary neurons is
modified as following.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013M 1
 m 0  mi  mj
wij  

0


if

i j

when the distributions of two data are similar. The
similarity of the internal state distributions of neurons isevaluated by LSA here.

otherwise(5)

where   1 / M is learning coefficient,  mibipolar value of mth pattern, m  0,1,2,...,M  1 .

3.3. Hierarchical clustering
Hierarchical clustering (HC) is a clustering method that
iteratively merges clusters in higher similarity. The method
creates the clusters that have a tree-like structure. In thispaper, the nearest neighbor method is used in HC andEuclidean distance as the degree of similarity of the internal
state is used to classify the chaotic neurons. Shorter
Euclidean distance represents higher similarity between two
clusters. In this paper, the quantitative values calculated by
LSA in Q-Q plot between each chaotic neuron and all areused in HC. So the similarity between the internal state
distributions of each neuron are able to be represented in
the tree-like structure of HC.

is ith3 FEATURE EXTRACTION
In this paper, Q-Q plot, least squares approximation(LSA), hierarchical clustering (HC), and Hilbert transform(HT) are used for analyzing the characteristics of the
dynamical association in CNN. Firstly, Q-Q plot is used tocompare the distribution types of each chaotic neuron
internal sates in CNN. Secondly, the curve plotted by Q-Q
plot is approximated to a function by LSA. Finally, theapproximation errors and approximated parameters are
clustered by HC, and the clustered data as the similarity
between neurons is shown. Additionally, HT is used toobserve the synchronization between internal states ofchaotic neuron because the synchronization depends ontime, is not able to be represented with the internal state
distribution of each chaotic neuron.

3.4. Hilbert transform
Hilbert transform (HT) is a transform technique tocalculate the complex signal from an observed time series
signal. In this paper, the complex signal is transformed
from the internal state time series signal of each chaotic
neuron in CNN. And the synchronization between neurons
is observed with the complex signals. The method of HT is
defined as follows:

3.1. Q-Q plot
Q-Q plot is a graphical method to represent thesimilarity and it’s characteristics between the distributionsof two data sets. When the elements of a data set are sorted
in ascending order, are plotted on X-dimension in order,
and the elements of another data set are plotted on Ydimension either, then the correlation and the similarity ofthe two data sets are easily to be observed. If the plottedgraph has linearity, it means that their data distributions are
similarity. On other hand, if the graph has nonlinearity, itmeans that their data are not similarity. And the data thoseare equal to the other, are plotted on a line of the X-Y plane,i.e., a linear function that consists of one gradientcoefficient and one bias. In this paper, similarities of allobserved internal states of chaotic neurons in CNN are
investigated using Q-Q plot.

(6)yi ()  Reyi ()  I Imyi ()(7)y'i ()  Imyi ()  I Reyi ()

(8)

1

y'i () FT yiH (t )(9)where yi (t ) is an observed signal, FT is Fourier
1transform, I is the imaginary unit, FT is inverse Fouriertransform, yiH (t ) is the signal of HT.
The phase difference i (t )   j (t ) of two signals( yi (t ), y j (t ) ) is calculated by the following equation:
i (t )   j (t )  tan 1

3.2. Least squares approximation
Least squares approximation (LSA) is a method to find
an approximate fitting function using samples of data set. In
this paper, linear least square as a LSA method is used toquantitatively measure the plot graph (the sorted data set) in
Q-Q plot mentioned in 3.1 section. A polynomial equationof the first degree (linear function) as an approximation
function is used to measure the similarity (linearity)
between the distributions of two data in Q-Q plot. The
advantages of linear function are that squares error is less

© ISAROB 2013

yi (t ) FT yi ()yiH (t ) y j (t )  yi (t ) y jH (t )yi (t ) y j (t )  yiH (t ) y jH (t )(10)

4 COMPUTATER SIMULATIONS
The features of the internal states in CNN are observed
when the value of the internal parameter k r of CNN isdifferent ( kr  0.9, kr  0.8 ). Other parameters of theassociative model are set as N  100 (10x10 network), i (0)  0 , i (0)  0 , k f  0.2 ,   10 , a  2 ,  0.015 ,   0.25 . The stored patterns in CNN are“cross”, “star”, “triangle”, and “wave” as shown in Fig.1.
“initial” in Fig.1 means the initial state of CNN in

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(b) k r  0.8

recollection process. The internal states of chaotic neuronsin CNN were observed from t  2048 to t  4095 duringa recalling.
The HC results of the case k r  0.9 (which is thevalue used in the original paper [1]) and the case k r  0.8(which is an optional value used in this simulation) are
shown in Fig.2 (a) and (b) respectively.

Cluster’s No.
C0
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15

“cross”“star”“triangle”“wave”“initial”
Fig. 1. Stored patterns and initial pattern (width=10,height=10, black: mi  1 , white:  mi  1 ).

(a) k r  0.9

Neuron’s No.
14, 15, 86
71, 72, 77, 78, 81, 87
7, 42, 51, 60, 61
23, 32, 45, 55, 66
46, 47, 52, 57, 84, 95
12, 17, 22, 27, 64, 65, 76
8, 9, 11, 18, 19, 21, 80
5, 79, 83, 92, 93, 94, 96, 97
26, 35, 36, 54, 62, 63, 67, 73
6, 29, 38, 39, 40, 41, 48, 49, 50
2, 13, 69, 74, 75
82, 88, 89, 91, 98, 99
16, 24, 25 53, 58, 68, 85
3, 4, 20, 30, 31, 59, 70
33, 34, 37, 43, 44, 56
0, 1, 10, 28, 90

The phases between the neurons in the same cluster
showed no any difference ( i (t )   j (t )  0.0 ). Fig. 3 showssome examples of the phase comparison using neuron
No.23 in C0 via neuron No. 32, 45, 55, and 66 (The firstraw in Table 1 (a) where k r  0.9 ), and No. 14 via No. 15,
and 86 (The first raw in Table 1 (b) where k r  0.8 ). So
the value of parameter k r did not affect thesynchronization of the neurons in the same cluster.
Fig. 4 shows the phase differences between the cluster
C0 and other clusters (C1-C15). In Fig.4, the horizontal
axis indicates the time of recollection, the vertical axis is
the phase difference, and the depth axis corresponds todifferent clusters (from C1 to C15). So we can find that thephase difference happened between the neurons of different
clusters. In other word, it can be conclude that the value ofparameter k r affected the synchronization of the neurons in
the different clusters.

(b) k r  0.8

Fig. 2. The results of hierarchical clustering (HC).

In Fig. 2, 100 neurons are plotted on the vertical axis
which orders are according to their clusters. When the
distance between the clusters (horizontal axis) is the pointwith arrows in Fig.2 (a) and (b), the numbers of clusters are
the same16, but neurons in those clusters are different when
the parameter k r  0.9 and k r  0.8 . Neurons in 16
clusters yielded by the different parameters are listed inTable 1 (a) and (b) respectively.
Table 1. The results of clustered neurons which internal states were similar during recollection using HC.
(a) k r  0.9
Cluster’s No.
C0
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15

© ISAROB 2013

Neuron’s No.
23, 32, 45, 55, 66
14, 15, 86
33, 34, 37, 43, 44, 56
3, 4, 20, 30, 31, 59, 70
0, 1, 10, 28, 90
16, 24, 25 53, 58, 68, 85
8, 9, 11, 18, 19, 21, 80
12, 17, 22, 27, 64, 65, 76
46, 47, 52, 57, 84, 95
82, 88, 89, 91, 98, 99
2, 13, 69, 74, 75
7, 42, 51, 60, 61
71, 72, 77, 78, 81, 87
5, 79, 83, 92, 93, 94, 96, 97
6, 29, 38, 39, 40, 41, 48, 49, 50
26, 35, 36, 54, 62, 63, 67, 73(a) k r  0.9(b) k r  0.8
Fig. 3. The phase differences between the neurons ofcluster C0.

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value of the internal parameter. The chaotic neurons wereclustered according to their similarity of internal states, andthe characterization seemed affected by the parametervalues. So these results support our previous works whichsuggested the importance of the optimal parameter for the
dynamic association model CNNs.
Table 3. Recalling times of stored patterns of each CNN.
CNN
“cross”“star”“triangle”“wave”
Total(a) k r  0.9

6

2

8

14

30

k r =0.8

0

7

12

0

19

Table 4. The causality between elements combinations of sto
red patterns and clustered neurons.
Neuron’s output co
Clust Clu
rresponding to theerster
stored patterns
Neuron’s No.
( kr
( kr
(cross, star, triangl
0.9)
0.8)
e, wave)26, 35, 36, 54, 62,-1, -1, -1, -1
C15
C8
63, 67, 73
71, 72, 77, 78, 81,-1, -1, -1, +1
C12
C1
87
33, 34, 37, 43, 44,-1, -1, +1, -1
C2
C14
56
12, 17, 22, 27, 64,-1, +1, -1, -1
C7
C5
65,76
16, 24, 25 53, 58, 6+1, -1, -1, -1
C5
C12
8, 85
82, 88, 89, 91, 98,-1, -1, +1, +1
C9
C11
99
8, 9, 11, 18, 19, 21,-1, +1, -1, +1
C6C680-1, +1, +1, -1
23, 32, 45, 55, 66
C0C3+1, -1, -1, +1
14, 15, 86
C1
C0
46, 47, 52, 57, 84,+1, -1, +1, -1
C8C495+1, +1, -1, -1
2, 13, 69, 74, 75
C10C10-1, +1, +1, +1
0, 1, 10, 28, 90
C4C15
5, 79, 83, 92, 93, 9+1, -1, +1, +1
C13C74, 96, 97
3, 4, 20, 30, 31, 59,+1, +1, -1, +1
C3
C13
70
+1, +1, +1, -1
7, 42, 51, 60, 61
C11
C2
6, 29, 38, 39, 40, 4+1, +1, +1, +1
C14C9
1, 48, 49, 50(b) kr  0.8
Fig. 4. The phase difference between the neurons of different
clusters.

The maximum Lyapunov exponent of CNN withk r  0.9 was 0.290, and CNN with k r  0.8 was 0.245.
Both values of them were positive, nearby zero. Thesemaximum Lyapunov exponent suggested that either the
CNN with k r  0.9 or the CNN with k r  0.8 were able togenerate chaotic associative process, that is, dynamical
attracts were available to appear. Table 3 shows therecalling times of the different stored patterns. The CNN
with k r  0.9 which was used in [1] had a higher recallingrates.
Furthermore, to investigate the relationship between thestored patterns and the neuron spatial positions, we listed
the output of neurons in 16 clusters, and it was interested
that the number of clusters (i.e. 16) equaled to the numberof the combination of stored patterns (see Table 4). For
example, neuron No. 23, 32, 45, 55, and 66 in C0 whenk r  0.9 and in C3 when k r  0.8 , output “-1” when stored
pattern “cross” was recalled. “+1”, “+1”, “-1” were output
corresponding to the recalling of “star”, “triangle” and“wave” respectively.

REFERENCES[1] Adachi M., Aihara K. (1997), Associative dynamics in achaotic neural network, Neural Networks, 10(1):83-98[2] Kuremoto T., Watanabe S., Kobayashi K., Feng. L.-B., andObayashi M. (2011), Dynamical recollection of interconnectedneural networks using meta-heuristics (in Japanese), IEEJ
Transactions on Electronics, Information and Systems,
131(8):1475-1484[3] Kuremoto T., Watanabe S., Kobayashi K., Feng. L.-B., andObayashi M. (2012), Dynamical recollection of interconnectedneural networks using meta-heuristics, Electronics andCommunications in Japan, 95(6):12-23

5 Conclusion
In this paper, we investigated the effect of the internalparameter of CNN on the association process. The
spatiotemporal change of the internal states of neurons wasused to show the difference yielded by the change of the© ISAROB 2013

k r =0.9

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

An estimating method for missing values by using multiple SOMs
Yuui Kikuchi1, Nobuhiro Okada1, Yasutaka Tsuji1, and Kazuo Kiguchi1
1

1

Kyushu University, Japan(Tel: 81-92-802-3167)

2TE11313R@s.kyushu-u.ac.jp

Abstract: Recently, development of information technology grows the importance of data analysis. In actual data, however,instances will sometimes miss some of their values. Then, how to deal such missing values has become one of the important
subjects. Estimating and completing the missing values is required when analyzing the instances or attributes including thevalues. Using a self-organizing map (SOM) is one of such estimating method. This method is available for nonlinear data. In
the data which lacks instances which are not including missing values, however, it was difficult to estimate such values byusing conventional SOM method. To solve the problem, we propose a new method that uses multiple SOMs for estimating
missing values. To evaluate our method, we performed simulation using proposed and other conventional methods. By thesimulation results, we showed the advantages of our method.
Keywords: Self-Organizing Map, Data Processing, Missing Valueis frequently nonlinear. The same can be said about missing
values estimation. In conventional SOM method, however,
only instances which are not including missing values canbe used for learning the features or the laws of whole data.
Thus, in the data which lacks such instances, it is difficult
to estimate missing values by using SOM.
In this paper, to solve the problem, we propose a new
method that uses multiple SOMs for estimating missing
values. This method can achieve effective estimation evenfor data which lacks instances not including missing values.
Moreover, the system is still available for nonlinear data
because of using SOMs. To evaluate our method, we
performed simulation of estimating the missing values withincomplete data. From the simulation result, we proved that
our method is more effective than some conventional
method including a conventional SOM.

1 INTRODUCTION
In recent years, collection and accumulation of various
data becomes very easy because of development of
information technology. Due to this change, the importanceof data mining is increasing. Data mining is the work
finding the knowledge hid in data. In actual data, however,instances will sometimes miss some of their values forvarious reasons. Then, how to deal such missing values has
become one of the important subjects in data analysis. In
general, there are two methods to deal with them. One is theremoving instances or attributes including the missing
values, and another method is estimating and completingthem. The latter method is required when analyzing the
instances or attributes including the missing values. Such amethod is also applicable to data prediction problems. In
this method, at first, the features or the laws of whole data
are led from a part of data except for the missing values.
Then, the values are estimated basing on the features or thelaws.
Prior research on missing values is manifold. Edgar [1]investigated removing method and some estimating method.
He investigated their influence in data analysis also. Abe etal [2] compared some statistical-analysis techniques toestimate missing values. Using a self-organizing map(SOM) is one of estimating method. For example, Iwasaki
et al [3] studied application SOM to estimating missing
values in earthquake data.
The SOM is an artificial intelligence which is proposedby Kohonen [4], and it is applied to data analysis andvarious field [5]. This method is also available for nonlinear
data. The SOM method is very useful, because actual data

© ISAROB 2013

2 THE ESTIMATING METHOD BY SOM
In this section, we describe the method to estimate
missing values by using conventional SOM. We explain
SOM in the sections 2.1-2.3, and the estimating method in
the section 2.4.
2.1 A summary of SOM
SOM is the algorithm which makes a map reflecting thefeatures of high-dimensional input vectors. The map
consists of neurons latticed in low-dimensional space. Thus,
the position of the neuron on the map is expressed with thecoordinates in low-dimensions. Each neuron has a reference
vector. The reference vectors have same dimensions as
input vectors. In algorithm of SOM, the reference vectors

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

are updated, whenever an input vector is presented. By
repeating updating, a map gradually learns the feature of
input vectors. After sufficient learning, the map reflects thefeatures.

An input vector

2.2 The algorithm of SOM
The algorithm of SOM is described below. For
convenience, a number is assigned to each neuron and i-th
neuron’s reference vector is expressed as mi.
Each neuron is initialized by given an initial value to areference vector. The value is determined at random orbased on the result of principal component analysis (PCA).
When the vector x(t) is given as t-th input (t=1, 2...), a
reference vector is updated in the following procedures.
(1)|| x(t )  mc (t ) || mini || x(t )  mi (t ) ||(2)
mi (t  1)  mi (t )  hci (t ) {x(t )  mi (t )}At fast, a winning neuron which has the reference
vector with the greatest similarity to x(t) is chosen from allneurons. The number of a winner is set to c. Then, mi (t) is
updated by equation (2). By updating, mi is more similar tox(t). hci(t) is the function of t and lci which is distance in themap from c-th neuron to i-th neuron. An example of hci (t)using a Gaussian function is shown below.
(3)
hci (t )   (t )  exp[ l ci2 /{2 2 (t )}]mi of the neuron near a winner on the map changes a lot.
As learning proceed,α(t) and σ(t) is gradually decrease.
In the early stages of learning, a reference vector changes a
lot in the wide range on the map. The range and amount ofchange decrease as learning proceed.

A neuronFig. 1. The input vectors (2 dim) and the map (1 dim)
2.4 The estimating method by conventional SOM
In the method estimating missing values in data by
SOM, at fast, instances which are not including missing
values are used as input vectors for learning. The laws ofeach attribute of these instances are learned as the laws in
all instances. After learning, the laws expressed withneurons of the map discretely.
Then, to each instance including missing values, thewinner is determined. The winner is the neuron which has
the reference vector with the greatest similarity to theinstance. In the calculation of similarity, the attribute withmissing value is excluded from consideration. It is the same
also about the corresponding component of the reference
vector. After determining the winner to each instance,
missing value included in the instance is estimated. The
estimated value is the corresponding component of awinner’s reference vector.
In this method, however, only instances which are notincluding missing values can be used for learning. Thus, inthe data which lacks such instances, it is difficult toestimate missing values by using SOM.

2.3 The map after learning
In this section, we describe the map after learning. To
each input vector, a neuron with reference vector which isvery similar to it exists. When some two neurons arelocated close together on the map, their reference vectors
are also well alike. Thus, with the position change on themap, the reference vector of the neuron also changes
smoothly. By mapping the each input vector to the winningneuron, the relation of inputs in high-dimensional space canbe expressed with the coordinates in low-dimensions on themap. The law of each component of input vector isexpressed with all neuron’s reference vector discretely
whether the law is linear or not.
The example of a map after learning is shown in Fig.
1.The features or the laws of two-dimensional input vectors
are reflected in the one-dimensional map. The axes in the
figure correspond to each component of an input vector (orreference vector). A Fig. 1 shows that the map after learning
has the above-mentioned character.

© ISAROB 2013

3 PROPOSED METHOD
In proposed method, a SOM disregarding some
attributes are utilized. The SOM learn the relation between
only attributes which is not disregarded. In the SOM, theinstances including missing values can be used for learning,
so long as the attribute with the missing value isdisregarded. Thus, the SOM can use more instances thannormal one when estimating missing values in data. On theother hand, the SOM can’t learn the relation between thedisregarded attribute and other attributes. Therefore, theSOM can’t estimate missing values in the disregarded
attribute.

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We propose the method using multiple disregarding
SOMs. The attribute disregarded by a SOM is notdisregarded by other SOM. In multiple SOMs method, therelation of all the attributes is learned by the whole ofmultiple SOMs.
After learning, a missing value is estimated by each
SOM. At this time, to one missing value, some estimatedvalues are obtained from some maps. The average of theseestimated values is used as a final estimated value.
The concrete way of using the proposed method is
explained in section 4.4.

We made the data incomplete by removing some values.
At fast, we determined the rate X(%) of the instances
including missing values, and then, equally divided theinstances into 4 groups at random. In each group, the valueof the attribute 1, the attribute 2, the attribute 3, and theattribute 4 was made missed, respectively. These groups
call Group1, Group2, Group3, and Group 4, respectively.
The group of instances which are not including missing
value calls Group0.
We created four kinds of incomplete data (Data1-Data4)
by grouping the instances four times to each X ( = 10, 20,…, 90).
When X = 90, to each incomplete data, we applied eachestimating method, and calculated the average of the error,
respectively. Furthermore, we compared proposed method
and conventional SOM method when X = 10, 20, …, 90.

4 SIMULATION
4.1 A summary of simulation
To evaluate the proposed method, we performed
simulation of estimating the missing values with incomplete
data. In the simulation, the data for benchmark of data
analyzing was utilized. The data had no missing values
originally. And then we made the data incomplete byremoving some values. We applied proposed method,
conventional method that uses SOM and other methods tothe incomplete data, and compared the results. We
evaluated the results based on the average of each error of
the estimated value to the original value. The error is arelative error to variance of the attribute with missing value.
In the algorithm of SOM in this simulation, we
determined the initial value of reference vectors of neurons
based on the result of principal component analysis (PCA).
We used equation (3) as hci(t) when updating a neuron.

4.4 The estimation by proposed method
To estimate missing values by proposed method, we
prepared four kinds of disregarding SOMs (SOM-A, SOMB, SOM-C, and SOM-D) to each group of instances
including missing value. SOM-A, SOM-B, SOM-C, andSOM-D disregards attribute1, attribute2, attribute3, andattribute4, respectively.
Then, each SOM learns the relation between attributes
which are not disregarded. Each SOM can use moreinstances for learning than conventional one. For example,
SOM-A can use not only instances of Group0, but also
instances of Group1 which are including missing values in
the attribute1 disregarded by SOM-A.
After learning, each SOM estimate a missing value inthe attribute which are not disregarded. For example, themissing value of attribute3 is estimated by SOM-A, SOM-B,
and SOM-D which don’t disregard attribute3. The averageof these estimated values is used as a final estimated value.

4.2. The conventional techniques other than SOM
As the conventional technique for comparing with theproposed one, we adopted the method which used averagevalue, and hot-deck imputation. In the method using
average, the average value of an attribute with a missing
value is adopted as an estimated value. In the latter method,at fast, the instance with no missing which is most similar
to the instance with missing value is determined. In thecalculation of similarity, the attribute with missing value is
excluded from consideration. After determining the instance,
the value which is corresponding missing values is adopted
as estimated value.

4.5 The simulation result
In the following Table 2, we show the simulation result
when X = 90. The table 2 shows that the error of the
proposed method (multiple SOMs) is the smallest.
In the following Fig. 2, we show the simulation result of
estimation by the proposed method (multiple SOMs) andthe conventional SOM method when X = 10, 20, …, 90.
The Fig. 2 shows that the method using multiple SOMs ismore effective than conventional SOM when X is at least60(%).

4.3 The data for validation
In this simulation, we used the iris data which isreleased as a benchmark of analyzing [6]. The data has 150
instances and 4 attributes (attribute1-attribute4). The data
had no missing values originally.

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

0, No.7, pp. 639-647

Table 1. The error of estimated value when X = 90

Multiple
SOMs
Conventional
SOM
Using
the average
hot-deck
imputation

Data1
(%)
32.8

Data2
(%)
36.7

Data3
(%)
35.5

Data4
(%)
31.6

Ave
(%)
34.1

40.3

48.2

56.9

42.6

47.0

87.5

88.1

81.7

83.1

85.1

50.0

50.9

54.0

42.8

49.4

[2] Abe T, Inaba Y, Iwasaki M (2005), The StatisticalAnalysis Techniques and Comparison of Software of
Incomplete Data (in Japanes). Japanese Society ofComputational Statistics，Vol. 18, No. 2，pp. 79-94[3] Iwasaki T, Genei M (2001), Estimation of theearthquake information with missing by use of a selforganizing map (in Japanese). Technical paper summaries
of Architectural Institute of Japan, B-2, p.185[4] Kohonen T (2005), Self-Organizing Map (inJapanese), Springer-Verlag, Tokyo.
[5] George K (2010) Self-Organizing Maps, Publication
In-Tech.
[6] University of California Irvine, Iris Data Set,
http://archive.ics.uci.edu/ml/datasets/Iris

Fig. 2. The input vectors (2 dim) and the map (1 dim)

5 CONCLUSION
From the simulation result, we proved that our method
is more effective than some conventional method including
a conventional SOM especially in data with many instances
including missing values.
In future, we have to perform more simulation invarious data and conditions. For example, we have to check
whether the proposed method has advantage even if themissing pattern becomes more complex. Moreover, we
would like to consider any better method to determine a
final estimated value based on some estimated values fromeach SOM.

REFERENCES[1] Edgar A, and Caroline R (2004), The Treatment ofMissing Values and its Effect on Classifier Accuracy.
Classification, Clustering, and Data Mining Applications, Vol.

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Proposal and evaluation of information gathering methods
that consider the MANET’s load in a disaster
Keisuke Gohara1 and Hiroyuki Nishiyama1
1

Graduate School of Science and Technology，Tokyo University of Science，
2641 Yamazaki，Noda-shi，Chiba-ken，Japan(Tel:+81-47122-1106; Fax:+81-47122-1106)(j7412610@ed.tus.ac.jp)

Abstract: When a disaster occurs, victims must be rescued quickly and their safety confirmed. However, current informationgathering systems may become unusable if their infrastructures are destroyed. The Mobile Ad Hoc Network (MANET) isa rescue-support system. MANET typically uses the flooding strategy. In flooding, the source node broadcasts the Route
Request Control Packet (RREQ) to discover a route to the destination. However, this generates a high number of redundant
retransmissions, so flooding places a huge burden on the devices that compose MANET. In this paper, we propose a MANET
system that uses location information and the current energy status of the node. By simulating a disaster, we demonstrate that our
proposed system outperforms the existing systems in terms of the total number of RREQs and the average power consumption.
Keywords: Mobile Ad Hoc Network, Broadcast, Location Information, Energy Status
1 INTRODUCTION

the current energy status of the node.
The rest of this paper is organized as follows. Section
2 reviews related work on MANETs. Section 3 provides our
proposed system. Section 4 presents the simulation setup andperformance evaluation of our proposed system. Finally, section 5 concludes the paper and outlines future work.

Recently, growth of mobile devices and the Internet has
spurred development of individual information communications. Information communication in disasters such as earthquakes, volcanic eruptions, floods, and terrorism has become
more and more important because it is necessary to rescue
the victims quickly and confirm their safety. Existing rescuesupport systems for disasters use hazard maps and emergency
call systems such as 119. Hazard maps anticipate damage
and victims’ responses. However, damage from disasters
may be worse than expected. In this case, systems employing
hazard maps would be useless. The emergency call system
is regularly used by many people. In the event of a disaster, however, the system may be unusable due to destroyed
infrastructure.
The Mobile Ad Hoc Network (MANET) is a current
rescue-support system. MANET is a self-configured wireless network formed by a collection of wireless mobile nodes
without a fixed infrastructure or a centralized administrator.
A rescue-support system using MANET can gather information on victim safety dynamically in a disaster. MANET typically employs a flooding strategy. In flooding, the source
node broadcasts the Route Request Control Packet (RREQ)
to discover a route to the destination. MANET currently has
two problems. The first is the increased number of devices
composing MANET. The increase in the number of devices
increases the total number of RREQs and degrades the availability of MANET. Second is the difficulty of ensuring electric power. If it is difficult to ensure electric power, the energy status of device must be considered. In this paper, we
propose a MANET system that uses location information and© ISAROB 2013

2 MOTIVATION
Disseminating routing protocol control packets (e.g.,
RREQ packets) with blind flooding may entail a huge overhead as mentioned previously. Therefore, increased attention
is focused on reducing the routing overhead associated withroute discovery by increasing the effectiveness of broadcasting.
2.1 Route discovery using Location Information
In Iwasaki’s Location Aided Routing middle (LARmiddle) algorithm [1], mobile nodes use location information
obtained from global positioning system (GPS) receivers andtransmit RREQ packets in a circle with a diameter of the distance between the source node and the destination node inorder to determine the route. This approach effectively reduces the number of broadcast packets, but also reduces thenumber of communication paths to the destination node anddecreases the fraction of packets delivered.
2.2 Route discovery using gossip
Haas et al. proposed an adaptive gossip-based protocolthat employs a predefined probability p to forward an RREQ
packet if there is the node more than a certain distance fromthe source ; otherwise it will gossip with probability 1 [2].

472The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

They conducted experiments to demonstate how simple gossiping uses up to 35% fewer packets than flooding for large
networks and that the performance of AODV routing relying
on a gossip-based search is improved even in a small networkof 150 nodes.
2.3 Route discovery using directional gossip
Beradi exploited a directional gossip algorithm to improvethe efficiency of route discovery. When a node receives apacket for the first time, it rebroadcasts with a higher gossip
probability if the node is estimated to be closer to the destination than the sending node, otherwise it will forward thepacket with a lower probability or discard it. However, thealgorithm must under the assumptions that any two neighbornodes can estimate the difference of their current distancefrom the destination and that such an estimation is done fromthe inside of the network by using periodic beacons [3].

Fig. 1. Advanced LAR-middle algorithm

2.4 Route discovery using energy-based gossip
3.2 Advanced EBG algorithm

Nitnaware and Verma introduced the Energy-Based Gossip (EBG) Routing Algorithm [4]. The intermediate nodes
forward the RREQ packets with a probability based on thecurrent energy status of that node. If the node s remaining
energy is 80% of the initial energy, then the probability will
be set to 0.8, if it is 60% then the probability will be 0.6 andso on. The simulation demonstrates that energy consumption
due to routing packets and routing overhead can be reduced
10% to 30%, but they do not consider the RREQ packets dying when the probability is too small.

The advanced EBG algorithm is a route-discovery algorighm that reduces packet loss more than EBG.
When an intermediate node receives the RREQ packet
broadcast from the source node, the intermediate node forwards the RREQ packet with the probability calculated basedon the current energy status of that node. The probability of
the node i is given by
Pi = (1 − α) × φi + α

3 PROPOSED SYSTEM

where α is the minimum relay probability and φi is thepercentage of the residual energy of node i. This can prevent a node with little energy from consuming its battery andreduces packet loss more than EBG.

In our proposed system, RREQ packets are broadcast to
the destination node based on the advanced LAR-middle algorithm and the advanced EBG algorithm.
3.1 Advanced LAR-middle algorithm
The Advanced LAR-middle algorithm is a routediscovery algorithm in which the source node receives morecommunication paths to the destination node than with the
LAR-middle algorithm.
First, the source node transmits a RREQ packet to an intermediate node in a circle with a diameter of the distance
between the source node and the destination node and in acircle in which the source node can transmit radio. The intermediate node receiving the RREQ packet also transmits
the RREQ packet to the node in a circle with a diameter ofthe distance between the intermediate node and the destination node and in a circle in which the intermediate node cantransmit radio. Thus, the source node receives more communication paths (paths through Node E or Node F in Fig.1) tothe destination node than with the LAR-middle algorithm byflexibly changing the transfer range.

© ISAROB 2013

Fig. 2. Advanced EBG algorithm

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3.3 Algorithm for Proposed System
The following is our algorithm for the proposed system.

appliction uses the MAC layer protocol IEEE 802.11, and themobility model is based on the random waypoint model [5] in
1000m × 1000m. We randomly placed many victim nodes,
moving at randomly chosen speeds (uniformly chosen from
1 to 15 m/s) and a rescue node that collects the RREQ packet
from victim nodes. Fifty victim nodes transmit the RREQ
packet to the rescue node. We set the pause time to zero toachieve the dynamic topology and set the minimal speed to 1
to prevent mobility from going asymptotically to zero. The
simulation time was set to 300s. Each data point represents
an average of 10 different randomly generated mobility scenarios. Table 1 shows other simulation parameters from theexperiment.

Algorithm 1 Algorithm for Proposal System
Set the threshold k of the distance from the source node to
the distance node;
while each RREQ received by the node do
Set a random number r between 0.0 and 1.0;
Obtain the distance h from the node to the distancenode;
if h <= k thenCalculate probability p using remaining energy;
if r < p thenBroadcast RREQ;
Update k to the value of h;else
Drop RREQ;
end ifelse
Drop RREQ;
end ifend while

Table 1. SIMULATION PARAMETERS
Simulation Parameter
Topology size
Transmitter range
Number of nodes
Simulation time
max Speed
Initial energy
Energy consumption during reception
Energy consumption during sending

4 PERFORMANCE EVALUATION
We conducted a simulation based on the supposition of adisaster and evaluated our proposed system.

Value
1000m × 1000m
250 meters
50,60,…,100
300 sec
15 m/sec
1000 J
0.1 W
0.5 W

4.1 Simulation Setup
4.2 Performance Result
We compared our proposed system with existing systems (flooding, LAR-middle, gossip) in four broadcasts of
the RREQ packet (the total number of RREQ packets, average energy consumption, packet delivery fraction, and delaytime). Also, the minimum relay probability in our proposedsystem and the relay probability for gossiping are 50%.
Figure 4 depicts the total number of RREQ packets. The
results demonstrate that our proposed system uses fewer
RREQ packets than existing systems.

We used network simulator ns-2 [7] to evaluate our proposed system. Network simulator ns-2 is an object-oriented,
discrete event-driven network simulator developed at UC
Berkeley and USC ISI as part of the VINT project. It is avery useful tool for conducting network simulations involving local and wide-area networks and wireless networks.

Fig. 3. The network simulator ns-2
This paper uses default radio propagation model ns-2 witha 2Mbps bit rate and transmission range of 250 meters. Our© ISAROB 2013

Fig. 4. The total number of RREQ packets

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5 CONCLUSION

Figure 5 presents the average energy consumption. The
results indicate that our proposed system has lower energy
consumption than existing systems and fewer RREQ packets.

This paper proposes a MANET routing protocol using
location information and current energy status of the nodeand evaluates the performance of broadcasting RREQ packets based on a supposed disaster. Experiment results provedthat the proposed system is superior to the existing systems in
the total number of RREQ packets and average energy consumption. However, experiment results also proved that the
proposed system is inferior to flooding and LAR-middle algorithms in packet delivery fraction and delay time.
Future work will improve our proposed system’s packet
delivery fraction and delay time and add a new protocol for
sending data to our proposed system to achieve this paper’s
purpose.

Fig. 5. Average energy consumption

REFERENCES[1] Midori Iwasaki, Yoshikatu Oota, Hidetomo Suzuki(2009), A Method for Reducing Control Packets of
AODV using the Location Information, Information
Processing Society of Japan SIG Technical Report ,Proceedings of the National Conference 71st(3):125126．

Figure 6 plots the percentage of packet deliveries. Our
proposed system delivers more packets than the gossip system but fewer packets than the two other systems.

[2] Z. J. Haas, J. Y. Halpern, and L. Li (2006), Gossipbased ad hoc routing, IEEE/ACM Trans. on Networking, vol.14:479-491.
[3] R. Beraldi (2006), A directional gossip protocol forpath discovery in MANETs, in Proc. 26th IEEE Int.
Conf. on Distributed Computing Systems Workshops,
Lisboa, Portugal, Jul.:82-82.
[4] D. Nitnaware and A. Verma(2010), Energy Based Gossip Routing Algorithm for MANETs, in Proc. 2010 Int.
Conf. on Recent Trends in Information, Telecommunication and Computing:23-27.

Fig. 6. Packet delivery fraction[5] J. Broch, D. A. Maltz, D. B. Johnson, Y. C. Hu, and J.
Jetcheva(1998), A Performance Comparison of MultiHop Wireless Ad Hoc Network Routing Protocols, in
Proc. 4th Annu. ACM/IEEE Int. Conf. on Mobile Computing and Networking, Dallas, TX, USA,:85-97.

Figure 7 plots the packet delay time. Our proposed system
has less delay time than the gossip system but more delay
time than the two other systems.

[6] TC Huang, SC Chen, L Tang(2011), Energy-Aware
Gossip Routing for Mobile Ad Hoc Networks, High
Performance Computing and Communications:955959.
[7] The Network Simulator - ns (Verision 2). Available:
http://www.isi.edu/nsnam/ns/
Fig. 7. Delay time

© ISAROB 2013

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Simultaneous Recognition of Multiple Actions using CHLAC features
Kyosuke Masumitsu, Takayasu Fuchida
Kagoshima University, Japan(Tel: 099-285-3408, Fax: 099-285-3408)
sc108067@ics.kagoshima-u.ac.jp
Abstract: In this study, we propose simultaneous recognition of multiple actions in moving image using sub-space method
and CHLAC features. As the CHLAC features are integral value of entire the image, it has the additivity. Thus, when multiple
objects exist, it is possible to use the added-feature which is made by adding the each action of the object. But if you use suchan added-feature, it becomes difficult to distinguish each action respectively. Then we introduce a method to trace the region ofeach object in the image. From the computer simulation by using these 8 kinds of moving images, we show that our proposed
method can recognize these 8 kinds of actions.
Keywords: CHLAC, Color-HLAC, CLAFIC method, Projection distance method.

1 Introduction
Recently, as the performance of computers improves,the demand of action recognition using features of moving
image has risen and different method has been proposed. In
recent features descriptor methods, without descripting thefeatures from one only image in the moving image, manymethods that utilize time and space that consider the current
frame, the previous and the next frame is proposed[1][2][3].
CHLAC(Cubic Higher-order Local Auto Correlation)[3] is
one of the amount of image feature. This is what Higherorder Local Auto Correlation[4] is extended and cansimultaneously evaluate features of both moving and shapereflected in the camera. This features extraction method is,in general, performed by calculating the frequency ofmatching between the pixels which has moved in the threebinary images obtained by the frame difference from four
successive frames in the moving image and the mask
patterns prepared in advance. Consequently, the general
CHLAC feature has 251 dimensions.
As the CHLAC features have the additivity, when
multiple objects exist, it is possible to use the added-feature
which is made by adding the each action of the object. It
can be simultaneously multiple objects using the addedfeatures, but when the number of object increases, variation
becomes enormous and configuring the discriminator isdifficult. In previous research which resolves the
problem[5], the method using multiple regression analysis
has been proposed and it enables simultaneous recognitionof multiple moving objects. But in this method, it isn’t able
to current location of the object and to track the object. So,
in this study, we propose the method which traces theregion of each object in the image and extract CHLAC
features from each object and distinguish the moving.

© ISAROB 2013

We used color-information for tracking the objects. We
adopted the features which combined Color-HLAC[6] andHLAC for this information.
We used sub-space method to the discrimination of
motion. This is one of the pattern recognition methods
which utilizes the bias of input vectors and takes relatively
small amount of calculation at the time of the identification.
So we think that it suits the motion recognition which
requires the real-time processing. In this research, we adopt
the sub-space method which uses the bias of distribution of
the CHLAC features which are extracted for each motion of
objects in the movies.
And, we validate the effectiveness of our method throughthe computer simulations using some movies which include
multiple objects in them.

2 extraction of CHLAC features
We will easily explain how to extract CHLAC.
2.1 HLAC feature
HLAC(Higher-order Local Auto Correlation)[4] is theamount of statistical features based on Higher-ordercorrelation the 𝑁 dimensional autocorrelation for 𝑓(𝑟) isexpressed as following equation.
x(𝒂1 , ⋯ , 𝒂𝑁 ) =∫ 𝑓(𝒓)𝑓(𝒓 + 𝒂1 ). . . 𝑓(𝒓 + 𝒂𝑁 )𝑑𝑟 (1)Here, 𝑓 is the image which is retrieved in time series
and 𝑟 is the reference position and 𝒂𝑖 is the vector thatindicates the displacement. Since the correlation between
the pixel of interest and its neighboring regions is expected
to be high, we usually will not calculate the correlation for

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registered object and the features quantity obtained fromthe current object region. The region which has smallest
difference becomes the updated position of the object, andwe update the previous feature value.

all areas, but for only neighbor pixels, this feature is usually
set as degree－𝑁 = 2. Consequently, it has 25 dimensions.
2.2 Extraction of CHLAC features
We will extract the features which are used to recognize
motion from moving image. First, we convert the moving
image to YCbCr color-space in order to remove theillumination variation, and then remove component Y ofluminance signal. After this, we perform smoothing
processing in order to reduce the noise in the image. Next,
we make frame differences from moving image and create
the subtraction binary images and then calculate CHLAC
feature from its subtraction binary images.
HLAC which we described in sub-section 2.1 is thecorrelation in the two-dimensional local region, but
CHLAC is feature value which introduces the time axis tothe local area and then calculates correlation in the local
region of 3 × 3 × 3.
In general, degree 𝑁 is set {0, 1, 2} and the feature has
251 dimensional vector.

3.2 Features amount by combining the color-HLAC and
HLAC
Color-HLAC is the feature which is extended higherorder local auto correlation to color image. Similarly HLAC,
we will consider mask pattern of 3 × 3 sizes. This isexpressed as the combination of each color component in
the local region of pixel of interest. In this study, we used
45 dimensional features that have degree {0, 1} only.
In addition, we also added HLAC considering theluminance. Consequently, the feature amount of a
combination of the HLAC and color-HLAC becomes 70
dimensions.

4 Motion determination using sub-space method
In this research, we use sub-space method which is themulti-class classification for the input patterns.

3 Tracing the region of the object
In this chapter, we will explain about tracing the regionof the object and tracking using color-information.

4.1 CLAFIC method and projection distance method
Sub-space method is to calculate sub-space of eachlearning class using bias of distribution of the feature andrecognize input pattern by projecting it onto the sub-space.
CLAFIC method is one of the sub-space methods.
CLAFIC is, at the time of learning, a method which puts theorigin of sub-space on the origin of original feature spaceand performs KL expansion for each class and thendetermines sub-space of each class. Then, when an
unknown pattern is input, its pattern class is determined as
the sub-space class which the length of the projection
vector for each class is maximum.
We will describe how to configure the sub-space by thismethod.
We calculate autocorrelation matrix 𝑅𝑖 using CHLAC
feature vector 𝒙𝑖,𝑗 ∈ C𝑖 (1 ≤ j ≤ N𝑖 ) which belongs to theclass C𝑖 as the learning data.
The feature vector is normalized so that its norm
becomes 1 using 𝒙𝑖,𝑗 /‖𝒙𝑁𝑖 ‖.

3.1 Tracing the region of object and tracking
In order to simultaneously recognize each location ofmultiple objects and motion, we trace the each region of
objects. First, we prepare the background image in advance.
Next, we extract the subtraction image between the current
frame and background image. We divide the whole ofsubtraction image into the many vertical narrow rectangles
and then check the all pixels in each segmented region
whether there are any differences. If the different pixel isfound in the segmented region, its region becomes acandidate for the presence of the object region and we will
check the adjacent pixels of this region.
If the candidate of the object is found in the rectangle
which divides the image lengthwise, we divide thecandidate region horizontally and explore the region withthe subtraction as same as the vertical segmentation.
Moreover, since we use the difference image between
two sequential frames in calculating CHLAC feature, we
are able to make a more accurate segmentation of the object
by examining whether there is a pixel which has motion in
the subtraction image within the candidate region of theobject. If there is an area of noise, we update the previous
pixel value to the current pixel value in order to denoising.
If the presence of the object is confirmed, we register thecolor-information of the object in order to distinguishbetween each object of the next frame, and in order to trackthe object. We have been utilized color-histogram andcolor-HLAC to the color-feature. Then distinction between
objects is performed by Euclidean distance and then we
calculate the subtraction between the features quantity of© ISAROB 2013

𝑅𝑖 =

1
𝑁𝑖

𝑁

𝑖
∑𝑗=1
𝒙𝑖,𝑗

(2)

Sub-spaceiscomposedusing
eigenvalues
𝜆𝑖,𝑗 (𝜆𝑖,1 ⋯ 𝜆𝑖,251 ) obtainedfromtheeigenvalue
decomposition of the autocorrelation matrix 𝑅𝑖 and thecorresponding part of the eigenvectors 𝒖𝑖,1 , ⋯ , 𝒖𝑖,251 .
In order to compose the sub-space, we calculate principal
component of the consisted sub-space.

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To set the number of principal components, we use acumulative contribution ratio 𝑎(𝑑𝑖 ) of the dimension 𝑑𝑖shown in equation (3).
𝑑𝑎(𝑑𝑖 ) =

𝑖 𝜆
∑𝑗=1
𝑖,𝑗

(3)

∑251
𝑗=1 𝜆𝑖,𝑗

𝑑𝑖 is selected to satisfy the following conditions,𝑎(𝑑𝑖 ) < 𝑘𝑑 < 𝑎(𝑑𝑖 + 1)

(4)

where 𝑘𝑑 is the threshold and in this study, we use
𝑘𝑑 = 0.998 . After determining 𝑑𝑖 , we select the 𝑑𝑖eigenvectors from the top of the value. Then, we calculate
projection matrix 𝑃𝑖 of sub-space 𝐿𝑖 using equation (5).
Figure 1. Corresponding to the scaling
𝑃𝑖 = [𝒖𝑖,1 , ⋯ , 𝒖𝑖,𝑑𝑖 ](5)
In recognition of projection distance method, the total of
projection distance to the sub-space is calculated byfollowing:

In contrast to CLAFIC method, projection distancemethod is the method which the center of each class is theorigin.
While
CLAFIC
methodperform
eigenvalue
decomposition of autocorrelation matrix obtained by theequation (2), projection distance method calculate averageof feature vectors of each class and calculate the variancecovariance matrix 𝑅𝑖 according to the following formulas.
And sub-space 𝑆𝑖 using projection distance method is
configured by eigenvalue decomposition of this matrix.
𝒎𝑖 =
𝑅𝑖 =

1
𝑁𝑖
1
𝑁𝑖

𝑁

𝑖
∑𝑗=1
𝒙𝑖,𝑗

𝑁

2

𝑎𝑟𝑔𝑚𝑖𝑛𝑖 {𝐷𝑖 } = ∑𝑛𝑖=1 {‖𝒒 − 𝒎𝑖 ‖ − ‖𝑆𝑖 𝑡 (𝒒 − 𝒎𝑖 )‖ } (10).
The class which has smallest 𝐷𝑖 is the decision class of
the unknown pattern.
4.3 Configuring the discriminator corresponding to thescaling
CHLAC isn’t invariant feature quantity to scaling.
Therefore, we configured the sub-space for each scale ofobject at the time of learning. We configured to determine
whether any portion of the scale corresponding to the spaceaccording to the size of extracted region of the object(Figure 1).

(6)
𝑡

𝑖
∑𝑗=1
(𝒙𝑖,𝑗 − 𝒎𝑖 ) (𝒙𝑖,𝑗 − 𝒎𝑖 )𝑆𝑖 = [𝒖𝑖,1 , ⋯ , 𝒖𝑖,𝑑𝑖 ](7)

5 Experiments(8)

To evaluate the effectiveness of our proposed method, we
have conducted some experiments.

4.2 Recognition
In this section, we will explain how to identify the
unknown pattern 𝒒 according to each method of CLAFIC
method and projection distance method.
Recognition of unknown pattern 𝒒 using CLAFIC
method is executed by calculating the square of the lengthof 𝑃𝑖 𝒒 as the degree of similarity between the unknown
pattern 𝒒 and the class 𝐶𝑖 by using projection matrix 𝑃𝑖
which is determined for each class. Sub-space which
projection length is greatest becomes unknown pattern that
determines the class of.
𝑎𝑟𝑔𝑚𝑎𝑥𝑖 {𝐷𝑖 } = ‖𝑃𝑖 𝒒‖2 = 𝒒𝑡 𝑃𝑖 𝒒2𝑑𝑖𝑑𝑖
= 𝒒𝑡 (∑𝑗=1(𝒖𝑡𝑖𝑗 𝒖𝑖𝑗 ))𝒒 = ∑𝑗=1
(𝒒𝑡 𝒖𝑖𝑗 )© ISAROB 2013

5.1 Conditions of the experiments
We have set up a camera on the street and have taken thevideo which was assumed to detect each motion andlocation of the multiple entering objects. The video wastaken with the state of the objects entering the lateral ofcamera.
We prepared 8 kinds of moving images, such like"bicycle left", "bicycle right", "walk left", "walk right","crouch", "stand up", "raise hand" and "hand down".
In addition, preparing video such that maximum threeobjects exist, we have experimented using them.
The experiments were conducted with six subjects.
The motion at each scale was learned two per person.
Moving images were taken with 30fps and size of them was
set to 320×240 pixels.

(9)

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by frame difference. Then, we used CLAFIC method andprojection distance method as the classifier. And we utilized
a combination of feature quantity color-CHLAC and HLAC.
We have performed two evaluation experiments forvideos that have eight types of behavior and at most threepeople who consist of person and bicycle. For the video oflearned objects, the result of CLAFIC method was 89%,
and the result of projection distance method was 92%. In
addition, for the video of unlearned objects, the result of
CLAFIC method was 82%, and the result of projection
distance method was 84%.
In this study, we proposed a method that corresponds tothe scaling and have confirmed its effectiveness, but wehave not been able to respond to changes in speed yet. So,
for the future work, we also need to propose a method that
corresponds to the action speed of the object. And many
type of improved sub-space method are proposed, we areable to consider the implement of variant of sub-spacemethod.
And we think that the accuracy of motion recognition
will be improved using the variant of CHLAC[7] which isextended to cross correlation which takes the timing of the
motion of an object into consideration.

1

Recognition rate

0.9
0.8
0.7
0.6
0.5

0.4
0.3
0.2
0.1
0

learned

CLAFIC

unlearned

Projection distance

Figure 2. Action recognition rate by each classifier
5.2 Experiment using only objects who were learned
We have carried out an experiment using two videos inwhich only learned persons exist. Each video has thecontent that at most three persons are entering.
5.3 Experiment using only objects who were not learned
We have taken two videos which were consists of only
persons who were not learned, and conducted anexperiment. Each video has the content that at most three
persons are entering.

REFERENCES

5.4 Result
The success rate of segmentation and object tracking was
95.4%. There was a slight mistake when an object enters
and when the object goes out the screen. However, at thetime of occlusion between the objects occurred, we haveconfirmed that detecting the regions of the objects were
performed well and also tracking was performed correctly
after the occlusion.
The action recognition success rate using each classifier
shows at the Figure 2. On the process of identification,
mainly erroneous identification occurred when the object
entered and went out. In addition, we confirmed thatincorrect identification rate didn’t change much if an
occlusion occurs or not.

[1] Yeffet, L., Wolf, L., Local trinary patterns for humanaction recognition. In: ICCV, 2009[2] Nazli, I., Stan, S., Object, Scene and Actions:
Combining Multiple Features for Human Action
Recognition, ECCV, 2010[3] T.Kobayashi, and N.Otsu, Three-way auto-correlation
approach to motion recognition, Pattern Recognition Letters,
Volume 30, Issue 3, pp.212 221, 2009.
[4] N.Otsu and T.Kurita, “A new scheme for practical,
flexible and intelligent vision systems”, Proc.IAPR
Workshop on Computer Vision, pp431-435, 1988.
[5] Y.Simohata, N.Otsu, “Simultaneous Recognition ofMultiple Moving Objects Using Cubic Higher-Order Local
Autocorrelation”, PRMU, 2006.
[6] T.Kurita, T.Kato, I.Fukuda, A.Sakakura, “Sense
Retrieval on a Image Database of Full Color Paintings”,
Information Processing Society of Japan Vol.33, 1992.
[7] T.Matsukawa, T.Kurita, “Action recognition using cu
bic cross-correlations features of local motion attributes”, PRMU2009-285, 2010.

6 Conclusion
In this research, we proposed the method for recognizing
the location and the motion of multiple objects in moving
images captured from a camera which was set on the street.
In our proposed method, first, we extract a difference image
between one frame image and background image that has
been prepared beforehand, and extract regions of objects in
the image. Then, we performed the motion recognition by
sub-space method that matches the size of each object
region. As the feature quantity of the motion recognition,
we used CHLAC obtained from the binary image sequence© ISAROB 2013

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Hand gesture recognition using subunit-based dynamic time warping
Yanrung Wang1, Atsushi Shimada1, Takayoshi Yamashita2, and Rin-ichiro Taniguchi1
1

2

Kyushu University, JapanOMRON Corporation, Japankenyou@ limu.ait.kyushu-u.ac.jp
Abstract: A subunit-based Dynamic Time Warping (DTW) approach is introduced for hand movement recognition. Two major
contributions distinguish the proposed approach from conventional DTW. (1) A set of hand movement subunits is constructedusing a data-driven method. The learning is based on subunits instead of the whole hand movement for more efficient learning.
(2) A more accurate similarity measure is offered using subunit-to-subunit matching to absorb the difference between two
similar sub-sequences belonging to the same subunit, and only keeping the distances between sub-sequences that relate todifferent subunits. Compared with the conventional DTW approach, the proposed approach is experimentally demonstrated to
be both accurate and efficient for locally collected datasets.
Keywords: hand gesture, gesture recognition, subunit

1 INTRODUCTION

2 OVERVIEW OF THE PROPOSED APPROACH

Vision-based hand gesture recognition has attracted
considerable attention because of its new and fascinating
applications such as interactive human-machine interfaces,
sign language interpretation, and virtual environments [1].
Features such as appearance, shape, and orientation often
play an important role in hand gesture recognition. In thispaper, we consider hand gestures as movement trajectories
and focus on recognition of the movement trajectories.
Dynamic time warping (DTW) [2] is widely used torecognize movement trajectories, because it simultaneouslyaligns time-variable data and computes a likelihood ofsimilarity. Generally speaking, there are two major
limitations to the use of DTW in hand movement
recognition. (1) DTW matching uses information about
individual training examples that it is sensitive to variations
in training data. Hence, it is difficult to support efficientpersonalized gesture recognition. (2) DTW is sensitive tonoise and unable to distinguish movement trajectories thathave similar subsequences, as it requires continuity along
the warping path. The use of DTW consequently requires
the development of many prototypes to achieve proper
performance, leading to an expensive computational load.
To address these issues, we develop an effective
recognition approach that combines the use of the DTW
distance metric and subunits, widely investigated in thefield of sign language [3][4]. Subunits are elementary units
in a language and there are far fewer subunits than words inthe vocabulary of the language, which is expected to lead tosmaller data size in training and a smaller search space inrecognition.

Our system handles color image sequences in real time
to recognize numbers from 0 to 9 by the hand movement
trajectories. In the training phase, all training data aremapped to sequences of digits between 0 and 7 according totheir orientation feature and then segmented into the set ofbasic motion units according to changes in orientation. Next,
subunits are selected via clustering and set as the yieldedcluster centers. In this case, each training sequence ismapped to a sequence of subunits. In the testing phase, thetest sequence is also represented as a sequence of subunits
and then classified according to DP matching between thetest sequence and training sequences. Specifically, DTW
distance is measured by subunit-to-subunit matching to
improve recognition accuracy and online learning is used toadapt the training set to the user’s individual habits.

© ISAROB 2013

3 HAND MOVEMENT REPRESENTATION
Hand movement trajectories are obtained by detecting
the top most point of the hand skin region as the fingertip.
To represent these trajectories, we use the orientation
feature, which has been shown to provide high accuracy inhand movement recognition in previous work [5]. A hand
movement is a spatio-temporal trajectory that consists offingertip positions ( , ), t = 1,2, … , − 1 , where T
indicates the length of trajectory. Similar to [5], we
calculate the orientation feature according to the positionsof fingertips between consecutive frames as follows.
=

480

−
−

; = 1,2, … ,

−1

(1)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The orientatiion
is quaantized into a set of codewoords
fro
om 0 to 7 by dividing itt by 45◦. Th
herefore, a hhandmoovement can be representted by a seq
quence of diigits
acccording to thee yielded codeewords as sho
own in Fig. 1 andthee similarity beetween two movementsmcann be measuredd bythee DTW distannce [6]. The Examples
Eof th
he DTW distaance
bettween movem
ments are illusttrated in Fig. 2. The exampples
shoow that the measurement prrocedure of ad
dding the currrent
disstance to the sm
mallest one caan result in similar trajectoories
beiing treated ass dissimilar, whichw
leads too inaccuraciess inhan
nd movement recognition.

Fig. 1. Orieentation codew
words and an example ofmovementm
reppresentation uusing orientatioon codewordss

4 HANDH
MOV
VEMENT SUBUNITS
COONSTRUCTTION(a) disstance betweeen ’0’ and ’0
0’ is 7

Motivated byy [3] and [4], we consider a hand movem
ment
as a sequence of basic mottion units, reeferred to as thecom
mmon patternn of hand movvements, and carry out a sselforg
ganization proocess to selectt a representative set of mottion
uniits from the trraining set as subunits. All training data areseggmented into a set of suub-movementts. Clusteringg isperrformed for thhese sub-moveements (basicc motion unitss) tofind the commonn pattern of haand movemennts.

(b) distancedbetweeen ’0’ and ’6’’ is 6
Fig
g. 2. Examples of the DTW
W distance betw
ween movemeents
The yielded cluster centerrs are then ussed as subunitts tong data to suubunit sequennces. We builld amaap the trainin
subbunit-to-subun
nit distance mmatrix during
g constructionn ofsubbunits and usse it as a loook-up table to speed up thereccognition proccedure.

4.1
1 Motion unitt segmentatioon
Motion unitt segmentatioon can be thhought of a s abouundary detecction problem
m. The usee of trajecttory
disscontinuity annd motion sppeed discontinuity has bbeen
shoown to be efffective in deteecting the subunit boundaryy insig
gn language recognition [7].
[We empploy changess inorientation as traajectory discoontinuity metrrics to detect uunit
bouundaries wheen the currentt orientation is very differrent
fro
om that in a neighboring frame or thaat in the startting
frame of the mottion unit.

5 SUBUNIT-B
BASED REECOGNITIOON
We proposee two-step submovement-to-subunit andsubbunit-to-subun
nit matching in the recogn
nition processs to
imp
prove the performancepof recognitio
on and empploy
subbunit-based leearning to oveercome sensitiivity to variatiionsin thet training daata.

4.2
2 Subunit clusstering
To select a set of reprresentative su
ubunits from allsubbmovements oof the traininng set, we perrform k-medooids
cluustering using the DTW distance metricc. The k-medooids
alg
gorithm, a vvariant of k--means clustering, compuutes
meedoids insteadd of centroids as cluster cennters to minim
mizethee sum of intraa-class distancces. To determmine the num
mberof clusters, we eemploy an iterrative clusterin
ng that selectss allsubb-movements of the trainning set as th
he initial cluustercennters and itteratively meerges similarr clusters uuntil
connvergence to obtain the ”optimal””
num
mber of clustters.
Furrthermore, w
we use a km
means-like allgorithm for kmeedoids clusterring [8] to overcomeothee drawback tthat
parrtitioning aroound medoidss (PAM, K-medoids) woorks
ineefficiently for large data setss because of th
he complexityy.

© ISAROB 2013

5.1
1 Subunit-bassed learning
Instead of traaining entire hhand movemeents composedd oforiientation codewords, we train each movement as aconncatenation of subunits. Thhe advantages are as folloows.
(1)) The amount of training mmaterials needded is reducedd as
all training data are composeed of a limitedd set of subunnits.
(2)) A simplified enlargement of training daata is achievedd bycom
mposing new training data uusing the exissting subunits..
5.2
2. Submovem
ment-to-subunnit matchingLet
be a testing sequuence and S =, ,…, | |be a set of | | subunits coonstructed fro
om training data.
dSimmilar to trainiing sequencess, the test seq
quenceis alsoamaapped to a seequence of diigits accordin
ng to changess in

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

significant improvement when there were only nine training
data. The findings indicate that the proposed approach isable to overcome the sensitivity to training data of
conventional DTW to offer high recognition accuracy evenwhen there are few training data. The two main reasons forthe improvement are as follows.
6.1.1 Increase in the variety of training data
To train each movement as a concatenation of subunits
increases the variety of training data such that it is possible
to recognize new training patterns not seen in training.
For instance, we might have a training set of three
training data
=
,
,
, =
,
, and
=
,
,
, and
are segmented, wheresubmovements and are clustered into three subunits
=
,
=
,
,
and
=
,
.
According to the yielded subunit set, training data aremapped to sequences of subunits
=
, ,
,
=
,
=
, .
and
In the example, we only train two training prototypes
andfor three training data becauseandare mapped to the same prototype. The reductionof training prototypes improves learning efficiency while
maintaining the variety of training data to avoid loss ofrecognition accuracy. In addition, training patterns that canbe represented by the training prototype
are not only
andbut also,, and so on. That is,the variety ofandis increased to | || | training
patterns because of the use of existing subunits that include
motion units from the other training data. It is thus also
possible to recognize new patterns, even though they are
not seen in the training. These merits achieve an
improvement of the recognition rate without requiring highcomputational complexity.
6.1.2 A more accurate similarity measure
The conventional DTW distance metric is sensitive tonoise and unable to find movement trajectories that have
similar sub-sequences. Therefore, similar trajectories may
be treated as dissimilar, leading to inaccurate recognition.
As illustrated in Fig. 4, the proposed approach offers a moreaccurate similarity measure because it absorbs thedifference between two similar sub-sequences belonging to
the same subunit and only keeps the distances between subsequences that relate to different subunits.

orientation and then segmented into
submovements.
We calculate DTW distances between submovement
and all subunits to find the nearest subunit
and then use
these subunits to recompose the testing sequence . The
yielded testing sequence=,…, ,…,is usedto perform subunit-to-subunit matching with training data.
5.3. Submovement-to-subunit matching
Hand movement trajectories are recognized throughdynamic
subunitsequence
matching.
Let=,…, ,…,
be a training sequence consistingof n subunits. The distance
,
= (
,
) is
calculated as follows.
,
(

,

,

) = min

+
,

= min

+
+

(2)

=

0
,

≠

(3)

Here,
,
is obtained using the look-up table
generated during the construction of subunits.

6 EXPERIMENTS
To test the proposed approach for hand movement
recognition and to compare with conventional DTW, we
perform evaluations in terms of the recognition rate andaverage computational time for a locally collected hand
movement corpus. Here, the average computation time isthe average time taken to calculate the distance.
The constructed corpus contains 10 different classes ofhand movement trajectories from 0 to 9, performed byseven subjects in our laboratory environment. Each of the
10 classes of trajectories is repeated 25 times by eachsubject. To evaluate the performance for datasets ofdifferent size, we randomly select 9, 15, and 30 training
samples from each class, performed by three subjects, toconstruct the training set. The other data corresponding tothe other four subjects are used as a test set. To obtain
results that are more reliable, the construction of subunits
and evaluation of recognition performance were repeatedfive times using different datasets constructed relating todifferent subjects.

6.2 Evaluation of average computation time
The average computation time and the number oftraining prototypes when using subunit-based learning are
given in Fig. 5. The results indicate that a significant
improvement in computational complexity was obtained.

6.1 Evaluation of the recognition rate
Recognition rates classified according to three different
sizes of training set are compared in Fig. 3. Compared withconventional DTW, the proposed approach showed a© ISAROB 2013

482

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013’ is 0(a) diistance betweeen ’0’ and ’0’(a) average recognition raate using conv
ventional DTWW(a) distance betwee
een ’0’ and ’0’ is 8
Fig. 4. Examp
ples of the subbunit-based DTW
D
distance
between m
movements(bb) average reecognition ratte using subu
unit-based DT
TW
Fig. 3. Comparison ofo the recognittion rate
g prototypes, due
The reductioon of the numbber of training
to thet fact that mmultiple traininng data were mappedm
to sinngle
traiining prototyype, was oneoof the causes of theimpprovement inn computationnal cost. The majormreason forthee improvemennt is that the distancedbetweeen subunits w
was
rap
pidly obtainedd using the loookup table in the proceduree ofsubbunit-to-subunnit matching. These finding
gs shown in F
Fig.
3 and
a Fig. 5 suppport the claim
m that the proposed approoach
imp
proves recoggnition accuraacy while noot increasing thecom
mputational looad.

Fig. 5. Averaage computatiion time of reccognition fordifferent sizees of datasets
A Theodoraki
kis S, Pitsikalis V, et al (2010),[3] Roussos A,
Haand tracking and affine shape-appearrance handshhape
subbunits in continuouscssign languaage recognition.
Pro
oceedings of Internationall Workshop ono Sign, Gesttureandd Activity, EC
CCV 2010, pp..258-272[4] Bauer B, Kraiss KF ((2002), Towarrds an autom
matic
sig
gn language recognition system usinng subunits. In
Reevised Papers fromf
the Interrnational Gestture Workshopp on
Geesture and Sign Languuages in Human-Compu
H
uter
Intteraction, vol.14, pp.64-75
n M, Al-Ham
madi A, Michhaelis B (20008),[5] Elmezain
Reealtime capable system for hhand gesture recognitionr
ussinghid
dden markov models in sttereo color im
mage sequences.
Th
he Jour al of WSCG'08,
W
vol..16, no.1, pp.6
65-72
[6] Cha SH, Shin YC, Shiihari SN (19999), Approxim
mate
stro
oke sequencee string matcching algorith
hm for charaacterreccognition andd analysis. Inn Proceedingss of the ICD
DAR
199
99, pp.53-56
2009), Modellling[7] Han JW, Awad G, Suttherland A (2andd segmenting
g subunits foor sign langu
uage recognittion
bassed on hand motionmanalysiis. Pattern Reecognition Lettters
30((6): 623-633
J CH (20099), A simple anda fast algoritthm[8] Park HS, Junforr k-medoids cllustering. Exppert Systems with
w Applicatiions
36:3336-3341

7 CONCLUSIC
ION
This paper proposes a subuunit-based ap
pproach to hhandmoovement recoggnition. In coontrast to con
nventional DT
TW
appproaches, we share subuniits across han
nd movementss to
obttain a smalleer training daata size and search spacee to
imp
prove recognnition perform
mance. In ad
ddition, a mmorerobbust similariity measuree, using su
ubunit-to-subuunit
maatching, is offfered. The expperimental ressults demonstrrate
thaat the proposedd approach is both accuratee and efficientt forhannd movement recognition. Our future research will foocus
on incremental learning for the subunit itself to suppport
effficient personaalized recognition.

RE
EFERENCE
ES[1][ Wachs JP, Kölsch M Sttern, et al (20111), Vision-baasedhannd-gesture appplications. Coommun. ACM 54:60-71[2][ Okada S, Hasegawa O (2008), Mo
otion recognittion
bassed on dynnamic-time warpingw
meth
hod with sselforg
ganizing incrremental neurral network. Proceedings of
ICP
PR’08, pp.1-44© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Mathematical modeling of mirror based passive moiré target for orientation
sensing
M.M. Hasan1, Satoshi Tanemura1, and Kenbu Teramoto1
1

Department of Advanced Technology Fusion
Saga University, Japan
E-mail: mahbub_eeekuet@yahoo.com(Tel: 81-80-39017956)

Abstract: Mirror based passive moiré target has been studied mathematically and numerically. Mathematical model of thepassive moiré target at CCD camera sensor plate has been developed mathematical considering its orientations. Based on themathematical model, the moiré patterns are simulated for the proposed passive target. The simulated resulted are compared tothe moiré pattern produced by the existing passive moiré target. From the numerical results, it is found that the proposed
passive moiré target exhibits better sensing capacities.
Keywords: Mirror-based moiré passive target, specimen grating, reflected grating, low-frequency moiré pattern.
the orientation of the moiré target. The mathematical
relations indicate the intensity of moiré fringes captured by
the CCD sensor. The specimen grating function and thereflected grating functions are multiplied to form the moiré
pattern. The low frequency term represents the moiré
pattern.
The article is organized as follows: in section 2, themathematical relations have been developed, in section 3,
the simulated results from the mathematical relations will
be shown, and at last we will draw a conclusion.

1 INTRODUCTION
Moiré fringes based sensing technology plays animportant role in shape and position control, medicare,biomechanics, liquid wave analysis and sophisticatedorientation determination, due to the non-contact nature of
the measurement [1-3]. The moiré technique amplifies thesmall changes into sensible ranges by low cost and simple
arrangement. For the position and orientation sensing
system, the moiré arrangement which changes its patternaccording to the observer position and orientation without
using light source is desirable. This type of moiré
arrangement is called passive moiré. On the other hand, themoiré sensing systems require lighting arrangement tomake grating pattern are called active moiré [4-5]. The
research on passive moiré has been reported recently bydifferent authors in which, a moiré target has been proposedby using two same types of specimen gratings attaching in
opposite sides of a transparent substrate [6-7]. In this case,the moiré fringes become visible due to interaction of thetwo gratings which response to the position and orientation.
The linear gratings are utilized in these reports andmathematical formulations have been developed for thearrangements. In this report, mirror based passive moirétarget has been proposed in which only one specimen
grating is required. In the two gratings passive moiré
arrangement, two precisely identical gratings should be
attached similarly in opposite sides of a substrate. But, thepresent mirror based one grating arrangement is free fromthese limitations. To the best of our knowledge, themathematical or experimental reports on mirror based
moiré fringes have not published yet. In this paper,
mathematical formulations have been developed based on© ISAROB 2013

2 Mathematical Formulations
For developing mathematical formulation of the moiré
fringe, the equation of 2D target plane in three dimension
space has been derived. Then the equation of the line thatpasses through camera image plane point and focal point of
the camera (projection line) are also derived. Theintersection point of the target plane and projection line has
been calculated. This intersection point grating intensity is
the image intensity. For calculating the second grating(reflected grating) image intensity the optical ray reflection
theory has been used. Then the image intensity function has
been multiplied for getting moiré fringes image.
For deriving the mathematical equation consider the fig.1.
First the formulations are developed based on target planeorigin. Here ( x, y, z ) is a point on the rotated target
plane. The X and Y-axis rotational angles are θ and β
respectively. The relation between the rotated plane point( x, y, z) and corresponding before rotation plane point( x' , y ' , z ' ) can be expressed by the following equation:
cos β
0
sin β
x'
 x
 y' =  sin β sin θ
cos θ − cos β sin θ   y  (1)  
 z' − sin β cos θ sin θ cos β cos θ   z 
From the fig.1, we can write z ' = 0 as we consider the484

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig1. One-dimensional moiré target

target plane canter as origin. Putting z ' = 0 in equation (1),
we have:

Fig.2: Camera image scanning processax − by; a = sin β cosθ ; b = sin θ ;c
c = cos β cosθ(2)z=This is the equation of the rotated target plane considering
origin at target plane centre. But if we consider origin atcamera image sensor centre, then we have to modify theequation (2) by the following:ax − by
z=
+h
c

(3)

X max
tan( FOV / 2)

cyi ( f − h)(cf − byi + axi )z=(axi − byi )( f − h)(cf − byi + axi )(6 )rotation coordinate is ( x' , y ' , z ' ) , we are interested in only

x ' and y ' . We can write the expression of x ' andfollowing:x' = xcosβ + zsin β(4)y ' as(7)y ' = xsin β sin θ + y cosθ − z cos β sin θ
If the target plane grating is Fresnel zone plate (FZP), theintensity of first grating at camera image sensor plane canbeexpressedbyequation(8)plane point ( xi , yi ) . So, the equation of the straight line is x' 2 + y ' 2
G1( xi , yi ) = cos
 lλ

(5)

(8)

The second grating is the reflected image of the attachedgrating (i.e. first grating). The simplified one-dimensional
representation of optical ray diagram of target considering
Y-axis rotation is shown in fig.3. This figure shows how
grating illuminates the camera image sensor plate byreflection. So, from fig.3, it is found that image sensor point
A is illuminated by the intensity of grating point B and C.
Here, the target rotates around Y-axis and the rotation angle
is β .In case of X-axis rotation, the situation is shown in
fig.4. From fig.3 and fig.4, the Z-axis rotation will not

To determine the point of rotated target plane whose
intensity are scanned by the image sensor plane point( xi , yi ) , we have to find out the intersection point ofequation (3) and (5). After some calculation andsimplification, the coordinate of the intersection point on
rotated target plane has been evaluated as following:© ISAROB 2013y=finding the before rotation point of point ( x, y, z ) . If before

The straight line from target plane to image sensor plane
passes through focal point (0,0, f ) and image sensorx − xi y − y i z
=
=
− xi
− yi
f

cxi ( f − h)(cf − byi + axi )It is easier to obtain the intensity at point ( x, y, z ) by

One dimensional model of camera image scanning process
is shown in fig.2. In fig. 2, FOV is the field of view of thecamera, f is the focal length of the camera. From fig.2,
we can write the following equation:

f =

x=

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.4: One-dimensional view of reflected grating
considering X-axis rotation
Fig.3: One-dimensional view of reflected grating
considering Y-axis rotation
moiré fringes are produced in this case for the two grating
moiré target which is shown in fig.6. Three closed contour
and one open contour moiré fringes are observed whenrotation around X-axis is 2 degree (θ = 2 degree) whichis shown in fig.7. For the 2 degree angular rotation, twoclosed contour moiré fringes are produced for the two
grating moiré target as shown in fig.8. When both X(θ = 2 degree) and Y-axis ( β = 2 degree) rotational
angles are present, the number of contour remains same, but
the moiré fringes orientation has been changes according to
the ratio of θ and β as shown in fig.9. So, moiré fringes
with higher number of contours are observed in our
proposed moiré target. As the sensitivity depends on thenumbers of contour, the proposed passive moiré target ismore sensitive.

affect on the expression of dx and dy . So, the image
sensor point ( xi , yi ) will also illuminated by the point( x'+dx, y '+dy) of the grating by reflection. If weconsider x ′′ = x ′ + dx and y ′′ = y ′ + dy , the equation of
the intensity of the reflected grating can be expressed
according to the equation

 x′′ 2 + y ′′2
G 2( xi , yi ) = cos
lλ


(9)

The equation of the moiré fringes with grating can be
expressed by the following equation

I ( xi , yi ) = G1( xi , yi )G 2( xi , yi )(10)

we are interesting only in low frequency moiré. So, thecaptured moiré can be modeled by the equation

 2 x' dx + 2 y ' dy + dx 2 + dy 2
(11)
I ( xi , yi ) = coslλ

4 Conclusion
The moiré patterns produced from the proposed mirror
based moiré target are compared with the moiré target
described in [6]. For the same values of rotational angles,
the proposed moiré target produced more contours moiré
pattern, which enhances the sensing capacity and increases
sensitivity.

3 Simulated Results
Then numerical experiment has been performed based on
the developed mathematical relations for the proposedmoiré target. The simulated results for the proposed moirétarget have also been compared with the two gratings moirétargets [6]. For numerical experiments, the parameters are
selected considering the experimental conditions. The
parameters are: field of view of the camera is 20 degree,
both width and height of CCD sensor is 24mm, focal lengthis 30mm. From the simulated results, it is found that threeclosed contour moiré fringes are produced when distancefrom CCD to sensor target is 100mm and rotational angles
are set to zero, which is shown in fig.5. Two closed contour© ISAROB 2013

REFERENCES[1] E.U. Wagemann and T. Haist and M. Schऺnleber and
H.-J. Tiziani, Fast shape and position control by Moiréfiltering and object-adapted fringe projection, Optics
Communications,vol. 165, no.1-3, pp.7-10, 1999.
[2] Jinyou Shao and Yucheng Ding and Hongmiao Tian,and Xin Li and Xiangming Li and Hongzhong Liu, Digitalmoire fringe measurement method for alignment in imprint
lithography, vol. 44, no.2, pp. 446-451, 2012.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[3] Pollyanna F. Gomes and Meinhard Sesselmann andChristina D.C.M. Faria and Priscila A. Araujo and Luci F.
Teixeira-Salmela, Measurement of scapular kinematics withthe moiré fringe projection technique, vol.43, no.6,
pp.1215-1219,2010.
[4] Daniel Post and Bongtae Han and Peter Ifju, HighSensitivity Moire, Springer, 1994.
[5] Chia-Ming Liu and Lien-Wen Chen, Using the digitalphase-shifting projection Moiré method and wavelet
transformation to measure the deformation of a PMMA
cantilever beam, Polymer testing,vol.24, no.5, pp. 576582,2005.
[6] Glenn P. Tournier, Six degree of freedom estimation
using monocular vision and moire patterns, M.Sc thesis,
Massachusetts Institute of Technology, 2006.
[7] Eric Feron, Jim Paduano, A passive sensor for
position and attitude estimation using an interferometrictarget, Proc. of 43rd IEEE conference on Decision andControl,vol.2,pp.1663-1669,2004.

Fig.7: Low frequency moiré fringes fromproposed target when

θ = 0, β = 2, h = 100mm

Fig.5: Low frequency moiré fringes
from proposed target when

Fig.8: Low frequency moiré fringes fromexisting target when

θ = 0, β = 0, h = 100mm

θ = 0, β = 2, h = 100mm

Fig.6: Low frequency moiré fringes
from existing target when

θ = 0, β = 0, h = 100mm
Fig.9: Low frequency moiré fringes fromproposed target when

θ = 2, β = 2, h = 100mm© ISAROB 2013

487

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

A study about interactive musical editing system for automatic piano：Inferring performance expression by considering tempos
Tomohiro Inoue1, Eiji Hayashi2
1

Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering, Japan
Kyushu Institute of Technology Department of Mechanical Information Science and Technology, Japan(Tel:0948-29-7775)

2

1

inoue@mmcs.mse.kyutech.ac.jp
2
haya@mse.kyutech.ac.jp

Abstract
We developed a system that allows a piano to perform automatically. To enable an automatic piano to play musicwith the emotional expression that a skilled human pianist can provide, all of the notes in the score must be arranged.
The simulation of the emotional expression in an actual performance by a highly skilled pianist would take a great dealof time by a music editor, and in the present research, we structured an interactive music editing system to edit music
more efficiently.
The system is based on a concept, “Similar-patterned phrases within the same composition are performed withsimilar emotional expression,” and is composed of two systems: the "searching system of similar-patterned phrases" andthe "inferring system of emotional expression in a performance."
In the searching system, we use dynamic programming (DP) matching as the method for searching for similarphrases. In the inferring system, we use Musical Rules Databases that contain information regarding the musicalsymbols used by pianists for inferring emotional expression in a performance.
Key words: automatic piano, knowledge database, computer music, DP matching, Music Interface
of a “searching system of similar-patterned phrases” andan “inferring system of emotional expression in a
performance.”
In the searching system, we use dynamic
programming (DP) matching. This method maps two
character strings dynamically and calculates the degreeof disagreement of the strings, which is used as an index
to determine resemblance.
In the inferring system, we use Musical Rules
Databases that are programmed with the musicalknowledge of pianists. Their data express the influenceof musical symbols numerically. Our interactive musicalediting system refers to these databases and infers similarphrases automatically.
Here we describe and evaluate the results ofinferring similar phrases when using the parameter called“Step,” which determines the tempo.

1. Introduction
We developed a system that allows a piano toperform automatically. In this system, 90 actuators are
installed in the keys and pedals of a grand piano. Theseactuators perform the key strokes and pedaling during
the piano’s performance (Fig. 1).
To enable an automatic piano to play music asperformed by a human pianist, we must arrange all of thenotes in scores. In the case of piano music, the averagescore contains 1,000 or more notes. Moreover, a skilled
pianist is able to play an unfamiliar piece of music bysight, even if the performance is not completely inaccord with a specific musical interpretation. In contrast,
current computing systems, such as MIDI, cannot truly
perform a new piece of music by sight in that they cannot
simulate the emotional expression of a human pianist’s
performance without direction. Therefore, a simulationof the emotional expression in a performance by a highly
skilled pianist would take a great deal of time by a music
editor. In the present research, we developed aninteractive music editing system to edit music more
efficiently and to enable the automatic piano to simulate
the emotional expression of a skilled human pianist." [1]This system is composed of a concept that wearrived at by analyzing pianists’ expressive tendencies:“Similar-patterned phrases within the same composition
are performed with similar emotional expression.” Inaccord with this concept, the editing system is composed© ISAROB 2013

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Fig.1. The automatic piano.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

matching is applied through the following steps.
Step 1. Draw a route consisting of the strings.

2. Music Editing Support System
2.1. System architecture
The structure of the music editing support system isshown in Figure 2. The human user edits music via theuser’s interface on a computer display. The interface
edits automatically by referring to the Musical Rules
Database which contains information regarding themusical symbols used by pianists. The user’s editorial
work is greatly reduced and efficient editing becomes
possible.

S: Start, G: Goal
Fig.3. A route consisting of the strings.
Step 2. Set the costs for calculating the minimum
value.
Fig.2. Structure of the editing system.
2.2. Format of performance information
The parameters of the performance information are
shown in Table 1.
The data for our automatic piano system are similar
to those used by MIDI(Musical Instrument Digital
Interface). We defined the performance information by
dividing it into two categories: the notes and the pedals.
The note information is comprised of the six parameters
involved in producing a tone: Key (note), Velo (velocity),
Gate, Step, Bar, and Time. “Velo” is the dynamics, givenby the value of 1 to 127. “Gate” is the duration of thenote in milliseconds (ms). “Step” is the interval of time
between notes, and it also depends on the tempo. “Bar”
is the vertical line placed on the staff to divide the music
into measures.
The pedal information is comprised of fourparameters: Key (indicating the type of pedal: Damper or
Shifting), Velo (the pedaling quantity), Time (theduration for which the pedal is applied), and Bar.

Red spot: Spots composed of different charactersFig.4. The costs for calculating the minimum value.
Step 3. Calculate the minimum value to achieve thegoal.
In this example, the minimum value is 4 and theshortest route is shown by the red arrow.

Fig.5. Total costs for reaching the goal.

Table.1. Parameters for automatic piano.
systemStep 4. Check the one-to-one correspondence between
the strings.
Pursuing the shortest route, we obtain a one-to-one
correspondence of the strings. The character string,“abb”, is expanded to “aabb” and this string corresponds
to ”abbb” (Fig.6).

2.3. Searching system of similar phrases
2.3.1. Dynamic programming (DP) matching
We used dynamic programming (DP) matching as
the method for searching for similar phrases. Thismethod maps two character strings dynamically andcalculates the degree of disagreement of the strings,
which is used as an index to determine resemblance.
As an example, we’ll explain how the resemblanceis determined between two strings, ”abb” and “aabb.” DP© ISAROB 2013

Fig.6. Correspondence between the strings.
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2.3.2. Converting the notes into character strings
For use with the DP matching method, we built a
Score Database which contains information regarding themusical score, with a field called “Note Value.” The datain this field indicates a particular type of note, e.g., aquarter note, a triplet, and so on. Our interactive musicalediting system converts notes into character strings using
the “Note Value data.” (Fig. 7).

in performance by a human pianist: thus, we introduce
the parameter “StepRate”

StepRate =𝑆𝑡𝑒𝑝[𝑚𝑠]
𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑇𝑖𝑚𝑒[𝑚𝑠](1)

To compare the tendencies of each note, we store
this parameter in the relevant Musical Rules Database.
2.4.2. The Musical Rules Databases
The Musical Rules Databases contain the musicalknowledge of pianists. We analyzed the MIDI data ofperformances of highly skilled pianists in order toobserve the pianists’ expressive tendencies, and we used
these tendencies and musical knowledge to build these
databases.
Musical Rules Databases are composed of fivetables: Dynamics marks, Articulation marks, Symbols of
Changing Dynamics or Changing Tempo (symbols that
affect the speed of a note or the increase or decrease of
the volume), Time signature, and Tempo marks. Eachdatabase has data that expresses the influence of musicalsymbols numerically.

1. Note or Rest (0: Note, 1: Rest)
2. Note Value (000: A whole note, 001: A half note
…etc.)
3. Tie (0: No tie, 1~: The number of ties)
4. Ornament (1: This note has an ornament)
5. The number of dots (except that if it is "11" then thenote is a tuplet.)
6. Additional Information
The number of tuplets (Triplets: 0011)
The type of ornament (Trill: 0101), etc.
Fig.7. Note values.
2.3.3. Process of searching similar phrases
First, we designate an input phrase for searchingsimilar-patterned phrases and we set a threshold for the
total costs.
Then, the searching system searches for phrases that
resemble the input, using DP matching. Throughout thisprocess, this system outputs all phrases that have aminimum cost less than the threshold assimilar-patterned phrases. In our study, we called thesephrases Similar Phrases.
These phrases are not edited, so we infer theemotional expression in a performance by using theinferring system.

Fig.8. Example of a Musical Rules Database.
2.4.3. The inferring process
Here we explain the process of inferring emotional
expressions in a performance, using an example with twophrases: part of the first movement of Beethoven's'Sonata Pathétique' as an input phrase, and the firstmovement of his sonata Appassionata as a similar phrase(Fig.9a. and Fig.9b).
Inferring emotional expression in a performance ofsimilar-patterned phrases is accomplished through thefollowing steps.

2.4. The inferring system of emotional expression in a
performance
2.4.1. Parameters used in the inferring system
In this example of the inferring system, we use theparameter called “Step,” which is the interval of time
between notes and which determines the tempo.
Each note, e.g., a quarter note, has an absolute timethat depends on the tempo. We called this time the“Phonetic Value.”
However, increases and decreases in this time exist© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) Input phrase

By following the above steps, we obtain results suchas those shown in Figure 11. The results provide threevalues: the “Input phrase,” which is non-edited data, the“Result of inferring” and the “Pianist Data,” which is theactual emotional expression in a performance by apianist in the similar phrase.
We have consistently found that the emotional
expression represented by the “Result of inferring”
resembles the “Pianist Data” more than it resembles the“Input phrase.”(b) Similar phrase(c) Correspondence between the two phrases
Fig.9. Phrases for inferring system.
Step1. Apply the input phrase data to the similarphrase.
We change the similar phrase’s value of “StepRate”
into the same value of the input phrase considering thecorrespondence between the two phrases (Fig. 9c).
Step2. Revise by using “Dynamics marks.”
We changed the dynamics marks of the input phrase
into the same dynamics marks of the similar phrase.
As illustrated in Figure 9c, we changed the
dynamics marks ”p” into “pp.”Fig.11. Results of inferring.

3. Conclusions
We developed an interactive musical editing
system to edit music more efficiently. This system iscomposed of a "Searching system of similar-patternedphrases" and an "Inferring system of emotional
expression in a performance. The Inferring system infers
the emotional expression in a performance of similarphrases by referring to the databases automatically,
greatly reducing the time needed for editing. We
improved the Inferring system by using StepRate, whichis the ratio of the Step to the PhoneticValue and is stored
in the Musical Rules Databases.
Moreover, the interactive musical editing systemprovides edited phrases that resemble the "Pianist Data,"
which are the actual emotional expressions in a
performance by a skilled human pianist.

Step3. Revise by using “Articulation marks.”
We changed the articulation marks of the input
phrase into the same marks of the similar phrase.
As shown in Figure 9c, we changed the articulation
marks of all of the notes to “slur.”
Step4. Consider the positions of the notes.
“A note before a rest,” “First note of a bar” and“Last note of a bar” are all notes with aparticulartendency (Fig. 10).
A note with a tendency has these values if the“StepRate” of the standard note is 1. (Fig. 10)
In this case, note No. 4 in the similar phrase must bechanged to the value of the "StepRate" by considering
this relation.

References[1] Hayashi, E. et al, “Behavior of piano-action in a
grand piano.I”, Journal of acoustical Society ofAmerica, Vol.105, pp.3534-3544, 1999.

Fig.10. Values of notes with a tendency.

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A simple wall-climbing mechanism for a window cleaning robot
Takafumi Soeda1, Nobuhiro Okada2, and Kazuo Kiguchi1,21

Kyushu University, Japan
Kyushu University, Japan(Tel: 81-92-802-3167)

2

1

2TE11331E@s.kyushu-u.ac.jp

Abstract: In this paper, a new type of wall climbing robot to clean window is proposed. The developed robot can climb thepane by using a crawler with ordinary suckers. The suckers of the crawler sequentially stick to the window and the robot climb
the pane. The robot will be low-cost and simple. The robot was tested to see whether it have performance to climb the window
and to carry cleaning equipments. As the result, it was confirmed that the developed robot can move on the window. This paper
gives motivation of this development, concept design of this robot, the details of the system, the experimental result,conclusions and future work.
Keywords: robot, window cleaning, crawler, ordinary sucker
adsorption force, easily put the window and easy to control.
However, the robot has danger of falling when the trouble
is caused in the supply of electrical energy because thevacuum pump of the robot needs the electric power.
Thus, the proposed mechanisms of climbing the wall
have the fault respectively, and the development of a newmechanism for wall climbing robot is demanded.
In our laboratory, from such back ground, develop
climb the window by rotating a crawler that has general
purpose suckers. This robot has the mechanism that climb
the wall with an easy mechanism that does adsorb and peel
off the glass by the suckers, and the following merits exist.

1 INTRODUCTION
Recently, the automation of the window cleaning isrequested. The number of high-rise building which uses
large-scale panes is increasing. And window cleaning has
been done by human or by large equipment. They are very
dangerous and expensive. It is important that developing
safer and cheaper robot to clean the window [1][2].
Many small robots to clean the window have beendeveloped. Such robots can be classified into four types
according to the adhesion methods [8]: magnetic, hand-hold,biologically inspired and vacuum suction pad type.
The magnetic method is used magnet for absorbing andwheel for driving [3]. This method has the can steadily
absorb the wall and not need electric power for absorbing.
However, it has the problem that it is difficult to put thewindow and that the robot makes the window dirty likestripes by stick slip motion [4] because the frictional force
between the magnet and the window. The other, there is amechanism that adsorbs the wall of iron by permanent
magnets and move in itself as wheel [5]. But it is possible
to use only for the wall of iron, and can’t adopt for windowcleaning robot.
The hand –hold method can support very strong
adhesion force but it requires special equipment like knobs.
The biologically inspired method does not need noelectric power for creating the absorb force. However the
possible payload is very small.
The vacuum suction pad type method is a mechanismthat adsorbs onto the wall by negative pressure of thevacuum pump, and to run with the wheel for the drive[6][7]. The robot have this mechanism can freely set the© ISAROB 2013

1.

There is no fear of the fall by the electric power
cutting.
2. It is suitable for lightening and lowering the costbecause the robot has easy mechanism
3. It contributes to conservation of energy because it
doesn’t need electric energy for adsorb the window.
From the above reason, it is thought that the robot will
be lower-cost and simpler than other methods.
We perform some experiment to evaluate this robot.

2 CONCEPT DESIGN OF THE ROBOTThis paper takes the building that has ruggedness on the wall because it is difficult to adapt by the largescale cleaning system.
The function demanded from the window cleaning
robot is to adsorb outside in the window that is neede
d cleaning in some means and to wipe up the window
while automatically moving the all aspects of the win
dow. For this function, function that moves from objec
ted window to another window and that moves with adsorbing the window are needed.
In the case of the large-scale cleaning system, thehanging crane mechanism must set up in the rooftop t

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adsorb the suckers onto the window and rotate the crawler.
The idea suitable to trim weight and to develop cheaper bycompared with negative pressure adsorption mechanism and magnet adsorption mechanism. And it doesn’t have the ris
k of fall out by electricity failure.
For the above- mentioned reasons, we decided to adopt
this mechanism for this plan.
In our laboratory, one robot was made on the basisof this plan [9].The length of the robot that has been
made before in the laboratory is 630mm. It is too bi
g. In addition, the weight of robot was 4.0kg, it was
not working properly. Then, a new smaller robot isdeveloped. The goal was reduced to about 200mm, wh
ich is about one-third the size of the robot prior to asize that can easily carry. Sucker which is used in theprevious robot was 56mm in diameter. In order to red
uce the size of the robot, a sucker diameter of 42mm
is selected in ordinary suckers. The size is the smaller
one in the ordinary suckers.
Experiment was carried out as shown in Fig.2 in order toknow the performance of this sucker. Fig.3 shows the resultof the experiment. It can be seen that approximately 8.2kg
withstand the load of one suction cup to be used from theexperimental result. Design of the robot is performed basedon the performance of this sucker.

o fix the cleaning mechanism to the vicinity of the pa
ne, which makes this system more expensive.
On the other hand, small window cleaning robot ne
eds to adsorb window and move with adsorbing windo
w steadily. Additionally, it also needs the function thatwipes all aspects of the window without the lack ofwipe and the wipe mark to clean the window perfectly.
Then, it is hoped that the robot clean the window wh
ile climbing it autonomously.
A small window cleaning robot is needed to move fromobjected window to another window while getting over theruggedness, but it is very difficult to develop such function.
Then, in this case, we make the approach to develop suchfunction a deferment, and try to achieve to plan that a user
set the robot in each window and the robot clean each one
window sequentially.
Fig.1 shows the plan that we summarized such idea. We
presume that this plan becomes the following procedures.
① The user takes out the robot from the inventory
location, and supply to the robot for window cleaningsuch as water.
② The user adsorbs the robot onto the window and startup the robot. The robot cleans the window
autonomously and returns the first position. In this case,the provision of the electrical energy for the robot
assumes the power source from indoor by power code
which also behaves as a safety rope.
③ The user gets out the robot from the window, andreturns it the inventory location.
Thus, we try to trim the weight and to reduce the risk of
fal out by using power code from indoor power source and combine with the safety rope.

Fig.2. The outline of a perpendicular load experimentFig.3. The result of a perpendicular load experimentFig.1. Concept of automated window cleaning

3 THE DEVELOPMENT OF THE ROBOTThe robot needs the following condition because many
robots are needed to carry out such plan and general public
use it.
１. The costs about one robot are cheap.
２. It is easy to maintenance.
３. It is a safe for all the people.
Then, we spot the mechanism use the crawler installed some ordinary suckers, and climb the vertical pane by© ISAROB 2013

3.1 The spec of the robot
Fig.4 shows the picture of the developed robot. And Fi
g.5 shows the diagrammatic illustration of a wall cleaning
robot. This robot is composed of a crawler that has suckers
that have a knob, sprocket, and pulley. Then a DC motor isused to drive the crawler.
Total weight of system is 0.8kg. Therefore, the robot can

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afford to have equipment such as cleaning unit because theone of the suckers can hold 8.2kg. The robot’s dimensions
are 100mm width, 210mm length, and 150mm height. Thissize is about a third of test model which was made in ourearlier study, and the weight is about one-fifth of it. We
have refined the concept design so that the size will become
suitable to the performance of the suckers. It also
contributes in a downsizing of the robot.

Fig.6. The outline of system

4 EXPERIMENT
Fig.4. The picture of developed robot

In experiment, t h e r o b o t wa s adsorbed onto thewindow and started up. Fig.7 shows the outline of theexperiment. Then, we made sure whether the robot could
climb the window.

Fig.5. Overall view of robot of climbing the window

3.2. The system of the window climbing
The robot climbs the pane by using a crawler withordinary suckers. The suckers of the crawler sequentiallystick to the window and the robot climb the pane. The
suckers are assembled to the crawler so that one or more
suckers stick to the window at all times. Total weight ofsystem is 0.8kg and one of the suckers can hold 8.2kg. It
means that the robot doesn’t fall down if one sucker falls of
the window.
Fig.6 shows the outline of the system to climb the pane.
When the crawler rotates, the sucker at adsorbing step ispressed to the wall by the rollers moving along the ledge.
Then the sucker at holding step keeps absorbing the wall
until the roller passes the ledge, also, push the sucker atadsorbing step. The sucker at peeling step peels off when
the knob is pushed up by the movement of the crawler.
Thus, the robot keeps absorbing and moves ahead.

© ISAROB 2013

Fig.7. The outline of experiment

5 CONCLUSION
In this paper, the research and development of thewindow cleaning robot was described as an approach of thewindow cleaning on the automation. Firstly, we view anidea about the window cleaning using a small wall climbing
robot. And propose the robot including the mechanism canclimb the pane with simple suckers. Then we actuallyfabricated the robot and performed an experiment that madesure whether it could climb the window.
Future works are assembling equipments to turn the
robot and to control the trajectory of the robot. For exampl
e, we try to turn the robot by attach omni-wheel backwardof the robot. And try to control the robot by attach thetouch-sensitive switch and acceleration sensor to clean the494

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window more suitably.

REFERENCES[1] Furuhashi (2004)，Maintenance and structural skill
with glass architecture No. 653, pp.182-183[2]Nishijima, Mizusima (1992), The development of a
glass cleaning robot “Canadian Club”, Journal of the Japan
Society for Mechanical Engineers
Vol. 10 No. 5 pp.596-598[3]Miyake T (2006), Research and development of w
indow cleaning robot WallWalker-Present conditions an
d perspective for commercial viability-, JSME Conferen
ce on Robotics and Mechatronics, Waseda, Japan[4]Nakano K, Kikuchi Y(2006), Conditional
Expression for the Occurrence of Stick-Slip Motion basedon the Coulomb Friction Model, Tribologist Vol.51 pp.131139[5]Zui-yuan Qian, Yan-zheng Zhao, Zhuang Fu (2006),
Development of Wall-climbing Robots with Sliding Suction
Cups, IEEE/RSJ International Conference on Intelligent
Robots and Systems, Beijing, China[6]Hirose S Tsutsumitake H, Development of Disk
Rover, Wall-Climbing Robot using Permanent Magnet Disk,
Journal of the Robotics Society of Japan Vol. 10 No. 7, pp.
992-997, (1992)[7]Tsuru K, Yoneda K (2007), Window Cleaning Ro
bot with Magnetic Synchronous Drive, Journal of the
Robotics Society of Japan Vol. 25 No. 5, pp. 738-744[8] Young Kouk Song, Chang Min Lee, Ig Mo Koo,et al (2008), IEEE/RSJ International Conference on Intelligent Robots and Systems[9]Okada N, Yamanaka K, Kondo E (2009), A Wall
Climbing Robot with Simple Suckers, ICCAS-SICE© ISAROB 2013

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Topological Graph Based Boundary Coverage Path Planning for a Mobile
Robot
Bong Keun Kim, Hideyuki Tanaka, and Yasushi SumiIntelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, Japan(Tel: 81-29-861-3282, Fax: 81-29-861-3493)
bk.kim@aist.go.jp
Abstract: A path planning method for the optimal boundary coverage of building interiors using a mobile robot is proposed.
First, the data association problem of indoor modeling caused by localization uncertainties of an information gathering systemsuch as a mobile robot is explained. Then, an expanded obstacle map is proposed to cope with the data association problem, inwhich the boundaries of the expanded obstacles are utilized as the path of a mobile robot. And also, the path enables a mobile
robot to gather information on the obstacles at a certain distance. Next, a topological graph is utilized for the optimization of
the travel path. Finally, simulation results are shown to verify the proposed algorithm.
Keywords: Boundary coverage path planning, Expanded obstacle map, Indoor modeling, Topological graph.
the image stitching error can be reduced. But the indoor
environment with narrow hallways and a complex indoor
structure prevents optimal positioning of a mobile robot forcollecting ambient information.
Given these considerations, an expanded obstacle map
which consists of obstacles with constant offset boundary
and a path planning method using a topological graph areproposed to cope with these problems. As a result, a mobile
robot can gather environment information at a certain
distance continuously and come back to the start locationby the minimum traversing distance via all boundaries of
obstacles since it navigates the boundary coverage path
which is an Eulerian circuit.
The rest of the paper is organized as follows. The
following section introduces the data association problem
of indoor modeling. Section 3 addresses the algorithm forboundary coverage path planning. Final section sums up
with some concluding remarks.

1 INTRODUCTION
This paper aims at developing a boundary coverage path
planning method which allows a robot to gather ambient
information at a certain distance in various structures ofindoor environments.
The typical coverage path planning for cleaning andexamination of indoor environments is focus on planningthe path which allows a robot to pass all accessible area in agiven environment. The cell decomposition method is oneof the famous approaches for the area coverage path
planning. Boustrophedon Cellular Decomposition (BCD)
based method carries out optimal coverage of known
environments using Reeb graph based critical points [1]. In
order to explore surface using UAV, BCD based coveragepath planning method was also proposed [2]. In order tosolve a boundary coverage problem using multi-robot withan omnidirectional inspection sensor, K. Easton et al.
proposed solution of kRPP(k-Rural Postman Problem)
using simplified sensor model based graph representation[3]. This approach is most suitable for boundary inspection.
But the inspection is not conducted at a certain distance.
On the other hand, the boundary coverage proposed inthis paper is for the point cloud generation which expresses
environments based on a set of vertices and for gathering
information for texture mapping. In order to generate
accurate point cloud, a robot should travel via points whichallow the robot to collect relative position information of agiven environment. And if texture information is collectedsuccessively at a regular distance, the frame transformusing projective geometry can be reduced and the pixel
density of images can be maintained uniformly and finally© ISAROB 2013

2 DATA ASSOCIATION PROBLEM OF INDOORMODELING
The point cloud generation using a mobile robot can besummarized as the following three steps.
1.
Localization and mapping are conducted basedon encoders, a gyroscope, and a horizontal LRF.
2.
If the travel distance is long, the position error
can be accumulated and it causes a loop closure problem. In
order to cope with this problem, the graph SLAM can beutilized for minimizing the accumulated error [4]. As aresult, if all the paths of a mobile robot are obtained, thepose of a robot at each sampling time can be calculated.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) Boundary following at a certain distance.

Fig. 1 Indoor modeling using a mobile robot.
(b) The path of a robot center becomes the boundary of an
expanded obstacle.

3.
Integrating the information from a vertical LRF
based on a robot pose, the point cloud can be generated.
As a result, in order to generate the point cloud and togather image information for texture mapping, it is requiredthat a mobile robot navigates along the path on which it cangather ambient information optimally based on the
structural characteristics of the surrounding space. Also, inorder for reducing the frame transform using projective
geometry and keeping the pixel density of visual image
uniformly, it is required that image information for texture
mapping is gathered continuously at a certain distance. But,
the navigation with no regard for changing space disturbs
the generation of point cloud with uniform density, and theinaccurate data association between the image and theposition data may cause an image stitching problem in the
texture mapping.
Fig. 1 shows the scanning patterns of a mobile robot
with two laser scanners when it navigates in a corridor forgathering information required for the point cloud
generation. The LRF which scans xy horizontal plane canbe used for building an obstacle map based on 2D
information of the environment. On the other hand, theinformation from the LRF mounted along z axis isdependent on the robot pose because it gathers information
based on line scanning which is vertical to the direction of
the robot. That is, since the line scanning is carried out as
the blue dot line shown in Fig. 1, the information gathering
is seriously affected by the structure of environment and thepose of the robot. Hence, the navigation of the mobile robot
should be conducted in order for the second LRF to nevermiss the boundary of the obstacle.
To cope with these problems, the path planning method
for the boundary coverage which allows a robot to gather
environment information optimally using a given 2D map is
proposed in the next section.

© ISAROB 2013(c) Minkowski sum.

Fig. 2 Expanded obstacles.

3 BOUNDARY COVERAGE PATH PLANNING
The boundary coverage path planning method aims at
generating the shortest path which allows a robot to travel
via all the boundaries of obstacles in various indoor
environments. In this paper, the boundaries of obstacles are
generated based on expanded obstacles, and then theboundary coverage path is planned by using a topologicalgraph. Hence, the proposed path planning method iscomposed of the following three steps.
First, expanded obstacles are generated based on the
optimal distance which is required for information
gathering. If a mobile robot navigates around an obstacle ata certain distance, then the path is generated as shown in
Fig. 2 (a). The radius of the robot is the optimal distance. If
the center points of the robot are connected, a certain
distance offset from the obstacle is generated as shown in
Fig. 2 (b). Hence, a robot with optimal sensing area in the
obstacle map can be dealt with as a point robot in the
expanded obstacle map. This means that if a point robot
navigates along the boundary of an expanded obstacle, thenthe robot can gather environment information optimally. In
this paper, an expanded obstacle is calculated by
Minkowski sum, which is described by Eq. (1). Fig. 2 (c)shows Minkowski sum for various polygonal obstacles [5].

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Intersection area
Point robot

Intersection point(a) Obstacles with intersection area(a) Obstacle map(b) Critical points and edges

Fig. 4 Critical points and edges for overlapping
polygonal obstacles.

(b) Critical points and edges for disjoint obstacles

Fig. 3 Critical points and edges for disjoint polygonal
obstacles.

⊕

∈ ,

∈

points are included in the vertices of the graphbecausethe point robot can gather environment information along
the intersection boundary. Fig. 4 (b) shows critical points
and edges for overlapping polygonal obstacles.
Finally, the boundary coverage path is calculated by
Chinese postman problem with the topological graph [6],which is an Eulerian circuit. Since the Eulerian circuit is an
Eulerian path which starts and ends on the same vertex, theobtained path is a closed path traversing via every edge atleast once. The boundary coverage path can be obtained by
the following linear programming problem:(1)

Next, the critical points based on Boustrophedon
Cellular Decomposition (BCD) [1] are founded and theedges which connect critical points are replaced by theboundaries of expanded obstacles. Then the critical points
and the boundaries are utilized as vertices and edges of the
topological graph,. If expanded obstacles
overlap each other and the intersection area doesn’t overlap
the obstacle, then the intersection points are included in thevertexes of the graphbecause the point robot can gather
environment information along the intersection boundaries.
Fig.3 (a) shows the expanded obstacle map of inner andouter obstacles. And Fig. 3 (b) shows critical points andedges of the expanded obstacle map. Without loss ofgenerality, if the search is started from the left side, the lasttwo critical points are not included in the vertex of the input
graph because they are connected to the boundary of the
obstacle which is already connected by other critical point.
Fig. 4 (a) shows the overlap of the boundaries in the
expanded obstacle map caused by that two obstacles arelocated too close in the obstacle map. If the intersection
area doesn’t overlap the obstacle, then the intersection© ISAROB 2013∑∈

Minimize(2)

And the following conditions should be satisfied.
0,
0,
∑∈

∈
∈

(3)
2

,

∈

where
is a real number representing the cost of edge ,is 1 if edge
meets node , and 0 otherwise,isan integer, the number of extra times the edge
istraversed,
is 0 when degree ofis even, 1 otherwise,andis an optional integer, which can be thought asadjoining loops to the graph at each node.

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013(a) Topological graph for disjoint obstacles.
(a) Topological graph for overlapping obstacles

10

7

8
9
1

11

0

5
3

6
4

2

(b) The boundary coverage path for disjoint obstacles.

(b) The boundary coverage path for overlapping obstacles.

Fig. 5 Boundary coverage path planning using a
topological graph for disjoint obstacles.

Fig. 6 Boundary coverage path planning using a
topological graph for overlapping obstacles.

As a result, the boundary coverage paths which have thecharacteristics of an Eulerian circuit are obtained. Fig. 5
shows the result of disjoint polygonal obstacles. Fig. 5 (a)shows the topological graph of the boundary coverage pathand Fig. 5 (b) shows the boundary coverage path, in which
the gray polygonal area is an obstacle, the green bold line isthe boundary of the expanded obstacle, the green thin line isthe edge which connects the critical points of disjoint
polygonal obstacles, the yellow circle is the critical point,
and the number means the order of the path following. Fig.
6 (a) shows the topological graph of the boundary coveragepath of overlapping polygonal obstacles. Fig. 6 (b) shows
the boundary coverage path.

ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 24300082.

REFERENCES[1] Mannadiar R, Rekleitis I (2010), Optimal Coverageof a Known Arbitrary Environment. Proc. 2010 IEEE Int.
Conf. Robotics & Automation (ICRA 2010), 2010, pp.
5525-5530.
[2]
Xu A, Viriyasuthee C, Rekleitis I (2011),
Complete Optimal Terrain Coverage Using an Unmanned
Aerial Vehicle. Proc. 2011 IEEE Int. Conf. Robotics &Automation (ICRA 2011), 2011, pp. 2513-2519.
[3]
Easton K, Burdick J (2005), A Coverage
Algorithm for Multi-robot Boundary Inspection. Proc. 2005
IEEE Int. Conf. Robotics & Automation (ICRA 2005), 2005,
pp. 727-734.
[4]
Grisetti G, Grzonka S, Stachniss C, Pfaff P,
Burgard W (2007), Efficient Estimation of Accurate
Maximum Likelihood Maps in 3D. Proc. 2007 IEEE/RSJ
Int. Conf. Intelligent Robots and Systems (IROS 2007),
2007.
[5]
Berg M, Cheong O, Kreveld M, Overmars M(2008), Computational Geometry - Algorithms andApplications, 3rd Ed, 2008.
[6]
Edmonds J, Johnson EL (1973), Matching, Euler
Tours and the Chinese Postman. Mathematical
Programming 5: 88-124.

4 CONCLUSION
In this paper, a mobile robot with cameras and laser
scanners for indoor modeling has been introduced and aboundary coverage path planning algorithm using expanded
obstacles and topological graphs has been proposed for the
information gathering of building interiors at a certain
distance. The topological graph based on the critical points
and the boundaries of the expanded obstacles was utilized
as an input graph for Chinese postman problem forplanning the optimal boundary coverage path, which allows
a mobile robot to traverse all the boundaries of obstacles atleast once. Case study results have been shown forverifying the proposed algorithm.

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Arc/line segments extraction from unknown indoor
environment with laser sensor
Rui-Jun Yan1, Jing Wu1, Ming-Lei Shao2, Ji-Yeong Lee2, and Chang-Soo Han2#
1

Department of Mechatronics Engineering, Hanyang University, Korea
Department of Mechanical Engineering, Hanyang University, Korea(Tel: 82-031-400-0462)

2#

corresponding author: cshan@hanyang.ac.kr

Abstract: This paper proposed an arc/line extraction algorithm from the unknown indoor environment with a laser sensor.
After getting the raw sensor data from laser sensor, the data is divided into different groups by checking the distance of two
adjacent points. If the distance is bigger than a limit value, the sensor points in this group will be separated into two groups atthese two points. Then the angle of three adjacent points is calculated and compared with another limit value. The group willbe split into twos at the middle one of three points if the limiting condition is not satisfied. Moreover, the points in every groupare attempted to extracting as a line segment firstly. If the extracted line is not smooth enough, the points in this group are
extracted as an arc segment. After the segmenting process, these segments are merged by using the segments merging
algorithm. In addition, the experiment results of arc/line extraction in unknown indoor environment by using a HOKUYO laser
sensor located on a Pioneer mobile robot are presented.
Keywords: feature extraction, indoor environment, laser sensor and arc/line segments.
Two algorithms about arc/line extraction are proposedin this paper, arc/line segments segmentation algorithm andarc/line segments merging algorithm. Firstly, the sensor
data are separated into different groups by checking the
distance between two adjacent sensor points. For the data inevery group, the angle of three sequential points iscompared with a limit value. The group is divided into twogroups if the condition is not satisfied. After finish theseparation of raw sensor data, the data points in every groupare extracted as a line segment or an arc segment. Somesegments belong to the same segment actually maybe
separated into different parts, which are merged by using
the segments merging algorithm if the overlap distance
between the segments is long enough.
There are five sections in this paper. The arc/line
segments segmentation algorithm is shown in Section 2 andthe segments merging algorithm is presented in Section 3.
The experiment results prove the feasibility of the proposed
in Section 4. Finally, this paper is concluded in the last
Section.

1 INTRODUCTION
The extracted segments can be considered as landmarks
which take an important effect in the robot localization,navigation, perception and mapping environment. Most
experiments, with laser sensor or vision sensor, of robot
localization and mapping environment estimate the positionof mobile robot using man-made landmarks, like Cup in [1]and Green Circle in [2]. However, it is difficult to put thelandmarks into unknown complex environment. The reasonof using landmark in these applications to correct theposition of mobile robot is its low accuracy with only
odometry, integrating the angular velocity and linear
velocity over time for measuring the incremental distanceand angle.
The data from the laser sensor are a collection of points
with angle and distance, which must be extracted as line orarc segment because of the features cannot be identified bypoints alone. The natural corners are extracted from thenatural indoor environment in [3], which are chosen as
landmarks applied in the SLAM experiment for realizing
the robot localization and mapping environment in [4].
Although the environment is constructed by using themobile robot and laser sensor successfully, the curves in theexperiment are expressed by some line segments which are
not very accurate. To express the geometric shape of thefeature appropriately, the raw sensor data are extracted asline segments and arc segments in this paper.

© ISAROB 2013

2 SEGMENTS SEGMENTATION
The goal of this part is separating the raw sensor data
into different groups and extracting the corresponding
segments. The distance between point pi and pi+1 iscomputed for checking whether these two points come fromtwo different objects. If the distance is bigger than a limit
value, the data set is divided into two sets. When the sensor
points are from several objects, the distance of the end

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distance λi will be found when the data is extracted as line
segment and arc segment separately. By comparing the
distance λ of these two points in Fig. 3, this data set isdivided into two sets at the point with bigger λ. Moreover,
all of these steps are executed repeatedly until the raw
sensor data in different groups are extracted as a line or an
arc segment successfully.

points of two data sets from two different objects must bevery long.
After finishing the first step, the angle between three
sequential points pi, pi+1 and pi+2 are calculated in Fig. 1
and compared with another limit value. If the angle issmaller than the restricted condition, the data set isseparated into two new sets at the point pi+1. When there isa corner in the environment, the angle between the points
from this corner must be smaller and these points should bein two data sets.

Fig. 3. Two points with biggest distance λ when the sensor
data in this group is extracted as line segment and arc
segment separately

Fig. 1. Calculation of the angle between three sequential
points: pi, pi+1 and pi+2

3 SEGMENTS MERGINGFor every group of the sensor data, first of all, the line
segments are extracted based on the data in this group. At
the same time, the averaged distance between all the sensor
points in the group and the extracted line segment iscalculated, shown in Fig.2. The detailed calculation of
average distance is expressed in Equation 1. After the
extraction of line segments, the average distance iscompared with a limit average distance. The arc segment is
extracted based on the same group of data if the average
distance of line segment is bigger than the limit value. The
average distance of arc segment is also calculated andcompared with the same limit value.

Some segments extracted from raw sensor data by using
segments segmentation algorithm maybe belong to thesame segment, which should be merged as one segment. If
the overlap distance between two line segments or two arc
segments is bigger than a limit distance as in Fig.4 andFig.5, these segments are merged as a new segment basedon the sensor data in these two groups. The merging
process is the same as the extraction part in thesegmentation algorithm.

Fig. 4. Overlap distance of two arc segments which belong
to the same segment actually
Fig. 2. Average distance between the sensor points and theextracted line or arc segment

ave   

1 k
 i
k i 1(1)

If both of the two average distances of the group isbigger than the limit value, the two points with the biggest© ISAROB 2013

Fig. 5. Overlap distance of two line segments which belong
to the same segment actually

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4 EXPERIMENT
4.1 Experiment 1
The experiment 1 is done in the first underground floorof Engineering building #5 in Hanyang University, shownin Fig. 6, including the pictures of real experimentenvironment. The extracted line segments and arc segments
by using the proposed algorithms in environment 1 ispresented in Fig. 7. There are three figures of the segments
extraction results when the mobile robot moves to different
position. The circular objects and walls are extracted as thecorresponding arc and line segments successfully. The
green dash line in the figures is the range of laser sensor, of
which the range is 270 degrees.

Fig. 8. Real picture of experiment environment 2

Fig. 6. Flow chart and real picture of experimentenvironment 1

Fig. 9. Extracted arc segments and line segments from envir
onment 2 with the different position of mobile robot
4.2 Experiment 2
The experiment 2 is done in the first floor ofEngineering building #5 in Hanyang University in Fig. 8,
the real picture of experiment. There are some columns andglass in this environment. However, the glass cannot be
detected by laser sensor which can be seen in Fig. 9. The
grey points in the figure are the raw sensor data and theblue line is extracted line segments. Arc segment isexpressed by a red curve with an origin point. The circularcolumns are extracted as arc segments and glass are
extracted as line segments successfully. The bigger grey
point with two dash lines stand for the mobile robot and therange of laser sensor.

Fig. 7. Extracted arc segments and line segments fromenvironment 1 with the different position of mobile robot© ISAROB 2013

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5 CONCLUSION
In this paper, the arc/line segments extraction algorithm
are proposed, including segments segmentation algorithm
and segments merging algorithm. The extracted segments
can be chosen as landmarks applied in robot localization,
path planning and mapping environment. Two experiments
are done for checking the feasibility of proposed algorithm.
In the experiments, the objects in unknown complex
environment are extracted as line segments and arc
segments successfully.

ACKNOWLEDGE
This research was supported by the Ministry ofKnowledge Economy, Korea, under the Industrial
Foundation Technology Development Program supervisedby the Korea Evaluation Institute of Industrial Technology(No. 10038660, Development of the control technology
with sensor fusion based recognition for dual-arm work andthe technology of manufacturing process with multi-robot
cooperation).

REFERENCES[1] Choi KS and Lee SJ (2010), Enhanced SLAM for a
Mobile Robot using Extended Kalman Filter, International
Journal of Precision Engineering and Manufacturing, Vol.
11, No. 2, pp. 255-264[2] Mata M, Armingol JM, Escalera A, et al (2001). “A
Visual Landmark Recognition System for Topological
Navigation of Mobile Robot,” Proceeding of the 2001
ICRA/IEEE International Conference on Robotics andAutomation, Vol. 2, pp. 1124-112[3] Yan RJ, Wu J, Wang WJ, et al (2012), Natural
Corners Extraction Algorithm in 2D Unknown Indoor
Environment with Laser Sensor, Proceedings of 12th
International Conference on Control, Automation and
Systems, Korea, pp.983-987[4] Yan RJ, Wu J, Lim SJ, et al (2012), Natural Cornersbased SLAM in Unknown Indoor Environment,
Proceedings of 9th International Conference on Ubiquitous
Robots and Ambient Intelligence, Daejeon, Korea, pp. 259261© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Study on the development of an open motion control platform for a differential
mobile robot based on ROS
Jiwu Wang1, Fangbo Liao1,Sugisaka Masanori 2
1

Department of Mechanical and Electrical Engineering, Beijing Jiaotong University, Beijing, China, 100044
2
Alife Robotics Corporation Ltd., 1068-1 Oaza Oshino, Oita, Japan

Abstract: Robust motion control is prerequisite for an advanced robot. Based on the ROS open source platform, wedeveloped
a reliable platform for any differential mobile robot. This platform can be modified according to practical various requirements
conveniently. That is, it is an open control platform for any sensors. The developed system has been verified with a mobile
robot developed in our lab. The experiment results show its reliability and robustness.
Keywords: mobile robot, motion control
Figure2 depicts the relation between translation androtation. The control of the robot at time t is expressed aslinear and angular velocities.
According to planar motion characteristics of the robot,
the robot control at time t is expressed as the linear andangular velocities

1 INTRODUCTION
Traditional robotic software structure is often
determined by the specific hardware. As the variety of the
hardware, this led to the difficulty of the common andreusable code platform.ROS (Robot Operation System) is aschedule platform published by Stanford and maintained by
Willow Garage with BSD license. Robot developers canbuild their own app or deploy the shared libs, middleware,and tools provided by ROS. Here a new mobile robot isdeveloped with a completed differential mobile robot basedon ROS requirement.
Figure1 shown in the system using differential drive, asa platform of nonholonomic vehicles, two drive wheelsare
paralleland a balance wheel realize differential movement
and the body balance. Vehicle hardware components
include: the two motors, the speed detection unit, the motor
drive unit, the motion control unit, the power supply unit,
and so on.

Figure2 Geometric relationships of the vehicle motion
Ro b o t po s e P h a s th r ee d e g r e e s of f r e e do m：

(x

y θ)

ontrol input

T，The mathematical relationship betweenc

ut and pose P is as follows[1]：2 Motion Analysis
Since the trajectory of the differential mobile robot canbe decomposed into three categories : linear, rotary, arc.
Therefore, this system facilitates the kinematics conclusion
to achieve the control on the three forms of motion[2].

Figure1 Differential drive wheeled mobile robot© ISAROB 2013

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Linear motion: when the speed of the two differential
wheelsequals anddirections are the same, the trajectory oft∫the robot is a straight line： x (t ) = vr (t ) dt
0

Rotary motion: when the speed of the two differential
wheelsequals and in the opposite direction, the robot moves
around the original position： θ (t ) =t2
vr (t )dt
L ∫0

Circular motion: when the directions of the two wheels are
identical, speed remains unchanged, and the speed
difference is constant, the trajectory of the robot is an arc.

Figure3 vehicle motion analysis
Below is the algorithm to compute xt :δ rot1 = arctan( y '− y, x '− x) − θ

2.1 Prediction model based on control
Suppose

the

input

is

⎧vt ⎫
⎨ ⎬ ， the
⎩ωt ⎭

state

δ trans = ( x '− x) 2 + ( y '− y )2is

δ rot 2 = θ '− θ − δ rot1

xt = ( x ', y ', θ ' ) after ∆t , also suppose the input isδ$ rot1 = δ rot1 − sample(α1δ rot1 + α 2δ trans )Tδ$ trans = δ trans − sample(α 3δ trans + α 4 (δ rot1 + δ rot 2 ))invariant in time ∆t ，sov = ω ∗r

δ$ rot 2 = δ rot 2 − sample(α1δ rot 2 + α 2δ trans )νxc = x − sin θων
yc = y + cos θωδ$ rot1 , δ$ trans , δ$ rot 2 stand for the true motion ， whereinsample(b) represents triangular or Gauss distribution withvariance b zero mean ， α1,α2 ,α3 ,α4 are the robot-

The pose is as following after ∆tspecific error parameter ， which characterize the robot
cumulative error.

ν⎛⎞⎜ xc + ω sin(θ + ω∆t ) ⎟⎛ x'⎞ ⎜⎟ν⎜ ⎟ ⎜xt = ⎜ y ' ⎟ = yc − cos(θ + ω∆t ) ⎟⎜⎟ω⎜θ ' ⎟ ⎜⎟⎝ ⎠ θ + ω∆t
⎜⎜
⎟⎟
⎝
⎠

So the state xt is computed as:

x ' = x + δ$ trans cos(θ + δ$ rot1 )y ' = y + δ$ trans sin(θ + δ$ rot1 )θ ' = θ + δ$ rot1 + δ$ rot 2
T
Wherein xt = ( x ', y ', θ ' )2.3 Interface implementation
ROS controls the robot motion through the two
messages ： cmd_vel message implements the motion
control from the high layer to the drive level；Odommessage is collection of the motion state.

2.2 Robot measurement model based on encoders
For the differential drive robot，robot state is
input isxt ，control

ut ， measurement is zt ， so the conditional

probability of the robot state can be expressed as[3,4]：p( xt | x0:t −1, z1:t −1, u1:t )cmd_vel is of twist data type，twist data type is as thefollowing：

For convenience，we simplify the robot motion process
to three stages，shown in figure 3：rotation-translationrotation ， represent the measured motion as ：geometry_msgs/Vector3 linear：float64 x，float64 y，
float64 z
geometry_msgs/Vector3 angular：float64 x，float 64(δ rot1 , δ trans , δ rot 2 )T© ISAROB 2013

y，float64 z
As shown above，Twist message contains two Vector3
type sub messages，in right-handed coordinate system,
respectively represent the three directions of the line speed

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and the angular velocity. For differential mobile robot ，only the translation x and angular z are nonzero because of
the freedom degree. The following analyses how the sent
message is resolved and implemented:
As mentioned geometric motion analysis，when z
is zero ， x is the robot translation speed, low-level
controller sends two identical channel PWM drive signal，
via the motor drive amplifier，the robot pose is ：( xi + di cos θθ)yi + d i sin θ

其中

velocity can be deduced from the vector U .
The detail of the pose type:geometry_msgs/Point position：
float64x ，
float64 y，float64 z
geometry_msgs/Quaternion orientation ： float64 x ，float64 y，float64 z，float64 w
float64[36] covariance
Therein,the values of x and y in positionare computed asfollows:td i = ∫ v(t ) dt

∆x = [vx (t ) cos θ − v y (t ) sin θ ] ∗ dt

0∆y = [vx (t ) sin θ + v y (t ) cos θ ] ∗ dt

When x is zero，z is the angular speed of therobot，the low-level controller send two channel PWM
drive signal with opposite direction and same duty cycle.

⎡⎣

The robot pose is ⎢ xi

yi θ i −2d Li ⎤L ⎥⎦∆θ = ω ∗ dt

T

xi +1 = xi + ∆xyi +1 = yi + ∆y

When x， z are not simultaneously zero ， we need toθi +1 = θi + ∆θcompute the value of vx and vy from the known x andAnd orientation can be gotten:
odom.pose.pose.orientation=tf::createQuaternionMsgFromYaw( θ );known z:Reverse solution vector U :⎡ 1⎡vl (t ) ⎤ ⎢ 2⎢ v (t ) ⎥ = ⎢ 1⎣ r ⎦ ⎢− L
⎢⎣ 2

1 ⎤
2 ⎥ ⎡v ⎤⎥1 ⎥ ⎢⎣ω ⎥⎦L
2 ⎥⎦3 Experiment design
In order to verify the accuracy of the robot motion, we
design two forms of motion to check the constancy of theimplemented and the desired results. The two algorithms
are respectively translation motion drivex(distance，speed)，rotation motion turnx(angle，angularSpeed).

Send the velocity command of the two motors，the desiredtranslation velocity angular velocity can be achieved.
Odom message represents the robot state，ROS
publishes Odom message to indicate the estimate of the

divex(distance，speed)algorithm：send velocity command
to robot ， record the motion start point:startPose ， andupdate the current pose in real time: CurrentPose，so the
distance is：dx = currentPose.x–startPose.x
dy =currentPose.y–startPose.ydistanceMoved = math.sqrt(dx * dx + dy * dy)compare the value of distanceMoved and desired distance，
controlling the robot to a desired distance can be realized.

position and velocity，assuming we get tick (t ) from theleft encoder within measured period
applied encoder parameter isη( t , t + ∆t ) ， theη1 ， wheel diameterparameters is 2 ，further computation on these parameterand measured data，we can obtain one side translation andangular velocity,，similarity the right side[5]：tick (t )∗η1∆ttick (t )vl (t ) =∗η1 ∗η 2∆t

ωl (t ) =Turnx(angle ， angularSpeed)algorithm ： send angularvelocity command cmd_velto robot，record the motion
start point:startPose，and update the current pose in realtime: CurrentPose，so the rotation angular is：dx = currentPose.x–startPose.x
dy = currentPose.y–startPose.yangleTurned = currentAngle + angleOffset–startAngle

And Odom message in ROS belongs to
nav_msgs/Odometry type ， the detail of thenav_msgs/Odometry is as following:
Header header
stringchild_frame_id
geometry_msgs/PoseWithCovariance pose
geometry_msgs/TwistWithCovariance twist
twist is as the previously described, we gain

© ISAROB 2013

from the two encoders ， and translation and angularwhereinangleOffset is offset to 2nπ ,we can control therobot at desired angular pose through comparing
angleTurned and angle.
The Experiment design the trajectory of the robot is square,
we evaluate the motion error by comparing the pose of thevl and vr
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start point and the end point.
Design trajectory: square with 1m side length, translation
velocity is 0.2m/s, angular velocity is 0.7rad/s.
Pseudo code of test Algorithm :Repeat 4 times:
divex（1，0.2）
turnx（radius(90)，0.7）
End repeat
The experiment result shows our approach is stable andapplicable. Figure4 is the actual trajectory of our robot,
from the trajectory we can conclude that our means canconstruct the desired differential wheeled mobile robot
platform.

Figure4 vehicle motion analysis
In the next step, some further tests will be carried out,
such as navigation with complicated trace, obstacle
avoidance etc.
}

REFERENCES[1] Gregor Klanč ar , DragoMatko, Saš o Blaž ič , A
control strategy for platoons of differential drive wheeledmobile robot[J],Robotics and Autonomous Systems 59(2011) 57–64.
[2] Soonshin Han, ByoungSuk Choi, JangMyung Lee. A
precise curved motion planning for a differential driving
mobile robot[J]. Mechatronics 18 (2008) 486–494.
[3]Thrun S，Fox D, Burgard Probabilistic Algorithms andtheInteractive Museum Tour-guide Robot Minerva[J],InternationalJournal ofRobotics Research,2000,19(1-1) ：972-999．
[4]Thrun S, Fox D, Burgard W. Probabilistic Robotics [M].
Massachusetts:MIT Press,2005. 167-193.
[5]Ming Tan, De Xu. Advanced Robot Control[M].Beijing:Higher Education Press,2007. 400-410.

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The small eye and tongue module for an android robot head
Dongwoon Choi, Dong-Wook Lee, Duk Yeon Lee, BYEONG KYU AHN and Hogil Lee
Department of Applied Robot Technology, Korea Institute of Industrial Technology(Tel: 81-031-8040-6317; Fax: 81-031-8040-6370)(cdw｜dwlee｜proldy｜bk.ahn｜leehg@kitech.re.kr)

Abstract: In this paper, the small size eye and tongue module for an android robot head are presented. There are narrow spaces
inside the android robot head by its human like shape and size, so to use small size parts is very important to design an android
robot head. We tried to design an android robot head with 33 D.O.F and human like shape, but as the large numbers of D.O.F
and human like shape, it was needed to make small size of each module to achieve our goal. The eye module presented has 2
D.O.F for eyeball and 1 D.O.F for eyelid, so there are roll, pitch motion for eyeball and pitch motion for eyelid like human eye.
A gimbal was generally used in robotic eye, but a spherical joint was selected to reduce space occupying by its simple structure.
The spherical joint is very simple and can manufacture small one, but it has 3 D.O.F even if there are only 2 D.O.F in eyes, sorotation motion of eye can be occurred. To avoid rotation of eyeball by using spherical joint, spring column was installed incenter of eyeball. There has been no active tongue for an android robot or humanoid robot until now. The tongue is very
important factor to design human like robot because it can be shown when the robot speaks, so an active tongue give more
reality to an android robot when it speaks. Additionally, there are many emotional expressions to use tongue like ridicule, so
tongue can help to make more various emotional expressions in android robot research. The tongue module has a flexible
silicon complex skin for natural motion and human like looks. It has 2 D.O.F for stick out-put in and bending. The wire andrack gear were used to make that motions. With this small eye and tongue module, an android robot head which is highlysophisticated and more similar to human head can be designed.
Keywords: Android, robot, robot eye, robot tongue

I.

INTRODUCTION

D.O.F to the android head. Takuya Hashimoto made afemale android robot SAYA and its eyes are gimbal type, so
it has only 19 D.O.F in head, because eyes take so many
spaces [4]. Minoru Hashimoto used artificial muscles tomake an android robot face. This face is based on anatomy,
so it is very good trial to make an android robot, but it ishard to make and maintain [5]. Jong Won Kwak usedpolymer artificial muscles to make eyes. These eyes arevery small and simple, but it needs separate controller and
DC-DC converters, so it is not suitable to our robot [6]. Our
android robot has been used in commercial areas, so it isvery important factors like maintenance, cost and parts
supply, so we tried to develop small but simple structure [7].
To make small and simple eyes, spherical joint and springcolumn was used. Lorenzo Jamone used spring as a neckjoint and bone to make their humanoid robot [8]. From thisspring joint, the idea of spring column which prevent a
rotation of the spherical joint can be drawn. The sphericaljoint is the simplest structure to make 2 axis motions, butthere is a rotation of axis and using a spring column canhold this rotation. The main role of a tongue to an organism
is degustation, but robots don’t need to taste, so it is notexist or generally used as a decoration. The android robot
has human like appearance, so the tongue is necessary, but
it is just a dummy generally. In case of humans, the tongue

As researches in robotics have been developed, theresearches of a humanoid robot and an android robot havebeen increased. An android robot is one of humanoid robots,
but it has human likely appearance than one of humanoid
robot. Because an android robot has human likely
appearance, it can be used generally like a secretary, anannouncer, an actor, etc. [1]. The appearance and size of
android robots are similar to human, so the inner spaces ofthem are very narrow and this is one of the hardest things todesign an android robot. Especially, in case of a head, there
are many actuators to make facial expressions, so securing
spaces is the most important to make a face with large
numbers of D.O.F. The best method to secure spaces inside
head is to reduce the size of each part, but most of parts likeactuators, bolts are standardized, so it is not easy to reduce
their size. For this reason, to downsize structures bysimplification can be one of the best solutions. The eyeparts are usually a gimbal which has yaw, pitch joint andthey are most complicate and large sized parts in a head.
The eye used a gimbal has 2 D.O.F in each eye and it isvery common structure as a robotic eye, but it has frame inoutside of eye ball and it is not good to downsize [2] [3].
Downsizing an eye means increasing of the number of© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013is used when they are speaking, express emotions, so we
focused its role to emphasize emotional expression. This is
the first trial for android and humanoid robots. Toshiya
Kawamura made a robotic tongue to research flexibility andsoft motions of robots, but it was made for researches, so its
size is too big and appearance is not matched to one ofhumans [9]. The robotic tongue presented has 2 D.O.F(stick out – put in, bending) and it was made by siliconcomplex, so it has human like appearance. To make natural
bending motions, wires were used and a rack-pinion gear
was used to make stick-out and put in motions. This tongue
module was designed small size enough to install in a jaw,
because there are no actuators for skin in a jaw even if its
space is narrow.

II.
1.

eyelid are designed similar to human eyelid andmanufactured by CNC not bending, because the gap
between an eyeball and an eyelid is very important tolooking, so the gap is just 0.5mm. The eye ball and eyelid isconnected to servo motors by ball joints. These parts areassembled as a module, so it is easy to maintain. The
module structure is easy to maintain and attach to other face.
The size of a presented 3 D.O.F eye module is very small as
64x40x40mm (Fig.2). Because of using small eye modules,
more spaces inside of head can be occupied, so 33 D.O.F
head can be developed.

Hardware design

Design of the eye module

The purpose of a presented robotic eye is to make small
size, simple structures and easy to maintain. In addition, thesize and appearance must be exactly same to human eyes.
There are pitch and yaw motions in human eyes and in caseof both eyes, the yaw motion is separated but the pitch
motion is constrained, so both eyes have 3 D.O.F. In case of
the android robot, it has eyelids and they moveindependently, so the eyes of android robot need 5 D.O.F
totally. The gimbal is used generally to make pitch, yaw
motions of eyes, but this structure has frame outside of eyes
and this is not good structure to miniaturize. In addition,
this structure needs standard parts like bearings, bolts, etc.
and there is limit to find small parts among them. The
simplest structure to make 2 D.O.F is a spherical joint. The
spherical joint is consist of only 2 parts, cup and ball joint,
so it is very easy to downsize and manufacture, but there isa problem to use for eyes. The joint of spherical joint is not
fixed to rotate motion unlike a gimbal or universal joint, so
the eyes can rotate. The spring column is used to prevent
rotation of eyes. The spring can be used as a joint because it
can be bent to any directions. The presented eye ballconsists of a cup joint, hollow ball joint and spring column.
The spring column is connected with ball and cup joint, and
it is located in hollow of ball joint, so the eyeball has only 3parts but it has 2 D.O.F, small size and the simplest
structure (Fig.1). 3 RC servo motors (Dymond D47, HiTec
Hs5085mg) are used as actuators to move an eyeball and aneyelid, because RC servo is easy to use and small. The© ISAROB 2013

Fig 1 Structure of eyeballFig. 2 3 D.O.F eye module
2.

Design of the tongue module

A tongue is one of the most complicate parts in human
body, so it is very difficult to make human like tongue. The
motions of a tongue are divided outside of mouth motions
and inside of mouth motions. In case of an android robot,
the role of a tongue is only for expressions or as adecoration, so inside of mouth motions are not needed. The
outside of mouth motions can be simplified as stick out-put
in, bending up-down, bending left-right, so there are 3
D.O.F. The tongue is located in a jaw and the space inside a
jaw is very narrow, so 2 D.O.F (stick out-put in, bending

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up-down) is selected for downsizing. The silicon complex
is used to make human like appearance and elasticity. Thereis a plastic bone inside the silicon tong and wires are used
to make motions. Three wire holders are attached the bone
and by changing of the location of these holders, thebending shape can be adjusted. A rack- pinion gear is used
for stick out-put in motion, because it is very simple andcheap. The actuators for tongue are RC servo motors(Dymond D47, Dymond D 60). This robotic tongue is also
designed as a module, so it is easy to maintain and apply toother head. The size of this tongue module is small as
100x60x60mm and the range of stick out-put in is 40mm.
Fig. 3 shows mechanism of tongue module. These small
sized eye modules and tongue module can supply spaces
enough to make 33 D.O.F inside an android robot head(Fig.4).

subminiature analog types, so it is hard to receive data fromdevices. The most important factor to the android robot is
how the android robot is similar to human, so we focused tocheck its shape when it operated. We attached new eye andtongue modules to our hardware, android robot EveR-3,
with new head which has 33 D.O.F and test. Fig. 5 shows
yaw and pitch motions of eyeballs and a pitch motion ofeyelids. The stick out-put in, bending up-down motions arealso checked (Fig.6). All motions with eye, tongue modules
are operated without trouble and similar to one of human.
The presented eye, tongue modules have small size andsimple structure, so the 33 D.O.F android robot head withhigher expressions and easy maintenance can be developed.

Fig. 3 Tongue module and its mechanism

Fig. 5 Eye motions (default, left, up, down gaze)

Fig. 6 Tongue motion (stick out, bending down)
Fig. 4 Assembly of each module and 33 D.O.F head

IV.
III.

The small and simple eye, tongue modules for new 33
D.O.F android robot head are presented. The android robot
needs many actuators to make human like facial
expressions, so inside spaces of head are very narrow. In
this reason, downsizing of each part is very important todecide its D.O.F. In addition, our android robot has beenused in commercial area, so easy maintenance andreliability is very important. To satisfy these needs, small

Experiments were taken to check whether the eye module,
tongue module work or not when it is attached to skin andin that time, how the appearance is similar to one of human.
There are no cameras in eye module, so accuracy doesn’t
matter in this device and the RC servo motors used are© ISAROB 2013

CONCLUSION

Experiment and result

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and simple eye, tongue modules are developed. The eyes
used a spherical joint and spring column and these canmake small, simple structure and it is made as a module, so
it is easy to maintain. The tongue consists of the siliconcomplex tong, plastic bone and rack-pinion gear. This
tongue is also made as a module and small sized. The small
sized RC servo motors are used as actuators. By this eye,tongue modules which are downsized, the android robot
head with 33 D.O.F can be developed.

Intelligent Robots and Systems, Aug. 2-6, pp.1041-1046

Acknowledgement
This research is supported by Ministry of Culture,
Sports and Tourism (MCST) and Korea Creative Content
Agency (KOCCA) in the Culture Technology (CT)
Research & Development Program 2010

References[1] H. G. Lee, M. H. Baeg, D. W. Lee, T. G. Lee, and H. S.
Park (2006), Development of an Android for Emotional
Communication between Human and Machine, Proc. of Int.
Symp. on Advanced Robotics and Machine Intelligence[2] Nobutsuna Endo, Shimpei Momoki, Massimiliano

Zecca, Minoru Saito, Yu Mizoguchi, Kazuko Itoh, andAtsuo Takanishi (2008), Development of Whole-bodyEmotionExpression
Humanoid
Robot,IEEE
International Conference on Robotics and Automation,
Pasadena, CA, USA, May, pp.19-23[3] Chyi-Yeu Lin, Chun-Chia Huang and Li-Chieh Cheng(2011), A Small Number Actuator Mechanism Design for
Anthropomorphic Face Robot, IEEE International
Conference on Robotics and Biomimetics, Phuket, Thailand
Dec. 7-11, pp.633-638[4] Takuya Hashimoto, Masato Suzuki and Hiroshi
Kobayashi (2009), Development of Remote Class Support
System and Field Trial using an Android Robot, IEEE
International Conference on Mechatronics and Automation, Changchun, China, August 9 – 12, pp.2042-2047[5] Minoru Hashimoto, Chisaki Yokogawa and Tsugutake
Sadoyama (2006), Development and Control of a Face
Robot Imitating Human Muscular Structures, IEEE/RSJ
International Conference on Intelligent Robots and Systems,
Beijing, China, October 9- 15, pp.1855-1860[6] Jong Won Kwak, Ho June Chi, Kwang Mok Jung, Ja
Choon Koo, Jae Wook Jeon, Youngkwan Lee, Jae-do Nam,
Youngsun Ryew and Hyouk Ryeol Choi (2005), Journal of
Mechanical Science and Technology, Vol 19, No 2, pp.
578~ 588[7] Dongwoon Choi, Dong-Wook Lee, Duk Yeon Lee, Ho
Seok Ahn and Hogil Lee (2011), Journal of Artificial Lifeand Robotics, Vol 16, No 3, pp.315~ 318[8] Lorenzo Jamone, Giorgio Metta, Francesco Nori and
Giulio Sandini (2006), James: A Humanoid Robot Acting
over an Unstructured World, IEEE-RAS International
Conference on Humanoid Robots, Dec. 4-6, pp.143-150[9] Toshiya Kawamura, Tadayoshi Tandai, Hideaki

Takanobu (2005), IEEE/RSJ International Conference on© ISAROB 2013

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Design of robotics arm’s behavior in imitation animal consciousness:
Development of altruistic behavior
Kyoko Tanaka

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: Our research has been focused on developing a robot with a “consciousness” like that of a person or an

animal to enhance the user affinity of service robots. Our laboratory previously conceived a model of themechanism of consciousness and action and a related software architecture, called Consciousness-based
Architecture (CBA), by which this model can be used to control the action of an artificial animal. Here, we newly
theorized a “motivation model” which assumes that certain motives inhere in the actions of sentient beings, andthat the motivational processes involved could become part of how a robot determines what action to take. Our
motivation model is based on the dopamine-generating mechanism of sentient beings. In the present study, we
focus on the altruistic behavior of animals so that the consciousness and behavior of the robot approximate thoseof an animal. We added altruistic behavior to the CBA and tried to construct a system in which the robot
cooperates with the user and chooses altruistic behavior. Our goal is to enhance the user affinity of service robots
with this system.
Keywords: CBA, Robot consciousness, Robot consciousness

1. INTRODUCTION

developed a software architecture we call
Consciousness-based Architecture (CBA). CBA
introduces an evaluation function for behavior selection.
Here, we elaborate on the evaluation function, using the
dopamine-based motivational system as its basis.
For the present study, we developed a robotic armthat has six degrees of freedom, so that the arm could
autonomously adjust to a target position. Fig. 1 shows
an overview of the robotic arm, which has a handconsisting of three fingers in which a small monocular
web camera is installed. The landmark object is detectedin the image acquired by the web camera. In a previous
study, as an autonomy action experiment, we applied
CBA to the robot arm and inspected the arm's behavior.
We thus determined that the arm could successfully
perform the maneuvers used in the present study.

At present, not only industrial robots but also socalled “service” robots are being developed swiftly bymany researchers around the world. There are various
types of service robot, including business robots,
research robots, welfare robots, and domestic robots.
The operation of these robots requires not only the basic
functions of robots, such as a high level of intellectual
activity, but also the function of user compatibility oraffinity, so that a user can feel close to the robot as aresult of its appearance and behavior. User compatibility
implies that the user is easily able to operate the given
robot, without getting bored with its use, and can readily
develop a sense of closeness with it. One design goal isthat the user will ultimately receive from service robots
the emotional benefits experienced in relationships withother conscious beings.
Although a robot may gain user compatibility by
being genuinely modeled after a face, it is far morechallenging to achieve user compatibility through its
behavior and actions, including human-like “capricious
behavior.” The attempt to give robots “consciousness”
such as that identified in humans and animals is a partof these requirements.
Our laboratory has studied animals’ adjustments totheir environments in an attempt to emulate animal
behavior. We constructed a hierarchic structural modelin which consciousness and behavior were
hierarchically related. Based on this model, we© ISAROB 2013

Fig. 1 Overview of robotics arm

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2. SYSTEM STRUCTURE

3.1 Recognize the situation using a web camera

Figure 2 shows the appearance of the robot arm andits degrees of freedom. The robot arm has 7 levels of
flexibility {shoulder (Joint1, Joint2), elbow (Joint3,
Joint4), wrist (Joint5, Joint6), and finger (Joint7)} at its
full length of 450 [mm]. The hand part has 3 fingers
with one level of flexibility; the weight of the main part
is about 0.8 kg. A small web camera, installed at the tipof the arm, can recognize the external situation. The
web camera and the robot arm’s actuator are controlled
by USB communication.

The first step of the “humanized” robot’s system
would be to recognize a situation. For this purpose wedevised a labeling image (Fig. 4), taken by the web
camera installed on the robot hand. We programmed thesystem to divide the image into green, blue, red andflesh-colored blobs and extract the shape, size, andcenter of gravity position (Fig. 4). From this informationand the posture of the robot arm, the robot could
recognize the position and its distance from the targetcolor object. Furthermore, the system memorized thecentral point for three frames.

Fig. 4 Simplified image and labeling image
Fig.2 Robot arm and degree of freedom in the robot arm

3.2 Determine motivation
based on the situation and on dopamine input

3. AUTONOMOUS BEHAVIOR

When a human and an animal interact, changes occur
in the dopamine level in the brains of both the humanand the animal. We used the dopamine-generating locus
as the model for determining the robot's motivation andcopied it for use in the control model. The control modelis shown below. In Fig. 5, the graph shows sample
changes in n ,  and T .

Most robots are pleasing to people because of theirunique movements. However, the action choices ofrobots are very objective in orientation, and we believe
action choices that resemble those of subjective humanbeings and animals are needed to enhance user
compatibility. Therefore, we at first considered thestructure of a sentient action. When an animal, including
a human being, takes an action, it can be represented bya series of steps, such as "Recognition →Comprehension → Motivation → Action." Typically,
the action of the robot tends to eliminate motivation andinstead engage in the simple flow "Recognition(Comprehension) → Action.” Figure 3 shows the ideal
series of steps for a robot with both objective andsubjective behavior.

Rising
Decaying

t

ye T

n : natural angular frequency: earliness of a rise in dopamine : braking rate

: height of the peak of a rise in dopamine
T : time constant : attenuation performanceFig. 5 Dopamine modelFig. 3 Flowchart of this system© ISAROB 2013y"2 ny' n2 y   n2 u (t )  0

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3.4 Activate the motor to accomplish the action

In the control model, dopamine is generated withreference to pleasant or disagreeable stimuli represented
in variables.
n and T are determined by the outside environment
and the internal state, respectively. Moreover, a robot's
motivation is defined by asking for the total of thegenerated dopamine and calculating the secondary delay
response in such a way that the total is considered as the
input for the robot's motivation.

The action level was set up by dividing a robot's
motivation by a fixed value. In this way, the actions that
could be chosen for each action level were limited.
Figure 8 shows the robot's choices in this system.

Fig. 8 Choice of action based on motivation

4. ALTRUISTIC BEHAVIOR

Fig. 6 Motivation model

Our robot acts in proportion to the value of the
motivation. For example, when the robot sees a favorite
object, the value of the motivation rises and the robot
approaches or catches the object. On the other hand,when the robot sees an object it dislikes, the value of the
motivation drops and the robot avoids the object. Thisbehavior of the robot can be considered to be selfish. In
the present study, we focus on the altruistic behavior ofanimals so that the consciousness and behavior of therobot approximate those of an animal. We added
altruistic behavior to the CBA so that the robot
cooperates with the user, and our goal was to construct asystem in which the robot would choose altruistic
behavior.

3.3 Choose an action using CBA
Figure 8 shows a diagram of CBA, which relates
consciousness to behavior hierarchically. In this model
the consciousness behavior fields are built separately. Ina dynamic environment, this model determines the consciousness level appropriate to the environment, and therobot then selects the behavior corresponding to that
consciousness level and performs the behavior. Whencertain behavior corresponding to the consciousness
level is discouraged by some external environmental
factor, the consciousness level approaches an upper
level so that the robot can select an advanced behavior.
Alternately, while operating at upper-levelconsciousness the robot can choose to perform a lowlevel behavior. The mechanism of this model is that it
selects the optimum behavior within the low-level
behaviors, to achieve the robot’s goals.

4.1 Flow of imitated action
The altruistic behavior that we added to the CBA is
an imitation of the action sit, which is a typicalcommand given to a dog. The owner says “Sit.”
whereupon the dog sits on his hind legs and waits forthe owner’s direction. The dog acts altruistically during
the waiting time, knowing that the appropriate behavior
will get him what he wants. In this case, the altruistic
behavior includes conquering his desire to simply chasethe ball and instead cooperating with his owner. We
imitated the sit dog command and established an
altruistic behavior that the robot could perform, as
shown in Fig. 9.

Fig. 7 Consciousness-based Architecture (CBA)© ISAROB 2013

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robot to catch the red object by bringing the subsequent(D). The robot approached the red object (E) and caught
it (F).

Fig. 9 Flow of altruistic behavior

(A)

(B)

(C)

(D)

4.2 Algorithm for altruistic behavior
The robot recognizes the outside situation using the
web camera installed on its hand. We programmed therobot to act based on switching from selfish
consciousness to altruistic consciousness when the robot
sees a red object.
If the robot sees a red object, the robot throws itself
into a posture, like the position assumed by a dog when
the sit command is given. When the user moves the red
object, the robot keeps the object in its sight as if itwishes to chase the object. The robot waits until the user
gives directions. If the value of the motivation changes
while the robot is waiting, the robot doesn’t choose its
action based on the motivation; instead, it follows theuser’s direction. If the user brings the object near to therobot, the robot judges that it could catch the object, andit thus tries to catch it. On the other hand, if the user
keeps the red object at a distance from the robot, therobot judges that it could not catch the object, and it
thus tries waits for the user’s direction.

(E)(F)
Fig. 11 The action of a robot arm

6. CONCLUSION
We created a system of altruistic behavior for a robot,
which acted based on not selfish consciousness but
altruistic consciousness. In this case, the robot cooperated
with the user when it saw a red object, as shown in the
result of the verification experiment. We thus confirmed
the effectiveness of this system.
Our system of altruistic behavior will enable the robot
to act sophisticated. For example, when the robot sees anobject it does not like as well as a red object, the robot
will appear to have difficulty following the user’s
directions. Eventually we would like the robot to express
embarrassment at such a predicament. We believe such
behavior would enhance the user affinity of the robot.

7. ACKNOWLEDGEMENT
This research was partially supported by the Ministry of
Education and Science,Sports and Culture, Grant-in-Aid for Scientific Research,2012.

Fig. 10 Choice of altruistic behavior
based on altruistic consciousness

8. REFERENCES

5. VERIFICATION EXPERIMENT[1] Kouichirou Kurogi, Kei Ueyama, Eiji Hayashi, Designof robotic behavior that imitates animal consciousness:
Emotion expression of robotic arm based on eyeball
movement AROB 16th '2011, Bepu, Oita, Japan, 2011

We added the system of altruistic behavior to the
CBA and verified the subsequent action of the robot
experimentally. Figure 11 shows a result of theverification experiment. The robot saw a red object andsat down (A). Then, the robot stared at the red object
and waited for the user’s direction to catch it (B). If the
motivation rose during this time, the robot went on
waiting without being selfish (C). The user directed the© ISAROB 2013[2]Motoki Yoshida, Eiji Hayashi, Design of robotic
behaviorthat
imitatesanimalconsciousness:
Construction of the user-recognition system, AROB 17th
'2012, Beppu, Oita, Japan, 2012

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Network Based Multiple Mobile Robots with Subsumption
Architecture Supporting Swarm Behaviors
Maki K Habib1, Fusaomi Nagata2, Akimasa Otsuka2 and Keigo Watanabe3
1

The American University in Cairo, Egypt
Tokyo University of Science, Yamaguchi, Japan
3
Okayama University, Yamaguchi, Japan2

1

maki@ieee.org

Abstract: This paper presents the use of dynamic sampling period to evaluate the basic behavior performance of networkbased multiple mobile robots system with cooperative swarm behaviors. Network-based subsumption architecture with PC
server is proposed to minimize the total cost for designing multiple mobile robots system by maximizing the groupperformance of robots with limited hardware and software capabilities rather than optimizing the behavior performance of a
individual robot. This enables to develop high-level group behavior architecture such as a complex schooling behavior. Suchcapability is based on decomposing a complex behavior into simple and basic behaviors that are organized into layers ofsubsumption architecture. Finally, this paper, the basic performance of the network-based subsumption architecture isexperimentally evaluated in association with the measurement of the dynamic sampling period.
Keywords: Multiple mobile robots, Subsumption architecture, Swarm intelligence, Group behaviors.
these challenging application domains, multi-robot systems
can often deal with tasks that are difficult, if not impossible,
to be accomplished by an individual robot. The following
paragraph highlights some examples of the work in the fieldof multi robot systems.
Parsons and Canny proposed an algorithm for planningthe motions of several mobile robots which share the sameworkspace containing polygonal obstacles [7]. Each robot
has an ability of independent translational motion in two
dimensions. The algorithm computes a path for each robot
which avoids all obstacles in the workspace as well as theother robots. Barman et al. developed an extensible facility
for multiple mobile robots [8]. The system consists of nineradio-controlled mobile robots, two CCD color video
cameras, a video transmitter and tuner, radio controllers,and so on. Software for tracking control is described. Kube
and Zhang examined the problem of controlling multiple
behavior-based autonomous robots [9], [10]. Based on
observations made from the study of social insects, they
proposed five simple mechanisms used to invoke group
behavior in simple sensor based mobile robots. They also
constructed a system of five homogeneous sensor-based
mobile robots with capability of achieving simple collective
task. Noreils described architecture for cooperative andautonomous mobile robots [11]. The cooperation iscomposed of two phases. One is the collaboration where atask is decomposed into subtasks. The other is thecoordination where robots coordinate their activities tofulfill the initial task using the notion of coordinatedprotocols. This architecture showed benefits of modularity,robustness and programmability.
In addition, Azarm and Schmidt presented a novel
approach to do conflict-resolution for multiple mobile
robots [12]. A framework for negotiation is developed byusing the online motion planning, which permits quick
decentralized and parallel decision-making. The key

1 INTRODUCTION
Nature continually aspire the development of new ideas
and system. Many simple biological species influence
directly the development of new multi robot systems. Someof these species include insects such as ants [1 - 4], bees,
wasps, and termites [5]. A multi robot system is a widely
progressive research fields that went from simulation,
prototyping and recently real applications [`1, 6]. It
represents a team of robots that are either heterogeneous or
homogeneous in their physical structure and functionality.
The importance of multi robot systems is reflected in its
high task reliability, fault tolerance and flexibility, spatiallydistributed, cost effective by using simpler individual
structures, high application demand and the researchchallenges facing it [6, 22].
While robotics community witness recognizable
progress in research and developments in the field of multirobot systems and its applications, there are still many
research challenges which need to be addressed andefficiently solved by applying new innovative techniques.
These research challenges may include: task allocation,
cooperative mapping and localizations, sensor integration
and real time sensory information fusion, real time based
control architectures, local and global behaviors, resources
utilization and deadlock solving, heterogeneous anddistributed intelligence (think, reason, decide and learn),effective reconfigurable modular functionality, autonomy
and cooperation, awareness and coordination, inter process
communication and human machine interface, etc. [1aa, 1a,
1b].
The application areas of multi robot systems may coverareas such as, military and security, underwater and space
exploration, hazardous environments, service robotics inboth public and private domains, entertainment, and soforth, can benefit from the use of multi-robot systems. In© ISAROB 2013

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objective of the negotiation procedure is dynamic
assignment of robot motion priorities. The performance was
evaluated experimentally using only two mobile robots.
Bennewitz and Burgard considered the problem of path
planning for teams of mobile robots [13] using a decoupledand prioritized approach to coordinate the movements ofthe mobile robots in their environment. The proposed
algorithm computes the paths for the individual robots inthe configuration-time space. To estimate the risk ofcolliding with other robots, it uses a probabilistic model ofthe robots motions. Guo and Parker proposed a distributedand optimal motion planning algorithm for multiple robots,in which computation cost was decomposed into two
modules, i.e., path planning and velocity planning [14]. The
D* search method was applied in both modules, based oneither geometric formulation or schedule formulation. The
algorithm was implemented and tested in a group of Nomad
200 indoor robots. Parker outlined the project that
demonstrated a team of 100+ heterogeneous robots solving
an indoor reconnaissance and surveillance task [15]. The
focus was the impact of heterogeneity on the collaborative
solution approach that the robot team must take. Pimentel
and Campos addressed the problem of multi-mobile robot
cooperation with strict communication constraints which
are considered indispensable for successful task execution[16]. The problem is modeled as a minimization of anenergy functional which accounts for network connectivity,
other relevant robot and task requirements in order to selectlocally optimal actions for each robot.
Antonelli et al. presented two experimental casestudies performed using a multi-robot system made of six
Khepera II mobile robots [17]. The experiments are aimed
at testing the performances and the robustness of abehavior-based technique, called the null-space-based
behavioral control (NSB). The NSB approach wasdeveloped to control a generic team of autonomous vehicles
and it was implemented on a centralized architecture tocontrol a platoon of autonomous mobile robots at akinematic level. Also, the experimental validation of the
NSB in the presence of static and dynamic obstacles wasevaluated with a team of grounded mobile robots [18].
In this paper, network-based subsumption architecturewith PC server is proposed to minimize the total cost for
designing multiple mobile robots system by maximizing thegroup performance of robots with limited hardware andsoftware capabilities rather than optimizing the behavior
performance of individual robot. This enables to develop
high-level group behavior. A server supervisory control
with networked-based subsumption architecture is
implemented and tested to realize a schooling behavior byrelying only on information from the PSD sensors. Further,
the dynamic sampling period is introduced to evaluate thebasic performance of developed system.

wheels driven by DC motors and six PSD (Position
Sensitive Detector) sensors.. It is produced by TosaDenshi
LTD. A MicroConverter ADuC814ARU provided by
Analog Devices is mounted on the control board of themobile robot. In order to measure the distance to any object
in real time, di(k) = [di1(k)...di6(k)]T is the distance vector of
the i-th mobile robot at the discrete time k. The PSD sensor
is mainly composed of LEDs, electrical resistances andphotodiodes, and can calculate the distance to an object
through triangulation technique. Figure 2 shows themeasurement graph of the PSD sensor, In order to copewith the problem of narrow directivity, the required numberof the PSD sensor was decided to be six. In order to reducethe dead zone angle in the sensor view, more PSD sensors
is required. Besides, each robot has a Bluetooth wirelessdevice to communicate with the PC server.

2 MOBILE ROBOT WITH SIX PSD SENSORS

Fig.3. Bluetooth module through a serial interface AGB65-BT.

Fig. 1. The mobile robot with three wheels and six PSD sensors.

Fig.2. Shows the functional graph of PSD distance measurements.

A. Basic Hardware
Figure 1 shows the developed mobile robot [19], [20].
The robot is an omnidirectional mobile robot with three© ISAROB 2013

The Bluetooth is connected to each mobile robot throughserial port to support data communication, so that text
codes can be transmitted to and received from the Bluetooth

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module called BlueMaster through a serial interface
AGB65-BT as shown in Fig. 3. The small Bluetooth device
is provided by Asakusagiken Co., Ltd. The AGB65-BT isconnected to the serial port of MicroConverter
ADuC814ARU mounted on a mobile robot.

The software development environment of the selectedmobile robot using a free C language has two limitations
since we are using robots with limited capabilities. The firstis having limited memory and communication overhead,
the flash ROM of the mobile robot is only 8 kB, so that it isdifficult to deal with huge amount of state information. The
second is, the mathematical functions within the standard
library such as “exp ( )” is not supported by the complier
while the computation resources is not enough to realize
advance behavior. Thus, for example, it is impossible todirectly program potential field technique supporting
navigation and planning. In order to cope with such
limitations of the software development environment, a nefficient strategy was adopted to have two levels of
behaviors. The first level of behaviors constitutes simple
reflexive swarm behaviors implemented directly on themobile robot itself. The other level of behaviors represents
a complex set of smarm behaviors implemented on a PC
server expressing a supervisory control scheme. In theserver control mode, each mobile robot collects all sensory
information from the six PSD sensors and transmits it to the
PC server, di(k) = [di1(k)...di6(k)]T through Bluetoothcommunication. The subscript i denotes the identification
number of a robot. Then the server decides and returns a
simple swarm command/behavior to the robot whileconsidering the overall swarm behavior (current and next).
By means of the proposed supervisory control of the PC
server, a complex swarm behavior can be executed bedeciding the selection of such behavior by the PC server
according to the situation needs and decompose it into setsof simple behaviors that are sent to each robot to execute.
In addition, functions demanding high computation, such as
potential field technique, can be placed at the PC serverside where the Windows Visual Studio runs (representing
the software development environment).

B. Kinematic Control of Three-Wheeled Mobile Robot
Next, the kinematic control method of the mobile robot
is explained. Figure 4 illustrates the kinematic model of themobile robot in robot coordinate system ΣR(O − RXRY).
ωi (i = 1, 2, 3) is the angular velocity of each wheel. Also,
by using the radius r of each wheel, vri = rωri is the forward
velocity of each wheel. If the position and orientation
vector of the robot is given by xr y r r T , then thevelocity is represented by vr  xr y r r T . The following
kinematic relation is obtained from Fig. 3 [21].
 1

 2
 1 
  1 1
2      r  2
 3
 1



3
2
3
2
0


L
 x r 
L   y r   
L   r 



(1)

where L is the distance between the center O of the robot
and the center of each wheel. By using Eq. (1), the robot
can be controlled kinematically, i.e., the is desired behavior
designed by vr  xr y r r T can be performed by making
three wheels rotate with the angular velocity vector ω = [ω1ω2 ω3]T. As special cases, Table 1 shows the basic velocity
components to move in the direction of each PSD sensor.
When designing the schooling mode using multiple mobile
robots, the six basic velocities are used. The important pointis that the direction of velocity, a mobile robot generates in
ΣR, depends on the ratio x ri : y ri . The velocity norm can bearbitrarily changed as αω = [αω1 αω2 αω3]T with a scalar α.

B. Server Supervisory Control Based on Subsumption
Architecture
The proposed PC server based supervisory control isdesigned coordinate the required behavior for multiple
homogeneous mobile robots each with three wheels and six
PSD sensors. This system is used to support study needs offourth year students at Tokyo University of Science,
Yamaguchi, such as, to learn the subsumption control
architecture for schooling behavior. The subsumption
control architecture was first proposed by Brooks [23].
Students can practically know the basic concept andeffectiveness of subsumption control architecture which
provides a method for structuring reactive systems fromlower level to higher level using layered sets of rules, i.e.,
reactive behaviors according to the change in the situation
within robot’s environment. Accordingly, the PC server
returns a set of simple behavior associated with short
execution time, e.g., 200 ms to the corresponding robot.
Nine kinds of the most simply subdivided reaction
behaviors, i.e., reflex actions, are implemented for themobile robots as tabulated in Table 2. When a set of
behavior codes and an execution time is transmitted fromthe PC server to a mobile robot, the mobile robot conducts
the motion exactly within the specified execution time.

Fig. 4. Kinematics of the mobile robot with three wheels.

Table 1
Velocity components to move in the direction of each PSD sensor.

3 NETWORK-BASED MULTI ROBOT SYSTEM
A. Software Development Environment and Its Limitation© ISAROB 2013

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Three agents called “Avoid objects ”, “Turn to left or right”
and “Move forward ” are designed as a composite of a setof basic behaviors/commands/actions Ai (i = 0, 1, 2, ….. , 8)
shown in Table 2 that are used to realize a schoolingbehavior.

4 SUBSUMPTION ARCHITECTURE
IMPLEMENTED ON PC SERVER
The software development environment is Windows
Visual Studio C#, which is used to develop and implement
high level software architecture such as subsumption
control architecture according to application requirements
and can be adapted based on robot’s hardware capabilities.
Subsumption is an efficient way to decompose complex
behavior into a set of simple behaviors. Based on sensory
information, only one behavior is selected as a highest
priority when a new set of sensory information is presented.
Figure 5 shows the subsumption control architecture
implemented on the supervisory PC server. The controller
includes the three agents. The upper level agent has ahigher priority to be dispatched. This section introduces thethree kinds of agents and the corresponding output
command codes from the list shown in Table 2. The ninecommands shown in Table II are simple and basic motions
but important reflex actions for each mobile robot toconsequently produce the competences relevant to the three
agents. The PC server receives PSD sensory informationdi(k)(1 ≤ i ≤ N) from all mobile robots periodically every
dynamic sampling period, in which N is the number of theavailable mobile robots. By analyzing di(k), the controller
dispatches the current execution to one of the three agentsfor the i-th mobile robot according to triggering priority.
In the schooling mode, all mobile robots try regularly
move along the inner of a circular fence keeping the
distance to both the fence and other mobile robots. Thismode enables the robots to behave like carps in a Japanese
artificial circular pond.

Fig. 5. Network-based subsumption architecture for a schooling behavior,
which is implemented on a PC server.

Fig. 6. Layout configuration of the developed system.

TABLE 2The basic list of simple reactive behaviors/commands/Actions that can be
executed at the robot side together with the corresponding motion.

Fig. 7. Experimental school behavior demonstration.

To coordinate the relation between each robot and the PC
server, there is an agent dispatcher supervises the controller
shown in Fig. 4. The dispatcher does not immediately movethe execution right to a higher priority agent when a lower
priority agent is running, because each agent works as asimple reactive behavior every sampling period according
to the sensory information. Instead of this, whenever areaction behavior is executed during a specified execution
time, the dispatcher checks and updates the activity of eachagent and gives the next execution right to a newly updated
active agent with the highest priority.

Figure 6 and 7 show the layout configuration and theexperimental scene of schooling behavior respectively, inwhich multiple mobile robots are controlled based on thesubsumption control architecture incorporated in the PC
server associated with supervisory control capability as
shown in Fig. 5. It was confirmed from the experiment thatthe multiple mobile robots performed the desirable
schooling behavior successfully.

© ISAROB 2013

5 DYNAMIC SAMPLING PERIOD
The dynamic sampling period is an important factor toevaluate the performance of the multiple mobile robots

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013[7] Parsons, D and Canny, J, “A motion planner for multiple mobile
robots,” in Proc. IEEE International Conference on Robotics andAutomation, Cincinnati, OH, USA, May, 1990, 8–13.
[8] A. R. Barman, A R, Kingdon, S J, Little, J J, et al, “DYNAMO:
Real-time experiments with multiple mobile robots,” Intelligent
Vehicles Symposium, Tokyo, Japan, July, 1993, 261–266.
[9] Kube, C R and Zhang, H, “Collective robotic intelligence,” inProc. The Second International Workshop on Simulation of
Adaptive Behavior, 1992, 460–468.
[10] Kube, C R and Zhang, H, “Collective robotics: From social
insects to robots,” Adaptive Behavior, vol. 2, no. 2, 1993, 189–219.
[11] Noreils, F R, “An architecture for cooperative and autonomous
mobile robots,” in Proc. IEEE International Conference on
Robotics and Automation, Nice , France, May, 1992, 2703–2710.
[12] Azarm, K and Schmidt, G, “Conflict-free motion of multiple
mobile robots based on decentralized motion planning andnegotiation,” in Proc. IEEE International Conference on
Robotics and Automation, Albuquerque, NM , USA, April, 1997,
3526–3533.
[13] Bennewitz, M and Burgard, W, “A probabilistic method forplanning collision-free trajectories of multiple mobile robots”,
Proc. of the Workshop Service Robotics - Applications andSafety Issues in an Emerging Market at the 14th European
Conference on Artificial Intelligence, Berlin, Germany, August,
7 pages, 2000.
[14] Guo, Y and Parker, L E, “A distributed and optimal motion
planning approach for multiple mobile robots”, Proc. IEEE
International Conference on Robotics and Automation,
Washington, DC, USA, 2002, 2612–2619.
[15] Parker, L E, “The effect of heterogeneity in teams of 100+ mobile
robots,” Multi-Robot Systems: From Swarms to Intelligent
Automata, Volume II, Kluwer Academic Publishers, 2003, 205–
215.
[16] Pimentel, B and Campos, M, “Cooperative communication in ad
hoc networked mobile robots,” in Proc. IEEE/RSJ International
Conference on Intelligent Robots and Systems, Las Vegas, NE,
Oct., 2003, 2876–2881.
[17] Antonelli, G, Arrichiello, F, Chakraborti, S and Chiaverini, S,“Experiences of formation control of multi-robot systems with
the null-spacebased behavioral control,” in Proc. IEEE
International Conference on Robotics and Automation, Roma,
April, 2007, 1068–1073.
[18] G. Antonelli, F. Arrichiello and S. Chiaverini, “Experiments offormation control with multirobot systems using the null-spacebased behavioral control,” IEEE Transactions on Control
Systems Technology, Vol. 17, No. 5, pp. 1173–1182, 2009.
[19] F. Nagata, T. Yamashiro and K. Watanabe, “Cooperative swarm
control for multiple mobile robots using only information from
PSD sensors,” Artificial Life and Robotics, vol.16, no. 1, pp.
116–120, 2011.
[20] N. Kitahara, F. Nagata, A. Otsuka, K. Sakakibara, K. Watanabe
and M. K. Habib, “A proposal of experimental education systemof mechatronics,” in Proc. of 17th International Symposium on
Artificial Life and Robotics, Beppu, Oita, Japan, January, pp.
166–169, 2012.
[21] K. Watanabe, Y. Shiraishi, S. G. Tzafestas, J. Tang and T.
Fukuda, “Feedback control of an omni-directional autonomous
platform for mobile service robots,” Journal of Intelligent &Robotic Systems, vol. 22. nos. 3/4, pp. 315–330, 1998.
[22] Asama, H , Ishida, Y, Ozaki, K, Habib, MK, Matsumoto, A,
Kaetsu, H, and Endo, I “Communication system between
multiple robotic agents.,” the 1992 Japan-USA Symposium on
Flexible Automation Part 1(of 2), 1992, 647-654.
[23] R. A. Brooks, “A robust layered control system for a mobile
robot,” IEEE Journal of Robotics and Automation, vol. 2, no. 1,
pp. 14–23, 1986.

system. The dynamic sampling period is defined for a PC
server as a variable time starting from submitting acommand packet to a mobile robot until receiving a
response packet including the new sensory information.
Figure 9 shows the handshake process to measure thesampling period Tk = tk+1 − tk. The processor time tk at thediscrete time k in the PC server is used as a timestamp tosynchronize the time between the PC server and each
mobile robot. The processor time tk can be obtained byreferring the Windows parameter “int smillisec =Environment.TickCount;” in a timer interrupt process at the
PC server side. The size of the packet used in Bluetoothcommunication between the PC server and a mobile robot
is 30 bytes. When a command packet is transmitted fromthe PC server to a mobile robot, necessary information
about the command is written at the offset positions frombyte 0 to 11. On the other hand, when returning to the PC
server from the mobile robot, the sensory information of thesix PSD sensor are set at the offset positions starting frombyte 12 to 29. It should be noted that the execution time in
Fig. 4 is set to 0 ms in this measurement process. It isobserved experimentally that there exists some timedispersion around 62.4 ms and the resolution of the timedispersion is about 15.6 ms. The timer interval of Microsoft
Visual C# used in the measurement.

6 CONCLUSION
In this paper, network-based subsumption architecture
has been presented to realize high level behavior such as,
schooling behavior using only information from PSD
sensors. Experimental results showed interesting behavior
among the multiple mobile robots, such as following,
avoidance, and schooling. Further, how to simply measure
the dynamic sampling period has been introduced toevaluate the basic performance of network-based multiple
mobile robots system. The dynamic sampling period isdefined for the PC server as the variable time. It is expectedthat the dynamic sampling period becomes one of importantcriterion for network-based multiple mobile robots system.

REFERENCES[1] Asama, H, Habib, M K, Endo, I, Ozaki, K, Mastumoto, A Ishida, Y,"Functional distribution among multiple mobile robots in anautonomous and decentralized robot system", IEEE Robotics andAutomation 1991 conf. (ICRA 1991), USA, pp.1921-1926.
[2] Moffet, M W, “Cooperative Food Transport by an Asiatic Ant,”
National Geographic Research, 4(3), 1988, 386-394.
[3] Anderson, C and Franks, N R., “Teams in animal societies,”
Behavioral Ecology, 12(5), 2001, 534-540.
[4] Gautrais, J., Theraulaz, G., Deneubourg, J.L., and Anderson, C.,“Emergent polyethism as a consequence of increased colony size
in insect societies,” Journal of Theoretical Biology, 215, 2002,
363-373.
[5] Mori, H, Saito, Y, and Tho, Y P, “Cooperative group predation ina sit and wait cheyletid mite,” Experimental and Applied
Acarology, 23, 1999, 643-651.
[6] Chow, M-Y, Chiaverini, S, and Kitts, C, “Introduction to the
Focused Section on Mechatronics in Multirobot Systems,”
IEEE/ASME Transactions on Mechatronics, VOL. 14, No. 2,
2009, pp.133 – 140.

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A precision position arrangement of the SCARA robot by H∞robust
control
Yasuyuki Hirofuji, Zhang Feifei, Masanori Ito
Tokyo University of Marine Science and Technology, Japan(Tel:+81-3-5245-7422, Fax: +81-3-5245-7422)
Abstract: The SCARA (Selective Compliance Assembly Robot Arm) robot is playing an active role in many manufacturing
stages of electrical machinery, electronic components etc. In those days, improvement of productivity is becoming important
subject. And it will be realized with improvement in a capacity utilization rate, and industrial accident prevention, developmentof the technology for speed up of motion, accuracy of operation, or abnormality detection. Especially speed, accuracy andsafety are most important items for it. We applied modern control theory, such as H∞ robust control and confirmed theperformance with experiments.
Keywords: H∞Robust Control SCARA Robot
The mass of each joint, the moment of inertia, friction
coefficient are identified with experiment.

1 INTRODUCTION
Currently, the control method of industrial robots, suchas a SCARA robot is PID control, which is classified as aclassical control. As PID control is relatively simple andeasy to use, it is widely used in various situations and not
limited. On the other hand, modern control theory is not soused in the field with the reason of difficulty to use becauseof its stringency and complexity. However, various control
theory have been studied and many advanced control theory
have been established until now.
As the factors that cause to parameter change of
SCARA robots, some mass change at the time of holding
the work, posture change, interference from other joints,
and aging of the device are pick-upped. Modern control isdifferent from classical control, such as PID control, andable to handle multiple-input multiple-output systems. With
applying the H∞ robust control, robust stabilization forparameter variations, disturbance from peripheral inhibitory
effect can be expected. Then it is possible to improve theperformance such as high-precision positioning withsmooth operation of the arm, stabilization of the system,
and reduction of maintenance.

Y軸
X軸

Fig. 1 SCARA Robot

3 H∞ROBUST CONTROL
Control design to stabilize the closed-loop system
including the control target is to minimize the H∞ norm of
the closed-loop transfer function between the input andoutput.
The actual system is complex, and it can’t be
represented with a single transfer function. So, we adopted
the method to estimate the uncertainly from the difference
between actual and model system.
Model for this design is that of the nominal plant. Realsystems are included in the plant.
Therefore, on the design of the controller, instead ofthinking the actual system, we considered the collection of

2 CONTROLLED OBJECT
In this study, we used a SCARA robot (very small)YX-XG series by Yamaha Motor CO. Ltd.
Table 1. Spec. of controlled object
Shaft X length
45(mm)
Shaft Y length
75(mm)
Motor capacity
30(W)Maximum rotation
8000(rpm)
Reduction ratio
1/50© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

this plant model, to guarantee the stability and control, also
ensures the stability and control to the real system.

Next, from the moment of inertia calculation, mass canbe estimated as follows,

4 SCARA ROBOT MODELING
At first, from the experiment, we calculated mass,
moment of inertia of each axis and the friction coefficient
including a motor and load.
Equation of motion and the transfer function of the
motor is as follows,J  D  Jw  Dw  kV
G(s)  w(s) / V (s)  k /( Js  D)

Gain： g  k / D

D  V  50  50  k / A(6)hJy  Iy  My( ) 22(7)

Unknown parameters can be obtained with those
processes.

Mx  1.5(kg) My  2.4(kg)

(1)
(2)

Jx  0.04(kgm2 ) Jy  0.02(kgm2 )Dx  0.486 Dy  0.226

 ：Angle of rotation of the arm
J ：Moment of inertia of the base of the arm
D ：Coefficient of friction of the arm
V ：Input voltage
k ：Motor parameters

Time constant： T  J / D

Jy  Dy  Ty

From those data, we can fix model of target.
The transfer function from the input voltage to therotation angle of each of the X-axis, Y-axis of controlled
object is as follows.
(3)89.39s  5959
s  187.1s 2  4755s
159.8s  6561
Py  3s  175.6s 2  4253s
Px 

(4)

(5)
Fig.2 and 3 are the output data of the angular velocity ofeach arm when input to the X-axis is 0.25 (V), and input tothe Y-axis is 0.5 (V),
From measured data graph, we can read Time constant:T, the Gain: g, and calculate the unknown J and D.

(8)

3

(9)

5 DESIGN OF H∞ROBUST CONTROLLER
Using MATLAB, we designed a H∞ robust controller.
The design procedure is as follows,
1) Selection of nominal model
2) Formulation as a mixed sensitivity problem3) Configuration of the general plant4) H∞ robust controller designFirst, for the step 1) each transfer function of X axes and,Y axes uses.
Next, in order to design a H∞ control system, theproblem of determining the controller K is considered tominimize following evaluation function.
.

Fig. 2 Experiment x

W1 PK (1  PK ) 1
W2 (1  PK ) 1

→Minimization
Fig. 4 Evaluation function
As shown in the Fig.5, it corresponds to the transfer
function from W1 to z, and from W2 to z.
Fig. 3 Experiment y© ISAROB 2013

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z

W1
u
K

 A Bk 
K  kC k Dk  0.3104  0.5645 1.529e  004 
 0.001603
 0.004666
 1.545
 57.34 3.452e  004 
Ak   0.4592
166.4
141.8  3.899e  006
 279.8 
 2.64e  005 0.0003646 0.02252

W2
ｙ

P

Fig. 5 Mixed sensitivity problem

 9.008e  005  0.0002033 Bk  
 0.02296 
 5.725e  005C k   7.997 1577 6319  7.508e  007Then, as shown in the following figure, we configure
the general plant.
w
u

z
G

x

K

Dk  0.4422

Fig. 6 General plant

6 RESULTS OF EXPEREMENT· How can we choose the weighting function?
It was depicted as follows. The frequency response
variation were estimated on bode diagram, and so as tocover this, it was determined with the low-order transfer
function on the graph. At this time, we determine theweighting function as shown in the broken line withapproximation. Then, at the same time we draw the Bodeplots of the variation and weight, and make sure that the topof the Bode plot of weight variation. In addition, as thecontrol is not performed in a high frequency band, we set
the frequency as high as possible and increase the gain of
the variation. Then vibration input is reduced.
In Fig.7, P is solid line, W1 is broken line, and W2 isalternate long and short dash line.

We performed comparative experiments of H∞
controller and PID controller. Confirming the robustness,
we experimented to change positioning speed. Fig. 8 to 11
are the results of PID control. Fig 12 to 15 are the results of
H∞ control. On each control, we experimented withdifferent positioning speed at 1.4 seconds to three times,
four times, five times. The horizontal axis is Time (s). The
vertical axis is angle of each link (rad.). Solid line is thetarget angle. The dotted line is the measured angle

Fig. 8 Experiment result of PID control (1.4sec)

Fig. 7 Bode diagram

Fig. 9 Experiment result of PID control (0.47sec)© ISAROB 2013

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Fig. 10 Experiment result of PID control (0.35sec)

Fig. 14 Experiment result of H∞ control (0.35sec)

Fig. 11 Experiment result of PID control (0.28sec)

Fig. 15 Experiment result of H ∞ control (0.28sec)

7 CONCLUSION
In this study, we applied H∞ robust control to SCARA
robot, in order to confirm the possibility of control
performance improvement. We performed comparative
experiments of H∞ controller and PID controller withactual device, and the following conclusions were obtained.
1) To get faster operation speed of SCARA robot, robust
H∞ control obtained good results in target joint angle
tracking capability and we confirmed the robust stability.
In addition, for the parameter variations due to load fluctuation or change in the attitude of the arm, the stability could
be confirmed as well.

Fig. 12 Experiment result of H ∞ control (1.4sec)

2) Based on the results of this study, we want to expand thecontrol to multi-input multi-output system from the singleinput single-output system.

REFERENCES[1] Y. Liu (2002), Linear robust control[2] Hideki Kimura (2000), H∞control

Fig. 13 Experiment result of H ∞ control (0.47sec)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Hybrid control with adaptive and state feedback control for Robot Hand
Kazuma.Funahashi, Hiroki.Goto, Feifei.Zhang and Masanori.Ito
Tokyo University of Marine Science and Technology,
2-1-6 Echujima, Koto-ku, Tokyo 135-8533, JAPAN(Tel : 81-3-5245-7422; Fax : 81-3-5245-7422)
kazuma9@hotmail.co.jp
Abstract: In this research, we aimed to get an optimum control performance for any kinds of system with the combination ofadaptive and state feedback control which are one of modern control theory. Here we used "Gifuhand" which is a hand typerobot of 5 fingers as an experimental device. It is optimum experimental device for the purpose of this research to develop thecontrol method which does not spoil controllability under any condition. Because it is the structure imitating human's hand and
it is possible to be applied in various scenes. The results of performance such as robustness and accuracy are satisfiable.
Keywords:

1

robot hand, adaptive control, state feedback control, hybrid
no offset, saving time and effort of design andmaintaining high robustness.
The validity of control and the robustness of adaptation
proposed control method were confirmed with theexperiments of position control for the robot hand of 5
fingers, 16 DOF and 20 joint and called "Gifuhand" andcomparison with conventional method.

INTRODUCTION

An adaptive control is expected as next-generation ofcontrol method with its very high robustness andeasiness for actual use. For example, there is nonecessity of parameter tuning in a design phase, andcontrollability can be maintained to status change of thetarget in the control process.
However, on the other side it has weak points. Since
there exist much computational complexity, the reaction
tends to be overdue and the offset is inevitable forsystem operation.
A state feedback control has the features that are
possible to extend to the formula of various
optimizations, such as type 1, and type 2 and has very
high controllability with adjusting precisely in a designphase except low robustness.
In this research, we propose a hybrid control method
which is combined each strong point of above mentioned
two control theory, and have high performance such as

2

CONTROL DESIGNThe feature of the proposed control method is to makea good input to make a set variable follow while itreplies the optimum gain and an integral gain for every
sampling period with a pole assignment method applying
the discrete numeric model of the system which is it
obtained with iterative least squares technique and theeigenvalue specified beforehand to integral type servo
system of discrete state feedback.
Hereinafter, the block diagram of a control system isshown in. Fig.1

Fig.1 Block diagram of hybrid system© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Here Δ is a sampling period. Eq.(4.6) integrates withthe difference of the present output and a target output
for every sampling period, and, finally suppress offset

2.1 Discrete-time model
In a basic adaptive control (the RLS method), they
perform optimum control with following steps that it
calculating the coefficient of the input and output ofthe difference equation of a system with a least-squares
method and substituting them to the equation and adesired value for the future.
To apply this method, we designed control system
based on the difference equation using adaptive control
in this experiment.
And we designed difference equation as quadraticsystem considering for the time and effort for a design,accuracy of control, computational complexity, and etc,
in the difference equation of the system. Eq.(1)2.2 Control systems
Eq.(5) is given to Eq.(4.1) ~ (4.6) as an input. With apole placement method and eigenvalue ω, the value of kis calculated, respectively. Eq.(6.1) ~ (6.3).
..(5)..(6.1)..(1)..(6.2)And in order to extend an Eq.(1), a discrete stateEq.(2.1) ~ (2.3) is made to correspond as follows.
..(2.1)

Here each Ω is given from Eq.(7.1) ~ (7.3).

X and y are the followings.

..(6.3)..(7.1)..(2.2)..(7.2)..(2.3)..(7.3)

From Eq.(1) and Eq.(2.1) ~ (2.3), a state variable and A
and B can be found uniquely. Each Eq.(3.1) ~ (3.3) canbe derived.

Since it is a discrete system, the system is stabilized
while all eigenvalues are between -1 and 1. However, in
this study, all ω are set to 0, in order to save the time andeffort of a control design.

..(3.1)

2.3 Identification..(3.2), , and, a iterative least
In calculatingsquares mean method (the RLS method) is used. θ[n]and φ [n] are defined as follows.

..(3.3)The integration clause of a discrete integration typeservo system is added into the state equations Eq.(1) andEq.(2.1) ~ (2.3). The results are Eq.(4.1) ~ (4.2).

..(8.1)..(4.1)..(4.2)

And the value of θ is presumed with iterative leastsquares mean technique. At every sampling period, thiscalculation is repeated and each value is derived.
The algorithm is as follows.

Here X[n] is given from the following equations. And adesired value is given to a state variable in order to converge
on it.

..(9.1)..(8.2)..(9.2)..(4.3)..(9.3)..(4.4)..(9.4)..(4.5)..(9.5)..(4.6)© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

3

Table 1 Specifications of robot hand
Speed reduction
148.48:1[deg]
Encoder resolution
Max torque0.20[Nm]
Frequency characteristic
7.0[Hz]Rating output
1.2[W]
Idling speed
17700[Rpm]Starting current
673[mA]
Rotor moment-of-inertia

EXPERIMENTAL SYSTEMThe Gifuhand consists of a thumb and four fingers.
The thumb and a finger are modularized for every joint,
and unitized for every finger, respectively. The thumbhas 4 joint and 4 DOF and other fingers have 4 joint and3 DOF. It has a total of 20 joint 16 DOF for single hand.
This is almost equal to man's DOF of hand, and canimitate a motion of man's hand. In addition, as for an
joint is connected to the
jointeach finger, thewith a parallel link mechanism.

4

RESULTS AND EVALUATIONThe experiments were done for sampling period is
0.01second. All eigenvalues were set to 0.The joint
which was used for the experiment was a root joint of alittle finger. We gave order to this joint to move 60 deg
within 1 sec to the direction of grasping. In every
experiment, we set the data of control parameterobtained with pre-operation as initial value.（ =1,=0.1, =0, =0）
In this experiment we compared following threemethods(1) Conventional adaptive control(2) Hybrid control which we are proposed(3) Hybrid control under loading

Adaptive Control
80

Fig.2 Photo of robot hand

3rd joinangle [deg]60
2nd join

4th joint

40
Sv
20

1st link
2nd link 3rd link
1st motor encode
1st joint
1st motor
3rd motor encode Parallel link mechanis

0

Pv

0

3rd motor

2nd moto
2nd motor encode

1000

2000
time [ms]4000

Fig.4 Adaptive control

Asymmetric type reduction gear

Hybrid Control

Fig.3 Finger mechanism & size

80

60
angle [deg]Fig.2 is the photo of the hand and Fig.3 is themechanism of the little finger of the experimental device.
Moreover, in this experiment, in order to move a robot
hand, a visual C ++ is used for programing and a D/Aboard, an A/D board, an encoder counter board, and 16channel power amplifier are used as interface.

40

Sv
Pv

20

The specification of the third joint of the finger usedin this experiment is introduced in Table 1.

0

0

1000

2000
time [ms]Fig.5 Hybrid control© ISAROB 2013

3000

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig.4 is the result of conventional adaptive control.
Input is a solid line, and output is a dotted line.
Although the outputs follow to the input stably, thesmall offset error occurs. The detail is shown in Fig.6.

moment changes with the motion of finger. It is a kind of
hard condition for control. However it shows highrobustness that the speed change and shock of motion
was not so large to the big change of moment.

Fig.5 is the result of the Hybrid control. Input is a solidline, and output is a chain line.
Although there is some overshooting of about 2
seconds, it has a high traceability to the change of input.
The detail is shown in Fig.6.

5This study showed that hybrid control has highrobustness and easiness for adjustment, and higher
controllability and accuracy than conventional adaptive
control. In particular, the problem of the steady-state
error which was a problem until now was solved, andprecise control was also attained. Moreover, it can besaid that it is very effective in the equipment used forvarious uses like a robot hand with very high robustness.
However, the shortage of speed which comes from thenumerousness of the computational complexity ofadaptive control is remained yet. Although this problem
is being solved with evolution of a computer, still, it is
not enough for high speed control. Solving of thisproblem will become a future subject.

angle [deg]Hybrid and Adaptive control in detail
61
60.8
60.6
60.4
60.2
60
59.8
59.6
59.4
59.2
59
1000

Sv
HybridAdapt
2000

time [ms]3000

REFERENCE
4000[1] System Identification for Control, Shuuichi Adachi,
Tokyo Denki Univ Press.
[2] Theory ＆ Practice of Adaptive Control Systems, I.
D. Landau and M. Tomizuka, OHM Co. Ltd.
[3]Design of Multirate Digital Servo Systems, JunnichiHara, Graduate school of Information Sciences, Tohoku
University, Instrument and control engineers, vol.67,
No.3, 2001, (PP.466~472)[4]Auto-Tuning of Digital Controllers for DC servo
motors, Hiromitsu HIKITA, The Japan Society for
Precision Engineering, vol.192, No.192-17, 2000

Fig.6 Hybrid and Adaptive in detailFig.6 shows the enlarged comparison of theconventional method and a hybrid method. Input is asolid line, Hybrid output is a chain line and conventional
output is a dotted line.
This shows the overshoot of Hybrid control is 0.4deg
and offset error is very small (0.01deg) and offset errorof conventional adaptive control is about 0.8 deg.

Hyblid Control with load
80

angle [deg]

60
40
Sv

20
0

Pv

0

1000

2000
time [ms]

3000

4000

Fig.7 Hybrid control with load
Fig.7 is the result of the Hybrid control under 1kg load.
Input is a solid line, and output is a chain line.
Since the load of 1kg is hung to the fingertip, the load

© ISAROB 2013

CONCLUSION

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Hexa-Quad Transformation Control for Hexapod Robot Based on Support
Polygon Pattern
Addie Irawan1, Yee Yin Tan2, Mohd Syakirin Ramli3, Mohd Riduwan Ghazali4 and Kenzo Nonami5
1,2,3,4

Robotics & Unmanned System (RUS) Group, Faculty of Electrical & Electronics Engineering,
University Malaysia Pahang, Malaysia
5

Division of Artificial Systems Science, Department of Mechanical Engineering,
Chiba University, Chiba, Japan

addieirawan@ump.edu.my, 2yeeyin1228@gmail.com, 3syakirin@ump.edu.my, 4riduwan@ump.edu.my,5
nonami@faculty.chiba-u.jp

1

Abstract: This paper presents a leg reconfigurable technique to optimize the multi-legged robot operation and walking
performances. A hexapod-to-quadruped (Hexa-Quad) transformation technique is proposed to optimize hexapod legs on certain
situation that need some legs to be disabled as a leg to do other tasks/operations. By separating two legs from the others,
hexapod robot is able to be configured as quadruped robot configuration. Quadruped robot configuration is stand within
dynamically and statically stable criteria if compare to the hexapod robot that has only statically stable criteria. Thus, it is very
crucial to have a stable transformation technique during walking and operation session. Therefore Hexa-Quad is proposed withreference to the defined support polygon that based on its body area. A real-time based model of hexapod robot (4-DOF/leg)control architecture with Hexa-Quad transformation is designed and verified using separated 3D simulators.
Keywords: Hexa-Quad Transformation, center of mass, support polygon
statically stable walking or static stability. Static stability
occurs when CoM lies completely within the support
polygon and the polygon’s area is greater than zero, andhence static stability requires at least three points of groundcontact [3]. Robot’s CoM represented a significant aid inmaintaining the stability[4] and as additional source of
information in identified process and stability indicator.
Moreover, CoM is calculated to provide critical to accessrehabilitation success in pathology detection and indescribing gaits[5]. In reconfiguration aspect, the CoM’s oflegged robot is will be reallocated since the changing of in
the structure or leg configuration of the robot.
Therefore in this study, control technique on hexapod
configuration to quadruped configuration for a hexapod
robot (Hexa-Quad) is proposed. Hexapod is one of thestatically stable configurations of multi-legged robot thathas potential to be reconfigured into less than six legs suchas quadruped and bipedal configuration. Transforming
hexapod to bipedal configuration is considered as criticalconfiguration for hexapod unless there have a special
design on leg configuration and robot body’s shape itself(other than common hexapod’s body shapes; square,
trapezium, round or hexagon body). The quadruped
configuration is selected since this configuration is in
between statically and dynamically stable and suitable forany common shape of hexapod robot’s body. Static stability
assumes the vertical projection of the CoM always remain

1 INTRODUCTION
Multi-legged robot or so called active suspension
vehicle (ASV) has significant advantages if compare to thewheel type robot especially on facing irregular andmountainous terrain. The advantages of multi-legged or
legged robot can be seen obviously on inspired life living
form; legged creatures. Raibert in his book has mentionedthat only about half of the earth ‘s landmass is accessible toexisting wheeled and tracked vehicles ,whereas a muchlarger fraction can be reached by animals on foot[1].
In multi-legged robot research and development, several
studies have been done to achieve good adaptability,
function, high flexibility and extensibility with extreme andunknown terrain. The progress emphasized in all expects
and hierarchy of multi-legged system such as systemmechanism, structure design/configuration, softwaredevelopment/control technique and electronics unit design.
In control technique level, reconfiguration technique is oneof the important parts in legged robot control, which isemphasized on recovery action [2] and multi-tasking.
Therefore stability become a main point in this research thatinvolving center of mass (CoM) of the legged robot and its
support polygon. The larger the support polygon developedby the robots the bigger the probability for the robot toremain upright without overturning when it stops walking
at any moment during the walking period, and this is called© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

inside the support polygon with an adequate stability
margin during all phase of movements [6]. On the other
hand, dynamically stable depends on the stability during therobot is moving which demands on active actuation tomaintain the balance and performing faster motion [7].
Quadruped legged robot configuration also practical on
performing locomotion for complex terrain, due to theamount of researches that have been done on practicalwalking and motion control such as reported in [8-10].

z
Body instable
range

ϕ

φ

x

00
y

(a)

CONRO from Polymorphic Robotics Laboratory of
USC Information Science Institute is one of the examplesof hexapod robot that performing proposed hormone-based
distributed control to implement its gait reconfiguration
between caterpillar and spider gait mode [2]. Shen et. al.
mentioned that the number of supported leg must meet thestability criteria according to the number of leg thatavailable for walking used. In this article, the focus will beon the proposed Hexa-Quad transformation with two
different forms namely center legs disable (CLD) and side
legs disable (SLD). The form is decided based on common
application for the hexapod robot such as converting legs to
the free manipulators. The proposed transformation
technique is created by inspired from the CoB and leg
shoulder angle symmetrical concept proposed previous in[11]. The proposed transformation was modeled andapplied on hexapod robot real-time model with 4 degree of
freedom (DOF) and verified using separated 3D model
developed previously in [12].

zBody instable
range

ϕ

φ

x

00
y

(b)
Fig 1. The proposed form of Hexa-Quad transformation;(a) CLD form, (b) SLD form.
CLD is realized by lifting up two center legs as in sit
down mode. This form is not critical to control if compare
to the SLD (Fig.1(b)) that required a proper initial standing
position for other legs. Therefore, this proposed techniqueintroduced separated calculation for CLD and SLD as
shown in Fig.2 and Fig.3 respectively.

2 HEXA-QUAD TRANSFORMATION TECHNIQUE
Most of the proposed transformation techniques for
multi-legged walking robot are due to the specificconfiguration of the robot itself. In this study, thetransformation is proposed for general hexapod robot
configuration with any number of DOF legs. The proposed
Hexa-Quad transformation technique is designed byconsidering the support polygon or stability area of therobot as shown in Fig. 1 and Fig.2. The larger the support
polygon developed by the robots the bigger the probability
for the robot to remain upright without overturning when it
stops walking at any moment during walking period, andthis is called statically stable walking or static stability [3].
Therefore in proposed Hexa-Quad transformation
technique, two forms of transformation are proposed byconsidering the support polygon and CoM as shown in
Fig.1; center legs disable (CLD) and side legs disable(SLD). Disable here mean the legs are disable fromwalking command and separated from walking sequence.

© ISAROB 2013

c

a
Leg1

SupportPolygon
Leg4

Leg2

Leg5
b

d

Leg3

Leg6
CoM

Fig. 2. Shoulder angle determination for CLD
transformation mode.
As shown in Fig.2, the CoM is at the center of the body
(CoB) of the robot and the support polygon is followed by
the shape of the standing legs. The shape of support

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

polygon is depends on the number of touching leg on the
ground (red dotted line) as shown in Fig.2 and Fig.3. Thus
the new angle of shoulder for each supporting legs (enabledlegs) (𝜃𝑎 ) after transformation can be determined by using
is the length (𝑙) and width (𝑤) of the robot body as follows;

Transformation
Mode

Reinitializedside legs(Leg 1,3,4,6)

x 
l 
=
− | θ no | 0.5 tan −1  o  − | θ no | (1)θ a 0.5 tan −1  = w
 yo 

Center Legs(leg 2 & 5)
Releasedan initial value for each shoulder. This rule is applied withreference to the shoulder-based coordination system [13]and CoB-based symmetrical approach[11]. Therefore, inthis situation, the stability of the Hexa-Quad is achieved
which CoM is within the support polygon and the area ofthe polygon is greater than zero. It is different to the SLD
form mode whereby side legs are disabled from walking
used and other legs initial angle for each shoulder is reset
using Eq.(1). As shown in Fig.3, example situation of twoside legs (leg 1 and leg 4) is disabled and other four legs
(leg 2,3,5 and 6) is reinitialized by using Eq.1. The full
proposed Hexa-Quad transformation flow is illustrated via
finite state machine (FSM) as shown in Fig. 4.

Leg2

c

Leg3
SupportPolygon

Quadruped
Walking Mode

Shoulder-based coordination system (SCS) wasestablished implemented in the previous progresses forhexapod configuration such reported in [13,11] but not for
quadruped configuration. For this transformation walking
pattern, the combination of transverse and trot gait patterns[14] are used for the quadruped walking algorithm. The
sequences of the legs for quadruped and hexapod walking
are presented in finite state machine (FSM) as shown in Fig
5 (a). On hexapod configuration or hexapod mode (see Fig.
5(b)), tripod walking gait pattern is used since it performs
fastest walking with minimum area of support polygon inhexapod robot stability.

Leg5

Transformed

Leg6

To hexapod
walkingsequences

Not
Transformed

Transformation state(QuadHexa)
Shoulder angles
reinitialized

CoM

Leg b,c,d : Support
Phase
Leg a: Swing Phase

All legs:
Transient
Phase

No

Fig. 3. Shoulder angle determination for SLD
transformation mode.
Yes

Leg a,b,c : Support
Phase
Leg d: Swing Phase

STOP:
All legs on
groundLeg a,d: Support
Phase
Leg b,c: Swing Phase(a)© ISAROB 2013

Side Legs (leg1 & 4 or leg 3& 6) released

3 WALKING PATTERN AND SHOULDER-BASEDCOORDINATION SYSTEMdbSide Legs (leg1 & 4 or leg 3& 6) disabled

Fig. 4. FSM of proposed Hexa-Quad transformation forhexapod robot model.

Leg4

a

Reinitializedside legs(Leg
2,5,1/3,4/6)

Center Legs(leg 2 & 5)
disabled

where the area of robot’s body 𝑙 ≅ 2𝑤, 𝑥0 is the verticallength from the center of the robot body while 𝑦0 is thehorizontal length from the center of the body and θ no is

Leg1

From Hexapod
Mode

To Hexapod
Mode

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All legs:
Transient
Phase

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Transformed

To quadrupedwalkingsequences

Transformation state(HexaQuad)
Shoulder angles
reinitialized

Leg 2,4,6 : Support
Phase
Leg 1,3,5: Swing
Phase
All legs:
Transient
Phase

No

Yes

Both Eq.2 and Eq.3 were created to realize the motion
shape as shown in Fig. 6. This motion shape is important
for the force control implementation on each robot foot forwalking on irregular terrain. As shown in Fig.6, (1) leg
standing up, (2) swing phase (first step), (3) support phase,and (4) swing phase (next step).

Not
Transformed

Z

STOP:
All legs on
groundLeg 2,4,6: Support
Phase
Leg 1,3,5: Swing
Phase

θ2
L1

(b)
Fig. 5: FSM for (a) traverse and trot gait pattern and (b)tripod gait pattern in proposed model of hexapod robot with
Hexa-Quad transformation.

θ1

Y

L2

θ3

X

L4

As shown in Fig. 5, in transformation state, 𝜃𝑎 ischanged and used on both 𝑥 and 𝑦 position of leg on thenext sequences. 𝑥 and 𝑦 for each 𝑛 -leg are part ofmoving frame and kinematics element for each link on eachleg as shown in Fig.6. Both positions including vertical leg
position (𝑧) is determined differently in each support andswing phase by using Eq.2 and Eq.3 respectively.
(Support Phase – Step and push on the ground) 0 ≤ t ≤ Tc
2

 4π t  S  2t 1
xsn (t ) =x0n + o  −sin   cos θ an
4  Tc 2π
 Tc   4π t  S  2t 1
ysn (t ) =y0n + o  −sin   sin θ an
4  Tc 2π
 Tc  
zsn (t ) = z0n

Z-Axis (m)Shoulder point (potentiometer)(1)
FIRST-phase

Sh

MOVE-phase (k)

(4)
(2)

Sensing Point(3)

Y-Axis (m)Fig. 7. A leg motion shape used in proposed model HexaQuad robot(2)

xsn (t ) =x0n +4 SIMULATION AND RESULTIn this study, several simulations running have been doneto analyze the potential of the proposed method to be
implemented in the real system. The first simulation in doneon CLD method by simulating the real-time system modelwith the 3D model that designed separately [12] as shownin Fig.8. On Fig.8 it shows that center legs (Leg 2 and 5)
are disabled after robot stop walking with hexapod mode.
In this case of transformation, side legs become main legs
that functional for quadruped mode walking. The initial
angle of each main leg for quadruped mode doesn’t changemuch due to the calculation using Eq.1.

(3)

where

Tc = walking cycle time (s),t = update time (real-time) (s),tex = additional period for applying extra force (s),S0 = distance of foot placement for one cycle (m), and
H 0 = height of leg lift from the initial position (m).

© ISAROB 2013

4

Fig. 6. SCS trajectory kinematics motion for a 4-DOF legof hexapod robot model(Swing Phase) 0 ≤ t ≤ Tc
2

 2π  So t   cos θ an1 − cos 2  Tc   2π  S 
ysn (t ) =y0n + o 1 − cos t   sin θ an
2 
 Tc   2π 
zsn (=t ) z0n + H 0 sin t Tc 

L3 θ

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Hexapod Mode
Quadruped ModeZ Coordination [m]

1.5

1

0.5

0
4
2

(a)
(b)
Fig. 8. 3D model simulation result for CLD transformation,(a) hexapod walking stop, (b) center legs disabled

0
Y Coordination [m]

-2

-25

-20

-15

-10

-5

0

5

X Coordination [m]Fig.10 Omnidirectional walking simulation results forboth hexapod and quadruped walking mode.

It is different to the SLD transformation whereby certain
steps of initialization need to be done on the main legs thatwill be used for quadruped mode walking. As shown in Fig.
9, center legs (Legs 2 and 5) and side legs are reinitialized(Fig. 9(b) and (c)) to appropriate angle before another side
legs (Leg 1 and 4) are flipped to the front and disabled (Fig.
9(d)). This procedure of transformation to make sure robot
are in stable range and overturning avoidance.

5 CONCLUSION
The performance of proposed Hex-Quad transformation
technique has been presented. Through the series ofsimulations, it was shown that the proposed method
performed stable reconfiguration on hexapod robot system
to quadruped configuration with proposed reinitializing
calculation. On the next step progress, the proposed method
will be customized and applied on real-time hexapod
system walking on actual environment.
6 ACKNOWLEDGEMENT(a)

I would like to thanks to Research and Innovation
Centre, University Malaysia Pahang on funding support on
this research project. Also deep appreciation and thanks to
my former supervisor, Prof. Kenzo Nonami from Chiba
University, regarding his support on this research studies.
Last but not least, also thanks to my students and associateresearchers that involved in this research project.

(b)

REFERENCES(c)(d)
Fig. 9. 3D model simulation result for SLD transformation,(a) hexapod walking stop, (b) center legs shoulder angle
reinitialized, (c) side legs shoulder angle reinitialized,(d) target legs disabled.

[1] Raibert MH (1986) Legged Robots That Balance.
MIT Press,[2] Wei-Min Shen, Salemi B, Will P (2002) Hormone-I
nspired Adaptive Communication and Distributed Control for CONRO Self-Reconfigurable Robots. IEEE Transactions On Robotics and Automation 18 (5):700-712[3] Siegwart R, Nourbakhsh IR (2004) Introduction toautonomous mobile robots. MIT Press, Massachusetts I
nstitute of Technology Cambridge, Massachusetts[4] Muscolo GG, Recchiuto CT, Laschi C, Dario P, Ha
shimoto K, Takanishi A A method for the calculationof the effective Center of Mass of humanoid robots In:
11th IEEE-RAS International Conference on Humanoid
Robots (Humanoids), 2011 Bled, Slovenia, 2011. pp 3
71 - 376[5] Cotton S, Murray AP, Fraisse P (2009) Estimation

Quadruped mode walking is verified by simulating theperformance of robot CoB [12] with comparison to thehexapod walking mode as shown in Fig.10. CoB
representing body way point shows that both modes
perform almost same results with the same omnidirectional
angles inputs.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of the Center of Mass: From Humanoid Robots to Human Beings IEEE/ASME Transactions on Mechatronics
14 (6):707-712[6] Shugen M, Takashi T, Hideyuki W (2005) Omnidir
ectional Static Walking of a Quadruped Robot. IEEE T
ransactions On Robotics 21 (2)[7] Freyr H (1997) Locomotion for difficult terrain. Ro
yal Institute of Technology, Stockholm, Sweden,[8] Raibert M, Blankespoor K, Nelson G, Playter R, T
heBigDogTeam BigDog, the Rough Terrain Quadruped
Robot. In: Proceeding of 17th World Congress, The Int
ernational Federation of Automatic Control, Seoul, Korea, 2008.
[9] Hirose S (1984) A Study of desing and control ofa quadrupped walking vehicle. International Journal of
Robotics Research 3 (2):113-133[10] Byl K (2008) Metastable Legged-Robot Locomotio
n. Massachusetts Institute of Technology, Massachusetts
Institute of Technology Cambridge, Massachusetts[11] Irawan A, Nonami K Force Threshold-Based Omn
i-directional Movement for Hexapod Robot Walking on
Uneven Terrain. In: 2012 Forth International Conferen
ce on Computational Intelligence, Modelling and Simul
ation (CIMSiM), Kuantan, Pahang, Malaysia, 2012. vol127-132. IEEE Computer Society,[12] Ohroku H, Irawan A, Nonami K (2009) A 3D m
odeling for Hydraulic-drive Hexapod Walking Robot us
ing 3D Geometric Technique with distributed Numerica
l Model. International Journal of Automation, Robotics
and Autonomous Systems 9 (1)[13] Irawan A, Nonami K (2011) Compliant Walking
Control for Hydraulic Driven Hexapod Robot on Rough Terrain. Journal of Robotics and Mechatronics 23(1):149-162[14] Tsujita K, Tsuchiya K, Onat A (2001) Adaptive g
ait pattern control of a quadruped locomotion robot. Pa
per presented at the IEEE/RSJ International Conferenceon Intelligent Robots and Systems, 2001, Maui, USA,© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Design of a special cam-based high speed manipulator system
Jing Wu1, Rui-Jun Yan1, Kyoosik Shin2, Chang-Soo Han2#
1

Department of Mechatronics Engineering, Hanyang University, Korea
2
Department of Mechanical Engineering, Hanyang University, Korea(Tel: 82-031-400-0462)#

corresponding author: cshan@hanyang.ac.kr

Abstract: In this paper, we develop a mechanism that mainly used a special cam and gearbox to utilize a DC servo motor
powerfully and speedily for a linkage manipulator. The mechanisms can be charged slowly, store the energy in a spring andrelease it on demand using a click mechanism. By compared the motions with many cams, we designed our special cam thatcan output an explosive movements. Two torsion springs, used as elastic elements, are located on the manipulator axis which isconnected to the main link and the mechanism. Consider about the output of a manipulator, we choose to implement a four bar
linkage design for the motion and force control. The manipulator’s mathematic models are built for purposed of analysis andcontrol.
Keywords: High-speed manipulator, series-elastic actuation (SEA), five bar linkage, explosive movements.
different applications and different scales. In the following
subsections, we will review the principles of how these
existing robots address the challenges of high-speed
problem in manipulators.
By the literature survey the general classification is
given in Fig. 1. Five common high-speed structures are
introduced in this figure, (i) the spring systems [4], (ii) the
cam system [5], [6], (iii) four-bar spring linkage system [5],[7], (iiii) the electric system [8], (iiiii) hydraulic system.
Every structure has their advantages and disadvantages. The
most skillful spring system is applied in gums, combine ofkinds of springs and the bullet,

1 INTRODUCTION
Since robots work in place of humans in many fields,
the developing of high-performance robots has increased
rapidly, such as those in industry fields, automobile
assembly plants, precision works, etc. Depends thedevelopment of multiple motors, about the high speed robot,
one interesting and challenging research topic is the energy
saving problems.
However, multitudinous of techniques are proposed inmany aspect to achieve the high speed motions. For amanipulator, the motion of high-speed could consist of
three patterns, 1) Fast-forward (FF); 2) Fast-reverse (FR);3) Fast forward and reverse (FFR). There is a developed
golf robot [1] whose swing simulates human motion is
proposed in order to reach ultra Fast-forward speed. Imitate
to human motion ability, this robot consists of a shoulder
joint with a high-power direct-drive motor and a wrist joint
with a low-power direct-drive motor to realize ultrahighspeed dynamic manipulation using a dexterous mechanism.
The most representational Fast-reverse technique is utilized
in jumping robot. It is generally known that the high powerhumanoid is desired for application of running or jumping
motions. The dynamic motion of jumping is characterized
by large instantaneous forces and short duration [2],depends of those conditions, some jumping structures usedsprings both in improve the velocity and energy
conservation [3]. The crucial factor in a high speed
manipulator is the motor.
There is a relatively large robots related to using highspeed mechanisms or methods for manipulators for© ISAROB 2013

2 DESIGN OF HIGH-SPEED MANIPULATOR
The driving principle of this system is described.

Fig. 1. The illustration of motion flow chart

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 2. Classification of the existing high-speed manipulators
force control. Using this design offers the possibility tomodify the rotating angle by adjustment the parameters of
the special cam and the trajectory of the manipulator tip byadjustment of the gear ratio.

2.1 Mechanical design
The mechanisms can be charged slowly, store theenergy in a spring and release it on demand using a clickmechanism. This mechanical principle is used by several
small animals and insects.
By compared the motions with many cams, we designed
our special cam that can output an explosive movements.
The manipulator, consisting of a four bar linkage structure
connected by springs and travels quickly using this special
cam that is mounted on actuator which are connect by gears.
The shape of the cam has been specifically designed toyield a constant torque on the motor through a three stage
gearbox system.
Two torsion springs, used as elastic elements, arelocated on the manipulator axis which is connected to themain link and the mechanism. To charge these two springs,
a small DC motor actuates the special cam and rotates themain link lever manipulator by a constant angle (can been
calculate by the given cam parameters) for one charge cycle.
Consider about the output of a manipulator, we choose
to implement a four bar linkage design for the motion and© ISAROB 2013

Cam

Gear 2

Gear 1ba
Main

Fixation platerGear 3

Gear 4

Fig. 3. The mechanical design of the high speed structure.
The structure for connect the main link and the torsion
springs:

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013y

S0 , S and e are parameters of cam.
If clockwise, Iw  1; anticlockwise, Iw  1.
k1 and k2 are parameters of torsion spring.

Ll

For the Torsion springs, we know that



t 

x

TorsionspringF  x  k2 ( So  S ) cos( Iw  ) e sin( Iw  )    (3)k1  y  k1  ( So  S )sin( Iw  ) e cos( Iw  ) Where, the force is calculated

F  k2
So, we get:

F

t 

Fig. 4. The torsion spring fixing mechanism
2.2 Kinematic model
Mechanical design
introduced in this paper.

andkinematic(4)

analysisk2  x k  x
 2 
k1  y 
k1  y =

k2 ( So  S ) cos( Iw  ) e sin( Iw  )  
k1  ( So  S ) sin( Iw  ) e cos( Iw  ) 

k2  ( So  S ) sin( Iw  ) e sin( Iw  ) k1  ( So  S ) cos( Iw  ) e sin( Iw  ) 

are

(5)

2.3 Dynamic model
The manipulator’s mathematic models are built for
purposed of analysis and control. The dynamic model isbuilt and many simulation models are built for testing thevelocity and explosive force of the manipulator.
Suppose the system is in balance, we have,
Tt   0(6)
In this redundant actuator system, suppose there are two
independent joint 1 and 4, the equation between the
independent joint and the dependent joint is:P  GaP  a(7)

Fig. 5. The kinematic model of the high speed structure.

The equation of the dynamic model as,
1 g 4t   Tt   Tt  

 
4 
0 htt   T4   0 

(8)

Here,
 gt2  G    gt3  gt4  pa

l3l4 sin t  2  3


1 

l4 (l2 sin t  2  l3 sin t  2  3 ) l2 l3 sin 3  l2 (l3 sin 3  l4 sin t 2 ) 

Fig. 6. The four bar linkage structure.
For the input of the motor, we have :

2 =

R1 z1
Rz
m  2  1 1 m
z2
z2

(1)

htt4 

For the gear1 and gear2, we get:

 x  cos( Iw  )  sin( Iw  )   So  S 
 y    sin( Iw  ) cos( Iw  )   e 
  



© ISAROB 2013

( gt4 )
tl cost  2 (1  gt2 )sin 3  l4 sin t  2 cos3 gt3 4l3 (sin 3 ) 2

(2)

537

(9)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

The testing results show that the requirements withrespect to velocity, acceleration, working space and energy
are successfully achieved.

3 CONCLUSION
This mechanism witch is designed to be characterized
as high speed manipulator could also form the propulsion
system of a jumping robot or be used for self deployment ofsensors. Our future research can combining some moredegree of freedom manipulator and developed as abiomimetic arm that may attach more applications.

ACKNOWLEDGEMENT
This research was supported by the MKE (The Ministryof Knowledge Economy), Korea, under the 'Advanced
Robot Manipulation Research Center' support programsupervised by the NIPA (National IT Industry Promotion
Agency). (NIPA-2012-H1502-12-1002)

REFERENCES[1] Maruyama T, Xu CQ, Ming A and Shimojo M (2006),
Motion Control of Ultra-High-Speed Manipulator with a
Flexible Link Based on Dynamically Coupled Driving,
Journal of Robotics and Mechatronics, Vol. 18, No.5,
pp.598-607[2] Urata J, Nakanishi Y, Okada K and Inaba M (2010),
Design of High Torque and High Speed Leg Module forHigh Power Humanoid, in Proceedings of International
Conference on Intelligent Robots and Systems, pp. 44974502[3] Wang M, Zang XZ, Fan JZ and Zhao J (2008),
Biological Jumping Mechanism Analysis and Modeling for
Frog Robot, pp.181-188[4] Shimoda S, Kubota T and Nakatani I (2002), New
Mobility System Based on Elastic Energy under
Microgravity. in Proceedings of International Conferenceon Robotics and Automation, pp. 2296-2301[5] Mirko K, Martin F, Andr G, et al (2008),A miniature
7g jumping robot, in Proceedings of International
Conference on Robotics and Automation, pp.373-378[6] Okada M and Murakami K (2007), Robot
Communication Principal by Motion Synchronization using
Orbit Attractor, pp. 2564-2569[7] Reddy N. S., Ray R, Shome S.N. (2011), Modelingand Simulation of a Jumping Frog Robot, in Proceedings ofInternational Conference on Mechatronics and Automation,
pp.1264-1268[8] Jerry E. Pratt, Benjamin T. Krupp (2004), Series
Elastic Actuators for legged robots, Storage and Retrieval
for Image and Video Databases,© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Estimating the clothes characteristics with the image and depth sensors
for developing virtual fitting room
Yuka Mastuba1, Jehan Jung2, Hiroyuki Funaya1, Kazushi Ikeda1, Rammohan Mallipeddi2, and Minho Lee2
1

Nara Institute of Science and Technology, Japan
Kyungpook National University, Republic of Korea
1(Tel: 81-743-72-5984, Fax: 81-743-72-5989)
2(Tel: 82-53-950-6436, Fax: 82-53-950-5505)

2

1{yuka-m,hiroyuki-fn,kazushi}@is.naist.jp
2{jjhan,mallipeddi,mholee}@knu.ac.kr

Abstract: Creating physically simulated animations of real clothes is needed for virtual fitting rooms. In this paper, we
propose a method for estimating the parameters of real clothes using the motion data obtained from a commercial RGB-depth
sensor. Our method determines the parameters using simulated annealing so that the distance between the spatiotemporal
trajectories of the real and the computer-simulated shapes of clothes. Here, the shapes of the clothes are expressed by several
points identified by a feature-selection algorithm. In our experiments, the method showed a good reconstruction of motions ofa skirt.
Keywords: clothes characteristics, optimization, simulated annealing, virtual fitting room
system requires a special and expensive device to measure
the characteristics of the fabric.
Bhat et al., for example, used video images to estimate
the static and dynamic parameters of fabrics [5]. Theirsystem swings a swatch of a fabric using a robot arm,
extracts features from video images and estimates theparameters of the fabric using an optimization algorithm.
Hasler et al. proposed a similar system using clothes
silhouette [6]. Their approaches generatively estimate thetrue parameters of clothes using swatches and applied themto generate animations. The systems have to prepare thesame swatch as the given real cloth in advance that seems
not realistic.
The essential difficulty of this problem lies on theestimation of the physical parameters of clothes. The
conventional systems estimate them from the movement ofswatches for known forces. To avoid this difficulty, we
estimate the parameters used in a 3D physical simulator,
instead. A 3D physical simulator generates computer
graphics according to given physical models. It uses notnecessarily a cloth model according to the material
mechanics but a simpler one. In that case, we need toestimate only the parameters in the simulator.
Our system estimates the parameters in the simulator bycomparing the real trajectory of clothes with the simulated
one. This idea makes it possible to employ any cloth modelin the simulator. The trajectory of the clothes is measured
using a commercial-use depth and image sensor, KinectTM[7], so that it is affordable at home.

1 INTRODUCTION
A virtual fitting room (VFR) enables customers to see
their fashion coordinates in online clothing shops bydisplaying computer graphics. VFRs have been in need anddeveloped recently because the main reason of customers’
hesitancy in buying stuff online is that they could not try onthem [1]. One example of VFR is H&M’s online dressing
room [2] which allows customers to change various clothes
on a fashion model and see the coordination. Digital
fashion ltd is a similar service but different from the
H&M’s in that they prepare a special studio where thefashion models in clothes are captured from all directions[3].
Although all those systems have been developed andcommercialized, there are still some limitations: 1. they
provide only the clothes sold in a shop, 2. displayed models
including garments are static although dynamics of theclothes also influences the purchase decision, and 3. an
expensive studio is need to register garments with sufficient
information. To make a dynamic model of garments and tocreate animations, their physical parameters should be
estimated.
There are several systems that estimate the physicalparameters of fabrics. Kawabata Evaluation System is astandard tool for a thorough evaluation of textile fabrics
deformability. Although it gives the behaviors of a fabric in
tension, shear, bending, compression and friction [4], the© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 2. The experiment condition for measuring. Yellow
and green lines present the vertical position of hanger.
Fig. 1. The procedure of our method
The detail of system is shown in Section 2 and its
effectiveness was confirmed by experiments in Section 3
and 4. Section 5 gives our conclusion.

2 METHOD
Our goal is to generate realistic 3D CG animations
based on the measurement of real clothes. To accomplish
this goal, our proposed method estimates the parameters ofclothes in a simulator by minimizing the distance between
the measured trajectory of clothes and the simulated one ofclothes. The term “trajectory” means time-series data of 3D
locations of five feature points of a cloth captured frame byframe in our system. The five feature points are defined as
the four corners and the center point of real clothes
One point of our method is that we need not to know theforce to clothes a priori. The force is measured through the
trajectory of the hanger that produces the movement of theclothes (Fig. 1). Hence we can swing the hanger by humanhand. The hanger has two markers so that we can easily
measure the trajectory from the depth and image sensor.
Note that we have to click the center point of markers for
detection in the first frame at this time. After the second
frame, our system can track the markers using the AR
toolkit [8] and OpenCV [9].
The measurement of clothes should be marker-less sinceclothes have no common markers. Hereafter, we consider
the cases of a skirt and a T-shirt for clothes (Fig. 2). We
use the depth image as a silhouette because some kinds ofclothes are difficult to extract feature points from thesurface. In the case of skirt, after eliminating thebackground from the depth image, we extract the four
corners of the skirt using the Harris corner detector, therein
we rotate 45 degrees clockwise so that the corners have the© ISAROB 2013

Fig. 3. The result of Harris corner detection and
Dijkstra algorithm.
maximum/minimum x-/y-coordinate. In the case of a T-shirt,
the leftmost and rightmost points in the original coordinate
correspond to the left-top and right-top points, respectively(Fig. 3).
We add the center point as a feature point, which isdefined as the max-min distant point from the four corners.
Here, the distances are calculated as the lengths ofgeodesics and minimized using the Dijkstra algorithm [10].
Our system compares the measured trajectories of thefeature points with the simulated ones. The simulated
trajectories are synthesized using a physical simulator,
Blender [11]. Blender has a cloth simulation function thathas six parameters (Table 1).
The parameters 𝛉 of the simulator are adapted so thatthe two sets of trajectories match. Here, the distance of thetwo sets is defined as the Euclidean distance,

𝐷(𝑅, 𝑆) ≡

540

𝑁

𝑀

𝑛=1

𝑖=1

1
1
∑ ∑‖𝑟𝑖𝑛 − 𝑠𝑖𝑛 ‖2.
𝑁
𝑀

(1)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013𝑣 is the perturbation range vector. Then, the parameter
vector is updated according toTable 1. Parameters in cloth simulation function
parameter

Description

Mass

The mass of the cloth material

StructOverall stiffness of the cloth
Wrinkle coefficient(higher creates more large folds)
Damping of cloth velocity(higher creates more smooth, less jiggling)
Have normally some thickness which slows
falling things down
Help with the cloth wobbling problem

Bend
SpringAir
Velocity𝐴 = min(1, 𝑒𝑥𝑝{(𝑐ℎ𝑎𝑛𝑔𝑒 ∙ 𝛾)⁄𝑇𝑡 })𝛉= 𝛉𝑡 + 𝑣 𝑖𝑓 𝑝2 ≤ 𝐴{ 𝑡+1
𝛉𝑡+1 = 𝛉𝑡𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

where 𝑝2 is also a random number between 0 and 1, and γ
is a factor.

3 EXPERIMENTThe effectiveness of our method was confirmed byexperiments. In the first experiment, we evaluated theaccuracy of the estimation of the center point. In the second
experiment, we evaluated the estimation error of our
method. The six parameters 𝛉 for a cloth simulation
function in the 3D physical simulator, Blender, are Mass,
Structural, Bending, Spring, Air and Velocity (Table 1).
Their initial, minimum, maximum and interval values wereset as shown in Table 2 according to the preset parametersin Blender and our preliminary experiments.
The parameters for the simulated annealing, the starting
temperature, the maximum loop count and the temperature
decrement parameters (α, β) were set to 1000, 350, and(0.003, 1.0), respectively. Other parameters in (2) ware set
to 𝜇 = 0.1 and 𝜎 = 0.6. These values were chosen so thatthe annealing algorithm can search all parameter spacewithin the maximum iteration count.

Table 2. The definition of parameter values
valueparameter

initialminimum

maximuminterval

Mass

1.0

0.15

10.0

0.5

Struct

30.0

5.0

80.0

15.0

Bend

20.0

0.1

150.0

10.0

Spring

3.0

0.0

25.0

4.0

Air

5.0

1.0

10.0

5.0

Velocity

0.95

0.9

1.0

0.01

where 𝑟𝑖𝑛 and 𝑠𝑖𝑛 denote the position of the 𝑖th featurepoint (a corner or the center point) in the 𝑛 th frame.
Because, the function D is no-convex, our systemminimizes it with a simulated annealing method, that is,perturbs the parameters randomly within a range depending
on a decreasing temperature. The perturbation is acceptedwith a probability also depending on the temperature.
The details of the setting are as below.
The perturbation range 𝑣𝑖 of 𝑖th parameter 𝜃𝑖 was set
to1
𝑣𝑖 = 𝜃𝐬𝑖 tan {𝜋 (𝑝1 − )} ∙ 𝜇𝑇𝑡 𝜎 ,2

4 RESULTS
The result of the first experiment showed that theestimated center point had errors of 1.9 cm (4.30 % of theclothes width) on average from the measured center point
using an additional marker for the ground truth. This value
is accurate enough compared to the estimation error of thecorners shown in the second experiment below.
In the second experiment, a short-and-hard skirt wasused (Fig. 5). The annealing method reduced both the
maximum and mean Euclidean distances in the swung
direction (Fig. 6).
The animation in 3D simulator was also consideredreasonable and proper by looks. We made anotheranimation of human walking motion in which a humanmodel wore on the optimized clothes (Fig. 7).

(2)

where 𝜃𝐬𝑖 is the standard value of the perturbation range of
𝜃𝑖 , 𝑝1 represents a random number between 0 and 1, and𝜇 and 𝜎 are gain factors. The temperature 𝑇𝑡 at the 𝑡thiterations is set to
𝑇𝑡 =

𝑇𝑡−1
,
(1 + 𝛽𝑡)𝛼(3)where 𝑇0 is the initial value and α, β are gain factors.
The probability that the perturbations are accepted
depends on the change of the distance,𝑐ℎ𝑎𝑛𝑔𝑒 = D(R, S(𝛉𝑡 )) − 𝐷(R, S(𝛉𝑡 + 𝒗))

5 CONCLUSION
This paper proposed a method for generating realistic
3D CG animations using a depth image sensor and a 3D
physical simulator. We successfully captured the motion ofclothes as trajectories and optimized the physical(4)

where 𝛉𝑡 is the parameter vector at the 𝑡th iteration, and© ISAROB 2013

(5)

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REFERENCES

parameters by comparing the real and simulated motions.
Our method will work as a part of VFR system bycombining to other techniques such as a body measurement
system [12].
At this time, however, our system cannot treat occlusionthat occurs due to bends. We need to change feature points
to cope with this problem to make our system applicable tovarious clothes.

[1] Brian Beck, Key Strategic Issues in Online Apparel
Retailing, yourfit.com, 2000, Version 1.0[2] H&M: http://www.hm.com/[3] digital fashion ltd: http://www.digitalfashion.jp/[4] Kawabata S (1980), The standardization and analysisof hand evaluation. Textile Machinery Society of Japan[5] Bhat K, Twigg C, Hodgins J, Khosla P, Poppvic Z,
Seitz S (2003), Estimating cloth simulation parametersfrom video. In Proceedings of the 2003 ACM
SIGGRAPH/Eurographics symposium on Computer
Animation (SCA 2003), pp.37-51[6] Hasler N, Asbach M, Rosenhahn B, Ohm J.-R, Seidel
H.-P (2006), Physically based tracking of cloth. In
Proceedings of VMV 2006, Aachen, Germany, pp.49-56[7] Microsoft KinectTM: http://www.microsoft.com/enus/kinectforwindows/[8] ARToolkit: http://www.hitl.washington.edu/artoolkit/[9] OpenCV: http://opencv.org/[10] Plagemann C, Ganapathi V, Koller D, Thrun S(2010), Real-time identification and localization of bodyparts from depth images. In Proceedings of ICRA, Alaska,USA[11] Blender: http://blender.org/[12] Weiss A, Hirshberg D, Black M.-J (2011), Home 3D
body scans from noisy image and range data. IEEE
International Conference on Computer Vision, Barcelona,pp.1951-1958

Fig. 5. The detail of clothes used for experimentFig. 6. Errors before (A) and after (B) the optimization
experiment. The maximum errors in all frames are 0.2290(A) and 0.0807 (B), the average errors are 0.1276 (A) and
0.0467 (B), respectively.

Fig. 7. The animation of human walking motion© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Spatial operation using skeletal recognition for virtual 3D work space
Yusuke Saito1 and Hiroyuki Nishiyama1
1 Graduate School of Science and Technology，Tokyo University of Science，

2641 Yamazaki，Noda-shi，Chiba-ken，Japan(Tel:+81-47122-1106; Fax:+81-47122-1106)(j7412611@ed.tus.ac.jp)
Abstract: 3D desktop application software for the general environment as well as professional work environmenthas been increasing. However, the input method for existing applications requires large-scale facilities and special
equipment. This study proposes an operation method using skeletal recognition technology with depth information
only. In addition, the method compares placement and specifying rotation tasks with the wheel mouse to verify its
operability and to evaluate its efficiency and usability. The efficiency of the method, excluding the adjustment andsome usability aspects was found to be good, but the overall efficiency was poor compared with using a mouse because
the operation of the space is unstable and the estimated position of the skeleton was inaccurate.
Keywords: 3D operation, Input device, Skeletal recognition, kinect
2 RELATED WORK

1 INTRODUCTION
In recent years, there have been improvements inworking environments and applications that use 3D
space represented on a 2D plane screen. 3dsMax1 and
GoogleEarth2 are current examples. However, a virtual
3D work environment like this increases the degrees
of freedom required for operation compared to a 2D
workspace. Several operations in space have been studied to resolve these problems. However, these studies
had to employ hand-held devices, large-scale systems
with a marker at the site, or a camera in a room.
This paper proposes an operation method using
skeletal recognition technology[4]. this technology canacquire the depth of an object by a single infrared sensor
device.3 With the skeletal recognition technology forvirtual 3D workspace on the computer, at operation inreal 3D space that is no tabletop surface, 3D coordinateof the dominant hand corresponds to the transition, wesuggest a technique employing a complex device consisting of a 2D cursor and a 3D cursor. With respect
to efficacy, we create tasks that mimic the operation ofplacement and of rotation in workspace virtual 3D, participants did the experiment compared with the mousewheel, to consider and feeling its efficiency also.

Interfaces suitable for a 3D virtual working environment have been proposed. These include the GlobeFish and the Globe mouse[1], which decouple translation and rotation at the device level and Wii Remote[5],which achieves high degrees of freedom in space by employing infrared and tilt sensors. Operating in virtual
3D space requires much zooming and rotation, even forsimple point of view (POV) and objects. However, input devices have a limited number of buttons, whichhinders scalability for these mode changes to perform
complex tasks in a virtual 3D work environment.
On the other hand, operations with the body havebeen actively researched. Examples include Kimura
et al.[2] who designed and implemented a gestureinterface system for a wide-view electronic workspaceand gesture operation in a large space called the ”gstalt”[6]. In addition, Nancel et al.[3] conducted a comparative study considering input devices and differences
between the dimension of the method of manipulation.
In this study, we selected operation on a tabletop
surface and in space for a comparative evaluation andimplemented operations in space with skeletal recognition assuming that a two-button click of the mouse isa pseudo hand gesture. During the evaluation, we decided to use only mouse buttons because hand gesturerecognition accuracy can be a factor.

1

3dsMax, http://usa.autodesk.com/
GoogleEarth, http://www.google.com/intl/en/earth/index.html
3
Kinect, http://www.xbox.com/en-US/kinect
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3 DESIGN PRICIPLES
3.1 Operation target

This study seeks to implement POV and object manipulation with reference to manipulations in 3D spaceof modeling software Metasequoia4 .
• Changing the POV (camera control)– Vertical and parallel movement of the POV– Changing POV in the depth direction– Moving POV along a curve (Rotating thescene)• Changing object positions– Vertical and parallel movements of the object

Fig. 2. Systemflow– Rotating to change the object orientation

the sensor. Thus, it is able to acquire rate of positions
for the 2D image of each skeletal site, by orthogonal
projection that 3D position converted to 2D image.

Operations on a tabletop surface, such as mouse
movement, are restricted to a plane, and be forced tochange view when adjusting or moving the depth of theobject in the above operation. However, for operation in
space, if both objects and the target point are displayedon the screen, there will be less need to change the POV
before moving the target point of the object.

3.3.2 Complex cursor control module

The position of the 2D cursor is determined by multiplying rate of 2D position that is determined by theorthogonal projection of the 3D hand and the screen
size. tmpX(tmpY) is referred to as ratio of the dominant position in the image depth for the x (y) axis, andthe position is represented by equation (1).
2DCusorP osition(X, Y )= (tmpX × DisplayW ide, tmpY × DisplayHeight)
The position of the 3D cursor is determined by the
position of the 2D cursor and the distance from the sensor to the dominant hand. In particular, SD is the distance from the work field to the sensor, HD is the distance between the dominant hand and the sensor, HS is
the sensitivity of the movement distance in virtual space
and the real operating distance of the hand, and equation(2) represents the position.

Fig. 1. Example of complex cursor.
3.2 Proposed method

In operation in space, it is difficult to control the pointof view (POV) with only a 3D cursor. Therefore, wesought to design a system suitable for controlling objects with a 3D cursor and controlling the POV with a
2D cursor. Figure1 illustrates an example of using acomplex 3D and 2D cursor. The user move objects andspecify the direction directly with a 3D cursor. The 2D
cursor controls the POV until the arrow is reached.

3DCusorDepth = {(SD × 2) − HD}HS(2)

3.3.3 State-acquiring module

3.3 Systemflow

Information for switching operation mode is needed
when working with objects and viewpoints. In this system, we assume that an object is clicked when the 3D
cursor touches the object and that the POV is clicked
when the 3D cursor is not touching the object.

3.3.1 Skeletal information acquiring module

3.3.4 application-execution moduleThis module employs a depth sensor to obtain the
distance between the sensor and something in sight ofThis module reflects the complex cursor and themode change process of the state-acquiring module inthis application.

4

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placement, to fit the direction completely, to adjust thedirection, and to complete both tasks. In this experiment, participants performed three consecutive tasks after some practice. We recruited nine participants (onefemale), ranging in age from 20 to 24 years old. All
are right-handed daily computer users. None are colorblind. We measure each of Operation time and number of overshoots for each of 486 trials: 2interf ace ·3distance · 3rotation · 9participants · 3replications.
We asked questions based on the five-point Likert
scale. We ask that question a total of 10 times per participant in the entire experiment: each distance of placement task, each difficulty of rotation adjustment, andeach of 2 interfaces. Participants rated the usability ineach operation object and POV and in each of the twotasks, and of fatigue. We also encouraged comments after every question.
In both the mouse and the proposed control methods, the distance between the participant and the screen
was a uniform 3[m]. Depth sensors are placed as near
1.3 [m] is about space and work from the sensor. The
mouse had a wheel and was made by Logicool, using akinect depth sensor. The system and the trial were implemented on the XNA framework using C# language.

4 EXPERIMENT
Participants continuously perform the two tasks ofplacing an object in the specified location and turning
the object to the specified direction. They performed
these tasks using both the proposed method and themouse wheel, with placement distance and difficulty ofangle adjustment divided into three levels. The difficulty of adjusting the setting angle is divided into threelevels, beginning from the easiest: less than 45 degrees,
sideways, and diagonal front. As representing the efficiency, not only in the overall time for consecutive task,
it took time to adjust location and direction of the object, to reach the goal and such as measuring the numberof errors.

Fig. 3. Set of tasks.

4.3 Results and Consideration

4.1 Consecutive tasks

In the placement task, participants transported thegreen sphere to the target (purple cube). Immediately
after the start, the POV is located just in front of theobject. Verifying the target area requires pulling someof the POV, it has become random whether the left or
right (figure3(a)). When the controlled object remains
in the range of the target for 1.0 seconds, we assume theobject position is stable (figure3(b)). When the rotation
task is specified, the target (purple cube) will disappear
when the placement task is completed (figure3(c)). Purple spheres that has become random whether the left or
right indicate the direction in which the occurrence ofthe specified. As with objects, the consecutive task is
considered completed when the red light indicating thedirection of the object intersects the sphere and is stablefor 1.0 second (figure3(d)).

Fig. 4. Operation time : Placement task.
4.3.1 Operation Time

Figure4 and Figure5 presents the operation time foreach task. First, in placement task, when placement
distance is short, the average time has reduced by 23%
compared to using the mouse (average 9,673[ms] vs.
12,492[ms]). However, as the distance increased, thetime for moving to the target and the time for adjustment increased because time is lost in the proposed

4.2 Experiment environment

To measure the performance efficiency, we recorded
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4.3.3 Cause of cursor shaking instability

We will now address the issue of specific shaking
instability of skeletal recognition that is considered tohave the greatest impact on efficiency and usability. In
placement and rotation tasks, the cursor became unstable when approaching the goal, and this did not simply seem to relate to holding the hand steady in space.
When skeletal positions closely overlapped, the middleware s estimated 3D coordinates became inaccurate,
and the tracking of the hand s 3D position information
became unstable in the final stages.
Fig. 5. Operation time : Rotation task.

5 SUMMERY AND FUTURE WORKIn this study, we designed and implemented a methodof controlling virtual 3D objects and POV based onskeletal recognition. The proposed system used a complex cursor composed of 2D and 3D cursors. Experiments compared the complex cursor and mouse cursor
with respect to efficiency and usability. In addition, using skeletal recognition technology in virtual 3D spaceclarified the issues that require precise operation. The
future direction of this study will include designing analgorithm to account for the instability of the skeletal
position recognition and improving the POV control in
virtual space.

method from the time the participant presses the button to change the perspective until he/she moves the 2D
cursor to the icon to change the POV. Second, the number of overshoots increases considerably as the distanceincreases. Cursor shaking instability is a possible cause.
Third, the overall efficiency using our method to specify
a rotation task is less than that using mouse. However,as regards suitable time in the approximate direction,
our method is not affected by the complexity of direction. This can be specified directly in spatial orientation,
which means that users can easily suited to a complex
direction.
4.3.2 Operability

REFERENCES[1] Froehlich, B et al.(2006), The globefish and theglobe mouse: two new six degree of freedom input
devices for graphics applications. In: ACM Conference on Human Factors in Computing Systems(CHI), pp. 191―199. ACM Press[2] Kimura, A et al.(2005), Design and Implementation of Wide-view Electronic Working Space, In:
Proceedings of Interaction 2005, pp. 143-150[3] Nancel, M et al.(2011), Mid-air Pan-and-Zoom
on Wall-sized Displays. In: ACM Conference on
Human Factors in Computing Systems (CHI), pp.
177-186, ACMPress[4] J. Shotton et al(2011), Real-time human pose
recognition in parts from a single depth image. In
Proc. CVPR
[5] WiiRemote, http://wii.com/[6] Zigelbaum, J. et al.(2010), g-stalt: a chirocentric,
spatiotemporal, and telekinetic gestural interface.
In Proc. TEI 10, pp.261-264, ACM

To evaluate the operability, participants evaluatedease of controlling the object and POV on a five-point
scale with 5 being comfortable and 1 being uncomfortable; fatigue was rated on a similar scale with 5 being
most fatiguing. The proposed method was harder to use
for moving the object (mouse 327 points vs. proposed
method 284 points). In comments, some participants
say that moving the object to the target was easy, but
it could seem difficult due taking a long time to adjust
and the instability of the complex cursor. And, there
were no significant differences between the mouse andthe proposed method for vertical and lateral control of
the POV (339 points vs. 304 points) or for scene rotation (319 points vs. 301 points). This indicates that
2D feedback such as with an arrow icon is more efficient. About tiredness, The standard deviation of the
proposed method is also greater than that of the mouse(3.57 points vs. 7.87 points). Some participants didn’t
feel tired, and were able to control the object and direction smoothly in space.

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Breast Cancer Palpation Training System
Using Five‐Fingered Haptic Interface Robot and GPGPU
Takamitsu Kawai1, Tetsuya Mouri2, Takahiro Endo2, and Haruhisa Kawasaki2
1

Organization for Research and Community Development, Gifu University, Japan2
Department of Human and Information Systems, Gifu University, Japan(Tel: 81-58-293-2675, Fax: 81-58-293-2675)
1

takawai@gifu-u.ac.jp

Abstract: As the number of breast cancer patients grows, teaching more medical students is highly demanded than ever.
However, seeking for real patients and actually having a training session with them are very difficult due to the nature of the
training involving touching their body. Our approach to this problem is to develop a virtual breast cancer palpation training
system that can completely eliminate the need for real human patients for the training. Our training system uses HandHIRO, a
five-fingered haptic interface robot to create haptic sensations of the deformations of a virtual breast including tumors. The
deformations of the virtual breast are entirely computed on a GPU using an FEM and the computational results are renderedboth haptically using a HandHIRO and graphically using a 3D display system with a half mirror. In this paper, the design andimplementation of our training system and preliminary experimental results are presented.
Keywords: breast cancer palpation, haptic interface robot, 3D display, deformable body simulation, FEM, GPGPU
with arbitrary shapes and position, up to the meshresolution. Introducing GPGPU (General Purpose
computing on Graphics Processing Units) technologies, thesimulator runs entirely on a GPU. The use of GPUs allows
us to simulate large-scale models in realtime that are
intractable to conventional CPU implementations. Thesefeatures enable the user to quickly change the virtual
models without lengthy offline precomputations. Thismeans that the users can train using a wide variety of virtual
patients with different medical conditions.
This paper presents the design and implementation of
our training system featuring the HandHIRO, 3D display
system, and a GPU-based FEM simulator. Preliminary
experimental results are also presented.

1 INTRODUCTION
Training environments for breast cancer palpation thatare easily accessible by medical users have been greatly indemand reflecting the increasing number of breast cancer
patients. Creating a virtual human breast model utilizing
VR technologies such as haptic interfaces and 3D displays
can completely eliminate the need for real human patients.
Based on this idea, we have been developing a virtual
training system for breast cancer palpation.
Takaiwa et al. [1] created a breast cancer palpation
simulator based on a real off-the-shelf breast training modeland a pneumatic parallel manipulator to create force
feedback. Jeon et al. [2] created an augmented reality
system based on a real silicone breast model and a
PHANToM haptic device. Although these systems create
realistic haptic sensations utilizing real breast models, they
are required to replace the breast model when simulatingbreasts of different geometry. Moreover, they do not
simulate multi-fingered touch sensations. The basic idea of
our training system is to create a complete virtual model ofa breast as a deformable body and simulate its deformationin realtime. The haptics are rendered for each finger
individually by a five-fingered haptic interface robot called
HandHIRO (Endo et al. [3]) and the graphics are rendered
by a 3D display system with a half mirror. The simulator isbased on an FEM (Finite Element Method) with acorotational formulation. Our system allows the user tospecify the stiffness for each finite element to place tumors© ISAROB 2013

2 SYSTEM OVERVIEW
Fig. 1. shows the overview of our training system. It iscomprised of a HandHIRO installed at the bottom, and a 3D
display based on a LCD (Liquid Crystal Display) attachedat the top, and a half mirror in between. As shown in Fig. 2.,
a user wearing a pair of 3D shutter glasses sits down infront of the half mirror and sees the 3D images of a female
chest including breasts which are projected on the half
mirror as a stereoscopic image. At the same time, all the
five fingers of the user are connected to the HandHIRO thatis placed behind the half mirror so that he/she can receive
haptic sensations for each finger individually, while seeing
his/her own hand through the half mirror. The 3D images ofthe deformed virtual breast generated by a PC with GPUs

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are overlaid on top of their own hand, thereby a mixed
reality effect is created. This effect is expected to enhance
the user’s training experience.
2.1 HandHIRO: five-fingered haptic interface robot
Fig. 3. shows theappearance of HandHIRO.
HandHIRO is a five-fingered haptic interface robot thatfeatures the ability to present high-precision threedirectional forces to each of the user’s five fingertips
individually. HandHIRO has many DOFs: 15 DOFs for thehaptic hand and 6 DOFs for the interface arm. Thanks tothis arm, HandHIRO can be used in a large workspace. A
user connects his/her five fingertips to HandHIRO throughpassive spherical permanent magnet joints. These joints
will automatically come off when excessive amount offorces are exerted, thereby HandHIRO allows to touch avirtual object with five fingers in a safe manner. Fig. 4.
shows an example of graphics rendering of a virtual breast.
The white spheres represent the user’s five fingertips. As
the user pushes the breast, dents are made and reaction
forces are given to the user through HandHIRO. The breast
deforms in realtime in response to the movements of theuser’s fingertips.

Fig. 1. Proposed breast cancer palpation training system

3 DEFORMABLE BODY SIMULATION FOR
VIRTUAL BREAST CANCER PALPATION

Fig. 2. Actual use of our training system

Many parts of a human body, including organs and/or
tissues are modeled as a deformable body. We focus on
simulating the breast part only as a deformable body.
Deformable body simulation is generally a difficult task
since deformable bodies have infinite DOFs. Therefore,
usually we have to discretize the deformable body using
numerical methods such as FEM. Our past breast palpation
simulator (Daniulaitis et al. [4]) is also based on an FEM. It
realized realtime simulation of a deformable body based on
precomputation of the body’s displacement and reaction
forces: the deformations of the entire body and the reaction
forces are precomputed for unit displacements for eachvertex of the FE model. At runtime, the deformation of theentire body and the reaction forces for each finger arecomputed as a linear combination of these precomputed
displacements, given a set of contact points. The drawbackof this method is the need for the lengthy precomputationand lack of dynamics. Our method simulates the dynamics
of a deformable body in realtime using GPGPU and at thesame time interacts with HandHIRO for haptic rendering sothat more realistic results can be obtained on the fly.

Fig. 3. HandHIRO, five-fingered haptic interface robot

Fig. 4. Deformations of breast by multiple fingertips© ISAROB 2013

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3.1 Simulation algorithm overview
Our simulation algorithm is comprised of (1) collision
detection and response and (2) FEM computation. The firststep for the algorithm is to receive the current position of
fingertips from HandHIRO and perform collision detection
between the fingertip spheres and the deformable bodysurface. If a collision is detected, its contact forces arecomputed and used as boundary conditions for the FEM. At
the same time, the contact forces will be sent to HandHIRO
for haptic rendering. Finally, the time step in the simulator
is proceeded and the next time step’s physical states arecalculated.

rendering. Note that in some cases we observed somenoticeable vibrations due to the penalty force when a sphereand the deformable body collide. To suppress thesevibrations, we apply a low-pass filter for the computed
forces.
3.4 GPGPU: General Purpose computing on GPUs
The entire FEM and collision detection and response are
implemented based on GPGPU by CUDA technology(NVIDIA [8]). In the GPU kernels, shared memory isextensively utilized to maximize memory bandwidth
utilization. Most of vectors in the algorithms are mapped toa vertex buffer object and made interoperable with
OpenGL. This allows direct graphics rendering of thecomputational results by GPU without copying the resultsback to the CPU memory.

3.2 Corotational FEM
In our simulator, the deformation of breast is simulatedusing an FEM based on a corotational formulation on the
GPU (Allart et al. [5], Parker and O'Brien [6], Nesme [7]).
The corotational formulation accounts for geometric
nonlinearities of the deformations. It can avoid so-called“inflation” artifacts when subject to large deformations. The
idea of corotational FEM is to rotate the stiffness matrix bya rotational matrix that brings it to the original coordinate
space before deformation. This eliminates the bad influenceof the rotational component of the deformation to the linear
strain that leads to an inflation effect. The system ofordinary differential equations is solved by implicit time
integration based on backward Euler scheme for stability.
The backward Euler scheme updates the velocity using theacceleration at the end of the time step. This requires tosolve a system of linear equations at every time step. This isdone by a conjugate gradient solver.

4 EXPERIMENTAL RESULTS
A preliminary evaluation was conducted using a PC
with Intel Xeon E5606 2.13GHz, 48GB main memory, and
NVIDIA Tesla C2075 for running GPGPU, and NVIDIA
Quadro 2000 for stereoscopic 3D graphics rendering withNVIDIA 3D vision technology. The frequency of the haptic
control loop is kept to 1 [kHz] and the graphics frame rate
is set to 30 [Hz].
4.1 Constructing a virtual breast model
A virtual breast model was constructed as follows: A
real silicone palpation training model was first scanned.
Then after the data is cleaned up, it is processed with meshgeneration software to generate a triangular surface meshand a tetrahedral volumetric mesh. Fig. 5. shows thebreast model represented with a surface mesh (6004
triangles). A volumetric mesh (44208 tetrahedra, 9097
nodes) is constructed from the surface mesh. According to
Krouskop [9], the Young’s modulus of the entire breast
except the tumor part is set to E=19000 [Pa] and the tumor
is set to 107000 [Pa], so that the tumor part can be felt
stiffer than other parts of the breast. Fig. 5. also shows thesize and location of the tumor used in this experiment. The
tumor’s radius is set to 1.5 [cm] and is located on the x axis
at x = 0.125 [m]. Only one of the breasts (the one on theright hand side in the Fig. 4.) is modeled by an FEM andsimulated. Other body parts are static and not included in
the simulation. The base part of the breast is given Dirichletboundary conditions to fix the breast part on top of thechest part.

3.3 Modeling fingertip contact
HandHIRO has 5 fingers and each fingertip is modeled
as a rigid sphere. The spheres and the deformable bodyneed to avoid interpenetration. We employ a penalty
method for simplicity. Proximity is checked for each spherecenter against all the vertices within the deformable mesh.
If the distance is less than the sphere radius r , then thevertex is considered in contact and proceeds to penalty
force computation. This process is efficiently done on the
GPU in parallel. The penalty force is given by(1)
F  ks d  kev
where d is the penetration depth and v is the rate ofchange of the penetration distance, k s and k e are a spring
constant and a damping constant, respectively. For eachsphere, all the penalty forces exerted on the sphere areapplied to the deformable body as external forces at eachcontact point. The penalty forces for each sphere aresummed and converted into one 3D force vector for haptic© ISAROB 2013

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5 CONCLUSIONz

We have discussed the implementation of our virtualbreast cancer palpation training system based on a fivefingered haptic interface robot HandHIRO, a 3D display,and GPGPU. The deformation of breast is modeled using a
corotational FEM and is computed on a GPU. This enables
large-scale realtime simulation of the deformations of avirtual breast. The preliminary experimental results showthat our system can simulate the physical behavior of abreast and is effective for breast cancer palpation training.

x

ACKNOWLEDGMENTS

Fig. 5. Tetrahedral mesh and tumor (red part: E=107000[Pa]). E=19000 [Pa] for the rest of the mesh.

This paper was supported by the Ministry of Internal
Affairs and Communication R & D Promotion Programme(SCOPE) and Grant-in-Aid for Scientific Research from
JSPS, Japan ((B) No. 23360184).

4.2 Results from virtual breast palpation
A virtual palpation session was conducted using theconstructed model. Although the user can generally use up
to 5 fingers simultaneously, here we show only the resultsof 1 finger case for simplicity. The palpation is done using
his/her index finger by periodically repeating a push/pull
motion along z direction (in Fig. 5., +z points towards thereader) translating his/her hand along the x axis from theleft to the right, and then from the right to the left (repeat
this 2 times). Fig. 6. shows the changes in the index
finger’s x position (dotted line) and the corresponding z
component of the target reaction forces (solid line) that arecomputed by the computational module. These target forces
are sent to the HandHIRO and rendered to the user’s
fingertip. As shown in the figure, the reaction force
becomes noticeably high at positions around x = 0.12 [m].
This means that, around this location, the user should bereceiving significant reaction forces in +z direction andfeeling the existence of the tumor. The position x = 0.12 [m]approximately corresponds to the x position of the tumor
that is predefined as shown in Fig. 5. This verifies that thelocation of the tumor is reasonably well represented, thus
showing the effectiveness of our system.

REFERENCES[1] Takaiwa M, Noritsugu T (2007), Development ofbreast cancer palpation simulator using pneumatic parallel
manipulator. SICE Annual Conference, 2007, pp.823-827[2] Jeon S, Knoerlein B, Harders M, et. al. (2010), Haptic
simulation of breast cancer palpation: a case study of haptic
augmented reality. 9th IEEE International Symposium on
Mixed and Augmented Reality (ISMAR), 2010, pp. 237238[3] Endo T, Kawasaki H, Mouri T, et al. (2011), Fivefingered haptic interface robot: HIRO III. IEEE Transactionof haptics, 4(1):14-27[4] Daniulaitis V, Alhalabi O, Kawasaki H, et al. (2004),
Medical palpation of deformable tissue using physics-basedmodel for haptic interface Robot (HIRO). Proc of 2004
IEEE/RSJ Internatinoal Conference on Intelligent Robots
and Systems, pp. 3907-3911[5] Allart J, Courtecuisse H, Faure F (2011), Implicit
FEM solver on GPU for interactive deformation simulation.
In GPU Computing Gems Jade Edition. Elsevier. 2011, pp.
281-294[6] Parker EG, O'Brien JF (2009), Real-time deformationand fracture in a game environment, In: Fellner D, Spencer,
S (ed) Symp. on Computer Animation (2009) ACM, New
York, pp. 156-166[7] Nesme M, Marchal M, Promayon E, et al. (2005),
Physically realistic interactive simulation for biological soft
tissues, Recent Res. Developments Biomech 2 (2005) pp. 122[8] NVIDIA Corporation, CUDA toolkit documentation.
http://docs.nvidia.com/cuda/index.html[9] Krouskop TA, Wheeler TM, Kallel F, et al. (1998),
Elastic moduli of breast and prostate tissues undercompression. Ultrason. Imaging, 20, 1998, pp. 260-74

Fig. 6. X-component of index fingertip position vs. Zcomponent of reaction force of index fingertip measured ina virtual palpation session© ISAROB 2013

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ALife approach for body-behavior predator-prey coevolution:body first or behavior first?
Takashi Ito1 , Marcin L. Pilat2 , Reiji Suzuki3 and Takaya Arita4
1,2,3,4

Graduate School of Information Science, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
1
takashi@alife.cs.is.nagoya-u.ac.jp, 2 marcin.pilat@gmail.com, 3 reiji@nagoya-u.jp, 4 arita@nagoya-u.jp
Abstract: We present the results of morphology-behavior predator-prey coevolution in a 3D physically simulated environment.
The morphology and behaviors of virtual creature predators and prey are evolved using a genetic algorithm and random one-onone encounters in a shared environment. We analyze the evolutionary dynamics on the basis of quantitative characterization ofmorphology and behavior. Specifically, we pose and answer the question: Which precede the other, morphology or behavior,during the evolutionary acquisition of predator and prey strategies?
Keywords: Coevolution, Predator-prey, 3D physical simulation, Artificial life
1 INTRODUCTION
Prey-predator interactions are the key element of ecological systems [1]. Predation pressures in food chains shapediversity and functions of organisms [2]. Many predators
employ various strategies capturing their prey and at thesame time, many prey employ various protective mechanisms
against their predators. These strategies arose through thecoevolution between predators and prey. Furthermore, in the
coevolution, morphology and behavior have been tightly coupled in each species. Therefore, the process can be regarded
as double coevolution of morphology-behavior and predatorprey couplings (Fig. 1).

virtual creature models in Artificial Life, following the pioneering study [5], allow us to analyze the morphology andbehavior coevolution.
In our previous study as a first step, we observed theemergence of various morphological and behavioral prey defensive strategies. This paper focuses on the morphologybehavior coevolution in terms of the evolution of strategies.
Specifically, we pose a question: Which preceded the other,morphology or behavior, during the evolutionary acquisitionof new strategies? We attempt to answer it by analyzing two
example cases of evolutionary experiments: acquiring a new
strategy as the first move or the countermove against the newstrategy of the other species.
2 MODELWe used the Morphid Academy open-source simulation
system [6] to evolve virtual creatures in a 3D physically simulated environment (Fig. 2). The presented coevolution of
predators and prey provides the example of simulating several agents in a shared environment of Morphid Academy ina coevolutionary context.

Fig. 1. Double coevolution of morphology-behavior andpredator-prey.
The purpose of this study is to understand the process
and dynamics of strategy emergence in the context of thisdouble coevolution. We performed double coevolution ofmorphology-behavior and predator-prey by using a simple
predator-prey scenario in a 3D physically simulated environment. Predator-prey coevolution has been studied mainly inmathematical biology using mathematical methods typicallyto analyze the change in population size of each species [4].
However, these studies have not focused or have not beenable to focus attention on coevolution of morphology andbehavior of individual virtual creatures. On the other hand,© ISAROB 2013

Fig. 2. Virtual creatures in Morphid Academy.
The agents are virtual creatures comprised of several 3D
rectangular solid body parts connected with simple hinge

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

joints. Their physical phenotype is developed from a directed
graph (Fig. 3). The nodes represent body parts and the links
represent joints. The genotype graph undergoes evolution
based on a genetic algorithm, and the phenotype tree represents the connected body parts. We termed the root bodypart as the torso, and all the other parts as limbs.

The fitness of the prey is defined by Eq. 2. If it escaped fromits predator without caught in the simulation time steps, itgets 5000 and gets another maximum of 5000 fitness points
proportional the distance it moved ln . Otherwise, the fitness
is calculated according to the ratio of the time the prey escaped during tn over the time limit t.
l 5000 + 5000 × ln (escaped, ln ≤ l)
Fprey =
10000
(escaped, 0 ≤ l < ln ) (2) 5000 × t−tn(being caught)t
The random predator and prey are positioned above thesimulation plane and allowed to free-fall due to gravity during a stabilization phase. Once they are stable from movement and resting on the ground surface, the evaluation encounter begins and lasts for S simulation time steps. Capturing is defined as the predator touching the torso of the preywith any of the predator’s body parts. A captured creature isdisabled and cannot be sensed.
We calculated several indices to characterize the morphology or behavior of the creatures quantitatively. As for themorphology, we used these four indices: volume (VOL), center of the mass (COM), agent body width (WID) and numberof body parts (NUM). Comparing morphology and behavior, it is more difficult to characterize the latter quantitatively
because, in general, the behavior heavily depends on the former, and is too complex to know what kind of indices willclearly represent the progress of coevolution in a 3D physicalsimulation environment. We decided to use these two simple
indices: the maximum output of sensory neurons (SEN) andthe average output of effector neurons (EFF), which are intended to approximately represent the sensitivity to the environment and the mobility, respectively. These indices do notdepend directly on the morphology of the agents. In orderto answer the question posed in Introduction, concerning theevolutionary order of morphology and behavior, we use theidea of cross-correlation methods.

Fig. 3. The development from genotype to phenotype.
The controller of a virtual creature is a recurrent neuralnetwork embedded in body nodes. There are three typesof neurons: input, calculation and output. The input neurons represent sensory information from the environment, thecomputational neurons process the input and the results are
fed into the output neurons as joint effectors that power thejoints, making the creature move. The creature sensor detects other living agents nearest to the virtual creature withina sensing range r . This virtual creature model is a simplification of Sims’ Blockies model [5] and is fully described in[6]. The simplification in body and neural structure decreases
the evolutionary search space and has been demonstrated toperform well for various evolutionary task.
Two populations are concurrently evolved, representing
the predators and the prey. A steady-state genetic algorithm
is used with tournament selection of size 3. Fitness of eachagent is calculated from the result of an encounter between arandomly selected predator-prey pair. For each tournament,
one or two individuals with the best fitness can produce achild through one of the genetic operators of copy, crossoveror grafting. The child replaces the worst performing of the
3 individuals. Mutation is applied to the resulting child individual and includes: mutation of the morphological nodeor link parameters, addition of morphological nodes, and the
addition or removal of morphological links.
The fitness of each agent is calculated after the encounter
through a fitness function. The fitness of a predator is definedby Eq. 1. It gets 5000 if it has captured the prey and gets another maximum of 5000 points proportional to the early capturing. Otherwise, its fitness is proportional to the distancegained towards the prey, based on the initial distance r0 andthe final distance rn .
t−t 5000 + 5000 × t n (caught)
r0 −rn
5000 × r0(missed, r0 ≥ rn ) (1)Fpred =

 0
(missed, r0 < rn )© ISAROB 2013

3 RESULTS
We evolved predator and prey populations, each of sizei = 30 and initially random individuals, for g = 10000 tournaments. Each evaluation of an encounter was performed for
S = 100000 simulation time steps with an initial distancer = 700 between the agents. For each tournament, a child
was created by asexual copy (probability of 40%), crossover(30%), or grafting (30%). Mutation of the child was performed with prob. of 80% with each mutation able to apply
small changes to the whole genome (prob. of 5% per gene).
The vision radius of predators was 5000 while the prey were
only able to see within 500 distance units. Therefore, thepredator can sense the prey much earlier than the prey.
In previous studies, we classified the evolved prey’s defensive strategies into two types, each with an assortment

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013of evolved morphologies and behaviors: Runaway Strategy
which involves fleeing from the predator and Guard Strategy which relies on their morphologies and typically stationary behavior to provide protection from predation [7]. It iseasy to detect the emergence of the Guard Strategies as theytend to evolve with a sharp increase in the fitness. Therefore,
we investigate the relationship between morphology and behavior evolutions by focusing on the course of the evolutionof Guard Strategies. To control the movement of prey andto promote the emergence of Guard Strategies, we used themodified fitness function of the prey (Eq. 2) and the environmental parameter l = 100.
We performed 30 trials. Among them, the prey evolved
some Guard Strategy to prevent the predator from capturing it in 17 trials and the prey did not evolved any defensive
strategies in the other trials. We used 12 results with a sharp
increase in fitness for analysis out of the 17 results. Fig. 4
shows a typical evolution in which Guard Strategy emerged.
It is shown that firstly, the predator fitness increased, and at
the same time, the prey fitness decreased. At some point, theprey increased the fitness sharply by evolving a strong defensive strategy.

Fig. 5. Fitness and two indices change of the prey in theevolution shown in Fig. 4.
tal. Fig. 6 shows a result of the coefficient between VOL asa morphological index and EFF, SEN as behavioral indicesfor a typical trial. Note that we used the absolute value ofthe coefficient in order to calculate the result in Fig. 6. The
maximum time-lag was set to 500 evolutionary time steps
before and after the emergence of Guard Strategy and focused on in analysis, which was identified by the maximum
increase in fitness of the prey. The time-lag maximizing thecoefficient can be used to estimate the evolutionary order of
the two indices. If it is positive or negative, the behavioral
changes precede the morphological changes or vice versa,
respectively. In this way, we see from Fig. 6 that EFF change
preceded VOL change, and on the other hand, VOL change
preceded SEN change, which means that the evolutionary order between morphological and behavioral indices depends
on which index was used for calculation even in one trial. We

Fig. 4. Fitness change in a typical predator-prey coevolution.
The prey evolved strong Guard Strategies at 4500 evolutionary time step.
Fig. 5 shows the changes in a morphological index, VOL,a behavioral index, EFF and the fitness of the prey in the same
evolution with the one shown in Fig. 4. We see a certain tendency that when the fitness value increased, the morphological index also increased, but the behavioral index decreased.

Fig. 6. Cross-correlation coefficients with evolutionary timelags.

3.1 Morphology first or behavior first?
Next, we investigate the evolutionary order relation between morphological and behavioral evolutions by repeatedly calculating the cross-correlation between the evolutionary sequences of index and another index with a changed
time-lag. We performed 12 trials and calculated 8 crosscorrelation (4 morphological indices × 2 behavioral indices)per trial and obtained 96 cross-correlation coefficients in to-© ISAROB 2013

thus classified all 96 cases into the cases in which morphology changes preceded the behavior changes and the reverse
cases by checking if the time-lag maximizing the coefficientis positive or negative. Table 1 shows the results. It shows aweak tendency for morphological change to precede behavioral change.
The tendency of morphology evolution to precede the behavior evolution might partly depend on the evolved strate-

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Table 1. Frequency of the evolutionary order (precursor).
Morphology Simultaneous Behavior
41.6%
17.7%
31.3%

gies. This paper focused on the emergence of Guard Strategies in the prey evolution. They rely heavily on their morphology and do not need specific behavior. Therefore, theincrease in fitness during the emergence of these strategies
tends to depend on a few mutations which modify the morphology giving a greater fitness. On the other hand, the behavior is less important for these strategies, and therefore, thebehavior modification adapting to the new morphology tends
to follow the morphological evolution.
It was also shown that the morphological or behavioral indices tended to change in advance of the change in fitness byusing the same cross-correlation based method. This result
is reasonable because morphology and behavior emergences
did not occur simultaneously as shown in the first experiment, and in order to increase the fitness, some morphology
might need corresponding behavior or vice versa.

Fig. 7. Frequency of the cases in which the cross-correlation
coefficient was more than 0.4 with evolutionary time-lags.
to precede behavior in the independent emergence of new
strategies and behavior tends to precede morphology in the
response to the new strategies of the opponents. This difference might indicate that the former and the latter were driven
mainly by big changes and small changes, respectively.
In general, there is an obvious asymmetry between predator and prey at the inter-species level. The results shown inthis paper indicate that an asymmetry between morphology
and behavior at the intra-species level does produce complex
dynamics in the coevolution between predators and prey.

3.2 By morphology or by behavior?
The previous analysis treated evolutionary change in thetraits only of the prey. Here, we focus on the evolution of
predator strategies responding to the emergence of new preystrategies.
We calculated the cross-correlation coefficient value between the same indices of the predator and of the prey.
Fig. 7 shows the frequency of the cases in which the crosscorrelation was more than 0.4 in all trials. We see a clear tendency that the cross-correlation coefficient for behavior indices was larger than that for morphological indices. In other
word, they tend to respond to new strategies of the opponent
by changing behavior and not by changing morphology.
This might be due to the difficulty of morphologicalchange. In general, a morphological change tends to be drastic, and thus non-adaptive without the corresponding change
in behavior. By contrast, a behavioral change has a greater
tendency to lead to a small improvement for the strategy.

REFERENCES[1] P. Legreneur, M. Laurin and V. Bels, “Predator-preyinteractions paradigm: a new tool for artificial intelligence,” Adaptive Behavior, 20 (1), 3-9, 2012.
[2] A. A. Agrawal, “Phenotypic plasticity in the interactions and evolution of species,” Science, 294 (5541),
321-326, 2001.
[3] M. Edmunds, Defence in Animals: A Survey of Antipredator Defences. Longman Group Limited, 1974.
[4] J. Murray, Mathematical Biology I: An Introduction.
Springer-Verlag, 2003.
[5] K. Sims, “Evolving virtual creatures,” in 21st Annual
Conf. on Computer Graphics and Interactive Techniques (SIGGRAPH 94), 15-22, 1994.
[6] M. L. Pilat and C. Jacob, “Creature academy: A system for virtual creature evolution,” in Proc. of theIEEE Cong. on Evolutionary Computation (CEC 2008),
3289-3297, 2008.

4 CONCLUSION
We presented the results of evolutionary experiments
showing morphological and behavioral changes under apredator-prey coevolutionary scenario in a 3D physicallysimulated environment. We defined the indices to characterize the morphology or behavior of the creatures quantitatively and analyzed their dynamics in order to answer thequestion: Which preceded the other, morphology or behavior, during the evolutionary acquisition of new strategies?
The answer is summarized as follows. Morphology tends© ISAROB 2013[7] T. Ito, M. L. Pilat, R. Suzuki and T. Arita, “Emergence of defensive strategies based on predator-prey coevolution in 3D physical simulation,” in Proc. of the 6th
Int. Conf. on Soft Computing and Intelligent Systems,
and the 13th Int. Symp. on Advanced Intelligent Systems 2012 (SCIS-ISIS2012), 890-895, 2012.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Application of an actor–critic method to a robot using state representation
based on distance between distributions
Manabu Gouko
Dept. of Mechanical Engineering and Intelligent Systems,Faculty of Engineering,Tohoku Gakuin University, Japan(Tel&Fax: 81-97-594-0181)
gouko@tjcc.tohoku-gakuin.ac.jp
Abstract: In this study, an actor–critic learning method was applied to a mobile robot. The method adopts a state representation based on distances between probability distributions. This state representation is less affected by the environment i.e., sensor signals maintain an identical state even under certain environmental changes. A simulation was performed and verified thatthe mobile robot can learn action relationship in the suite state using the actor–critic method.
Keywords: Reinforcement learning, actor–critic method, state representation, mobile robot

1 Introduction
Over the past few decades, several researches have been
conducted on autonomous robots. Given the wide variety of
external environments, robot’s adaptability has become primarily importance. In a robot system, it is important to determine how the outside environment is processed as a statefrom sensor information.
In a previous study, a state representation was developedon the basis of noisy sensor data using distances between
probability distributions[1]. This state representation is insensitive to the environment, i.e., sensor signals maintain an
identical state even under certain environmental changes.
Sensor signals are assumed to be expressed by probability
distributions, and states are defined in terms of distances
between distributions.
In the previous study, reinforcement learning to the autonomous mobile robot was applied using the proposed state
representation. Then, by repeated trial and error, it was confirmed that the robot can learn a suitable state–action relationship that helps it to perform a given task. Specifically,
the robot was trained by Q-learning method to move forward
along walls. However, Q-learning cannot usually be applied
to discrete state and action spaces. In such cases, the discrete
state and the action of the robot must be defined prior to
robot learning.

Fig. 1. State representation using distance between
distributions [1].
A simulation was performed and verified that the mobile
robot can learn action relationship in the suite state using theactor–critic method. The robot’s ability to perform the moving task was confirmed.
2 State representation using distance between distribu
tions
In this section, the proposed state representation was defined.
In theoretical information and statistics, the distance between two probability distributions is expressed in several
ways. f-divergence (f-div) is a family of measures introduced
by Csiszár and Shields [2] that includes the well-known
Kullback–Leibler divergence. The f-div of probability distribution
fromis defined as

In this study, an actor–critic method was applied to amobile robot, which uses the proposed state representation.
Actor–critic is a reinforcement learning algorithm that canprocess a continuous state and action space, demanding theneed to define the discrete state and action prior to robot
learning.

© ISAROB 2013

,

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,

(1)

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

where
is a convex function defined for0
and 1
0. Qiao and Minematsu [3] proposed that f-div isinvariant to invertible transforms and showed that all invariant measures are of the f-div form. Furthermore, they applied the invariant of measures to speech recognition [4].
3 Mobile Robot Application and Behavior Learning
Subsection 3.1shows how the proposed state representation
is applied to a mobile robot. Subsection 3.2 describes behavior learning by the actor–critic method.
Fig. 2. Autonomous mobile robot e-puck and its
infrared distance sensors [1].

3.1 Mobile Robot Application
Figure 2 shows the autonomous mobile robot, named epuck, used in our experiments.
The robot has eight infrared distance sensors. In our experiments, I used the six sensors labeled in Fig. 2. The next
section describes an experiment in which the mobile robot
performs a wall-following task. It was assumed that the robot’s sensors respond only to differences in the wall colorand not to wall position. Figure 3 shows how the state representation is acquired. First, while a robot moves in time ∆ ,each sensor memorizes
data. Next, the distances between
the distributions of each sensor’s data are calculated. In thisstudy, the sensor distribution 1, ⋯ , , ⋯ is assumed toand standard deviation σ . I
be Gaussian with meanused the Bhattacharyya distance (BD), a widely used measure of f-div, as the distance between two distributions. The
BD between the distributions of the sensors
and( and ) is given by the following formula
1
4

,

1
ln
2
2

.

Fig. 3. Proposed state representation using e-puck.
While a robot moves in time ∆ , data are memorized by every sensor. Next, the distances between the
distributions of each sensor’s data are calculated [1].

framework, a robot learns a suitable state–action mapping
without prior knowledge of its dynamics or environment.
The actor–critic learning method is applied, which is areinforcement learning algorithm that can handle a continuous state and action spaces. This method needs a critic,
which estimates a reward expectation from a state. It also
needs an actor as a controller. The actor outputs a motor
commanding response to the state.

2

BD is calculated from the sensor signal distributions acquired by the moving robot from time
∆ to . The
state vector at time contains distances and is defined asfollows:
, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,

, ,
,

, ,

,

4 Experimental Result and Discussions

3

In this section, I present and discuss the experimental r
esult. The robot executed behavior learning and obtained state–action mapping. After the learning process, thesensor signal was artificially transformed and the robot
can perform a task using the acquired mapping was ver
ified.
The robot was then placed in the experimental environmentshown in Fig. 4.

where , is,
. In this formulation, when an object
is outside the sensing range of a sensor and the distribution
is 0, the distance between distributions cannot be calculated.
In such a situation, the distance between the distributions ofthat sensor and other sensors is set to 0.
3.2 Behavior learning by actor–critic method
This subsection explains behavior learning using reinforcement learning [5]. In the reinforcement learning© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Fig. 5. Normalized rewards.
Fig. 4. Experimental environment.
i.e., sensor signals maintain an identical state even undercertain environmental changes. A simulation was performed
and verified that the mobile robot can learn action relationship in the suite state using the actor–critic method.

Behavior learning was assessed in a wall-following task.
When all conditions given below were satisfied at timet, the reward was defined as follows,
,

.

,

4
Acknowledgements

Here s , and s , are signal outputs by sensors 6 and 3, respe
ctively, andandare the respective motor comman
ds of the left and right wheel. In this experiment, ∆was set to 1 sec andwas set to 20.
The learning time was 10,000 steps (one step = ∆ ). The
robot was placed near the wall at every 500 steps during
learning. After learning, I confirmed the success of the learning behavior. Figure 5 shows the obtained reward data forthe three nonlinear transformations shown below (Equations
5, 6, and 7).
′
,

10

,

′
,

,

′
,

,

5

i

1, ⋯ ,65i

1, ⋯ ,66i

1, ⋯ ,6

7

This research was partially supported by the Ministry of
Education, Science, Sports and Culture, Grant-in-Aid forYoung Scientists (B), 24700196, 2012.
References[1] Gouko M. and Kobayashi Y. (2012) A State Rep
resentation Model for Robots Unaffectedby Envir
onmental Changes. International Journal of Social
Robotics, DOI:10.1007/s12369-012-0164-9.
[2] Csiszár I, Shields PC (2004) Information theory
and statistics: a tutorial. Now, Boston[3] Qiao Y. and Minematsu N. (2008) f-divergence isa generalized invariant measure between distribut
ions. In: Proceedings of 10th annual conference of the international speech communication associati
on, pp 1349–1352.z[4] Qiao Y. and Minematsu N. (2010) A study on invariance of f-divergence and its application to
speech recognition. IEEE TransSignal Process, 58(7): 3884–3890.
[5] Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. The MIT Press, Cambridge.

In this figure, the total rewards accumulated over 200 steps
are normalized by the total rewards obtained by the robot
with normal sensor signals.
The performance of the robot using our proposed state representation showed minimal degradation. Note that these
nonlinear transformations did not correct for physical environmental changes. The results indicate that the proposed
state representation model is applicable to all invertible
transformations, including nonlinear transformations.
5 Conclusions
In this study, I applied an actor–critic learning method toa mobile robot. The method uses a proposed state representation based on distances between probability distributions.
This state representation is insensitive to the environment,© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Color Halftoning Using the Error Diffusion Method by the Edge Adjustment
and the False Colored Limit
Kazuya Kadoya1, Ken-ichi Tanaka2
Meiji University, Japan1,2(Tel: 090-7724-0425 , 2090-2146-7873)
11

ce11025@meiji.ac.jp, 2tanaken@meiji.ac.jp

Abstract: In the device that the usable number of the colors and density of printer or facsimile communication are limited, thedigital halftoning is an important technique. When you use error diffusion method for mix of color and monochrome images,
false color appear in monochrome parts. The images were evaluated using the Mean Squere Error. The image evaluation of
MSE had rise. And so the evaluation with the monitor, a very good value was in this way provided. Full-color images grayscale
is not contaminated and black and white images, images of the experimental results of different patterns, withoutcompromising the quality of the original error diffusion method, we solve the problem.
Keywords: color halftoning, error diffusion method, grayscale

1 INTRODUCTION

2.2 The adjustment of the contrast
The adjustment of the contrast adjusted the bright place
becomes brighter, the dark place becomes darker with thesigmoid tone curve [6] called the highlight shadow todarken to apply to a variety of original images.
In this study is adjusting the contrast by Equation (2) tocoordinate the contrast.

Digital halftoning [1] includes many methods. For
example, there are the applications of the combination
optimization problem, dither method, the error diffusion
method [2]. Error diffusion method is applied easy for color
image halftoning, and has already been put to practical use.
However, as for the problems of the error diffusion
method, a domain of the grayscale [3] is that a color
unrelated at all occurred when the image in a part of thecolor image is processed though it is necessary for whiteand black to express a domain of the gray scale. In otherwords it means that a false color [4] occurred.
Among others, there is a problem that this processing
scatters an error, some outline parts become dim. wesuggested the new error diffusion method that weemphasized an edge, Contrast adjustment and limited afalse color, to solve these two problems.

(2)This expression (2) supports only numerical value to 0128 of the brightness before the processing. The subsequent
brightness changes the degree of the increase rate
symmetrically.
Not only a hue improves by doing contrast adjustment,
but also the place that brightness is remarkable and is highor is low becomes easy to make a hit for the halftoning of
the false color limit.
256

2 TECHNIQUE
After processing

224

We will show a method used in this experiment here.
2.1 The emphasis of the edge
The emphasis of the edge used a Gaussian [5] filter withunsharp mask on this occasion again.
The unsharp mask is a method to add the difference
share with the original image after shading off an image
once. When blur the image, equation (1) used a Gaussian
filter using a Gaussian functions.
ｘ

ｙ

c=1.0

160

c=1.2

128

c=1.5

96

c=2.0

64

c=3.0

32
0
0

32 64 96 128 160 192 224 256

Before processing(1)

σ of the Gaussian coefficient assumed it 1 then.

© ISAROB 2013

192

Fig. 1. The illustration of something

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013nberg which we showed in table 1. The pixels that sc
attered an error by scattering an error in the pixel that
does not perform threshold processing become easy to
exceed the threshold. In addition, the scanning assume
d it raster scanning.

2.3 False color limit
2.3.1 Basic concept of error diffusion method
Fig.2 shows basic concept of the error diffusion me
thod. Data f (n) is assumed that the value within the range of -1 ~ +1 is taken.
A. Data f (k) in n=k is read.Value g(k) ,which the qua
ntization error is added, is given as follows,(3)
where, if k is equal to zero, quantization error
is equal to zero. This is because that n isgreater that zero.

g(-1,0)=0
g(-1,1)=3/16

2.3.3 New concept of error diffusion method
When the level of the original image color components
R is equal to G, we keep that R is equal to G in the same
position of the halftoning image. When the level of theoriginal image color components R is equal to B, we keepthat R is equal to B in the same position of the halftoning
image. When the level of the original image color
components B is equal to G, we keep that B is equal to G in
the same position of the halftoning image. In case of a fullcolor image is constructed in grayscale, Halftone images
should not be generated in colors that are quit irrelevant inblack and white images.
Figure 3 shows relations of Red, Green, Blue, Cyan
Magenta and Yellow.
Table 2 shows the list of the false color limit.

B. The values
are compared with threshold (=0)
and binary number
is obtained as follows.
(4)
C. Quantization error
which generated between
andis given as follows.
(5)
D. Value, which the quantization errors added, given as follows.

Table 1. Floyd & Steinberg
g(0,0)=0g(1,0)=7/16
g(0,1)=5/16
g(1,1)=1/16i(6)E. Ass data
are quantized by a similar processthat consisting of the procedure 2~4, mentioned above. Output binary number
is obtained.

Fig. 3. Relations of RGB and CMY

Input
data

Fig. 2. Basic concept of error diffusion method
2.3.2 Extension to color image
The color image is composed by the combination of each Red, Green, and Blue (three primary colors). At
the color image the case of Windows Bit Map, the three primary colors of R, G and B has density inform
ation of 0-255 respectively. Therefore, the color data is
first divided into each primary color element. And, processing similar to the case of a monochrome image is done. Each primary color results are united at the end. Then, the color image can be treated.
In this study used error diffusivity of Floyd & Stei

© ISAROB 2013

R=G=B
R=G
R=B
B=G

559

Table 2. False color limit
The color that I canFalse color
outputRed, Green, Blue,White, Black
Cyanide, Magenta, Yellow
White, Black, Blue,
Red, Green, Cyanide,
Yellow
Magenta
White, Black,
Red, Blue, Cyanide,
Green, Magenta
YellowWhite, Black, Red,
Green, Blue, Magenta,
CyanideYellow

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2.4 Evaluation method
It is a picture evaluation of the halftoning about the color image.
The valuation basis of a color image carries out convolution of the Gaussian filter to the RGB ingredientof the quantized image, and to create a pseudo-color
image is represented. In this study, minimizing the me
an squared Euclidean distance in RGB color space of t
he image and the original image.
The MSE is defined as the equation (7).
(7)
Inin original image,
decoding image,
M and N show the size of the image.
It may be said that a high quality image was provided if
a MSE level is small.

(a) Conventional method(b) Proposed method
Fig. 5. Enlargement of the right eye
Fig 5 shows an enlargement of the right eye of girlhaving Balloon in Fig 4.
In this study compare it with six kinds of original
images "Aerial" "Airplane" "Balloon" "Earth" "Mandrill""tulip". Table 4 shows an evaluation value for each image.
Good results were obtained with the monitor and MSE.
There were not many differences of the value of MSE.
However, the difference of the number of people with themonitor was clearly.
Here is an example "Balloon" and "Mandrill" image
shows the results.
Fig 6 shows the original image. Fig 7 shows an image
subjected to unsharp mask and contrast adjustment. Fig 8shows an image of the conventional error diffusion method.
Fig 9 shows images of the proposed error diffusion method.
The left image "Balloon", the right image "Mandrill"
images show.

3 EXPERIMENTAL RESULT
Comparing the processed image with conventional andsuggestion method error diffusion method.
At first the right half compared it with an image of"Balloon" which became the grayscale. Fig 4 shows theresults.
The left image of Fig 4 is the image which it processedby the conventional error diffusion method. The right imageis the image which it processed by the error diffusion
method of the proposed method.
Proposed method understands that a false color does notoccur. The grayscale area is the error diffusion processing is
not only black and white.
Table 3 shows an evaluation value for this image. 63
people were monitored. Good results were obtained withthe monitor and MSE.

Table 3. Experimental results
MSE
AerialAirplane
Balloon(a) Conventional method(b) Proposed method
Fig. 4. Comparison image of "Balloon" image

Earth
Mandrill

Table 3. "Grayscale Balloon" experimental results
MSE
Monitor[people]
Conventional method
57.41701
Proposed method
56.1591
62© ISAROB 2013

Tulip

560

conventional method
proposed method
conventional method
proposed method
conventional method
proposed method
conventional method
proposed method
conventional method
proposed method
conventional method
proposed method232.1830
197.6562
227.3057
188.3521
57.4170
56.1591103.9658
94.1132
382.2701
355.2142420.8529
409.8079

Monitor[people]
5
58
2
61
1
62
7
56
2
61
3
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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

4 CONCLUSION(a) Ballon

The advantages of unsharp mask
 It was improvement that “large part of the change
in gradation blurred” is defect of error diffusion
method.
 Easy to handle.
The advantages of contrast adjustment
 Easier to hit when you try to limit false color.
 An objective visual evaluation increase.
The advantages of false color limit
 False color is eliminated.
 An objective visual evaluation increase.
 The grayscale is expressed more beautifully.
Good images were provided visually by taking abovementioned process.
In the future, we will try to do a thing as a variety ofvalue. For example, there are Gaussian coefficients andcontrast adjustment coefficients. We want to obtain a good
image and more.

(b) Mandrill
Fig. 6. Original image

REFERENCES(a) Ballon(b) Mandrill
Fig. 7. Unsharp mask and contrast adjustment images[1] T. Umemura, H. Aguirre, K. Tanaka, “Multi-Level
Image Halftoning Technique with Genetic Algorithms,”
IEICE transactions on fundamentals of electronics,
communications and computer sciences E85-A, no.8,
pp.1892-1897 (2002).
[2] K. Miyake, M. Saito, “An Evaluation Method for the
Images Obtained by Multi-Level Error Diffusion,” J.
Imag .Sci. Technol. 42, 155-120 (1998).
[3] R. W. Floyd, L. steinberg “An adaptive algorithm for
spatial gray-scale,” Prpc. Soc. Inf Disp., 17, pp.75-77(1976).
[4] K. Tanaka, “Error Diffusion Method for Color Images
without False Color,” The Institute of Image Information
and Television Engineers, 33(51), pp45-48 (2009).
[5] F. Catté, P.-L. Lions, J.-M. Morel, and T. Coll,“Image selective smoothing and edge detection by
nonlinear diffusion” in SIAM J.Numer. Anal., vol. 29, pp.
182–193 (Feb. 1992).
[6] K. Tanaka and H. Arai, “Color Halftoning without
Generation of False Color,” IWAIT2011, (2011)(a) Ballon(b) Mandrill
Fig. 8. The conventional error diffusion method(a) Ballon(b) Mandrill
Fig. 9. The proposed error diffusion method© ISAROB 2013

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The three optimization methods of searching the effective
combination dither matrix
Shohei Niinuma and Ken-ichi Tanaka
Meiji University, Japan(Tel:044-934-7562)
ce11068@meiji.ac.jp
Abstract: In this paper, we proposed the dither matrix for color image with optimization methods. A dither method displays agrayscale picture that is expressed two colors. They are black and white values. Thereby, in a picture, although it is two values,in view of a distant view, it seems that there is a monochrome shade. I used three methods that are Genetic Algorithm (GA),
Simulated Annealing (SA), and taboo searching (TS). The criterion of comparing data is cost E that is the evaluation value of
reproduction of gray image. And it is addition of evaluated value of gray level and evaluated value of contrast.
Keywords: Dither method,GA,SA,TS.

1 INTRODUCTION(1)Dither method [1] is the methods used by digital
halftoning. Nowadays, There are some popularly metho
ds of dither matrix. Bayer dither, mesh dither, and soon. These methods are actually good on a viewpoint of image quality. However, there is difference of evaluat
ion value for using different images. In addition, conve
ntional methods, Bayer dither for example, are not really optimal combination on a viewpoint of combinationa
l optimization problem.
In this paper, we proposed the dither matrix for color
image with optimization methods. I used three methods
that are Genetic Algorithm (GA) [2],[3],[4], Simulated
Annealing(SA) [5], and taboo searching(TS) [6]. Pro
cessing procedure is follows,(1) The color image is composed of eight bits respect
ively of red, green, blue, and becomes 24 bits in total.
Therefore, the color image is dividing into a red ele
ment, a green element, a blue element.
(2) And, the divided each element is optimized severa
lly.
(3) The optimized element is synthesized and the color halftoning image is made.
The criterion of comparing data is cost E that is the evaluation value of gray level. And it is addition of cost E_m and cost E_c. Individuals that have small cost
E are prior carried over.

But
(2)
(3)
In this case, it is the operation method, andmeans the rest that divided, A in B.

Fig. 1. Principle of the organized dither method

2 DITHER METHOD
Organized dither method, as shown in Fig.1. below,
the original image dither matrix (or Dither pattern) called
intentional noise
are added,
and then quantized. Now, the original image is shown asof M×M.
The
handling of Fig.2. is equivalent with choosing the thresholdas expression (1).

© ISAROB 2013

Fig. 2. Threshold change image

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3 OPTIMIZATION METHODHOD

In this study, the conditions are that temperature isunder number which is set at hand or evaluated value of SA
is better than Bayer method.

3.1 Genetic Algorithm
In this study, individuals are themselves. Elements ofindividuals are threshold level of gray level from 0 to 255.
The threshold levels are Equation.1. “I[x]” is position x ofthe matrix. "i” is numerical number from 0 to 15. The
numeral numbers of elements of Table 1 are “i” of equation(1).
The same number is not set in the same individual. GA
is to convert grouping and to search what combination ofelements is the optimal combination. In this study, theoperation of GA which shows is as below.
(1) The matrices which have elements set in at random
are prepared(2) Set up solution of cost function E. Cost function E isthe addition of costand cost. Costisevaluated value of gray level. Cost
is Evaluated valueof contrast. Equation of cost function E is as below.
(3) The individuals of next generation are decided byusing the tournament method.
(4) Selecting two individuals and doing single point
Crossover.
(5) Two elements of the individual which is selected at
random are exchanged.
(6) Running over from (2) to (5) until the numbers ofgenerations reach the generation number.
The number of individual is 200, the number ofgeneration is 40, the probability of shakeout is 0.1 and theprobability of mutation evolution is 0.01.
Single point Crossover is exchanging the position ofelemental number from 0 to X of individual. The positionof elemental number X is selected at random.
If the same elements are in the same individual, empty
number of element sets one of duplicative element.

3.3 Taboo searching
The method of taboo searching is as below.
(1) The matrices which have elements set in at random
are prepared (it is named individual A)(2) Making some individuals based the individual A(3) Evaluating and comparing these individuals(4) The best evaluated value individual (it is named
individual B) is selected by these individuals(5) If combination of individual A and B has be selected
before, return to operation (2)(6) Comparing individual A and individual B(7) If individual A is better than individual B, return tooperation (2)(8) If individual B is better than individual A, making
some individuals based the individual B(9) Running over from (3) to (8) until numbers of repeat
time surpass numbers of generations

4 FLOW CHART
Figure 3 shows the steps from original image to halftone
image.

3.2 Simulated Annealing
In this paper, the algorithm of SA is as below.
(1) An individual that have matrices which is set
elements in at random is prepared.
(2) New individual is made by changing element of theindividual which is made at operation (1).
(3) Comparing two individuals(4) New individual is made by exchanging element of
the individual getting better evaluated value(5) Running over from (3) to (4) until the satisfaction of
the conditions.

© ISAROB 2013

Fig. 3. Flow of optimized halftone image

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5 EXPERIMENTAL RESULT

Table 2. Experimental results using SA
Image name

The following shows experimental results that was used
this time. The picture is "Balloon" and "Mandrill" and"Parots" Using. In this study, I used the panmagic square
dither because panmagic square dither was better than thebayer dither. Evaluation was performed by comparing thevalues of E. visually good Pseudo-gray image is given thatby reducing the Evaluation value of contrast
and theEvaluation of the tone reproduction. From the above,the evaluation value is represented by Expression (4).
An above expression's "α_m" and "α_c" are Weighting
coefficients."α_m" and "α_c" is related to the following.
Changes in the tone reproduction and contrast of theimage obtained according to the weighting coefficients.

E

Blue element"Balloon"

6.9416127.0003

102.9885

Green element"Balloon"

7.0533127.2086

103.1775

Red element"Balloon"

6.8086127.3100103.2097

Blue element"Mandrill"

16.6493123.7304

102.3142

Green element"Mandrill"

16.4153

123.7034

102.2458

Red element"Mandrill"

14.2207125.1569

102.9696

Blue element"Parrots"

9.1002125.8621

102.5097

Table 1. Experimental results using GA
Image nameBlue element"Balloon"

7.4926

127.6042

103.5820

Green element"Parrots"

8.6757126.1537

102.6581

Green element"Balloon"

6.5050127.1885

103.0519

Red element"Parrots"

8.4334126.0297

102.5104

Red element"Balloon"

6.8001

127.2975

103.1981

Blue element"Mandrill"

16.5764

123.7008

102.2760

Green element"Mandrill"

16.2016124.0656

102.4928

Red element"Mandrill"

13.8551

125.0254

102.7914

Blue element"Parrots"

8.5978125.8586

102.4065

Green element"Parrots"

9.2138126.0804

102.7071

Red element"Parrots"

8.5727126.0877

102.5848

Figure.4 shows the original image used in theexperiment. The blue element of the original image wasshown in Figure 5 and the green element of the original
image was in Figure 6, the red element of the original
image was in Figure 7. In addition, Figure 8 shows asynthesis of three elements. And Figure.9 shows an image
using a panmagic square dither. Finally, Table 4 shows theevaluation of the best things in GA and SA and TS, whichwere compared with the panmagic square dither method's
images.

Fig. 4. Original image© ISAROB 2013

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Fig. 5. Blue element

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Table 3. Experimental results using TS
Image name
E_m
E_c
E

Table 4. Comparison of evaluation values about eachelement in “Balloon”
B l u e G r e e n Redelement
element
element
p a n magic
103.3915
103.5731
103.3127
squareBlue element"Balloon"

9.0856126.9960

103.4139

Green element"Balloon"

11.2213127.2162

104.0172

Red element"Balloon"

8.7723

127.3087

103.6014

ditherThe
optimized
valueBlue element"Mandrill"

18.4120123.9069

102.8079

4 CONCLUSION

Green element"Mandrill"

17.5246

124.0483

102.7435

Red element"Mandrill"

14.3682

125.1369

102.9832

Blue element"Parrots"

10.4983

125.8647

102.7915

Green element"Parrots"

11.6400

126.2159

103.3007

Red element"Parrots"

10.7327126.1812

103.0915

102.9885

103.0519

103.1981

By using optimization techniques, in this study,
obtained more good evaluation value than the conventional
evaluation. Then, in the three methods, considering both
calculation time and the evaluation value, we think that SA
is the most good in the three methods. Because SA spent
the shortest time in three method as the calculation and got
good evaluation values on the average. Then, we obtainedsome opinions that this technique has better thanconventional method in visually. In the future we would
like to strive to improve the assessment by changing the
input values during optimization. In addition, We would
like to shorten the time of optimization about GA,SA andTS.

REFERENCES

Fig. 6. Green element

Fig. 7. Red element

Fig. 8. Synthetic image

Fig. 9. panmagic image© ISAROB 2013

[1] Tsuneyasu Kato and Ken-ichi Tanaka, ”The optimal
method of searching the effective combination of dither
matrix” ARTIFICIAL LIFE AND ROBOTICS No.16
pp140.(2011)[2] D.E.Goldberg, ”Genetic Algorithm in Search,
Optimization, and Machine Leaming”, Addison-Wesley,
Reading, Mass(1989)[3]
S.Kirtpatrick,
C.D.Gelatt
and
M.P.Vecchi, ”Optimization by Simulated Annealing”,
Science, vol.220, pp.671-680(1983)[4] T.Umemura, H.Aguirre,and K.Tanaka,”Multi-Level
Image Halftoning Technique with Genetic Algorithms”,
IEICE Trans. Fundamentals, Vol.E85-A, No.8, pp.18921897,Aug 2002
[5]K. Tanaka, "halftone processing of images for SA"
Journal of ITEJ, Vol.61 No.6 pp828-837 (2007)[6] F.Glover. ”Tabu search II”. ORSA Journal on
Computing, 2:4-32,(1989)

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Inverse halftoning of color image using unsharp mask
Hiroki Matsuo1 and Ken-ici Tanaka2
Meiji University, Japan(Tel: 044-934-7562)
1

ce11082@meiji.ac.jp
tanaken@meiji.ac.jp

2

Abstract: The inverse halftoning is image restoration technique to make the grayscale image from binary image that isprocessed by digital halftoning. Inverse halftoning techniques need the removal of the noise. This is because the noise is added
to reconstruct the pseudo grayscale image using black and white. In inverse halftoning image, important information also
blurred when the noise in inverse halftoning image is reduced. In other hand, when the edge in inverse halftoning image is left,
reducing the noise becomes insufficient. The goal of the inverse halftoning technique that can reduce the noise on inverse
halftone image and that can emphasize the edge on inverse halftoning image. Therefore we propose inverse halftoning toemphasize the edge using unsharp mask. As a result of using unsharp mask, SNR became higher than conventional method andbecame easy to look visually.
Keywords: inverse halftonig, unsharp mask, canny method.
expected as for doing the two highly accurate at the same
time.
Therefore, we removed the noise by a mean filter andthe gaussian filter, and performed unsharp mask to the edge
part. From this, we are aiming that get the highly precise
reconstruction image which satisfied both removal of thenoise and emphasis of the edge.

1 INTRODUCTION
The inverse halftoning is processing that restores theimage made binary by the half tone processing to former
gray-scale image. Inverse halftoning displays the pixel
value with 0 (white) or 255 (black) bordering on a thresholdvalue from 0 to 255. And, when the image is made binary, it
is an expression technique as mixing the noise, and doing
the one that is not a continuous step, etc. using the mistakeof eyes of man who shows it. The one that is called thedither method and a random dither as a past technique isenumerated in the half tone processing. And, because thenoise is mixed when the half tone was processed, it isnecessary to remove it though the inverse halftoning returns
the image made binary thus to former step value. Evenperfection has not lived yet as a past technique of theinverse halftoning though the restoration of the image of thelevel that is only these past techniques can expect though asmoothing filter, a median filter, and gaussian filter, etc. areproposed. Because there is an evil such as growing dim of
the edge part of the image to remove even the highfrequency elements ,in a word, necessary information in theimage other than the noise when it tries to obtain a smooth
image by the inverse halftoning. Oppositely, the removal of
the noise becomes insufficient when it tries to emphasize
the edge part of the image, and the great result is not
obtained so much as restoration accuracy of the image. The
removal of the noise and the emphasis of the edge relate
closely in the restoration of the image like this, and thedesign of processing where it can be difficultly achieved is© ISAROB 2013

2 INVERSE HALFTONING
Inverse halftoning is processing to return to thegradation of original image which is binarized byhalftoning, and to become easy to look visually.
Representative inverse halftoning processing includes asmoothing filter, a gaussian filter, a median filter.
In this paper, halftoning image is used Floyd &Steinberg..
2.1 Smoothing filter
The Smoothing filter is processing to let the mean ofattention pixel and the pixel of the eight pixels ofneighboring pixels be a pixel value of a new attention pixel.
Fig. 1 shows that pixel (x,y) with a focus on nine pixels canbe expressed as following equation..
𝑓′(𝑥, 𝑦) = 𝑓(𝑥, 𝑦) ∗ 𝑀(𝑥, 𝑦)(1)Here, * is the convolution of the above equation, M(x,y)
is the mask of Fig. 2, f(x,y) is the attention pixel and theneighboring pixels, f'(x,y) represents a new pixel that.

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Fig. 1. operator

Here, f(x,y) is the image before blurring, g(x,y) is theimage blurred.
"Range" specifies the range of processing in pixel value.
when the range is small, processing is only reflected inneighboring pixels. But, when the range is large, processing
is reflected in far pixel. Therefore, an effect becomes large,
so that the range is large. In this paper, we perform an
operation on nine pixels of 3 × 3.
"Threshold" specifies processing, when there is thegradation difference that is how much. If the threshold ishigh, the noise can be cut neatly, but cut it to the edge when
too high. On the contrary, when the threshold is too low, anoise is left and becomes the dirty image. Therefore, it isnecessary to determine the appropriate threshold. However,it cannot isolate the noise and the edge with one threshold,because inverse halftoning image has much noise.
Therefore, this paper describes unsharp mask only edge partby canny method.

Fig. 2. smoothing filter

2.2 Gaussian filter
Gaussian filter is a process of determining the weighting
factor of the neighborhood surrounding the pixel of interest
using a Gaussian distribution. Gaussian distribution can be
expressed as follows.
f(x, y) =1√2𝜋𝜎exp⁡(−

𝑥 2 +𝑦 2
2𝜎 2

)

(2)

In this paper, σ=0.53. Gaussian filter is obtained byconvolving a mask that obtained by calculating the above
equation as 3 × 3 pixels and attention pixel. Because
Gaussian distribution is a normal distribution, the sum of
the coefficients is always 1. Therefore, the pixel values
before and after remains unchanged, so it can be folded in
the coefficients.

2.5 Canny method
Canny method is a method to detect edge that is thinned by combining the Sobel filter and Gaussian filt
er. Canny method algorithm is processing of the image
smoothing; the calculation of the gradient direction ed
ge strength, edge thinning process is performed in theorder of Hysteresis thresholding. Each step of the Can
ny method is described as follows.
 Image smoothing

2.3 Median filter
Median filter is a processing of decreasing order ofpixel values in the neighborhood surrounding the pixel ofinterest, and let the value of middle be attention pixel.

Performed by the Gaussian smoothing filter to the
input image image, and reduce the effects of noise.

2.4 Unsharp mask
Unsharp mask, when interpreted literally, "Sharp
eliminate" becomes a means, but it is actually sharpen theimage processing. Unsharp masking is often used to correct
blurry images. Unsharp mask algorithm is as follows.
Means to sharpen the image is that increasing thedifference between the pixel values of adjacent pixels.
Applying a blur to the contrary is a process that continuous
gradation is smoothly. Plus the blurred images to original
image minus the difference and original image then can geta large picture of the difference gradation. This is a basic
algorithm of unsharp mask. However, when the entire
image to be processed, it is too sharp, and sharpens thenoise, the picture may become ugly. Therefore, unsharp
mask determine "quantity" and "Range" and "threshold",and processes into the portion which fulfilled conditions.
"Quantity" specifies the percentage K of change in pixel
value. K is determined how much a sharp is used. "Quantity" can be expressed as follows.
f ′ (x, y) = f(x, y) + (f(x, y) − g(x, y)) ∗ K(3)© ISAROB 2013

The calculation of the gradient direction edge strength
After obtaining the derivative in each direction bySobel filter, the strength and direction of each pixel
are calculated. Sobel filter to calculate differential
value of the horizontal fx and vertical fy by
convoluting the mask in Fig. 3,4 and the attention
pixel. Edge intensity E and gradient direction θ
obtained by the following equation is obtained from fx
and fy.
e⁡ = ⁡ √𝑓𝑥 2 + 𝑓𝑦 2(4)θ⁡ = ⁡𝑓𝑦𝑓𝑥(5)

The gradient direction θ calculates the pixel edge
direction in 3 × 3, can be classified into four directions,as shown in Fig. 5,6,7. Four-way can be classified by
splitting in the range of θ is calculated.
0°⁡direction ∶ −0.4142⁡ < ⁡θ⁡ ≤ ⁡0.4142
45°⁡direction ∶ ⁡⁡⁡⁡⁡⁡0.4142⁡ < ⁡θ⁡ < ⁡2.4142{
90°⁡direction ∶ ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡ |𝜃| ⁡ ≥ ⁡0.4142
135°⁡direction ∶ ⁡ −2.4142⁡ < ⁡θ⁡ ≤ −0.4142

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

Here, the red line in Fig. 5,6,7,8 is the direction of anedge pixel, the black pixels is adjacent pixels in thevertical direction of the edge pixels.
Edge thinning
Proccess of edge thinning slim thickened edge by
Gaussian filter. Comparing the pixel value of pixels
adjacent vertical edges as in Fig. 5,6,7,8, and edge
value to 0 if pixel value of edge is not max. Then,edge can be made thin.
Hysteresis thresholding
Two thresholds are set as high value and as low values.
High threshold is detected as edge pixel, and low
threshold is detected as there is no edge pixel. When alower threshold than under a high threshold, attention
pixel is detected as edge pixel if edge pixel exists inthe neighborhood and attention pixel is detected as
there is no edge pixel if edge pixel doesn’t exist in theneighborhood. In this paper, high threshold to 128,
low threshold to 20.

images, the best thing is to use Gaussian filter twice.
Therefore, gradate processing uses Gaussian filter twice inthis paper.

Fig. 3. Horizontal direction fx Fig. 4. Vertical direction fx

Fig. 9. Process of inverse halftonig

4 EXPERIMENTAL RESULT

Fig. 5. 00 direction

We tested it in a procedure of Fig. 9 and verified themost suitable value of the unsharp coefficient K.
Verification is used a representative sample images,"Airplane", "Lenna", "Milkdrop", "Parrots", "Pepper","Sailboat".
Experiment is used SNR as a method for evaluation of
the accuracy of image reconstruction. SNR can becalculated as follows.

Fig. 6. 450 direction

SNR = 10 log10

Fig. 7. 900 direction⁡⁡⁡⁡⁡[𝑑𝐵](6)Here, f(x,y) is the original image, f'(x,y) is the processed
image.
Fig. 10 shows that coefficient of variation of 6 imagesof images corresponding to SNR as a graph. SNR can't use
as an evaluation function for inverse halftoning. So,
coefficients K should be constant for any image. After wecomputed the mean of coefficient K when SNR of each
image was the highest, and handling it, it was shown intable 1. Table 1 shows SNR when the coefficient K isaverage, comparison between maximum SNR and the SNR

Fig. 8. 1350 direction

3 PROCESS OF PROPOSAL METHOD
Fig. 9 shows the experimental procedure in this paper.
The unsharp mask gradates processing use that smoothingfilter, gaussian filter, a combination of three filter medianfilter. As a result of having inspected it with six pieces of© ISAROB 2013√∑ 𝑓(𝑥,𝑦)2√∑(𝑓(𝑥,𝑦)−𝑓′(𝑥,𝑦))2568

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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when the coefficient K is average, SNR of the conventional
method. The conventional method used the good following
combinations of Halftoning image affinity by Floyd &Steinberg.
1st ∶ smoothing⁡filter(2nd ∶ gaussian⁡filter⁡⁡
3rd ∶ gaussian⁡filter⁡⁡⁡"Airplane" and "Sailboat" is greatest difference between
SNR of the conventional method and SNR of the proposal
method in Table 1. Therefore, we show a disposal result of
two images in Fig. 11,12,13,14,15,16.

Fig. 13. Unsharp mask

Fig. 14. Conventional method

Fig. 15. Unsharp mask

Fig. 16. Conventional method

25
24
23

Airplane

SNR [dB]

22

Lenna

21
milkdrop20
19

Parrots18

Pepper17

5 CONCLUSION
In this paper, inverse halftoning was applied unsharp
mask, it was confirmed that it is possible to restore somelevel of any image. In addition, the maximum difference
between difference between the average and maximum
values of the coefficient K wasn't little difference that was
0.04[dB]. Comparing the proposed method and theconventional method, SNR of each images are 0.5 [dB]higher, and became easy to look visually.

Sailboat

16
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Coefficient K

Fig. 10. Coefficient K corresponding to SNR

REFERENCES

Fig. 11. Original image Airplane Fig. 12. Original image Sailboat[1] J.Z.C Lai and J.Y. Yen (1998), Inverse error-diffusion
using classified vector quantization. IEEE Trans. Image
Process[2] John Canny(1986) , A Computational Approach To
Edge Detection.
[3] Naoto Suzue, and shinishi yoshida (2009), Edge Feature
for Monochrome Image Retrieval.

Table 1. Average value of the coefficient K and conventional method

unsharp mask
Average value ofthe coefficient K
AirplaneLenna
milkdrop
Parrots
PepperSailboat© ISAROB 2013

2.41

conventional method

SNR [dB]

Difference between the average andmaximum values of the coefficient K

24.18
20.92
21.25
19.17
17.59
22.62

0
0
0.01
0
0
0.04

SNR [dB]
23.36
20.36
20.75
18.65
16.79
21.51

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Development of mobile robot navigation systemusing simplified map based on place recognition
Satoshi Yamanaka 1, Kazuyuki Morioka 2
1,2

Dept. of Electrical Engineering, School of Science and Technology,
Meiji University, Japan1ce11093@ meiji.ac.jp, 2 morioka@isc.meiji.ac.jp

Abstract: This paper introduces a mobile robot navigation system using simplified hybrid maps based on occupancy grid mapsof important places and relationships among them. In order to achieve autonomous navigation with the hybrid maps, additional
information, such as waypoints in important places with occupancy grid maps, are learned in SLAM process. Also, navigation
system based on state transition between inside and outside of the important places is proposed. Finally, experimental resultswith actual robot systems in indoor environments are shown.
Keywords: Mobile robot navigation, Hybrid map, Position estimation

1 INTRODUCTION
Recently, various kinds of robots, such as cleaning
robots, have been available in human living environments.
In order to perform mobile robot navigation, environmental
maps for robots are needed. Generally, most of studies for
robot navigation use detailed occupancy grid maps
representing all the environments that the mobile robots
will move[1]. However, such global metric maps based on
occupancy grids are not necessarily required for the
purpose of navigation. At least, humans do not need suchgrid maps for navigation. Then, a number of navigation
approaches consider using simple maps. Especially, hybrid
maps that combine metric and topological maps have been
widely proposed[2]. This study assumes the hybrid maps
with geometrical relations between important places via
straight lines are enough for representing the simple
environments that consist of straight lines such as corridors.
Then, this study aims to achieve autonomous navigation of
the mobile robot using the hybrid maps at indoor
environment.

Fig.1 Outline of the proposed SLAM and hybrid mapof the proposed SLAM algorithm, exact geometrical
relationships between important places are required. In
other words, the robot has to judge whether the important
place is visited for the first time or not in the SLAM
process. Then, geometrical relationships between the place
and the other places must be obtained. In order to recognize
revisit or not, images are acquired at a fixed intervals in theimportant places and SURF features of the images are
stored in databases. And, image features newly acquired ineach place are compared with image features stored in the
databases. According to matching results, “new” or “revisit”
is recognized. Thus, the geometrical relationship between
important places is recognized. In this study, two cameras
are attached to the robot around the height of human's eyes(about 160 cm) in order to capture images. Cameras are set
to right and left directions. This is because we can see
specific information of each place, such as room numbers,
information maps, etc. on walls or doors around the heightof human eyes. These are easy to extract as image featuresand effective for image matching. The hybrid map isgenerated by the above method.

2 SIMPLIFICATION OF THE MAP
2.1 Hybrid map
The robot’s navigation in this study is performed usinga hybrid environmental map as shown in Fig.1. In the
hybrid map, occupancy grid maps are generated using
FastSLAM[3] at important places with complicated shapes
like crossing points among corridors. On the other hand, atthe places with simpler shapes, such as corridors, detailed
maps are not generated and only geometrical relationships
between important places are learned. Then, the map
representation does not lose necessary global relationships
among important places as maps for robot navigation. In
order for the robot to learn the map, we previously
proposed a novel SLAM algorithm based on acquisition
and matching of images in each important place[4]. Also,
integration of occupancy grid maps in revisit places and etc.
was added to the proposed SLAM algorithm. As the results© ISAROB 2013

2.2 Information acquisition for navigation
The hybrid map simply expresses relationships among
important places and occupancy grid maps of each
important place. In order to perform autonomous mobile
robot navigation with this map, additional information isrequired. For example, how to move in the important places
should be considered. When the robot enters in the570

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

important places, robot positions and orientations must be
accurately estimated in the coordinate system of each
occupancy grid map. Waypoints in the each occupancy grid
map are also required for going to the other place. Justlearning relationships among important places are notenough for navigation because distances among places arealso required for actual navigation. Then, in order to use theproposed hybrid map for navigation, the robot acquires thefollowing information and adds the information to thehybrid map while map generation process with theproposed SLAM algorithm.
A) Waypoints: A starting point and a terminal point basedon geometrical relationships among places, and relay
points in the grid maps are used as waypoints fornavigation. These points are called bridge points asshown in Fig.2. Relay points are defined as the middle
waypoints between bridge points. The robot moves
toward a bridge point via a relay point first in eachplace. A relay point is set in a position where the robotchanged its heading 70 degrees in each place during
map learning process.
B) Gate: Gates in each important place are straight lines
for judging moving out from the place. The lines arecalculated from the LRF scan data on bridge points.
C) Distance between places: It can be roughly calculated
by odometry while map learning process.
D) Acquisition positions of database images: Images
stored in database are mentioned in Chapter 2.1.
Position of the robot in each place is estimatedaccording to acquisition positions of database images.

Outside B: Movement distance of the robot from theprevious gate exceeds distances between the last place andnext places before localization in a next place.

	
  	
  	
  	
  	
  Fig.3 State transition diagram
3.2. Localization
Localization in three states is described below.
Inside: Localization is performed with particle filter using
scan data of LRF and the grid map of each place.
Outside A: In this study, courses between places are
assumed as corridors. First, position relationships between
the robot and gates are calculated, when a robot state has
just transited from Inside to Outside A. Coordinate systemafter transition to Outside A is defined according torelationships between the robot and the gate (Fig. 4). And,navigation along wall is performed. Moving distances alongwalls are also calculated from base information.

Fig.2 Information acquisition for robot navigation
Fig4. Wall following method

3 NAVIGATION SYSTEM

Outside B: When a moving distance along walls exceeds
distances between places memorized in the hybrid map, astate of the robot transits to Outside B. In this state, there
are possibilities that the robot is in next places. Then,
recognition of a place number and accurate localization in
the place should be performed in this state. First, the roughself-position of the robot in the place is estimated by image
matching with database images of places stored in the
hybrid map. Next, the accurate self-position of the robot in
the place is estimated by ICP matching with the grid map of
the place. Details of this process are explained below. As
described above, rough position estimation based on image
matching is performed. Since images in the robot arecaptured in short sampling rate, successive captured images
match same one of database images. In that case, an image
with the highest voting rate can be regarded as the mostsimilar image with one of database images. It is considered

In this chapter, navigation system using the hybrid map
is explained. In order to perform navigation using the
hybrid map, it is necessary to change localization andmoving methods according to robot conditions. In thenavigation system, robot conditions based on places are
divided into three states: Inside, Outside A and Outside B.
Details of the navigation system are explained as follows.
Finally, the configuration of the system is shown.
3.1 Robot’s state transition
The definition of three states in a navigation system isshown below. State transition diagram is shown in Fig. 3.
Inside: The robot is in a place under localization.
Outside A: The robot is out from a place through a gate. Its
movement distance from the gate is less than distances
between the last place and next places.

© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013that the position of the robot is the closest to the acquisition
position of database image. Then, the acquisition positionof database image is exploited as the position, where theimage with the highest voting rate is captured, as shown in
Fig.5. In addition, recognition of the place number is alsoachieved from image matching. However, this position
estimation is too rough to control the robot. So, accurate
position estimation based on the scan data of LRF and agrid map is also performed. ICP matching[5] between thegrid map and the scan data of LRF is performed. Roughposition estimation results based on image matching areused as initial positions of ICP matching. The accurate selfposition of the robot in the place is estimated from thismatching. Although several images are saved in database as
representing the places in the hybrid map, the first database
image matched with one of successive captured images isonly used for position estimation in each place. When theaccurate self-position of the robot in the place is estimated,
the state transits from Outside B to Inside. Image matching
method in this position estimation is similar with themethod of recognition of relationships between places forthe hybrid map generation[4] except the followings.

the destination place. Then, the robot moves according to
the determined path. In navigation, the same movement isperformed in Outside A and Outside B. Movement methods
in Inside and Outside (including Outside A and Outside B)
are shown below. The configuration of the navigation
system actually used in this study is shown in Fig. 6.

Step2.All feature points in captured images from two
cameras of the robot are not saved in a database. If
features extracted in one image are less than 20, theimage is deleted as out of matching.

Fig.6 Navigation system

Inside: First, the robot moves to a relay point in each place.
Next, the robot moves toward a bridge point connected to
the next place.
Outside: The robot keeps moving along a wall.

4 EXPERIMENT
4.1 Experiment setup
In this study, the mobile robot carries two cameras(Logicool® HD Pro Webcam C910) (Fig.7(b)) around theheight of human's eyes and one LRF (HOKUYO electricalmachinery UTM-30LX) (Fig.7(c)) ahead of the robot asshown in Fig.7(a). The experimental environment is one
floor of university buildings (Fig.8). In this study, four
corners or intersections are defined as important places in a
hybrid map by a human operator before map learning. Also,in advance, the robot moved manually by a controller andthe hybrid map was generated.

Step3.Two votes are given for the image when Euclid
distance between features is the smallest in order tofind the nearest image with the corresponding
database image. In this voting, two votes are needed
for compatibility with calculation of the voting rate.
Step4.The images with more than 10% share of the voting
rate in the databases are regarded as candidates ofmatching images.

(b)

(a)
(c)
Fig.7 Mobile robot with LRF and camera

Fig.5 Image matching in Outside B
3.3. Navigation
A destination place number, a current place number anda rough self-position of the robot in the current place are
given as initial information. Then, autonomous movementof the robot is carried out from the current place to thedestination place. First, global path planning is performed
using relationships of places. The shortest path is calculatedby the Dijkstra method from the initial information. Pathplanning includes places transition from the current place toFig.8 Experimental environment© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

4.2 Experiment method
In this experiment, it was tested whether autonomous
navigation of the mobile robot could be actually achieved
using the hybrid map. Followings are given to the robot inadvance as initial information.
1) Initial place number
2) Rough initial position of the robot in the current place3) Destination place number
After ICP matching based on the rough position toestimate for an exact position in the initial place, the robot
starts movement. Each parameter setup is as follows. In
localization with the particle filter, the number of particle
was set to 30. In ICP algorithm, the maximum loop count
was set to 500 times. Also, when the distance between eachpoint of one point group and the nearest point of anotherpoint group to the point was shorter than 600 mm, thenearest point was considered a corresponding point.

	 4.3 Experimental result
In the example, the robot is placed at place “1” at first
and the destination place is place “4”. As a result of the
Dijkstra method, the robot moved from place “1” throughplace “2” and place “5” to place “4”. At place “2”, place “5”and place “4”, image matching and ICP matching was
performed when the robot state is Outside B. Fig.9 shows
images captured by the robot during the navigation,
matched database images in the places of the hybrid map
and voting rate in the case of the highest voting rate inimage matching at place “2” and place “5”. The result of
ICP matching at place “2” and place “5” is shown in Fig.10.
In this figure, red points represent grids whose occupation
probabilities are over 50 percent in the occupancy grid mapof each place, blue points represent scan data of LRF at
robot position estimated roughly by image matching, andgreen points represent scan data of LRF at accurate robot
position corrected by ICP matching. The result of statetransition in navigation is shown in Fig.11. Fig.9 showsthese two images are comparatively similar. That means
place recognition based on image matching was successful.
Fig.10 shows ICP matching in the initial rough place wasalso successful since the suitable position is searched byimage matching. As a result, localization of the robot wasachieved and navigation to the destination place was
successful, carrying out a state transition as shown in Fig.11.

Captured images

Matched images

Fig.11 State transition of navigation

5 CONCLUSIONSThe navigation system using the hybrid map wasdeveloped. And, autonomous navigation using the hybrid
map was achieved. Experimental results showed validity of
the hybrid map and the navigation system. The experimentof the map generation and navigation in more complicatedenvironment is conducted from now on, and detailed
evaluation of the approach should be performed.

REFERENCES[1] Ato Araki, Kentaro Takemura, Junichi Ido et al,“Generating Individual Maps for Mobile Robots from
Universal 3D Environment Map”, Journal of the
Robotics Society of Japan, Vol.28, No.1, pp.106-111,
2010[2] Kurt Konolige, Eitan Marder-Eppstein, and Bhaskara
Marthi“NavigationinHybridMetricTopologicalMaps”, International Conference on
Robotics and Automation (ICRA), pp.3041-3047, 2011[3] D. Haehnel, D. Fox, W. Burgard et al, “A highly
efficient FastSLAM algorithm for generating cyclic
maps of large scale environments from raw laser range
measurements”, In Proc. of the Conference onIntelligent Robots and Systems (IROS), pp.206-211,
2003[4] Satoshi Yamanaka, Kazuyuki Morioka, “Mobile robot
navigation using hybrid simplified map withrelationships between places and grid maps”, In Proc.
10th IFAC Symposium on Robot Control (SYROCO),
2012[5] F.Lu, E.Milios, “Robot Pose Estimation in Unknown
Environments by matching 2D Range Scans”, Journalof Intelligent and Robotic Systems, Vol.18, pp.249-275,
1997

Voting rate

Place2

26.1%

Place534.4%
Fig.9 Matching image of navigation© ISAROB 2013

Place2
Place5
Fig.10 ICP matching of navigation

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Real-time 3D-Shape Reconstruction System
in Intelligent Space Based on Networked Vision Sensors
Takashi Ishimaru 1, Kazuyuki Morioka 2
1,2

Dept. of Electrical Engineering, School of Science and Technology,
Meiji University, Japan1ce11008@ meiji.ac.jp, 2 morioka@isc.meiji.ac.jp

Abstract: In recent years, intelligent spaces based multi-viewpoint cameras and range image sensors have been developed. Especially, realtime human 3D shape reconstruction system with multiple sensors in intelligent space is considered in this study. In order to achieve
application of human shape reconstruction in intelligent space, it is necessary to integrate several kinds of sensors efficiently. This paper
focuses on roles and functions required to each sensor node, and proposes systematic structures with RT-component for sensor integration.

Keywords: Intelligent Space, 3D-Shape Reconstruction, RT component
simple. Also, the configuration for accepting additional
sensors or new sensors should be required for intelligent
spaces as platform of various sensors integration. This
paper describes followings. At first, integration methods forconfiguring sensor nodes from distributed sensors aredescribed. And, an application of 3D shape recognition
system for accepting sensor nodes is described. Finally, a
total system including sensor nodes and application isshown.

1 INTRODUCTION
Intelligent space is a platform, which organically integrates
various sensors, computers and robots via network[1].
Especially, intelligent spaces, which multiple cameras andrange image sensors are distributed, have been widely
proposed. These systems aim to achieve various
applications, such as supporting mobile robot navigation or
human tracking with position estimation. In addition tohuman positions estimation, 3D shapes of humans can bereconstructed by cooperation of image sensors. Positioning
and shape reconstruction of humans in intelligent space arepromising for human-system interaction. In this study, realtime 3D shapes recognition systems in intelligent spaces are
considered. This paper focuses on sensor nodes
configuration in 3D-shape reconstruction systems for atarget person by integrating image data captured by
distributed sensors.
In order to acquire 3D shape of a target human in intelligent
space, it is important how to process sensor data fromsensors contributed to 3D shape reconstruction. However,intelligent space is a platform including various kinds ofsensors. There are various kinds of sensors for contributing
3D shape reconstruction in intelligent spaces. Then,intelligent spaces require the system configuration for
integrating various sensors effectively. This paper considers
how to assign roles between intelligent space andapplication of 3D shape reconstruction. Especially, thispaper proposes the system configuration that consists ofhierarchical sensor nodes in intelligent space andapplication of 3D shape reconstruction independent ofsensors. In the intelligent space side, various kinds ofsensor data is integrated through a network as shown in
Fig.1. This figure means that all sensor nodes provide samekinds of sensor data to application. In order to provide samekinds of sensor data, two or more sensors are hierarchicallyintegrated for sensor nodes. On the other hand, theapplication side is independent to kinds of distributed
sensors. Then, software configuration of application can be© ISAROB 2013

Fig 1. 3D shape reconstruction system

2 APPLICATION IN INTELLIGENT SPACEAs mentioned above, the intelligent space is a platform for
integrating various sensors via network. Sensor data is
integrated in order to achieve applications with networked
sensors. The 3D shape reconstruction in this paper is also
one of the applications in intelligent space. Sensors
distributed in the intelligent space are not necessarily
exploited only for the purpose of 3D shape reconstruction.
These sensors should afford the other applications. Thismeans that output of distributed sensors must not depend on
the specific application. So, each sensor in intelligent spacecannot provide suitable information for the application. On
the other hand, the application should not include processes

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013that depend on kinds of sensors, so that the application can
accept different sensors added in intelligent space. The 3D
shape reconstruction application needs following elementsfrom distributed sensors. The first is 3D point cloud data ofa target human. The second is color information
corresponding to 3D points. The last is a target position in
the intelligent space. Some sensors, such as Kinect sensors,
can provide these elements by themselves. The othersensors, such as cameras or LRFs, cannot provide them.
However, the sensors such as cameras or LRFs can provide
same kinds of elements by integrating several sensors. In
the proposed application, sensor nodes integrating several
sensors are considered in order to provide information ofsame form to applications. Such same form informationfrom sensor nodes will make the applications that flexibly
accept several kinds of sensors in the intelligent space.
In this study, a system based on networked cameras andKinect sensors is developed, because information requiredfor 3D shape reconstruction can be easily acquired. Also,
the system includes different kinds of sensors. Then, thesystem will show flexible acceptance of different sensors.
As mentioned above, Kinect sensors can provide
information required for 3D shape reconstruction. A
standalone Kinect sensor can be regarded as a sensor nodein 3D shape reconstruction application. On the other hand,raw data obtained by cameras cannot be directly used for3D shape reconstruction. This raw data should be changed
into the same data format in order to be accepted by 3D
shape reconstruction application. In this case, a sensor
group integrating several cameras can be regarded as asensor node in the application. In this study, a systemdesign based on RT component is performed.

with the infrared camera, color information corresponding
to 3D point clouds can be acquired. Then, data required for3D shape reconstruction application is acquired by eachstandalone Kinect sensor. This means that each Kinect
sensor can be regarded as a sensor node for 3D shapereconstruction application.
In addition, in order to integrate data between sensors nodes
in intelligent space, each sensor node must be geometricallycalibrated in common world coordinates. Each distributed
Kinect has calibrated manually in advance in this system.
Internal parameters and external parameters can be obtainedby calibration in each sensor.
Although 3D point clouds obtained from a Kinect sensor
are numerical values in a camera coordinate system of each
Kinect sensor, 3D point clouds are transformed to world
coordinates with internal and external parameters. Then,suitable data integrated in 3D shape reconstruction
application is acquired by a sensor node by a Kinect sensor.
3.2 Component based on several cameras
Color information of a target human can be acquired with
one camera. However, 3D point clouds and humanpositions in world coordinates cannot be acquired using one
camera. Therefore, two or more cameras should be
integrated for satisfying minimum requirements of 3D
shape reconstruction application. Such integrated cameras
can be treated as a sensor node to output the same data
formats with a sensor node based on a Kinect sensor. Then,the application built on the platform of intelligent spaceshould have following functions. That is a dynamical
configuration of a sensor node according to kinds ofapplications.
A sensor node component including several cameras is
explained below. First, this component obtains images fromseveral cameras. Next, 3D point clouds of a target humanare calculated using images from cameras connected to thiscomponent. Also, color information corresponding to thepoint is obtained from images.
Image processing methods in this component are as follows.
At first, in order to obtain a human region in each camera
image, background subtraction is performed between input
images and a background image. Geometrical calibration ofeach camera is performed in advance in the similar way
with Kinect sensors. Camera parameters obtained bycalibration can be used for a volume intersection method
integrating several camera images. Calculation results ofthe volume intersection method are regarded as 3D point
clouds of a target human.
Details of the volume intersection method in the developed
component are as follows. First, a 3D point in world
coordinate system is transformed into a pixel position on
image coordinate system of each camera. It is judged
whether the transformed position is inside a human region
in the image of each camera.
This judgment is applied to all cameras in a sensor node.
When the transformed positions exist in human regions of
all cameras, the 3D point is recorded as one of the points
representing a target human. At this time, color information
corresponding to that point is also recorded. This

3 SENSOR NODE COMPONENTAt first, every sensor data acquired in intelligent spaceshould be generalized so that it becomes same data formats
for 3D shape reconstruction application. For this purpose,processing in each sensor is integrated and wrapped inorder to generalize data formats for 3D reconstruction
application. Specifically, RT-component[2] based sensor
nodes including several sensors are developed for 3D shapereconstruction application. In this application, threeelements mentioned in Section.2 are same data formats for
3D data reconstruction. Therefore, same data formats fromsensor nodes must be accepted and processed in 3D shapereconstruction application. Design and development ofcomponents of sensor nodes, which output same data
formats, are explained below. Especially, sensor nodes
based on cameras or Kinect sensors are described.
3.1 Component based on Kinect
A sensor node to output the same data format using a
Kinect sensor is configured as follows.
Kinect sensors can acquire RGB image data and depth
image data respectively. A target human on the image istracked using depth data and human posture pattern. 3D
point clouds of human can be also acquired from depth dataaround the target human position. Since Kinect sensors can
RGB data in synchronization with the depth data acquired© ISAROB 2013

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calculation is applied to points in a given cuboid aroundhuman position. These processes are implemented using
GPU. Then, data formats for 3D shape reconstruction
application can be obtained in real-time with a sensor node
based on several cameras. These data formats are same witha sensor node based on a Kinect sensor. That is, thiscomponent based on cameras is acceptable as a distributed
sensor node in 3D shape reconstruction application.

reconstruct around a target human. This makes processing
time of integrating data short. So, this is efficient for realtime shape reconstruction.
4.2 Real time reconstruction through a network
The information acquired by each distributed sensor is unifi
ed through a network. When the data volume transmitted isexcessive, it has bad influence on processing of each compo
nent. As an example, if a component based on Kinect passed a network, it was investigated what bad influence it would
have to the processing. The result of the influence is shownin Table 1.Table 1 expresses comparison of the processing ti
me at one cycle in a component based on Kinect. The top ofa table is a case where it processes independently without
unifying data. The middle of a table is a case where the data
through a network is unified. The lower berth of a table is acase where data integration which uses the compression component explained in Section 3.3 is performed. Data was compared in this table. Time of the case not compressed andtime of the case compressed is compared. The case of data c
ompressed is 14 times as quick as the case of data not compressed. This has influence good for the performance of 3D shape reconstruction application.

3.3 Data integration through a network
Two sensor node components mentioned above output dataof same formats. A 3D shape reconstruction system isachieved by integrating them.
However, processing performance of sensor nodes wasdecreased when integrating them through a network. For
example, a sensor node component based on Kinect cangenerate 3D point clouds and color information at about
100 milliseconds cycle. This loop cycle is sufficientperformance for real-time 3D shape reconstruction. On theother hand, when integrating this component to the othercomponent through a network for data sharing, a processing
loop cycle decreases to 600 milliseconds. This degradation
makes sensor node performance worse in the viewpoints ofreal-time 3D shape reconstruction.
In order to stabilize tracking human moving at natural
speed, it is important to keep short processing cycle.
Therefore, the following components are added for
integration of sensor nodes through a network. First, thedata compression components and decoding components
are prepared among sensor node components and 3D shapereconstruction application. These components are efficientfor short loop cycle of each sensor node component. In thisstudy, several compression methods are evaluated toward
executing a loop at about 100 milliseconds cycle in sensor
node components.
As a result of evaluation, image data including color
information is compressed with JPEG of high compression
ratio because color information does not greatly affect theaccuracy of shape reconstruction. Since 3D point clouds
affect to reconstruction accuracy of human, PNG was
adopted for them. PNG is lossless compression, so 3D
shape reconstruction application can obtain data calculated
in each sensor node without any changes.

Table 1. Processing time in a component based on Kinect
Time of one cycle
Case of only component
20[ms]
Case of not compressed
831[ms]Case of compressed
60[ms]

Fig 2. Sensors placements in an initial intelligent space
4.3 Flexible system and 3D shape reconstruction
In the proposed system, output of each distributed sensor
node is the same format for the application.
This means that same frameworks to all sensor nodes are
adopted in this system. The following advantages of the
proposed system can be explained from this viewpoint.
Even if a new sensor node is added to the system, thesensor node is easily integrated to the application. This
makes the system more scalable. In order to show theadvantages of the proposed system, addition of a newsensor node in this system is performed.
First, an initial intelligent space as shown in Fig. 2 wasconfigured. In this space, four USB cameras and one Kinect
sensor, which are geometrically calibrated in the common
world coordinate, have been installed. Four USB cameras
are integrated and it is regarded as one distributed sensor

4 FLEXIBILITY OF THE SYSTEM
4.1 Application integrating distributed sensor nodes
Each distributed sensor node component outputs following
data formats as mentioned above. The first is 3D point
cloud data of a target human. The second is color
information corresponding to 3D points. The last is a target
position in the intelligent space. 3D shape reconstruction
application receives these from sensor node components
through a network. This application execute following
processes. First, this application integrates the 3D points on
the world coordinate sent from sensor nodes. And, color
information is put to a corresponding 3D point. Then,integrated 3D shapes are displayed using OpenGL. The
range of reconstruction does not need to be the whole
intelligent space. It is enough for this application to© ISAROB 2013

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node. One Kinect is also regarded as a distributed sensor
node. Fig. 3 shows the experiment environment seen fromthe Kinect sensor installed in intelligent space. Each line on
this figure expresses an axis of the world coordinate set inadvance. The blue line is the X-axis on the world
coordinate. The green line is the Y-axis. The red line is the
Z-axis. Each voxel size in 3D shape reconstruction is set as
1cm x 1cm x 1cm.

(a)Front viewpoint(b)Back viewpoint
Fig 5. 3D shape reconstruction at environment of Fig2

KinectA(a)Front viewpoint(b)Back viewpointFig 6. 3D shape reconstruction at environment of Fig4

Fig 3. Intelligent space seen from KinectA
KinectA

5 CONCLUSION
In this paper, a flexible system configuration for the 3D
shape reconstruction application in intelligent space wasproposed.
The proposed system was based on sensor nodes with samefunctions respectively. And each sensor node integrated
several sensors in order to achieve the same function for theapplication.
Sensor nodes and the application were implemented as RT components. Then, an experiment for adding a new sensor to
the system was performed. Accuracy of 3D shapes of targe
t humans depend on placements of sensors. The results sho
w that a system for satisfying a purpose of the application iseasily constructed with the proposed platform of sensor inte
gration.

KinectB
Fig 4. An intelligent space with an additional sensor
Next, the system as shown in Fig. 4 was configured. In thissystem, a sensor node based on one Kinect is added to the initial intelligent space as shown in Fig.2. Fig.5 shows 3D sh
ape reconstruction results in the initial intelligent space of Fig. 2. Fig.5 (a) is the result of seeing a target human from afront viewpoint. Fig.5 (b) is the result of seeing a target hu
man from a back viewpoint.Fig.6 shows 3D shape reconstru
ction results in the improved intelligent space of Fig. 4.
The viewpoints of 3D shape reconstruction results are samewith Fig. 5.These results show that shapes of Fig.6 are moreaccurate. Since the new sensor node was installed so thatdead angles in intelligent space might be reduced, theseresults were previously expected. Generally, optimal
placements of sensor nodes depend on scales or shapes ofenvironments or purposes of the applications. Whenbuilding such systems, developers usually configure thesystems by trials-and-errors of sensor nodes placements. On
the other hand, the proposed system configuration, whichmeans integration of same format data from distributed
sensor nodes, can contribute to flexibly adding new sensor
nodes or new kinds of sensors to the systems. This data
integration platform is required for intelligent spaces
configured with various kinds of sensors. This also meansthat the intelligent spaces evolve according to addition of
new sensors.

© ISAROB 2013

REFERENCES[1] Joo-Ho Lee, Hideki Hashimoto, “Intelligent Space concept and contents”, Advanced robotics, Vol.16,
No.3, pp.265-280, 2002
[2] Noriaki ANDO, Takashi SUEHIRO, Kosei
KITAGAKI, Tetsuo KOTOKU, Woo-Keun Yoon,"RT-Middleware: Distributed Component Middlewarefor RT (Robot Technology)", 2005 IEEE/RSJ
International Conference on Intelligent Robots andSystems (IROS2005), pp.3555-3560, 2005

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Using a GA-based Extension Recognized Method for
Fault Diagnosis in Car Engines
Meng-Hui Wang*, Pi-Yun Chen
National Chin-Yi University of Technology, Taiwan(TEL: +886-4-23924505, FAX: +886-4-23924419)*

wangmh@ncut.edu.tw

Abstract: Due to the passenger’s security, the recognized hidden faults in car engines are the most importantwork for a maintain engineer, so they can regulate the engines to be safety and improve the reliability of
automobile systems. In this paper, we will present a novel fault recognized method based on the genetic
algorithm (GA) and the extension theory, and also applies this method in the fault recognition of a practical car
engine. The proposed recognized method has been tested on the practical tested records of the Nissan CEFIRO
2.0 engine and also compared with other traditional classified methods. Experimental results are of great
effective for the hidden faulting recognition of car engine, the proposed method also suits application in otherindustrial apparatus.
Keywords: genetic algorithm (GA), extension theory, fault diagnosis, pattern recognition.

1. INTRODUCTION

2. SUMMARY OF EXTENSION THEORY

Car is an important tool in human life currently, andthe traffic accident has become a part of human life. An
engine fault not only damages the engine itself but also
causes a break in the car system. Usually, the component
module of the engine generates natural loss and improper
maintenance that will make the engine oil consumption
to increase gradually and let the thickness of exhaust to
be increased. The cylinder vibration and the temperatureof engine exhaust will come to abnormal situation. The
type of the hidden defect is due to the gradual formation,
so it is difficult to recognize in the normal inspection. So
how to detect engine fault signs early, and to
immediately repair or remove them, is very necessary.
In the past, various pattern clustering techniques,
including, expert systems (ES) [1], fuzzy clustering [2]and neural networks (NN) [3] have been extensively
used in pattern recognition. Combinations of personal
computers (PC), expert and fuzzy systems bring up
possibilities of automating recognition. However, it ishard to use these rule-based methods to acquire pictorial
knowledge and hard to maintain the database of decisionrules. A limitation of the MNN approach is the inability
to produce linguistic output, because it is difficult tounderstand the content of network memory.
To overcome the limitations of the ES and MNN
mentioned above, a new recognition method, based on
the GA and extension theory, is presented for fault
diagnosis of car engine in this paper. The extension
theory concept was first proposed by Cai in 1983 [4].
Now extension theory has been used in the research fieldof artificial intelligence (AI) and its relevant sciences.
Experimental results show that the GA-based extension
recognized method not only has a high accuracy andmuch suitable as a practical solution of diagnosis
problem [5].

There are two main points in extension theory thatare matter-element model and extension set [6]. The hardcore of extension theory is two theoretical pillars that
include matter-element theory and the theory ofextension set.

© ISAROB 2013

2.1 Matter-element theory
In extension theory, a matter-element uses anordered triad as the basic element for describing things asfollows:
R = ( N , c, v )(1)
Where N represents the matter, c the characteristics;
v is N’s measure of the characteristics c, where v can be avalue or an interval. A multi-dimensional matter-element
is defined as follows: R1   N , c1 , v1 
  

R
c ,v
R = ( N , C ,V ) =  2  =  2 2         Rn   c n , v n 

(2)

2.2 Summary of extension set
Definition1.Let U be a space of objects and x a generic~element of U, then an extension set E in U is defined as aset of ordered pairs as follows:~
E = ( x, y ) x ∈U , y = K ( x) ∈ (−∞, ∞)
(3)

{

}

Where y = K (x ) is called the correlation function~
for extension set E . The K (x ) maps each element of U
to a membership grade between − ∞ and ∞. The
extended membership function is shown in Fig.1. WhenK ( x ) > 0 , it indicates the degrees to which x belongs to578

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

x0 . When K ( x ) < 0 it describes the degree to which x
does not belong to x0 . When −1 < K ( x ) < 0 , it is called theextension domain, which means that the element x still
has a chance to become part of the set if conditions
change.

the proposed method. Setting Patterns= {p1, p2,…pn}with i-th as follow: pij={c1,c2,…,ck}.In the patterns, i is
the total number of genes, and j is the type of pattern.
Using the proposed method can be simply described asfollows:
Step1: Set the epoch, the crossover rate Cr, the mutation
rate mu, the tolerance of error rate Er, and thechromosome rate Ra .

K(x)Step2: Find the gene of lower limit and upper limit value.
jvaj = min(ckn
)
(4)

1
g

f
a

x

b

j
vbj = min(ckn
)

-1

(5)

j

v =< vaj , vbj >Fig. 1.The extended membership function
2.3 The basic theory of genetic algorithm
The best-known evolutionary algorithm (EA) is thegenetic algorithm (GA), which transposed the notion ofevolution in Nature to computers and imitates natural
evolution and selection [8,9].The genetic algorithm
generally includes the following five parts:
1. Gene coding:
Combining all genes into a chromosome ofsequence 0 and 1.
2. Fitness function:
It describes the capability of a certain individual
gene to reproduce and is usually equal to the
proportion of the individual's genes in all genes of
the next generation.
3. Selection mechanism:
It is the intentional manipulation by chromosome of
the fitness of individuals in a population to produce
a desired evolutionary response.
4. Crossover:A process in which chromosomes exchange genes
through the breakage and reunion of two
chromosomes.
5. Mutation:
A change in a gene resulting in new or rearranged
hereditary determinants, mutations are rare, random
events in which the base sequence of the gene ischanged.

vaj − Ra ≤ GLj ≤ vaj + Ra

(7)

vbj − Ra ≤ GLj ≤ vbj + Ra

(8)

Step4: The genes make up the chromosome.
jk
11 12 12chrom = {G11
L , GL , GL , GL ,..., GL }(9)

The amount of gene in a chromosome is calculatedby the function 2*k*j.
Step5: Building the matter-element model from gene.
 N , c , < G1 , G1 > 
1
L U 

 c ,< G2 ,G2 >
L U  j = 1,2,..., m
Rj =  2






 c , < G k , G k >
n
L U 


(10)

Step6: Input the training of data that is the value of gene.
x j = {c1, c2 ,..., ck }(11)

Step 7: Calculate the correlation function.
kz k = (GLk + GU)
K nk =jkjkn  xj −z
jk − (GU − G L ) / 2
 nk

∑ 
i =1



(GUjk − GLjk ) / 2

(12)


+ 1



(13)

Step8: Normalizing the value of correlation function forthe matter-element model to be between 1 and -1.
Step9: Input the next training of data to repeat Step6 to
Step8.
Step10: Input the next matter-element model, and repeat
Step5 to Step9.
Step11: Calculate the fitness function.
N
Fitness = r(14)
Na

3. GA-BASED EXTENSION RECOGNIZEDMETHODIn this paper, the proposed recognized method
involves a combination of the genetic algorithm (GA)and extension theory. The extension theory provides ameans for distance measurement in the classification
process. The genetic algorithm has the ability to search
for an optimal solution within a wide space.

N r is the right amounts, and N a is the total
mounts.
Step12: The selection of the parental chromosomes put
into the mating pool, and the genes implement
crossover mechanism.
Step13: Let the next generation of chromosomes to

3.1 The training stage
The chromosomes propagate next generation ofchromosomes to combine the matter-element models in© ISAROB 2013(6)

k is number of characteristic.
va is the upper limit, and vb is lower limit.
Step3: Produce new gene of lower limit and upper limit
value with chromosome rate. The chromosome
rate is produced with random generator.

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replace the chromosomes, and implement
mutation mechanism.
Step14: Calculate the correct rate.
Er = (1 − Fitness) × 100%(15)
Step15: Until the training is finished. If training process
is not finished; otherwise go to Step3.

4. FAULT DIAGNOSIS OF CAR ENGINE
The tested object of this research is the engine of
the Nissan Cefiro2.0 as shown in Fig. 2, the engine
temperature is working between 80~95℃, and the baseconfiguration of the engine has about 1.0mm spark-plug
gap. In the experiment time, the car is parked gear whenthe engine is the normal condition or the fault tests
condition.

Fig. 4. The LabView recorder of the fault
diagnosis system
4.2 Testing results and discussion
In this paper, we use 208 sets of tested dataaccording to the reference [13] to test the practicabilityof the proposed method. In the training stage, the training
data are 160 sets, and the other data (48 sets) are used totest pattern. The input data of a fault diagnosis system
would unavoidably contain some uncertainties and noise.
The sources of error include environmental noise,transducers, human mistakes, etc., which could lead todata uncertainties. To take the noise and uncertainties
into account, 1,800 sets of testing data were created byadding ±5% to ±15% of random, uniformly distributed
error to the training data to appraise the fault-tolerant
abilities of the proposed method. To take into accountthe noise and uncertainties, and 48 sets of testing data
were created by adding ±5% to ±20% of random,
uniformly distributed, error to the training data toappraise the fault-tolerant abilities of the proposed
method. The reason is the input data of engine system
would contain some noise and uncertainties. Table 1
shows recognition results of different methods, we canfind, when using the multilayer neural network (MNN)
and k-means-based method to diagnose the faults ofengine, the maximum accuracy of the MNN-based
method is 95% and the accuracy is 85% in
k-means-based method. The accuracy of the proposed
diagnostic method is 98%, and the accuracy of the
proposed method is quite high and batter than the othermethods. The input data of a fault diagnosis system
would unavoidably contain some uncertainties and noise.
The sources of error include environmental noise,transducers, human mistakes, etc., which could lead todata uncertainties. To take the noise and uncertainties
into account, 1,800 sets of testing data were created byadding ±5% to ±20% of random, uniformly distributed
error to the training data to appraise the fault-tolerant
abilities of the proposed method. The test results using
different numbers of added errors are given in Table 2.
Usually, the error-containing data indeed degrade therecognition capabilities in proportion to the number of
errors added. This table shows that these methods all
bear remarkable tolerance to the errors contained in theFig. 2. The engine of Nissan Cefiro 2.0
4.1 The tested configuration
The back pressure is received on digital storage
oscilloscope by using pressure transmitter; the exhaust
temperature was received by temperature sensor. The
exhausted component was received by exhaust gas
analyzer. The experimental structure is shown in Fig. 3,
the signals of engine are all delivered by the sensors to
the diagnosis system, and then the detailed records ofsignals can be easy designed by a LabView8.5 software[12], the typical windows of fault diagnostic software arealso shown in Fig. 4. Here, the fault types are divided
into 16 kinds (including no fault), and there are 8
characteristics to be the input data. The components ofengine exhaust include HC (ppm), CO (%) and CO2 (%),
and this study separately installed the temperature sensor
in T1, T2, T3 and T4, so that we can promptly monitoring
the temperature for every exhaust position to speculated
engine fault.
2

1
3

V6engine

5

4
6

T2
temperature
sensorT3
temperature
sensorT1
temperature
sensorPressuretransmitter
catalytic
converter
AmplifierSilencertemperature
display

T4
temperature
sensorC
SDSO
V

Exhaust gas
analyzer

PC Fault
diagnosis
Systemmemorystick

Fig. 3. The experimental structure© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

data. The proposed method shows good tolerance to theadded errors, and has high accuracy rates of 65% inextreme error of ±20%.Usually, the error containing data
indeed degrade the recognition capabilities in proportion
to the amounts of error added.

problem,” Journal of Scientific Exploration, l (1)(1983) 81-93.
[5] M. H. Wang, “A novel extension method fortransformer fault diagnosis, ”IEEE Transactions on
Power Delivery, 18 (1) (2003) 164-169.
[6] W. Cai, “Extension theory and its application, journalof scientific exploration,”44 (7) (1999) 673-682.
[7] B. Das, “Fuzzy logic-based fault-type identificationin
unbalanced
radialpower
distribution
system, ”IEEE Transactions on Power Delivery, 21(1) (2006) 278-285.
[8]Renner G,Ekárt A. “Genetic algorithms in computer
aided design.”Computer-Aided Design, 35 (8)(2003) 709-726.
[9] S. Jesús Aguilar-Ruiz, Raúl Giráldez, and José C.
Riquelme, “Natural encoding for evolutionary
supervised learning, ”IEEE Transactions on
Evolutionary Computation, 11(4) (2007) 466-479.
[10] M. H. Wang, Y. F. Tseng, H. C. Chen, and K. H.
Chao, “A novel clustering algorithm based on theextension theory and genetic algorithm, ”Expert
Systems with Applications,1(36) (2009) 8269-8276.
[11]Hwang S. F, He R. S. “A hybrid real-parameter
genetic algorithm for function optimization.”
Advanced Engineering Informatics, 20 (1)(2006)
7-21.
[12] Xuejun, Xiang Ping, Xia Yang, Sheng. Liu,
“Real-time digital simulation of control system withLabVIEW simulation interface toolkit, ”Control
Conference, (2007) 318-322.
[13] Y. Y. Chen, “An artificial neural network model forthe diagnosis of automotive engine, ”National
Chung Hsing University (2006)

Table 1. Diagnosis performances of method compare
MethodTraining time

Accuracy rate (%)

Proposed method

1000

98%

K-means method

N/A

85%

MNN-I (8-8-16)

1000

62%

MNN-II (8-10-16)

1000

80%

MNN-III (8-15-16)

1000

95%

Table 2. Diagnosis performances of proposed method
Noise percentage (%)
± 0%
± 5%
± 10%
± 15%
± 20%

Accuracy rate (%)
98%
95%
87%
77%
65%

5. CONCLUSIONSThis paper presents a novel fault diagnosis method
based on the GA and extension theory for car engine.
The calculation of the proposed recognized method isfast and very simple. It can be easily implemented by PC
software. When a diagnosed data input the proposed
diagnosis system, the proposed recognized method will
output the possibility of all fault types. It provides useful
information to engine fault diagnosis and maintenance.
Test results shows that the proposed method cannot only
diagnose the main fault types; it can also detect useful
information for future trends and multi-fault analysis by
the relation degrees. Moreover, the proposed method has
a significantly high degree of diagnosis accuracy andshows good tolerances to errors added.

REFERENCES[1] E. Styvaktakis et al, “Expert system for power quality
disturbance classifier,” IEEE Transactions on Power
Delivery, 22 (3) (2007) 1979-1989.
[2] Tovar et al, “Fuzzy neural modeling via clusteringand support vector machines,” IEEE 22ndInternational Symposium on Intelligent Control,(2007) 24-29.
[3] B. Kerezsi, and I. Howard, “Vibration fault detectionof large turbine generators using neural networks,”
IEEE International Conference on Neural Networks,l (1) (1995) 121-126.
[4] W. Cai, “The extension set and incompatibility© ISAROB 2013

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Design of a rehabilitation robot suit with hardware-based safety devices- Proposal of the basic structure Yoshihiro Kai1, Satoshi Kitaguchi1, Wenlong Zhang2 and Masayoshi Tomizuka2
1

2

Tokai University, Japan
University of California, Berkeley, USA1

kai@tokai.ac.jp

Abstract: Safety is one of the most important issues in rehabilitation robot suits. We present a new rehabilitation robot suitequipped with two hardware-based safety devices. The robot suit assists a patient’s knee joint. The safety devices consist of
only mechanical components without actuators, controllers, or batteries. We expect that the safety devices guarantee thesafety even if the computer does not work. We call one device the “velocity-based safety device” and the other device the“torque-based safety device”. The velocity-based safety device can switch off the motor of the robot suit if the device detects
an unexpected joint angular velocity. Also, the torque-based safety device can switch off the motor if the device detects an
unexpected joint torque.
Keywords: Human-machine cooperative systems, Human-welfare robotics, Rehabilitation robot, Robot suit, Safety device.
patient’s leg at an unexpected high velocity and (2) the
robot suit may provide an unexpected high torque to thepatient’s joint, before the patient’s joint is stopped by thejoint limiter, as mentioned in section 2.
In this paper, we present a new rehabilitation robot suitwith two hardware-based safety devices – “velocity-based
safety device” and “torque-based safety device”. The robot
suit assists a patient’s knee joint. The safety devices consistof only mechanical components without actuators,
controllers, or batteries. The velocity-based safety device
can switch off the motor of the robot suit if the device
detects an unexpected joint angular velocity. Also, thetorque-based safety device can switch off the motor if thedevice detects an unexpected joint torque. The safety
devices can work even if the computer does not work.
This paper is organized as follows. In section 2, we
briefly describe the safety problems in rehabilitation robot
suits and two hardware-based safety devices. In section 3,
we explain the basic structure and mechanism of therehabilitation robot suit with two hardware-based safety
devices. Section 4 concludes this paper.

1 INTRODUCTION
In gait rehabilitation of patients with neurologicaldiseases such as spinal cord injury, stroke, and traumaticbrain injury, physical therapists often assist the patient leg
movement by using a treadmill and a partial body-weightsupport system [1]. In Japan, the number of aged people is
rapidly increasing [2]. Predictably, it is expected thatelderly patients who need gait rehabilitation will increase,that the physical therapists’ burdens will increase and thatthe time that can be allotted to gait rehabilitation of eachpatient will become insufficient. In order to relieve thetherapists from manually moving the legs of the patients,
rehabilitation robot suits are needed that can assist thepatient leg movement [3], [4].
In the design of rehabilitation robot suits, safety
consideration for patients is one of the most important
issues. Computer-aided control techniques can improve thesafety of the robot suits [5], [6]. However, the computer
may break down, which makes the software-based safetymeasure inadequate. Therefore, a robot suit with hardwarebased safety devices would be desirable to guarantee safety
even under breaking down of the computer.
Emergency switches and joint limiters are often used as
hardware-based safety devices [7], [8]. When the computer
does not work, emergency switches are useful for stopping
the robot suits. Also, joint limiters can prevent thehyperextension of the patient’s joints. However, the patient
and/or the physical therapists may not be able to push the
emergency switch in case of emergency. Joint limiters
involve risks such as (1)the robot suit may move the© ISAROB 2013

2 REHABILITATION ROBOT SUIT WITH
HARDWARE-BASED SAFETY DEVICES
We consider a rehabilitation robot suit which assists apatient’s knee joint. The robot suit is controlled by acomputer.
Assume that the computer of the robot suit breaks down.
If an unexpected high velocity of the motor occurs, thepatient’s foot may collide with the ground at high speed asshown in Fig.1(a). If the motor generates an unexpected582

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013device. The robot suit is worn on a patient’s leg by twothigh braces and a calf brace.
Fig.3 shows the basic structure. A motor is mounted on
Frame A. The motor torque is transmitted to Calf Brace via
Worm Gear, Worm Wheel, Shaft A, Plate A, Torsion Spring,Gear C, Gear D, Shaft B, and Frame B. The velocity-based
safety device consists of Gear A, Rotary Dumper, Bar, two
Springs, two Pins, and two Switches.
Fig.4 shows the details of velocity-based safety device.
Gear A is attached to Shaft A. Gear B meshes with Gear A.
Rotary Dumper is connected to Gear B. Bar is connected tothe axis of Rotary Dumper. Bar is also attached to two
Springs. The ends of the Springs are connected to Frame A
by two Pins.

high torque, the patient may feel pain as shown in Fig.1(b).
Therefore, we present a rehabilitation robot suit equippedwith two safety devices (velocity-based safety device andtorque-based safety device).

(a) High Velocity(b) High TorqueFig. 1. Problems in computer failure

The characteristics of the velocity-based safety device
are as follows:(1) If the angular velocity of the motor exceeds a preset
threshold level, then the safety device can switch off
the motor. We call the preset threshold level the“detection velocity level”.
(2) The detection velocity level is adjustable.
By (1), we can expect that the velocity-based safety
device prevents from moving the patient’s leg at a highspeed. Furthermore, by (2), we can adjust the detection
velocity level according to the requirement of each patient’s
gait exercise.
Also, the characteristics of the torque-based safety
device are as follows:(3) If the torque which the robot suit provides to patient’s
knee exceeds a preset threshold level, then the safety
device can switch off the motor. We call the preset
threshold level the “detection torque level”.
(4) The detection torque level is adjustable.
By (3), we can expect that the torque-based safety
device prevents from providing an unexpected high torqueto the patient. Furthermore, by (4), we can adjust thedetection torque level according to the requirement of eachpatient’s gait exercise.

Fig. 2. The rehabilitation robot suit with safety devices

Hole

Bearing

3 BASIC STRUCTURE & MECHANISM
3.1. Basic Structure
Fig.2 shows the rehabilitation robot suit with thevelocity-based safety device and the torque-based safety© ISAROB 2013

Fig. 3. The basic structure583

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Next, we explain the structure of torque-based safety
device by using Fig.3. Plate A is attached to Shaft A. The
Torsion Spring is directly installed between Plate A andGear C. Four Wire Ropes are connected to Plate A and
Plate B through 4 Holes of Gear C. Gear C is attached to
Shaft A via Bearing. Compression Spring is installed
between Plate B and Gear C. Shaft A is inserted to Hole ofPlate B. Two Guide Bars attached to Plate B are inserted to
two Guide Holes of Gear C. By the geometric restriction,
Plate B and Gear C can rotate around Shaft A together, andPlate B can slide along Shaft A when Plate B is pulled byfour Wire Ropes.

Fig. 6. The switch-off mechanism in the velocity-based
safety device (in the opposite direction)

3.2. Mechanism(1) Velocity-based Safety Device
Fig.5 shows the mechanism which mechanically detects
the unexpected robot suit motion on the basis of the angularvelocity of the motor. The damping torque by Rotary
Damper and the spring torque by Springs act on Bar, when
Gear B is rotated by the motor. As the motor velocity
increases, the damping torque increases. If the motor
velocity exceeds the detection velocity level, Bar rotates by
the torque difference between the damping torque and thespring torque and switches off the motor. Also in the
opposite direction, Bar can switch off, as shown in Fig.6.
The detection velocity level is adjustable by changing theattachment positions of Springs as shown in Fig.7.

Fig. 7. Setting of the detection velocity level

Shaft A

q1

Fig. 4. The velocity-based safety device

q2
Fig. 8. The switch-off mechanism in the torque-based
safety device

Fig. 5. The switch-off mechanism in the velocity-based
safety device© ISAROB 2013

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(2) Torque-based Safety Device
Fig.8 shows the mechanism which mechanically detects
the unexpected robot suit motion on the basis of the torquewhich the robot suit provides to patient’s knee. When thetorque acts on Torsion Spring, the angle difference (q1-q2)
between Plate A and Gear C generates (q1: the angle ofPlate A, q2: the angle of Gear C) and Plate B is pulled byfour Wire Ropes. If the torque exceeds the detection torquelevel, Plate B switches off the motor. Also in the opposite
direction, Plate B can switch off as shown in Fig.9. The
detection torque level is adjustable by changing theattachment position of Switch as shown in Fig.10.
Fig. 10. Setting of the detection torque level

ACKNOWLEDGMENT
This work was supported by Overseas Research Visit
Program of Tokai University (2012).

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study of Lokomat versus manual-assisted treadmill training
for locomotor recovery post-stroke, Journal of
NeuroEngineering and Rehabilitation 2009, 6:18.
[2] Cabinet Office, Government of Japan (2011), Annual
Report on the Aging Society: 2010 (Summary), Cabinet
Office Japan, 2011, pp.2-5.
[3]Robert Riener (2012), Technology of the Robotic Gait
Orthosis Lokomat, Neurorehabilitation Technology, 2012,
pp.221-232.
[4]Kenta Suzuki, Gouji Mito, Hiroaki Kawamoto,
Yasuhisa Hasegawa, Yoshiyuki Sankai (2010),”IntentionBased Walking Support for Paraplegia Patient with Robot
Suit HAL”, Climbing and Walking Robots, 2010, pp.383408.
[5] Kong K, Bae J, Tomizuka M (2012), A Compact
Rotary Series Elastic Actuator for Human Assistive
Systems,
IEEE/ASME
TRANSACTIONSONMECHATRONICS, VOL. 17, NO. 2, 2012, pp.288-297.
[6] Bae J, Kong K, and Tomizuka M (2011), Control
Algorithms for Prevention of Impacts in Rehabilitation
Systems, 2011 IEEE/ASME International Conference on
Advanced Intelligent Mechatronics (AIM2011), pp.128-133.
[7] Kawamoto H., Lee S., Kanbe S., Sankai Y., “Power
Assist Method for HAL-3 using EMG-based Feedback
Controller”, Proc. of Int’l Conf. onSystems, Man andCybernetics (SMC2003), 2003, pp.1648-1653.
[8] Feng Chen, Yong Yu, Yunjian Ge, Jian Sun and
Xiaohong Deng, A PAWL for Enhancing Strength andEndurance during Walking Using Interaction Force andDynamical Information”, Climbing and Walking Robots:
towards New Applications, 2007, pp.417-428.

Fig. 9. The switch-off mechanism in the torquebased safety device (in the opposite direction)

4 CONCLUSION
We presented a new rehabilitation robot suit with two
hardware-based safety devices (velocity-based safety
device and torque-based safety device). In the future, wewill build the robot suit and experimentally examine theusefulness of the two hardware-based safety devices.

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Through-Wall Imaging for a Metallic Cylinder by Dynamic Differential
Evolution
Chung-Hsin Huang1

Lung-Fai Tuen4

Department of Information Technology and Mobile
Communication, Taipei College of Maritime Technology,
New Taipei City, Taiwan, R.O.C.
e-mail: havehuang@hotmail.com

Electrical Engineering Department, Tamkang University,
New Taipei City, Taiwan, R.O.C.
e-mail:justintuen@pchome.com.tw

Chi-Hsien Sun2

Ching-Lieh Li5

Department of Electronic and computer engineering,
National Taiwan University of Science and Technology,
Taiwan, R.O.C.
e-mail:laisingsun@yahoo.com.tw

Electrical Engineering Department, Tamkang University,
Taipei County, Taiwan, R.O.C.
e-mail: chingliehli1@gmail.com

Pin-Ru, Lai6

3

Ping-Chieh,Chiang

Electrical Engineering Department, Tamkang University,
Taipei County, Taiwan, R.O.C.
e-mail: cynically6667@livemail.tw

Department of Electrical Engineering
National Taiwan University of Science and Technology,
Taiwan, R.O.C.
e-mail:cfes2579@hotmail.com
Abstract— Through wall imaging for estimating shape ofa metallic cylinder is proposed. The ability of dynamic
differential evolution (DDE) stochastic searchingalgorithm for shape reconstruction of 2-D conducting
targets hidden behind a homogeneous building wall isdemonstrated by using simulated backscattered fields.
After an integral formulation, a discretization using themethod of moment (MoM) is applied. The through-wall
imaging (TWI) problem is recast as a nonlinear
optimization problem with an objective function definedby the norm of a difference between the measured andcalculated scattered electric field. Thus, the shape ofmetallic cylinder can be obtained by minimizing theobjective function. Simulations show that DDE cansuccessfully reconstruct the through-wall imaging for a
metallic cylinder.

a gradient-type method, it is well known that theconvergence of the iteration depends highly on the initial
guess. If a good initial guess is given, the speed of theconvergence can be very fast. On the other hand, if the initial
guess is far away from the exact one, the searching tends toget fail [7]. In general, they tend to get trapped in local
minima when the initial trial solution is far away from theexact one. Thus, some population-based stochastic methods,
such as genetic algorithms (GAs) [8], differential evolution(DE) [9],[10] and particle swarm optimization (PSO) [11][13] are proposed to search the global extreme of the inverse
problems to overcome the drawback of the deterministic
methods for TWI or buried problems.In our knowledge, there
are still no numerical results by the DDE algorithm formetallic cylinder scatterers hidden behind a homogeneous
wall. The DDE algorithm is used to recover the shape of thescatterer. The outline of the paper is as follows. In Section II,
we describe the forward scattering for TWI. Section III and
IV present inverse problem and dynamic differential
evolution (DDE), respectively. Two numerical examples arepresented in Section V. Section VI is the conclusion.

Keywords- Inverse Scattering, Dynamic Differential Evolution.

I. INTRODUCTION
Through-wall imaging (TWI) consists of imaging objects
hidden behind an obstacle by using electromagnetic (EM)waves at the microwave frequencies. This problem is of greatinterest, as the aim of detecting and localizing hidden objects
is shared in many applicative contexts (i.e., both military andcivilian) such as in search and rescue, security, peacekeeping,and law enforcement operations [1]-[3].
The focus of the present work is on the reconstruction
using inverse scattering approach. Among the inverse
scattering approaches, several numerical reconstruction
methods have been applied for through-wall imaging.
General speaking, two main kinds of approaches have beendeveloped. The first is based on local searching schemes
such as the contrast source inversion (CSI) [4]. Recently,
subspace-based optimization method (SOM) [5]-[6] has been
proposed to solve inverse scattering problems. However, for© ISAROB 2013

II. FORWARD PROBLEM
Let us consider a two-dimensional slab structure asshown in Fig.1, where ( ε i ,σ i ) i = 1,2,3, denote thepermittivities and conductivities in each region. Here thepermeabilities of all three regions are assumed to be µ o anda conducting cylinder is buried in region 3. In our simulation,a priori information is assuming that scatterer is a metallic
cylinder. The cylinder is of an infinite extent in the z
direction, and its cross-section is described in polar
coordinates in the x, y plane by the equation ρ = F (θ ) (i.e.,the object is of a star-like shape.) is illuminated by an
incident plane wave whose electric field vector is parallel to586

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measured at the receivers. This problem is resolved by an
optimization approach, for which the global searching
scheme DDE is employed to minimize the following
objective function (OF):theaxis (i.e., TM polarization). We assume that the timedependence of the field is harmonic with the factor
exp( jωt ). Let E inc denote the incident field form region
1 with incident angle θ1 as follow:

12

Einc = E1+ e jk1 cos θ1 y e− jk1 sinθ1 x zˆAt an arbitrary point ( x, y ) (or ( r ,θ ) in polar22 1 MtOF =  ∑ Esexp (rm ) − Escal (rm ) / Esexp (rm )  Mt m=1


(1)

exp

(4)

cal

Where E z and E z are experimental electric fields
and the calculated electric fields, respectively.

coordinates) in regions 3 the scattered field, Es = E − Ei ,can be expressed as

Es (x, y) =− j ωµ0 ∫ G (x, y, F(θ′),θ′) Js (θ′) F(θ) + F′(θ)dθ

IV.

(2)

Dynamic Differential Evolutionc

Where J s (θ ' ) is the induced surface current density, which is

DDE algorithm starts with an initial population ofpotential solutions that is composed by a group of randomly
generated individuals which represents shape function of thecylinders. Each individual in DDE algorithm is a Ddimensional vector consisting of D optimization parameters.
The initial population
may be expressed by{xi : i = 1, 2, L, Np} , where Np is the population size.

proportional to the normal derivative of the electric field on
the conductor surface. F (θ ) is the shape function, andF ′(θ ) is the differentiation of F (θ ) . c is cross section for
detect domain. Note that G1 and G3 denote the Green’s
function which can be obtained by tedious mathematic
manipulation for the line source in region 3. More detail on
subgriding FDTD can be found in [9]The direct scattering problem is to calculate the scatteredelectric fields while the shape and location of the scatterer
are given. The shape function F (θ ) of the scatterer is
described by the trigonometric series in the direct scattering
problem
N /2
N /2
(3)
F (θ ) =B cos( n θ ) +C sin( n θ )

∑

∑

n

n =0

After initialization, DDE algorithm performs the genetic
evolution until the termination criterion is met. DDE
algorithm, like other EAs, also relies on the genetic
operations (mutation, crossover and selection) to evolve
generation by generation.
The key distinction between a DDE algorithm and atypical DE [12] is on the population updating mechanism. In
a typical DE, all the update actions of the population are
performed at the end of the generation, of which theimplementation is referred as static updating mechanism.
Alternatively, the updating mechanism of a DDE algorithm
is carried out in a dynamic way: each parent individual will
be replaced by his offspring if the offspring has a better
objective function value than its parent individual does. Thus,
DDE algorithm can respond the progress of population status
immediately and to yield faster convergence speed than the
typical DE. Based on the convergent characteristic of DDE
algorithm, we are able to reduce the numbers of cost function
evaluation and reconstruct the microwave image efficiently.

n

n =1

where Bn and Cn are real coefficients to expand the shapefunction.
III. INVERSE PROBLEM
For the inverse scattering problem, the shape of the metallic
cylinder is reconstructed by the given scattered electric fieldY

R e g io n 1
E 1+

H 1+

µ1 ε1 σ 1

E 1−

θ1

H 1−

y= 2 aθ2

R e g io n 2

E 2+

H

µ2

ε2

H

σ 2

ε3

σ 3

NUMERICAL RESULT

We illustrate the performance of the proposed inversion
algorithm and its sensitivity to random noise in the scatteredfield.
Consideralosslessthree-layer
structure( σ 1 = σ 2 = σ 3 = 0 ) and a perfectly conducting cylinder

−
2

y= a
θ3

µ3

V.

E 2−

+
2

R e g io n 3

EVOLUTIONAL ALGORITHMS

E 3+
H 3+

buried in region 3. The permittivity in each region isθcharacterized by,X

ε 1 = ε 0 , ε 2 = 8.0ε 0 and ε 3 = ε 0respectively, as shown in Fig. 1. The incident angles equal to

45 o , 90o and 315 o , respectively. The frequency of theincident wave is chosen to be 3GHz. The width of thesecond layer is 0.1 m. There are seven measurement points
are equally separated on a circle of radius 3m about center at

Fig. 1. Geometrical configuration of the problem.

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equal spacing in region 1. Thus there are totally 21
measurements in each simulation. The number of unknowns
is set to be 7 (i.e., N+1=7). To save computing time, thenumber of unknowns is set to be 7. Moreover, to avoid
inverse crime, the discretization number for the direct
problem is two times that for the inverse problem in thesimulation. In forward problem, the shape function F (θ )is discretized to 30. Reconstruction is carried out on an IntelPC (3.4 GHz/ 4G memory /500 G). The software isdeveloped on FORTRAN VISION 6.0 in WINDOWS XP
system environment. The search range for the unknown
coefficient of the shape function is chosen to be from 0 to 0.1.
The operational coefficients for the DDE algorithm are set as
below: The crossover rate CR=0.8. Both parameters λ andξ are set to be 0.8. The population size Np is set to be 70. It
should be noted that the termination criterion is set to 2000
generations in our simulation based on our empirical rule.
In the first example, the shape function is chosen tobeThe
F (θ ) = (0.025 + 0.015 cos 3θ ) (unit:m).
reconstructed shape function for the best population member
is plotted in Fig.2 with the shape error shown in Fig.3. It isobserved that DDE algorithm can be quickly achieved within
150 generation to minimize the relative errors of the shapeand has good convergences. The typical CPU time for eachexample is about 2 minutes. Here, the shape function
discrepancy is defined as
1 N'
DF = { ∑ [ F cal (θ i ) − F (θ i )] 2 / F 2 (θ i )}1 / 2 × 100 % (5)N ' i =1
where N ' is set to 1000. The relative error is about 0.01%.

1

Relative Error (%)

DF

10

10

10

0

-1

-2

10

0

10

1

10

2

10

3

10

4

Generation
Fig. 3. Shape function error at different generationsIV.

CONCLISIONSIn this paper, a computational approach to microwave
imaging of homogeneous dielectric scatterer with arbitrary
cross section in time domain has been presented. Scattering
fields are obtained by FDTD method. The subgridding
scheme is employed to closely describe the shape of thecylinder for the FDTD method. The approach has beenformulated as a global nonlinear optimization problem and
NU-SSGA has been applied. It has been shown that thelocation, shape and permittivity of the dielectric object canbe successfully reconstructed even when the dielectric object
with fairly large permittivity

ACKNOWLEDGEMENT

0.06

Exact
InitialGen.=30th
Final

0.04

Y-axis (m)10This work was supported by National Science Council,
Republic of China, under Grant NSC 101-2221-E-229-001REFERENCES

0.02[1] F. Soldovieri, R. Solimene, A. Brancaccio, and R. Pierri, “Localizationof the interfaces of a slab hidden behind a wall,” IEEE Transactions on
Geoscience and Remote Sensing, vol. 45, no. 8, pp. 2471–2482, Aug.

0

2007.
[2] M. Dehmollaian, K. Sarabandi, “Refocusing Through Building Walls-0.02

Using Synthetic Aperture Radar,” IEEE Transactions on Geoscienceand Remote Sensing, vol. 46, pp. 1589-1599, 2008.

-0.04
-0.04

[3] Y. Wang and A. E. Fathy, “Advanced System Level Simulation Platform

-0.02

0
0.02
X-axis (m)

0.04

0.06

for Three-Dimensional UWB Through-Wall Image” IEEE Transactions

Fig. 2. The reconstructed cross section of the cylinder of theexample at different generations.

[4] Abubakar, A. and P. M. van den Berg, “The contrast source inversion

on Geoscience and Remote Sensing, vol. 50, pp. 1986-2000, 2012.

method for location and shape reconstructions,” Inverse Problems, Vol.
18, No. 2, 495-510, Mar. 2002.
[5] Chen, X., “Subspace-based optimization method for solving inverse
scattering problems,” IEEE Transactions on Geoscience and Remote© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Sensing, Vol. 48, 42-49, 2010.
[6] Ye, X., X. Chen, Y. Zhong, and K. Agarwal, “Subspace-based
optimization method for reconstructing perfectly electric conductors,”
Progress In Electromagnetic Research, PIER 100, 119-128, 2010.
[7] T. Moriyama, Z. Meng, and T. Takenaka, "Forward-backward timestepping method combined with genetic algorithm applied to breast
cancer detection", Microwave and Optical Technology Letters, Vol. 53,
No. 2, pp.438-442, 2011.
[8] C. H. Sun, C. L. Li, C. C. Chiu and C. H. Huang, “Time Domain Image
Reconstruction for a Buried 2D Homogeneous Dielectric Cylinder
Using NU-SSGA.”, Research in Nondestructive Evaluation, Vol. 22,
No.1, pp. 1-15, Jan. 2011.
[9] M. Dehmollaian, “Through-Wall Shape Reconstruction and Wall
Parameters Estimation Using Differential Evolution,” IEEE Geoscienceand Remote Sensing Letter, Vol. 8, 201-205, 2011.
[10] C. H. Sun, C. C. Chiu, W. Chien and C. L. Li, “Application of FDTD
and Dynamic Differential Evolution for Inverse Scattering of a TwoDimensional Perfectly Conducting Cylinder in Slab Medium”, Journalof Electronic Imaging. Vol. 19, 043016, Dec. 2010.
[11] C. H. Sun, C. C. Chiu and C. L. Li, “Time-Domain Inverse Scatteringof a Two- dimensional Metallic Cylinder in Slab Medium Using
Asynchronous

Particle

Swarm

Optimization.”,

Progress

In

Electromagnetic Research M. PIER M Vol. 14, pp. 85-100. Aug. 2010.
[12]

C. C. Chiu, C. H. Sun and Y. S. Fan “Shape Reconstruction of 2-D
Perfectly Conducting Cylinder Targets Using the Particle Swarm
Optimization.” Imaging Science Journal. Vol. 60, No 2, pp. 83-89, Apr.
2012.

[13] C. C. Chiu, C. H. Sun and W. L. Chang “Comparison of Particle Swarm
Optimization and Asynchronous Particle Swarm Optimization for
Inverse Scattering of a Two- Dimensional Perfectly Conducting
Cylinder.”, International Journal of Applied Electromagnetics andMechanics Vol. 35, No.4, pp. 249-261,Apr. 2011

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Analysis of Effects of Exercises on Middle-aged and Elderly Arthritics by
Thermal Imagery and Automatic Auxiliary Diagnosis
1,aChin-Lun Lai* 2,bTsung-Ching Lin and 1,cTung-Lin Yang

1

Institute of Information and Communication Engineering, Oriental Institute of Technology, Taipei, Taiwan
2
Department of Physical Medicine and Rehabilitation, Far Eastern Memorial Hospital, Taipei, Taiwan
Email: afo001@mail.oit.edu.tw, btclin0331@gmail.com, cmaniya.ntcb@msa.hinet.net,

Abstract: In this paper, an automatic infrared thermal image analysis method is proposed to analyze the rehabilitation effects,
after simple exercise intervention, on middle-aged and elderly osteoarthritic patients, while an automatic CV diagnosis criteria
for distinguishing the patients from the healthy persons is also established, to assist medical personnel's diagnoses andsuggestions. To approach this goal, the questionnaires from the Western Ontario and McMaster Universities are also used as
basis for evaluating physiological changes before and after rehabilitative exercises and the data analyzed may also be used as
the reference for subsequent exercise adjustment by the subject. Experimental results shows that the proposed method performs
well in automatic judgement of degenerative arthritis hence can be a good reference for clinical diagnosis.
Keywords: Infrared thermal imaging, osteoarthritic, automatic diagnosis , WOMAC
exploring biomedicine via thermal imagery [1]. For
example: evaluation of hand osteoarthritis andrheumarthritis, effect of lesion of nerves around the sole ofthe diabetics’ foot soles on blood circulation [2-3]. The
detection methods above are based on symptoms such as
inflammatory responses, swell, fever, pain etc. Thesepathological processes will affect blood vessels andsurrounding tissues. But, the heat radiation is the energy
diffused naturally from the body temperature and can beremotely sensed to form thermal imagery data and analyzed.
Hence the thermal image can be used to diagnose diseases
occurring to body surface or proximity of body surface
without invasive behavior or radiation dose problem, which
is a highly safe physiological exploring approach. Thus
great importance has gradually been attached to theapproach in the medical circle. It is required that attention
shall be paid to the operating errors when the thermal image
assessment diagnosis is used for conditions. The errors will
not be decreased unless the operating conditions forinfrared thermograph are complied [4]. It is attributed thatthe experimental environment, equipment and physiologicalcondition of the subject at the moment will affect the result.
In addition, the assessment scale for OA patients is used in
the study the Western Ontario and McMaster Universities
Osteoarthritis Index (WOMAC) [5]. However, theassessment scale can only objectively reflect the conditionsof the patient after exercise and a thermal imager isrequired for pathological detection in combination with theassessment scale. The heat generated can be used for
detecting the skin surface temperature indicating pain
intensity by the thermal imager since the area below the

1 INTRODUCTION
Some chronic diseases occur with age increasing and
Osteoarthritis (OA) is just one of the most common ones.
Degenerative gonitis is also referred to as knee
osteoarthritis which is the most common joint diseaseacross the world. It has gradually spread from the elderly tothe young in recent years. The main symptom is articularcartilage damaged. Therefore some people hold that thedegenerative arthritis is joint weak and degeneration andthe degenerative joints are vulnerable to pain, hydrarthrosis
and swelling resulting in the range of motion decreased.
The arthritis may occur to various parts of the whole body,in particular to the weight bearing joint or joints with alarge range of motion, such as cervical vertebra, lumbar
vertebra, hip joints, knee joints and hand joints. Suchsymptoms will significantly affect daily motions of thepatient, such as waking tread, surmounting obstacles,
walking up and down the stairs, change from sitting tostanding, etc. In the past, such physiological conditions
relied on X-ray examination. The result is accurate while
the issue of radiation dose exists. Infrared imaging is also
called temperature-difference photographing and is a
detection technique for observing shell temperature
distribution using the infrared radiation photographingprinciple. The local heat balance will be affected when ahuman being contracts a disease or the physiologicalcondition is abnormal and clinically the tissue temperature
will increase or decrease accordingly. Thus themeasurement of body temperature variation is one of theimportant indicators for clinical disease diagnosis. And thenmany studies focus on the application and limitation of© ISAROB 2013

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patella is most sensitive to pain for the arthritic [6]. Besides,
it will be very difficult to distinguish from other arthritis or
symptoms and the temperature detected by the thermal
imager may approximate the temperature of a normal
subject if no change is observed or only slight stenosis ofarticular cavity is noted on the X-ray film for the initialdegenerative arthritis. In such a case, it is difficult to judge
the subject as an arthritic or a healthy person. Hence, thepaper presents a set of rehabilitation exercise method to
induce the difference between an arthritic and a healthy
person after exercise and calculate the center point [7] ofpatch below the patella with the image captured
automatically by the thermal imager. Compare thedifference (if any) between the skin surface temperate of
the experimental group and control group after thetemperature of the area is obtained, and the rehabilitation
effect after exercise for two weeks. Then overlap the twodifferent images (visible image and thermal image), adjust
the contrast after overlapping [8], to allow the location of
local abnormal temperature to be more easily observed on
the overlapped image. With such detections, the medical
personnel are able to judge the patients’ conditions evenmore accurate and quicker.
The paper will introduce methods in the following
section, the third section will present and discuss andexperimental results, the fourth section will present
conclusions and suggestions.

Fig. 1.

Step 1: Doctor diagnosed the medical history, pain siteand severity of the patient and delivered the statistical study
data to the study personnel for photographing the pain site.
Step 2: The study personnel are required to inform thesubject of precautions in advance. The subject shall stay inthe isolation room for 15 minutes prior to the start of thefirst-week exercise. Apply a patch with diameter of about
2cm to the central point of the knee patella as the locating
point and photograph the temperature before the first-time
exercise. The subject shall do warm-up exercises first andthen take rehabilitation exercise under the direction of thecoach in the fitness center (annular oil pressure resistancetraining, eight apparatus in total, one minute for eachapparatus, and three circles in total). Take note of theexercise effect of each subject at all times. The subjects willbe lead to the isolation room for experiment following
exercise. They shall also stay in the isolation room for 15
minutes before the experiment, understand the experiment
procedures to be performed and fill in the WOMAC
questionnaires. All procedures will be performed at a room
temperature of about 23±1℃. In addition, be sure to makeinfrared image measurement after the body temperaturereaches the balance state to avoid temperature errors causedby heat accumulation. Maintain low humidity for the bodyand prevent the measurement being influenced by theconvection and heat radiation.
Step 3: Repeat the steps in A and B above , retrieve the
Image Data of the experimental group and the control groupand input them to the data base. Then write computer
identifying programs based on the images in the database.
The flow chart for the automatic computer identifying
system operation is showed as the Fig .2 below the
automatic identifying program is as follows:

2 Experimental Methods
According to the bibliography, it is known that the
infrared thermal image can be used to detect degenerative
arthritis. But the detection effect of the person with slight
symptom is not necessarily significant compared with thatof a healthy person. Therefore, the study is conducted incooperation with a doctor from the rehabilitation division.
Annular oil pressure resistance training movement as
induced to detect the difference between the degenerative
arthritics and healthy persons, and collected data as thebasis for establishing an automated auxiliary discrimination
program, in order to make the process more clear, the flow
chart for the system operation is showed as the Fig. 1 below
and then the function of each block is described as follows:

Fig. 2.

© ISAROB 2013

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Automatic computer identifying System Diagram.

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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(a)

(b)

(c)

(d)

point. Repeat the above steps for four times to obtain the
average temperature, maximum temperature and minimum
temperature of the pixel sum in the blue box.
Step 5: The coordinates of the central points from thetwo images shown in Fig. 3(a) and Fig. 3(c) subtract andthe resulting new central point is exactly the distance
between the two cameras shown in Fig. 3(e). Perform
image overlapping for conveniently viewing the abnormalsites on the skin. Finally, retrieve the temperatures of theexperimental group and the control group before and after
exercise and find out the difference.
Step 6: Input the collected data of subjects to the
database and apply Gaussian distribution statistical data to
the experimental group and control group. Normalize thederived average value and standard deviation to determine
the probability of each observed value in the data
distribution as the basis for inferring whether the data froma certain population. In the database, one-point density
function is applied per 0.01℃ for the data of ten groups ofpatients and ten groups of healthy persons collected basedon the average value and standard deviation. The curve
derived is distributed as shown in Fig. 4 (the red points on
the left and right curves are the actual temperatures of ten
subjects). The two curves significantly show thetemperature difference between the arthritics and healthy
persons. In addition, collect the data of four subjects withcondition unknown and input it to the database (purple
round points) as probability for testing the system prognosis,
which can assist the medical personnel in diagnosis.

(e)Fig. 3.
(a) Original visual image, (b) thermal image, (c)the black patch marked as 2 cm, applied to the center of thepatella, the red box for confirming the patch’s registration
with the central point, and the stance during photographing,(d) the blue box for calculating the temperature below thepatella, (e) the overlapped image is convenient for
identifying the variation of skin surface.

3 Experimental Results

Step 4: Reference [16], set the black patch as the desired
object, calculate the central point of the patch, in addition,
stick the patch with diameter about 2cm to the central pointof the knee patella as the positioning point to confirm thatthe positions of the thermal image and visible image areconsistent the mark displays as a red block on the thermal
imager shown in Fig. 3(b). Therefore, it is easy to identify
the registration of the two images. Finally, adjust thethermal imager to the optimal angle and height forphotographing the knee joints. Fig. 3(c) is for confirming
whether the patch is selected, whether the standing position
for photographing is excessively close to or away from thecamera. The range of the black patch will be beyond the redbox and affect the judgment if the standing position forphotographing is excessively close to the camera. Calculate
the temperature of the feet down from the central point of
the patch. The blue box below the central point (below thepatella) can be simply defined as:
S(x, y) = I(c + d, r + i) < k 

In order to achieve the expected results of the study, listed t
he following precautions: infrared thermal imager, subjects
information, shooting distance, and environmental factors.
A. Subject Information
The study is approved by Institutional Review Board for coo
perating with the doctor from rehabilitation division of Far E
astern Memorial Hospital.
B. Operating Way
The sequences: wait for 15 minutes, stand by the footmat, researchers subjects paste patch to automate capture
images show the temperature of the subjects, and analysis
belongs to which part of the ethnic groups with aprobability value.
In WOMAC questionnaires for evaluating pain intensity(knee pain, stiff degree, motion difficulty) from patientswith objective conditions, the pain intensity of second timeis weaker than that before and after the exercise of first time
and the second time and the temperature before and after
exercise decreases significantly. Therefore it is determinedthat the rehabilitation exercise can generate rehabilitation
effects as shown in Table 1. The result of experimental
numerical analysis shows that partial subjects are not easily
judged as arthritics due to the fact that their temperatureThe green box in Fig. 3(d) is the patch range set,variables c and r are the central points of the patch, d is the
distance between the upper and the lower end of the central
point, i is the distance between the left end and the right endof the central point, k is the value of background pixel.
Record the coordinate value of the previous point if the
pixel of the following point is less than the pixel value of k

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differences before exercise are not significant. Therefore,
the temperatures after induced rehabilitation exercise areused to plot the normal distribution curve in the form. The
temperature of the left foot is higher than that of the rightfoot among all subjects, which may relate to footedness.
Therefore, the temperatures of left feet are uniformly
adopted for the distribution curve for more convenient
difference distinguishing as show in Table 2. Observe theNormal distribution curve established with the experimentalgroup and control group in Fig. 4 if the prediction
demonstrates that the method after simple exercise is able
to identify the range value for the difference temperature
between the patients and healthy persons. It is assumed thata subject with condition unknown is measured to be
33.17℃ as shown in Table 3. It is predicted that theprobability of the subject in the database range fordegenerative arthritics accounts for 25.30% while theprobability of healthy persons accounts for 0.10%. But theprobability of patients is much higher than that of healthy
persons. Hence the subject is judged as an arthritic finally.
And the temperature is within the range of patient databaseand partial area is within the range of healthy persons.
Hence the part is also judged as error rate.

Fig. 4.

4 Conclusions And Suggestions
The experiment result verifies the following arguments.
The thermal imager can be used to detect the difference
between the degenerative arthritics and healthy persons
after rehabilitation exercise and it is obviously observedthat the rehabilitation exercise is significantly helpful forthe patients. In addition, the questionnaire assessment also
shows that the pain index of the patients involved inrehabilitation is decreasing.

REFERENCES[1] L. J. JIANG, E. Y. K. NG, A. C. B. YEO, S. WU, F. PAN, W.
Y. YAU, J. H. CHEN, and Y. YANG, “A perspective on
medical infrared imaging,” Journal of Medical Engineering& Technology, vol. 29, no. 6, pp. 257-267, 2005.
[2] S. Bagavathiappan, J. Philip, T. Jayakumar, B. Raj, PN.
Rao, M. Varalakshmi, and V. Mohan, “Correlation between
Plantar Foot Temperature and Diabetic Neuropathy: A Case
Study by Using an Infrared Thermal Imaging Technique,”
Journal of Diabetes Science and Technology, vol. 4, pp.
1386-1392, November 2010.
[3] N. Borojevic, D. Kolaric, S. Grazio, F. Grubisic, S.
Antonini, I. A. Nola, and Z. Herceg, “Thermography ofRheumatoid Arthritis and Osteoarthritis,” ELMAR, pp. 293295, Zadar, Croatia, September 2011.
[4] N. Kakuta, S. Yokoyama, and K. Mabuchi, “Human thermal
models for evaluating infrared images,” IEEE Trans. on
Engineering in Medicine and Biology Magazine, vol. 21, no.
6, pp. 65-72, 2002.
[5] S. McConnell, P. Kolopack, and A. M. Davis, “The Western
Ontario and McMaster Universities Osteoarthritis Index(WOMAC): A review of its utility and measurement
properties,” Arthritis Care & Research, vol. 45, no. 5, pp.
453-461, October 2001.
[6] Anna E. Denoble, Norine Hall, Carl F. Pieper, and Virginia B.
Kraus, “Patellar Skin Surface Temperature by Thermography
Reflects Knee Osteoarthritis Severity,” Departments ofMedicine, pp. 69-75, October 2010.
[7] F. Essannouni, R. Oulad Haj Thami, D. Aboutajdine, and A.
Salam, “Simple noncircular correlation method for
exhaustive sum square difference matching,” OpticalEngineering, vol. 46, no. 10, October 2007.
[8] G. Schaefer, R. Tait, and S. Y. Zhu, “Overlay of thermal andvisual medical images using skin detection and image
registration,” Int. Conf. of Engineering in Medicine andBiology Society, 2006. EMBS '06, New York City, USA, pp.
965-967, 2006.

Table 1. Differences after Exercise
Arthritic
WOMAC(pain intensity)
Temperature before exercise(left/right)
Temperature after exercise(left/right)

First Detection
12.66

Second Detection
10.92

32.44/33.85

30.78/32.92

31.93/33.84

30.48/32.80

Table 2. Characteristics of the study participants

1066.8±14.2

Without
Degenerative
Arthritis10
20.8±3.2

32.44

28.41

31.93

28.30

33.85

29.35

33.84

29.28

Grade Ⅰ-Ⅱ

Grade 0Degenerative
Arthritis
Quantity
Age
Average temperature before
exercise (left foot)Average temperature before
exercise (right foot)Average temperature after
exercise (left foot)Average temperature after
exercise (right foot)
Grade

Table 3. Analysis of System auxiliary Discriminant
Temperature
29.35
30.19
33.17
33.85

© ISAROB 2013

Probability ofPatient
5.20e-06%
1.64e-04%
25.30%
50.12%

Probability of
Healthy Person
47.46%
24.21%
0.16%
0.03%

Normal distribution of subjects

Cross Error Rate
5.20e-06%
1.64e-04%
0.16%
0.03%

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Enhancing electrical communication through reconstructed nanowire
electrodes for implantable enzymatic biofuel cell (EBFC)
Deby Fapyane1, Soo-Jin Lee1, Duhyun Lim2, Jou-Hyeon Ahn2 and In Seop Chang1
1School of Environment Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdangwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea; Telephone: 82-62-715-3278
2
Department of Chemical and Biological Engineering, Gyeongsang National University, Jinju 660-701, Republic of Korea
Corresponding author: ischang@gist.ac.kr
Abstract: Enzymatic biofuel cell (EBFC) is a promising continuous energy supply powered by abundant and essential
components in human physiological fluid; glucose and oxygen, for powering implanted device. Important aspects in BFC areestablishing efficient electron transfer pathways between enzyme redox center and electrodes and maintaining enzyme stability.
In this study we characterized of electron transport from enzyme as core of BFC to the nanowire by edge-immobilization (EI)and sidewall-immobilization (SI). Through immobilization in both aspects of nanowire surface, electrical transport throughedge is responsible for efficiency meanwhile sidewall immobilization benefits for enzyme stacking compartment. We alsostudied the functional group exist in nanowire may act as bridge to individual nanowire. Nanowire which is lacking of EWG
diminished electron transport enhancement between C-C bonds through resistance barrier. Further study which factor limitingand enhancing electrical transport through pristine and constructed carbon nanotube is still being assessed.
Keywords: Implantable EBFC, carbon nanotube network, electrical communication, reconstructed nanowire

1 INTRODUCTION
accompanied by high charge transfer resistance (Wang2006) and electrolyte diffusion limitation due to its
hydrophobicity. Hence, optimization of nanowire condition
is practically needed to enhance electron transfer efficiency.
In this study we assessed the electrical network
through CNTs in the EBFC system. Nanowire network arebuilt in the edge-edge (E-E), edge-sidewall (E-S) andsidewall-sidewall (S-S) to support EBFC performance.
Utilization of pristine carbon nanotube (CNTs) with its
native high electrical conductivity needs to be improved toachieve maximal use of nanowire network throughreconstructed
electrical
networkwithchemicalfunctionalization. Acid treatment creates defect sites for
activating –COOH functional groups in carbon nanotube toenhance electron transport from and to individual nanowire
as –COOH is an electron withdrawing group (EWG). We
also assessed the characteristic of electron transport fromenzyme (glucose oxidase, GOX) as core of BFC to thenanowire as supporting material to avoid ambiguousinterpretation of over-potential occurrence during electrontransport.

Enzymatic biofuel cell (EBFC) is a system which
generates electrical power from conversion of substrate
through redov reaction in the enzyme redox center ((Mano,
Mao et al. 2003; Soukharev, Mano et al. 2004; Cracknell,
Vincent et al. 2008; Moehlenbrock and Minteer 2008).
Being able to generate micropower, EBFC has beendrawing out the attention for real application and developed
the power source for implanted devices (MacVittie,
Halamek et al. 2012) by using human essential compound;glucose and oxygen. As near future promising power source,
EBFC is expected to be able to power developed implanted
devices such as pacemakers, neuron stimulator andimplanted neurophysiological monitoring devices (Justin,
Yingze et al. 2004; MacVittie, Halamek et al. 2012).
Before Heller and Cosnier’s group conducting
the real application of implanted enzymatic in plant andanimal, EBFC was less attractive due to its low voltage
which thermodynamically limited by the potential of
enzymes substrate (MacVittie, Halamek et al. 2012). Basedon the EBFC main limitation, many studies had been
conducted to overcome the critical points of EBFC which isthe communication between enzyme and electrode (Ferreira,
Iost et al. 2012). Achieving the high current and power
density of EBFC is the main objective of the electrodes
construction.
Conductive nanowires have been studied as amatrix for electrode architecture of EBFC. Nanowire suchas carbon nanotubes (CNTs) are able to act as enhancement
factor of electrical communication between enzyme andelectrode superficial (Ferreira, Iost et al. 2012) and also as
enzyme stacking compartment due to its large surface area.
However, like other nanoparticle characteristic, CNTs also
carry concerning drawback as electrode matrix where it is© ISAROB 2013

Scheme1. Near future application of implanted BFC in
human powering implanted medical devices

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2. MATERIAL AND METHOD

0.00060.00100.00052.1. Material
Glucose oxidase from Aspergillus niger (typeVII-S, 192 U/mg). Laccase from Trametes versicolor (13.6
U/mg), β-D Glucose, 1-pyrenebutanoic succinimidyl ester,N-(-3-dimethylaminoprophyl) N’-ethylcarbodiimide, 2thioethanol, cystamine sodium diphosphate and disodium
phosphate and Si/Au wafer as substrate from Sigma Aldrich.
Pristine, purified and acid treated multi walled carbon
nanotube (MWNTs) was provided from Gyeongsan
National University, Gyeongsan, South Korea. Phosphatebuffer solution (PBS, 100 mM, pH 7.0) was prepared as
supporting electrolyte for BFC performance. D-glucose
solutions (10mM) prepared at least a night before to allow
mutarational equilibrium. All solutions were prepared with
deionized water.

current,Acurrent, A

0.00030.00060.00040.00020.00020.00010.00000.0000
-1.0

-0.8

-0.6

-0.4

-0.2

0.0

-1.0

-0.8

potential, V

-0.6

-0.4

-0.2

0.0

potential, V

Fig. 1. Cyclic voltammograph of vertically (left) andhorizontally (right) aligned CNT for GOX immobilization
3.2. XPS profile
Each immobilization method was evaluatedthrough XPS which showed the emerge peak of C=O
bonding in the 286.25 eV for horizontally aligned CNTs
showing carbonyl groups mainly present in the edge of
CNTs which will react with amine group of enzyme. In theother hand, we can observe increase of π-π* stacking pointin the 291 eV or also called satellite peak as the result ofheterobifunctional polymer use for sidewall modification

2.2. Method
Characterization of enzyme immobilization as
Edge-immobilization (EI) and sidewall-immobilization (SI)were constructed by covalent bonding and utilizing
heterobifunctional
polymer,
respectively.
Eachimmobilization method was evaluated through XPS(ThermoFisherScientific,
U.K)
spectroscopy.
Characterization of each CNT material was done by FTIR(JASCO, U.S.A), Raman spectroscopy (Andor, U.S.A) and
XPS. All electrochemical measurements for EBFC
performance were performed in PBS buffer at 37oC.
Electrochemical method was performed using
three electrodes system (BAS-EC epsilon, U.S.A) consistsof Si/Au wafer, platinum and Ag/AgCl as working, counterand reference electrode respectively. Anode (CNT-GOX)
and cathode (CNT-laccase) composites were placed in one
membraneless compartment biofuel cell. Stirring andaeration was done for cathode reaction during performance
test with electrolyte flow 0.1mL/min. A surface area of
0.196 cm2 used for calculation of the current and power
density.

Fig. 2. XPS spectra of vertically (left) and horizontally(right) aligned CNTs on Si/Au wafer
3.3. Reconstructed nanowire with functionalization
Nanowire network are built in the edge-edge (EE), edge-sidewall (E-S) and sidewall-sidewall (S-S) to
support EBFC performance. Utilization of pristine carbon
nanotube (CNTs) with its native high electrical conductivity
needs to be improved to achieve maximal use of nanowire
network through reconstructed electrical network withchemical functionalization.
Acid treatment creates defect sites for activating –COOH functional groups in carbon nanotube to enhanceelectron transport from and to individual nanowire as –COOH is an electron withdrawing group (EWG). As
expected, higher current was achieved for higher degree offunctionalized nanowire approximately increased 50%
every 4h of strong acid treatment (Fig. 3)

3. RESULT AND DISCUSSION
3.1. Vertically and horizontally aligned CNTs
Direct electron transport can be achieved by enzyme
immobilization in the edge of CNT shown at potential of
FAD as glucose oxidase redox center, which was -0.4 V(against Ag/AgCl). EI enzyme wiring achieved electrontransfer with Ks value of 1620.5/s whereas SI enzyme Ks is953.6/s. Furthermore, SI enzyme wiring contributed to theover-potential happened in the electron transport fromredox center to the electrode characterized by cathodic peakat -0.3 V instead of -0.4 V (Fig.1).
However, SI produced 1.5 times higher current EI
indicating, more load of enzyme in the sidewall than in theedge which can be confirmed through nanowire diameterlength ratio. Through immobilization in both aspects ofnanowire surface, electrical transport through edge is
responsibleforefficiency
meanwhile
sidewallimmobilization benefits for enzyme stacking compartment© ISAROB 2013

0.00080.00040.000100.00005

Current (A)0.00000-0.00005-0.00010-0.00015

GCS 8 hGCS 4 hGCS 2 h-0.00020-0.00025
-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Potential (V)Fig. 3. Current enhancement after acid treatment

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3.4. Performance of EBFC
Constructed network of CNT disk increased
current and power density of BFC. By utilizing 4800 U of
glucose oxidase, constructed nanowire current density was
863.00±49.33 µA/cm2 while non-constructed network
showed 67% of performance which was 583.58±49 µA/cm2.
Fig. 4. showed the performance of reconstructed andpristine CNTs-enzyme in EBFC. Reconstructed nanowire
resulted 30% higher power density compared with pristine
CNTs composites.

5. REFERENCES[1] Mano, N., F. Mao, and A. Heller, Characteristics
of a miniature compartment-less glucose-O2
biofuel cell and its operation in a living plant.
Journal of the American Chemical Society, 2003.
125(21): p. 6588-6594.
[2] Soukharev, V., N. Mano, and A. Heller, A fourelectron O2-electroreduction biocatalyst superior
to platinum and a biofuel cell operating at 0.88 V.
Journal of the American Chemical Society, 2004.
126(27): p. 8368-8369.
[3] Moehlenbrock, M.J. and S.D. Minteer, Extended
lifetime biofuel cells. Chemical Society Reviews,
2008. 37(6): p. 1188-1196.
[4] Cracknell, J.A., K.A. Vincent, and F.A.
Armstrong, Enzymes as working or inspirational
electrocatalysts for fuel cells and electrolysis.
ChemInform, 2008. 39(41): p. no-no.
[5] MacVittie, K., et al., From "Cyborg" Lobsters toa Pacemaker Powered by Implantable Biofuel
Cells. Energy & Environmental Science, 2012.
[6] Justin, G.A., et al. Biofuel cells: a possible powersource for implantable electronic devices. in
Engineering in Medicine and Biology Society,2004. IEMBS '04. 26th Annual International
Conference of the IEEE. 2004.
[7] Ferreira, F., et al., Intravenous Implantable
Glucose/Dioxygen Biofuel Cell with Modified
Flexible Carbon Fiber Electrodes. Lab on a Chip,
2012.
[8] Wang, P., Nanoscale biocatalyst systems. Current
Opinion in Biotechnology, 2006. 17(6): p. 574579.

Power density (µW/cm 2)

500

400

300

200

100

0

0

200

400

600

800

1000

Current density (µA/cm2)

Fig. 4. I-V curve of reconstructed CNTs-GOX (red) andpristine CNTs-GOX anode composites operated in EBFC
system with reconstructed CNTs-Laccase as cathode

Measurement of impedance by EIS revealed that
non-functionalized CNT disk has less conductivity (3mmthick; 15,000 Ω) by higher resistance than functionalized
CNT disk (3 mm thick; 9,000Ω). We suggest that functionalgroup acts as bridge to individual nanowire which islacking of EWG diminished electron transport enhancement
between C-C bonds in CNT junction through resistancebarrier. Advance assessment for the factor which limitingand enhancing electrical transport through pristine andconstructed carbon nanotube is still being assessed.
4. CONCLUSION
We successfully examined the effect of enzyme
stacking site to nanowire interface. Edge of CNTs is more
active to enhance the electrical communication rather thansidewall. However, sidewall of CNTs is important site forenzyme stacking compartment.
Reconstructed nanowire by functionalization to
make edge-like site in the sidewall will accommodate theenhancement of electrical communication by minimizing
resistance between nanowire interfaces
Further factor relating to EBFC operation which
affects composites performance such as electrolyte
diffusion barrier and its chance to decrease the electrontransfer is still being assessed.

© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Object Tracking Based on Adaboost Classifier and Particle Filter
Chin-Lun Lai1 , Li-Yin Lee2
1,2

Communication Engineering Department, Oriental Institute of Technology, Taipei, Taiwan
1

2

fo001@mail.oit.edu.tw
990130108@mail.oit.edu.tw

Abstract: In this paper, a hybrid structure combining adaboost classifier and particle filter is proposed toautomatically detect and track the pedestrian targets. The adaboost detection process is adopted first to target
candidate objects, and then the particle filter is applied for confirming and tracking of targets. Experiment results
show that via the proposed method, the drawback of the current particle filters which requires specifying an object tobe tracked in advance can be overcome, while performing good also in cases of target missing, occlusion, andidentifying the previously appeared objects.
Keywords：object tracking, adaboost detection, particle filter

1 INTRODUCTION

locations of the target are preset[2]. However, adding
the adaboost algorithm helps to solve this issue [3].
Therefore, this study proposed a combined structurewith adaboost detection and particle filtering method toresolve the problems mentioned above for thepedestrian tracking problems.

Application of object tracking has always been an
important issue in computer vision or image processing
applications. In the early stages, object tracking had
been applied to air traffic control. Recently, it has often
been applied to with security monitoring related fields.
There are various types of methods for object tracking.
Generally, these methods can be divided into time
domain methods and space domain methods. Timedomain methods include such as motion estimation[1]or optical flow, etc., while space domain methods
include k-means, mean-shift, kalman filter, and particle
filters [2].
In a time domain system, the target must be able toshow time differences. In other words, the target has tomove so that judgments can be made. In a space domain
system, judgments are made based on image
characteristics of the targets. And usually judging
methods based on characteristic information are more
complex and diversified.
However, if only a time domain method is applied,the only thing that can be confirmed is that whether thetarget is moving or not. It is difficult to find out if thistarget is the interest one. Methods like k-means andmean-shift require manual settings and object sizechanges are not allowed. Kalman filter is used for linear
systems thus is not appropriate for real-world systems
which are usually no-linear systems. On the other hand,particle filter is a good solution for target tracking but isonly applicable when the scene is known and possible© ISAROB 2013

2 SYSTEM FRAMEWORKThe proposed system framework is shown in figure
1 and the detailed steps are described as follows:
Import next image

Adaboost detection
Motion Estimation

Adaboost & Motion Estimation
position target

Particle Filter tracking

Compare PDF to determine
object state

Object position correction
Fig. 1. system framework

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2.1 Adaboost detection
To improve the prediction rate and reduce the errorrate, four adaboost cascades were combined to form theinitial conditions for pedestrian detection. The types ofthe four cascades were upper body, lower body, front
side of whole body, and back side of whole body. Allfour cascades were objects or parts of objects, resolving
the issue of imprecise detection with only one cascade.
The training sequences included 2094 positive
samples and 1333 negative samples. The width andheight were 10 and 24 (pels), using all upright rectangle
feature and 45 rotated rectangle feature [4].
Two of the four cascades were used to detect upper
body and lower body. Therefore, whole bodynormalization was required. An upper body includes
head and shoulders. Thus, a bounding box may containhead and shoulders. And in the cascade training, the size
was 22x20, with the shape being close to a square.
Since humans’ upper bodies are rectangles, it isnecessary to normalize bounding boxes for upper bodies
as follows:
BBoxup(x) = upbody(x) +3

upbody(w)BBoxup(w) = × upbody(w)44BBoxup(h) = √2 × upbody(h)range of an object.
2.3 Adaboost & motion estimation
After applying motion estimation to the normalizedbounding boxes, the areas of the bounding boxes arecalculated. The bounding boxes of small sizes wereconsidered as noises, while the centers of those whichwere not noises were obtained.
With a given size threshold, the bounding boxes
with areas over the average were removed. Among thebounding boxes left, the one with the smallest area wasconsidered as the most possible target. Then, target
positioning was performed with the most possible
bounding box. And the positioning information,
including the center and the range of the target, was sent
to the particle filter.
2.4 Particle filter tracking
Ones the object was confirmed, the particles of theparticle filter are initialized using the positioning
information of the object to perform particle filter
tracking[2]. The re-sampling method adopted was thesystematic method[5]. The new model was based on the
average of the two previous models.
After that, the PDF of the target at t-1 was comparedwith that at t through the corresponding correlation
coefficient. If the difference was too large, the target
was considered disappeared or lost. In that case,adaboost was applied again for re-positioning.

(1a)
(1b)
(1c)

where BBoxup is the normalized bounding box andupbody is the bounding box detected using adaboost.
Parameters for upbody, (x, y, w, h), represent thex-coordinate, y-coordinate, width, and height,
respectively.
Similarly, as for the lower body training, the size
was 19x23. Thus whole body normalization was
performed by (2) as follows.

2.5 Adaboost & particle filter correction
In order to prevent particle distribution from being
too scattered due to large changes of PDF of the target,
adjustments needed to be made based on the adaboost
detection results when applying particle filter. In
continuous images of the target, the functions of the size
and the distance were supposed to be continuous.
Therefore, by referencing continuous sizes anddistances from adaboost and particle filter, targets ofabnormal sizes were removed and targets of large
distances were repositioned according to corresponding
adaboost information while particle filter was
reinitialized.

1

BBoxlow(y) = lowbody(y) − lowbody(h) (2a)2BBoxdown(h) = √2 × lowbody(h)(2b)

where BBoxlow is the normalized bounding box andlowbody is the bounding box detected using adaboost.

3 EXPERIMENTAL RESULTS ANDDISCUSSIONS

2.2 Motion estimation
By adopting adaboost, many candidate targets were
found hence further filtering was required to remove themisjudged ones. It can be done by adding simple motion
estimation process. First, binary threshold for threecontinuous images was calculated. And thenmorphology erosion and closing, with 3x3 and 5x5
masks, were applied to these 3 images to obtain a binary
image using OR gate, in order to determine the moving© ISAROB 2013

Two detection results with different scenes areshown in figure 2. It is noted that, in figure 2(a)(c),squares with different colors represent the adaboost
detection results corresponding to different training
database, where red, green, blue, and cyan squares
resulted from upper bodies, lower bodies, front/back ofwhole body, and sideways of whole body respectively.
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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

In figure 2(b)(d), the red squares are generated from (a)and (c) after filtering, while the blue points and green
ellipses represent the particles and average statuses ofparticles respectively.

a

b

c

d

rate is shown in Table 1 to illustrate the efficiency
differences between the proposed method and thecurrent tracking methods. The detection rate is definedas: the number of successful tracking of the object in the
507 images (there’re a total of 701 images from thevideos for the two testing scenes, and after the ones
with the target disappearing were removed, a total of
507 images were left).
Table 1. The detection rate and false alarm rate ofcompared algorithms in test scenarios
Detection
False alarm
Methodrate
rate
Adaboost[3]0.40650.1572
Motion0.53410
Estimation[1]Adaboost &0.57560.0356
Motion Estimation
particle filter[2]0.75660.2433
proposed method
0.91390.1483Fig. 2.(a)(c) original image with adaboost
detection results, and (b)(d): thecorresponding final detection result andtarget location of particles.

According to Table 1, compared with otheralgorithms, the detection rate of the proposed method
was higher and the false alarm rate was lower. However,its performances in other experiment scenes weren’t allas outstanding. For example, in a scene with complex
environment and weak lights, as shown in figure 5,
tracking was not possible. The reason was the high error
rate and low accuracy of adaboost, making successful
positioning impossible. In the future, before adaboost isapplied, preprocessing using histogram equalization canbe done to reduce issues caused by weak lights and low
contrast. In addition, by increasing the number ofnegative sample for cascade training, more features
which don’t belong to the object can be learned to breakthe limitation of this system.

Figure 3 shows the number of detection and that offalse alarm for each method in scene1.

Fig. 3. Detection and false alarm results of comparedalgorithm in test scene 1.
Figure 4 shows the number of detection and that offalse alarm for each method in scene2.

Fig. 4. Detection and false alarm results of comparedalgorithm in test scene 2.

a b
c d

The statistical result of the detection and false alarm© ISAROB 2013

599

Fig. 5. Failure positioning examples. (a)(b)
low contrast conditions, and (c)(d) complex
environment. In both cases the adaboost
results low detection rate and high false
alarm.

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

4 CONCLUSIONS
An efficient and effective pedestrian tracking
method is proposed to overcome the drawbacks of theexisting methods based on particle filter. With adaboost
appended, the positioning information can be obtainedin advance for particle filter initiation while the tracking
results can be adjusted automatically. Via this structure
the limitation of having to specifying target positions inadvance manually is broken and it also solved issues
such as object disappearing, object being masked, formchanges, and object re-appearing. Therefore, thismethod can be applied in various scenes thus can be
widely applied in different areas with high efficiency.
Like other current methods, the issue of bad
performances in detection and tracking in a complex
environment still exists with the object tracking method
proposed by this study. In the future, we will add apreprocessing step of image enhancement before target
detection and increase the number of negative samples
in the sample for cascade training, to solve the issue
above, so this system can be applied more widely withefficiency.

REFERENCES[1] Saxena V., Grover S., and Joshi S.(2008), A realtime face tracking system using rank deficient facedetection and motion estimation. Cybernetic Intelligent
Systems, 2008, pp.1-6[2] Katja Nummiaro, Esther Koller-Meier, and Luc
Van Gool.(2002), An Adaptive Color-Based Particle
Filter”, Image and Vision Computing, 2002, pp.1-22[3] Viola, P., and Jones M.(2001), Robust Real-Time
Face Detection. Computer Vision, ICCV 2001. vol 2,
2001, pp. 137-154[4] Lienhart R., and Maydt J.(2002) An Extended Set
of Haar-like Features for Rapid Object Detection”,
Image Processing, vol. 1, 2002, pp.I-900 - I-903[5] Gordon N, Salmo D J, and Smith A F M(1993),
Novel approach to nonlinear/ non-Gaussian Bayesianstate estimation, IEE Proceedings F Radar and Signal
Processing, 1993, pp.107-113© ISAROB 2013

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A metaheuristic for huge scale quadratic assignment problems
Hatsumi Nakaura1, Fubito Toyama1, Hiroshi Mori1, Kenji Shoji1, and Juichi Miyamichi1
1

Graduate School of Engineering, Utsunomiya University, Japan(Tel: 81-28-689-6244, Fax: 81-28-689-6244)
1

hatsumi.nakaura@gmail.com

Abstract: The quadratic assignment problem (QAP) is one of the most difficult problems in the NP-hard class. Many
metaheuristics have been proposed for the QAP. To evaluate the performance of these algorithms, QAPLIB which is a library
of QAP instances is used. But QAPLIB does not have large scale QAP instances. QAPLIB cannot be used to evaluate theperformance for large scale instances. Thus, these metaheuristics have been only applied to small scale QAP instances. It isdifficult to apply these algorithms to large scale QAP instances because the number of combinations is huge. In this paper, we
propose a metaheuristic approach for large scale QAP. The computational results showed that the proposed method
outperformed conventional methods for huge scale QAP instances.
Keywords: Combinatorial optimization, Metaheuristics, Quadratic Assignment Problem, Tabu searchneighborhood solutions. If the problem size n is small, thefirst calculation of evaluation values of neighborhood
solutions does not consume much time. But if the problem
size n is huge, its calculation consumes huge time.
Therefore it is hard to apply 2-opt local search to huge scale
QAP instances. On the other hand, Genetic algorithms (GA)are often used for the QAP. GA works effectively for the
problems in which the size of instances, n, is around 100.
However, it is hard to apply GA to huge scale QAP
instances (n is over 1000) because it takes too much timefor GA to converge. Moreover, in order to improve searchability, most algorithms based on GA use hybrid GA(Memetic algorithm [5]) with 2-opt local search [6].
Therefore, GA cannot be applied for huge scale QAP
because it is difficult even to apply 2-opt local search.
In this paper, a metaheuristic approach is proposed forthe huge scale QAP. Robust Tabu Search (RoTS) [7] andthe construction of difference value list are performed
simultaneously in the proposed method. The construction ofdifference value list means to calculate evaluation values of
the neighborhood solutions. Moving current solution is
generally performed after the construction of the difference
value list. But it is performed using the difference value list
which is not constructed completely in the proposed
method. The difference value list is constructed graduallywhile RoTS is performed. The proposed method can search
for a relatively good solution in short time. Thus, it appears
to be an appropriate algorithm for huge scale QAP
instances.
In our experiments, the proposed method was compared
to a fast local search and RoTS. The problem size n used inour experiments was set from 1000 to 10000. The

1 INTRODUCTION
The quadratic assignment problem (QAP) [1] is one of
the most difficult problems in the NP-hard class [2]. In the
QAP, n facilities have to be assigned to n locations at
minimum cost when a n×n distance matrix and a n×n flow
matrix are given. A solution is generally defined as a
permutation of {1, 2, ..., n}, and the evaluation value of thesolution is calculated using the two matrices. QAP has alarge number of applications, including printed circuitdesign, facility layout, network design, and others.
Many metaheuristics have been proposed for the QAP[3][4]. To evaluate the performance of these algorithms,
QAPLIB which is a library of QAP instances is used. But
QAPLIB does not have large scale QAP instances. QAPLIB
cannot be used to evaluate the performance for large scaleinstances. Thus, these metaheuristics have been only
applied to small scale QAP instances. It is difficult to apply
these algorithms to large scale QAP instances because thenumber of combinations is huge. 2-opt local search whichuses the best improvement strategy is incorporated into themost of previous methods. 2-opt local search finds a better
solution in a neighborhood of a current solution. The
neighborhood is defined as the set of all solutions that canbe reached from the current solution by swapping twoelements in a permutation. Therefore, the number ofsolutions in the set of neighborhood is n(n-1)/2, where nrepresents the size of the problem. The computational costof calculating neighborhood is O(n2). But after once
evaluation values of the neighborhood are calculated, thecomputational cost of it can reduce to O(n) by thedifference value list between the current solution and the© ISAROB 2013

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swapping two elements i and j in the current solution. At
the beginning of a search, any gain values have not been
calculated. Therefore the elements of the difference value
list do not have the value. In the construction step, if swap
elements 1 and 2 are first selected to obtain a neighborhood
solution, gain value δ1,2 is calculated. Then the difference
value list is shown as follows.

computational results showed that the proposed method
outperformed the local search and RoTS for huge scale
QAP instances.

2 QUADRATIC ASSIGNMENT PROBLEM

⎡*
⎢*
∆=⎢
⎢*
⎢
⎣*

The QAP [7] is one of the most difficult problems in the
NP-hard class. In the QAP, n facilities have to be assigned
to n locations at minimum cost when a n × n distancematrix, and a n × n flow matrix are given. A solution is
generally defined as a permutation of {1, 2, …, n}, and theevaluation value of the solution is calculated using the two
matrices as follows:n(1)

i =1 j =1

where π is a permutation of {1, 2, …, n}, aij is an elementof flow matrix and bkl is an element of distance matrix. The
element aij represents the flow quantity between thefacilities i and j. The element bkl represents the distance
between the locations k and l. If the facilities i and j areassigned to locations k and l, the part of permutation will be
π(i)=k, π(j)=l. If π is the permutation that minimizes theobjective expression (1), it is the optimum solution of the
problem.
The most of benchmark instances of QAP are shown in
QAPLIB. The largest instance in QAPLIB is tai256c inwhich the size n is 256. We set the size n from 1000 to
10000 for our experiments as larger instances than
QAPLIB’s.

3 PROPOSED ALGORITHM
The proposed algorithm is composed of two steps; theconstruction of the difference value list and Robust Tabu
Search (RoTS). In the construction step, a gain between
current solution and a 2-opt neighborhood solution which is
randomly selected is calculated. Then its value is assigned
to an element of the difference value list. 2-opt
neighborhood is defined as a solution obtained by swapping
two elements in a current solution. Therefore, in 2-opt
neighborhoods, the number of neighborhood solutions isn(n-1)/2 (n: size of the problem). The difference value list
(gain list) is defined as follows:⎡* δ 0,1 δ 0, 2 δ 0,3 ⎤⎢* * δ
δ1, 3 ⎥⎥
1, 2
(2)
∆=⎢
⎢* *
* δ 2,3 ⎥
⎢
⎥
*
* ⎦
⎣* *
where δi,j represents a gain value obtained by subtracting
the evaluation value of a current solution from theevaluation value of a neighborhood solution obtained by© ISAROB 2013

*

*

*⎤
*⎥⎥
*⎥
⎥
*⎦

(3)

Once a gain was assigned to the difference value list, thesame two swap elements are not selected for the assignment
process. When a current solution moves to a neighborhood
solution, the assigned gain value is updated in the RoTS
step. The computational cost required to calculate a gain isO(n) and the cost of update a gain is O(1).
The RoTS step is executed only if the assigned gain wasa negative value which means the exchange makes current
solution better. In the RoTS step, current solution moves to
2-opt neighborhoods within the limit of the difference value
list. The tabu tenure which is the parameter of the RoTS ischanged according to the number of assigned elements inthe difference value list.
The pseudocode for the proposed method is shown in
Fig. 1. n in Fig. 1 is the size of a problem, π is apermutation of current solution, πbest is a permutation of thebest solution found so far, P is a set of pairs of elements
which indicates the position of a permutation, and Δ is thedifference value list: When p={i,j}, Δ(p)=δi,j. Afterinitializing P at the first double loop (lines 4– 8 in Fig. 1.),
P becomes as follows:
P = {{0,1},{0,2},...,{0, n},...,{n − 1, n}} (4)The while loop between line 9 – 21 of Fig.1 is the main
loop. The construction of the difference value list is
performed in the first step of the main loop (line 10– 14).
The RoTS is applied in the next step (line 15 – 20). The
construction of the difference value list and RoTS are
repeated until some criterion is satisfied. In the construction
step, first, a pair p={i,j} is selected randomly from the set P.
Then the gain value, δi,j is calculated by SwapGain(π, p). In
SwapGain(π, p), a difference value (gain value) between
current solution and a 2-opt neighborhood solution obtainedby swapping i and j of current solution is calculated. This
gain value is assigned to the difference value list Δ. Then,the selected pair p is eliminated from the set P. The RoTS is
performed if Δ(p)<0 or P=∅. Otherwise the performance of
the RoTS is skipped. Δ(p)<0 means the evaluation value ofthe neighborhood solution obtained by swapping pair p isbetter than current evaluation value. This skipping strategy
can make the current solution better. The other condition to
perform the RoTS, P=∅, means the difference value list is
constructed completely. Therefore, after the difference valuen

F (π ) = ∑ ∑ a ij bπ ( i )π ( j )* ** δ1, 2
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list was completed, the proposed method works the same as theRoTS. When the termination criterion was satisfied, the best

RoTS is always executed after a gain value is assigned tothe difference value list (even though Δ(p)>0). We call thismethod “Proposed Algorithm (unconditional)’’. In theexperiment, the parameters of RoTS were the same as thoseused in the literature [7].

solution, πbest, found in the search is returned.
Procedure Proposed Algorithm
begin
1: generate a random solution π
2: πbest := π;
3: P := ∅;4: for i := 1 to n－1 do
5:for j := i+1 to n do
6:
P := P∪{{i,j}};7:
endfor8: endfor
9: while (termination criterion not satisfied) do
10:
if P≠∅ then11:
select p from P randomly;
12:Δ(p) := SwapGain(π, p);
13:
P := P－{p};14: endif15:
if Δ(p)<0 or P=∅ then16:
π := TabuSearch(π, Δ);17:
if π<πbest then18:
πbest := π;
19:
endif
20:
endif
21: endwhile
22: return πbest;
end
Fig. 1. Process of proposed algorithm

Fig. 2. Variation in evaluation value of best solution for
n=5000 instance

4 EXPERIMENTAL RESULTS
To evaluate the performance of the proposed method,
we compare the performance of the proposed method withthose of other methods on huge scale QAP instances. The
size of huge instances used in our experiments was set from1000 to 10000. The flow matrix and the distance matrix
were asymmetric and their diagonals had non-zero elements.
Values of these matrices were decided randomly between 0
and 99. Simple RoTS and 2-opt Local Search (2-opt-F)
which uses first improvement strategy were applied tocompare the performance. In simple RoTS, the complete
difference value list is constructed at the beginning ofsearch. In 2-opt Local Search using first improvement
strategy, a current solution moves to 2-opt neighborhood
obtained randomly if its gain value is better than that ofcurrent solution. The 2-opt-F does not make difference
value list. Thus it does not consume much time to construct
the difference value list and the convergence speed is very
fast. A proposed algorithm without any conditions to
execute RoTS is also tested for comparison of theperformance of the proposed algorithm. In this method,© ISAROB 2013

Fig. 3. Variation in evaluation value of best solution for
n=8000 instanceFirst solutions are all the same for the methods on each
instance. The experiment is executed on the same computer
whose CPU is Intel Xeon X5650 2.67GHz and main
memory has 64GB capacity. We used gcc4.4.6 as thecompiler with –O3 option.
The results are shown in Table 1. n shows a size of
instances, Time shows the the maximum run time, Bestvalue shows the best evaluation values from all of 4
methods. “Deviation from best value” defined as the resultof subtracting the “best value” from the evaluation values
obtained by each method. Lower values are better thanhigher values, and 0 is the best value of 4 methods. As
shown in Table 1, the proposed algorithm shows the best
performance in 7 instances. Therefore the proposed
algorithm is definitely very effective method for the hugescale QAP instances.
Fig. 2 and Fig. 3 show the changes in the evaluation
value of the best solution against processing time for the603

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Table 1. Performance comparison to other methods
Deviation from best value
Proposed
Proposed
2-opt-FBest value
Algorithm
Algorithm(unconditional)

Time [h]

1000
2000
3000
4000

1
2
3
4

2376195206
9577858020
21657258081
38593328252

0
3.98×10400

8.34×106
2.57×107
5.94×107
8.90×107

7.68×106
2.42×107
4.84×107
6.54×107

6.14×105
0
6.73×106
8.37×1065000

560438065514

01.01×108

6.81×107

1.70×1078

7

6.43×1076000

687132554267

0

1.33×10

7.50×107000

7118734259691

0

1.30×108

8.57×107

1.41×1088000

8155253875423

01.22×108

3.03×107

3.12×1089000

9196707554616

4.32×107

1.14×1080

6.40×108242888642072

8

8

0

9.72×108

10000

10

1.05×10

Table 2. Time to complete the difference value listsnTime [h]Time rate [%]
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000

9.20×10-3
0.109
0.294
0.736
1.37
2.36
3.81
5.96
-

1.74×102-opt selection and Robust Tabu Search. Experimental
results show the effectivities of the proposed algorithm forhuge scale QAP instances. In order to use proposed
algorithm effectively, the conditions to execute RoTS are
required.

0.920
5.44
9.78
18.4
27.5
39.3
54.4
74.5
-

REFERENCES[1] Koopmans TC, Beckman M. (1957), Assignment
problems and the location of economic activities.
Econometric 25:53-76[2] S. Sahni, T. Gonzalez (1976), P-Complete
Approximation Problems. Journal of the ACM 23(3):555565[3] A. S. Ramkumar, S. G. Ponnambalam, N. Jawahar etal (2008), Iterated fast local search algorithm for solving
quadratic assignment problems. Robotics and ComputerIntegrated Manufacturing 24:392-401[4] Nilgun Fescioglu-Unver, Mieczyslaw M. Kokar(2011), Self Controlling Tabu Search algorithm for the
Quadratic Assignment Problem. Computers & Industrial
Engineering 60:310-319[5] Moscato P. (2002), Memetic Algorithms. In:
Handbook of Applied Optimization, P. M. Pardalos and M.
G. C. Resende (eds), Oxford University Press, New York,
pp. 157-168[6] P. Merz, B. Freisleben (2000), Fitness landscapeanalysis and memetic algorithms for the quadratic
assignment problem. IEEE Transactions on Evolutionary
Computation 4:337-352[7] E. D. Taillard (1991), Robust taboo search for thequadratic assignment problem. Parallel Computing 17:443455

instances of n=5000 and n=8000, respectively. The
horizontal axis represents the processing time and thevertical axis represents the evaluation value for the bestsolution obtained with each method. As shown in Fig. 2 andFig. 3, the proposed method seems to find good solutions inshorter times than the previous method and theunconditional proposed method. The above results
confirmed that the condition (Δ(p)<0) to execute RoTS is
an important factor to search huge QAP efficiently.
The time to complete the difference value lists on the
proposed method is shown in Table 2. Time rate means theratio of completion time to whole search time. On the
instances of n=9000 and n=10000, the difference value lists
were not completed until the ends of searches.

5 CONCLUSION
In this paper, we proposed an algorithm for huge scale
QAP instances. The proposed algorithm is based on random© ISAROB 2013

RoTS

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Competition-based Particle Swarm Optimizer for Solving Numerical
Optimization Problems
Sheng-Ta Hsieh and Bo-Han Chen
Department of Communication Engineering, Oriental Institute of Technology, Taiwan(Tel: +886-2-7 738-0145 Ext. 2325, Fax: +886-2-7738-7411)
fo013@mail.oit.edu.tw
Abstract: In this paper, a new variant of particle swarm optimizer is proposed for solving numerical optimization problems.
The main difference between proposed method and common PSO is the swarm structure. In general, the PSO has only one
swam and each particle of the swarm will share their information for guiding other particles toward to potential solution space.
The proposed method is to separate swarm into two sub-swarm. The size of two sub-swarms will be adjusted according to theirperformance. It can drive increased the diversity of the particles and prevent particle to fall into the local optimum. For testing
the performance of proposed method, fifteen of CEC 2005 test functions were selected for experiments. From the result, it canbe observed that the proposed method performs better than SPSO2011.
Keywords: computational intelligence, optimization, particle swarm optimizer, population.
simply, and reduce computational consumption. But is will
also decrease particles’ ability for deep search. Somemethods were focus on parameters adjustment. Due todifferent setting of parameters will affect optimizer’s
performance directly. In [8] Bratton and Kennedy proposed
two kinds of topology type which are Gbest and Lbest
topology. The Gbest type is for solving uni-modal problems;and Lbest type is much suitable for solving multi-modal
problems. Ghosh et al. proposed hierarchical dynamic
neighborhood PSO [9]. It will arrange particles for define
their neighborhood under a dynamic hierarchical structure.
Neighborhoods of a particle will changes constantly
according to their solution quality. The changing of thearrangement (relationship of particle’s neighborhoods) canpreserve particle’s diversity for easier finding optimal
solution.
In general, the main advantage of PSO is fast
convergence, but the weakness is easier to fall into local
optimum. Due to particles will toward around global
optimal solution. In order to overcome this situation,
Blackwell and Branke proposed multi-swarm PSO[10],which divided population into several sub-swarms. Eachswarm has its own parameters and will perform evolution
independently. After a few generations, sub-swarm will
perform information sharing. Since there are more than one
global optimal solution, It can avoid particles form fall into
local optimum.
In order to further improve PSO’s performance, acompetition-based particle swarm optimizer is proposed.
The population is separated as two sub-swarms. Afterseveral generations, performance of two sub-swarms will

1 INTRODUCTION
In last 3 decades, evolutionary algorithms were applied
for solving variety of real world problems. Since 1975,
John Holland proposed the first evolutionary based
optimizer, genetic algorithm (GA)[1]. More and more
optimizer was then proposed, such as, ant colony
optimization (ACO)[2], particle swarm optimization(PSO)[3], differential evolution (DE)[4], and artificial bee
colony algorithm (ABC)[5], etc. The main feature of PSO is
it has simply equations to generate new moving vector andhas and very few parameters to be set. The Particle swarm
optimization (PSO) was introduced by Kennedy andEberhart in 1995. The PSO Simulated foraging behavior of
birds or fishes to solve optimization problems. Particles ofthe swarm will move in a D-dimensional space according to
new moving vector. The positions of particles represent
potential solutions. Each particle will have chance to share
their better experience (global best solution, Gbest) for
guiding other particle toward to unsearched or potential
solution space. The global best solution is selected from thebest performance of the personal best solutions (Pbest) of
all particles. Both of the Gbest and Pbest will then guideparticles’ for searching optimal solution.
In last decade, more and more variants of PSO havebeen proposed to improve PSO’s efficiency and make itmuch robust. Omran et al. introduced a hybrid of particle
swarm optimization and differential evolution [6]. Chen and
Yeh presented a new search strategy [7]. They keep only
personal best position for providing moving vector but noglobal best position. It will make vector update much

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take into comparison. The swarm size will be adjusted
according competition results.
The paper is organized as follows: In Section II, asimply introduce of standard PSO will be described.
Section III presents the detail of proposed method. Section
IV presents the experiments including test functions andparameter settings. Finally, the conclusion is described in
Section V.

Velocity update for Sub-swarm1:

2 PARTICLE SWARM OPTIMIZATION

and the inertia weight is assigned according following
equation.
( MAX _ FEs  FEs)w  ( w1  w2 )  w2(5)
MAX _ FEsvid (t  1)  w  vid (t )  c1  r1  ( pbid (t )  xid (t )) c2  r2  (lbid1 (t )  xid (t ))Velocity update for Sub-swarm2:

vid (t  1)  w  vid (t )  c1  r1  ( pbid (t )  xid (t )) c2  r2  (lbid2 (t )  rpbid (t ))2.1. Original PSO
Since the original PSO proposed in 1995[3], researchers
are focus on improve its solution searching ability andapplied it on various applications. In order to extend its
ability of exploration and exploitation, Shi and Eberhart [11]introduced inertia weight into the original PSO, forenhancing particles’ search abilities and drive particle keep
closer on potential solution space. The modified PSO (withinertia weight) will make PSO much robust and increase its
searching performance.
In PSO, particle’s movement are represented by position
vector x and velocity v. The new velocity for particle i isgenerated by equation (1). Then, the ith particle’s new
position will be updated by equation (2).

vid (t  1)  w  vid (t )  c1  r1  ( pbid (t )  xid (t )) c2  r2  ( gbid (t )  xid (t )xid (t  1)  xid (t )  vid (t  1)

(4)

Where, MAX_FEs is the maximum fitness evaluations, thec and c are acceleration coefficients, and r and r
are random number between 0, 1 .The lb is the local
best solution of s sub-swarm, and pb is the personal best
particle. The rpb is the random selected neighborhoodpb, and rpb is not equal the current pb. The w1 and w2
denotes the lower and upper bound of inertia weight. The w
for sub-swarm1 will decrease linearly, and the w for s
ub-swarm2 is generated randomly. The range of two subswarms are set as [0.4, 0.9] and [0.45, 0.75] respectively.
3.2 Information
Adjustment(1)

ExchangeandSub-swarm

Size

In general, each sub-swarm will perform its own
solution search process independently. After several
generations, the sub-swarm with better performance will
share its own gbest information to other sub-swarms. The
pseudo code of information exchange is given as follows:(2)

Where, d denotes dimension, gb is the global best particle,
and pb is the personal best particle. The w represents inertia
weight, c1 and c2 are acceleration coefficients, r1 and r2 are
random number between [0, 1].
The velocity update equation is consists of three parts,
including the previous velocity, personal experience andsocial experience. They are controlled by inertia weight andtwo acceleration coefficients respectively.

If lbf1 < lbf2
count = count + 1;
else
count = count - 1;
End
If count == 10
lb2 = lb1;
count = 0;
regrouping = regrouping + 1;
elseifcount == -10
lb1 = lb2;
count = 0;
regrouping = regrouping - 1;End

3 COMPRTITION-BASED PARTICLE SWARM
OPTIMIZATIONIn this paper, competition-based particle swarm
optimizer is proposed for solving numerical optimization.
For the proposed method, the population is divided into two
sub-swarms. Either of sub-swarm will apply different
parameters setting.

*lbf denoted fitness value of local best particle.

In order to provide better sub-swarm has much resource
to keep finding better solutions, the sub-swarm size will be
adjustment according its performance. Thus, after several
generations, the better sub-swarm will have a new joined
particle and the worse sub-swarm will reduce one. However,in order to keep sub-swarm’s basic ability and to prevent all

3.1 Sub-Swarm Setting
The velocity update of proposed is listed as follows.

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the particles of worse swarm be removed. The minimum
sub-swarm size is set 10. The pseudo code of sub-swarm
size adjustment is given as follows:f

If regrouping == 20
If np2 > 10
{regrouping sub-swarm}End
regrouping = 0;
Elseif regrouping == -20
If np1 > 10
{regrouping sub-swarm}End
regrouping = 0;
End

f11

Rotated Hybrid Composition Function 2 (f18)

f12

Rotated Hybrid Composition Function 2 with a Narrow Basin for
the Global Optimum (f19)f13

Rotated Hybrid Composition Function 2 with the Global Optimumon the Bounds (f20)

f14

Rotated Hybrid Composition Function 3 with High Condition
Number Matrix (f22)f15

Rotated Hybrid Composition Function 4 without Bounds (f25)

4

EXPERIMENTAL RESULTS

In order to test proposed method and compare it to
standard particle swarm optimization (SPSO2011) [12],fifteen test functions of the CEC 2005 [13], which includes
included uni-model functions (f1 ~ f3), multi-model
functions (f4 ~ f8), expanded functions (f9), and hybrid
composition functions (f10 ~ f15), are listed in Table 1. The
global optimum, initialization and search range of the 18
test functions are listed in Table 2. All algorithms are
implemented by MATLAB 2010a and are executed on
platform with Intel Core i3-2120 and 4GB RAM.

3.3 Flowchart of the CSPSO
The procedure of the CSPSO is given as follows:
Step 1: Initial population.
Step 2: Divide population into two sub-swarms.
Step 3: Calculate Fitness value for each sub-swarm.
Step 4: Perform solution search by two sub-swarms.
Step 5: Update each swarm local best particle(lb) andpersonal best particle(pb).
Step 6: Determine condition for information exchange(local best position).

TABLE 2. THE GLOBAL OPTIMUM, INITIALIZATION RANGE AND SEARCHRANGE OF TEST FUNCTIONS.

Step 7: Determine condition for regrouping sub-swarms.
Step 8: Stop evolution if meet the stop condition, else jump
to step 4.
TABLE 1. TEST FUNCTIONSf

Test Functionsf

Global Optimum

Initialization

Search Range

f1

0[-100, 100]D[-100, 100]Df2

0[-100, 100]D[-100, 100]D

f3

0[-100, 100]D[-100, 100]D

f4

0[-100, 100]D[-100, 100]D

f5

0[-100, 100]D[-100, 100]D

f6

0[-32, 32]D[-32, 32]Df7

0[-0.5, 0.5]D[-0.5, 0.5]D

f8

0[-100, 100]D[-100, 100]Df9

0[-3,1]D[-3, 1]Df10

0[-5,5]D[-5, 5]Df11

0[-5, 5]D[-5, 5]D

f12

0[-5, 5]D[-5, 5]Df13

0[-5, 5]D[-5, 5]D

Test Functions

f1

Shifted Sphere Function (f1)

f2

Shifted Schwefel's Problem 1.2 with Noise in Fitness (f4)f3

Schwefel's Problem 2.6 with Global Optimum on Bounds (f5)

f4

Shifted Rosenbrock's Function (f6)

f5

Shifted Rotated Griewank's Function without Bounds (f7)

f6

Shifted Rastrigin's Function (f9)f7

Shifted Rotated Weierstrass Function (f11)f8Schwefel's Problem 2.13 (f12)f14

0[-5, 5]D[-5, 5]D

f9

Expanded Extended Griewank's + Rosenbrock's Function (f13)f15

0[-2, 5]D[-2, 5]D

f10

Hybrid Composition Function 1 (f15)

All the test functions are set as 30-dimension andexecuted for 25 times. Both the population size of SPSO© ISAROB 2013

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

5 CONCLUSION

2011 and proposed method are set as 40, and the maximum
fitness evaluations (FEs) are set 300,000. The details of
related parameters are listed in Table 3.

In this paper, a new variant of PSO is proposed forsolving numerical optimizations. The two sub-swarms will
increase population’s diversity for prevent particles fromfall into local optimum. In the experimental results, fifteen
test functions of CEC 2005 are selected. In order to test theperformance of proposed method, the SPSO 2011 is taken
into comparison. From the results, it can be observed thatthe proposed method is able to explore better solutions.

TABLE 3. PARAMETER SETTINGS
Algorithm

Proposed Method

SPSO 2011

Inertia weight[0.4,0.9] for sub-swarm 1[0.45,0.75] for sub-swarm 2

0.7213Population
size

40

Dimensions30

c1, c2

1.1931

Max FEs

300,000

Run

25

ACKNOWLEDGEMENT
This work was supported in part by National Science
Council of Taiwan, Taiwan, R.O.C. under Grant NSC 1012221-E-161-011.

REFERENCES
The experimental results are listed in TABLE 4 which
includes mean and standard deviation of 15 selected test
functions of 25 independent runs. The better performanceof two PSO variants is shown in bold. From the results, it
can be observed that the proposed method perform better inmost test functions, especially in the hybrid problems.

[1] J. H. Holland , Adaptation in Natural and Artificial Systems ,University of Michigan Press, 1975
[2] A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed
optimization by ant colonies,” in Proc. of the 1st European
Conference on Artificial Life, pp. 134-142, 1991.
[3] J. Kennedy and R. Eberhar, “Particle Swarm Optimization,”in Proc. of IEEE Int. Conf. on Neural Network, pp. 19421948, 1995.
[4] R. Storn and K. Price, “Differential evolution—A simple andefficient heuristic for global optimization over continuous
spaces,” Journal of Global Optimization, vol. 11, pp. 341–
359, 1997.
[5] D. Karaboga, “An idea based on honeybee swarm fornumerical optimization,” Technical Report TR06, 2005.
[6] M. G. H. Omran , A. P. Engelbrecht and A.Salman, "Differential Evolution Based Particle Swarm Optimization,"in Proc. of IEEE Swarm Intelligence Symposium, pp.112-119,
2007.
[7] C. H. Chen and S. N. Yeh “Personal Best Oriented
Constriction Type Particle Swarm Optimization,” in Proc. ofIEEE Conference on Cybernetics and Intelligent Systems,
pp.1-4, 2006.
[8] D. Bratton and J. Kennedy "Defining a Standard forParticle Swarm Optimization," in Proc. of IEEE SwarmIntelligence Symposium, pp.120-127, 2007.
[9] P. Ghosh, H. Zafar, S. Das and A. Abraham, “Hierarchicaldynamic neighborhood based Particle Swarm Optimization
for global optimization,” in Proc. of IEEE Congress on
Evolutionary Computation (CEC), pp.757-764, 2011.
[10] T. M. Blackwell and J. Branke, “Multiswarms, Exclusion,and Anti-Convergence in Dynamic Environments,” in Proc.
of IEEE Transactions on Evolutionary Computation, vol.
10/4, pp. 51-58, 2004.
[11] Y. Shi, and R. Eberhart, “ A modified particle swarm
optimizer”, in Proc. of Conference on Evolutionary
Computation: IEEE Press. Piscataway. , pp.69 –73, 1998.
[12] [Online]. Available: http://particleswarm.info[13] [Online]. http://www.ntu.edu.sg/home/EPNSugan

TABLE 4. EXPERIMENT RESULTS FOR 30-D PROBLEMS
Algorithms
Functions

SPSO 2011

Proposed method

f1

1.3717e-027(1.7744e-028)

f2

6.0104e+001(3.4593e+001)

2.0195e-030(9.8934e-030)
1.0867e+002(1.4697e+002)

f3

4.7958e+003(8.2740e+002)

f4

3.9496e+002(2.8910e+002)

f5

4.8127e+003(4.4511e+001)

f6

6.2365e+001(1.4530e+001)

f7

2.9061e+001(1.2393e+000)

f8

9.1062e+005(1.2477e+005)

f9

1.0825e+001(1.1886e+000)

f10

4.5201e+002(4.9538e+001)

f11

9.2079e+002(2.2923e+001)

f12

9.1608e+002(1.3797e+001)

f13

9.1667e+002(9.0488e+000)

f14

9.1363e+002(1.6512e+001)

f15

9.9539e+002(9.5791e+000)© ISAROB 2013

3.8486e+003(1.8659e+003)
6.6767e+001(2.4387e+002)
2.1846e-002(1.6000e-002)
7.0721e+001(1.5324e+001)
3.8296e+001(1.0916e+000)
8.7510e+005(1.3349e+005)
5.5308e+000(1.3665e+000)
3.4388e+002(3.1475e+001)
8.2431e+002(3.3237e+000)
8.2650e+002(5.9936e+000)
8.2615e+002(3.4888e+000)
5.3446e+002(8.6952e+001)
2.1832e+002(5.3872e+000)

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
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Elitist Differential Evolution for solving Numerical Optimization Problems
Sheng-Ta Hsieh, Huang-Lyu Wu, Tse Su
Department of Communication Engineering Oriental Institute of Technology Taiwan(Tel: +886-2-7738-0145 Ext. 2325, Fax: +886-2-77380145)
fo013@mail.oit.edu.tw
Abstract:
In this paper, an elitist strategy is proposed for enhancing solution searching performance of Differential Evolution (DE). Also,a new variant of mutation for DE is proposed to improved population’s exploration and prevent particles form fall into local
optimum. In the experiments, 10 hybrid composition functions of CEC 2005 test functions are selected for testing performanceof proposed method and compare it with 4 DE variants. From the results, it can be observed that the proposed method exhibits
better than related works.
Keywords: Differential Evolution, elitist, optimization, population.
according current status. In the same year, Zhang and
Sanderson proposed adaptive differential evolution named
JADE [15]. The mutation strategy in JADE is refer to
DE/current to best/1. Due to the particles will toward toaround the global best particle due to the natural of current
to best/1 mutation. It will make particles’ convergence inearly stage. Thus, in JADE, DE/current to best/1 ismodified as DE/current to pbest. The pbest is selected form
several better target vectors randomly. It will keep particles’
diversity and improved DE’s robust. Besides, the selected
and unselected trail vector will also be recorded as areference for parameters adjustment in next generation.

1 INTRODUCTION
In last four decades, more and more heuristic-basedalgorithms were proposed for solving numerical
optimization, such as genetic algorithm (GA) [1] andparticle swarm optimizer (PSO) [2], etc. In 1996, theconcept of original differential evolution (DE) was
proposed by Storn and Price [3]-[6]. The DE algorithm is a
powerful population-based optimizer with simply concept
to be implemented.
Due to there are many parameters in DE need to be
decide before apply it on solving optimization problems.
These parameters are very sensitive to different type ofproblems. Thus, in order to increase robust of DE,
researchers are aiming to fine tune these parameters in last
decade [8]-[12]. Similar to PSO, particles in DE will be
moved according new moving vector, which is generated
by variety mutation and crossover strategies. The selection
will then be performed to preserve better particles for next
generations.

Recently, Islam et al. proposed a new variant of DE
named MDE_pBX [16]. They proposed a new mutation
strategy named DE/current to gr_best/1 for solving early
convergence. The gr_gebest is a better target vector form q
randomly selected target vectors. It can also prevent
particles be clustered around the global best particle andavoid particle fall into local optimal. On the other hand, thecrossover in MDE_Pbx is to random select two target
vectors from p better vectors for information exchange. The
p% value will decrease linearly by generations. It will
enhance DE’s convergence (deep search) in last stage ofsolution search process. However, a smaller population will
decrease solution searching ability and diversity of
DE/current to gr_best/1.

In 2006, Brest et al. proposed self-adaptive differential
evolution (jDE) [13], which is focus on adjust control
parameters, such as crossover rate and scale factor, etc. In
2009, Huang and Suganthan proposed a DE variant named
SaDE [14]. The trail vector generating strategies in SADE
will change according to current solution search status for
dealing with different kinds of problems. It is archived byanalyzing the better and worse vectors to provide usefulclue for selecting mutation strategy in next generation.
Furthermore, the related parameters such as scale factor,
crossover and population size, etc. will also self-adjust© ISAROB 2013

In this paper, a new variant of DE is proposed forsolving numerical optimization problems. The proposed
elitist strategy is referred to the DE/current to best/1
mutation, and to replace the best particle with elitist
particles. It’s an efficient way to solving early convergence609

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

dimension. While the random number is smaller than orequal to CR, the trial vector (u , , ) will inherit mutatedvector ( V , ). Otherwise, the trial vector ( u , , ) will
duplicate current particle’s moving vector (X , ). Finally,
the selection in DE is performed by following equation.

problem and can avoid particles from fall into local
optimum. Besides, the two of elitist particles will berandomly selected for crossover to discover more potential
solution space. Finally, the scale factor is adjusted by
Gaussian distribution.
This paper is consisted of five sections. The basic
concept of differential evolution (DE) is described in
section 2. The detail of proposed method is introduced in
section 3. The experiments are presented in section 4.
Finally, the conclusions are described in section 5.

,

V,

,

DE/best/1
,

,

,

,

)

DE/current to best/1
,

4.

,

,

,

(3)

,

DE/rand/2
,

5.

,

,

,

,

,

(4)

,

DE/best/2
,

,

,

,

,

,

(5)

Unlike GA, the mutation process is executed before
crossover. Due to the mutation in DE is main approach to
explore better solutions in solution space. Thus, either
mutation is adopted for generating new trail vector; thecrossover will be performed by following equation.
CR or jV , , , if rand , 0,1
X , , , otherwise

j

,

,

X,

F X

X

,

(8)

,

,

,

,

,

,

(9)

where X , is a random generated particle, which is notbelonging to current. If particles can find one or more
solutions to update the global best solution in k consecutive
generations, the DE/current to real-rand/1 will be activatedto generate potential moving vectors. It will allow more

(6)

where CR ∈ (0,1) denotes crossover rate, j denotes© ISAROB 2013

F X

In traditional DE/current to rand/1, the random selectedparticle is belonging to current population. It seems areasonable way to generate random vector. In fact, after
several generations, particles will get closer to search
specific area. The random selected particles may not beable to provide useful information to help other particles,
which could trap in local optimum, to jump out tounsearched solution space. The proposed DE/current toreal-rand/1 is listed as follows.

where i, r1, r2, r3, r4, r5 denote current selected particle andfive random selected particles of population, respectively.
And i. The G represent current
generation, Xbest denote best particle of population and F
denote scale factor where F∈(0,1).

u,,

X,

3.2. Current to real-random
In this paper, there are two mutation strategies areproposed for deal with complex problems, especially the
problems contain many local optimum solutions in solution
space. In previous sub-section, the elitist mutation is
introduced. In order to prove particles higher ability tojump out from local optimum, the DE/current to real-rand/1
is then proposed.

(2)
3.

(7)

where X , denotes target vector，V , represents donor
vector，scale factor must be a positive integer between 0
and 1. The X , and X , are two random selectedparticles, and X, is the first E% better particles of
population. Note that the X , must not be the same asX, . Thus, the selected elitist particle will be different
for current selected particles. It can avoid particles are
guided to the same position (global best particle) and canprevent particles fall into local optimum or early
convergence.

(1)
2.

,

In this paper, a new mutation structure is proposed,
named DE/current to elitist/1. The vector update equation islisted as follows.

DE/rand/1
,

,

,

,

3.1 DE/current to elitist/1

Differential Evolution (DE), in recent years, is one of the
popular optimizers. Its main advantages include have a few
parameters, simply structure and fast convergence, etc. The
DE is a population-based optimization algorithm. The
members of population in DE are called parameter vectors.
Particles’ movement is according to trial vectors. The fivecommon mutation strategies are listed as follows.。

,

, if

3 PROPOSED METHOD

2 Differential Evolution

1.

,

,

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

generation and will be generated by following equation.

potential solutions to be found during the solution
exploration, and find unsearched solution space. In thispaper, the k is set as 50.

F

Gaussian F , 0.1

where the F is set as 0.5 in initial stage. After that, the F
will be generated according to following equation.

Start

F

N

50

1

u

F ∗ mean R

∗F

(11)

where u is a random generated number between 0.5 and 1.
And the[17] is used to adjust F value, where R
is survival rate in current generation.

Initialization

k

(10)

3.5 Flowchart of proposed methodY

The flowchart of proposed is presented in figure 1 andthe procedure of proposed method is listed as follows.
Step 1：Initialization and set the generation number G=0.
Step 2：If k >= 50 then execute step 3.1, else execute step
3.2
Step 3.1 ： DE/current to elitist/1, to generate mutatedvector by (8)Step 3.2：DE/current to real-rand/1, to generate mutatedvector by (9)Step 4：Elitist Crossover
Step 5：Selection by (7)Step 6: Fitness Evaluations.
Step 7：Repeat step 2 to step 6, until meet the stop criteria.

Elitist Mutation
Real Rand Mutation

Elitist Crossover
Selection
Fitness Evaluations

Gbest

Gbest

N

4 EXPERIMANTS RESULTS
4.1. Test functions

Y
In order to test the performance of proposed method andcompare it with other variants of DE, ten hybrid
composition functions, which are f16 ~ f25 of CEC 2005 [18],are selected for experiments. All the test functions are set as
30 dimensions. The global optimum, initialization range
and search range of ten test functions are listed in table 1.

k=k+1

Reach Stop Criteria?
N
YEnd

TABLE 1. GLOBAL OPTIMUM AND SEARCH RANGE OF TEST FUNCTIONSFunctionsf1 ~ f 2

Fig. 1. Flowchart of this paper

f3 ~ f 5

3.3. Elitist crossoverf6 ~ f 8

In original DE, crossover is to combine mutated particle
and random selected particle to produce new particles. In
fact, the random selected particle could contain poor
information and may not be able to generate better particle.
In this paper, one of the C% better particles will be selected
for join crossover with the mutated particle. It will increase
particles’ deep searching ability and speed up convergence.

f9
f10

0
0
0
0

Initialization
Range[-5, 5]D

Search Range[-5, 5] D[-5, 5]D[-5, 5] D[-5, 5]D[-5, 5] D[-5, 5]D[-5, 5] D

D

No Boundary[2, 5]

4.2. Parameters Settings
In the experiments, the population size for all DE variants
are set as 100, each approaches are executed for 25
independent runs, and the maximum fitness evaluations(FEs) are set as 300,000. For fair comparison, the original
parameters setting of jDE variants are adopted. For thebasic DEs, which include DE/Rand/1, DE/best/1, andDE/Current to best/1, both the mutation and crossover rate

3.4 Parameter adjustment
In this paper, the scale factor will be adjusted in each

© ISAROB 2013

GlobalOptimum
0611

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

are set as 0.9. For the proposed method, the F is set as 0.5，
CR is set as 0.9, E is set as 15 and C is set as 25.
4.3. Experimental Results

optimization problems.

ACKNOWLEDGEMENT
This work was supported in part by National Science
Council of Taiwan, Taiwan, R.O.C. under Grant NSC 1012221-E-161-011.

The experiment results are listed in table 2 which
presents the mean, standard deviation, and averagecomputation time of 25 runs of the five variants of DE
approaches on the 10 test functions with 30 dimensions.
The best results among the five methods are shown in bold.

REFERENCES[1]

J. H. Holland, “Adaptation in Natural and Artificial Systems,[2]

J. Kennedy and R. C. Eberhart, “in: Particle Swarm Optimization,” inUniversity of Michigan Press,” Ann Arbor, MI, 1975.

From the results, the proposed method performed with
better results on most test functions can be observed. The
jDE [13] performed best result on function 1 and 2.

Proc. of 1995 IEEE International Conference on Neural Networks,
vol. 4, pp. 1942–1948, 1995.
R. Storn and K. V. Price, Differential evolution—A simple andefficient adaptive scheme for global optimization over continuous
spaces, ICSI, Berkeley, CA, Tech. Rep. TR-95-012. [Online].
Available:http://http.icsi.berkeley.edu/~storn/litera.html[4] R. Storn and K. Price, “ Differential evolution — A simple andefficient heuristic for global optimization over continuous spaces,”
J. Global Optim.,vol. 11, no. 4, pp. 341–359, Dec. 1997.
[5] K. Price, R. Storn, and J. Lampinen, Differential Evolution—A
Practical Approach to Global Optimization. Berlin, Germany:
Springer-Verlag,2005.
[6] R. Storn and K. V. Price, “Minimizing the real functions of the
ICEC 1996 contest by differential evolution,” in Proc. IEEE Int.
Conf. Evol. Comput.,Nagoya, Japan, 1996, pp. 842–844.
[7] J. Liu and J. Lampinen, “On setting the control parameters of thedifferential evolution method,” in Proc. 8th MENDEL, R. Matoušek
and P. Ošmera, Ed.s, 2002, pp. 11–18.
[8] J. Liu and J. Lampinen, “A fuzzy adaptive differential evolution
algorithm,”Soft Comput., Fusion Found. Methodologies Appl., vol. 9,
no. 6,pp. 448–462, Jun. 2005.
[9] J. Liu and J. Lampinen, “Adaptive parameter control of differential
evolution,”in Proc. 8th MENDEL, R. Matoušek and P. Ošmera, Eds.,
2002,pp 19–26.
[10] J. Rönkkönen and J. Lampinen, “On using normally distributed
mutation step length for the differential evolution algorithm,” in Proc.
9th MENDEL, Brno, Czech Republic, 2003, pp. 11–18.
[11] M. M. Ali and A. Törn, “Population set based global optimization
algorithms: Some modifications and numerical studies,” Computers
and Operations Research, Elsevier,vol. 31, no. 10, pp. 1703–
1725,Sep. 2004.
[12] W. Gong, Z. Cai, C. X. Ling, and H. Li, “Enhanced differential
evolution with adaptive strategies for numerical optimization,” IEEE
Trans. Syst.,Man, Cybern. B, Cybern., vol. 41, no. 2, pp. 397–413,
Apr. 2011.
[13] J. Brest, S. Greiner, B. Boškovi´c, M. Mernik, and V. Žumer,“ Selfadapting control parameters in differential evolution: A
comparative study on numerical benchmark problems, ” IEEE
Trans. Evol. Comput., vol. 10,no. 6, pp. 646–657, Dec. 2006.
[14] A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution
algorithm with strategy adaptation for global numerical
optimization,”IEEE Trans. Evol. Comput., vol. 13, no. 2, pp. 398–
417, Apr. 2009.
[15] J. Zhang and A. C. Sanderson, “ JADE: Adaptive differential
evolution with optional external archive, ” IEEE Trans. Evol.
Comput., vol. 13, no. 5,pp. 945–958, Oct. 2009.
[16] S.M. Islam, S. Das, S. Ghosh, S. Roy, P.N. Suganthan, “An Adaptive
Differential Evolution Algorithm With Novel Mutation and
Crossover Strategies for Global Numerical Optimization,” IEEE
Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 2, pp. 482–500,
Apr. 2012.
[17] Evans, M., N. Hastings, and B. Peacock. Statistical Distributions. 2nded., Hoboken, NJ: John Wiley & Sons, Inc., 1993, pp. 147–148[3]

TABLE 2. EXPERIMENT RESULTS
f1

f2

f3

f4

f5

Mean

2.8143E+02

3.4813E+02

8.3249E+02

8.3518E+02

8.3087E+02

Std.

3.1917E+01

5.6843E+01

5.2431E+00

7.4596E+00

4.6088E+00

Mean

9.1785E+02

9.6942E+02

1.3100E+03

1.3252E+03

1.3051E+03

Std.

4.0104E+01

5.7750E+01

2.8452E+01

1.9686E+01

3.0350E+01

Current Mean
to best/1 Std.

1.4592E+02

1.8311E+02

8.1645E+02

8.1639E+02

8.1642E+02

4.6975E+01

4.8953E+01

2.5121E-01

4.3215E-01

3.5078E-01

Mean

1.0541E+02

2.0837E+02

8.1738E+02

8.1738E+02

8.1740E+02

Std.

2.4564E+01

4.8086E+01

5.9315E-01

5.2053E-01

5.2154E-01

Proposed Mean
Method Std.

1.8257E+02

2.5625E+02

8.1600E+02

8.1605E+02

8.1606E+02

1.9297E+01

3.6249E+01

2.9137E-02

7.4758E-02

5.6439E-02

Methods Results

f6

f7

f8

f9

f10

Mean

8.6260E+02

7.8090E+02

8.6636E+02

2.1275E+02

2.1236E+02

Std.

1.0110E+00

1.7051E+02

1.3069E+00

1.6850E+00

1.2230E+00

Mean

1.4328E+03

1.5227E+03

1.4392E+03

1.4446E+03

1.4577E+03

Std.

2.6894E+01

1.0783E+02

2.9659E+01

1.7320E+01

1.4929E+01

Current Mean
to best/1 Std.

8.6570E+02

5.0009E+02

8.7159E+02

2.1098E+02

2.1086E+02

4.7043E+00

2.6955E-01

2.7118E+00

7.7643E-01

5.4458E-01

Mean

8.5873E+02

5.0109E+02

8.6621E+02

2.1080E+02

2.1118E+02

Std.

9.7320E-01

1.5504E+00

1.4459E+00

6.6982E-01

7.4161E-01

Proposed Mean
Method Std.

8.2198E+02

5.0001E+02

8.3194E+02

2.0925E+02

2.0904E+02

1.8666E+01

1.2824E-02

1.4056E+01

1.7458E-01

3.4315E-01

Methods Results

Rand/1

Best/1jDE

Rand/1

Best/1jDE

5 CONCLUSION
In this paper, two mutation strategies are proposed,
which are DE/current to elitist/1 and DE/current to realrand/1, to replace DE’s mutation process. The DE/current toelitist/1 can produce more potential particles for improving
particles’ searching abilities, and can easier to find theglobal optimal solution. The DE/current to real-rand/1 canprevent particles from fall into the local optimum.
The experimental results proved that the proposed method
can find better solutions than four DE variants. Ten hybrid
composition functions were adopted for testing through areasonable average. Form the results; it can be observed thatthe proposed has high ability to solve complex numerical© ISAROB 2013[18] http://www.ntu.edu.sg/home/EPNSuga

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Artificial Bee Colony Algorithm with Crossover Strategies for Global
Numerical Optimization
Sheng-Ta Hsieh and Jhih-Sian Chen
Department of Communication Engineering Oriental Institute of Technology, Taiwan(Tel: +886-2-7738-0145 Ext.2325, Fax: +886-2-7738-7411)
fo013@mail.oit.edu.tw
Abstract: In this paper, a new variant of artificial bee colony (ABC) algorithm is proposed for solving numerical optimization
problems. In order to increase population’s solution searching ability, the crossover operation of genetic algorithm (GA) is
involved to produce new potential offspring. In the experiment, the CEC 2005 test functions are adopted for test proposed
method and compared it to related works. From the results, it can be observed that the proposed method performed better
performance than two variants of ABC approaches.
Keywords: artificial bee colony, crossover, genetic algorithm, optimization.
bees. Further, it also combined an opposition-based strategy
to replace scouts bee. Both strategies will speed up solution
searching process. In the same year, Guo et al. proposed
GABCS [10] which refer to PSO moving behavior tomodify bees’ moving vector. Also, a mutated strategy is
involved to generate new bees for increasing bees’ solution
searching ability. In 2012, El-Abd proposed an interesting
ABC variant named GOABC [11], which introduced a
opposition-based learning and involved generalized concept
into ABC for enhancing optimizer’s performance. The
GOABC exhibit good results on solving both uni-modaland multi-modal test functions.

1 INTRODUCTION
In order to deal with complicate real-world
optimization problems, more and more researchers arefocus on creating new optimizers or improving its
performance. Such as Genetic Algorithm (GA) [1], Ant
Colony Optimization (ACO) [2], Particle Swarm
Optimization (PSO) [3] and Differential Evolution(DE) [4]etc.
Artificial bee colony algorithm was first proposed by
Karaboga [5]-[6] in 2005. It simulated Bees’ forage
behavior. Then, Tereshko and Loengarov [7] proposed theforaging model for ABC algorithm.

However, an optimizer may has higher ability forsolving uni-modal problems due to its deep search
approach, but it could make optimizer has a weak ability on
widely search for solving multi-modal problems, and viceversa. In other word, for the deep and widely search ability,
improved one will worsen the other one. Thus, how to makechoose a suitable strategy to deal with current problem will
be an important issue of optimizer improvement.

The ABC algorithm is consisted of three basic
elements, employed bees, onlooker bees and scouts bees.
The employed bee, onlooker bee will try to find food.
While employed and onlooker bees cannot find any food,then, the scout bee will start over for following foodsearching process and looking for new food source.
In recent years, more and more researchers are
studying ABC algorithms either enhancement orapplications. In 2010, Banharnsakun et al. proposed
distributed population [9]，which divide whole population
into several small sub-populations. Each sub-population
will perform its own solution search process in parallel. The
sub-swarm will exchange their information after several
iterations for improving solution quality and improving
solution searching process. In 2011, Bi and Wang proposed
FMABC [8], which introduced a free search algorithm toreplace traditional roulette wheel for selecting onlooker© ISAROB 2013

In order to enhance ABC optimizer’s solution
searching ability. In this paper, the crossover of GA is
involved into to generate more potential individual. Also,
the elitist strategy is involved to speed up convergence.
This paper is consisted of five sections. The basic
concept of ABC algorithm is described in section 2. The
detail of proposed method is introduced in section 3. The
experiments are presented in section 4. Finally, theconclusions are described in section 5.

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2 ARTIFICIAL BEE COLONY

wheel and keep looking for better food by (1).
Step 6: Evaluate xi and v for select better food source.
Step 7: Record the best food source.
Step 8: If no better be found in limited generations, use (4)to find new food source.
Step 9: Repeat step 3 to 8, until terminal condition is reached

2.1 Introduction to ABC algorithm
Artificial bee colony (ABC) algorithm is a novel
optimizer which simulated Bees’ forage behavior insolution space and try to looking for optimal solution inreasonable computational consumption In ABC, there are
2 kinds of bees, employed and onlooker bees, are response
for finding food source (also named solutions). The
employed bees are going to explore new food and onlooker
bees are response for better food source. The food searchingprocess will be performed by following equation.
∅

3 PROPOSED METHOD
In order to enhance ABC optimizer’s solution
searching ability. In this paper, the crossover of GA is
involved into to generate more potential individual. Also,
the elitist strategy is involved to speed up convergence.
3.1 Crossover
In genetic algorithm, crossover is going to produce
offspring by re-combine parents’ information. In this paper,
multi-point crossover is involved to generate new individualfor searching potential/unsearched solution space.

(1)

where both i and k are random integer, whose range are
between 1, s , and i is not equal to k. The s denotes
population size. In other word, i and k are random selected
bees. The j is also a random integer whose range is between
1, D . The D denotes dimension. ∅ is a normal
distribution number which is between [-1, 1], x denotes
current food source and v is the new food source.

The multi-point crossover will applied on random
selected dimension and perform information exchange forselected bees. Thus, it will drive bees toward to right
direction and perform deep and widely search. The same as
GA, the crossover rate Cr is also set.

Onlooker bees will then select a food source according
to equation (2) and perform food searching process byequation (1).

In order to prevent solution from fall into local optimum,
the multi-point crossover will not perform every generation
but every g generations.

(2)∑

where fiti is fitness value and i denotes the ith bee. The fitness
value will be updated by following equation.

1

abs

,

0

,

0

3.2. Elitist strategy
The elitist strategy is to select the best bee for guideother bees toward to right direction and perform solution
search for finding better food source. Thus, the equation (1)is modified as following equation.

(3)

where fi represents objective value of ith bee. After several
generations, if there is no better food source be found. The
scout bees will take over the food search process and try tolooking for new food source (better solutions) by following
equation.
0,1∅

where xbest is the best bee of population in current
generation.
Although the elitist strategy will speed up convergenceof solution searching process, it could also make all the bees
are clustered in a small area of solution space. In order toavoid this situation, the elitist strategy will not be activatedevery generation but by an activation rate Er.

(4)where xrand denotes random selected bee, and xmin and xmax
present minimal and maximal boundary of search range
respectively.
The procedure of ABC algorithm is listed as follows.
Step 1: Initialization for generated food source randomly.
Step 2: Calculate fitness value for each food source.
Step 3: Employed bees looking for new food source by (1)and evaluate xi and v for select better food source.
Step 4: Calculate probability of fitness value by (2).
Step 5: Onlooker bees will select food source by roulette© ISAROB 2013(5)

3.3. Procedure of proposed method
The procedure of proposed method is listed as follows.
Step 1: Initialization for generated food source randomly.
Step 2: Calculate fitness value for each food source.
Step 3: If the random value is smaller than Er, jump to step5, else execute step 4.
Step 4: Employed bees looking for new food source by (1)614The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

TABLE 2. EXPERIMENT RESULTS

and evaluate xi and v for select better food source.
Step 5: Employed bees looking for new food source by (5)and evaluate xi and v for select better food source.
Step 6: Calculate probability of fitness value by (2).
Step 7: Onlooker bees will select food source by roulette
wheel and keep looking for better food. If therandom value is smaller than Er, use (5), else use(1).
Step 8: Evaluate xi and v for select better food source.
Step 9: If the random value is smaller than Cr, perform
crossover, else jump to step 10.
Step 10: Record the best food source.
Step 11: If no better be found in limited generations, use (4)to find new food source.

Algorithms Results

Std.

F4

F5

0.00E+00 2.87E+03 2.50E+06 6.74E+03 1.45E+03

Mean 0.00E+00 2.72E+03 6.15E+06 3.18E+04 1.20E+04
GOABC
Std.
Proposed
Method

0.00E+00 9.13E+02 2.07E+06 6.52E+03 1.61E+03

Mean 0.00E+00 2.98E+02 1.89E+06 9.59E+03 3.21E+03
Std.

Algorithms Results

0.00E+00 1.57E+02 6.83E+05 2.86E+03 6.63E+02
F6

F7

F8

F9

F10

Mean 5.17E+00 4.70E+03 2.08E+01 2.89E-11 3.89E+02
ABC
Std.

7.93E+00 1.74E-12 4.65E-02 1.41E-10 5.59E+01

Mean 8.46E+00 3.29E+03 2.08E+01 0.00E+00 3.48E+02
GOABC
Std.

4.1 Test functions
In order to test the performance of proposed method andcompare it with other variants of ABC, the twenty five test
function of CEC 2005 [12] are selected which include unimodal functions (f1 ~ f5), multi-modal functions (f6 ~ f12),
expanded functions (f13 ~ f14) and hybrid composition
functions (f15 ~ f25). All the test functions are set as 30
dimensions. In order to easier compare the performance
between optimizers, the error e between the real global
optimum f * and function value f found by optimizer will bepresented. Thus, the bias of CEC 2005 test functions is not
involved.
e = f – f*

Proposed
Method

1.23E+01 2.22E+02 4.05E-02 0.00E+00 5.63E+01

Mean 1.93E+01 4.70E+03 2.04E+01 0.00E+00 8.73E+01
Std.

Algorithms Results

1.89E+01 5.75E-13 4.81E-02 0.00E+00 2.10E+01
F11

F12

F13

F14

F15

Mean 2.83E+01 1.27E+04 1.10E+00 1.31E+01 1.85E+01
ABC
Std.

1.44E+00 5.83E+03 1.50E-01 1.31E-01 5.47E+01

Mean 2.97E+01 8.43E+03 8.08E-01 1.30E+01 2.94E+01
GOABC
Std.
Proposed
Method

1.47E+00 3.86E+03 1.48E-01 1.13E-01 6.29E+01

Mean 2.37E+01 2.84E+03 8.59E-01 1.26E+01 4.00E+00
Std.

Algorithms Results(6)

1.09E+00 2.19E+03 1.85E-01 1.35E-01 1.96E+01
F16

F17

F18

F19

F20

Mean 3.43E+02 2.75E+02 9.24E+02 9.22E+02 9.21E+02

The search range and global optimum of all test
function is listed in Table 1.

ABC
Std.

TABLE 1. GLOBAL OPTIMUM AND SEARCH RANGE OF CEC 2005

3.99E+01 3.61E+01 6.32E+00 5.19E+00 6.95E+00

Mean 3.47E+02 3.75E+02 9.25E+02 9.23E+02 9.25E+02

TEST FUNCTIONS

GOABC
Std.

Search range
Proposed
MethodD

F1 ~ F60[-100, 100]F7

0[0, 600]DF8

0[-32, 32]DF9 ~ F10

0[-5, 5]DF11

0[-0.5, 0.5]DF120[-100, 100]D

0[-3, 1]F140[-100, 100]DF15 ~ F24

0[-5, 5]DF25

0[-2, 5]D

3.05E+01 4.30E+01 6.00E+00 4.39E+00 6.20E+00

Mean 1.64E+02 2.07E+02 9.08E+02 9.08E+02 9.04E+02
Std.

Algorithms Results

2.93E+01 5.82E+01 1.45E+00 2.12E+00 7.96E+00
F21

F22

F23

F24

F25

Mean 5.00E+02 1.12E+03 5.34E+02 2.00E+02 1.02E+03
ABC
Std.

3.68E-03 4.41E+01 6.08E-04 8.76E-13 7.47E+00

Mean 5.00E+02 1.09E+03 5.33E+02 2.33E+02 1.26E+03
GOABC

D

F13

© ISAROB 2013

F3

Mean 0.00E+00 6.00E+03 8.81E+06 3.04E+04 1.26E+04

4 EXPERIMENT RESULTS

Global Optimum

F2

ABC

Step 12: Repeat step 3 to 11, until terminal condition isreached

Test Functions

F1

Std.
Proposed
Method615

1.09E-12 4.63E+01 7.86E-04 2.97E+01 1.17E+01

Mean 5.00E+02 9.43E+02 5.34E+02 2.56E+02 1.23E+03
Std.

4.62E-13 2.18E+01 3.23E-04 3.29E+01 2.00E+01

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Council of Taiwan, Taiwan, R.O.C. under Grant NSC 1012221-E-161-011.

4.2 Parameter settings
In order to fair comparison, all the parameters areaccording to their original settings. The population size of
proposed method is referring to [11]. The crossover will be
activated every 300 generations, and the crossover rate is
set as 0.5. The maximum Fitness Evaluations (FEs) are set
as 300,000.

REFERENCES[1] J. H. Holland, “Adaptation in Natural and Artificial
Systems, University of Michigan Press,” Ann Arbor,
MI, 1975.
[2] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant System:
Optimization by a colony of cooperaing agents," IEEE
Trans. on SMC, pp. 29-41, 1996.
[3] J. Kennedy and R. C. Eberhart, “in: Particle Swarm
Optimization,” in Proc. of 1995 IEEE International
Conference on Neural Networks, vol. 4, pp. 1942–1948,
1995.
[4] R. Storn and K. Price, “Differential evolution – asimple and efficient daptive scheme for global
optimization over continuous spaces,” Technical report,
International Computer Science Institute, 1995.
[5] D. Karaboga, “An idea based on honeybee swarm fornumerical optimization,” Technical Report TR06, 2005.
[6] D. Karaboga and B. Akay, "Artificial bee colony(ABC), harmony search and bees algorithms on
numerical optimization," in Innovative Production
Machines and Systems Virtual Conference, 2009.
[7] V. Tereshko and A. Loengarov, “Collective DecisionMaking in Honey Bee Foraging Dynamics,”
Computing and Information Systems Journal, vol. 9,
No 3, ISSN 1352-9404, 2005.
[8] X. Bi and Y. Wang, "An improved artificial bee colonyalgorithm," in Proc. of 2011 3rd International
Conference on Computer Research and Development(ICCRD), vol.2, pp.174-177, 2011.
[9] A. Banharnsakun, T. Achalakul and B. Sirinaovakul,"Artificial bee colony algorithm on distributed
environments," in Proc. of 2010 Second World
Congress on Nature and Biologically Inspired
Computing (NaBIC), pp.13-18, 2010.
[10] P. Guo, W. Cheng and J. Liang , "Global artificial bee
colony search algorithm for numerical function
optimization," in Proc. of 2011 Seventh International
Conference on Natural Computation (ICNC), vol.3,
pp.1280-1283, 2011.
[11] M. El-Abd, "Generalized opposition-based artificial
bee colony algorithm," in Proc. of 2012 IEEE
Congress on Evolutionary Computation (CEC), pp.1-4,
2012.
[12] http://www.ntu.edu.sg/home/EPNSugan

4.3 Experiment results
Table 2 presents the mean, standard deviation, andaverage computation time of 25 runs of the 3 variants of
ABC approaches on the 25 test functions with 30
dimensions. The best results among the three approaches
are shown in bold.
From the results, the proposed method performed with
better results on most test functions can be observed. The
proposed method surpasses all other algorithm in solving
all functions and a significant improvement on the results offunctions 2, 4, 5, 15 and 22. The Proposed method
performed the same best result as the ABC [5] and GOABC[11] on function 1. The Proposed method performed thesame best result as the GOABC on function 9.

5 CONCLUSIONSIn this paper, both the crossover and elitist strategy are
involved into ABC algorithm for solving numerical
optimizations. The crossover can produce more potential
individuals for improving bees’ searching abilities, for easier
to find the global optimal solution. It also makes ABC morerobust, prevents bees from falling into the local optimum.
The experimental results proved that the proposed
method can find better solutions than other ABC variants.
Twenty five test functions were adopted for testing througha reasonable average. Form the results; it can be observedthat the proposed has high ability for solving uni-modal
functions, multi-modal functions, expanded functions andhybrid composition functions.

ACKNOWLEDGEMENT
This work was supported in part by National Science© ISAROB 2013

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Microwave Imaging of Dielectric Cylindrical Target Using Dynamic Differ
ential Evolution and Self-Adaptive Dynamic Differential Evolution
Ching-Lieh Li1
Electrical Engineering Department, Tamkang University,
New Taipei City, Taiwan, R.O.C.
e-mail:li@ee.tku.edu.tw
Chi-Hsien Sun2
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology
E-mail:laisingsun@yahoo.com.tw
Chung-Hsin Huang3
Department of Information Technology and Mobile Communication, Taipei College of Maritime Technology, New Taipei
City, Taiwan, R.O.C.
e-mail: havehuang@hotmail.com
Lung-Fai Tuen4
Electrical Engineering Department, Tamkang University,
New Taipei City, Taiwan, R.O.C.
e-mail: justin_tuen@hotmail.com
Huang-Wei Wong5
Electrical Engineering Department, Tamkang University,
New Taipei City, Taiwan, R.O.C.
e-mail: mayday72001@hotmail.com
Abstract: The inverse problem under consideration is to reconstruct the characteristic of scatterer from the scattering E field.
Dynamic differential evolution (DDE) and self-adaptive dynamic differential evolution (SADDE) are stochastic-type
optimization approach that aims to minimize a cost function between measurements and computer-simulated data. Thesealgorithms are capable of retrieving the location, shape and permittivity of the dielectric cylinder in a slab medium made oflossless materials. The finite-difference time-domain (FDTD) is employed for the analysis of the forward scattering.
Numerical results indicate that both optimization methods are reliable tools for inverse scattering applications. In the particularcase of small-scale problems investigated in this paper, SADDE outperforms the DDE a little in terms of reconstruction
accuracy.
Keywords: Inverse Scattering, Differential Evolution, Optimization.
were retrieved using the spherical harmonic decomposition
1 INTRODUCTION
approach [4]. The gradient descent optimization method
and the Method of Moments (MoM) were combined to
The inverse scattering scattering problem of unknown
determine the coefficients of the spherical harmonic
objects has many applications including microwave
decomposition. A lossless dielectric 2-D object withimaging, ground penetrating radar, nondestructive
irregular shape was simulated nondestructive evaluation [5].
evaluation, and biomedical engineering. Numerous inverse
However, these papers are focused on frequency-domain.
problem techniques for 2-dimensional or 3-dimensional
As compared to frequency-domain approach, thetargets were reported [1]-[10].
interaction of the entire medium in the time domain withFor example, single frequency plane waves at a fixed
the incident field needs to be considered. In contrast, timeangle were used in [1] to illuminate the perfect electricdomain approaches can exploit causality to limit the region
conductors (PEC), and the observation domain was locatedof inversion, potentially reducing the number of unknowns.
in the far zone. Upon using a linear distributional model,
The genetic algorithm (GA) was reported as a fast and very
the unknown parameters were retrieved. A Kirchhoff-based
effective technique for shape reconstruction problems [6].
approximation was employed in [2] to retrieve the shape ofIn this method, the shape of the scattering object was
2-D dielectric cylinders from aspect-limited multiretrieved upon solving cost function. However, large
monostatic backscattering data. The Tikhonov regularizedamount of computation load was generally needed.
Gauss-Newton framework was implemented in [3] toImprovements in the GA were reported in [7] where goodreconstruct the boundary and the inhomogeneity parameters
reconstruction results were demonstrated using fewerof 2-D scatterers. For the breast cancer application, thesearching time. Optimization methods, such as theshape and the location of a 3-D breast cancer tumor-likesynchronous particle swarm optimization (SPSO)© ISAROB 2013

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techniques were reported in [8] where the shape of 2-D
PEC target was reconstructed using the asynchronous
particle swarm optimization (APSO). In the 2010, thedynamic differential evolution (DDE) was first proposed todeal with the shape reconstruction of homogeneous
dielectric cylinders under time domain [9]. The DDE
algorithm is a potentially trend to obtain the global
optimum of a functional whatever the initial guesses are. In
recent decade years, some papers have compared different
algorithm in inverse scattering [10]-[15]. To the best of ourknowledge, there is still no comparative study about theperformances of SADDE and DDE to inverse scattering
problems is also investigated. I n this paper, thecomputational methods combining the FDTD method [16]and the DDE and SADDE is presented. The forward
problem is solved by the FDTD method. The shape of
scatterer is parameterized by closed cubic spline expansion.
The inverse problem is formulated into an optimization one
and then the global searching scheme DDE and SADDE isused to search the parameter space. In section II, thesubgridding FDTD method for the forward scattering arepresented. In sections III and IV, the inverse problem andthe DDE and SADDE of the proposed inverse problem aregiven, respectively. In V section, the numerical result of the
proposed inverse problem is given. Finally, in VI sectionsome conclusions are drawn for the proposed time domain
inverse scattering.

(1 , 1 )( 2 ,  2 )( r ,  r )( 3 ,  3 )Fig. 1. Geometrical configuration of the problem.

3 INVERSE PROBLEM
For the inverse scattering problem, the shape andlocation of the perfectly conducting cylinder arereconstructed by the given scattered electric field measured
at the receivers. This problem is resolved by an
optimization approach, for which the global searching DDE
and SADDE is employed to minimize the following
objective function (OF):
Ni

OF

n 0

n 1

M

Q

exp

cal

4 EVOLUTIONAL ALGORTHEMS
DDE algorithm starts with an initial population ofpotential solutions that is composed by a group of randomly
generated individuals which represents the center position
and the geometrical radiuses of the cylinders. Eachindividual in DDE algorithm is a D-dimensional vector
consisting of D optimization parameters. The initial
population
maybeexpressedby x : j  1, 2,, Np , where Np is the populationjsize. After initialization, DDE algorithm performs thegenetic evolution until the termination criterion is met.
DDE algorithm, like other EAs, also relies on the genetic
operations (mutation, crossover and selection) to evolve
generation by generation. The mutation operation of DDE
algorithm is performed by arithmetical combination of(1)

where Bn and Cn are real coefficients to expand theshape function.In order to closely describe the shape of thecylinder for both the forward and inverse scattering
procedure, the subgridding technique is implemented in the
FDTD code; More detail on subgriding FDTD can be foundin [16].

individual. For each parameter vector
generation, a trial vectorx j of the parent

v j is generated according tofollowing equation:(Vjg 1)i  (X gj )i  F  [(X gbest )i  (X gj )i]    [(X gr1)i  (X gr 2 )i] ,© ISAROB 2013

(2)

Where E z
and E zare experimental electricfields and the calculated electric fields, respectively. The Ni
and M are the total number of the transmitters and receivers,
respectively. Q is the total time step number of the recorded
electric fields.

The direct scattering problem is to calculate thescattered electric fields while the shape and location of thescatterer are given. The shape function F ( ) of thescatterer is described by the trigonometric series in the
direct scattering problem

F ( )   Bn cos(n )   C n sin(n )N i   E zexp (n, m, q  t)

Let us consider a two-dimensional metallic cylinder
buried in a slab medium as shown in Figure 1. The cylinder
is parallel to z axis, while the cross-section of the cylinder
is arbitrary. The object is illuminated by a Gaussian pulse
line source located at the points denoted by Tx and reflected
waves are recorded at those points denoted by Rx. The
computational domain is discretized by Yee cells. It shouldbe mentioned that the computational domain is surroundedby the optimized perfect matching layers (PML) absorber toreduce the reflection from the environment-PML interface
[17].

N /2

Q

n 1 m 1 q  0.2 FORWARD PROBLEM

N /2



M

   E zexp (n, m, q  t)  E zcal (n, m, q  t)n 1 m 1 q  0

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r1, r 2  [0, N p  1], r1  r 2(3)
where F and  are the scaling factors associated withat two different positions, Ni=2, which are located at the (143mm, 178.5mm) and (143mm, 178.5mm), respectively.
The scattered E fields for each illumination are collected at
the eight receivers, M=8, which are equally separated by
47.8mm along the distance of 48mm from the interface
between region 1 and region 2.
For the example, the dielectric cylinder with shapefunction F ( )  29.75  5.95 cos(4 ) mm and thethe vector differences (X gbest  X gj ) and (X gr1  X gr 2 ) ,respectively. The disturbance vector V due to the mutationgmechanism consists of parameter vector X j , the best
particle

X gbest and two randomly selected vectors.

error of the reconstructed shape F ( ) and the relative
error for permittity with respect to the exact values versus
generations are shown in Figure 3. It is clear that the
SADDE a little bit outperforms DDE for this example.
cal

(5)

60

rand 5 , if rand 6  0.1
CRi , G 1   CRi ,G 1 , otherwise (6)Y-coordinate (m m )40

where rand1, rand2, rand3, rand4, rand5 and rand6 are uni
form random numbers with thin values uniformly between
0 and 1. And Fl , Fu , l and u are the lower and theupper limits of F and  , respectively .Both

Fl and

20
0
-20
-40

l are set to 0.1 and both Fu and u are set to 0.9.
Based on the self-adaptive concept, the F ,  and CR

-60
-80

Exact
Best initial
SADDE
DDE
-60
-40

-20

0

20

40

X-coordinate (mm)parameters adjust automatically while the time complexity
does not increase. More details about the SADDE algorithm
can be found in [18]. It should be noted that the value ofFourier series used to describe the shape of the cylinder will
be determined by the DDE and SADDE scheme.

Fig. 2. The reconstructed shapes of the cylinder for
example by DDE and SADDE, respectively.
10

Relative Error (%)

5 NUMERICAL RESULTAs shown in Figure 1, the problem space is divided in

68  68 grids with the grid size x  y  5.95 mm.

The dielectric cylinder is buried in lossless slab medium(  1   2   3  0 ). The transmitters and receivers areplaced in free space above the homogeneous dielectric. The
permittivities in region 1, region 2 and region 3 arecharacterized by 1   0 ,  2  6 0 and  3   0 ,

10

10

10

10

10

respectively, while the permeability 0 is used for each

2

1

0

-1

Shape Function by SADDE
Permittivity by SADDE
Shape Function by DDE
Permittivity by DDE

-2

-3

10

0

10

1

10

2

Generation

region, i.e., only non-magnetic media are concerned here.
The scatterer is illuminated by plane waves with theelectric field polarized along the axis, while the timedependence of the field is of a one derivative Gaussian
pulse. The cylindrical object is illuminated by a transmitter© ISAROB 2013

isconsidered. The final reconstructed shapes by SADDE and
DDE at the 300 th generation are compared to the exact
shape in Figure 2. The r.m.s. error of shape function for
SADDE and DDE are about 2.31% and 2.51% in the final
generation, respectively. The relative error for permittivity
by SADDE and DDE are both less than 1%.The R.M.S.

 F  rand 1 * Fu , if rand 2  0 .1
Fi , G 1   l(4)
Fi ,G 1 , otherwise  rand 3 * u , if rand 4  0.1
i ,G 1   l
i ,G 1, otherwise r  3.5

relative permittivity of the object is

SADDE are based on DDE scheme. Each vector isextended with its own  , F and CR values. Therefore thecontrol parameters are self-adjusted in every generation foreach individual according to the following scheme:

Fig. 3. Error value versus generation for example by
DDE and SADDE, respectively.

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Image Reconstruction for Homogenous Dielectric Objects by

6 CONCLUSION

Dynamic Differential Evolution,” Electromagnetics. Vol. 30, No. 4,

In this paper, we study the time domain inverse
scattering of an arbitrary cross section dielectric cylinder.
Numerical results show that result by SADDE in accurate
reconstruction. Finally, SADDE leads to more precise
reconstruction results for the same population size andtotal number of iterations. It should be mentioned that thiscomparative study is indicative and its conclusion should
not be considered generally applicable in all inverse
scattering problems.

pp. 309-323, May. 2010.
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One-Dimensional[11]

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[13]

problems,” Inverse Problems. Vol. 925 pp. 1–42, 2009.
[14]

and

G.

Leone,“Multimonostatic

Scattering of a Two- dimensional Metallic Cylinder in Slabreconstruction of two-dimensional dielectric cylinders by aMedium Using Asynchronous Particle Swarm Optimization.”,Kirchhoff-based approach,” IEEE Transactions on Geoscience andProgress In Electromagnetic Research M. PIER M Vol. 14, pp. 85100. Aug. 2010.

Naik, J. Eriksson, P. de Groen, and H. Sahli, “A nonlinear iterative[15]

Swarm Optimization and Dynamic Differential Evolution forelectromagnetic tomography,” IEEE Transactions on Geoscience

Partially Immersed Conductor.” Waves in Random and Complex

and Remote Sensing., vol. 46, no. 5, pp. 1558–1574, May 2008.

Media. Vol. 21, No.3, pp. 485-500, Aug. 2011.

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MA, 2000

Oct. 2006.

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C. H. Huang, C. L. Liu, C. C. Chiu, Y. D. Wu, T. Wysocki and B. J

Propagation, vol. 45, No. 3, March 1997.

Scattering of a Two-dimensional Dielectric Object by Genetic[18]

Algorithm,” Electromagnetics Vol. 27, No. 5, pp. 241-251, June

Inverse Scattering of a Two- Dimensional Perfectly Conducting

X.-M. Zhong, C Liao and W. Chen, “Image reconstruction of

Cylinder in Slab Medium” IEEE Transactions on Geoscience andarbitrary cross section conducting cylinder using UWB pulse, ”

Remote Sensing. (in press)

Journal of Electromagnetic Waves Application,, vol. 21, No. 1, pp.
25-34, 2007
C. H. Sun, C. L. Li, C. C. Chiu and C. H. Huang, “Time Domain
Image Reconstruction for a Buried 2D Homogeneous Dielectric
Using

NU-SSGA.”,

ResearchinNondestructive

Evaluation, Vol. 22, No.1, pp. 1-15, Jan. 2011.
C. C. Chiu, C. H. Sun and W. L. Chang “Comparison of Particle
Optimization

andAsynchronous

Particle

Swarm

Optimization for Inverse Scattering of a Two- Dimensional
Perfectly Conducting Cylinder.”, International Journal of Applied
Electromagnetics and Mechanics Vol. 35, No.4, pp. 249-261,Apr.
2011.
[9]

C. H. Sun, C. C. Chiu, C. L. Li, and C. H. Huang, “Time Domain© ISAROB 2013

C. C. Chiu, C. H. Sun, C. L. Li and C. H. Huang, “Comparative
Study of Some Population-based Optimization Algorithms on

2007.

Swarm

M. W. Chevalier, R. J.Luebbers and V. P. Cable, “FDTD local gridwith material traverse,” IEEE Transaction on Antennas andWysock, “Electromagnetic Transverse Electric-Wave Inverse

CylinderA. Taflove and S. Hagness, “Computational Electrodynamics: The
Finite-Difference Time-Domain Method,” Artech House, Boston,tumor,” IEEE Trans. Med. Imag., vol. 25, no. 10, pp. 1258–1271,[8]

C. C. Chiu and W. C. Hsiao “Comparison of Asynchronous Particle

reconstruction and analysis approach to shape-based approximate

algorithm for shape and location reconstruction of breast cancer[7]

C. H. Sun, C. C. Chiu and C. L. Li, “Time-Domain Inverse

shape

Remote Sensing., vol. 48, no. 8, pp. 3152–3161, Aug. 2010.

[6]

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Differential

Optimization,” IEEE Transactions on Geoscience and Remote

C. C. Chiu and P. T. Liu, “Image Reconstruction of a Perfectly

Brancaccio

Using

Scatterer Using Differential Evolution and Particle Swarm

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Scatterers

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This work was supported by National Science Council,
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[2]

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ACKNOWLEDGEMENT[1]

A. Semnani, M. Kamyab, and I. T. Rekanos, “Reconstruction of620

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Application of Natural Language Processing for Information Retrieval
Su Mei Xi1, Dae Jong Lee2, Young Im Cho3
1

Department of Information, Shandong Polytechnic University, Jinan, 250353, China
College of Electrical and Computer Engineering, Chungbuk National University, Cheong Ju, 361-763, Korea
3
College of Information Technology, Suwon University, San 2-2, Bongdam-eup, Hwaseong-si, 445-743, Korea
21xiyanzi_79@sina.com.cn
bigbell@chungbuk.ac.kr
3
ycho@suwon.ac.kr

2

Abstract: Through a comprehensive analysis of using natural language processing in information retrieval, we compared theeffects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks ofmore suitable as well as accurate results of natural language processing.
Keywords: natural language processing, information retrieval, phrase identification, stemming
2. 1. 1 Stop Word Removal
Stop word refers to the word that lack actual meaning
and appear lot of times in the document, such as most ofEnglish prepositions, and articles etc. Because stop word
removal technique has no substantial help to improve theretrieval effect actual information retrieval systems such as
Web search engines often do not use this technique.
Moreover, using this technique could not lead to goodresults in dealing with some queries. The classic example isthe query of “to be or not to be”. So stop word also has
been reserved as index item in most actual retrieval system.
2.1.2 Word Segmentation
Word segmentation is a special problem in information
retrieval of Asian languages such as Chinese and Japanese.
Most of European languages need not word segmentation.
Word segmentation technique is widely used in Chinese
information retrieval systems.
Peng et al have made word segmentation and retrieve
experiments in the Chinese data set of TREC5 and TREC6[2]. Their experiments showed that word segmentation
accuracy and retrieval effect is not the monotonous directly
proportional. The best retrieval effect can be obtained
when word segmentation accuracy is 70% or so. If theWord segmentation accuracy is too high, it may lead to
decline in retrieval effect.
2. 1. 3 Stemming
Stemming can make the same stem word match thedifferent form word. Commonly used methods include rulebased stemming (e.g. Porter Stemmer) and dictionary-based
stemming (e.g. KSTEM).
Strzalkowski and Vauthey applied the stemming methodof dictionary-assisted in their retrieval system. The

1 INTRODUCTION
Many researchers have been trying to use natural
language processing (NLP) in information retrieval, but theresult is not satisfactory. Less complex basic natural
language processing techniques with small calculated
consumption and simple implementation help a small for
information retrieval, which including stop words removal,
word segmentation, stemming etc. But some techniques arestill recommended in the information retrieval experimental
platform, which can improve the retrieval effect such as
stop words removal and stemming etc. Senior highcomplexity of natural language processing techniques withhigh calculated consumption and low precision can’t help
the information retrieval even harmful to it, which
including parsing, phrase identification, named entity
recognition, concept extraction, anaphora resolution and
WSD etc [1].
Therefore, in this paper, through a comprehensive
analysis of using natural language processing in
information retrieval, we will compare the effects with thevarious natural languages. This paper is organized asfollows: Firstly we introduce the application of natural
language processing in information retrieval, and secondly
we compare the effectiveness of NLP for IR precision, andthird, we discuss and finally we conclude of natural
language processing in information retrieval.

2 Application of NLP in Information Retrieval
2.1 Application of Basic NLP Technique© ISAROB 2013

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Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

2. 2. 2 Named Entity Extraction
Named entity is a special phrase that identified aconcept or entity, such as proper nouns, place names,
organization name and so on. Obviously, the named entities
express more accurate information than the general phrases.
But the application of named entity in information
retrieval has not obtained the direct effect to retrieval. On
the one hand it existing error in the named entity
recognition technique itself, on the other hand, researchers
have also confused that how to match the named entity.
2. 2. 3 Concept Extraction
Concept is a more general special phrase than namedentity. Named entity identifies a concept, so we canconsider it belong to the concept. Concept also includes
other phrases that not belong to the named entity.
2. 2. 4 Anaphora and Co-references
An anaphora and co-references technique is to find theactual things for the pronoun or the unknown phrase thatappeared in the document. This technique seems to havecontributed to the information retrieval since it is able toeliminate the unclear expression in the document. However,
the truth is not the case. Anaphora and co-references also
can not improve information retrieval effect. On the one
hand anaphora resolution still has more errors, the other
hand pronoun and the unknown phrase does not actuallyaffect the results of information retrieval[12].
2. 2. 5 Word Sense Disambiguation
Voorhees used the word sense disambiguation techniquein treating word sense as an index item. It is found thatsome queries can indeed benefit from the word sense index,
however more decline after individually analyzing retrieval
results of each query. Almost all the declines are because of
the match fails of those words should be matched between
queries and documents, since word sense index is used.
Then what word sense disambiguation accurate could
help the information retrieval? The answer is 90% which is
given by Sanderson[13]. In his experiment he found that the
improvement of succeeded disambiguation for retrieval
would be offset by the negative effects of failed
disambiguation if there was 20% to 30% error rate of word
sense disambiguation.
Stokoe et al used Semcor corpus which released with
WordNet training the word sense disambiguation system.
The words of failed disambiguation would be assigned tothe meaning of the highest frequency appeared in the
WordNet[14]. Although the accuracy of the word sense
disambiguation was only 62%, the experimental results
which completed in TREC9 data show that thisunreasonable states were improved in the results ofstemming and the retrieval precision has 6% to 8% increase[3]. The corpus-based stemming approach improved theretrieval precision slightly to the Porter stemmer and
KSTEM put forward by Xu and Croft [4].
In practice, stemming technique was widely used in
information retrieval system for its high availability
although it can only improve the retrieval effect a little.
2. 1. 4 Part-of-Speech Tagging
It has no obvious usage about part-of-speech tagging to
information retrieval. The biggest problem is that we do notknow how to use it in retrieval even if it has a very highaccuracy .
One approach is that only index the certain parts-ofspeech. Kraaij and Pohlmann studied the importance of thedifferent part-of-speech words to retrieval [5]. Their result
is that 58% are noun, 29% are verb and 13% are adjective
among the document words that useful to retrieval. It can befound that 84% are noun among the useful words if we only
focus on those fronts of documents. Arampatzis et al onlyused nouns to complete the experiment and the result
showed that there was 4% improvement compared withusing all words [6].
Another use is to separate the different parts of speechof words. Let the words that have the same part of speech in
the query and document can be matched. Using TREC7 andTREC8 data sets, Su Qi and others examined the retrieval
effect of this use to the SMART system [7].The results
show that if we can tag the words that have the same form
but different part of speech we will improve retrieval
accuracy and decrease the matching noise. The words thathave the same meaning, the same stemming and different
parts of speech have no match, leading to the decline ofretrieval recall.
2.2 Application of Advanced NLP Technique
Advanced natural language processing technique
include parsing, phrase identification, named entity
recognition, concept extraction, anaphora resolution andword sense disambiguation etc.
2. 2. 1 Phrase Identification
Phrase identification technique used in information
retrieval mixed the results, largely depending on thespecific recognition technology, the phrase type and thematching strategy[8,9,10]. Nie and Dufort made the phrase
as an additional unit to combine with the traditional wordbased index. They placed the phrases and words in different
vectors, calculated the similarity of the query and document
and then added their weights[11].

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Natural language processing resources are constructedmanually or revised manually after generated by machine.
It has a very high accuracy and it is suitable to be used in
information retrieval. We should use it according to theactual situation for different problems.

disambiguation method can relatively improve the retrieval
effect 45%. However, their retrieval results are still worse
than the best result of TREC9 even if there is more
improvement about retrieval accuracy because theperformance of the benchmark system used in theirexperiments is poor.
Kim et al used a particular Word Sense Disambiguation
technique. They only consider the 25 most original word
meanings of a word in the WordNet and then assign ameaning to a word, which can insure the accuracy of
WSD[15].

3 Comparison of Different NLP Techniques
Applied in IR Systems
TREC NLP evaluation results (table1 and table2) showthat phrase identification, terminology, and stemming andother natural language processing Techniques used in
information retrieval can obtain better effect than the wordbased retrieval system.

2.3 Adding Natural Language Processing Technique into
Language Model
The nature of the language model is to determine therelevance between the query and document by computing
the probability of generating the query from the document.
Kumaran and Allan joined the stemming technique inthe language model. They thought that Stemming can beseen as smoothing [16]. They proposed a generative model.
In this model, they thought that it can be divided into two
steps from d to w. Firstly, d generates c, and then cgenerates w. This assumption comes from the writer writing.
When writers select the words they usually think to ameaning and then select the word with correct form.
Experimental results show that the average retrieval
accuracy of the new model has increased about 10%.

Table 1. The experimental results of basic NLP
techniques used in IR systems

Stop
word
removalWordsegmentation
Stemming
Part-of-speechtagging

TREC-6
Avg.Prec.
R.Prec.

No usenull

No usenull

0.3721

0.39880.50440.5072

0.328

0.356

0.273

0.304

0.46320.48040.26920.2712

Most of the trial had no good effect, which try to use
natural language processing in information retrieval, evenhad some little help, it was not satisfied for people.
Researchers have analyzed, and draw conclusions that itwas need to be optimized that natural language processing
for information retrieval tasks.

2.4 The Application Effect of NLP Technique inInformation Retrieval
TREC5 NLP evaluation results show that query
expansion, phrase identification, terminology, andstemming and other natural language processing
Techniques used in information retrieval can obtain better
effect than the word-based retrieval system. These systems
that applied these technologies are still not better than thesystems based on statistical [17].
Manning analyzed the roles of natural language
processing techniques in Web Retrieval. In fact, Web
retrieval technique has gained more progress through Web
link analysis techniques and AnchorText.

Table 2. The experimental results of advanced NLP
techniques used in IR systems

Phrase identification
Named
entityextraction
Concept extraction
Anaphora
coreferences
Word
sense
disambiguation

2.5 The Application of NLP Resources in Information
Retrieval
Natural language processing resources refer to thedictionary like WordNet and HowNet. Smeaton
experimented with WordNet after the failure that using
natural language technique to retrieval experiment such as
parsing. In addition, as mentioned above, WordNet iscommonly tools used in word sense disambiguation.

© ISAROB 2013

TREC-5
Avg.Prec.
R.Prec.

TREC-5
Avg.Prec. R.Prec.
0.23470.2939TREC-6
Avg.Prec. R.Prec.
0.24340.25740.28600.28350.19610.20140.26130.25450.3346

0.33210.29980.2876

0.32610.32110.32910.19940.2426

0.1980

4 CONCLUSION
For a long time, the development of natural language
processing is to be applied to the tasks which need precise
results such as machine translation, so the role of natural

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as recall enhancement ”, In: Proceedings of the 19th
Annual International ACM SIGIR Conference on Researchand Development in Information Retrieval ,ACM Press,
pp.40-48, 1996.
[6] A. T. Arampatzis , Th. P. van der Weide , C. H. A.
Koster and P. van Bommel , “ Text Filtering using
Linguistically-motivated Indexing Terms ” . Technical
Report CSI-R9901, Computing Science Institute,
University of Nijmegen , Nijmegen , The Netherlands ,1999.
[7] Qi Su,Hongying Zan, “ Effects of POS Tagging
on Performance of IR Systems ” , Journal of Chinese
Information Processing, vol.19,no.2 , pp.58-65,2005.
[8] Thorsten Brants, “Natural Language Processing
in Information Retrieval ” , In: Proceedings of 20th
International Conference on Computational Linguistics ,Antwerp, Belgium, pp.1-13, 2004.
[9] M. Mit ra , C. Buckley , A. Singhal , and C. Cardie,“An analysis of statistical and syntactic phrases”, In :Proceedings of the RIAO97 , pp.200-216,1997.
[10]
S. E. Robert son and S. Walker, “ Okapi/
Keenbow at TREC28”, In: Proceedings of the 8th Text
Retrieval Conference, NIST Special Publications 500-246,
Gaithersburg , pp.151-162, 1999.
[11]
Jian-Yun Nie and J ean-Francois Dufort,“Combining words and compound terms for monolingualand cross-language information retrieval”, In: Proceedingsof Information. Beijing, pp.453-458, 2002.
[12] James Allan, “Natural Language Processing for
Information Retrieval ” , tutorial presented at the
NAACL/ANL P language technology joint conference inSeattle ,Washington , April 29 , 2000.
[13]
M. Sanderson, “Word Sense Disambiguation
and Information Retrieval ”, In: Proceedings of the 17th
Annual International ACM SIGIR Conference on Researchand Development in Information Retrieval , ACM Press,
pp.49-57,1994.
[14] Christopher Stokoe , Michael P. Oakes and John
Tait , “ Word Sense Disambiguation in Information
Retrieval Revisited”, In: Proceedings of the 26th Annual
International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, ACM Press ,pp.159-166, 2003.
[15] Sang-Bum Kim , Hee-Cheol Seo and Hae-Chang
Rim, “Information Retrieval using Word Senses : Root
Sense Tagging Approach” , In: Proceedings of the 27th
Annual International ACM SIGIR Conference on Researchand Development in Information Retrieval,ACM Press,
pp.258-265, 2004.
[16] James Allan and Giridhar Kumaran, “Stemming in
the Language Modeling Framework”, In: Proceedings ofthe 26th Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval(poster) ,ACM Press , pp.455-456,2003.
[17] Canhui Wang , Min Zhang , Shaoping Ma, “A
Survey of Natural Language Processing in Information
Retrieval”, Journal of Chinese Information Processing,vol.21,no. 2,pp.40, 2007.

language understanding may be larger in the question
answering system, automatic abstract and information
extraction. In face, in these tasks, we have achieved goodresults through the interaction between the NLP and IR.
The results of TREC also show that Natural language
processing can improve the effect of these tasks.
It can be seen in table 3 that the tasks of longer length of
the query are more suitable for using natural language
processing, such as information extraction and question
answering system etc. It is conceivable that the tasks ofshorter length results more need to use natural language
processing because of the need for syntactic and semantic
processing in order to ensure its accuracy. Therefore,
compared to the document retrieval, information extraction
and question answering system are more suitable for the useof natural language processing.
Table 3. Classification of Document Retrieval, Passage
Retrieval, Question Answering, and Information Retrieval
according to query length and result length
QuerylengthResult length
Short
MediumLong

Short

MediumLong

QA/ IEPRDR

REFERENCES[1]
Ricardo Baeza-Yates, “ Challenges in theInteraction of Information Retrieval and Natural Language
Processing ” . In: Proceedings of 5th International
Conference on Intelligent Text Processing andComputational Linguistics , CICLing 2004 , Seoul , Korea ,February 15-21 , pp.445-456, 2004.
[2] Fuchun Peng , Xiangji Huang , Dale Schuurmans
and Nick Cercone, “ Investigating the Relationshipbetween Word Segmentation Performance and Retrieval
Performance in Chinese IR”, In: Proceedings of 19thInternational Conference on Computational Linguistics,
pp.72-78, 2002.
[3]
Tomek Strzalkowski and Barbara Vauthey,“ Information retrieval using robust natural language
processing ”, In: Proceedings of the 30th annual meetingon Association for Computational Linguistics, pp.104111,1992.
[4] J Xu and W. B. Croft , “Corpus-based stemming
using cooccurrence of word variants”, ACM Transactions
on Information Systems ( TOIS) , vol.16,no.1, pp.61-81,
1998.
[5] W. Kraaij and R. Pohlmann, “Viewing stemming© ISAROB 2013

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AUTHOR INDEX
Notation of session name: PL: Plenary Speech, IS: Invited Speech,
ISP: Invited Session on Playware, GS: General Session, OS: Organized Session
PS: Poster Session
A
Adachi

Yuto

Adaniya
Ahn
Ahn
AikawaAoki
Arita

Yoshiaki
Byeong-kyu
Jou-Hyeon
MasaruKenjiTakaya

Asada

Ayano

Taro

Hideki

D
OS6-1
OS6-6
OS12-1
GS15-2
PS1-13
OS2-4
OS2-2
GS2-1
GS2-2
GS11-2
GS17-4
OS8-1
OS8-2
OS8-3
OS8-4
OS6-4

P-12 / 130
P-13 / 152
P-28 / 258
P-17 / 508
P-19 / 594
P-27 / 49
P-27 / 41
P-21 / 316
P-21 / 320
P-23 / 448
P-25 / 551
P-24 / 176
P-24 / 180
P-25 / 186
P-25 / 192
P-13 / 142

OS7-4
OS7-5
OS8-1

P-24 / 168
P-24 / 172
P-24 / 176

GS5-2
OS6-1
OS6-6
PS1-13OS10-2OS10-3
PS1-16OS11-5
PS1-18OS10-7OS10-1
PS1-9
OS10-7OS10-3
PS1-11
PS1-20GS15-2
PS1-7
ISP3
OS11-1OS11-2OS1-1
OS1-2

P-18 / 364
P-12 / 130
P-13 / 152
P-19 / 594
P-14 / 218
P-15 / 222
P-19 / 605
P-17 / 254
P-20 / 613
P-15 / 238
P-14 / 214
P-19 / 578
P-15 / 238
P-15 / 222
P-19 / 586
P-20 / 621
P-17 / 508
P-19
P-12 / 9
P-16 / 242
P-17 / 246
P-22 / 21
P-23 / 25

Do

Radzi

Boku

Kanu

Endo

Takahiro

Jihao
Kosin

Chang
Chang
Chang
Chen
Chen
Chen
Chen
Chen

In seopJhih-Cheng
Yi-Chang
Bo-HanJhih-Han
Jhih-Sian
Kuo-Kung
Pi-YunChen
ChiangChiang
Cho
Choi
Choi
Christensen
Chung
Ciou
Cui

Po-Yu
Huann-Keng
Ping-Chieh
Young Im
Dongwoon
Taeyong
David J.
Cheng-Yun
Wei-YiChengyou© ISAROB 2013

P-17

GS17-3P-25 / 547

PS1-13OS10-2OS10-1OS10-2
GS1-1
GS13-1
GS8-4
OS4-7
GS3-3
OS9-4
GS16-2
OS3-1
GS17-1
OS2-1
OS2-2
OS2-3
OS14-1OS14-2
OS5-6

P-19 / 594
P-14 / 218
P-14 / 214
P-14 / 218
P-16 / 304
P-26 / 476
P-17 / 412
P-14
P-27 / 333
P-15 / 210
P-26 / 525
P-13 / 53
P-25 / 539
P-27 / 37
P-27 / 41
P-27 / 45
P-28 / 296
P-28 / 300
P-24 / 122

GS3-3
GS4-2
GS16-3
GS6-1
GS12-4
PS1-6
GS16-2
PS1-1
OS10-5

P-27 / 333
P-20 / 346
P-26 / 529
P-25 / 371
P-18 / 472
P-19
P-26 / 525
P-18 / 555
P-15 / 230

GS15-4
GS11-1
GS14-3
GS16-4
OS1-3
OS1-4
OS5-3
OS5-7

P-18 / 516
P-23 / 444
P-16 / 500
P-26 / 535
P-23 / 29
P-23 / 33
P-23 / 110
P-24 / 126

F
FapyaneFu
Fu

Deby
Yu-FenYu-Yi

FuchidaTakayasuFujiedaFujiwara
Fujiwara
Fukami
Funahashi
Funamoto
FunayaFurutani

HomareKei
TakeshiKazuhiko
KazumaSatoshi
Hiroyuki
HiroshiFuruya

Naoki

C
Cao
Chamnongthai

GS15-1E

B
Bin Ambar

ShaodongG
GaoGhazali

Xiang
Mohd RiduwanGO
GoharaGornev
Goto
Gouko
Guo

Sou
KeisukeEvgeny S.
HirokiManabuJr-Hung
H

I-1

Habib
Hagiwara
Han

Maki K.
ShingoChang-SooHan

Kun-Young

HanadaMasaki

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

HanadaMasakiHara
Hasan
HashimotoHattori

YusukeMd. Mahbub
MasatoTetsuoHayashi

Eiji

Higuchi

Kohji

HikasaHiraishi
Hirofuji
Hsia
Hsieh

Shigeki
Kunihiko
Yasuyuki
Kuo-Hsien
Sheng-Ta

Huang

Chung-Hsin

Huang

Tzu-Hsuan

Hung

Chung-Wen

OS9-1
OS9-2
OS4-5
GS13-3
GS9-2
OS13-1OS13-2OS13-3OS13-4OS13-5
GS13-4
GS15-3
OS6-1
OS6-6
OS3-4
GS4-1
GS16-1OS11-2
PS1-16
PS1-17
PS1-18
PS1-11
PS1-19OS12-3OS12-4OS11-4OS11-5

P-15 / 197
P-15 / 202
P-14 / 93
P-26 / 484
P-21 / 420
P-22 / 276
P-22 / 280
P-22 / 284
P-22 / 288
P-22 / 292
P-26 / 488
P-17 / 512
P-12 / 130
P-13 / 152
P-13 / 65
P-20 / 342
P-26 / 521
P-17 / 246
P-19 / 605
P-20 / 609
P-20 / 613
P-19 / 586
P-20 / 617
P-28 / 268
P-28 / 272
P-17 / 250
P-17 / 254

GS1-3
GS17-1
OS3-4
OS14-1OS14-2
OS2-3
OS13-5OS13-2OS13-4OS13-5
OS7-5
GS5-1
OS4-1
GS13-4
GS16-3
GS9-2
PS1-8
OS4-6
GS1-2
GS3-2
GS5-2
GS16-1
GS16-2
OS2-3
OS14-1OS14-2
GS17-4
OS4-6
OS5-7
OS9-2
OS13-1

P-16
P-25 / 539
P-13 / 65
P-28 / 296
P-28 / 300
P-27 / 45
P-22 / 292
P-22 / 280
P-22 / 288
P-22 / 292
P-24 / 172
P-18 / 360
P-14 / 78
P-26 / 488
P-26 / 529
P-21 / 420
P-19 / 574
P-14 / 97
P-16 / 308
P-27 / 329
P-18 / 364
P-26 / 521
P-26 / 525
P-27 / 45
P-28 / 296
P-28 / 300
P-25 / 551
P-14 / 97
P-24 / 126
P-15 / 202
P-22 / 276

Izumi

IchiroKazushi
MasaakiSatoshiImai
Imai

Masatoshi
YoshiroImaikeImamuraInoue
Inoue
IrawanIshikawa
Ishimaru
Ito
Ito
Ito

Kenichi
Hiroki
Hirofumi
Tomohiro
Addie
Tomo
Takashi
HiroshiKazuyuki
Masanori

Ito

Takao

Ito
IwasakiIwasakiTakashi
Hideo
ShinyaIzumi

Tetsuya© ISAROB 2013

OS13-2OS13-3

P-22 / 280
P-22 / 284

GS5-3
GS8-3
OS13-3
ISP1
GS8-1
GS8-2
GS10-3
GS7-3
GS8-2
GS8-1
OS1-1
GS3-2
GS17-1

P-18 / 367
P-17 / 408
P-22 / 284
P-12 / 1
P-17 / 400
P-17 / 404
P-21 / 436
P-21 / 391
P-17 / 404
P-17 / 400
P-22 / 21
P-27 / 329
P-25 / 539

PS1-2
PS1-10
OS6-1
OS6-6
OS4-3
GS12-1
GS7-3
GS4-4
GS17-3OS13-1OS13-2OS13-1OS13-3
OS5-6
GS17-3
PS1-6
GS6-4
GS12-3
GS14-1
GS12-3
GS14-2
GS5-3
GS1-2
GS9-3
PS1-10OS10-1OS10-2
GS4-1
GS11-3
GS12-2
OS7-2
OS2-1
GS4-3
OS4-6
OS4-6
OS8-4
OS4-4
PS1-6
GS2-2
GS11-3

P-18 / 558
P-19 / 582
P-12 / 130
P-13 / 152
P-14 / 85
P-18 / 460
P-21 / 391
P-20 / 356
P-25 / 547
P-22 / 276
P-22 / 280
P-22 / 276
P-22 / 284
P-24 / 122
P-25 / 547
P-19
P-25 / 383
P-18 / 468
P-16 / 492
P-18 / 468
P-16 / 496
P-18 / 367
P-16 / 308
P-22
P-19 / 582
P-14 / 214
P-14 / 218
P-20 / 342
P-23 / 452
P-18 / 464
P-24 / 160
P-27 / 37
P-20 / 352
P-14 / 97
P-14 / 97
P-25 / 192
P-14 / 89
P-19
P-21 / 320
P-23 / 452

J
Jang
Jareanpon
Javidi
JessenJia

Jaeho
Chatklaw
BahramJari DueYingmin

Jiang
Jiang
Jiang
Jin
Jin
Jung

GanwenSharu
Yuefei
Hu
Xing
Jehan
K

I
Iimura
Ikeda
Ikeda
Ikeda

TetsuyaI-2

KadoyaKai
Kajikawa

KazuyaYoshihiro
Tatsuyoshi

KamiuraKarakuzu
Kashima
Kawabata
Kawai
Kawakami

Moto
Cihan
Masayuki
Yuma
Takamitsu
YusukeKawanoHiromichi

KawanoKawasaki
Khomyakov
Kiguchi

Yoshihiro
HaruhisaIlya A.
Kazuo

Kikuchi
Kim
Kim
Kim
Kim
Kitaguchi
Ko

Yuui
Bong Keun
HyogonJinwook
Yeon-Seob
Satoshi
Chia-Nan

Kobayashi
Kobayashi

Koichi
Kunikazu

Koga
Koga
Koike
Kondo
Kori
Koro
Koyamada
Krasnikov
KubotaKuremoto

Hiroyuki
Kiminobu
Hisashi
Takao
Hiroshi
ChikotoKoji
Andrey G.
Yuchi
TakashiThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Kuremoto
KurogiKutsuna
Kwak

Takashi
Tatsuma

GS12-2
OS2-3
OS14-1OS14-2OS13-2
OS9-3
OS9-4

DaisukeYoungjoo

P-18 / 464
P-27 / 45
P-28 / 296
P-28 / 300
P-22 / 280
P-15 / 206
P-15 / 210

M
Ma
Mabu
MackinQinLianShingoKenneth J.

Maeyama

ShoichiMagnússon
MakinoArnþórKoji

Mallipeddi
Marti
Masumitsu
Masunari
MatobaMatsubaMatsuiMatsunaga
MatsuoMatsushita
MinamiMiura
Miya
MiyachiMiyakeMiyamichi
Mizoguchi
Moghadam
Mohd Tumari
Mori
Mori
Mori

RammohanPatrizia
KyosukeSaki
Ryuichi
Yuka
Yosihiro
Nobutomo
HirokiHarunaMamoruAsaki
Takuya
Hideo
Shuhei
Juichi
Fumio
MikaelMohd Zaidi Bin
Hayao
HiroshiKazumaMoriokaKazuyukiMoritohMotonaka
Mouri
Murao
MurataMurataMurayama

YoshioKimikoTetsuyaHajimeTakayuki
Yu
Yoriko

L
Lai
Lai
Lai

Chin-Lun
Chin-Lun
Li-Chun

Lai
Lee
Lee
Lee
Lee

Pin-RuDae Jong
Dongwook
Duckyeon
Hee-Hyol

Lee
Lee

Hogil
Jae Hoon

Lee
Lee
Lee

Ji-Yeong
Jongwon
Ju-Jang

Lee
Lee
Lee
Lee
Li

Li-YinMinho
Soo-JinSukwooChing-Lieh

Li
Li
Liao

Lu
Mingxing
FangboLiao

Yi-Lin

Lien
Lim
Lin
Lin
Lin
Lin
Lin

Shao-Fan
DuHyunBen-ZhongChien-Chou
Sheau-WenTsung-Ching
Wen-Bin

Lin
Liu
Long
Lu
Lu
Lund

Yung-Chin
Guan-Yu
ShichenCunwei
Yi-Yu
Henrik Hautop© ISAROB 2013

PS1-12
PS1-14OS10-5OS10-6
PS1-11
PS1-20GS15-2
GS15-2
OS1-1
OS1-2
OS1-3
OS1-4
GS15-2
GS4-3
GS8-4
GS10-1
GS10-2
GS14-3
GS5-3
GS1-4
GS9-3
PS1-14
GS17-1
PS1-13
GS5-3
PS1-11
PS1-19OS1-3
GS10-3
GS14-4
GS15-1OS10-5OS11-1OS11-2
PS1-13OS11-3OS11-3OS10-6
PS1-12OS10-3OS10-7OS11-1OS10-1
GS8-2
OS12-1OS10-7
ISP1
ISP3

P-19 / 590
P-19 / 597
P-15 / 230
P-15 / 234
P-19 / 586
P-20 / 621
P-17 / 508
P-17 / 508
P-22 / 21
P-23 / 25
P-23 / 29
P-23 / 33
P-17 / 508
P-20 / 352
P-17 / 412
P-20 / 428
P-20 / 432
P-16 / 500
P-18 / 367
P-16 / 312
P-22
P-19 / 597
P-25 / 539
P-19 / 594
P-18 / 367
P-19 / 586
P-20 / 617
P-23 / 29
P-21 / 436
P-16 / 504
P-17
P-15 / 230
P-16 / 242
P-17 / 246
P-19 / 594
P-17
P-17
P-15 / 234
P-19 / 590
P-15 / 222
P-15 / 238
P-16 / 242
P-14 / 214
P-17 / 404
P-28 / 258
P-15 / 238
P-12 / 1
P-12 / 9

OS2-1
GS11-3
OS5-5
OS5-7
OS9-2
OS3-1
OS3-2
ISP3
GS9-2
GS10-4
GS17-1
ISP2
GS13-1OS13-3
GS11-1
GS17-1
OS6-4
GS6-3
PS1-4
OS13-2
OS3-6
ISP4
OS5-6
OS4-4
GS3-3
PS1-15
GS11-4
ISP3
GS4-2
OS9-1
PS1-15
OS5-7
OS9-2
PS1-5
PS1-8
OS13-4
OS3-2
GS17-3
GS9-4
GS6-2
OS4-3
OS4-6

P-27 / 37
P-23 / 452
P-24 / 118
P-24 / 126
P-15 / 202
P-13 / 53
P-13 / 57
P-12 / 9
P-21 / 420
P-21 / 440
P-25 / 539
P-12 / 5
P-26 / 476
P-22 / 284
P-23 / 444
P-25 / 539
P-13 / 142
P-25 / 379
P-19 / 566
P-22 / 280
P-13 / 73
P-12 / 17
P-24 / 122
P-14 / 89
P-27 / 333
P-19 / 601
P-23 / 456
P-12 / 9
P-20 / 346
P-15 / 197
P-19 / 601
P-24 / 126
P-15 / 202
P-19 / 570
P-19 / 574
P-22 / 288
P-13 / 57
P-25 / 547
P-22 / 424
P-25 / 375
P-14 / 85
P-14 / 97

GS6-3
OS3-4
OS3-5
OS5-3
GS15-4
OS3-3
GS3-1
OS2-3
OS14-1OS14-2
GS9-4
OS4-1
OS4-2
OS12-2

P-25 / 379
P-13 / 65
P-13 / 69
P-23 / 110
P-18 / 516
P-13 / 61
P-26 / 325
P-27 / 45
P-28 / 296
P-28 / 300
P-22 / 424
P-14 / 78
P-14 / 81
P-28 / 264

N

I-3

Nabekura
Nagai

Kazuki
Isaku

Nagai
NagataYasuo
FusaomiNagataNagatomo

Yuichi
MakotoNagayoshi
NaitohMasatoKen

Nakamura

Kazunori

The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Nakamura
Nakamura
Nakanishi

Masahiro
Yoshikazu
Yuu

NakanoKazushiNakashima
NakauraNakayama
Narukawa
NiinumaNishimoto
Nishiyama

Naotoshi
HatsumiShigeru
Kyoko
Shohei
Keita
Hiroyuki

Nishiyama
Nogami
NomuraNonamiNunohiro

HiroyukiTsuyoshiShin-ichiro M.
Kenzo
Eiji

GS9-1
OS3-5
OS8-2
OS8-3
OS6-4
OS6-5
GS3-1
PS1-15
GS1-3
GS3-2
PS1-3
GS11-2
GS5-1
GS11-4
GS12-4
GS17-2OS4-2
OS4-7
GS16-3
OS5-1
OS5-2
OS5-7
OS9-1
OS9-2
OS9-3

P-21 / 416
P-13 / 69
P-24 / 180
P-25 / 186
P-13 / 142
P-13 / 146
P-26 / 325
P-19 / 601
P-16
P-27 / 329
P-19 / 562
P-23 / 448
P-18 / 360
P-23 / 456
P-18 / 472
P-25 / 543
P-14 / 81
P-14
P-26 / 529
P-23 / 101
P-23 / 106
P-24 / 126
P-15 / 197
P-15 / 202
P-15 / 206

GS11-3
GS12-2
OS5-6
GS10-2
OS5-1
OS5-2
GS9-2
GS6-4
GS12-3
GS14-1
GS6-3
GS10-1
GS4-3
GS8-4
GS10-1
GS10-2
GS3-4
OS7-2
GS7-4
GS3-1
GS6-4
PS1-6
GS15-4
OS3-3
GS10-1
OS7-1
OS7-2
OS7-3

P-23 / 452
P-18 / 464
P-24 / 122
P-20 / 432
P-23 / 101
P-23 / 106
P-21 / 420
P-25 / 383
P-18 / 468
P-16 / 492
P-25 / 379
P-20 / 428
P-20 / 352
P-17 / 412
P-20 / 428
P-20 / 432
P-27 / 338
P-24 / 160
P-21 / 395
P-26 / 325
P-25 / 383
P-19
P-18 / 516
P-13 / 61
P-20 / 428
P-24 / 156
P-24 / 160
P-24 / 164

ISP3
OS9-1
OS9-3

P-12 / 9
P-15 / 197
P-15 / 206

Park
Park
Park
Peng
Pilat

Masanao

Obu
Ohara
OhshiroYuka
Kazuyoshi
Masanori

OhyamaOkada

Yasuhiro
Nobuhiro

OkajimaOkamotoOkamoto

HiroshiKei
ShingoOkazakiOkumura
Olufs
Ono
Ono
Orlov
OtsukaNaonobu
Katuhiro
Sven
Isao
YosukeSergey N.
Akimasa

Oue
Oya

MasumiMasahiro

Ramli© ISAROB 2013

MoisesJonggeol

P-15 / 210
P-18 / 367
P-15 / 206
P-16
P-25 / 551

Mohd Syakirin

GS16-3

P-26 / 529

GS4-2
OS7-4
OS7-5
GS17-2
GS9-2
OS2-1
OS2-2
OS2-3
OS14-1OS14-2
OS4-4
GS3-3
GS4-4
IS1
GS7-3
OS6-1
OS6-6
OS4-7
OS6-2
OS6-3
GS7-1
GS14-3
GS9-2
OS10-6
GS13-2
GS11-2
ISP4
GS16-4
OS6-2
OS6-3
OS6-5
PS1-15OS7-1
OS7-3
OS13-3
GS14-1
ISP2
OS11-2OS10-2OS10-5OS10-6OS11-1
PS1-17
GS8-1
GS11-1
OS7-3
OS2-2
GS2-1
GS9-1

P-20 / 346
P-24 / 168
P-24 / 172
P-25 / 543
P-21 / 420
P-27 / 37
P-27 / 41
P-27 / 45
P-28 / 296
P-28 / 300
P-14 / 89
P-27 / 333
P-20 / 356
P-12
P-21 / 391
P-12 / 130
P-13 / 152
P-14
P-13 / 134
P-13 / 138
P-21 / 387
P-16 / 500
P-21 / 420
P-15 / 234
P-26 / 480
P-23 / 448
P-12 / 17
P-26 / 535
P-13 / 134
P-13 / 138
P-13 / 146
P-19 / 601
P-24 / 156
P-24 / 164
P-22 / 284
P-16 / 492
P-12 / 5
P-17 / 246
P-14 / 218
P-15 / 230
P-15 / 234
P-16 / 242
P-20 / 609
P-17 / 400
P-23 / 444
P-24 / 164
P-27 / 41
P-21 / 316
P-21 / 416

S

P
Pacheco
Park

OS9-4
GS5-3
OS9-3
GS1-3
GS17-4
R

O
Obayashi

Jonggeol
Sangdeok
Sooyoung
Gang
Marcin L.

I-4

SaealalSagaraMuhammad Salihin
Shinichi

Saito
Sakai
Sakamoto

YusukeMasashi
MakotoSakamoto
Sakurai
Sano
SapatySato
Sato

Naohisa
Naoko
ShigenoriPeter S.
Kiminori
Tomoaki

Sato
SawadaYusukeKenji

Serikawa
Shao
She
Shih
ShimadaShimohara
Shimokakimoto
Shin
Shin

SeiichiMing-Lei
Jin-Hua
Chia-Hung
Atsushi
Katsunori
TomoyaKyoosik
Seiichi

Shintaku
Shoji
Shu

SatoshiKenji
Panfeng

Shu
Soeda
Stienstra
Su
Su

QingyuTakafumi
Jelle T.
Juhng-Perng
Kuo-Lan

Su
Su
Sudo
Suetake
Sueyoshi
Sughimura
Sugimoto

Tse
Xiaofeng
Hiroki
YusukeKenji
Noritsugu
TsuyoshiThe Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

Sugisaka

Masanori

Sugita

Kaoru

Sumantri
Sumi
Sun

Bambang
YasushiChi-Hsien

SuzukiSuzuki

Kenji
Reiji

GS14-4
GS15-1OS12-2OS12-3OS12-4
GS4-4
GS14-2
PS1-11
PS1-19
ISP4
GS2-1
GS2-2
GS11-2
GS17-4P-16 / 504
P-17
P-28 / 264
P-28 / 268
P-28 / 272
P-20 / 356
P-16 / 496
P-19 / 586
P-20 / 617
P-12 / 17
P-21 / 316
P-21 / 320
P-23 / 448
P-25 / 551

OS8-1
OS8-2
OS8-3
GS1-2
GS3-4
GS9-4
OS2-4
GS6-1
GS6-2
GS16-3
GS14-2
PS1-2
PS1-3
PS1-4
GS15-3
GS13-3
GS11-2
GS13-2
OS3-6
GS6-1
GS6-2
GS3-4
GS13-3
GS12-1
GS1-1
OS10-4OS4-2
GS11-1
GS3-4
PS1-10
PS1-15
GS12-3OS12-1
OS9-1
PS1-11
PS1-19P-24 / 176
P-24 / 180
P-25 / 186
P-16 / 308
P-27 / 338
P-22 / 424
P-27 / 49
P-25 / 371
P-25 / 375
P-26 / 529
P-16 / 496
P-18 / 558
P-19 / 562
P-19 / 566
P-17 / 512
P-26 / 484
P-23 / 448
P-26 / 480
P-13 / 73
P-25 / 371
P-25 / 375
P-27 / 338
P-26 / 484
P-18 / 460
P-16 / 304
P-15 / 226
P-14 / 81
P-23 / 444
P-27 / 338
P-19 / 582
P-19 / 601
P-18 / 468
P-28 / 258
P-15 / 197
P-19 / 586
P-20 / 617

V
VinczeMarkus

Wada
Wahab
Wang
Wang
Wang

W
Hideki
Yasmin Binti Abdul
Huan-Sheng
Jeong Hyeon
Jiwu

Wang
Wang
WatadaWatanabeMeng-Hui
Yanrung
Shogo
Keigo

Watanabe
Watanabe
Wong
Wu
Wu
Wu

MutsumiShun
Huang-Wei
Chang-Hong
Huang-Lyu
Jing

T
TabuseMasayoshi

TabuseTachibanaTakatsuka
TamakiTamotoTamuraMasayoshi
Katsunori
KayokoHisashiKazuyuki
HirokiTan
TanakaTanakaYee Yin
HideyukiKen-ichiTanakaTanemura
Tanev
Taniguchi
Tanimoto
Tanno

Kyoko
Satoshi
Ivan
Rin-ichiro
Hiro
KoichiTanoueTeramoto
Tezel
Thi Aung
Ting
Tobe
Tojo
TomitaTomizuka
ToyamaTsuji
TsujinoTsukamoto
Tuen

Michihito
Kenbu
Gülay
Kathy
Ying-Yao
Takahiro
Satoshi
Shigeyuki
Masayoshi
FubitoYasutaka
Kazuhiro
Seiji
Lung-Fai

Yasuo

Uchiyama

Naoki© ISAROB 2013

OS2-3
OS14-1OS14-2
GS4-4

P-21 / 395

OS7-2
GS4-2
OS10-4
GS1-4
GS14-4
GS15-1
PS1-9
GS13-2
GS11-3
GS15-4
OS3-1
OS3-2
OS3-4
OS3-5
GS7-3
GS12-2
PS1-19OS11-3
PS1-17
GS14-3
GS16-4P-24 / 160
P-20 / 346
P-15 / 226
P-16 / 312
P-16 / 504
P-17
P-19 / 578
P-26 / 480
P-23 / 452
P-18 / 516
P-13 / 53
P-13 / 57
P-13 / 65
P-13 / 69
P-21 / 391
P-18 / 464
P-20 / 617
P-17
P-20 / 609
P-16 / 500
P-26 / 535

PS1-20GS10-3

P-20 / 621
P-21 / 436

GS5-2
GS12-1
GS3-4
GS9-1
OS5-4
OS5-1
OS5-2
OS5-3
OS5-7
OS9-1
OS9-2
OS9-3
OS7-4
OS2-1
OS2-2
OS2-4
GS9-1
PS1-5
GS2-3
GS3-3
GS13-2
GS7-1
GS6-1
GS14-3
GS16-4
GS2-3
PS1-12

P-18 / 364
P-18 / 460
P-27 / 338
P-21 / 416
P-23 / 114
P-23 / 101
P-23 / 106
P-23 / 110
P-24 / 126
P-15 / 197
P-15 / 202
P-15 / 206
P-24 / 168
P-27 / 37
P-27 / 41
P-27 / 49
P-21 / 416
P-19 / 570
P-21 / 324
P-27 / 333
P-26 / 480
P-21 / 387
P-25 / 371
P-16 / 500
P-26 / 535
P-21 / 324
P-19 / 590

X
Xi
Xu

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The Eighteenth International Symposium on Artificial Life and Robotics 2013 (AROB 18th ’13),
Daejeon Convention Center, Daejeon, Korea, January 30-February 1, 2013

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Akira
Sergey I.
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Nikolay A.
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Yonghu© ISAROB 2013

I-6

Artificial Life and Robotics
Editor-in-Chief: Hiroshi Tanaka▶ Publishing body: International Symposium on Artificial Life andRobotics.
▶ Presents the development of new technologies concerning artificial
life and robotics, especially computer-based simulation andhardware.
▶ Publishes technical papers and authoritative state-of-the-art reviews.
▶ Covers a broad multidisciplinary field.

4 issues/year

Electronic access▶ springerlink.com

Subscription information▶ springer.com/librarians

This is an international journal publishing original technical papers and authoritative
state-of-the-art reviews on the development of new technologies concerning artificial lifeand robotics, especially computer-based simulation and hardware.
The journal covers a broad multidisciplinary field, including such areas as artificial brain
research, artificial intelligence, artificial life, artificial living, artificial mind research,brain science, chaos, cognitive science, complexity, computer graphics, evolutionary
computations, fuzzy control, genetic algorithms, innovative computations, intelligent
control and modeling, micromachines, micro-robot world cup soccer tournament,
mobile vehicles, neural networks, neurocomputers, neurocomputing technologies
and applications, robotics, robus virtual engineering, and virtual reality. It also covers
hardware-oriented issues.
Publishing body: International Symposium on Artificial Life and Robotics
On the homepage of Artificial Life and Robotics at springer.com you can▶ Read the most downloaded articles for free▶ Sign up for our Table of Contents Alerts▶ Get to know the complete Editorial Board▶ Find submission information