Table Of ContentFuzzy Logic Techniques
for Autonomous Vehicle Navigation
Studies in Fuzziness and Soft Computing
Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw, Poland
E-mail: [email protected]
http://www.springer.de/cgi-binlsearch_book.pl?series =2 941
Vol. 3. A. Geyer-Schulz Vol. 14. E. Hisdal
Fuzzy Rule-Based Expert Systems Logical Structures for Representation of Knowledge
and Genetic Machine Learning, 2nd ed. 1996 and Uncertainty, 1998
ISBN 3-7908-0964-0 ISBN 3-7908-\056-8
Vol. 15. G.J. Klir and M.J. Wierman
Vol. 4. T. Onisawa and J. Kacprzyk (Eds.)
Reliability and Safety Analyses under Fuzziness, Uncertainty-Based Information, 2nd ed., 1999
1995 ISBN 3-7908-1242-0
ISBN 3-7908-0837-7 Vol. 16. D. Driankov and R. Palm (Eds.)
Advances in Fuzzy Control, 1998
Vol. 5. P. Bosc and 1. Kacprzyk (Eds.) ISBN 3-7908-1090-8
Fuzziness in Database Management Systems, 1995
ISBN 3-7908-0858-X Vol. 17. L. Reznik, V. Dimitrov and 1. Kacprzyk
(Eds.)
Vol. 6. E. S. Lee and Q. Zhu Fuzzy Systems Design, 1998
Fuzzy and Evidence Reasoning, 1995 ISBN 3-7908-1118-1
ISBN 3-7908-0880-6
Vol. 18. L. Polkowski and A. Skowron (Eds.)
Rough Sets in Knowledge Discovery I, 1998
Vol. 7. B.A. Juliano and W. Bandler
ISBN 3-7908-1119-X
Tracing Chains-of-Thought, 1996
ISBN 3-7908-0922-5 Vol. 19. L. Polkowski and A. Skowron (Eds.)
Rough Sets in Knowledge Discovery 2. 1998
Vol. 8. F. Herrera and J. L. Verdegay (Eds.) ISBN 3-7908-1120-3
Genetic Algorithms and Soft Computing, 1996
ISBN 3-7908-0956-X Vol. 20. J. N. Mordeson and P. S. Nair
Fuzzy Mathematics, 1998
Vol. 9. M. Sato et al. ISBN 3-7908-1\21-1
Fuzzy Clustering Models and Applications, 1997
Vol. 21. L. C. Jain and T. Fukuda (Eds.)
ISBN 3-7908-1026-6
Soft Computing for Intelligent Robotic Systems,
1998
Vol. 10. L.c. Jain (Ed.)
ISBN 3-7908-1147-5
Soft Computing Techniques in Knowledge-based
Intelligent Engineering Systems, 1997 Vol. 22. 1. Cardoso and H. Camargo (Eds.)
ISBN 3-7908-\035-5 Fuzziness in Petri Nets, 1999
ISBN 3-7908-1158-0
Vol. II. W. Mie1czarski (Ed.)
FuzzY Logic Techniques in Power Systems, 1998, Vol. 23. P. S. Szczepaniak (Ed.)
ISBN 3-7908-\044-4 Computational Intelligence and Applications, 1999
ISBN 3-7908-1161-0
Vol. 12. B. Bouchon-Meunier (Ed.)
Vol. 24. E. Orlowska (Ed.)
Aggregation and Fusion of Impe1ect Information,
Logic at Work, 1999
1998
ISBN 3-7908-1164-5
ISBN 3-7908-\048-7
Vol. 25. 1. Buckley and Th. Feuring
Vol. 13. E. Orlowska (Ed.) Fuzzy and Neural: Interactions and Applications,
Incomplete Information: Rough Set Analysis, 1998 1999
ISBN 3-7908-\049-5 ISBN 3-7908-1I70-X
continued on page 392
Dimiter Driankov
Alessandro Saffiotti
Editors
Fuzzy Logic Techniques
for Autonomous
Vehicle Navigation
With 171 Figures
and 16 Tables
Springer-Verlag Berlin Heidelberg GmbH
Ass. Prof. Dr. Dimiter Driankov
Ass. Prof. Dr. Alessandro Saffiotti
Orebro University
Department of Technology
Fakultetsgatan 1
701820rebro
Sweden
[email protected]
[email protected]
ISBN 978-3-7908-2479-7 ISBN 978-3-7908-1835-2 (eBook)
DOI 10.1007/978-3-7908-1835-2
Cataloging-in-Publication Data applied for
Die Deutsche Bibliothek - CIP-Einheitsaufnahme
Fuzzy logic techniques for autonomous vehicle navigation: with 16 tab les I Dimiter
Driankov; Alessandro Saffiotti. ed. -Springer-Verlag Berlin Heidelberg GmbH
(Studies in fuzziness and soft computing; VoI. 61)
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© Springer-Veriag Berlin Heidelberg 2001
Originally published by Physica-Veriag Heidelberg in 2001.
Softcover reprint ofthe hardcover 1 st edition 2001
The use of general descriptive names, registered names. trademarks. etc. in this pub
Iication does not imply, even in tlie absence of a specific statement, that such names
are exempt !'rom the relevant protective laws and regulations and therefore free for
general use.
Hardwver Design: Erich Kirchner, Heidelberg
Preface
In the past decade a critical mass of work that uses fuzzy logic for autonomous
vehicle navigation has been reported. Unfortunately, reports of this work are
scattered among conference, workshop, and journal publications that belong
to different research communities (fuzzy logic, robotics, artificial intelligence,
intelligent control) and it is therefore not easily accessible either to the new
comer or to the specialist. As a result, researchers in this area may end up
reinventing things while being unaware of important existing work.
We believe that research and applications based on fuzzy logic in the
field of autonomous vehicle navigation have now reached a sufficient level
of maturity, and that it should be suitably reported to the largest possible
group of interested practitioners, researches, and students. On these grounds,
we have endeavored to collect some of the most representative pieces of work
in one volume to be used as a reference. Our aim was to provide a volume
which is more than "yet another random collection of papers," and gives the
reader some added value with respect to the individual papers.
In order to achieve this goal we have aimed at:
• Selecting contributions which are representative of a wide range of prob
lems and solutions and which have been validated on real robots; and
• Setting the individual contributions in a clear framework, that identifies
the main problems of autonomous robotics for which solutions based on
fuzzy logic have been proposed.
Each chapter in the present collection focuses on one specific problem in
the area of autonomous navigation, and illustrates the development procedure
and the experimental setting in enough detail to allow others to reproduce
the results. Moreover, each chapter emphasizes the role of fuzzy logic in the
design, analysis, implementation, and performance evaluation of the proposed
solution to the target problem. It also clearly points out the advantages and
disadvantages of using fuzzy logic for the specific problem addressed.
The volume is aimed at readers with some knowledge of mobile robotics
but possibly very little knowledge of fuzzy logic. Thus, the volume includes a
"hands-on" quick introduction to fuzzy logic and fuzzy control. Readers al
ready familiar with these subjects will find with each contribution clear point
ers to literature sources where they can find details about the specific fuzzy
techniques used. For readers who are unfamiliar with robotics, the problems
related to autonomous navigation are systematized and described in such a
way so that the reader can understand the main difficult issues. To all our
readers we recommend a look at http://www.aass.oru.se/Liv ing/FLARI
where we maintain a set of resources on fuzzy logic in autonomous robotics.
VI Preface
The volume is structured in five parts. The first part contains two tutorial
chapters which introduce the reader to the problems of autonomous vehicle
navigation, and to fuzzy logic, respectively. The first chapter also acts as an
introduction to the volume, by presenting the framework that has been used
to structure this collection. The following four parts deal with the four issues
identified by this framework: the design of robust individual navigation be
haviors; the coordination between several independent navigation behaviors;
the use of sensor data and prior knowledge to build and maintain a global
map of the environment; and the integration between the different levels of
representation and reasoning that must be present in an autonomous vehicle.
Orebro, Sweden, Dimiter Driankov
July 2000 Alessandro Saffiotti
Contents
Part I: Tutorials
Fuzzy Logic in Autonomous Navigation. .. . .. . ... . . . . . . . . . . .. . 3
Alessandro Saffiotti
A Reminder on Fuzzy Logic .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 25
Dimiter Driankov
Part II: Design of Individual Behaviors
Mobile Robot Path Tracking and Visual Target Tracking
Using Fuzzy Logic ........................... " . . . . . . . . . . . . . .. 51
Annibal Ollero, Joaquin Ferruz, Omar Sanchez, Guillermo Heredia
Fuzzy Logic for Fusion of Tactical Influences on Vehicle Speed
Control ....................................................... 73
Robin R. Murphy
Neuro-Fuzzy Control for Basic Mobile Robot Behaviours.. . .. 97
Jelena Godjevac, Nigel Steele
The Role of Fuzzy Logic Control in Evolutionary Robotics .... 119
Frank Hoffmann
Part III: Coordination of Behaviors
Resolving Conflicts Between Behaviors Using Suppression and
Inhibition ..................................................... 151
Franr,;ois G. Pin, Yutaka Watanabe
Multi-Layered Fuzzy Behavior Fusion for Reactive Control of
Autonomous Robots .......................................... 179
Steven G. Goodridge, Michael G. Kay
Fuzzy-Behavior Synthesis, Coordination, and Evolution in an
Adaptive Behavior Hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Edward W. Tunstel Jr.
Multiple Objective vs. Fuzzy Behavior Coordination .......... 235
Paolo Pirjanian, Maja Mataric
VIn Contents
Part IV: Mapping the Environment
Accurate Map Building via Fusion of Laser and Ultrasonic
Range Measures .............................................. 257
Elisabetta Fabrizi, Giuseppe Oriolo, Giovanni Ulivi
Map Generation by Co-operative Autonomous Robots Using
Possibility Theory ............................................ 281
Maite Lopez-Sanchez, Ramon Lopez de Mantaras, Carles Sierra
Integrating Linguistic Descriptions and Sensor Observations
for the Navigation of Autonomous Robots .................... 313
lorge Gasos
Part V: Layer Integration
MORIA: A Robot with Fuzzy Controlled Behaviour .......... 343
Hartmut Surmann, Liliane Peters
Integrating Deliberative and Reactive Strategies via Fuzzy
Modular Control ............................................. 367
lianwei Zhang, Alois Knoll
List of Contributors .......................................... 389
Part I
Tutorials
Fuzzy Logic in Autonomous Navigation
Alessandro Saffiotti
1 Introduction
The goal of autonomous mobile robotics is to build physical systems that can
move purposefully and without human intervention in unmodified environ
ments - that is, in real-world environments that have not been specifically
engineered for the robot. The development of techniques for autonomous
navigation constitutes one of the major trends in the current research on
robotics. This trend is motivated by the current gap between the available
technology and the new application demands. On the one hand, the tech
niques employed in current industrial robots lack the ability to provide flex
ibility and autonomy: typically, industrial robots perform pre-programmed
sequences of operations in highly constrained environments, and are not able
to operate in new environments or to face unexpected situations. On the
other hand, there is a clear emerging market for truly autonomous robots.
Possible applications include intelligent service robots for offices, hospitals,
and factory floors; maintenance robots operating in hazardous or inaccessi
ble areas; domestic robots for cleaning or entertainment; autonomous and
semi-autonomous vehicles for help to the disabled and the elderly; and so on.
Despite the recent advances in the field of autonomous robotics, many dif
ficulties have to be solved before we can deploy systems that exhibit truly au
tonomous navigation behavior. Most of the difficulties originate in the nature
of real-world, unstructured environment, and in the large uncertainties that
are inherent to these environments. First, prior knowledge about the envi
ronment is, in general, incomplete, uncertain, and approximate. For example,
maps typically omit some details and temporary features, spatial relations
between objects may have changed since the map was built, and the metric
information may be imprecise and inaccurate. Second, perceptually acquired
information is usually unreliable. The limited range, combined with the effect
of environmental features (e.g., occlusion) and of adverse observation condi
tions (e.g., poor lighting), leads to noisy and imprecise data; and errors in
the measurement interpretation process may lead to incorrect beliefs. Third,
real-world environments typically have complex and unpredictable dynamics:
objects can move, other agents can modify the environment, and relatively
stable features may change with time (e.g., seasonal variations). Finally, the
effect of control actions is not completely reliable: wheels may slip, and a
gripper may lose its grasp on an object.
Traditional work in robotics has tried to overcome these difficulties by
carefully designing the robot mechanics and sensors, or engineering the en
vironment, or both. This approach is systematically adopted in industrial
D. Driankov et al. (eds.), Fuzzy Logic Techniques for Autonomous Vehicle Navigation
© Springer-Verlag Berlin Heidelberg 2001
