Robot Teams: From Diversity to Polymorphism

436 Pages·2002·54.406 MB·\436

by  Tucker Balch (Editor), Lynne E. Parker (Editor)

Robot Teams

Robot Teams:
From Diversity to Polymorphism
Edited by
Tucker Balch
Camegie-Mellon University
Lynne E. Parker
Oak Ridge National Laboratory
Boca Raton London New York
CRC Press is an imprint of the
Taylor & Francis Group, an informa business
AN A K PETERS BOOK
First published 2002 by A K Peters
Published 2018 by CRCPress
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ISBN 13: 978-1-56881-155-0 (hbk)
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Library of Congress Cataloging-in-Publication Data
Robot teams : from diversity to polymorphism I edited by Tucker Balch, Lynne E. Parker.
p. em.
Includes bibliographical references and index.
ISBN 1-56881-155-1
1. Robotics. 2. Intelligent agents (Computer software) I. Balch, Tucker. II. Parker,
Lynne E.
TJ211 .R542 2001
629.8'92--dc21
2001038743
Contents
Preface ix
I Theoretical Foundations 1
1 A Taxonomy of Multirobot Systems 3
1.1 Why a Taxonomy Is Important............................................... 3
1.2 Dimensions of Robot Collective Taxonomies......................... 8
1.3 A Taxonomy of Robot Collectives........................................... 10
1.4 The Power of Robot Collectives: Case Studies...................... 17
1.5 Summary and Conclusions........................................................ 22
2 Taxonomies of Multirobot Task and Reward 23
2.1 Task Impacts Multirobot Performance.................................. 23
2.2 The Difference Between Task and Reward............................ 24
2.3 A Taxonomy of Multirobot Tasks........................................... 25
2.4 Classification of Example Tasks............................................... 30
2.5 A Taxonomy of Multirobot Reward........................................ 31
2.6 Summary and Conclusions........................................................ 35
3 A Survey of Multiagent and Multirobot Systems 37
3.1 MAS and DAI .......................................................................... 37
3.2 Multiagent Systems ................................................................. 39
3.3 Organization of Existing W ork............................................... 45
3.4 Homogeneous Non-Communicating Multiagent Systems . . 50
3.5 Heterogeneous Non-Communicating Multiagent Systems . . 58
3.6 Homogeneous Communicating Multiagent Systems............ 68
3.7 Heterogeneous Communicating Multiagent Systems............ 72
3.8 Robotic Soccer.......................................................................... 82
3.9 Summary and Conclusions........................................................ 92
v
VI Contents
4 Measuring Robot Group Diversity 93
4.1 Measuring Diversity Enables New Research......................... 93
4.2 The Meaning of Diversity........................................................ 94
4.3 Simple Social Entropy.............................................................. 96
4.4 Limitations of Simple Social Entropy.........................................101
4.5 Classification and Clustering ......................................................102
4.6 Hierarchic Social Entropy............................................................107
4.7 Behavioral Difference.....................................................................115
4.8 Simple Social Entropy in Simulated Soccer................................122
4.9 Hierarchic Social Entropy in Multirobot Foraging...................127
4.10 Summary and Conclusions............................................................134
II Enabling Technologies 137
5 A Polymorphic Robot Team 139
5.1 Advantages of Polymorphism for Robot Teams......................139
5.2 CONRO: A Polymorphic Robot..................................................143
5.3 Design of the CONRO modules..................................................145
5.4 Experimental Prototypes ............................................................155
5.5 The CONRO Modules and Robots............................................157
5.6 Summary and Conclusions............................................................159
6 Collaborative Multirobot Localization 161
6.1 Probabilistic Robot Localization...............................................161
6.2 Multirobot Localization...............................................................163
6.3 Sampling and Monte Carlo Localization...................................170
6.4 Probabilistic Detection Model.....................................................175
6.5 Experimental Results.....................................................................178
6.6 Related Work.................................................................................184
6.7 Summary and Conclusions............................................................186
7 Techniques for Learning in Multirobot Teams 191
7.1 Multirobot Learning.....................................................................191
7.2 Learning New Cooperative Behaviors.........................................193
7.3 Learning for Parameter Adjustment .........................................215
7.4 Summary and Conclusions............................................................235
8 Symbol Grounding for Communication 237
8.1 Shared Grounding Enables Symbolic Communication . . . . 237
8.2 Cooperation and Communication...............................................238
8.3 The System....................................................................................246
8.4 A Critical Examination ...............................................................264
8.5 Summary and Conclusions...........................................................269
Contents vii
9 Marsupial Robots 271
9.1 Motivation for Marsupialism........................................................271
9.2 Heterogeneity in Marsupial Teams............................................274
9.3 Roles and Core Behaviors............................................................279
9.4 Docking...........................................................................................282
9.5 Marsupials in the Field..................................................................287
9.6 Summary and Conclusions............................................................289
III Robot Team Case Studies 291
10 Heterogeneous Teams of Modular Robots
for Mapping and Exploration 293
10.1 Heterogeneous Robot Teams........................................................293
10.2 The Millibots.................................................................................295
10.3 Specialization and Collaboration...............................................297
10.4 Collaboration.................................................................................301
10.5 Mapping and Exploration...........................................................307
10.6 Results..............................................................................................309
10.7 Summary and Conclusions...........................................................313
11 Design and Evaluation of Robust
Behavior-Based Controllers 315
11.1 Requirements for Robust Robot Teams......................................315
11.2 Research in Foraging.....................................................................316
11.3 The Collection Task.....................................................................318
11.4 The Homogeneous Controller.....................................................322
11.5 Spatio-Temporal Interactions.....................................................325
11.6 The Pack Controller.....................................................................327
11.7 The Caste Controller.....................................................................331
11.8 Analysis...........................................................................................335
11.9 Summary and Conclusions............................................................343
12 Experiments with Cooperative Aerial-Ground Robots 345
12.1 Heterogeneous Teams for Surveillance and Reconnaissance . 345
12.2 Subsystems....................................................................................347
12.3 Control and Coordination Algorithms......................................350
12.4 Case Studies .................................................................................354
12.5 Discussion and Related W ork.....................................................366
12.6 Summary and Conclusions...........................................................367
viii Contents
13 Coordination of Heterogeneous Robots
for Large-Scale Assembly 369
13.1 When Coordination is Required..................................................369
13.2 Layered Architecture.....................................................................370
13.3 Related Approaches.....................................................................371
13.4 Testbed..........................................................................................372
13.5 Distributed Coordination............................................................373
13.6 Distributed Visual Servoing........................................................375
13.7 Summary and Conclusions...........................................................380
Bibliography 381
Preface
We are sometimes asked “why invest in research on multirobot teams when
we can hardly build a reliable individual robot?” Of course the answer
lies in the question—we are able to build more robust and reliable systems
by combining possibly unreliable but redundant components. There are
many other reasons for multirobot systems, not the least of which is per­
formance; many hands make light work. Successes in the field over the last
decade seem to have satisfied most of the skeptics. As of this writing, we
are experiencing an explosive surge in multiagent and multirobot systems
research.
In this book we explore the many aspects of diversity in multirobot
systems. There are a number of reasons heterogeneity might be important
for robot teams; one of the most compelling is the observation that it
is nearly impossible in practice to build truly homogeneous robot teams.
The realities of individual robot design, construction and experience will
inevitably cause a multirobot system to drift to heterogeneity over time.
This tendency has been recognized by experienced roboticists who have
seen that several copies of the same model of robot can vary widely in
capabilities due to the differences in sensor tuning, calibration, etc. Over
time, even minor initial differences among robots will grow due to indi­
vidual robot drift and wear-and-tear. Essentially, all multirobot teams are
heterogeneous whether we like it or not. This means that to employ robot
teams effectively we must understand diversity, predict how it will impact
performance, and enable robots to adapt to the divers capabilities of their
peers. In fact it is often advantageous to build on diversity as an important
feature of design.
There are a number of reasons for using heterogeneity as a design fea­
ture in a multirobot system, including economic and engineering issues
arising from the constraints of complex multirobot applications. In many
situations, for example, an application demands robotic capabilities that
are difficult, if not impossible, to build into a single robot. For instance,
robots cannot be both big and small at the same time, and payload lim­
itations may restrict a robot from carrying all the sensors needed for a
IX