Interleaving Planning and Execution for Autonomous Robots

152 Pages·1997·7.91 MB·English

by  Illah Reza Nourbakhsh (auth.)

INTERLEAVING PLANNING
AND EXECUTION
FOR AUTONOMOUS ROBOTS
THE KLUWER INTERNATIONAL SERIES
IN ENGINEERING AND COMPUTER SCIENCE
ROBOTICS: VISION, MANIPULATION AND SENSORS
Consulting Editor
Takeo Kanade
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INTERLEA VING PLANNING
AND EXECUTION
FOR AUTONOMOUS ROBOTS
by
fiah Reza Nourbakhsh
Stanford University
.
.,
~
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-7900-3 ISBN 978-1-4615-6317-4 (eBook)
DOI 10.1007/978-1-4615-6317-4
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright ~ 1997 by Springer Science+Business Media New York
Origina1ly published by Kluwer Academic Publishers in 1997
Softcover reprint of the hardcover 1s t edition 1997
AlI rights reserved. No part of this publication may be reproduced, stored in
a retrieval system or transmitted in any form or by any means, mechanical,
photo-copying, recording, or otherwise, without the prior written permission of
the publisher, Springer Science+Business Media, LLC.
Printed on acid-free paper.
to Noah and Zachary
Contents
Preface xiii
Acknowledgments xv
1 Introduction 1
1.1 Motivation 2
1.2 Contributions 3
1.3 Contents of Chapters 8
2 Perception and Action 9
2.1 The Situated Robot and its Environment 10
2.2 Defining State and State Update 11
2.3 Comparison to other Conventional Approaches 17
2.4 Conclusion 19
3 Formalizing Incomplete Information 21
3.1 Sources of Incomplete Information 22
3.2 Representing Incomplete Information 24
3.3 State-Set Tracking: Execution Monitoring 31
3.4 Related Work 33
4 Goal-Directed Control Systems 35
4.1 Defining a Problem Instance 36
4.2 Advance Planning Control Systems 38
4.3 Related Work 51
5 Interleaving Planning and Execution 53
5.1 Introduction: Interleaving 54
5.2 Premature Search Tennination 55
5.3 Experimental Results 62
5.4 Related Work 63
viii IIlah R. Nourbakhsh
6 Using Assumptions to Oversimplify 65
6.1 Introduction 66
6.2 Assumptions 67
6.3 Assumptive Control Systems 72
6.4 Continuous Selection Algorithm 78
6.5 The Cost of Assumptions 82
6.6 Large-scale Real-World Experimental Results 85
6.7 Related Work 92
6.8 Conclusion 94
7 Strategic Subgoaling: Using Abstraction Systems 97
7.1 Introduction 98
7.2 Problem Spaces 99
7.3 Abstraction 1,00
7.4 Examples of Abstraction Systems and their Cost 113
7.5 Related Work and Discussion 119
8 Generalizing beyond State Sets 123
8.1 Introduction 124
8.2 Representation Examples 125
8.3 Conclusion 129
9 Conclusions 131
Bibliography 137
Index 143
List of Tables
4.1 Search space size for five variations of two simulated problems 50
4.2 Running times for cond-plan without useless plan pruning (CPA) and 51
with pruning (CPAb)
5.1 The size of the search space for seven problems 62
5.2 The running times and execution lengths for each problem 63
6.1 A comparison of the planning cost of advance planning versus 83
Assumptive planning for three domain classes
6.2 A comparison of the conditional and Assumptive search space sizes 88
for the CS224 robot problem of figure 6.14
6.3 A comparison of the conditional and Assumptive search spaces for 92
the final contest at the AAAl1994 National Robot Competition
List of Illustrations
1.1 A comparison of advance planning and interleaving search space 2
1.2 A birds-eye view of a robot lost in a maze 5
1.3 A comparison of advance planning and Assumptive planning 6
1.4 A comparison of advance planning and an abstraction system 7
2.l A situated robot's actions and percepts 10
2.2 A finite automata view of Figure 2.1 12
2.3 The Mazeworld domain 13
2.4 A partial world state graph for the Mazeworld domain of Fig. 2.3 16
3.l An example of model error 22
3.2 A robot with an unreliable move action 23
3.3 A robot with an inconsistent percept 24
3.4 The state set representation of knowledge about world state 25
3.5 Graphical examples of effect 27
3.6 Effectory collapse 27
3.7 Perceptual collapse 27
3.8 Effectory expansion 28
3.9 Perceptual unreliability 28
3.10 Perceptual aliasing 28
3.11 A partial state-set graph for a Mazeworld 30
4.1 Goal sets 36
4.2 A conditional plan for changing highway lanes 39
4.3 A behavioral graph for a Mazeworld 40
4.4 State-set overlay 40
4.5 A flowchart depicting the CPA control system 43
4.6 An overhead view of the MJH domain 49
4.7 An example Wumpus World game 50
5.1 A flowchart depicting the DPA control system 56
5.2 Search horizon example 57
5.3 An example of a forced move 59
xii 111011 R. Nourbakhsh
5.4 Conditional planning with uncertain initial conditions 61
6.1 The search space of an Assumptive system 66
6.2 An assumption that reduces initial state set size 67
6.3 A Mazeworld example with initial uncertainty 68
6.4 The dangers of a deadly snake pit 69
6.5 A demonstration of nondeterministic move 70
6.6 An example of perceptual branching with false negatives 71
6.7 A flowchart depicting the basic Assumptive Algorithm 6.1 73
6.8 An example of state-set unreachability 74
6.9 An example of false goal detection 76
6.10 An example of assumptive overcommitment 79
6.11 A flowchart depicting Continuous Selection Algorithm 80
6.12 An example Mazeworld in which Indiana Robot faces snake pits 81
6.13 The CS 224 Final Contest maze 85
6.14 Three CS 224 students at work 86
6.15 The CS 224 Assumptive examination 87
6.16 Geometric and topological maps of Dervish 88
6.17 Dervish in its final configuration 89
6.18 Quantization of a geometric map 90
7.1 A comparison of a bipartite conditional graph and a hyperarc graph 100
7.2 A state graph-based example of abstraction 102
7.3 Abstract and ground conditional plan fringes 103
7.4 An example of useful abstraction 105
7.5 An example of abstraction systems 106
7.6 A flowchart depicting the problem space control system 109
7.7 An example run of a three-tier abstraction system 111
7.8 The horizon problem of geometric abstraction 115
7.9 Hyperarcs in an abstraction space 117
7.10 Hyperarcs demonstrating continent abstraction 117
7.11 Path nonoptimality example 118
7.12 TC-actions 120
8.1 An example of the property set representation 126
8.2 An example of polygonal position representation for mobile robots 127
8.3 A robot navigation problem solved using the Viable Plan Heuristic 129