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Planning Algorithms
Lavalle, Steven Michael
Hardcover:ハードカバー版  |
Preface xi
I Introductory Material 1 (62)
Introduction 3 (20)
Planning to plan 3 (1)
Motivational examples and applications 4 (10)
Basic ingredients of planning 14 (2)
Algorithms, planners, and plans 16 (4)
Organization of the book 20 (3)
Discrete Planning 23 (40)
Introduction to discrete feasible planning 24 (3)
Searching for feasible plans 27 (9)
Discrete optimal planning 36 (12)
Using logic to formulate discrete planning 48 (5)
Logic-based planning methods 53 (10)
II Motion Planning 63 (294)
Geometric Representations and 66 (39)
Transformations
Geometric modeling 66 (10)
Rigid-body transformations 76 (7)
Transforming kinematic chains of bodies 83 (10)
Transforming kinematic trees 93 (6)
Nonrigid transformations 99 (6)
The Configuration Space 105(48)
Basic topological concepts 105(15)
Defining the configuration space 120(9)
Configuration space obstacles 129(10)
Closed kinematic chains 139(14)
Sampling-Based Motion Planning 153(53)
Distance and volume in C-space 154(7)
Sampling theory 161(12)
Collision detection 173(7)
Incremental sampling and searching 180(9)
Rapidly exploring dense trees 189(7)
Roadmap methods for multiple queries 196(10)
Combinatorial Motion Planning 206(51)
Introduction 206(2)
Polygonal obstacle regions 208(10)
Cell decompositions 218(14)
Computational algebraic geometry 232(15)
Complexity of motion planning 247(10)
Extensions of Basic Motion Planning 257(47)
Time-varying problems 257(6)
Multiple robots 263(7)
Mixing discrete and continuous spaces 270(9)
Planning for closed kinematic chains 279(8)
Folding problems in robotics and biology 287(5)
Coverage planning 292(3)
Optimal motion planning 295(9)
Feedback Motion Planning 304(53)
Motivation 304(2)
Discrete state spaces 306(8)
Vector fields and integral curves 314(14)
Complete methods for continuous spaces 328(12)
Sampling-based methods for continuous 340(17)
spaces
III Decision-Theoretic Planning 357(230)
Basic Decision Theory 360(48)
Preliminary concepts 361(7)
A game against nature 368(10)
Two-player zero-sum games 378(8)
Nonzero-sum games 386(7)
Decision theory under scrutiny 393(15)
Sequential Decision Theory 408(54)
Introducing sequential games against 408(11)
nature
Algorithms for computing feedback plans 419(11)
Infinite-horizon problems 430(5)
Reinforcement learning 435(7)
Sequential game theory 442(13)
Continuous state spaces 455(7)
Sensors and Information Spaces 462(60)
Discrete state spaces 463(9)
Derived information spaces 472(8)
Examples for discrete state spaces 480(7)
Continuous state spaces 487(7)
Examples for continuous state spaces 494(13)
Computing probabilistic information states 507(5)
Information spaces in game theory 512(10)
Planning Under Sensing Uncertainty 522(65)
General methods 523(5)
Localization 528(12)
Environment uncertainty and mapping 540(24)
Visibility-based pursuit-evasion 564(6)
Manipulation planning with sensing 570(17)
uncertainty
IV Planning Under Differential Constraints 587(180)
Differential Models 590(61)
Velocity constraints on the configuration 590(16)
space
Phase space representation of dynamical 606(9)
systems
Basic Newton-Euler mechanics 615(15)
Advanced mechanics concepts 630(15)
Multiple decision makers 645(6)
Sampling-Based Planning Under Differential 651(61)
Constraints
Introduction 652(8)
Reachability and completeness 660(10)
Sampling-based motion planning revisited 670(8)
Incremental sampling and searching methods 678(15)
Feedback planning under differential 693(3)
constraints
Decoupled planning approaches 696(11)
Gradient-based trajectory optimization 707(5)
System Theory and Analytical Techniques 712(55)
Basic system properties 712(8)
Continuous-time dynamic programming 720(8)
Optimal paths for some wheeled vehicles 728(8)
Nonholonomic system theory 736(17)
Steering methods for nonholonomic systems 753(14)
Bibliography 767(44)
Index 811
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