計画アルゴリズム<br>Planning Algorithms

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計画アルゴリズム
Planning Algorithms

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  • 提携先の海外書籍取次会社に在庫がございます。通常約2週間で発送いたします。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合が若干ございます。
    2. 複数冊ご注文の場合、分割発送となる場合がございます。
    3. 美品のご指定は承りかねます。
  • ●この商品は国内送料無料です。
  • 製本 Hardcover:ハードカバー版/ページ数 826 p.
  • 言語 ENG
  • 商品コード 9780521862059
  • DDC分類 629.8932

基本説明

A coherent source for applications including robotics, computational biology, graphics, manufacturing, aerospace, and medicine. The first broad unification of planning-related topics, drawn together under a clearly explained mathematical framework. Emphasizes the powerful concept of information spaces, critical in the development of better robotic systems.

Full Description


Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning, but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the 'configuration spaces' of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Table of Contents

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