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| Preface | |
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| Introductory Material | |
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| Introduction | |
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| Planning to plan | |
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| Motivational examples and applications | |
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| Basic ingredients of planning | |
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| Algorithms, planners, and plans | |
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| Organization of the book | |
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| Discrete Planning | |
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| Introduction to discrete feasible planning | |
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| Searching for feasible plans | |
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| Discrete optimal planning | |
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| Using logic to formulate discrete planning | |
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| Logic-based planning methods | |
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| Motion Planning | |
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| Geometric Representations and Transformations | |
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| Geometric modeling | |
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| Rigid-body transformations | |
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| Transforming kinematic chains of bodies | |
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| Transforming kinematic trees | |
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| Nonrigid transformations | |
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| The Configuration Space | |
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| Basic topological concepts | |
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| Defining the configuration space | |
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| Configuration space obstacles | |
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| Closed kinematic chains | |
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| Sampling-Based Motion Planning | |
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| Distance and volume in C-space | |
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| Sampling theory | |
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| Collision detection | |
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| Incremental sampling and searching | |
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| Rapidly exploring dense trees | |
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| Roadmap methods for multiple queries | |
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| Combinatorial Motion Planning | |
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| Introduction | |
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| Polygonal obstacle regions | |
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| Cell decompositions | |
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| Computational algebraic geometry | |
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| Complexity of motion planning | |
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| Extensions of Basic Motion Planning | |
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| Time-varying problems | |
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| Multiple robots | |
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| Mixing discrete and continuous spaces | |
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| Planning for closed kinematic chains | |
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| Folding problems in robotics and biology | |
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| Coverage planning | |
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| Optimal motion planning | |
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| Feedback Motion Planning | |
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| Motivation | |
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| Discrete state spaces | |
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| Vector fields and integral curves | |
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| Complete methods for continuous spaces | |
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| Sampling-based methods for continuous spaces | |
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| Decision-Theoretic Planning | |
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| Basic Decision Theory | |
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| Preliminary concepts | |
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| A game against nature | |
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| Two-player zero-sum games | |
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| Nonzero-sum games | |
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| Decision theory under scrutiny | |
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| Sequential Decision Theory | |
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| Introducing sequential games against nature | |
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| Algorithms for computing feedback plans | |
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| Infinite-horizon problems | |
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| Reinforcement learning | |
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| Sequential game theory | |
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| Continuous state spaces | |
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| Sensors and Information Spaces | |
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| Discrete state spaces | |
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| Derived information spaces | |
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| Examples for discrete state spaces | |
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| Continuous state spaces | |
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| Examples for continuous state spaces | |
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| Computing probabilistic information states | |
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| Information spaces in game theory | |
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| Planning Under Sensing Uncertainty | |
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| General methods | |
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| Localization | |
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| Environment uncertainty and mapping | |
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| Visibility-based pursuit-evasion | |
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| Manipulation planning with sensing uncertainty | |
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| Planning Under Differential Constraints | |
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| Differential Models | |
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| Velocity constraints on the configuration space | |
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| Phase space representation of dynamical systems | |
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| Basic Newton-Euler mechanics | |
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| Advanced mechanics concepts | |
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| Multiple decision makers | |
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| Sampling-Based Planning Under Differential Constraints | |
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| Introduction | |
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| Reachability and completeness | |
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| Sampling-based motion planning revisited | |
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| Incremental sampling and searching methods | |
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| Feedback planning under differential constraints | |
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| Decoupled planning approaches | |
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| Gradient-based trajectory optimization | |
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| System Theory and Analytical Techniques | |
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| Basic system properties | |
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| Continuous-time dynamic programming | |
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| Optimal paths for some wheeled vehicles | |
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| Nonholonomic system theory | |
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| Steering methods for nonholonomic systems | |
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| Bibliography | |
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| Index | |