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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 | |