1 Introduction 2 Bug Algorithms 3 Configuration Space 4 Potential Functions 5 Roadmaps 6 Cell Decompositions 7 Sampling-Based Algorithms 8 Kalman Filtering 9 Bayesian Methods 10 Robot Dynamics 11 Trajectory Planning 12 Nonholonomic and Underactuated Systems A Mathematical Notation B Basic Set Definitions C Topology and Metric Spaces D Curve Tracing E Representations of Orientation F Polyhedral Robots in Polyhedral Worlds G Analysis of Algorithms and Complexity Classes H Graph Representation and Basic Search I Statistics Primer J Linear Systems and Control Bibliography Index
This text reflects the great advances in the field that have taken place in the last ten years, including sensor-based planning, probabilistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, relating low-level implementation details to high-level algorithmic concepts