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Robot Motion Planning

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TLDR
This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Abstract
1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.

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Proceedings ArticleDOI

Path Planning Based on Ant Colony Algorithm and Distributed Local Navigation for Multi-Robot Systems

TL;DR: Simulation results show that the proposed method can effectively improve the performance of the planned path, and the individual robots with collision-free can achieve to reach their goal locations by the simple local navigation strategies.
Journal ArticleDOI

Approximate distance fields with non-vanishing gradients

TL;DR: It is shown that the quality of an approximate distance field may be characterized locally near the boundary by its order of normalization and can be studied in terms of the field derivatives.
Journal ArticleDOI

Improving efficiency in mobile robot task planning through world abstraction

TL;DR: This paper proposes a new approach to task planning called "hierarchical task planning through world abstraction" that, by hierarchically arranging the world representation, becomes a good complement of Stanford Research Institute Problem Solver-style planners, significantly improving their computational efficiency.
Journal ArticleDOI

Reactive pedestrian path following from examples

TL;DR: In this article, the authors explore an approach for generating reactive path following based on the user's examples of the desired behavior, which is combined with reactive control methods to produce natural 2D pedestrian trajectories.