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Robot Motion Planning
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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.read more
Citations
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Journal ArticleDOI
Motion planning and control for Hilare pulling a trailer
TL;DR: The various components of an integrated architecture for the mobile robot Hilare pulling a trailer are presented and how to reduce the problem to a classical approach of trajectory tracking for a mobile robot moving forward only is shown.
Proceedings ArticleDOI
Steering flexible needles under Markov motion uncertainty
TL;DR: This work develops a motion planning algorithm based on dynamic programming where the path of the needle is uncertain due to uncertainty in tissue properties, needle mechanics, and interaction forces and generates motion plans for bevel-tip needles that reach targets inaccessible to rigid needles.
Journal ArticleDOI
Computation of configuration-space obstacles using the fast Fourier transform
TL;DR: This paper presents a new method for computing the configuration-space map of obstacles that is used in motion-planning algorithms, and is particularly promising for workspaces with many and/or complicated obstacles, or when the shape of the robot is not simple.
Journal ArticleDOI
Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach
Simon X. Yang,Max Q.-H. Meng +1 more
TL;DR: A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment and the stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory.
Book ChapterDOI
Multi-agent Path Planning and Network Flow
Jingjin Yu,Steven M. LaValle +1 more
TL;DR: In this paper, it was shown that when the goals are permutation invariant, the problem always has a feasible solution path set with a longest finish time of no more than n + V - 1 steps, where V is the number of vertices of the underlying graph.