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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
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Journal ArticleDOI
Path Planning for Autonomous Underwater Vehicles
TL;DR: This work develops an algorithm, called FM*, to efficiently extract a 2-D continuous path from a discrete representation of the environment and takes underwater currents into account thanks to an anisotropic extension of the original FM algorithm.
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From human to humanoid locomotion--an inverse optimal control approach
TL;DR: This paper applies inverse optimal control to establish a model of human overall locomotion path generation to given target positions and orientations, based on newly collected motion capture data, to establish optimal control models that can be used to control robot motion.
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On a class of O(n2) problems in computational geometry
Anka Gajentaan,Mark H. Overmars +1 more
TL;DR: A large class of problems is described for which it is proved that they are all at least as difficult as the following base problem 3sum: Given a set S of n integers, are there three elements of S that sum up to 0.
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The uses of fuzzy logic in autonomous robot navigation
TL;DR: This paper focuses on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules;How to use data from the sensors; and how to integrate high-level reasoning and low-level execution.
Journal ArticleDOI
Adaptive evolutionary planner/navigator for mobile robots
TL;DR: An adaptive evolutionary planner/navigator that unifies off-line planning and online planning/navigation processes in the same evolutionary algorithm that enables good tradeoffs among near-optimality of paths, high planning efficiency, and effective handling of unknown obstacles.