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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.read more
Citations
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Journal ArticleDOI
Exploiting Reactive Mobility for Collaborative Target Detection in Wireless Sensor Networks
TL;DR: A sensor movement scheduling algorithm is developed that achieves near-optimal system detection performance under a given detection delay bound and is validated by extensive simulations using the real data traces collected by 23 sensor nodes.
Proceedings ArticleDOI
Integrating symbolic and geometric planning for mobile manipulation
TL;DR: This work decomposes the manipulation problem into a symbolic and a geometric part that is implemented as a classical symbolic planner that tightly integrates a geometric planner enabling it to efficiently generate correct plans.
Journal ArticleDOI
A Neural Network Approach to Dynamic Task Assignment of Multirobots
Anmin Zhu,Simon X. Yang +1 more
TL;DR: A neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties, capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations.
Proceedings ArticleDOI
Localizing a robot with minimum travel
TL;DR: It is shown that the problem of localizing a robot with minimum travel is NP-hard, and a polynomial time approximation scheme is given that causes the robot to travel a distance of at most (k - 1)d, where k = |H|, which is no greater than the number of reflex vertices of P, and d is the length of a minimum length tour.
Journal ArticleDOI
On the design of an obstacle avoiding trajectory: Method and simulation
TL;DR: The paper suggests a new mathematical construction for the potential field used in the design of obstacle avoiding trajectories with the quickness of minimum computation and the compensation for the main drawbacks specific to the ''traditional approaches'' belonging to the possible field method in general.