Improving Motion-Planning Algorithms by Efficient Nearest-Neighbor Searching
The cost of nearest-neighbor (NN) calls is one of the bottlenecks in the performance of sampling-based motion-planning algorithms. Therefore, it is crucial to develop efficient techniques for NN searching in configuration spaces arising in motion planning. In this paper, we present and implement an...
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| Veröffentlicht in: | IEEE transactions on robotics 2007-02, Vol.23 (1), p.151-157 |
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| creator | Yershova, A. LaValle, S.M. |
| description | The cost of nearest-neighbor (NN) calls is one of the bottlenecks in the performance of sampling-based motion-planning algorithms. Therefore, it is crucial to develop efficient techniques for NN searching in configuration spaces arising in motion planning. In this paper, we present and implement an algorithm for performing NN queries in Cartesian products of R, S 1 , and RP 3 , the most common topological spaces in the context of motion planning. Our approach extends the algorithm based on kd-trees, called ANN, developed by Arya and Mount for Euclidean spaces. We prove the correctness of the algorithm and illustrate substantial performance improvement over the brute-force approach and several existing NN packages developed for general metric spaces. Our experimental results demonstrate a clear advantage of using the proposed method for both probabilistic roadmaps and rapidly exploring random trees |
| doi_str_mv | 10.1109/TRO.2006.886840 |
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Therefore, it is crucial to develop efficient techniques for NN searching in configuration spaces arising in motion planning. In this paper, we present and implement an algorithm for performing NN queries in Cartesian products of R, S 1 , and RP 3 , the most common topological spaces in the context of motion planning. Our approach extends the algorithm based on kd-trees, called ANN, developed by Arya and Mount for Euclidean spaces. We prove the correctness of the algorithm and illustrate substantial performance improvement over the brute-force approach and several existing NN packages developed for general metric spaces. Our experimental results demonstrate a clear advantage of using the proposed method for both probabilistic roadmaps and rapidly exploring random trees</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Configuration space</subject><subject>Control theory. 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| subjects | Algorithms Applied sciences Computer science control theory systems Configuration space Control theory. Systems Costs Data structures Euclidean space Exact sciences and technology Extraterrestrial measurements kd-trees Learning theory Metric space Motion control Motion planning nearest-neighbor (NN) searching Neural networks Packages Packaging Path planning Pattern recognition probabilistic roadmaps (PRMs) Queries rapidly exploring random trees (RRTs) Robotics Robots sampling-based motion planning Searching Topology Tree graphs |
| title | Improving Motion-Planning Algorithms by Efficient Nearest-Neighbor Searching |
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