Edge Nearest Neighbor in Sampling-Based Motion Planning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911007601852416 |
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| author | Ashur, Stav Amato, Nancy M. Har-Peled, Sariel |
| author_facet | Ashur, Stav Amato, Nancy M. Har-Peled, Sariel |
| contents | Neighborhood finders and nearest neighbor queries are fundamental parts of sampling based motion planning algorithms. Using different distance metrics or otherwise changing the definition of a neighborhood produces different algorithms with unique empiric and theoretical properties. In \cite{l-pa-06} LaValle suggests a neighborhood finder for the Rapidly-exploring Random Tree RRT
algorithm \cite{l-rrtnt-98} which finds the nearest neighbor of the sampled point on the swath of the tree, that is on the set of all of the points on the tree edges, using a hierarchical data structure. In this paper we implement such a neighborhood finder and show, theoretically and experimentally, that this results in more efficient algorithms, and suggest a variant of the Rapidly-exploring Random Graph RRG algorithm \cite{f-isaom-10} that better exploits the exploration properties of the newly described subroutine for finding narrow passages. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge Nearest Neighbor in Sampling-Based Motion Planning Ashur, Stav Amato, Nancy M. Har-Peled, Sariel Robotics Neighborhood finders and nearest neighbor queries are fundamental parts of sampling based motion planning algorithms. Using different distance metrics or otherwise changing the definition of a neighborhood produces different algorithms with unique empiric and theoretical properties. In \cite{l-pa-06} LaValle suggests a neighborhood finder for the Rapidly-exploring Random Tree RRT algorithm \cite{l-rrtnt-98} which finds the nearest neighbor of the sampled point on the swath of the tree, that is on the set of all of the points on the tree edges, using a hierarchical data structure. In this paper we implement such a neighborhood finder and show, theoretically and experimentally, that this results in more efficient algorithms, and suggest a variant of the Rapidly-exploring Random Graph RRG algorithm \cite{f-isaom-10} that better exploits the exploration properties of the newly described subroutine for finding narrow passages. |
| title | Edge Nearest Neighbor in Sampling-Based Motion Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.13753 |