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arXiv · 2506.13753

Edge Nearest Neighbor in Sampling-Based Motion Planning

Stav Ashur·Nancy M. Amato·Sariel Har-Peled
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Abstract

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.

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Stav Ashur, Nancy M. Amato, Sariel Har-Peled. 2025-06-16. Edge Nearest Neighbor in Sampling-Based Motion Planning. https://arxiv.org/abs/2506.13753

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