Edge Nearest Neighbor in Sampling-Based Motion Planning
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.
Cite
@article{arxiv.2506.13753,
title = {Edge Nearest Neighbor in Sampling-Based Motion Planning},
author = {Stav Ashur and Nancy M. Amato and Sariel Har-Peled},
journal= {arXiv preprint arXiv:2506.13753},
year = {2025}
}