Abstract
We present and implement an efficient algorithm for performing nearest-neighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd tree-based ANN algorithm, which was developed by Arya and Mount for Euclidean spaces. We argue the correctness of the algorithm and illustrate its efficiency through computed examples. We have applied the algorithm to both probabilistic roadmaps (PRMs) and Rapidly-exploring Random Trees (RRTs). Substantial performance improvements are shown for motion planning examples.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 632-637 |
| Number of pages | 6 |
| Journal | Proceedings - IEEE International Conference on Robotics and Automation |
| Volume | 1 |
| State | Published - 2002 |
| Externally published | Yes |
| Event | 2002 IEEE International Conference on Robotics and Automation - Washington, DC, United States Duration: May 11 2002 → May 15 2002 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Electrical and Electronic Engineering
- Control and Systems Engineering