Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k,K'), that first computes the K' closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and K'. We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. © 2012 IEEE.
|Original language||English (US)|
|Title of host publication||2012 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Oct 2012|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266, by THECB NHARPaward 000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and byAward KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST). Tapia supported in part by NIH Grant P20RR018754.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.