Local Randomization in Neighbor Selection Improves PRM Roadmap Quality

• Main Author: Boyd, Bryan 1985-
• Other Authors: Amato, Nancy M
• Format: Others
• Published: 2013
• Subjects: Motion Planning; Robotics
• Online Access: http://hdl.handle.net/1969.1/148341

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. This work proposes 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. A methodology for selecting the parameters k and k' is provided, and an experimental comparison for both rigid and articulated robots show that LocalRand results in roadmaps that are better connected than the traditional k-closest or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to the cost of k-closest.