Non-holonomic path planning using a quasi-random PRM approach

Abstract

The aim of this article is to compare experimentally the use of quasi-random sampling techniques for nonholonomic path planning. The experiments are evaluated in the context of the probabilistic roadmap methods (PRM). Two quasi-random variants of PRM-based planners are proposed: (1) a classical PRM with quasi-random sampling, and (2) a quasi-random lazy-PRM. Both have been implemented for car-like robots, and are shown through experimental results to offer some performance advantages in comparison to their randomized counterparts.