Adaptive Strategies for Probabilistic Roadmap Construction
Pekka Isto, Juha Tuominen, Martti Mäntylä · 2003
This paper presents an experimental study of prospects for using adaptable local search techniques in probabilistic roadmap based motion planning. The classical PRM approach uses a single fast and simple local planner to build a network representation of the configuration space. Advanced PRM planners utilize heuristic sampling techniques and combine multiple local planners. The planner described here uses a single local planner, but adjusts its competence during the roadmap construction stage according to the problem at hand. Two adjusting strategies are proposed and compared experimentally against using a static local planner at a set competence level. The results indicate that roadmap construction with an adaptive local planner can bring advantages including more robust performance and a reduction in planning cost variance.