Exploiting collision information in probabilistic roadmap planning

This paper develops a novel approach to combining probabilistic motion planners. Rather than trying to develop a single planner that works over a wide range of environments, we develop a strategy for combining different motion planners within a single framework. Specifically we examine how planners designed for open spaces and those designed for narrow passages can be integrated within a single planning framework. Information that is normally discarded in the planning process is used to identify regions as being potentially ´narrow´ or ´cluttered´, and we then apply the planner most suited for that region based on this information. Experimental results demonstrate our approach outperforms the basic PRM approach as well as a Gaussian sampler designed for narrow regions in three test environments.