Workspace importance sampling for probabilistic roadmap planning

Abstract

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but they behave poorly when a robot's configuration space contains narrow passages. This paper presents workspace sampling (WIS), a new sampling strategy for PRM planning. Our main idea is to use geometric information from a robot's workspace as importance values to guide sampling in the corresponding configuration space. By doing so, WIS increases the sampling density in narrow passages and decreases the sampling density in wide-open regions. We tested the new planner on rigid-body and articulated robots in 2-D and 3-D environments. Experimental results show that WIS improves the planner's performance for path planning problems with narrow passages.