A deterministic sampling approach to robot motion planning

Abstract

Probabilistic roadmap approaches (PRMs) have been successfully applied in motion planning of robots with many degrees of freedom. Narrow passages create significant difficulty for these planners. We do not propose a new sampling strategy; our main contribution is to replace random sampling with deterministic sampling. This work can be viewed as a complementary study to importance sampling. Our experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM.