Probabilistic Road Maps with Obstacle Avoidance in Cluttered Dynamic Environment

Abstract

The paper presents an experimental study of a probabilistic road map (PRM) based obstacle avoiding algorithm, for motion planning of a non-holonomic mobile robot in a cluttered dynamic environment. The PRM approach uses a fast and simple local planner to build a network representation of the configuration space. It trades off the distance to both static objects and moving obstacles in computing the travelled path. Our work has been implemented and tested on Player/Stage, a real time robotic software, in extensive simulation runs. The different experiments demonstrate that our approach is well suited to control the motions of a robot in a cluttered environment and demonstrates its advantages over other techniques.