Feasibility tests in virtual reality for nuclear power plant maintenance or dismantling operations are a source of problems for motion planning because finding a way in a cluttered environment is not easy for the bulky loads, mobile devices and robots used in such operations. Standard probabilistic roadmap methods (PRM) have been successfully used to answer such feasibility problems. These methods provide, at the most a single solution but do not provide a complete overview of the possible motions which have to be evaluated in a complete engineering task. We focus here on the open question of building probabilistic roadmaps which can provide an exhaustive list of all the solutions which can not be distorted from one to another while staying collision free. We call such roadmaps homotopy preserving probabilistic roadmap (HPPR). We propose a new algorithm for creating HPPR.

Presents a variant of a probabilistic approach to motion planning. The objective is to face large, complex industrial environments for maintenance purpose. The contribution is based on a structuring of the workspace into boxes that limits the size of the graph to be searched. The algorithm was integrated in a commercial CAD system and real-size experiments are presented.