Playing with several roadmaps to solve manipulation problems

We propose in this paper a resolution scheme that is aimed to be relevant for a large class of manipulation planning problems. This endeavor complements our efforts in developing manipulation planning algorithms. Indeed, we are convinced that a higher level of problems complexity, and particularly those involving multiple robots and multiple objects, will be accessible thanks to the introduction of a symbolic reasoning level. The resolution scheme relies on probabilistic roadmap methods (PRMs) and on a reasoning level that adaptively controls the construction and extension of a number of roadmaps. We consider this symbolic level as a step towards a systematic approach to integrate task planning and geometric planning in better conditions than through a gross, and somewhat, artificial hierarchical decomposition. This paper describes the main ingredients of the proposed framework, and its first results.