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Abstract

Guang Song, Shawna Thomas, Nancy M. Amato, "A General Framework for PRM Motion Planning," In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4445-4450, Taipei, Taiwan, Sep 2003.
Proceedings(ps, pdf, abstract)

An important property of PRM roadmaps is that they provide a good approximation of the connectivity of the free C-space. We present a general framework for building and querying probabilistic roadmaps that includes all previous PRM variants as special cases. In particular, it supports no, complete, or partial node and edge validation and various evaluation schedules for path validation, and it enables path customization for variable, adaptive query requirements. While each of the above features is present in some PRM variant, the general framework proposed here is the only one to include them all. Our framework enables users to choose the best approximation level for their problem. Our experimental evidence shows this can result in significant performance gains.