Kavraki Lab

Physical and Biological Computing

Keywords:

Abstract:

This paper shows how to effectively combine a
sampling-based method primarily designed for multiple-query motion
planning [probabilistic roadmap method (PRM)] with sampling-based
tree methods primarily designed for single-query motion planning
(expansive space trees, rapidly exploring random trees, and others)
in a novel planning framework that can be efficiently
parallelized. Our planner not only achieves a smooth spectrum
between multiple-query and single-query planning, but it combines
advantages of both. We present experiments which show that our
planner is capable of solving problems that cannot be addressed
efficiently with PRM or single-query planners. A key advantage of
our planner is that it is significantly more decoupled than PRM and
sampling-based tree planners. Exploiting this property, we designed
and implemented a parallel version of our planner. Our experiments
show that our planner distributes well and can easily solve
high-dimensional problems that exhaust resources available to single
machines and cannot be addressed with existing planners.

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