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Sampling-based roadmap of trees for parallel motion planning

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5 Author(s)
Plaku, E. ; Dept. of Comput. Sci., Rice Univ., Houston, TX, USA ; Bekris, K.E. ; Chen, B.Y. ; Ladd, A.M.
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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.

Published in:

Robotics, IEEE Transactions on  (Volume:21 ,  Issue: 4 )