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C-FOREST: Parallel Shortest Path Planning With Superlinear Speedup

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2 Author(s)
Michael Otte ; Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA ; Nikolaus Correll

C-FOREST is a parallelization framework for single-query sampling-based shortest path-planning algorithms. Multiple search trees are grown in parallel (e.g., 1 per CPU). Each time a better path is found, it is exchanged between trees so that all trees can benefit from its data. Specifically, the path's nodes increase the other trees' configuration space visibility, while the length of the path is used to prune irrelevant nodes and to avoid sampling from irrelevant portions of the configuration space. Experiments with a robotic team, a manipulator arm, and the alpha benchmark demonstrate that C-FOREST achieves significant superlinear speedup in practice for shortest path-planning problems (team and arm), but not for feasible path panning (alpha).

Published in:

IEEE Transactions on Robotics  (Volume:29 ,  Issue: 3 )