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Parallel Motion Planning Using Poisson-Disk Sampling
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DOI：10.1109/TRO.2016.2632160.png)
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Abstract
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We present a rapidly exploring-random-tree-based parallel motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach exploits the free-disk property of the maximal Poisson-disk samples to generate nodes and perform tree expansion. Furthermore, we use an adaptive scheme to generate more samples in challenging regions of the configuration space. The Poisson-disk sampling results in improved parallel performance and we highlight the performance benefits on multicore central processing units as well as manycore graphics processing units on different benchmarks.
Keywords:
Motion planning
parallel algorithm
Poisson-disk sampling
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