Author :
Chonhyon Park ; Jia Pan ; Manocha, Dinesh
Author_Institution :
Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
fDate :
May 31 2014-June 7 2014
Abstract :
We present an RRT-based 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. Our approach can be easily parallelized on multi-core CPUs and many-core GPUs. We highlight the performance of our algorithm on different benchmarks.
Keywords :
control engineering computing; graphics processing units; mobile robots; parallel processing; path planning; trees (mathematics); Poisson-RRT; RRT-based motion planning algorithm; adaptive scheme; configuration space; free-disk property; many-core GPUs; maximal Poisson-disk samples; maximal Poisson-disk sampling scheme; multicore CPUs; rapidly-exploring random trees; tree expansion; Algorithm design and analysis; Benchmark testing; Collision avoidance; Heuristic algorithms; Planning; Robots; Standards;
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICRA.2014.6907541
