DocumentCode
250957
Title
Poisson-RRT
Author
Chonhyon Park ; Jia Pan ; Manocha, Dinesh
Author_Institution
Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
fYear
2014
fDate
May 31 2014-June 7 2014
Firstpage
4667
Lastpage
4673
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location
Hong Kong
Type
conf
DOI
10.1109/ICRA.2014.6907541
Filename
6907541
Link To Document