Massively parallelizing the RRT and the RRT*
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Frazzoli_Massively parallelizing.pdf
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480.49 KB
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Author(s)
Karaman, Sertac
Frazzoli, Emilio
Bialkowski, Joshua John
Date Issued
September 2011
Journal
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bialkowski, Joshua, Sertac Karaman, and Emilio Frazzoli. “Massively parallelizing the RRT and the RRT.” In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3513-3518. Institute of Electrical and Electronics Engineers, 2011.
Version
Author's final manuscript
Abstract
In recent years, the growth of the computational power available in the Central Processing Units (CPUs) of consumer computers has tapered significantly. At the same time, growth in the computational power available in the Graphics Processing Units (GPUs) has remained strong. Algorithms that can be implemented on GPUs today are not only limited to graphics processing, but include scientific computation and beyond. This paper is concerned with massively parallel implementations of incremental sampling-based robot motion planning algorithms, namely the widely-used Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT*. We demonstrate an example implementation of RRT and RRT* motion-planning algorithm for a high-dimensional robotic manipulator that takes advantage of an NVidia CUDA-enabled GPU. We focus on parallelizing the collision-checking procedure, which is generally recognized as the computationally expensive component of sampling-based motion planning algorithms. Our experimental results indicate significant speedup when compared to CPU implementations, leading to practical algorithms for optimal motion planning in high-dimensional configuration spaces.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/IROS.2011.6095053
