9301

RealTime GPU-Based Motion Planning for Task Executions

Chonhyon Park, Jia Pan, Dinesh Manocha
Department of Computer Science, University of North Carolina at Chapel Hill
IEEE International Conference on Robotics and Automation (ICRA), 2013

@article{park2013realtime,

   title={RealTime GPU-Based Motion Planning for Task Executions},

   author={Park, Chonhyon and Pan, Jia and Manocha, Dinesh},

   year={2013}

}

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We present a realtime GPU-based motion planning algorithm for robot task executions. Many task execution strategies break down a high-level task planning problem into multiple low-level motion planning problems, and it is essential to solve those problems at interactive rates. In order to achieve high performance for the planning, our method exploits a high number of cores on commodity graphics processors (GPUs). We describe a parallel formulation of an RRT-based motion planning algorithm which is highly suited for single query motion planning. Our approach uses the properties of Poissondisk samples to achieve a high parallelism in order to exploit the computational capabilities of GPUs. Our approach can obtain 10-20X speedup over prior CPU-based motion planning algorithms, and we demonstrate the performance on a number of benchmarks.
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