We present parallel algorithms to accelerate collision queries for sample-based motion planning. Our approach is designed for current many-core GPUs and exploits the data-parallelism and multi-threaded capabilities. In order to take advantage of high number of cores, we present a clustering scheme and collision-packet traversal to perform efficient collision queries on multiple configurations simultaneously. Furthermore, we present a hierarchical traversal scheme that performs workload balancing for high parallel efficiency. We have implemented our algorithms on commodity NVIDIA GPUs using CUDA and can perform 500,000 collision queries/second on our benchmarks, which is 10X faster than prior GPU-based techniques. Moreover, we can compute collision-free paths for rigid and articulated models in less than 100 milliseconds for many benchmarks, almost 50-100X faster than current CPU-based planners. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Pan, J., & Manocha, D. (2010). GPU-based parallel collision detection for real-time motion planning. In Springer Tracts in Advanced Robotics (Vol. 68, pp. 211–228). https://doi.org/10.1007/978-3-642-17452-0_13
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