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Erschienen in:
2011 | OriginalPaper | Buchkapitel
GPU-Based Parallel Collision Detection for Real-Time Motion Planning
verfasst von : Jia Pan, Dinesh Manocha
Erschienen in: Algorithmic Foundations of Robotics IX
Verlag: Springer Berlin Heidelberg
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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.