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GPU-based parallel collision detection for fast motion planning

We present parallel algorithms to accelerate collision queries for sample-based motion planning. Our approach is designed for current many-core GPUs and exploits data-parallelism and multi-threaded capabilities. In order to take advantage of the 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 per second with our benchmarks, which is 10 times faster than prior GPU-based techniques. Moreover, we can compute collision-free paths for rigid and articulated models in less than 100 ms for many benchmarks, almost 50–100 times faster than current CPU-based PRM planners.