An introduction to parallel algorithms
An introduction to parallel algorithms
Robot motion planning: a distributed representation approach
International Journal of Robotics Research
Scalable Clustering Algorithms with Balancing Constraints
Data Mining and Knowledge Discovery
Planning Algorithms
Parallel white noise generation on a GPU via cryptographic hash
Proceedings of the 2008 symposium on Interactive 3D graphics and games
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Realtime Ray Tracing on GPU with BVH-based Packet Traversal
RT '07 Proceedings of the 2007 IEEE Symposium on Interactive Ray Tracing
Understanding the efficiency of ray traversal on GPUs
Proceedings of the Conference on High Performance Graphics 2009
GPU-based offset surface computation using point samples
Computer-Aided Design
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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 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.