We present novel randomized algorithms for solving global motion planning problems that exploit the computational capabilities of many-core GPUs. Our approach uses thread and data parallelism to achieve high performance for all components of sample-based algorithms, including random sampling, nearest neighbor computation, local planning, collision queries and graph search. This approach can efficiently solve both the multi-query and single-query versions of the problem and obtain considerable speedups over prior CPU-based algorithms. We demonstrate the efficiency of our algorithms by applying them to a number of 6DOF planning benchmarks in 3D environments. Overall, this is the first algorithm that can perform real-time motion planning and global navigation using commodity hardware.
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CITATION STYLE
Pan, J., Lauterbach, C., & Manocha, D. (2010). g-Planner: Real-Time Motion Planning and Global Navigation Using GPUs. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010 (pp. 1245–1251). AAAI Press. https://doi.org/10.1609/aaai.v24i1.7732