g-Planner: Real-time motion planning and global navigation using GPUs
Proceedings of the AAAI Conference on Artificial Intelligence, 2010•ojs.aaai.org
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. The approach can efficiently solve both the multi-query
and single-query versions of the problem and obtain considerable speedups over prior CPU …
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. The approach can efficiently solve both the multi-query
and single-query versions of the problem and obtain considerable speedups over prior CPU …
Abstract
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. The 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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