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Title: Towards GPU-Accelerated PRM for Autonomous Navigation
Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that the GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future.
Authors:
; ; ; ;
Award ID(s):
1719027
Publication Date:
NSF-PAR ID:
10188830
Journal Name:
International Conference on Information Technology–New Generations
Sponsoring Org:
National Science Foundation
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