Abstract
Path planning is a fundamental task in autonomous mobile robot navigation and one of the most computationally intensive tasks. In this work, a parallel version of the bacterial potential field (BPF) method for path planning in mobile robots is presented. The BPF is a hybrid algorithm, which makes use of a bacterial evolutionary algorithm (BEA) with the artificial potential field (APF) method, to take advantage of intelligent and classical methods. The parallel bacterial potential field (parallel-BPF) algorithm is implemented on a graphics processing unit (GPU) to speed up the path planning computation in mobile robot navigation. Simulation results to validate the analysis and implementation are provided; the experiments were specially designed to show the effectiveness and the efficiency of the parallel-BPF algorithm.
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