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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Acknowledgements
We thank Instituto Politécnico Nacional (IPN), the Comisión de Operación y Fomento de Actividades Academicas of IPN (COFAA), and the Mexican National Council of Science and Technology (CONACYT) for supporting our research activities.
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Orozco-Rosas, U., Montiel, O., Sepúlveda, R. (2018). Parallel Bacterial Potential Field Algorithm for Path Planning in Mobile Robots: A GPU Implementation. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_17
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