@inbook{30aab38379604520ad8991628a5d5891,
title = "Parallel bacterial potential field algorithm for path planning in mobile robots: A GPU implementation",
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.",
keywords = "Bacterial potential field, GPU, Mobile robots, Path planning",
author = "Ulises Orozco-Rosas and Oscar Montiel and Roberto Sep{\'u}lveda",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.",
year = "2018",
doi = "10.1007/978-3-319-71008-2_17",
language = "Ingl{\'e}s",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "207--222",
booktitle = "Studies in Computational Intelligence",
address = "Alemania",
}