Studies in Computational Intelligence

dc.creatorOrozco Rosas, Ulises
dc.creatorPicos, Kenia
dc.creatorMontiel, Oscar
dc.creatorCastillo, Oscar
dc.date.accessioned2021-04-06T17:33:20Z
dc.date.accessioned2022-10-14T15:42:30Z
dc.date.available2021-04-06T17:33:20Z
dc.date.available2022-10-14T15:42:30Z
dc.date.created2021-04-06T17:33:20Z
dc.date.issued2021-03
dc.identifierOnline ISBN 978-3-030-68776-2
dc.identifierhttps://repositorio.cetys.mx/handle/60000/1023
dc.identifierhttps://doi.org/10.1007/978-3-030-68776-2_13
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4255993
dc.description.abstractThis work presents a graphics processing unit (GPU) accelerated membrane evolutionary artificial potential field (MemEAPF) algorithm implementation for mobile robot path planning. Three different implementations are compared to show the performance, effectiveness, and efficiency of the MemEAPF algorithm. Simulation results for the three different implementations of the MemEAPF algorithm, a sequential implementation on CPU, a parallel implementation on CPU using the open multi-processing (OpenMP) application programming interface, and the parallel implementation on GPU using the compute unified device architecture (CUDA) are provided to validate the comparative and analysis. Based on the obtained results, we can conclude that the GPU implementation is a powerful way to accelerate the MemEAPF algorithm because the path planning problem in this work has been stated as a data-parallel problem.
dc.languageen_US
dc.publisherSpringer, link
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México
dc.subjectMembrane computing
dc.subjectGenetic algorithms
dc.subjectArtificial potential field
dc.subjectPath planning
dc.subjectMobile robots
dc.subjectGraphics processing unit
dc.titleStudies in Computational Intelligence
dc.typeBook chapter


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