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dc.contributor.authorOrozco Rosas, Ulises-
dc.contributor.authorPicos, Kenia-
dc.contributor.authorMontiel, Oscar-
dc.contributor.otherCETYS Universidades_ES
dc.date.accessioned2020-03-02T19:10:46Z-
dc.date.available2020-03-02T19:10:46Z-
dc.date.issued2020-02-28-
dc.identifier.issn1860-949X-
dc.identifier.uriDOI: 10.1007/978-3-030-35445-9_22-
dc.description.abstractIn this work, a mobile robot path-planning algorithm based on the evolutionary artificial potential field (EAPF) for non-static environments is presented. With the aim to accelerate the path planning computation, the EAPF algorithm is implemented employing novel parallel computing architectures. The EAPF algorithm is capable of deriving optimal potential field functions using evolutionary computation to generate accurate and efficient paths to drive a mobile robot from the start point to the goal point without colliding with obstacles in static and non-static environments. The algorithm allows parallel implementation to accelerate the computation to obtain better results in a reasonable runtime. Comparative performance analysis in terms of path length and computation time is provided. The experiments were specifically designed to show the effectiveness and the efficiency of the mobile robot path-planning algorithm based on the EAPF in a sequential implementation on CPU, a parallel implementation on CPU, and a parallel implementation on GPU.es_ES
dc.language.isoenes_ES
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/*
dc.subjectPath planninges_ES
dc.subjectEvolutionary artificial potential fieldes_ES
dc.subjectMobile robotses_ES
dc.subjectGraphics processing unites_ES
dc.subjectHeterogeneous computinges_ES
dc.titleIntuitionistic and type-2 fuzzy logic enhancements in neural and optimization algorithms: theory and applicationses_ES
dc.typeBook chapteres_ES
dc.subject.sedeSistemases_ES
dc.publisher.editorialSpringeres_ES
dc.title.chapterAcceleration of path planning computation based on evolutionary artificial potential field for non-static environmentses_ES
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