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dc.contributor.authorOrozco Rosas, Ulises-
dc.contributor.authorMontiel, Oscar-
dc.contributor.authorSepúlveda, Roberto-
dc.description.abstractIn this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.es_ES
dc.description.sponsorshipApplied Soft Computing Journales_ES
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México*
dc.subjectPath planninges_ES
dc.subjectMembrane computinges_ES
dc.subjectMembrane-inspired evolutionary algorithmes_ES
dc.subjectEvolutionary computationes_ES
dc.subjectMobile robotses_ES
dc.titleMobile robot path planning using membrane evolutionary artificial potential fieldes_ES
dc.title.alternativeApplied Soft Computing Journales_ES
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