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dc.contributor.authorTeso Fernández de Betoño, Daniel ORCID
dc.contributor.authorZulueta Guerrero, Ekaitz
dc.contributor.authorFernández Gámiz, Unai
dc.contributor.authorSáenz Aguirre, Aitor
dc.contributor.authorMartínez Rodríguez, Raquel ORCID
dc.date.accessioned2020-01-08T13:28:06Z
dc.date.available2020-01-08T13:28:06Z
dc.date.issued2019-08-26
dc.identifier.citationElectronics 8(9) : (2019) // Article ID 935es_ES
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10810/37530
dc.description.abstractThe aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters' values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the alpha(D), beta(D), and gamma(D) parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.es_ES
dc.description.sponsorshipThis research was financed by the plant of Mercedes-Benz Vitoria through PIF program to develop an intelligent production.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectDWAes_ES
dc.subjectANFISes_ES
dc.subjectmotion planninges_ES
dc.subjectmobile robotses_ES
dc.subjectobstacle avoidancees_ES
dc.subjectfuzzy logices_ES
dc.subjectMPCes_ES
dc.titlePredictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvementes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/8/9/935es_ES
dc.identifier.doi10.3390/electronics8090935
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoesIngeniería nuclear y mecánica de fluidoses_ES
dc.departamentoeuIngeniaritza nuklearra eta jariakinen mekanikaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).