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dc.contributor.authorZubizarreta Pico, Asier ORCID
dc.contributor.authorLarrea, Mikel ORCID
dc.contributor.authorIrigoyen Gordo, Eloy
dc.contributor.authorCabanes Axpe, Itziar
dc.contributor.authorPortillo Pérez, Eva
dc.date.accessioned2024-02-08T10:27:15Z
dc.date.available2024-02-08T10:27:15Z
dc.date.issued2018-01-03
dc.identifier.citationNeurocomputing 271 : 104-114 (2018)
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/10810/65319
dc.description.abstractThe reliable calculation of the Direct Kinematic Problem (DKP) is one of the main challenges for the implementation of Real-Time (RT) controllers in Parallel Robots. The DKP estimates the pose of the end effector of the robot in terms of the sensors placed on the actuators. However, this calculation requires the use of time-consuming numerical iterative procedures. Artificial Neural Networks have been proposed to implement the complex DKP equation mapping due to their universal approximator property. However, the proposals in this area do not consider the Real Time implementation of the ANN based solution, and no approximation error vs computational time analysis is carried out. In this work, a methodology that uses Artificial Neural Networks (ANNs) to approximate the DKP is proposed. Based on the 3PRS parallel robot, a comprehensive study is carried out in which several net- work configurations are proposed to approximate the DKP. Moreover, to demonstrate the effectiveness of the approach, the proposed networks are evaluated considering not only their approximation capabili- ties, but also their Real Time performance in comparison with the traditional iterative procedures used in robotics.es_ES
dc.language.isoenges_ES
dc.publisherElsevier
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectparallel robotses_ES
dc.subjectkinematic problemes_ES
dc.subjectartificial neural networkes_ES
dc.titleReal Time Direct Kinematic Problem Computation of the 3PRS robot Using Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.rights.holder© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0925231217312225
dc.identifier.doi10.1016/j.neucom.2017.02.098
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES
dc.identifier.eissn1872-8286


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© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/