Red neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimie

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dc.contributor.authorCarreón Díaz de León, Carlos Leopoldo
dc.contributor.authorVergara Limón, Sergio
dc.contributor.authorGonzález Calleros, Juan Manuel
dc.contributor.authorDiozcora Vargas Treviño, María Aurora
dc.contributor.orcidCarreón Díaz de León, Carlos Leopoldo [0000-0002-9953-9561]spa
dc.contributor.orcidVergara Limón, Sergio [0000-0002-5215-9262]spa
dc.contributor.orcidGonzález Calleros, Juan Manuel [0000-0002-9661-3615]spa
dc.contributor.orcidDiozcora Vargas Treviño, María Aurora [0000-0001-7188-2782]spa
dc.date.accessioned2024-09-12T16:22:35Z
dc.date.available2024-09-12T16:22:35Z
dc.date.issued2021-09-15
dc.description.abstractLas redes neuronales artificiales son útiles para procesar datos con información incompleta para obtener una salida deseada. En los sistemas de medición de robots manipuladores, solo se toman muestras cuantificadas de la posición y, por lo tanto, no se puede ejecutar en un tiempo razonable algoritmos deterministas para extraer los parámetros del robot. En el estado del arte, se abordan algoritmos de extracción de parámetros basados en la suposición de que no existe la cuantificación de las señales del movimiento del robot y que la primera y segunda derivada de la posición son muestreadas y no estimadas. En este trabajo, se propone un algoritmo basado en una red neuronal entrenada para extraer los parámetros de un determinado robot para reducir el tiempo de caracterización del robot, además, con la metodología propuesta se pueden extraer parámetros dinámicos del mismo tipo de robot con el que se ha entrenado la red neuronal.spa
dc.description.abstractenglishThe artificial neural networks are suitable for processing incomplete data to achieve the desired output. The acquisition system of the manipulator robots takes quantified samples of the position; therefore, it is not possible to execute deterministic algorithms of parameter extraction in a reasonable time. State of the art describes algorithms based on the assumption that the motion signals are not quantified, and the first and second derivatives of the position are sampled instead of estimated. In this paper, a trained neural network-based extraction parameter algorithm for a determined robot is proposed to reduce the robot characterization time. Also, with the proposed methodology is possible to extract the parameters of the same kind of robot used for training the neural network.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/25392115.4298
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26478
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4298/3506spa
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dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/276spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourceVol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 37-47spa
dc.subjectRed neuronalspa
dc.subjectRobotsspa
dc.subjectParámetros dinámicosspa
dc.subject.keywordsNeural networkeng
dc.subject.keywordsRobotseng
dc.subject.keywordsDynamic parameterseng
dc.titleRed neuronal artificial para la extracción de parámetros dinámicos de robots a partir de información incompleta de su movimiespa
dc.title.translatedArtificial neural network for the extraction of dynamic parameters of robots from incomplete information of their movementeng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

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