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dc.contributor.authorPedreño Molina, Juan Luis 
dc.contributor.authorMolina Vilaplana, Javier 
dc.contributor.authorLópez Coronado, Juan 
dc.date.accessioned2010-12-14T13:47:38Z
dc.date.available2010-12-14T13:47:38Z
dc.date.issued2005-09
dc.identifier.citationPEDREÑO MOLINA, J.L., MOLINA VILAPLANA, J., LOPEZ CORONADO, J. A Modular Neural Network linking Hyper RBF and AVITE models for reaching moving objects. ROBOTICA, vol. 23, Issue 5 : 625-633, 2005. ISSN 0263-5747
dc.description.abstractIn this paper, the problem of precision reaching applications in robotic systems for scenarios with static and non-static objects has been considered and a solution based on a neural architecture biologically inspired has been proposed and implemented. The goal of this solution is to combine robustness and capability mapping trajectories from two biologically inspired neural networks: HypRBF and AVITE. The Hyper Basis Radial Function (HypRBF) neural model solves the inverse kinematic in redundant robotic systems, while the Adaptive Vector Integration to End-Point (AVITE) visuo-motor neural model quickly mapping the difference vector between current and desired position in both spatial (visual information) and motor coordinates (propioceptive information). The anthropomorphic behaviour of the proposed architecture for reaching and tracking tasks in presence of spatial perturbations has been validated over a real arm-head robotic platform.en_US
dc.formatapplication/pdf
dc.language.isoengen_US
dc.publisherCambridge University Pressen_US
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleA Modular Neural Network linking Hyper RBF and AVITE models for reaching moving objectsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.subjectBilogical Modelen_US
dc.subjectVisuo-motor robotic systemen_US
dc.subjectReachingen_US
dc.subjectLearning inverse Kinematicsen_US
dc.subject.otherTeoría de la Señal y las Comunicacionesen_US
dc.identifier.urihttp://hdl.handle.net/10317/1477
dc.peerreviewen_US
dc.contributor.investgroupElectromagnetismo y Materiaen_US
dc.contributor.investgroupNeurotecnología, Control y Robóticaen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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