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dc.date.accessioned2021-11-24T20:26:27Z
dc.date.available2021-11-24T20:26:27Z
dc.date.issued2021-10-02es_MX
dc.identifier.isbn978-3-030-77938-2es_MX
dc.identifier.isbn978-3-030-77941-2es_MX
dc.identifier.isbn978-3-030-77939-9es_MX
dc.identifier.urihttp://cathi.uacj.mx/20.500.11961/19243
dc.description.abstractThis work presents a planning model control for a 6-axis robot manipulator simulation assembling task. This work’s purpose is to plan trajectories for locking cable harnesses in palettes using nylon ties. This work is motivated by two biologically inspired approaches. The general 𝜏τ - J erk theory for trajectory tracking and a recurrent bi-layer Hopfield artificial neural networks (HANN) for visual feedback of multiple palette’s elements. Equidistant Cartesian points describing free-collision paths between the robot and target positions are generated. Nonlinear regression-based 3th grade polynomials are obtained by multidimensional least squares as assembling trajectories. The Cartesian paths between robot and target position are chosen based on optimization with derivatives, where the path’s height is a criteria to minimize a route. This work validated the proposed method through computer simulations, which showed feasibility and effectiveness for assembling tasks.es_MX
dc.description.urihttps://link.springer.com/chapter/10.1007/978-3-030-77939-9_10#citeases_MX
dc.language.isoen_USes_MX
dc.publisherSpringeres_MX
dc.relation.ispartofProducto de investigación IITes_MX
dc.relation.ispartofInstituto de Ingeniería y Tecnologíaes_MX
dc.subjectRobotic-armes_MX
dc.subjectRobot-assemblinges_MX
dc.subjectModel-based-controles_MX
dc.subjectTau-theoryes_MX
dc.subjectArtificial-visiones_MX
dc.subjectHopfield-neuronses_MX
dc.subjectMulti-layer-ANNes_MX
dc.subject.otherinfo:eu-repo/classification/cti/7es_MX
dc.titleBioinspired Robotic Arm Planning by Tau-Jerk Theory and Recurrent Multilayered ANNes_MX
dc.typeCapítulo de libroes_MX
dcterms.thumbnailhttp://ri.uacj.mx/vufind/thumbnails/rupiiit.pnges_MX
dcrupi.institutoInstituto de Ingeniería y Tecnologíaes_MX
dcrupi.cosechableSies_MX
dcrupi.subtipoInvestigaciónes_MX
dcrupi.nopagina355-382es_MX
dcrupi.alcanceInternacionales_MX
dcrupi.paisSwitzerlandes_MX
dc.identifier.doihttps://doi.org/10.1007/978-3-030-77939-9es_MX
dc.contributor.coauthortorres cordoba, rafael
dc.contributor.coauthorcarrillo, victor
dc.contributor.coauthorMartinez-Garcia, Edgar
dc.contributor.alumno182871es_MX
dcrupi.estadoChames_MX
dcrupi.titulolibroDeep Learning for Unmanned Systemses_MX
dc.contributor.authorexternoCarvajal, Ivan
dcrupi.impactosocialFormacion de recurso humano, tesis de maestriaes_MX
dcrupi.vinculadoproyextnoes_MX
dcrupi.pronacesEducaciónes_MX
dcrupi.vinculadoproyintnoes_MX


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