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dc.creatorPérez Hurtado de Mendoza, Ignacioes
dc.creatorPérez Jiménez, Mario de Jesúses
dc.creatorZhang, Gexianges
dc.creatorOrellana Martín, Davides
dc.date.accessioned2021-03-17T11:08:44Z
dc.date.available2021-03-17T11:08:44Z
dc.date.issued2018
dc.identifier.citationPérez Hurtado de Mendoza, I., Pérez Jiménez, M.d.J., Zhang, G. y Orellana Martín, D. (2018). Robot Path Planning using Rapidly-Exploring Random Trees: A Membrane Computing Approach. En ICCCC 2018: 7th International Conference on Computers Communications and Control Oradea, Romania: IEEE Computer Society.
dc.identifier.isbn978-1-5386-1934-6es
dc.identifier.urihttps://hdl.handle.net/11441/106207
dc.description.abstractMethods based on Rapidly-exploring Random Trees (RRTs) have been in use in robotics to solve motion planning problems for nearly two decades. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers. These controllers handle the power of motors according to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by presenting a model for path planning of mobile robots based on the bidirectional RRT algorithm. A software for RENPSM has been developed within the Robot Operating System (ROS) and simulation experiments have been conducted by means of the Pioneer 3-DX robot simulation platform.es
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad TIN2017-89842-Pes
dc.description.sponsorshipNational Natural Science Foundation of China 61672437es
dc.description.sponsorshipNational Natural Science Foundation of China No. 61702428es
dc.description.sponsorshipSichuan Science and Technology Program 18ZDYF2877es
dc.description.sponsorshipSichuan Science and Technology Program 18ZDYF1985es
dc.formatapplication/pdfes
dc.format.extent10es
dc.language.isoenges
dc.publisherIEEE Computer Societyes
dc.relation.ispartofICCCC 2018: 7th International Conference on Computers Communications and Control (2018).
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleRobot Path Planning using Rapidly-Exploring Random Trees: A Membrane Computing Approaches
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.projectIDTIN2017-89842-Pes
dc.relation.projectID61672437es
dc.relation.projectID61702428es
dc.relation.projectID18ZDYF2877es
dc.relation.projectID18ZDYF1985es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8390434es
dc.identifier.doi10.1109/ICCCC.2018.8390434es
dc.contributor.groupUniversidad de Sevilla. TIC193: Computación Naturales
dc.eventtitleICCCC 2018: 7th International Conference on Computers Communications and Controles
dc.eventinstitutionOradea, Romaniaes
dc.relation.publicationplaceNew York, USAes
dc.contributor.funderMinisterio de Economia, Industria y Competitividad (MINECO). Españaes
dc.contributor.funderNational Natural Science Foundation of Chinaes
dc.contributor.funderSichuan Science and Technology Programes

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