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dc.contributor.authorChávez, Luisa
dc.contributor.authorCortez, Angel
dc.contributor.authorVinces, Leonardo
dc.date.accessioned2022-08-08T01:59:59Z
dc.date.available2022-08-08T01:59:59Z
dc.date.issued2022-01-01
dc.identifier.issn21903018
dc.identifier.doi10.1007/978-3-031-08545-1_43
dc.identifier.urihttp://hdl.handle.net/10757/660562
dc.description.abstractThis article focuses on the development of an autonomous navigation system by generating real-time 3D maps of different urban environments with different properties within simulation software. This system used the Pioneer 3-DX vehicle, a LiDAR sensor, GPS, and a gyroscope. For the elaboration of the trajectory, the mathematical tool of artificial potential fields was used, which will generate an attractive field to a dynamic goal identified by the robot and repulsive to the obstacles present in the environment, recognized with great precision thanks to the use of a neural network. The topology neural network 8–16–32 was developed using forward propagation, reverse propagation, and gradient descent algorithms. By combining the tools of potential fields and neural networks, a path was traced through which the robotic system will be able to move freely under an off-center point kinematic control algorithm. Finally, a 3D map of the environment was obtained to provide information on the morphology and most outstanding characteristics of the deployment environment to users who use the system.es_PE
dc.formatapplication/htmles_PE
dc.language.isoenges_PE
dc.publisherSpringer Science and Business Media Deutschland GmbHes_PE
dc.relation.urlhttps://link.springer.com/chapter/10.1007/978-3-031-08545-1_43es_PE
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_PE
dc.sourceUniversidad Peruana de Ciencias Aplicadas (UPC)es_PE
dc.sourceRepositorio Academico - UPCes_PE
dc.subject3D mapes_PE
dc.subjectArtificial potential fieldses_PE
dc.subjectAutonomous navigationes_PE
dc.subjectAutonomous systemes_PE
dc.subjectLiDARes_PE
dc.subjectNeural networkses_PE
dc.subjectUGVes_PE
dc.titleA Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environmentses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.eissn21903026
dc.identifier.journalSmart Innovation, Systems and Technologieses_PE
dc.identifier.eid2-s2.0-85135008074
dc.identifier.scopusidSCOPUS_ID:85135008074
dc.source.journaltitleSmart Innovation, Systems and Technologies
dc.source.volume295 SIST
dc.source.beginpage452
dc.source.endpage460
dc.identifier.isni0000 0001 2196 144X


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