A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments
dc.contributor.author | Chávez, Luisa | |
dc.contributor.author | Cortez, Angel | |
dc.contributor.author | Vinces, Leonardo | |
dc.date.accessioned | 2022-08-08T01:59:59Z | |
dc.date.available | 2022-08-08T01:59:59Z | |
dc.date.issued | 2022-01-01 | |
dc.identifier.issn | 21903018 | |
dc.identifier.doi | 10.1007/978-3-031-08545-1_43 | |
dc.identifier.uri | http://hdl.handle.net/10757/660562 | |
dc.description.abstract | This 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.format | application/html | es_PE |
dc.language.iso | eng | es_PE |
dc.publisher | Springer Science and Business Media Deutschland GmbH | es_PE |
dc.relation.url | https://link.springer.com/chapter/10.1007/978-3-031-08545-1_43 | es_PE |
dc.rights | info:eu-repo/semantics/embargoedAccess | es_PE |
dc.source | Universidad Peruana de Ciencias Aplicadas (UPC) | es_PE |
dc.source | Repositorio Academico - UPC | es_PE |
dc.subject | 3D map | es_PE |
dc.subject | Artificial potential fields | es_PE |
dc.subject | Autonomous navigation | es_PE |
dc.subject | Autonomous system | es_PE |
dc.subject | LiDAR | es_PE |
dc.subject | Neural networks | es_PE |
dc.subject | UGV | es_PE |
dc.title | A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments | es_PE |
dc.type | info:eu-repo/semantics/article | es_PE |
dc.identifier.eissn | 21903026 | |
dc.identifier.journal | Smart Innovation, Systems and Technologies | es_PE |
dc.identifier.eid | 2-s2.0-85135008074 | |
dc.identifier.scopusid | SCOPUS_ID:85135008074 | |
dc.source.journaltitle | Smart Innovation, Systems and Technologies | |
dc.source.volume | 295 SIST | |
dc.source.beginpage | 452 | |
dc.source.endpage | 460 | |
dc.identifier.isni | 0000 0001 2196 144X |