TY - GEN
T1 - A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments
AU - Chávez, Luisa
AU - Cortez, Angel
AU - Vinces, Leonardo
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - 3D map
KW - Artificial potential fields
KW - Autonomous navigation
KW - Autonomous system
KW - LiDAR
KW - Neural networks
KW - UGV
UR - https://www.scopus.com/pages/publications/85135008074
U2 - 10.1007/978-3-031-08545-1_43
DO - 10.1007/978-3-031-08545-1_43
M3 - Contribución a la conferencia
AN - SCOPUS:85135008074
SN - 9783031085444
T3 - Smart Innovation, Systems and Technologies
SP - 452
EP - 460
BT - Proceedings of the 7th Brazilian Technology Symposium, BTSym 2021 - Emerging Trends in Systems Engineering Mathematics and Physical Sciences
A2 - Iano, Yuzo
A2 - Saotome, Osamu
A2 - Kemper Vásquez, Guillermo Leopoldo
A2 - Cotrim Pezzuto, Claudia
A2 - Arthur, Rangel
A2 - Gomes de Oliveira, Gabriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Brazilian Technology Symposium, BTSym 2021
Y2 - 8 November 2021 through 10 November 2021
ER -