A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments
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Issue Date
2022-01-01Keywords
3D mapArtificial potential fields
Autonomous navigation
Autonomous system
LiDAR
Neural networks
UGV
Metadata
Show full item recordJournal
Smart Innovation, Systems and TechnologiesDOI
10.1007/978-3-031-08545-1_43Additional Links
https://link.springer.com/chapter/10.1007/978-3-031-08545-1_43Abstract
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.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engISSN
21903018EISSN
21903026ae974a485f413a2113503eed53cd6c53
10.1007/978-3-031-08545-1_43
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