A Strategy of Potential Fields and Neural Networks in the Control of an Autonomous Vehicle Within Dangerous Environments

Chávez L.; Cortez A.; Vinces L. · Smart Innovation, Systems and Technologies (2022) · DOI: 10.1007/978-3-031-08545-1_43

Resumen

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.

Instituciones: Universidad Peruana de Ciencias Aplicadas