Neural network-based optimal control for autonomous mobile vehicle navigation

This work addresses the problem of generating autonomously an optimal control action sequence for an autonomous vehicle based on artificial neural networks (ANN). The principal objective is to autonomously design an optimal controller that steers the center of the vehicle through a number of via points in a particular order using a minimum amount of time. In general, the steering of robotic vehicles depends on the interactions between the vehicle and its supporting medium. Planning for the future encounters with the via points should be part of the current control decision, since the vehicle's position and orientation as it moves through one gate greatly alter the case of navigation through successive points. The proposed neural network-based intelligent controller learns to guide the vehicle through a set of points autonomously. The simulation results show the performance of the proposed approach for a simple case.