Learning Deep Neural Network Controller for Path Following of Unicycle Robots

This paper investigates the scope of deep neural network (DNN) based controller in the path following task for unicycle mobile robots. A DNN-based controller is trained to follow paths with arbitrary curvature in two-dimensional space. The training process does not require initialization or supervision from any other known expert controller. Rather, the training of the DNN controller is guided by another predictive neural network that represents a path following error dynamics which is exponentially stable at the origin. The two DNNs are trained jointly in a simulated environment. The learned DNN controller is then employed as a standalone controller in a real unicycle robot for the tasks of following various linear and curved paths.