Smart Navigation of Mobile Robot Using Neural Network Controller

The field of autonomous navigation of mobile robot is advancing so fast especially with the development of machine learning algorithms. This study aims to introduce a neural network controller that controls the trajectory and the obstacle avoidance of a non-holonomic mobile robot. We train the robot in environment containing multiple obstacles with different places. This paper includes both a kinematic and a dynamic study of a mobile robot. Different training schemes have been studied that tackle the learning objectives differently. The trained controller is producing the Pulse Width Modulation (PWM) signals that could be implemented in a microprocessor and validated by simulations. Unlike some other recent approaches, this work was validated by a 3D simulation which is similar to the real model.