Neural Network Controller Design for a Mobile Robot Navigation; a Case Study - Tresna Dewi; Pola Risma; Yurni Oktarina; M. Taufik Roseno (Proceeding of the Electrical Engineering Computer Science and Informatics, 2017)
Mobile robot are widely applied in various aspect of human  life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task  when applied autonomously in dynamic and uncertain environment. The  ap- plication of artificial intelligence, namely neural   network,  can provide a ”brain” for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be  reduced by choosing the right model of the system, either   from mathematical modeling or directly taken from the input of sensory data  information. In this study, we compare the presented methods of previous  researches that applies neural network to mobile robot navigation. The comparison  is started  by considering  the right  mathematical model for the robot, getting the Jacobian  matrix  for online training, and giving the achieved input model to  the designed neural network layers in order to get the estimated position of the robot. From this literature study, it  is concluded that the consideration of both kinematics and dynamics modeling  of the robot will result in better performance since the exact parameters of the system are known.