Control of a differentially driven mobile robot using radial basis function based neural networks

Abstract

This paper proposes the use of radial basis function neural networks approach to the solution of a mobile robot orientation adjustment using reinforcement learning. In order to control the orientation of the mobile robot, a neural network control system has been constructed and implemented. Neural controller has been charged to enhance the control system by adding some degrees of award. Making use of the potential of neural networks to learn the relationships, the desired reference orientation and the error position of the mobile robot are used in training. The radial basis function based neural networks have been trained via reinforcement learning. The performance of the proposed controller and learning system has been evaluated by using a mobile robot that consists of a two driving wheels mounted on the same axis, and a free wheel on the front for balance.