Comparison between fuzzy, neural and neuro-fuzzy controllers for mobile robot path tracking

This paper describes the use of soft computing approaches to the robot behavior design. In many applications, the robot's environment changes with time in a way that is not predictable by the designer in advance. In addition, the knowledge about the environment is often imprecise and incomplete due to the limited perceptual quality of sensors. In order to equip the robot by capacity of autonomy and intelligence in its environment, the control system must perform much complex information and processing tasks in real time, and it is well suited to use the soft-computing techniques. The objective of this paper is to elaborate and compare simple intelligent control systems for the path following behavior by an autonomous mobile robot using the most known approaches of the artificial intelligence science: fuzzy, neuro and neuro-fuzzy controllers. The proposed controllers are used for pursuing a moving target. The obtained simulation results show the effectiveness of the designed controllers. The results are discussed and compared.