Path Generation and Obstacle Avoidance of an Autonomous Mobile Robot Using Intelligent Hybrid Controller

Abstract

Intelligent soft computing techniques such as artificial neural network and fuzzy logic approaches are verified to be efficient and appropriate when implemented to a variety of systems. Recent years both techniques has been rising interest and as a result Neuro-Fuzzy techniques have been developed. It has also taken the advantages of neural network and fuzzy inference system. This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance of an autonomous mobile robot in an unknown static and dynamic environment. In this architecture different sensor based information such as front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) and target angle (TA) are given as input to the adaptive controller and output from the controller is steering angle of the mobile robot. Outcome from the simulation results using MATLAB demonstrated that the ANFIS model could be used as a suitable and effective technique to navigate the mobile robot safely both in static and dynamic environment, find and reach to target objects.