Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.