Path Planning Strategy for Mobile Robot Navigation Using MANFIS Controller

Abstract

Nowadays intelligent techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. The hallmark of this paper presents a new intelligent hybrid technique (Multiple Adaptive Neuro-Fuzzy Inference System) based on the combination of fuzzy inference system and artificial neural network for solving path planning problem of autonomous mobile robot. First we develop an adaptive fuzzy controller with four input parameters, two output parameters and five parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), Heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) of the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been tested by means of simulation results. It has been observed that the proposed path planning strategy is capable of avoiding obstacles and effectively guiding the mobile robot moving from the start point to the desired target point with shortest path length.