A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

Nowadays intelligent tools 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. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two output parameters and three 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) for the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been demonstrated by means of simulation experiments. The real time experimental results were verified with simulation experiments, showing that the proposed navigational algorithm consistently performs better results to navigate the mobile robot safely in a completely or partially unknown environment.