Neuro-Fuzzy Navigation Technique for Control of Mobile Robots

Abstract

Navigation of multiple mobile robots using neuro-fuzzy controller has been discussed in this paper. In neuro-fuzzy controller the output from the neural network is fed as an input to fuzzy controller and the final outputs from the fuzzy controller are used for motion control of robots. The inputs to the neural network are obtained from the robot sensors (such as left, front, right obstacle distances and the target angle). The neural network used consists of four layers and the back propagation algorithm is used to train the neural network. The output from the neural network is initial-steering-angle. Inputs to the fuzzy-controller are initial-steering-angle (the output from neural network) and left, front, right obstacle distances. The outputs from the fuzzy controller are the crisps values of left and right wheel velocity. From the left and right wheel velocity final-steering-angle of a robot is calculated. The neuro-fuzzy controller is used to avoid various shaped obstacles and to reach target. A Petri-net model has been developed and is used to take care of inter-robot-collision during multiple mobile robot navigation. A piece of software has been developed under windows environment to implement the neurofuzzy controller for robot navigation (appendix-1). Six real mobile robots are built in the laboratory for navigational purpose (appendix-2) in reality. By using the above algorithm it is visualised that, multiple mobile robots (up-to one thousand) can navigate successfully avoiding obstacles placed in the environment.