Effects of membership functions for fuzzy logic controlled autonomous mobile robot

There is a growing trend in autonomous robotics research. A consistent collision avoidance and path following method is needed for an intelligent and operative mobile robot navigation. Usually robots are fitted with sensors for detecting the surrounding. Nevertheless, they still are unreliable due to ambiguity in the surroundings. Fuzzy logic has been long-established as an suitable tool for handling ambiguity that arises from vague data. Many Studies have presented Fuzzy logic models for obstacle avoidance wheeled robots frequently leading to a dead zone and inability to avoid obstacles. We presented a model with 8 inputs, 2 outputs and 27 rules for the robot movement. The research investigates the possibility of upholding uncertainty by changing controller membership functions to achieve optimum results. The study was implemented and tested through simulation by V-REP and MATLAB software. The outcomes reveal that tuning of membership functions enhance controller performance.