Fuzzy and neural controllers for acute obstacle avoidance in mobile robot navigation

Abstract

Robot navigation is the technique to guide the mobile robot move towards the desired goal where dynamic and unknown environment is involved. The environment is distinguished by variable terrain and also certain objects which are known as obstacles that may block the movement of the robot in reaching the desired destination. Fuzzy Logic (FL) and Artificial Neural Network (ANN) are used to assist autonomous mobile robot move, learn the environment and reach the desired goal. This research study is focused on exploring the four combinations of training algorithms composed of FL and ANN that avoid acute obstacles in the environment. Path Remembering algorithm proposed in this paper will assist the mobile robot to come out from acute obstacles. Virtual wall building method also is proposed in order to prevent the mobile robot reentering the same acute obstacle once it has been turned away from the wall. MATLAB simulation is developed to verify and validate the algorithms before they are implemented in real time on Team AmigoBot™ robot. The results obtained from both simulation and actual application confirmed the flexibility and robustness of the controllers designed in avoiding acute obstacles and a comparison of all the four combinations of algorithms is done to find the best combination of algorithms to perform the required navigation to avoid acute obstacles. ©2009 IEEE.