Optimal Mobile Robot Navigation for Obstacle Avoidance Based on ANFIS Controller

Over the past 20 years, there has been a lot of research done on the movement control issue of an automated wheeled movable robot. This paper suggests navigation and collision avoidance in a new setting by utilizing the sensor-based steering angle control method, the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller has already been introduced for the safety of navigation of single and multiple movable robots in cluttered surrounding areas. The front, right, and left obstruction distances have been measured using the sharp infrared reported significant and the ultrasonic distance finder sensor. This paper proposes navigating and collision avoidance in a unique environment. It uses the sensor-based angle of steering control approach. The acute ultraviolet detected is significant, and an ultrasound distance finder sensor was utilized to determine front, right, and left jump distances. In this study, a multi-layer ANFIS controller is used, with two levels for movement and the others for hurdle avoidance. The proposed ANFIS controller must be tested using a Matlab simulation. In six separate test situations, six obstacles in the surrounding region are used in a simulation, and then the robot can reach the objective without collisions in the shortest course.