Design of an Adaptive Neuro-Fuzzy Path Tracking and Obstacle Avoidance System for a Mobile Robot

Abstract

Over the last two decades, mobile robot navigation has remained a challenge. Mobile robots must operate in unfamiliar and complex situations, and their employment in material handling has expanded dramatically in recent years. Trajectory tracking and control is often a difficult problem in mobile robot systems. An Adaptive Neuro-Fuzzy Inference System Controller is created for tracking the trajectory of a mobile robot in this proposed work. To begin, the trajectory is pre-planned with a few precise places in the work space to match the shape requirements. Adaptive Neuro Fuzzy Inference System uses these via-points as a training set to synthesize the smooth curve. Furthermore, due to the enormous number of via-points, the technique is straightforward to calculate. Finally, two examples are simulated to see how the ANFIS structure affects the produced trajectory. The simulation results show that the suggested method can generate a smooth trajectory that is a good match with the desired trajectory, even if the desired trajectory has a convoluted shape, by utilizing the appropriate structure of ANFIS. Impediment avoidance tactics are another difficult topic in mobile robot systems. This device is meant to help wheeled mobile robots avoid front collisions by employing a fuzzy logic controller (FLC). A robust controller is installed in a mobile robot as it studies the input and output to help it navigate without colliding with any impediments. In this research, I offer a fuzzy logic controller-based obstacle avoidance control algorithm for mobile robots. With an efficient fuzzy logic look-up table, traditional approaches can be replaced in terms of efficiency. Even the output of a wheel's fuzzy logic controller can be utilized to adjust the direction and speed of obstacle avoidance. In the simulation, place a number of obstacles in the way. Allow the robot to reach the target on its own. Experiments demonstrate that the robot has an effective obstacle avoidance path.