Fuzzy Dynamic Feedback Linearization for Efficient Mobile Robot Trajectory Tracking and Obstacle Avoidance in Autonomous Navigation

Mobile robots are increasingly used in various applications that require precise trajectory tracking and efficient obstacle avoidance. Dynamic Feedback Linearization (DFL) is powerful method, however, it’s has limitations such as increased computational requirements, model dependency, inability to avoid obstacles, and reduced robustness. In this paper, we address the challenges of trajectory tracking and obstacle avoidance for non-holonomic mobile robots in certain static environments subjected to the challenge of the robot to follow the reference trajectory accurately while avoiding the known obstacle in the trajectory of the robot by switching the two behaviors. The proposed scheme leverages the adaptive performance control to minimize the error between the reference and actual trajectories and avoid the static obstacle successfully. Firstly, the Dynamic Feedback Linearization (DFL) concept is used to develop an efficient tracking control system. Secondly, a Fuzzy Logic Controller (FLC) is used to avoid obstacles in the reference trajectory of the robot . Finally, the simulations are conducted using MATLAB software and the TurtleBot2 mobile robot within the 3D Gazebo simulator. According to the simulation results, the proposed approach cuts tracking accuracy and obstacle avoidance success rate by 93% and 95%, respectively. Additionally, experimental validation is carried out with the Adapt Mobilerobots Pioneer-3DX mobile robot, the results obtained from the Robot Operating System (ROS) prove the efficacy of the proposed approach for efficiency and precision.