Integration of RFNN-EKF Algorithm with ROS2 for Autonomous Mobile Robot Navigation

This research proposes an improved RFNN-EKF (Recurrent Fuzzy Neural Network-Extended Kalman Filter) algorithm and integrates it with Robot Operating System 2 (ROS2) to achieve autonomous navigation for physical mobile robots in obstacle-filled environments. While previous studies have validated the feasibility of the RFNN-EKF algorithm through simulations, physical testing introduces additional challenges such as motor dynamics, sensor noise, and environmental disturbances. To address these issues, this study modifies the RFNN-EKF algorithm to better suit real-world applications. The system employs a distributed architecture: A PC handles the computationally intensive RFNN-EKF algorithm and processes data from LiDAR and IMU, while an ESP32-based microcontroller manages motor control and speed monitoring. Communication between the PC and microcontroller is facilitated through Micro-ROS, ensuring real-time data transmission. Experimental results demonstrate that the proposed system achieves effective path planning and obstacle avoidance in dynamic environments, enhancing the autonomy and adaptability of mobile robots.