Research on trajectory tracking of wheeled mobile robots using fuzzy PID based on TD3

Abstract

Abstract This study addresses the issues and limitations of trajectory tracking for Non-holonomic wheeled mobile robot (NWMR) under traditional PID control, including low accuracy, high response delay, poor robustness, and stability concerns. We propose a strategy that combines the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with fuzzy PID control to optimize PID parameter tuning for more precise robot trajectory tracking. Fuzzy logic is introduced to adjust the PID controller parameters, making them more adaptive and robust. The bidirectional long short-term memory (BiLSTM) network enhances the time series processing capability of the Actor-Critic network, improving the system’s state representation and prediction abilities. A curiosity-driven exploration method is employed to increase policy diversity and avoid premature convergence. Simulation experiments using the ROS Noetic and Gazebo platforms demonstrate that this method significantly outperforms traditional PID and TD3 algorithms in terms of trajectory alignment, training time, accuracy, and stability.