Research on Port AGV Path Tracking Control Based on Deep Reinforcement Learning

Abstract

To improve the path-tracking accuracy and motion stability of port Automated Guided Vehicles (AGVs), this paper proposes a centralized coupled control strategy for the lateral and longitudinal motion of AGVs based on deep reinforcement learning. The proposed strategy is developed based on a twodegree-of-freedom dynamic model of the AGV, where a deep reinforcement learning controller is constructed and the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted to obtain the optimal control actions for the front-wheel steering angle and longitudinal velocity. Through an end-to-end learning mechanism, the controller directly establishes a joint mapping between lateral and longitudinal actions from the overall vehicle dynamics, naturally adapting to the strong coupling characteristics of the system and avoiding the accuracy loss caused by traditional decoupled control methods. Simulation and real-vehicle experiments were conducted under typical port AGV operating conditions in the Matlab/Simulink simulation environment and on a self-developed port experimental platform. The simulation results show that the lateral error at the docking point reaches 0.01 m. In the realvehicle experiments, the lateral error at the docking point is $0.01 \mathbf{m}$ and the heading angle error is $\mathbf{0. 0 3 ~ r a d}$. The experimental results demonstrate that the proposed control strategy can significantly improve the docking accuracy and cornering stability of AGVs, providing a new technical approach for addressing the poor steering stability of port AGVs.