Deep Reinforcement Learning-Based Trajectory Tracking Framework for 4WS Robots Considering Switch of Steering Modes

The application scenarios of automated robots are undergoing a paradigm shift from structured environments to unstructured, complex settings. In highly constrained settings like factory inspections or disaster rescue, conventional steering systems show clear drawbacks. While the four-wheel independent drive and independent steering (4WS) robot provides a variety of steering modes, which can effectively meet the needs of complex environments. However, how a 4WS robot autonomously selects different steering modes based on trajectory point information during trajectory tracking remains a challenging problem. This paper proposes a multi-modal trajectory tracking method considering the switch of steering modes, which decomposes the trajectory tracking task into two parts: mode decision-making and tracking control. The corresponding method is designed based on deep reinforcement learning. Additionally, a target trajectory random generator and corresponding training interaction environment are designed to train the model in a data-driven manner. In the designed scenario, our tracker achieve more than a 30% improvement in average tracking error across all motion modes compared with model predictive control, and the decider's average decision position error is less than 2 cm. Extensive experiments demonstrate that our method achieves superior tracking performance and real-time capabilities compared to current methods.