Deep Reinforcement Learning-Based Trajectory Tracking Framework for 4WS Robots Considering Switch of Steering Modes
Runjiao Bao, Yongkang Xu, Lin Zhang, Haoyu Yuan, Jinge Si, Shoukun Wang, Tianwei Niu
Abstract
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.
BibTeX
@inproceedings{iros2025_deepreinforcemen,
title = {Deep Reinforcement Learning-Based Trajectory Tracking Framework for 4WS Robots Considering Switch of Steering Modes},
author = {Runjiao Bao and Yongkang Xu and Lin Zhang and Haoyu Yuan and Jinge Si and Shoukun Wang and Tianwei Niu},
booktitle = {IROS 2025},
year = {2025}
}