Reinforcement Learning-Based Control of a 4-Wheel Independent Steering Mobile Robot for Robust Path Tracking in Outdoor Environments

This paper proposes a reinforcement learning (RL)-based control method for robust path tracking of a 4-wheel independent steering (4WIS) mobile robot in outdoor rough terrain environments. Traditional wheeled robots typically suffer from limitations including mobility constraints in narrow spaces, path deviations caused by ground slip, and reduced traction on rough terrain. To address these challenges, we designed a 4WIS mobile robot and implemented an architecture that independently controls the steering and driving of each wheel. The RL state space is defined by look-ahead path information, robot pose, velocity, and tracking errors, while the action space consists of target angular velocity and steering angle. To ensure robust performance, we applied random path and terrain generation and implemented domain randomization for sensors and actuators based on empirical GPS and motor data. The proposed controller was validated against the Pure Pursuit algorithm through dynamic simulations and real-world experiments. In simulations mimicking outdoor terrain, the controller reduced lateral and heading RMSE by 6.32% and 16.00%, respectively. In actual outdoor environments, it reduced these errors by 21.54% and 4.78%, respectively. These results demonstrate that the proposed controller provides superior robust tracking performance in unstructured outdoor environments.