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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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

Hyoseok Lee¹, Hyun-Min Joe¹

¹Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)

|March 28, 2026

Summary

This study introduces a reinforcement learning (RL) controller for robust path tracking in 4-wheel independent steering (4WIS) robots on rough terrain. The RL method significantly improves tracking accuracy compared to traditional algorithms in simulations and real-world tests.

Keywords:

4-wheel independent steering Sim-to-Real mobile robot path tracking reinforcement learning

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Traditional wheeled robots face challenges in rough terrain, including limited mobility and path deviations due to ground slip.
Existing control methods struggle with the complexities of unstructured outdoor environments.

Purpose of the Study:

To develop a reinforcement learning (RL)-based control method for robust path tracking of a 4-wheel independent steering (4WIS) mobile robot.
To enhance the robot's performance in outdoor rough terrain environments.

Main Methods:

Designed a 4WIS mobile robot with independent control for each wheel's steering and driving.
Defined RL state space using path information, robot pose, velocity, and tracking errors; action space includes target angular velocity and steering angle.
Applied random path/terrain generation and domain randomization for sensors/actuators, validated against Pure Pursuit algorithm.

Main Results:

In simulations, the RL controller reduced lateral and heading Root Mean Square Error (RMSE) by 6.32% and 16.00%, respectively.
In real-world experiments, lateral and heading RMSE were reduced by 21.54% and 4.78%, respectively.
Demonstrated superior robust tracking performance in unstructured outdoor environments.

Conclusions:

The proposed RL-based controller significantly enhances robust path tracking for 4WIS mobile robots in challenging outdoor terrains.
The method offers a promising solution for autonomous navigation in unstructured environments.
Independent wheel control combined with RL provides superior adaptability and accuracy.