This paper proposes an algorithm to improve path planning and tracking performance for autonomous robots using a Four- Wheel Steering (4WS) system in constrained environments. Traditional Ackermann steering systems face limitations in narrow spaces, which the 4WS system aims to address. By extending the Hybrid A* algorithm to adapt to the unique characteristics of the 4WS system, and integrating it with Model Predictive Control, the study achieves efficient path planning and precise tracking in complex environments. A distinctive aspect of the proposed approach is its adaptive control strategy, dynamically switching between three modes—Normal driving, Pivot, and Parallel movement—based on the vehicle's motion state, thus enhancing both flexibility and efficiency. The algorithm's performance was validated through MATLAB simulations in a logistics warehouse setting, showing high path tracking accuracy in confined spaces. The study effectively demonstrates the feasibility of the proposed method in a simulated environment.