Model Predictive Path Tracking with Dynamic Obstacle Avoidance for Mobile Robots
Rishabh Satish Changwani · 2025
Efficient trajectory tracking is critical for autonomous mobile robots operating in dynamic environments. This study presents a model predictive control (MPC)-based approach for path tracking, integrating an artificial potential field (APF) method for real-time obstacle avoidance. The system employs both single-input single-output (SISO) and single-input multiple-output (SIMO) control models, optimizing navigation accuracy while adhering to kinematic constraints. Comparative simulations evaluate MPC against conventional PID controllers, demonstrating superior stability and adaptability. Results highlight the advantages of predictive control in generating feasible trajectories with minimal error, paving the way for improved autonomous navigation strategies.