Emergency Avoidance: Model Predictive Control Based Path Tracking for Unmanned Ground Vehicles with Active Obstacle Avoidance

Abstract

Autonomous driving is a high-performance, safety-critical task. Effectively controlling autonomous vehicles to enhance both performance and safety is crucial, especially in complex and dynamic environments. However, in real-time obstacle avoidance (OA) scenarios, the planning layer often fails due to high computational complexity and response delays. This trade-off between computational efficiency and safety performance presents a key challenge: how to achieve an optimal balance between autonomous driving safety and real-time performance. In recent years, addressing OA at the control layer has become a major research focus for improving the safety of autonomous vehicles. Given the advantages of Model Predictive Control (MPC) in prediction and constraint handling, this paper integrates an OA safety distance constraint into MPC to effectively handle OA in Unmanned Ground Vehicles (UGVs). First, a Taylor expansion is used to construct the UGV’s error model. Then, safe distance constraints for obstacle avoidance are formulated, considering both tracking errors and proximity to obstacles. Additionally, a Safe Obstacle Avoidance MPC (SOAMPC) is developed by integrating safety distance constraints and physical limitations. Furthermore, key control-theoretic properties are established, including recursive feasibility, guaranteed collision avoidance, and system stability. Simulations and experiments in a multi-obstacle environment validate SOAMPC’s effectiveness. Results show that SOAMPC not only ensures obstacle avoidance and stability but also outperforms other methods in efficiency and path tracking accuracy.