Obstacle Avoidance Path Planning and Trajectory Tracking of Unmanned Logistics Vehicle Based on MPC

To address the obstacle avoidance challenges faced by unmanned logistics vehicles during trajectory tracking, this paper presents a hierarchical control approach that integrates obstacle avoidance path planning and trajectory tracking, utilizing model predictive control (MPC). Firstly, an upper-layer obstacle avoidance path planning controller is designed using the vehicle kinematics model. The artificial potential field function is incorporated to represent the collision risk based on the relative states of obstacles and the vehicle. Secondly, a lower-layer trajectory tracking controller is developed based on the vehicle dynamics model, utilizing MPC to ensure accurate tracking of the obstacle avoidance path. Finally, a CarSim-Simulink cosimulation platform is established to evaluate and validate the performance of obstacle avoidance path planning and trajectory tracking across two different scenarios and three vehicle speed levels. The simulation results demonstrate that, compared to the classical MPC, the proposed hierarchical controller reduces lateral acceleration amplitude by 26.18% when avoiding a single obstacle and by 18.56% when navigating multiple obstacles. This indicates that the proposed approach achieves superior obstacle avoidance and trajectory tracking performance compared to the classical MPC controller.