Artificial potential field based motion planning for autonomous tractor-trailer vehicles - Ngoc Trong Hoang; Huu Viet Nguyen; Ngoc Duy Le; Huu Hai Ho; Tung Lam Nguyen (Results in Engineering, 2025)
The motion planning of autonomous tractor-trailer vehicles (TTVs) presents significant challenges due to their underactuated dynamics with nonholonomic constraints. This paper proposes a unified trajectory planning and tracking approach that integrates Artificial Potential Field (APF) into Model Predictive Control (MPC) to improve maneuverability and safety of TTVs in complex driving environments. The APF framework is first designed to model the interactions between the TTV and its surrounding environment by generating attractive forces toward the target and repulsive forces to avoid obstacles. To ensure dynamic feasibility and optimal motion planning for the TTV, the APF is then incorporated into the MPC by embedding potential field values into the MPC's objective function. This integration enables the system to generate optimal trajectories and enhance tracking performance while considering multiple constraints, including vehicle kinematics, environmental factors, and traffic safety requirements. The effectiveness of the proposed approach is evaluated through diverse scenarios in Matlab/Simulink such as overtaking maneuvers, obstacle cut-ins, emergency braking situations, and multi-obstacle avoidance. The simulation results demonstrate the robustness and efficiency of the proposed method in achieving collision-free navigation and smooth trajectory execution for the TTV.