<p>This paper introduces a novel methodology for minimum-time trajectory planning of autonomous heavy vehicles operating in structured environments. The proposed approach generates optimal and dynamically feasible trajectories by explicitly accounting for traction and braking torque limits, wheel non-sliding constraints, and obstacle avoidance requirements. The methodology is organized into two main stages. In the first stage, a non-holonomic reference trajectory is generated using the Random Profile Approach (RPA) to determine the optimal trajectory of both the tractor and trailer during task execution. It is important to note that the dynamic model employed at this stage neglects tire elasticity. The second stage serves as a correction phase, where the vehicle’s control parameters are refined through an appropriate control law to minimize tracking errors relative to the reference trajectory. In this phase, the dynamic model incorporates tire elasticity to enhance accuracy. Simulation results confirm that the proposed approach achieves fast convergence towards high-quality trajectories that satisfy all geometric, kinematic, and dynamic constraints, demonstrating its effectiveness and robustness in optimizing the motion of heavy articulated vehicles.</p>