A Hierarchical Framework of Local Planning and Motion Control for Unmanned Off-Road Vehicles in the Presence of Unforeseen Obstacles

In an unknown environment, moving to the destination safely and effectively is of paramount importance for an unmanned off-road vehicle. However, the strong nonlinear dynamic characteristics of off-road vehicles and their interactions with unforeseen obstacles pose challenges to vehicle trajectory planning and motion control. To address this issue, this paper proposes a hierarchical framework for local planning and motion control for off-road vehicles. Specifically, for local planning, the analytic hierarchy process and the comprehensive weighting method based on game theory are employed to optimize the reliability and index weights of the Morphin algorithm, thereby enhancing the quality of planned trajectories in perceptually constrained environments. For motion control, a stability priority adaptive law is incorporated into the MPC framework to coordinate tracking accuracy and stability. Finally, the feasibility and effectiveness of the proposed framework are verified using Matlab/Simulink and Carsim joint simulation platform. The results show that the improved Morphin algorithm can plan shorter and smoother trajectories for collision avoidance compared to the traditional Morphin algorithm. Additionally, the designed motion controller can improve tracking accuracy while maintaining the stability under emergency collision avoidance.