An improved trajectory planning method for heavy-duty unmanned tracked vehicle under undulating terrain environments

Abstract

For heavy-duty unmanned tracked vehicles (HDUTV), trajectory planning plays an indispensable role in high-mobility driving. When driving on undulating terrains, HDUTV experiences frequent attitude changes due to the unevenness of terrains. Neglecting these changes when applying trajectory planning methods designed for flat surfaces to undulating terrains can result in decreased trackability of the planned trajectories, which may pose safety concerns. To address these challenges, an improved trajectory planning method is proposed for HDUTVs under undulating terrain environments utilizing tube-based model predictive control. Firstly, the vehicle model is expanded with pitch and roll angles to reflect the vehicle response under undulating terrain environments. Based on the expanded vehicle model, a planning problem in model predictive control (MPC) form is formulated to generate a feasible and safe trajectory. Secondly, an initial guess considering terrain features is calculated for rolling optimization to reduce the changes in vehicle’s pitch and roll while driving along the trajectory. Thirdly, to improve the trackability of the trajectory, constraints in rolling optimization are tightened by the minimal robust positive invariant set, and a trajectory compensation mechanism is designed. Finally, validations are conducted through simulations and experiments. The results reveal that compared to the conventional MPC planning method, the tracking deviation of the trajectory is decreased by 42.3%, and the maximum pitch and roll angles are decreased by 23.3% and 16.9%, respectively.