Articulated tracked vehicles are used as special off-road transportation vehicles, and their mobility is gaining more attention now than before. As an important evaluation indicator of the mobility of articulated tracked vehicles, steering performance receives wide attention in particular. Most of the present studies focus on the planar steering performance; few studies employing current models concentrate on the slope steering performance of articulated tracked vehicles. To address this research gap, this study proposes a dynamic modeling method for analyzing the slope steering performance of articulated tracked vehicles. A kinematic model of a vehicle is initially constructed to analyze its kinematic characteristics during slope steering; these characteristics include velocity and acceleration. A dynamic model of a vehicle is then developed to analyze its mechanical characteristics during slope steering; these characteristics include vertical loads, driving forces, and driving moments of tracks. The created dynamic model is then applied to analyze the slope steering performance of a specific articulated tracked vehicle. A mechanical-control united simulation model and an actual test of an articulated tracked vehicle are suggested to verify the established steering model. Comparison results show the effectiveness of the proposed dynamic steering model.

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This paper focuses on the trajectory planning and trajectory tracking control of articulated tracked vehicles (ATVs). It utilizes the path planning method based on the Hybrid A-star and the minimum snap smoothing method to obtain the feasible kinematic trajectory. To overcome the highly non-linearity of ATVs, we proposed a linear-parameter-varying (LPV) kinematic tracking-error model. Then, the kinematic controller was formulated as the adaptive model predictive controller (AMPC). The simulation of the path planning algorithm showed that the proposed planning strategy could provide a feasible trajectory for the ATVs passing through the obstacles. Moreover, we compared the AMPC controller with the developed controller in four scenarios. The comparison showed that the AMPC controller achieved satisfactory tracking errors regarding the lateral position and orientation angle errors. The maximum lateral distance error by the AMPC controller has been reduced by 72.4% compared to the standard-MPC controller. The maximum orientation angle error has been reduced by 55.53%. The simulation results confirmed that the proposed trajectory planning and tracking control system could effectively perform the automated driving behaviors for ATVs.