Trajectory Planning for an Articulated Tracked Vehicle and Tracking the Trajectory via an Adaptive Model Predictive Control

Abstract

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.