Optimal Control for Trajectory Tracking of Mars Rover

This paper presents an integrated path planning and trajectory tracking framework for a Mars rover, where Model Predictive Control (MPC) and the$\mathrm{A}^{*}$algorithm are used in combination to provide obstacle avoidance navigation. The reduced kinematic bicycle model of the rover is utilized in the proposed control scheme, providing computational simplicity without compromising tracking accuracy. MPC provides stable and smooth trajectory tracking, while A* offers global path planning with dynamic avoidance of obstacles. Simulation outcomes on lemniscate and circular paths illustrate the system's capability of reducing tracking errors and adjusting to environmental limitations. The MPC and$\mathrm{A}^{*}$combined methodology is promising for future autonomous planetary exploration through the Martian surface with extensions to rough terrain and more complicated rover dynamics.