Optimal Control-Based Dynamic Obstacle Avoidance and Path Tracking for Unmanned Agricultural Vehicles

Path tracking and obstacle avoidance are challenging problems for autonomous vehicles moving in unknown environments. This paper presents a nonlinear model predictive control strategy for unmanned agricultural vehicles (UAVs), ensuring accurate path tracking while efficiently avoiding static and moving obstacles, as well as road boundaries. Compared to existing works, a new obstacle avoidance constraint is proposed to better delineate obstacle contours. The above constraints are incorporated into the optimal control problem. At the same time, a nonlinear optimization algorithm is used to deal with multiple constraints. The simulation outcomes confirm that the suggested method is highly effective for UAVs, ensuring accurate path tracking and proficient obstacle avoidance of both stationary and moving types.