Research on Trajectory Tracking and Obstacle Avoidance Methods for UAV Swarm Based on Model Predictive Control

UAV swarm systems have been widely used in many fields. When performing formation flight missions in complex airspace environments, UAV swarms often encounter sudden obstacle threats. UAV swarms need to track predetermined trajectories while avoiding sudden obstacles. To address the three-dimensional path tracking control and obstacle avoidance problem of UAV swarms, a nonlinear model predictive control algorithm combined with adaptive artificial potential field method is proposed. A trajectory tracking and obstacle avoidance model for UAV swarms is established, and a cost function considering internal collisions of UAV swarms and external obstacles is designed. The algorithm achieves UAV swarm tracking of predetermined trajectories while maintaining formation and avoiding sudden obstacles. The feasibility and effectiveness of the algorithm are verified through simulation calculations.