基于分布式模型预测控制的实时可交互 无人机群编队方法.

To meet the real-time interactive formation changes and personalized performance requirements of drone swarms, this paper proposed a new offline trajectory generation algorithm based on distributed model predictive control (DMPC). This method began with mathematical modeling of point-to-point formation changes in drone formations and solved the optimization problem. This paper introduced a new on-demand collision avoidance strategy for DMPC and proposed a rapid DMPC algorithm for multi-drone point-to-point transitions. By combining with intelligent target point allocation, the method reduced computational pressure while achieving more efficient flight trajectories. The study conducted simulations and compared the method with existing methods for performance evaluation. The results show that the proposed method significantly improves the success rate of trajectory generation, the rate of trajectory generation, and the efficiency of trajectory flight. The new algorithm quickly generates efficient flight trajectories for point-to-point formation changes in drone swarms, meeting the real-time interactivity and personalized needs of drone swarm flight performances. [ABSTRACT FROM AUTHOR]