动态优先级解耦的无人机集群轨迹分布式序列凸规划

In this paper, a Dynamic-Priority-Decoupled Sequential Convex Programming method (DPD-SCP) is proposed to alleviate the high-computational complexity burden for UAV swarm trajectory planning caused by high-dimensional and strong-coupling features. DPD-SCP splits a coupled swarm trajectory planning problem into several single-UAV convex programming subproblems, and the computational efficiency and scalability are enhanced by utilizing distributed computation. The flight-time-driven dynamic priority decoupled mechanism is designed to improve the convergence rate of swarm trajectory iterations. In this decoupled mechanism, the priority of UAVs with short flight time is lowered to explore the UAV's trajectory adjustment potential and eliminate the oscillation problem due to mutual avoidance of swarms. The time-consistency constraint update criterion is customized to avoid abnormal growth of swarm flight time. Furthermore, it is theoretically validated that DPD-SCP can generate the swarm trajectories that can satisfy the constraints of dynamics, collision avoidance, and time consistency. The simulation results show that the efficiency of DPD-SCP is significantly higher than that of the coupled SCP, serial-priority-decoupled SCP, and parallel-decoupled SCP methods.