Drone applications continue to expand across various domains, with flocking offering enhanced cooperative capabilities but introducing significant challenges during initial formation. Existing flocking algorithms often struggle with efficiency and scalability, particularly when potential collisions force drones into suboptimal trajectories. This paper presents a time-efficient prioritised scheduling algorithm that improves the initial formation process of drone flocks. The method assigns each drone a priority based on its number of potential collisions and its likelihood of reaching its target position without permanently obstructing other drones. Using this hierarchy, each drone computes an appropriate delay to ensure a collision-free path. Simulation results show that the proposed algorithm successfully generates collision-free trajectories for flocks of up to 5000 drones and outperforms the coupling-degree-based heuristic prioritised planning method (CDH-PP) in both performance and computational efficiency.
翻译:无人机应用持续扩展至各个领域,集群技术虽能提升协同能力,却在初始编队阶段带来显著挑战。现有集群算法常受效率和可扩展性制约,尤其在潜在碰撞迫使无人机采取次优轨迹时更为突出。本文提出一种时间高效的优先级调度算法,以改进无人机集群的初始编队过程。该方法根据每架无人机的潜在碰撞数量及其在不永久阻碍其他无人机的情况下抵达目标位置的可能性,为其分配优先级。利用此层级结构,每架无人机计算适当的延迟以确保无碰撞路径。仿真结果表明,所提算法成功为多达5000架无人机的集群生成无碰撞轨迹,并在性能和计算效率上均优于基于耦合度的启发式优先级规划方法(CDH-PP)。