This article presents Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is inspired by the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. PACNav relies solely on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity, which allow each swarm member to analyze the motion of others. PACNav is grounded on two main principles: (1) UAVs with little variation in motion direction exhibit high path persistence and are considered reliable leaders by other UAVs; (2) groups of UAVs that move in a similar direction demonstrate high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also incorporates a reactive collision avoidance mechanism to prevent collisions with swarm members and environmental obstacles. The method is validated through simulated and real-world experiments conducted in a natural forest.

翻译：本文提出了一种名为持久性管理集体导航（PACNav）的方法，用于实现无人机集群的分布式集体导航。该技术受自然界集群中观察到的聚集和集体导航行为启发，例如牛群、鸟群乃至大规模人群。PACNav仅依赖于对无人机相对位置的局部观测，使其适用于缺乏通信能力和外部定位系统的大规模集群。我们引入了路径持久性和路径相似性这两个新颖概念，使每个集群成员能够分析其他成员的运动模式。PACNav基于两个核心原则：（1）运动方向变化较小的无人机具有较高的路径持久性，被其他无人机视为可靠领航者；（2）沿相似方向运动的无人机集群表现出较高的路径相似性，此类集群被认为包含可靠领航者。该方法还集成了反应式避碰机制，以防止与集群成员及环境障碍物发生碰撞。通过模拟实验和真实森林环境中的实际实验对方法进行了验证。