This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only 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 that allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (1) UAVs with little variation in motion direction have high path persistence, and are considered by other UAVs to be reliable leaders; (2) groups of UAVs that move in a similar direction have high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

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（UAV）集群的去中心化集体导航。该技术基于自然界群体（如牛群、鸟群甚至大规模人类群体）中观察到的集群与集体导航行为。由于自然群体中无法获取所有成员的全局同步信息，这些系统通过局部观测实现预期行为。类似地，PACNav仅依赖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与路径相似性两个新概念，使每个集群成员能够分析其他成员的运动轨迹，从而决定自身的未来运动方向。PACNav基于两大核心原则：（1）运动方向变化较小的无人机具有高路径持久性，被其他无人机视为可靠领航者；（2）运动方向相似的无人机群组具有高路径相似性，此类群组被认为包含可靠领航者。该方法还嵌入反应式碰撞规避机制，以避免与集群成员及环境障碍物发生碰撞。该碰撞规避机制在确保安全性的同时，能减少对既定路径的偏离。通过多组仿真实验以及在自然森林环境中开展的真实世界实验，我们验证了所提集体导航方法在复杂环境中的有效性与可行性。相关源代码已开源，便于复现实验成果并推动学术界后续研究。