PACNav：增强通信受限环境下无人机集群的集体导航
PACNav: Enhancing Collective Navigation for UAV Swarms in Communication-Challenged Environments
AI总结：
本文提出PACNav方法，通过路径持久性和路径相似性实现无人机集群在通信受限环境下的去中心化集体导航，并结合碰撞避免机制，经仿真和真实森林实验验证有效。
摘要：
本文提出了持久性管理集体导航（PACNav），作为一种实现无人机（UAV）集群去中心化集体导航的方法。该技术受自然集群（如牛群、鸟群甚至大规模人群）中观察到的结群和集体导航行为启发。PACNav仅依赖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型集群。我们引入了路径持久性和路径相似性的新概念，允许每个集群成员分析其他成员的运动。PACNav基于两个主要原则：（1）运动方向变化小的无人机表现出高路径持久性，被其他无人机视为可靠的领导者；（2）朝相似方向移动的无人机群体表现出高路径相似性，假定此类群体包含可靠领导者。该方法还结合了反应式碰撞避免机制，以防止与集群成员和环境障碍物发生碰撞。该方法通过在自然森林中进行的模拟和真实世界实验得到了验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
