A novel approach for achieving fast evasion in self-localized swarms of Unmanned Aerial Vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects (interferers) that are actively approaching the group. The main objective of the proposed technique is the fast and safe escape of the swarm from an interferer ~discovered in proximity. This method is inspired by the collective behavior of groups of certain animals, such as schools of fish or flocks of birds. These animals use the limited information of their sensing organs and decentralized control to achieve reliable and effective group motion. The system presented in this paper is intended to execute the safe coordination of UAV swarms with a large number of agents. Similar to natural swarms, this system propagates a fast shock of information about detected interferers throughout the group to achieve dynamic and collective evasion. The proposed system is fully decentralized using only onboard sensors to mutually localize swarm agents and interferers, similar to how animals accomplish this behavior. As a result, the communication structure between swarm agents is not overwhelmed by information about the state (position and velocity) of each individual and it is reliable to communication dropouts. The proposed system and theory were numerically evaluated and verified in real-world experiments.

翻译：本文提出了一种新颖方法，用于实现受入侵移动物体威胁的无人机自定位集群的快速规避。受自然自组织系统启发，所提出的快速集体规避方法使无人机集群能够主动避开接近群体的动态物体（干扰体）。该技术的主要目标是使集群从邻近发现的干扰体中实现快速安全逃离。该方法灵感来源于某些动物群体（如鱼群或鸟群）的集体行为，这些动物利用有限的感知器官信息和分布式控制实现可靠有效的群体运动。本文提出的系统旨在执行大规模智能体无人机集群的安全协同。与自然集群类似，该系统通过快速传播检测到的干扰体信息冲击波来实现动态集体规避。所提系统完全分布式，仅使用机载传感器实现集群智能体与干扰体的相互定位，其原理与动物实现该行为的方式相似。因此，集群智能体间的通信结构不会因每个个体的状态（位置和速度）信息而过载，且对通信中断具有鲁棒性。所提出的系统与理论已通过数值仿真和实际实验进行了评估验证。