自定位无人机群中的快速集体规避
Fast Collective Evasion in Self-Localized Swarms of Unmanned Aerial Vehicles
AI总结：
本文提出一种受自然群体启发的分散式快速集体规避方法，使自定位无人机群利用机载传感器传播干扰器信息，实现安全逃离，并经过数值与实飞验证。
摘要：
本文提出了一种新颖的方法，用于在受到入侵移动物体威胁的自定位无人机（UAV）群中实现快速规避。受自然自组织系统的启发，所提出的快速集体规避方法使无人机群能够避开主动接近群体的动态物体（干扰器）。所提技术的主要目标是使群体从近距离发现的干扰器中快速安全地逃离。该方法受到某些动物群体（如鱼群或鸟群）集体行为的启发。这些动物利用其感觉器官的有限信息和分散控制来实现可靠有效的群体运动。本文所述系统旨在执行具有大量智能体的无人机群的安全协调。与自然群体类似，该系统在整个群体中传播关于检测到的干扰器的快速信息冲击，以实现动态和集体规避。所提出的系统完全分散，仅使用机载传感器来相互定位群体智能体和干扰器，类似于动物实现这种行为的方式。因此，群体智能体之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，并且对通信中断具有可靠性。所提出的系统和理论在数值上进行了评估，并在真实世界实验中得到了验证。
英文摘要：
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.
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。
