A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to decrease the inter-agent oscillations common in standard reactive swarm models employed in fast collective motion. The novel swarming methodology is supplemented with an enhanced Multi-Robot State Estimation (MRSE) strategy to increase the reliability of the purely onboard localization, which may be unreliable in real environments. Although MRSE and the neighborhood model may rely on information exchange between agents, we introduce a communication-less version of the swarming framework based on estimating communicated states to decrease dependence on the often unreliable communication networks of large swarms. The proposed solution has been verified by a set of complex real-world experiments to demonstrate its overall capability in different conditions, including a UAV interception-motivated task with a group velocity reaching the physical limits of the individual hardware platforms.

翻译：本文提出一种在无外部定位与通信的弱特征环境中实现无人机快速协同飞行的去中心化集群方法。通过构建新型无人机邻域模型，实现鲁棒的机载相互感知与集群状态反馈控制，旨在抑制高速集体运动时标准反应式集群模型中常见的个体间振荡。该新型集群方法辅以增强型多机器人状态估计策略，以提升在真实环境中可能不可靠的全机载定位可靠性。尽管MRSE与邻域模型可能依赖个体间信息交换，本文基于通信状态估计方法引入无通信版集群框架，以降低对大规模集群中常不可靠的通信网络的依赖。通过一系列复杂真实环境实验验证了所提方案在不同条件下的综合性能，包括群组速度达到单个硬件平台物理极限的无人机拦截任务。