This article presents a unique framework for deploying decentralized and infrastructure-independent swarms of homogeneous aerial vehicles in the real world without explicit communication. This is a requirement in swarm research, which anticipates that global knowledge and communication will not scale well with the number of robots. The system architecture proposed in this article employs the UVDAR technique to directly perceive the local neighborhood for direct mutual localization of swarm members. The technique allows for decentralization and high scalability of swarm systems, such as can be observed in fish schools, bird flocks, or cattle herds. The bio-inspired swarming model that has been developed is suited for real-world deployment of large particle groups in outdoor and indoor environments with obstacles. The collective behavior of the model emerges from a set of local rules based on direct observation of the neighborhood using onboard sensors only. The model is scalable, requires only local perception of agents and the environment, and requires no communication among the agents. Apart from simulated scenarios, the performance and usability of the entire framework is analyzed in several real-world experiments with a fully-decentralized swarm of UAVs deployed in outdoor conditions. To the best of our knowledge, these experiments are the first deployment of decentralized bio-inspired compact swarms of UAVs without the use of a communication network or shared absolute localization. The entire system is available as open-source at https://github.com/ctu-mrs.

翻译：本文提出了一种独特的框架，用于在现实世界中部署无显式通信的、去中心化且不依赖基础设施的同构无人机集群。这是集群研究中的一项基本要求，其预设全局知识与通信机制无法随机器人数量良好扩展。本文提出的系统架构采用UVDAR技术直接感知局部邻域，以实现集群成员间的直接相互定位。该技术使集群系统具备去中心化与高扩展性，类似于鱼群、鸟群或牛群中观察到的现象。所开发的仿生集群模型适用于在存在障碍物的室内外环境中大规模粒子群体的真实部署。该模型的集体行为源自一组基于仅使用机载传感器对邻域进行直接观测的局部规则。模型具有可扩展性，仅需智能体与环境的局部感知，且无需智能体间通信。除模拟场景外，本文还在多个室外全去中心化无人机集群真实实验中分析了整个框架的性能与可用性。据我们所知，这些实验是首批无需通信网络或共享绝对定位的去中心化仿生紧凑无人机集群部署案例。完整系统已开源发布在https://github.com/ctu-mrs。