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:本文为在现实世界中部署分散的、依赖基础设施的同质飞行器群集提供了独特的框架,而没有明确的通信;这是群群研究的一项要求,预计全球知识和通信将无法与机器人的数量相适应;本篇文章中提议的系统架构使用UVDAR技术直接认识当地社区,使群落成员直接地相互定位;该技术允许分散和高度扩缩群群落系统,如鱼类学校、鸟群或牛群群中可以观察到的。所开发的生物刺激升温模型适合在现实世界中部署有障碍的大型粒子组;该模型的集体行为来自一套基于仅使用机载传感器直接观测周边的当地规则。该模型可扩展,只需要当地对物剂和环境的认识,也无需在代理人之间进行交流。除了模拟假设外,整个框架的性能和可用性在几个现实世界的实验中,在完全分散的户外环境环境中部署大型粒子组群群群群;这些模型的集体行为源自一套基于直接观测的当地规则,仅使用机载传感器;这些分散式系统在地面上部署的地下网络,在不易爆的地下进行。</s>