This article presents the world's first rapid drone flocking control using natural language through generative AI. The described approach enables the intuitive orchestration of a flock of any size to achieve the desired geometry. The key feature of the method is the development of a new interface based on Large Language Models to communicate with the user and to generate the target geometry descriptions. Users can interactively modify or provide comments during the construction of the flock geometry model. By combining flocking technology and defining the target surface using a signed distance function, smooth and adaptive movement of the drone swarm between target states is achieved. Our user study on FlockGPT confirmed a high level of intuitive control over drone flocking by users. Subjects who had never previously controlled a swarm of drones were able to construct complex figures in just a few iterations and were able to accurately distinguish the formed swarm drone figures. The results revealed a high recognition rate for six different geometric patterns generated through the LLM-based interface and performed by a simulated drone flock (mean of 80% with a maximum of 93\% for cube and tetrahedron patterns). Users commented on low temporal demand (19.2 score in NASA-TLX), high performance (26 score in NASA-TLX), attractiveness (1.94 UEQ score), and hedonic quality (1.81 UEQ score) of the developed system. The FlockGPT demo code repository can be found at: coming soon

翻译：本文提出了首个通过生成式AI利用自然语言实现快速无人机集群控制的方法。该方法支持直观地编排任意规模的无人机集群以形成目标几何形状。其关键特性在于开发了一种基于大语言模型的新型交互界面，用于与用户通信并生成目标几何描述。用户在构建集群几何模型的过程中可进行交互式修改或提供反馈。通过结合集群技术与符号距离函数定义目标表面，实现了无人机集群在目标状态间的平滑自适应运动。基于FlockGPT的用户研究表明，用户能高度直观地控制无人机集群。从未接触过集群控制的受试者仅需数次迭代即可构建复杂图形，并能准确辨识生成的集群图形。结果显示，通过基于大语言模型的界面生成并由模拟无人机集群执行的六种几何图形（正方体与四面体图形识别率最高达93%，平均识别率为80%）具有高识别率。用户评价该系统具有低时间需求（NASA-TLX中得分19.2）、高绩效（NASA-TLX中得分26）、吸引力（UEQ中得分1.94）与享乐品质（UEQ中得分1.81）。FlockGPT演示代码仓库地址：即将发布