  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
