FlockGPT: Guiding UAV Flocking with Linguistic Orchestration
Artem Lykov, Sausar Karaf, Mikhail Martynov, Valerii Serpiva, Aleksey, Fedoseev, Mikhail Konenkov, Dzmitry Tsetserukou

TL;DR
FlockGPT introduces a novel natural language interface powered by large language models to intuitively control and orchestrate drone flocking, enabling users to create complex geometric formations with minimal training.
Contribution
The paper presents the first rapid drone flocking control system using natural language, integrating LLMs for intuitive user interaction and geometric target specification.
Findings
High recognition rate for geometric patterns (up to 93%)
Users could construct complex figures with few iterations
System scored well on usability and attractiveness metrics
Abstract
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…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Robotic Path Planning Algorithms · Robotics and Sensor-Based Localization
