# FlockGPT: Guiding UAV Flocking with Linguistic Orchestration

arXiv: https://arxiv.org/abs/2405.05872 · HTML: https://mlsanity.com/paper/2405.05872/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2024.

## Built on by (most cited first, 12 of 12)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour](https://mlsanity.com/paper/2503.02572/) (2025)
- [UAV-CodeAgents: Scalable UAV Mission Planning via Multi-Agent ReAct and Vision-Language Reasoning](https://mlsanity.com/paper/2505.07236/) (2025)
- [Industry 6.0: New Generation of Industry driven by Generative AI and Swarm of Heterogeneous Robots](https://mlsanity.com/paper/2409.10106/) (2024)
- [GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models](https://mlsanity.com/paper/2503.23875/) (2025)
- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)
- [Say the Mission, Execute the Swarm: Agent-Enhanced LLM Reasoning in the Web-of-Drones](https://mlsanity.com/paper/2605.03788/) (2026)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [Chat with UAV -- Human-UAV Interaction Based on Large Language Models](https://mlsanity.com/paper/2512.08145/) (2025)
- [PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs](https://mlsanity.com/paper/2606.00104/) (2026)
- [AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution](https://mlsanity.com/paper/2609.18520/) (2026)

## Abstract (arXiv)

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