Drone swarm performances -- synchronized, expressive aerial displays set to music -- have emerged as a captivating application of modern robotics. Yet designing smooth, safe choreographies remains a complex task requiring expert knowledge. We present SwarmGPT, a language-based choreographer that leverages the reasoning power of large language models (LLMs) to streamline drone performance design. The LLM is augmented by a safety filter that ensures deployability by making minimal corrections when safety or feasibility constraints are violated. By decoupling high-level choreographic design from low-level motion planning, our system enables non-experts to iteratively refine choreographies using natural language without worrying about collisions or actuator limits. We validate our approach through simulations with swarms up to 200 drones and real-world experiments with up to 20 drones performing choreographies to diverse types of songs, demonstrating scalable, synchronized, and safe performances. Beyond entertainment, this work offers a blueprint for integrating foundation models into safety-critical swarm robotics applications.
@article{arxiv.2412.08428,
title = {SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography},
author = {Martin Schuck and Dinushka Orrin Dahanaggamaarachchi and Ben Sprenger and Vedant Vyas and Siqi Zhou and Angela P. Schoellig},
journal= {arXiv preprint arXiv:2412.08428},
year = {2025}
}