  This paper presents Swarm-GPT, a system that integrates large language models
(LLMs) with safe swarm motion planning - offering an automated and novel
approach to deployable drone swarm choreography. Swarm-GPT enables users to
automatically generate synchronized drone performances through natural language
instructions. With an emphasis on safety and creativity, Swarm-GPT addresses a
critical gap in the field of drone choreography by integrating the creative
power of generative models with the effectiveness and safety of model-based
planning algorithms. This goal is achieved by prompting the LLM to generate a
unique set of waypoints based on extracted audio data. A trajectory planner
processes these waypoints to guarantee collision-free and feasible motion.
Results can be viewed in simulation prior to execution and modified through
dynamic re-prompting. Sim-to-real transfer experiments demonstrate Swarm-GPT's
ability to accurately replicate simulated drone trajectories, with a mean
sim-to-real root mean square error (RMSE) of 28.7 mm. To date, Swarm-GPT has
been successfully showcased at three live events, exemplifying safe real-world
deployment of pre-trained models.
