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This paper presents Swarm-GPT, a system that integrates large language models(LLMs) with safe swarm motion planning - offering an automated and novelapproach to deployable drone swarm choreography. Swarm-GPT enables users toautomatically generate synchronized drone performances through natural languageinstructions. With an emphasis on safety and creativity, Swarm-GPT addresses acritical gap in the field of drone choreography by integrating the creativepower of generative models with the effectiveness and safety of model-basedplanning algorithms. This goal is achieved by prompting the LLM to generate aunique set of waypoints based on extracted audio data. A trajectory plannerprocesses these waypoints to guarantee collision-free and feasible motion.Results can be viewed in simulation prior to execution and modified throughdynamic re-prompting. Sim-to-real transfer experiments demonstrate Swarm-GPT'sability to accurately replicate simulated drone trajectories, with a meansim-to-real root mean square error (RMSE) of 28.7 mm. To date, Swarm-GPT hasbeen successfully showcased at three live events, exemplifying safe real-worlddeployment of pre-trained models.

Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design