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arXiv · 2412.08428

SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography

Martin Schuck·Dinushka Orrin Dahanaggamaarachchi·Ben Sprenger·Vedant Vyas·Siqi Zhou·Angela P. Schoellig
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Abstract

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.

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Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger, Vedant Vyas, Siqi Zhou, Angela P. Schoellig. 2024-12-11. SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography. https://arxiv.org/abs/2412.08428

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