SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918158076477440 |
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| author | Schuck, Martin Dahanaggamaarachchi, Dinushka Orrin Sprenger, Ben Vyas, Vedant Zhou, Siqi Schoellig, Angela P. |
| author_facet | Schuck, Martin Dahanaggamaarachchi, Dinushka Orrin Sprenger, Ben Vyas, Vedant Zhou, Siqi Schoellig, Angela P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography Schuck, Martin Dahanaggamaarachchi, Dinushka Orrin Sprenger, Ben Vyas, Vedant Zhou, Siqi Schoellig, Angela P. Robotics Artificial Intelligence Systems and Control 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. |
| title | SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2412.08428 |