用大模型设计无人机编队表演，自动保证安全不撞机
SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography
- 用大语言模型理解自然语言指令生成编队方案
- 实测20架无人机在音乐中同步飞行无碰撞
- 非专业人士也能轻松设计复杂空中表演
无人机编队表演——随音乐同步、富有表现力的空中秀——已成为现代机器人学的吸引人应用。然而，设计流畅且安全的编排仍需专业知识，过程复杂。我们提出SwarmGPT，一种基于语言的编队设计师，利用大语言模型（LLMs）的推理能力简化无人机表演设计。该模型通过一个安全过滤器增强，可在违反安全或可行性约束时进行最小修正，确保可部署性。系统将高层编排设计与底层运动规划解耦，使非专家能通过自然语言迭代优化编排，无需担心碰撞或执行器限制。我们在模拟中验证了最多200架无人机的性能，并在真实实验中使用最多20架无人机，在多种音乐类型下完成编排，展示了可扩展、同步且安全的表演效果。本工作为将基础模型融入安全关键型群体机器人应用提供了蓝图。
原文摘要 · Abstract (English)
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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