SwarmGPT：结合大语言模型与安全运动规划的无人机群编舞
SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography
AI总结：
SwarmGPT利用大语言模型生成无人机编舞，并通过安全过滤器确保可部署性，实现非专家自然语言迭代设计，仿真与真实实验验证了其可扩展性和安全性。
摘要：
无人机群表演——同步、富有表现力的空中展示，配合音乐进行——已成为现代机器人技术中一个引人入胜的应用。然而，设计平滑、安全的编舞仍然是一项需要专业知识的复杂任务。我们提出了SwarmGPT，一种基于语言的编舞系统，利用大型语言模型（LLM）的推理能力来简化无人机表演设计。该LLM通过一个安全过滤器进行增强，该过滤器在安全性或可行性约束被违反时通过进行最小修正来确保可部署性。通过将高层编舞设计与低层运动规划解耦，我们的系统使非专家能够使用自然语言迭代地完善编舞，而无需担心碰撞或执行器限制。我们通过多达200架无人机的仿真实验以及多达20架无人机针对多种类型歌曲进行编舞的真实世界实验验证了我们的方法，展示了可扩展、同步且安全的表演。除了娱乐之外，这项工作为将基础模型集成到安全关键的群体机器人应用中提供了蓝图。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
