SwarmGPT: 大規模言語モデルと安全な動作計画を組み合わせたドローン群の振付生成
SwarmGPT: Combining Large Language Models with Safe Motion Planning for Drone Swarm Choreography
LLMに安全フィルタを組み合わせ、自然言語からドローン群の安全で実現可能な振付を生成するシステムを提案し、最大200機のシミュレーションと20機の実機実験で検証した。
著者: Martin Schuck, Dinushka Orrin Dahanaggamaarachchi, Ben Sprenger, Vedant Vyas, Siqi Zhou, Angela P. Schoellig
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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.
