Swarm-GPT：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振付設計
Swarm-GPT: Combining Large Language Models with Safe Motion Planning for Robot Choreography Design
LLMに音声データからウェイポイントを生成させ、衝突回避を保証する軌道計画と組み合わせることで、自然言語指示から安全なドローン群の振付を自動生成するシステムを提案した。
著者: Aoran Jiao, Tanmay P. Patel, Sanjmi Khurana, Anna-Mariya Korol, Lukas Brunke, Vivek K. Adajania, Utku Culha, Siqi Zhou, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
This paper presents Swarm-GPT, a system that integrates large language models (LLMs) with safe swarm motion planning - offering an automated and novel approach to deployable drone swarm choreography. Swarm-GPT enables users to automatically generate synchronized drone performances through natural language instructions. With an emphasis on safety and creativity, Swarm-GPT addresses a critical gap in the field of drone choreography by integrating the creative power of generative models with the effectiveness and safety of model-based planning algorithms. This goal is achieved by prompting the LLM to generate a unique set of waypoints based on extracted audio data. A trajectory planner processes these waypoints to guarantee collision-free and feasible motion. Results can be viewed in simulation prior to execution and modified through dynamic re-prompting. Sim-to-real transfer experiments demonstrate Swarm-GPT's ability to accurately replicate simulated drone trajectories, with a mean sim-to-real root mean square error (RMSE) of 28.7 mm. To date, Swarm-GPT has been successfully showcased at three live events, exemplifying safe real-world deployment of pre-trained models.
