CBFベース強化学習による安全でスケーラブルなマルチドローンペイロード輸送：ゼロショットsim-to-real転送
Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
マルチドローンによる協調ペイロード輸送を、安全制約を保証するCBFベースの強化学習とドメインランダム化で訓練した分散ポリシーにより実現し、実機でゼロショット転送と動的環境での安全性を実証した。
詳しい要約
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著者: Jaeyoun Choi, Oswin So, Songyuan Zhang, Cooper Taylor, Chuchu Fan
分類: cs.RO, cs.MA, cs.SY, eess.SY
原文アブストラクト
Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster response. However, the complex coupled dynamics among drones, cables, and payloads pose significant challenges, and existing approaches remain limited in safety and scalability, particularly in dynamic and unstructured environments. In this work, we propose a learning-based framework for safe and scalable multi-drone cooperative payload transport. We introduce a minimal 2D abstraction that preserves the task-relevant drone-payload coupling required for coordination and safety, while remaining computationally efficient for large-scale learning. Using domain randomization over team size and physical parameters, we train a fully distributed policy via Discrete Graph Control Barrier Function Proximal Policy Optimization (DGPPO), enabling robust zero-shot sim-to-real transfer without fine-tuning. Extensive real-world evaluations demonstrate that a single learned policy generalizes across varying team sizes and task scenarios. Furthermore, multi-group hardware experiments show that the same policy can safely operate in dynamic environments, where other drone teams act as moving obstacles. These results indicate that the proposed framework enables efficient, safe, and scalable multi-drone payload transportation with strong generalization to complex real-world conditions.
