通过基于CBF的强化学习实现安全且可扩展的多无人机载荷运输及零样本模拟到现实的迁移
Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
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中文总结 AI 辅助
研究多无人机载荷运输问题，提出基于学习的框架，引入二维抽象，用离散图控制障碍函数近端策略优化训练全分布式策略，实现零样本模拟到现实的迁移，能在不同团队规模和任务场景泛化，在动态环境安全运行。
中文摘要 AI 辅助
多无人机载荷运输是一个有前景的研究范式，在建筑、物流和灾难响应中有潜在应用。然而，无人机、电缆和载荷之间复杂的耦合动力学带来挑战，现有方法在安全性和可扩展性上有限。本文提出基于学习的安全且可扩展的多无人机协同载荷运输框架。引入最小二维抽象，通过离散图控制障碍函数近端策略优化训练全分布式策略，实现零样本模拟到现实的迁移。大量真实世界评估表明单一策略能在不同团队规模和任务场景中泛化，多组硬件实验显示能在动态环境安全运行，表明该框架能实现高效、安全且可扩展的多无人机载荷运输。
英文摘要
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.
发表机构
- Reliable Autonomous Systems Lab, Massachusetts Institute of Technology（可靠自主系统实验室，麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
