Collaborative transportation of cable-suspended payloads by teams of UAVs has the potential to enhance payload capacity, adapt to different payload shapes, and provide built-in compliance, making it attractive for applications ranging from disaster relief to precision logistics. However, multi-UAV coordination under disturbances, nonlinear payload dynamics, and slack-taut cable modes remains a challenging control problem. To our knowledge, no prior work has addressed these cable mode transitions in the multi-UAV context, instead relying on simplifying rigid-link assumptions. We propose CrazyMARL, a decentralized RL framework for multi-UAV cable-suspended payload transport. Simulation results demonstrate that the learned policies can outperform classical decentralized controllers in terms of disturbance rejection and tracking precision, achieving an 80% recovery rate from harsh conditions compared to 44% for the baseline method. We also achieve successful zero-shot sim-to-real transfer and demonstrate that our policies are highly robust under harsh conditions, including wind, random external disturbances, and transitions between slack and taut cable dynamics. This work paves the way for autonomous, resilient UAV teams capable of executing complex payload missions in unstructured environments. Code and videos can be found on the website: https://imrclab.github.io/CrazyMARL.

翻译：多无人机团队协作运输缆绳悬挂载荷具有提升载荷能力、适应不同载荷形状以及提供固有柔顺性的潜力，使其在从灾难救援到精准物流等应用中具有吸引力。然而，在扰动、非线性载荷动力学以及松弛-张紧缆绳模态下的多无人机协调仍是一个具有挑战性的控制问题。据我们所知，现有研究均未在多无人机场景中解决这些缆绳模态切换问题，而是依赖于简化的刚性连杆假设。本文提出CrazyMARL——一种用于多无人机缆绳悬挂载荷运输的去中心化强化学习框架。仿真结果表明，所学策略在扰动抑制和跟踪精度方面优于经典去中心化控制器，在恶劣条件下实现了80%的恢复率，而基准方法仅为44%。我们还成功实现了零样本从仿真到实机的迁移，并证明我们的策略在恶劣条件（包括风、随机外部扰动以及松弛与张紧缆绳动力学之间的切换）下具有高度鲁棒性。这项工作为能够执行非结构化环境中复杂载荷任务的自主、韧性无人机团队奠定了基础。代码和视频可在网站https://imrclab.github.io/CrazyMARL上获取。