CrazyMARL：用于缆绳悬挂载荷协同空中运输的分散式直接电机控制策略
CrazyMARL: Decentralized Direct Motor Control Policies for Cooperative Aerial Transport of Cable-Suspended Payloads
AI总结：
提出CrazyMARL，一种基于分散式强化学习的多无人机缆绳悬挂载荷运输框架，在抗干扰和跟踪精度上优于经典控制器，并实现了零样本仿真到实物迁移。
摘要：
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，使其在从灾害救援到精密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-绷紧缆绳模式下的多无人机协调仍然是一个具有挑战性的控制问题。据我们所知，先前的工作没有在多无人机背景下处理这些缆绳模式转换，而是依赖于简化的刚性连杆假设。我们提出了CrazyMARL，一个用于多无人机缆绳悬挂载荷运输的分散式强化学习框架。仿真结果表明，所学策略在抗干扰和跟踪精度方面优于经典分散式控制器，在恶劣条件下实现了80%的恢复率，而基线方法仅为44%。我们还成功实现了零样本仿真到实物迁移，并证明了我们的策略在恶劣条件下（包括风、随机外部扰动以及松弛和绷紧缆绳动力学之间的转换）具有高度鲁棒性。这项工作为能够在非结构化环境中执行复杂载荷任务的自主、弹性无人机团队铺平了道路。代码和视频可在网站https://imrclab.github.io/CrazyMARL上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
