This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventional navigation methods often prove inadequate. Leveraging the DJI M300 platform, our system is designed to autonomously navigate and transport cargo while overcoming these environmental challenges. In particular, this paper proposes an anchor-based localization method using ultrawideband (UWB) and QR codes facilities, which decouples the UAV's attitude from that of the moving landing platform, thus reducing control oscillations caused by platform movement. Additionally, a motor-driven attachment mechanism for cargo is designed, which enhances the UAV's field of view during descent and ensures a reliable attachment to the cargo upon landing. The system's reliability and effectiveness were progressively enhanced through multiple outdoor experimental iterations and were validated by the successful cargo transport during the 2024 Mohamed BinZayed International Robotics Challenge (MBZIRC2024) competition. Crucially, the system addresses uncertainties and interferences inherent in maritime transportation missions without prior knowledge of cargo locations on the deck and with strict limitations on intervention throughout the transportation.

翻译：本文提出了一种专门为在具有挑战性的海洋GNSS拒止环境中运行而设计的自主空中系统，旨在从目标船只上运输小型货物。这些环境的特点是纹理特征稀少的海面、波浪引起的甲板混沌振荡以及显著的阵风，传统导航方法在此类环境中往往效果不佳。基于DJI M300平台，我们的系统旨在克服这些环境挑战，实现自主导航和货物运输。具体而言，本文提出了一种基于超宽带（UWB）和二维码设施的锚点定位方法，该方法将无人机的姿态与移动着陆平台的姿态解耦，从而减少了由平台运动引起的控制振荡。此外，还设计了一种用于货物的电机驱动附着机构，该机构增强了无人机下降过程中的视野，并确保着陆时与货物的可靠附着。通过多次户外实验迭代，系统的可靠性和有效性逐步提升，并在2024年穆罕默德·本·扎耶德国际机器人挑战赛（MBZIRC2024）中成功完成货物运输，验证了其性能。至关重要的是，该系统解决了海上运输任务中固有的不确定性和干扰问题，这些任务在事先不知道甲板上货物位置且运输全程严格限制人工干预的条件下进行。