Autonomous landing of Unmanned Aerial Vehicles on maritime vessels is challenging due to the coupled motion of the vehicle and landing platform in open-sea conditions. This paper presents a reinforcement-learning-based approach for autonomous multirotor landing on moving maritime platforms without requiring explicit platform-state information. The proposed method uses multirotor state measurements together with local visual features, consisting of keypoints and associated descriptors extracted from the landing surface, to predict attitude and thrust commands. These commands are tracked by a conventional low-level controller. The policy is trained in simulation using synthetic keypoints with randomly generated normalized descriptors, enabling zero-shot deployment with different local feature extractors onboard the UAV. We evaluate the method in a realistic simulator and show that it outperforms a state-of-the-art Model Predictive Control baseline under platform motions corresponding to ``Very Rough'' sea conditions. Finally, we perform extensive real-world experiments, demonstrating autonomous onboard landing using two different local feature extractors. To the best of our knowledge, this is the first approach for agile multirotor landing on maritime platforms in turbulent waters that does not rely on an explicit platform-state representation.

翻译：无人机在开阔海域的自主降落面临飞行器与移动平台耦合运动的挑战。本文提出一种基于强化学习的多旋翼自主降落方法，无需显式获取平台状态信息即可在移动海事平台上实现降落。该方法利用多旋翼状态测量值与局部视觉特征（包括从降落表面提取的关键点及关联描述子）预测姿态与推力指令，并由传统底层控制器跟踪执行。策略训练采用包含随机生成归一化描述子的合成关键点进行仿真，这使得无人机可在不同局部特征提取器下实现零样本部署。我们在高逼真度仿真器中评估该方法，结果表明在对应“极恶劣”海况的平台运动条件下，该方法优于现行最优的模型预测控制基线方法。最终通过大规模实景实验，验证了使用两种不同局部特征提取器的自主机载降落能力。据我们所知，这是首个无需显式平台状态表征即可在湍流海域实现多旋翼敏捷降落的方案。