基于视觉的湍急水域敏捷着陆
Vision-Based Agile Landing on Turbulent Waters
AI总结：
提出一种基于强化学习的方法，利用多旋翼状态和局部视觉特征，在无需显式平台状态信息的情况下实现海上移动平台的自主着陆，在模拟和真实实验中优于模型预测控制基线。
摘要：
由于飞行器和着陆平台在公海条件下的耦合运动，无人机在海上船只上的自主着陆具有挑战性。本文提出了一种基于强化学习的自主多旋翼着陆方法，无需显式平台状态信息。该方法利用多旋翼状态测量以及局部视觉特征（包括从着陆表面提取的关键点和相关描述符）来预测姿态和推力指令。这些指令由传统的低层控制器跟踪。策略在仿真中使用合成关键点和随机生成的归一化描述符进行训练，从而能够在无人机上使用不同的局部特征提取器进行零样本部署。我们在逼真的模拟器中评估了该方法，并表明在对应于“非常恶劣”海况的平台运动下，它优于最先进的模型预测控制基线。最后，我们进行了广泛的实际实验，展示了使用两种不同局部特征提取器的自主机载着陆。据我们所知，这是首个在湍急水域中无需显式平台状态表示即可实现海上平台敏捷多旋翼着陆的方法。
英文摘要：
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 observations or estimation during deployment. 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 during deployment.
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。
