This letter describes autonomous landing of an unmanned aircraft system on a moving platform using vision and deep reinforcement learning. Landing on the moving platform offers several benefits, such as more mission flexibility and reduced flight time. In particular, the end-to-end vision approach (i.e., an input to the reinforcement learning is a raw image from the camera) with the deep regularized Q algorithm and custom designed reward is utilized. The custom reward was specifically devised to encourage useful feature extraction from the state space. Additionally, the proposed reinforcement learning algorithm has full 3D velocity control including the vertical channel. The simulation results show that the proposed approach can outperform existing approaches which use high-level extracted features (such as relative position and velocity of the landing pad). The simulation results are then successfully transferred to the real-world experiment by utilizing domain randomization.

中文翻译：

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使用基于视觉的深度强化学习在移动平台上自主着陆

这封信描述了使用视觉和深度强化学习在移动平台上实现无人机系统的自主着陆。在移动平台上着陆有很多好处，例如更大的任务灵活性和更少的飞行时间。特别是，利用了具有深度正则化 Q 算法和定制设计奖励的端到端视觉方法（即，强化学习的输入是来自相机的原始图像）。定制奖励是专门为了鼓励从状态空间中提取有用的特征而设计的。此外，所提出的强化学习算法具有完整的 3D 速度控制，包括垂直通道。仿真结果表明，所提出的方法可以优于使用高级提取特征（例如着陆场的相对位置和速度）的现有方法。然后利用域随机化将模拟结果成功转移到现实世界的实验中。