论文标题
强大的强化学习算法用于基于视觉的船舶登陆无人机
Robust Reinforcement Learning Algorithm for Vision-based Ship Landing of UAVs
论文作者
论文摘要
本文解决了开发一种用于垂直起飞和降落（VTOL）无人驾驶飞机（UAV）的算法的问题，仅使用无人机中的单眼相机进行跟踪和本地化。船舶着陆是一项具有挑战性的任务，这是由于较小的着陆空间，六个自由度的船甲板运动，有限的视觉参考以及对阵风的对抗环境条件（例如风阵）。我们首先开发了一种计算机视觉算法，该算法使用来自无人机上的单眼视觉摄像机的图像流来估计无人机相对于地平线参考栏的相对位置。我们的方法是由实际的船舶着陆程序进行的，然后是海军直升机飞行员在跟踪视觉提示的地平线参考栏时的动机。然后，我们开发了一种强大的增强学习（RL）算法，即使在存在诸如风阵的对抗性环境条件的情况下，也可以控制无人机朝着着陆平台。我们证明了与基准非线性PID控制方法相比，我们的算法表现出色，无论是在使用凉亭环境的仿真实验中还是在使用鹦鹉Anafi Quad-Rotor和Subscale Ship Platform进行6型自由度（DOF）甲板运动的模拟实验中。
This paper addresses the problem of developing an algorithm for autonomous ship landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs), using only a monocular camera in the UAV for tracking and localization. Ship landing is a challenging task due to the small landing space, six degrees of freedom ship deck motion, limited visual references for localization, and adversarial environmental conditions such as wind gusts. We first develop a computer vision algorithm which estimates the relative position of the UAV with respect to a horizon reference bar on the landing platform using the image stream from a monocular vision camera on the UAV. Our approach is motivated by the actual ship landing procedure followed by the Navy helicopter pilots in tracking the horizon reference bar as a visual cue. We then develop a robust reinforcement learning (RL) algorithm for controlling the UAV towards the landing platform even in the presence of adversarial environmental conditions such as wind gusts. We demonstrate the superior performance of our algorithm compared to a benchmark nonlinear PID control approach, both in the simulation experiments using the Gazebo environment and in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship platform undergoing 6 degrees of freedom (DOF) deck motion.
