Removing disabled satellites is essential to the efficient utilization of orbital resources. Approaching a target satellite is one of the critical stages of the whole removal process, requiring the chaser satellite to perform accurate control. In this article, we propose a method to establish a neural-network controller that utilizes an optical camera as the sole relative measurement device. To achieve this, we first create a set of optimal approach trajectories and generate a numerical dataset. We then modify this dataset to instruct the neural-network controller to generate additional corrective forces when the relative velocity deviates from its optimal value due to unexpected disturbances. By making use of the modified dataset and 3-D simulations, we create image sequences that are employed as training samples in deep learning. Finally, the neural-network controller established based on the 3D-ResNet-18 architecture is trained and obtained. The simulation results suggest that our approach significantly improves control accuracy under thruster output uncertainty.

中文翻译：

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通过深度学习仅基于视觉的接近失效卫星的控制


清除失效卫星对于有效利用轨道资源至关重要。接近目标卫星是整个清除过程的关键阶段之一，需要追踪卫星进行精确控制。在本文中，我们提出了一种建立神经网络控制器的方法，该控制器利用光学相机作为唯一的相关测量设备。为了实现这一目标，我们首先创建一组最佳进场轨迹并生成一个数值数据集。然后，我们修改该数据集，以指示神经网络控制器在相对速度由于意外干扰而偏离其最佳值时生成额外的校正力。通过利用修改后的数据集和 3D 模拟，我们创建了用作深度学习训练样本的图像序列。最后，训练并获得基于3D-ResNet-18架构建立的神经网络控制器。仿真结果表明，我们的方法显着提高了推进器输出不确定性下的控制精度。