Abstract The NASA Starlight and Breakthrough Starshot programs conceptualize fast interstellar travel via small relativistic spacecraft that are propelled by directed energy. This process is radically different from traditional space travel and trades large and slow spacecraft for small, fast, inexpensive, and fragile ones. The main goal of these wafer satellites is to gather useful images during their deep space journey. We introduce and solve some of the main problems that accompany this concept. First, we need an object detection system that can detect planets that we have never seen before, some containing features that we may not even know exist in the universe. Second, once we have images of exoplanets, we need a way to take these images and rank them by importance. Equipment fails and data rates are slow, thus we need a method to ensure that the most important images to humankind are the ones that are prioritized for data transfer. Finally, the energy on board is minimal and must be conserved and used sparingly. No exoplanet images should be missed, but using energy erroneously would be detrimental. We introduce simulator-based methods that leverage artificial intelligence, mostly in the form of computer vision, in order to solve all three of these issues. Our results confirm that simulators provide an extremely rich training environment that surpasses that of real images, and can be used to train models on features that have yet to be observed by humans. We also show that the immersive and adaptable environment provided by the simulator, combined with deep learning, lets us navigate and save energy in an otherwise implausible way.

中文翻译：

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通过模拟器和深度学习进行深空探索的进展

摘要 NASA Starlight 和Breakthrough Starshot 计划将通过由定向能驱动的小型相对论航天器实现快速星际旅行的概念化。这个过程与传统的太空旅行完全不同，用大而慢的航天器换取小、快、便宜和易碎的航天器。这些晶圆卫星的主要目标是在其深空之旅中收集有用的图像。我们介绍并解决了伴随这个概念的一些主要问题。首先，我们需要一个物体检测系统来检测我们以前从未见过的行星，其中一些包含我们甚至可能不知道宇宙中存在的特征。其次，一旦我们有了系外行星的图像，我们就需要一种方法来拍摄这些图像并按重要性对它们进行排序。设备故障和数据速率缓慢，因此，我们需要一种方法来确保对人类最重要的图像是优先进行数据传输的图像。最后，船上的能量是最小的，必须保存和谨慎使用。不应错过任何系外行星图像，但错误地使用能量将是有害的。我们引入了基于模拟器的方法，主要以计算机视觉的形式利用人工智能，以解决所有这三个问题。我们的结果证实，模拟器提供了超越真实图像的极其丰富的训练环境，可用于训练人类尚未观察到的特征的模型。我们还表明，模拟器提供的沉浸式和适应性环境与深度学习相结合，让我们能够以一种难以置信的方式导航和节省能源。