Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1% range-normalised and 1 deg, respectively. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure.

翻译：摄像机因其体积小、功耗低、质量轻且占用空间小，正迅速成为太空交会中星载传感器的首选。然而，在对接过程中，它们通常仅起辅助作用，主要工作仍由激光雷达等主动传感器承担。本文记录了一种基于人工智能（AI）的导航算法的开发过程，旨在推动将星载可见光摄像机作为对接和在轨服务（OOS）主传感器的应用成熟度，从而减少对激光雷达的依赖并大幅降低成本。具体而言，AI的使用能够将相对导航解决方案扩展到多种场景——例如不同目标或光照条件——而使用传统图像处理方法时，这些场景需逐一单独设计。本文对多种卷积神经网络（CNN）骨干架构进行基准测试，基于与国际空间站（ISS）对接过程的合成生成数据，实现了接近1%范围归一化的位置估计和1度的姿态估计。该解决方案已与加油机构物理原型集成，并在实验室中通过机械臂模拟对接流程进行了验证。