As the space domain becomes increasingly congested, autonomy is proposed as one approach to enable small numbers of human ground operators to manage large constellations of satellites and tackle more complex missions such as on-orbit or in-space servicing, assembly, and manufacturing. One of the biggest challenges in developing novel spacecraft autonomy is mechanisms to test and evaluate their performance. Testing spacecraft autonomy on-orbit can be high risk and prohibitively expensive. An alternative method is to test autonomy terrestrially using satellite surrogates such as attitude test beds on air bearings or drones for translational motion visualization. Against this background, this work develops an approach to evaluate autonomous spacecraft behavior using a surrogate platform, namely a micro-quadcopter drone developed by the Bitcraze team, the Crazyflie 2.1. The Crazyflie drones are increasingly becoming ubiquitous in flight testing labs because they are affordable, open source, readily available, and include expansion decks which allow for features such as positioning systems, distance and/or motion sensors, wireless charging, and AI capabilities. In this paper, models of Crazyflie drones are used to simulate the relative motion dynamics of spacecraft under linearized Clohessy-Wiltshire dynamics in elliptical natural motion trajectories, in pre-generated docking trajectories, and via trajectories output by neural network control systems.

翻译：随着空间领域日益拥挤，自主性被提出作为一种解决方案，使少量地面操作员能够管理大型卫星星座并执行更复杂的任务，如在轨或空间服务、组装与制造。开发新型航天器自主性的最大挑战之一是测试和评估其性能的机制。在轨测试航天器自主性可能风险极高且成本高昂。替代方法是使用卫星替代品在地面进行自主性测试，例如基于空气轴承的姿态测试台或用于平移运动可视化的无人机。在此背景下，本文开发了一种利用替代平台评估自主航天器行为的方法，该平台即Bitcraze团队开发的微型四旋翼无人机Crazyflie 2.1。Crazyflie无人机因价格低廉、开源、易于获取且包含扩展板（支持定位系统、距离/运动传感器、无线充电及人工智能功能）而在飞行测试实验室中日益普及。本文使用Crazyflie无人机模型模拟航天器在线性化Clohessy-Wiltshire动力学条件下的相对运动动力学，涵盖椭圆自然运动轨迹、预生成对接轨迹以及由神经网络控制系统输出的轨迹。