使用 Crazyflie 无人机作为卫星替代平台测试航天器编队飞行
Testing Spacecraft Formation Flying with Crazyflie Drones as Satellite Surrogates
AI总结：
本文提出使用 Crazyflie 2.1 微型无人机作为卫星替代平台，模拟航天器在 Clohessy-Wiltshire 动力学、对接轨迹及神经网络控制下的相对运动，以低成本地面测试航天器自主性。
摘要：
随着太空领域日益拥挤，自主性被提出作为一种方法，使少量地面操作人员能够管理大型卫星星座，并应对更复杂的任务，例如在轨或太空服务、组装与制造。开发新型航天器自主性所面临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高且成本极其高昂。另一种方法是利用卫星替代平台在地面测试自主性，例如使用气浮轴承上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，本工作开发了一种使用替代平台评估自主航天器行为的方法，该平台即由 Bitcraze 团队开发的微型四旋翼无人机 Crazyflie 2.1。Crazyflie 无人机在飞行测试实验室中日益普及，因为它们价格低廉、开源、易于获取，并且包含扩展板，可支持定位系统、距离和/或运动传感器、无线充电以及 AI 能力等功能。本文使用 Crazyflie 无人机模型来模拟航天器在以下情况下的相对运动动力学：线性化 Clohessy-Wiltshire 动力学下的椭圆自然运动轨迹、预先生成的对接轨迹，以及由神经网络控制系统输出的轨迹。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
