  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.
