Testing Spacecraft Formation Flying with Crazyflie Drones as Satellite Surrogates

TL;DR

The paper addresses the challenge of testing autonomous spacecraft formation flight in a safe, cost-effective manner by using Crazyflie 2.1 drones as satellite surrogates. It builds a complete surrogate pipeline that couples linearized Clohessy-Wiltshire relative-motion dynamics in Hill's frame, lab-scale trajectory scaling by $4\times10^{3}$, and a gym-pybullet-drones simulation framework, with reinforcement-learning–driven docking scenarios. The main contributions include a validated surrogate test bed for in-plane and out-of-plane CW trajectories, a lab-scale waypoint generation and tracking workflow, and RL-based docking demonstrations that are either offline or integrated with the simulator. This work provides a practical, scalable path to evaluating autonomous spacecraft control strategies and informs future on-orbit servicing and proximity operations, with a clear route toward physical flight validation and Sim2Real improvements.

Abstract

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.

Figures (16)

• Figure 1: Earth-Centered Inertial Frame (Black) and the Non-inertial Hill's Frame (Red). The Hill's frame x-axis points from the center of the Earth to the chief satellite's center of mass, the z-axis points in the same direction as the orbital angular moment (out of the page), and the y-axis completes the orthogonal frame.
• Figure 2: Noninertial Drone Body Fixed Frame.
• Figure 3: Crazyflie Simulation Control Architecture.
• Figure 4: Advanced Learning, Artificial Intelligence, and Control Flight Arena at the University of Houston
• Figure 5: Diagram of Flight Arena