Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft

Key points

• Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations.1
• A key enabler of such autonomy is the ability to run learning-based algorithms directly onboard.1
• In addition to this demonstration, we implement ROS 2 Foxy as a data transport middleware and integrate an open-source file system that provides strong data integrity guarantees and self-healing features.1
• Over the course of approximately one year, we performed 12 experiments on the spacecraft, advancing the state of the art in autonomous spacecraft systems by showing how modern learning-based pipelines, commercially available hardware, and open-source software can operate reliably under the compute and environmental constraints of Low Earth Orbit (LEO).1

Sources (1)

• [1]Learning in Orbit: Onboard Training and Closed-Loop Attitude Control with a Thermal Segmentation Model on a Spacecraft
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 07:57 PM

  In this work, we present a flight deployment on a 6U CubeSat that demonstrates (1) onboard training and inference of a thermal segmentation model on an NVIDIA Jetson TX2 NX GPU in space and (2) closed-loop attitude control using the segmentation model on a real-time, thermal image feed to track the Moon.

  Deep space missions increasingly require spacecraft capable of autonomously operating in environments with communication delays and bandwidth limitations.

Extractive summary: sentences quoted from the sources.