Mitigating Challenges of the Space Environment for Onboard Artificial
  Intelligence: Design Overview of the Imaging Payload on SpIRIT
Miguel Ortiz del Castillo Jonathan Morgan Jack McRobbie Clint Therakam Zaher Joukhadar Robert Mearns Simon Barraclough Richard Sinnott Andrew Woods Chris Bayliss Kris Ehinger Ben Rubinstein James Bailey Airlie Chapman Michele Trenti
Artificial intelligence (AI) and autonomous edge computing in space are
emerging areas of interest to augment capabilities of nanosatellites, where
modern sensors generate orders of magnitude more data than can typically be
transmitted to mission control. Here, we present the hardware and software
design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for
on-board computer vision experiments based on visible light and long wave
infrared cameras. This paper highlights the key design choices made to maximise
the robustness of the system in harsh space conditions, and their motivation
relative to key mission requirements, such as limited compute resources,
resilience to cosmic radiation, extreme temperature variations, distribution
shifts, and very low transmission bandwidths. The payload, called Loris,
consists of six visible light cameras, three infrared cameras, a camera control
board and a Graphics Processing Unit (GPU) system-on-module. Loris enables the
execution of AI models with on-orbit fine-tuning as well as a next-generation
image compression algorithm, including progressive coding. This innovative
approach not only enhances the data processing capabilities of nanosatellites
but also lays the groundwork for broader applications to remote sensing from
space.