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Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT

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 d...

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Main Authors: Del Castillo, Miguel Ortiz, Morgan, Jonathan, McRobbie, Jack, Therakam, Clint, Joukhadar, Zaher, Mearns, Robert, Barraclough, Simon, Sinnott, Richard, Woods, Andrew, Bayliss, Chris, Ehinger, Kris, Rubinstein, Ben, Bailey, James, Chapman, Airlie, Trenti, Michele
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creator Del Castillo, Miguel Ortiz
Morgan, Jonathan
McRobbie, Jack
Therakam, Clint
Joukhadar, Zaher
Mearns, Robert
Barraclough, Simon
Sinnott, Richard
Woods, Andrew
Bayliss, Chris
Ehinger, Kris
Rubinstein, Ben
Bailey, James
Chapman, Airlie
Trenti, Michele
description 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.
doi_str_mv 10.1109/CVPRW63382.2024.00672
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source IEEE Xplore All Conference Series
subjects Aerospace electronics
AI in Space
Computer vision
edge AI
Enhancing Nanosatellite Capabilities
Graphics processing units
Image coding
JPEGXL in space
Low Bandwidth AI
radiation resilience
Small satellites
Space missions
Temperature distribution
title Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
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