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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

TL;DR

The paper addresses the challenge of running robust onboard AI on nanosatellites with stringent limits on power, compute, bandwidth, and environmental risk. It presents the Loris payload architecture on SpIRIT, featuring a Jetson Nano, a multi-camera array (six visible, three LWIR), a camera multiplexer, and a shielding/thermal frame, augmented with on-board JPEG-XL compression and a Ground Truth Factory for remote fine-tuning. Key contributions include a cohesive design that tackles thermal and radiation resilience, bandwidth-aware data management with progressive, scalable image coding, and in-orbit adaptability through metadata-driven labeling and remote model updates. The work demonstrates practical viability with initial in-orbit commissioning, first JPEG-XL in-space usage, and favorable performance of the onboard AI stack, informing future missions aiming for autonomous, data-efficient space sensing.

Abstract

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.

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

TL;DR

The paper addresses the challenge of running robust onboard AI on nanosatellites with stringent limits on power, compute, bandwidth, and environmental risk. It presents the Loris payload architecture on SpIRIT, featuring a Jetson Nano, a multi-camera array (six visible, three LWIR), a camera multiplexer, and a shielding/thermal frame, augmented with on-board JPEG-XL compression and a Ground Truth Factory for remote fine-tuning. Key contributions include a cohesive design that tackles thermal and radiation resilience, bandwidth-aware data management with progressive, scalable image coding, and in-orbit adaptability through metadata-driven labeling and remote model updates. The work demonstrates practical viability with initial in-orbit commissioning, first JPEG-XL in-space usage, and favorable performance of the onboard AI stack, informing future missions aiming for autonomous, data-efficient space sensing.

Abstract

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.
Paper Structure (13 sections, 7 figures, 2 tables)

This paper contains 13 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Loris Payload Design
  4. Challenges and Mitigation Strategies
  5. Thermal Management
  6. Radiation Environment
  7. Limited Bandwidth
  8. Lossless vs Lossy Compression
  9. Progressive Coding
  10. Comparison with CCSDS Standard
  11. Computational Resources Limitations
  12. In Orbit Adaptability
  13. Conclusion and Future Work

Figures (7)

  • Figure 1: Loris payload architecture
  • Figure 2: Loris Camera and Multiplexing Electronics Sub-module
  • Figure 3: Results from worst case thermal simulation of Loris on board the host mission (Polar Sun Synchronous Orbit at approx. 500km).
  • Figure 4: Radiation environment for proton and electron fluxes for Loris
  • Figure 5: Loris Software Integrity Monitoring Architecture
  • ...and 2 more figures