Loading…
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...
Saved in:
Main Authors: | , , , , , , , , , , , , , , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 6798 |
container_issue | |
container_start_page | 6789 |
container_title | |
container_volume | |
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 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10678148</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10678148</ieee_id><sourcerecordid>10678148</sourcerecordid><originalsourceid>FETCH-ieee_primary_106781483</originalsourceid><addsrcrecordid>eNqFj81Kw0AURkehYNG8gcJ9gcaZTH7dSaw0C2lpiy7LmN5Mr0xmymSIdO-DW0HXrr7FgXP4GLsTPBaCV_f162r9lktZJnHCkzTmPC-SCxZVRVXKjMs8S4v0kk0TkfNZkYn8ikXD8ME5F7zMskpO2dcLBdIqkNVQH5QxaDUO4DoIB4TNUbUIczuSd7ZHG6BzHpb23Sm_h0cfqKOWlIHGBjSGNNoWH-AJB9IWliP6kfDzz9b0Sv90VupknNqDs-dAs262N2zSKTNg9LvX7PZ5vq0XM0LE3dFTr_xpJ87vSpGW8h_8DRZOVPA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT</title><source>IEEE Xplore All Conference Series</source><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</creator><creatorcontrib>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</creatorcontrib><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.</description><identifier>EISSN: 2160-7516</identifier><identifier>EISBN: 9798350365474</identifier><identifier>DOI: 10.1109/CVPRW63382.2024.00672</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>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</subject><ispartof>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024, p.6789-6798</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10678148$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27904,54533,54910</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10678148$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Del Castillo, Miguel Ortiz</creatorcontrib><creatorcontrib>Morgan, Jonathan</creatorcontrib><creatorcontrib>McRobbie, Jack</creatorcontrib><creatorcontrib>Therakam, Clint</creatorcontrib><creatorcontrib>Joukhadar, Zaher</creatorcontrib><creatorcontrib>Mearns, Robert</creatorcontrib><creatorcontrib>Barraclough, Simon</creatorcontrib><creatorcontrib>Sinnott, Richard</creatorcontrib><creatorcontrib>Woods, Andrew</creatorcontrib><creatorcontrib>Bayliss, Chris</creatorcontrib><creatorcontrib>Ehinger, Kris</creatorcontrib><creatorcontrib>Rubinstein, Ben</creatorcontrib><creatorcontrib>Bailey, James</creatorcontrib><creatorcontrib>Chapman, Airlie</creatorcontrib><creatorcontrib>Trenti, Michele</creatorcontrib><title>Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT</title><title>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</title><addtitle>CVPRW</addtitle><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.</description><subject>Aerospace electronics</subject><subject>AI in Space</subject><subject>Computer vision</subject><subject>edge AI</subject><subject>Enhancing Nanosatellite Capabilities</subject><subject>Graphics processing units</subject><subject>Image coding</subject><subject>JPEGXL in space</subject><subject>Low Bandwidth AI</subject><subject>radiation resilience</subject><subject>Small satellites</subject><subject>Space missions</subject><subject>Temperature distribution</subject><issn>2160-7516</issn><isbn>9798350365474</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFj81Kw0AURkehYNG8gcJ9gcaZTH7dSaw0C2lpiy7LmN5Mr0xmymSIdO-DW0HXrr7FgXP4GLsTPBaCV_f162r9lktZJnHCkzTmPC-SCxZVRVXKjMs8S4v0kk0TkfNZkYn8ikXD8ME5F7zMskpO2dcLBdIqkNVQH5QxaDUO4DoIB4TNUbUIczuSd7ZHG6BzHpb23Sm_h0cfqKOWlIHGBjSGNNoWH-AJB9IWliP6kfDzz9b0Sv90VupknNqDs-dAs262N2zSKTNg9LvX7PZ5vq0XM0LE3dFTr_xpJ87vSpGW8h_8DRZOVPA</recordid><startdate>20240617</startdate><enddate>20240617</enddate><creator>Del Castillo, Miguel Ortiz</creator><creator>Morgan, Jonathan</creator><creator>McRobbie, Jack</creator><creator>Therakam, Clint</creator><creator>Joukhadar, Zaher</creator><creator>Mearns, Robert</creator><creator>Barraclough, Simon</creator><creator>Sinnott, Richard</creator><creator>Woods, Andrew</creator><creator>Bayliss, Chris</creator><creator>Ehinger, Kris</creator><creator>Rubinstein, Ben</creator><creator>Bailey, James</creator><creator>Chapman, Airlie</creator><creator>Trenti, Michele</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20240617</creationdate><title>Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_106781483</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aerospace electronics</topic><topic>AI in Space</topic><topic>Computer vision</topic><topic>edge AI</topic><topic>Enhancing Nanosatellite Capabilities</topic><topic>Graphics processing units</topic><topic>Image coding</topic><topic>JPEGXL in space</topic><topic>Low Bandwidth AI</topic><topic>radiation resilience</topic><topic>Small satellites</topic><topic>Space missions</topic><topic>Temperature distribution</topic><toplevel>online_resources</toplevel><creatorcontrib>Del Castillo, Miguel Ortiz</creatorcontrib><creatorcontrib>Morgan, Jonathan</creatorcontrib><creatorcontrib>McRobbie, Jack</creatorcontrib><creatorcontrib>Therakam, Clint</creatorcontrib><creatorcontrib>Joukhadar, Zaher</creatorcontrib><creatorcontrib>Mearns, Robert</creatorcontrib><creatorcontrib>Barraclough, Simon</creatorcontrib><creatorcontrib>Sinnott, Richard</creatorcontrib><creatorcontrib>Woods, Andrew</creatorcontrib><creatorcontrib>Bayliss, Chris</creatorcontrib><creatorcontrib>Ehinger, Kris</creatorcontrib><creatorcontrib>Rubinstein, Ben</creatorcontrib><creatorcontrib>Bailey, James</creatorcontrib><creatorcontrib>Chapman, Airlie</creatorcontrib><creatorcontrib>Trenti, Michele</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Del Castillo, Miguel Ortiz</au><au>Morgan, Jonathan</au><au>McRobbie, Jack</au><au>Therakam, Clint</au><au>Joukhadar, Zaher</au><au>Mearns, Robert</au><au>Barraclough, Simon</au><au>Sinnott, Richard</au><au>Woods, Andrew</au><au>Bayliss, Chris</au><au>Ehinger, Kris</au><au>Rubinstein, Ben</au><au>Bailey, James</au><au>Chapman, Airlie</au><au>Trenti, Michele</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT</atitle><btitle>2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</btitle><stitle>CVPRW</stitle><date>2024-06-17</date><risdate>2024</risdate><spage>6789</spage><epage>6798</epage><pages>6789-6798</pages><eissn>2160-7516</eissn><eisbn>9798350365474</eisbn><coden>IEEPAD</coden><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.</abstract><pub>IEEE</pub><doi>10.1109/CVPRW63382.2024.00672</doi></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | EISSN: 2160-7516 |
ispartof | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024, p.6789-6798 |
issn | 2160-7516 |
language | eng |
recordid | cdi_ieee_primary_10678148 |
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 |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T04%3A42%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Mitigating%20Challenges%20of%20the%20Space%20Environment%20for%20Onboard%20Artificial%20Intelligence:%20Design%20Overview%20of%20the%20Imaging%20Payload%20on%20SpIRIT&rft.btitle=2024%20IEEE/CVF%20Conference%20on%20Computer%20Vision%20and%20Pattern%20Recognition%20Workshops%20(CVPRW)&rft.au=Del%20Castillo,%20Miguel%20Ortiz&rft.date=2024-06-17&rft.spage=6789&rft.epage=6798&rft.pages=6789-6798&rft.eissn=2160-7516&rft.coden=IEEPAD&rft_id=info:doi/10.1109/CVPRW63382.2024.00672&rft.eisbn=9798350365474&rft_dat=%3Cieee_CHZPO%3E10678148%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-ieee_primary_106781483%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10678148&rfr_iscdi=true |