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Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds SciencePlanetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.
Document ID
20220018175
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Bethany Theiling
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Matthew Brandt
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Lily Clough
(University of Tulsa Tulsa, Oklahoma, United States)
Gary Crum
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Evana Gizzi
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Cheryl Gramling
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Michael Johnson
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Asmita Korde-Patel
(Goddard Space Flight Center Greenbelt, Maryland, United States)
James MacKinnon
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Robert Morgenstern
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Lynnae Quick
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Conrad Schiff
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Elaine Stewart
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Mahmooda Sultana
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Sabrina Thompson
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Orenthal Tucker
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Wayne Hong Yu
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
December 1, 2022
Subject Category
Space Sciences (General)
Mathematical and Computer Sciences (General)
Meeting Information
Meeting: American Geophysical Union Fall Meeting 2022
Location: Chicago, IL
Country: US
Start Date: December 11, 2022
End Date: December 16, 2022
Sponsors: American Geophysical Union
Funding Number(s)
WBS: 981698.01.04.51.01.50.03
WBS: 981698.01.02.51.07.10.42
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
Distributed Systems
machine learning
ocean worlds
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