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NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗

NASA NTRS2006

Satellite-based Assessment of Global Warm Cloud Properties Associated with Aerosols, Atmospheric Stability, and Diurnal Cycle

This study examines variability in marine low cloud properties derived from semi-global observations by the Tropical Rainfall Measuring Mission (TRMM) satellite, as linked to the aerosol index (AI) and lower-tropospheric stability (LTS). AI is derived from the Moderate Resolution Imaging Spectroradiometer (Terra MODIS) sensor and the Goddard Chemistry Aerosol Radiation and Transportation (GOCART) model, and is used to represent column-integrated aerosol concentrations. LTS is derived from the NCEP/NCAR reanalysis, and represents the background thermodynamic environment in which the clouds form. Global statistics reveal that cloud droplet size tends to be smallest in polluted (high-AI) and strong inversion (high-LTS) environments. Statistical quantification shows that cloud droplet size is better correlated with AI than it is with LTS. Simultaneously, the cloud liquid water path (CLWP) tends to decrease as AI increases. This correlation does not support the hypothesis or assumption that constant or increased CLWP is associated with high aerosol concentrations. Global variability in corrected cloud albedo (CCA), the product of cloud optical depth and cloud fraction, is very well explained by LTS, while both AI and LTS are needed to explain local variability in CCA. Most of the local correlations between AI and cloud properties are similar to the results from the global statistics, while weak anomalous aerosol-cloud correlations appear locally in the regions where simultaneous high (low) AI and low (high) LTS compensate each other. Daytime diurnal cycles explain additional variability in cloud properties. CCA has the largest diurnal cycle in high-LTS regions. Cloud droplet size and CLWP have weak diurnal cycles that differ between clean and polluted environments. The combined results suggest that investigations of marine low cloud radiative forcing and its relationship to hypothesized aerosol indirect effects must consider the combined effects of aerosols, thermodynamics, and the diurnal cycle.

Matsui, Toshihisa↗

NASA NTRSDate not supplied

Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library

Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.

Ali Al↗

NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗

NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗

NASA NTRS2004

Applications for Mission Operations Using Multi-agent Model-based Instructional Systems with Virtual Environments

This viewgraph presentation provides an overview of past and possible future applications for artifical intelligence (AI) in astronaut instruction and training. AI systems have been used in training simulation for the Hubble Space Telescope repair, the International Space Station, and operations simulation for the Mars Exploration Rovers. In the future, robots such as may work as partners with astronauts on missions such as planetary exploration and extravehicular activities.

Clancey, William J.↗

NASA NTRS1988

Analyst-centered models for systems design, analysis, and development

Much has been written about the possible use of Expert Systems (ES) technology for strategic defense system applications, particularly for battle management algorithms and mission planning. It is proposed that ES (or more accurately, Knowledge Based System (KBS)) technology can be used in situations for which no human expert exists, namely to create design and analysis environments that allow an analyst to rapidly pose many different possible problem resolutions in game like fashion and to then work through the solution space in search of the optimal solution. Portions of such an environment exist for expensive AI hardware/software combinations such as the Xerox LOOPS and Intellicorp KEE systems. Efforts are discussed to build an analyst centered model (ACM) using an ES programming environment, ExperOPS5 for a simple missile system tradeoff study. By analyst centered, it is meant that the focus of learning is for the benefit of the analyst, not the model. The model's environment allows the analyst to pose a variety of what if questions without resorting to programming changes. Although not an ES per se, the ACM would allow for a design and analysis environment that is much superior to that of current technologies.

Bukley, A. P.↗

NASA NTRS2001

A Comparison of Seasonal and Interannual Variability of Soil Dust Aerosols Over the Atlantic Ocean as Inferred by the Toms AI and AVHRR AOT Retrievals

The seasonal cycle and interannual variability of two estimates of soil (or 'mineral') dust aerosols are compared: Advanced Very High Resolution Radiometer (AVHRR) aerosol optical thickness (AOT) and Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI), Both data sets, comprising more than a decade of global, daily images, are commonly used to evaluate aerosol transport models. The present comparison is based upon monthly averages, constructed from daily images of each data set for the period between 1984 and 1990, a period that excludes contamination from volcanic eruptions. The comparison focuses upon the Northern Hemisphere subtropical Atlantic Ocean, where soil dust aerosols make the largest contribution to the aerosol load, and are assumed to dominate the variability of each data set. While each retrieval is sensitive to a different aerosol radiative property - absorption for the TOMS AI versus reflectance for the AVHRR AOT - the seasonal cycles of dust loading implied by each retrieval are consistent, if seasonal variations in the height of the aerosol layer are taken into account when interpreting the TOMS AI. On interannual time scales, the correlation is low at most locations. It is suggested that the poor interannual correlation is at least partly a consequence of data availability. When the monthly averages are constructed using only days common to both data sets, the correlation is substantially increased: this consistency suggests that both TOMS and AVHRR accurately measure the aerosol load in any given scene. However, the two retrievals have only a few days in common per month so that these restricted monthly averages have a large uncertainty. Calculations suggest that at least 7 to 10 daily images are needed to estimate reliably the average dust load during any particular month, a threshold that is rarely satisfied by the AVHRR AOT due to the presence of clouds in the domain. By rebinning each data set onto a coarser grid, the availability of the AVHRR AOT is increased during any particular month, along with its interannual correlation with the TOMS AI The latter easily exceeds the sampling threshold due to its greater ability to infer the aerosol load in the presence of clouds. Whether the TOMS AI should be regarded as a more reliable indicator of interannual variability depends upon the extent of contamination by sub-pixel clouds.

Cakmur, R. V.↗

NASA NTRS1991

Design and Analysis Tools for Concurrent Blackboard Systems

A blackboard system consists of a set of knowledge sources, a blackboard data structure, and a control strategy used to activate the knowledge sources. The blackboard model of problem solving is best described by Dr. H. Penny Nii of the Stanford University AI Laboratory: "A Blackboard System can be viewed as a collection of intelligent agents who are gathered around a blackboard, looking at pieces of information written on it, thinking about the current state of the solution, and writing their conclusions on the blackboard as they generate them. " The blackboard is a centralized global data structure, often partitioned in a hierarchical manner, used to represent the problem domain. The blackboard is also used to allow inter-knowledge source communication and acts as a shared memory visible to all of the knowledge sources. A knowledge source is a highly specialized, highly independent process that takes inputs from the blackboard data structure, performs a computation, and places the results of the computation in the blackboard data structure. This design allows for an opportunistic control strategy. The opportunistic problem-solving technique allows a knowledge source to contribute towards the solution of the current problem without knowing which of the other knowledge sources will use the information. The use of opportunistic problem-solving allows the data transfers on the blackboard to determine which processes are active at a given time. Designing and developing blackboard systems is a difficult process. The designer is trying to balance several conflicting goals and achieve a high degree of concurrent knowledge source execution while maintaining both knowledge and semantic consistency on the blackboard. Blackboard systems have not attained their apparent potential because there are no established tools or methods to guide in their construction or analyze their performance.

McManus, John W.↗

NASA NTRS2012

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

Daniels, Taumi S.↗

NASA NTRS1987

An AI approach for scheduling space-station payloads at Kennedy Space Center

The Payload Processing for Space-Station Operations (PHITS) is a prototype modeling tool capable of addressing many Space Station related concerns. The system's object oriented design approach coupled with a powerful user interface provide the user with capabilities to easily define and model many applications. PHITS differs from many artificial intelligence based systems in that it couples scheduling and goal-directed simulation to ensure that on-orbit requirement dates are satisfied.

Castillo, D.↗

NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗

NASA NTRS1991

ECLSS predictive monitoring

On Space Station Freedom (SSF), design iterations have made clear the need to keep the sensor complement small. Along with the unprecendented duration of the mission, it is imperative that decisions regarding placement of sensors be carefully examined and justified during the design phase. In the ECLSS Predictive Monitoring task, we are developing AI-based software to enable design engineers to evaluate alternate sensor configurations. Based on techniques from model-based reasoning and information theory, the software tool makes explicit the quantitative tradeoffs among competing sensor placements, and helps designers explore and justify placement decisions. This work is being applied to the Environmental Control and Life Support System (ECLSS) testbed at MSFC to assist design personnel in placing sensors for test purposes to evaluate baseline configurations and ultimately to select advanced life support system technologies for evolutionary SSF.

Doyle, Richard J.↗

NASA NTRS1994

KATE: From the lab to the firing room

For the past 9 years, the AI group at NASA's Kennedy Space Center (KSC) has been developing a real-time model-based reasoning (MBR) system that can diagnose faults and provide control advisories. The system resulting from this effort is called KATE (Knowledge-based Autonomous Test Engineer). This article describes the transition of KATE from the applied research lab to the firing room (the firing room is the main control and monitoring center for all Shuttle ground support and launch processing). Because the problems that were countered along the way are not unique, we decided to share some of our experiences. This article also includes a brief overview of the latest application of KATE.

Pepe, Charles↗

NASA NTRS2022

A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rules in a factor graph. We show how this unified approach, presented both in probability space and in log of the probability space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options that lead to new smoother or sharper policy distributions. We propose original recursions such as: a generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and a modified versions of the Reward/Entropy algorithm. The discussion is carried over with reference to a path planning problem where the recursions that arise from various cost functions, although they may appear similar in scope, bear noticeable differences. We provide a comprehensive table of composition rules and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map.

Francesco A. N. Palmieri↗

NASA NTRS2017

Simulation of the Ozone Monitoring Instrument Aerosol Index Using the NASA Goddard Earth Observing System Aerosol Reanalysis Products

We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI aerosol retrieval algorithms, and its retrieved AI (OMAERUV AI) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600hPa and 1013.25hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.

remote sensing↗

NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗