Lunar Pit Exploration and Mapping via Autonomous Micro-Rover
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Technology and Mars exploration
The currently envisioned technology needs of the Space Exploration Initiative are surveyed. Earth-to-orbit transportation technology requirements are summarized. Space transportation needs regarding aerobraking, space-based engines, autonomous landing, autonomous rendezvous and docking, vehicle structures and cryogenic tankage, artificial gravity, nuclear propulsion, nuclear thermal propulsion, and nuclear electric propulsion. For in-space operations, cryogenic fluid systems, in-space assembly and construction, and vehicle processing and servicing are addressed. For surface operations on the moon and Mars, space nuclear power, resource utilization, planetary rovers, surface solar power, and surface habitats and construction are discussed. Regenerative life support, radiation protection, extravehicle activity, are considered along with factors pertaining to scientific activity in space and information systems and communications.
Mankins, John C.↗
NASA Tech Briefs, January 2007
Topics covered include: Flexible Skins Containing Integrated Sensors and Circuitry; Artificial Hair Cells for Sensing Flows; Video Guidance Sensor and Time-of-Flight Rangefinder; Optical Beam-Shear Sensors; Multiple-Agent Air/Ground Autonomous Exploration Systems; A 640 512-Pixel Portable Long-Wavelength Infrared Camera; An Array of Optical Receivers for Deep-Space Communications; Microstrip Antenna Arrays on Multilayer LCP Substrates; Applications for Subvocal Speech; Multiloop Rapid-Rise/Rapid Fall High-Voltage Power Supply; The PICWidget; Fusing Symbolic and Numerical Diagnostic Computations; Probabilistic Reasoning for Robustness in Automated Planning; Short-Term Forecasting of Radiation Belt and Ring Current; JMS Proxy and C/C++ Client SDK; XML Flight/Ground Data Dictionary Management; Cross-Compiler for Modeling Space-Flight Systems; Composite Elastic Skins for Shape-Changing Structures; Glass/Ceramic Composites for Sealing Solid Oxide Fuel Cells; Aligning Optical Fibers by Means of Actuated MEMS Wedges; Manufacturing Large Membrane Mirrors at Low Cost; Double-Vacuum-Bag Process for Making Resin- Matrix Composites; Surface Bacterial-Spore Assay Using Tb3+/DPA Luminescence; Simplified Microarray Technique for Identifying mRNA in Rare Samples; High-Resolution, Wide-Field-of-View Scanning Telescope; Multispectral Imager With Improved Filter Wheel and Optics; Integral Radiator and Storage Tank; Compensation for Phase Anisotropy of a Metal Reflector; Optical Characterization of Molecular Contaminant Films; Integrated Hardware and Software for No-Loss Computing; Decision-Tree Formulation With Order-1 Lateral Execution; GIS Methodology for Planning Planetary-Rover Operations; Optimal Calibration of the Spitzer Space Telescope; Automated Detection of Events of Scientific Interest; Representation-Independent Iteration of Sparse Data Arrays; Mission Operations of the Mars Exploration Rovers; and More About Software for No-Loss Computing.
Source record↗
Solar Torque Management for the Near Earth Asteroid Scout CubeSat Using Center of Mass Position Control
A novel mechanism, the Active Mass Translator (AMT), has been developed for the NASA Near Earth Asteroid (NEA) Scout mission to autonomously manage the spacecraft momentum. The NEA Scout CubeSat will launch as a secondary payload onboard Exploration Mission 1 of the Space Launch System. To accomplish its mission, the CubeSat will be propelled by an 86 square-meter solar sail during its two-year journey to reach asteroid 1991VG. NEA Scout's primary attitude control system uses reaction wheels for holding attitude and performing slew maneuvers, while a cold gas reaction control system performs the initial detumble and early trajectory correction maneuvers. The AMT control system requirements, feedback architecture, and control performance will be presented. The AMT reduces the amount of reaction control propellant needed for momentum management and allows for smaller capacity reaction wheels suitable for the limited 6U spacecraft volume. The reduced spacecraft mass allows higher in-space solar sail acceleration, thus reducing time-of-flight. The reduced time-of-flight opens the range of possible missions, which is limited by the lifetime of typical non-radiation tolerant CubeSat avionics exposed to the deep-space environment.
Orphee, Juan↗
Automated Multi-Robot Assembly of Compliance Optimized Structures
Autonomous assembly of large structures is one of the fundamental challenges on the way towards NASA’s objectives of deep space exploration. In this work, we propose an algorithmic framework to optimize the assembly process of a prescribed target structure by constraining the assembly effort as well as maintaining structural soundness throughout the process. This framework uses structural topology optimization with assembly effort metrics to generate checkpoints for robotic traversal algorithms. Assembly effort is quantified by the Wasserstein metric between consecutive structural configurations during the assembly process. The robotic assembly task is split into two subtasks, where we first optimize for a set of key frames, then perform reconfiguration between consecutive frames. Key frames are optimized by adopting topology optimization techniques to reduce assembly effort and maintain structural integrity during the assembly process, while reconfiguration between key frames is performed using a path planning algorithm with a minimum weight maximum matching approach on a bipartite graph. We employ a Crystalline robot model in which each structural element is capable of locomotion through the structure and locking into place with neighboring elements after reaching its destination. An example assembly of a two-dimensional cantilever beam under volume constraints and structural compliance considerations is presented to demonstrate the approach. Finally, we conclude by discussing possible future extensions to this work, including adoption of better metrics, extension to three-dimensional large-scale problems, and exacting finer control of structural integrity during the path-planning phase.
robotic assembly↗
Developing Technologies for Biological Experiments in Deep Space
NASA Ames Research Center is the leader in developing autonomous nanosatellites or CubeSats to address strategic knowledge gaps about the effects of space travel on biological organisms, including GeneSat, PharmaSat, and EcAMSat. Now that NASA has set its sights on human exploration in deep space, such missions require significant technological and biomedical countermeasures to protect astronauts from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms and/or biosensors. BioSentinel will be the first interplanetary CubeSat to study the biological response to space radiation outside low Earth orbit in almost 50 years. BioSentinel is an autonomous platform able to support biology and to investigate the effects of space radiation on a model organism in interplanetary deep space. It will fly onboard Artemis-1, from which it will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel, a 6U CubeSat (1U = 10-cm cube), will measure the DNA damage response to ambient space radiation in a model organism, which will be compared to information provided by an onboard radiation spectrometer and to data obtained on the ISS and on Earth. Even though the primary objective of the mission is to develop an autonomous spacecraft capable of conducting biological experiments in deep space, the 4U BioSensor science payload contained within the free-flyer is an adaptable instrument that can perform bio measurements with different microorganisms and in multiple space environments, including the ISS, lunar gateway, and on the surface of the Moon. Thus, nanosatellites like BioSentinel can be used to study the effects of both reduced gravity and space radiation and can house different organisms to answer specific science questions. In addition to their flexibility, nanosatellites also provide a low-cost alternative to more complex and larger missions, and require minimal crew support, if any
biosenses↗
Developing Technologies for Biological Experiments in Deep Space
NASA Ames Research Center is the leader in developing autonomous nanosatellites or CubeSats to address strategic knowledge gaps about the effects of space travel on biological organisms, including GeneSat, PharmaSat, and EcAMSat. Now that NASA has set its sights on human exploration in deep space, such missions require significant technological and biomedical countermeasures to protect astronauts from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms and/or biosensors. BioSentinel will be the first interplanetary CubeSat to study the biological response to space radiation outside low Earth orbit in almost 50 years. BioSentinel is an autonomous platform able to support biology and to investigate the effects of space radiation on a model organism in interplanetary deep space. It will fly onboard Artemis-1, from which it will be deployed on a lunar fly-by trajectory and into a heliocentric orbit. BioSentinel, a 6U CubeSat (1U = 10-cm cube), will measure the DNA damage response to ambient space radiation in a model organism, which will be compared to information provided by an onboard radiation spectrometer and to data obtained on the ISS and on Earth. Even though the primary objective of the mission is to develop an autonomous spacecraft capable of conducting biological experiments in deep space, the 4U BioSensor science payload contained within the free-flyer is an adaptable instrument that can perform bio measurements with different microorganisms and in multiple space environments, including the ISS, lunar gateway, and on the surface of the Moon. Thus, nanosatellites like BioSentinel can be used to study the effects of both reduced gravity and space radiation and can house different organisms to answer specific science questions. In addition to their flexibility, nanosatellites also provide a low-cost alternative to more complex and larger missions, and require minimal crew support, if any.
biosenses↗
Medical Data Architecture Platform and Recommended Requirements for a Medical Data System for Exploration Missions
The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically- relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions. In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.
Krihak, M.↗
Medical Data Architecture Platform and Recommended Requirements for A Medical Data System for Exploration Missions
Minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions.In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.
Krihak, M.↗
The Clementine Mission
The Clementine Mission is summarized from purposes of the mission through details of the instruments. The mission was a joint Department of Defense and NASA mission to use lighter instruments and other equipment developed by the military for space exploration. New lightweight items on Clementine include batteries, optical sensors, reaction wheels, star trackers, release systems, and inertial measurement units. Other items include a compression chip set, a solid state data recorder, autonomous navigation, and a 32-bit RISC computer.
space↗
The Space Operations Simulation Center (SOSC) and Closed-Loop Hardware Testing for Orion Rendezvous System Design
The exploration goals of Orion / MPCV Project will require a mature Rendezvous, Proximity Operations and Docking (RPOD) capability. Ground testing autonomous docking with a next-generation sensor such as the Vision Navigation Sensor (VNS) is a critical step along the path of ensuring successful execution of autonomous RPOD for Orion. This paper will discuss the testing rationale, the test configuration, the test limitations and the results obtained from tests that have been performed at the Lockheed Martin Space Operations Simulation Center (SOSC) to evaluate and mature the Orion RPOD system. We will show that these tests have greatly increased the confidence in the maturity of the Orion RPOD design, reduced some of the latent risks and in doing so validated the design philosophy of the Orion RPOD system. This paper is organized as follows: first, the objectives of the test are given. Descriptions of the SOSC facility, and the Orion RPOD system and associated components follow. The details of the test configuration of the components in question are presented prior to discussing preliminary results of the tests. The paper concludes with closing comments.
Milenkovic, Zoran↗
Cislunar space infrastructure: Lunar technologies
Continuing its emphasis on the creation of a cisluar infrastructure as an appropriate and cost-effective method of space exploration and development, the University of Colorado explores the technologies necessary for the creation of such an infrastructure, namely (1) automation and robotics; (2) life support systems; (3) fluid management; (4) propulsion; and (5) rotating technologes. The technological focal point is on the development of automated and robotic systems for the implementation of a Lunar Oasis produced by automation and robotics (LOARS). Under direction from the NASA Office of Exploration, automation and robotics have been extensively utilized as an initiating stage in the return to the Moon. A pair of autonomous rovers, modular in design and built from interchangeable and specialized components, is proposed. Utilizing a 'buddy system', these rovers will be able to support each other and to enhance their individual capabilities. One rover primarily explores and maps while the second rover tests the feasibility of various materials-processing techniques. The automated missions emphasize availability and potential uses of lunar resources and the deployment and operations of the LOAR program. An experimental bio-volume is put into place as the precursor to a Lunar Environmentally Controlled Life Support System. The bio-volume will determine the reproduction, growth and production characteristics of various life forms housed on the lunar surface. Physiochemical regenerative technologies and stored resources will be used to buffer biological disturbances of the bio-volume environment. The in situ lunar resources will be both tested and used within this bio-volume. Second phase development on the lunar surface calls for manned operations. Repairs and reconfiguration of the initial framework will ensue. An autonomously initiated, manned Lunar Oasis can become an essential component of the United States space program. The Lunar Oasis will provide support to science, technology, and commerce. It will enable more cost-effective space exploration to the planets and beyond.
W. Faller↗
The Intelligent Data Understanding Element of NASA's Intelligent Systems Program
Within the NASA Intelligent Systems Program, the Intelligent Data Understanding (IDU) element develops techniques for transforming data into scientific understanding. Automating such tools is critical for space science, space-based earth science, and planetary exploration with onboard scientific data analysis. Intelligent data understanding (IDU) is about extracting meaning from large, diverse science and engineering databases, via autonomous techniques that transform very large datasets into understanding. The earth science community in particular needs new tools for analyzing multi-formatted and geographically distributed datasets and for identifying cause-effect relationships in the complex data. Research within the IDU program element seeks to automate data analysis tasks so that humans can focus on creative hypothesis generation and knowledge synthesis. It may also enable NASA space missions in which autonomous agents must generate knowledge and take actions, and missions where limited bandwidth permits transmission of only the most interesting scientific observations, summaries, and conclusions. Twenty-seven research projects are-currently funded.
Coughlan, Joseph C.↗
Triton Hopper: Exploring Neptune's Captured Kuiper Belt Object
Neptune's moon Triton is a fascinating object, a dynamic moon with an atmosphere, and geysers. Triton is unique in the outer solar system in that it is most likely a captured Kuiper belt object (KBO), a leftover building block of the solar system. When Voyager flew by it was the coldest body yet found in our solar system (33 degrees Kelvin) and had volcanic activity, geysers, and a thin atmosphere. It is covered in ices made from nitrogen, water, and carbon-dioxide, and shows surface deposits of tholins, organic compounds that may be precursor chemicals to the origin of life. Exploring Triton will be a challenge well beyond anything done in previous missions; but the unique environment of Triton also allows some new possibilities for mobility. We developed a conceptual design of a Triton Hopping probe that both analyzes the surface and collects it for use to propel its hops. The Hopper would land near the South Pole in 2040 where geysers have been detected. Depending the details of propulsion chosen the Hopper should be able to jump over 300 kilometers in 60 hops or less, exploring the surface and thin atmosphere on its way. This craft will autonomously carry out detailed scientific investigations on the surface, below the surface (drilling) and in the upper atmosphere to provide unprecedented knowledge of a KBO-turned moon and expanding NASA's existing capabilities in deep space planetary exploration to include Hoppers using different ices for propellant. Triton is roughly 2700 kilometers in diameter with a surface of mostly frozen nitrogen, mostly water ice crust and core of metal and rock. Its gravity is half that of Earth's Moon and its atmosphere is 170,000th of Earth's or 0.3 of Mars.The mission concept studied investigated the full surface and atmospheric phenomenon: chemical composition of surface and near subsurface materials, the thin atmosphere, volcanic and geyser activity. Measurements of all these aspects of Triton's unique environment can only be made through focused in-situ exploration with a well-instrumented craft. And this craft will be provided revolutionary mobility, nearly global, using in-situ ices as propellants. While other concepts have looked at gathering gases at Mars to propel a hopper, long periods of time are needed to gather the thin CO2 atmosphere. Several gases, mainly nitrogen are on the surface in a readily dense ice form and just need to be picked up, vaporized and used for propellant.
low conductivity materials↗
Autonomous Navigation with Ground-to-Space Doppler Measurements Referenced to a Temperature-Compensated Crystal Oscillator
The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) has spent several years developing operational Onboard Navigation Systems (ONS's) to provide real-time autonomous, high-accuracy navigation products for spacecraft using NASA's space and ground communication systems. The highly successful Tracking and Data Relay Satellite System (TDRSS) ONS (TONS) experiment on the Explorer Platform/Extreme Ultraviolet Explorer (EP/EUVE) spacecraft, launched June 7, 1992, flight-demonstrated the ONS for high-accuracy navigation using TDRSS forward- link communications services, In late 1994, the ground station ONS (GONS) experiment, using the same EP/EUVE flight hardware, flight-demonstrated the feasibility of high-accuracy autonomous navigation using ground station forward-link communication services, with an ultrastable oscillator (USO) as the frequency reference (1 part in 10(exp 10) over 24 hours). This paper provides a follow-on analysis of GONS performance to assess the navigation accuracy achievable if GONS uses the significantly less stable (5 parts in 10(exp 8) over 24 hours, compared with a specification of 1 part in 10(exp 6)) Temperature-Compensated Crystal Oscillator (TCXO), which is integral to the transponder, as a frequency reference rather than an external USO. The GONS TCXO experiment results from a 20-day period are used to project the expected performance of an operational system. The GONS processes Doppler data derived from nominally scheduled ground station forward-link communication services using a sequential estimation algorithm enhanced by a sophisticated process noise model to provide onboard orbit and frequency determination. To evaluate the navigation accuracies achievable if a TCXO were used, actual experiment data (which used the USO as the frequency reference) were corrupted with errors from real TDRSS one-way return tracking measurements taken from EP/EUVE's TCXO. Analysis of the GONS TCXO experiment performance indicates that real-time onboard position accuracies of better than 300 meters (1 sigma) are achievable with as few as two tracking contacts per day for the EP/EUVE 525-kilometer altitude, 28.5-degree inclination orbit, provided the TCXO is as stable as that in EP/EUVE's TDRSS transponder. GONS using a TCXO frequency reference provides a viable option for many upcoming spacecraft missions with moderate position accuracy requirements and an interest in reducing their operational costs with autonomous navigation.
Radomski, M. S.↗
A method for diagnosing time dependent faults using model-based reasoning systems
This paper explores techniques to apply model-based reasoning to equipment and systems which exhibit dynamic behavior (that which changes as a function of time). The model-based system of interest is KATE-C (Knowledge based Autonomous Test Engineer) which is a C++ based system designed to perform monitoring and diagnosis of Space Shuttle electro-mechanical systems. Methods of model-based monitoring and diagnosis are well known and have been thoroughly explored by others. A short example is given which illustrates the principle of model-based reasoning and reveals some limitations of static, non-time-dependent simulation. This example is then extended to demonstrate representation of time-dependent behavior and testing of fault hypotheses in that environment.
Goodrich, Charles H.↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
J Lemery↗
Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions
Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.
Medical Operations↗