Results for “Autonomous Operations” — Page 39

NASA NTRS2010

The Evolution of Extravehicular Activity Operations to Lunar Exploration Based on Operational Lessons Learned During 2009 NASA Desert RATS Field Testing

This paper will present options to evolutionary changes in several philosophical areas of extravehicular activity (EVA) operations. These areas will include single person verses team EVAs; various loss of communications scenarios (with Mission Control, between suited crew, suited crew to rover crew, and rover crew A to rover crew B); EVA termination and abort time requirements; incapacitated crew ingress time requirements; autonomous crew operations during loss of signal periods including crew decisions on EVA execution (including decision for single verses team EVA). Additionally, suggestions as to the evolution of the make-up of the EVA flight control team from the current standard will be presented. With respect to the flight control team, the major areas of EVA flight control, EVA Systems and EVA Tasks, will be reviewed, and suggested evolutions of each will be presented. Currently both areas receive real-time information, and provide immediate feedback during EVAs as well as spacesuit (extravehicular mobility unit - EMU) maintenance and servicing periods. With respect to the tasks being performed, either EMU servicing and maintenance, or the specific EVA tasks, daily revising of plans will need to be able to be smoothly implemented to account for unforeseen situations and findings. Many of the presented ideas are a result of lessons learned by the NASA Johnson Space Center Mission Operations Directorate operations team support during the 2009 NASA Desert Research and Technology Studies (Desert RATS). It is important that the philosophy of both EVA crew operations and flight control be examined now, so that, where required, adjustments can be made to a next generation EMU and EVA equipment that will complement the anticipated needs of both the EVA flight control team and the crews.

Bell, Ernest R., Jr.↗

NASA NTRS2023

Progressively Enabling Earth Independent Medical Operations (EIMO)

This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, 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 is achievable.

Jay Lemery↗

NASA NTRSDate not supplied

Progressively Enabling Earth Independent Medical Operations (EIMO)

This panel presents the findings from a series of Technical Interchange Meetings (TIMs) hosted by the Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program. The topics for the TIMs were derived from a 2-day conference of senior leaders and subject matters experts that collectively outlined a multi-faceted strategy designed to optimize crew health and performance through an increasingly autonomous medical approach. The first abstract in this panel outlines the scope of issues related to data collection, usage, transmission and computing capacity to facilitate EIMO. The second presentation provides an overview of the challenges in developing curricula and advanced training tools to baseline knowledge, skills and abilities (KSA), verify clinical competency and assure retention during prolonged durations inherent in exploration-class missions. An overview of the complicated medical supply and resource chain necessary to facilitate EIMO is provided in the third presentation of this panel. The final presentation in this EIMO panel surveys the breadth and depth of demands on cognitive load expected to be experienced by crew on an exploration mission and proposes strategies to mitigate the prospect of cognitive overload through methods to shift task load from the crew to multi-modal artificial intelligence based medical support systems. Taken together, these presentations summarize the challenges to be expected and potential solution spaces to be explored and developed to progressively enable increasing autonomous medical operations to support crewed missions beyond low earth orbit. Through EIMO focused pre-mission planning, integrated data architecture design, innovative training development and AI-assisted task load management, 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 is achievable.

John Lemery↗

NASA NTRS1989

Orbital maneuvering vehicle teleoperation and video data compression

The Orbital Maneuvering Vehicle (OMV) and concepts of teleoperation and video data compression as applied to OMV design and operation are described. The OMV provides spacecraft delivery, retrieval, reboost, deboost and viewing services, with ground-control or Space Station operation, through autonomous navigation and pilot controlled maneuvers. Communications systems are comprised of S-band RF command, telemetry, and compressed video data links through the TDRSS and GSTDN networks. The control console video monitors display a monochrome image at an update rate of five frames per second. Depending upon the mode of operation selected by the pilot, the video resolution is either 255 x 244 pixels, or 510 x 244 pixels. Since practically all video image redundancy is removed by the compression process, the video reconstruction is particularly sensitive to data transmission bit errors. Concatenated Reed-Solomon and convolution coding are used with helical data interleaving for error detection and correction, and an error-containment process minimizes the propagation of error effects throughout the video image. Video sub-frame replacement is used, in the case of a non-correctable error or error burst, to minimize the visual impact to the pilot.

Jones, Steve↗

NASA NTRS2010

HyspIRI Intelligent Payload Module(IPM) and Benchmarking Algorithms for Upload

Features: Hardware: a) Xilinx Virtex-5 (GSFC Space Cube 2); b) 2 x 400MHz PPC; c) 100MHz Bus; d) 2 x 512MB SDRAM; e) Dual Gigabit Ethernet. Support Linux kernel 2.6.31 (gcc version 4.2.2). Support software running in stand alone mode for better performance. Can stream raw data up to 800 Mbps. Ready for operations. Software Application Examples: Band-stripping Algiotrhmsl:cloud, sulfur, flood, thermal, SWIL, NDVI, NDWI, SIWI, oil spills, algae blooms, etc. Corrections: geometric, radiometric, atmospheric. Core Flight System/dynamic software bus. CCSDS File Delivery Protocol. Delay Tolerant Network. CASPER /onboard planning. Fault monitoring/recovery software. S/C command and telemetry software. Data compression. Sensor Web for Autonomous Mission Operations.

Mandl, Daniel↗

NASA NTRSDate not supplied

The Lunar Regolith Terrain (LRT) Field: A New Lunar Surface Planetary Analog Facility at NASA Marshall Space Flight Center (MSFC)

Introduction: NASA is moving toward a new age of exploration and resource utilization of the lunar surface. Challenges related to exploration, resource utilization, and construction at the Lunar South Pole will require advanced technology and well-designed mission concepts and operations. NASA Marshall Space Flight Center (MSFC) has added new capabilities to support surface mobility and construction activities to meet industry, academia, and NASA research and development goals for lunar applications. The Lunar Regolith Terrain (LRT) field is a new, large-area, lunar regolith simulant planetary analog testing ground for users interested in surface mobility and lunar construction activities. The LRT complements NASA MSFC’s other lunar environment testing facilities such as the Lunar Surface Simulator (V20 dirty vacuum chamber), the Lunar Environment Testing System (LETS), among many others. Lunar Regolith Terrain (LTR) Description: The Lunar Regolith Terrain field is an outdoor planetary analog environment facility located on base at MSFC. The lunar regolith simulant is JSC-1A feedstock material (volcanic cinder sand sourced from Meriam Crater, Flagstaff, AZ). The field contains more than 500 tons of lunar regolith simulant confined within a 125 ft x 125 ft (38 m x 38 m) area. The field is placed ~ 50% over paved parking lot and ~ 50% over a natural ground. Currently, the depth of regolith ranges between ~ 5 in - ~ 4 ft (~ 13cm – 1.2m) but can be modified to suit user needs. The lunar regolith simulant that makes up the field has representative geotechnical, geochemical, and optical properties of lunar mare basalt. An area within the LRT of lunar highlands terrain simulant is planned. Additional Features of the LRT: The LRT was designed to allow rapid modification of the terrain’s topography obstacles in the field. The terrain can be reshaped to suit specific testing requirements that may require flat expanses, steep hills, or heavily cratered and rocky landscapes. Large rocky obstacles in Fig. 1 are artificial landscape boulders (faux-rocks) that can be easily placed by users or removed entirely. Areas of the field also contain buried fiducials, large sheets, bar stock, and pipes of various composition and dimensions to allow for possible ground penetrating radar and shallow seismic studies. Rapid modification capabilities will also allow for burial of additional user-specific materials to enable in-situ resource utilization detection (e.g., burial of hydrogen sources for neutron detection or other materials). The field is also equipped with on-site office space with an air-conditioned and heated trailer with 120/240V power and lighting. The site has Wi-Fi and Cellular signal coverage. Direct radio frequency communication with the Huntsville Operations Support Center (HOSC) is in development. Additional on-site workspace and secure equipment storage is available in adjacent buildings. Accessibility to the field is straightforward with on-site parking and access for delivery of instruments, payloads, and additional equipment. Community Availability: The LRTF provides an accessible planetary analog surface environment for surface mobility testing, autonomous roving operations, developing advanced navigation techniques and operations development. Interested parties can contact the abstract authors for additional details, tours, and scheduling.

Lunar Regolith↗

NASA NTRSDate not supplied

Marshall Space Flight Center: Lunar Regolith Terrain (LRT)

Introduction: NASA is moving toward a new age of exploration and resource utilization of the lunar surface. Challenges related to exploration, resource utilization, and construction at the Lunar South Pole will require advanced technology and well-designed mission concepts and operations. NASA Marshall Space Flight Center (MSFC) has added new capabilities to support surface mobility and construction activities to meet industry, academia, and NASA research and development goals for lunar applications. The Lunar Regolith Terrain (LRT) field is a new, large-area, lunar regolith simulant planetary analog testing ground for users interested in surface mobility and lunar construction activities. The LRT complements NASA MSFC’s other lunar environment testing facilities such as the Lunar Surface Simulator (V20 dirty vacuum chamber), the Lunar Environment Testing System (LETS), among many others. Lunar Regolith Terrain (LTR) Description: The Lunar Regolith Terrain field is an outdoor planetary analog environment facility located on base at MSFC. The lunar regolith simulant is JSC-1A feedstock material (volcanic cinder sand sourced from Meriam Crater, Flagstaff, AZ). The field contains more than 500 tons of lunar regolith simulant confined within a 125 ft x 125 ft (38 m x 38 m) area. The field is placed ~ 50% over paved parking lot and ~ 50% over a natural ground. Currently, the depth of regolith ranges between ~ 5 in - ~ 4 ft (~ 13cm – 1.2m) but can be modified to suit user needs. The lunar regolith simulant that makes up the field has representative geotechnical, geochemical, and optical properties of lunar mare basalt. An area within the LRT of lunar highlands terrain simulant is planned. Additional Features of the LRT: The LRT was designed to allow rapid modification of the terrain’s topography obstacles in the field. The terrain can be reshaped to suit specific testing requirements that may require flat expanses, steep hills, or heavily cratered and rocky landscapes. Large rocky obstacles in Fig. 1 are artificial landscape boulders (faux-rocks) that can be easily placed by users or removed entirely. Areas of the field also contain buried fiducials, large sheets, bar stock, and pipes of various composition and dimensions to allow for possible ground penetrating radar and shallow seismic studies. Rapid modification capabilities will also allow for burial of additional user-specific materials to enable in-situ resource utilization detection (e.g., burial of hydrogen sources for neutron detection or other materials). The field is also equipped with on-site office space with an air-conditioned and heated trailer with 120/240V power and lighting. The site has Wi-Fi and Cellular signal coverage. Direct radio frequency communication with the Huntsville Operations Support Center (HOSC) is in development. Additional on-site workspace and secure equipment storage is available in adjacent buildings. Accessibility to the field is straightforward with on-site parking and access for delivery of instruments, payloads, and additional equipment. Community Availability: The LRTF provides an accessible planetary analog surface environment for surface mobility testing, autonomous roving operations, developing advanced navigation techniques and operations development. Interested parties can contact the abstract authors for additional details, tours, and scheduling.

Lunar Regolith↗

NASA NTRSDate not supplied

Marshall Space Flight Center: Lunar Regolith Terrain (LRT)

Introduction: NASA is moving toward a new age of exploration and resource utilization of the lunar surface. Challenges related to exploration, resource utilization, and construction at the Lunar South Pole will require advanced technology and well-designed mission concepts and operations. NASA Marshall Space Flight Center (MSFC) has added new capabilities to support surface mobility and construction activities to meet industry, academia, and NASA research and development goals for lunar applications. The Lunar Regolith Terrain (LRT) field is a new, large-area, lunar regolith simulant planetary analog testing ground for users interested in surface mobility and lunar construction activities. The LRT complements NASA MSFC’s other lunar environment testing facilities such as the Lunar Surface Simulator (V20 dirty vacuum chamber), the Lunar Environment Testing System (LETS), among many others. Lunar Regolith Terrain (LTR) Description: The Lunar Regolith Terrain field is an outdoor planetary analog environment facility located on base at MSFC. The lunar regolith simulant is JSC-1A feedstock material (volcanic cinder sand sourced from Meriam Crater, Flagstaff, AZ). The field contains more than 500 tons of lunar regolith simulant confined within a 125 ft x 125 ft (38 m x 38 m) area. The field is placed ~ 50% over paved parking lot and ~ 50% over a natural ground. Currently, the depth of regolith ranges between ~ 5 in - ~ 4 ft (~ 13cm – 1.2m) but can be modified to suit user needs. The lunar regolith simulant that makes up the field has representative geotechnical, geochemical, and optical properties of lunar mare basalt. An area within the LRT of lunar highlands terrain simulant is planned. Additional Features of the LRT: The LRT was designed to allow rapid modification of the terrain’s topography obstacles in the field. The terrain can be reshaped to suit specific testing requirements that may require flat expanses, steep hills, or heavily cratered and rocky landscapes. Large rocky obstacles in Fig. 1 are artificial landscape boulders (faux-rocks) that can be easily placed by users or removed entirely. Areas of the field also contain buried fiducials, large sheets, bar stock, and pipes of various composition and dimensions to allow for possible ground penetrating radar and shallow seismic studies. Rapid modification capabilities will also allow for burial of additional user-specific materials to enable in-situ resource utilization detection (e.g., burial of hydrogen sources for neutron detection or other materials). The field is also equipped with on-site office space with an air-conditioned and heated trailer with 120/240V power and lighting. The site has Wi-Fi and Cellular signal coverage. Direct radio frequency communication with the Huntsville Operations Support Center (HOSC) is in development. Additional on-site workspace and secure equipment storage is available in adjacent buildings. Accessibility to the field is straightforward with on-site parking and access for delivery of instruments, payloads, and additional equipment. Community Availability: The LRTF provides an accessible planetary analog surface environment for surface mobility testing, autonomous roving operations, developing advanced navigation techniques and operations development. Interested parties can contact the abstract authors for additional details, tours, and scheduling.

Lunar Regolith↗

NASA NTRSDate not supplied

Simulating Microwave and Radar Signals in Severe Weather Conditions

The development of radiative transfer simulators for radar and microwave signals, spanning a range of frequencies from 10 to 800 GHz, is paramount for enhancing weather forecasting accuracy, particularly for severe weather events. These tools facilitate the assimilation of microwave and radar observations into numerical weather prediction models, thereby improving accuracy of weather forecasts. Additionally, they directly simulate radar signals crucial for autonomous vehicle operation, with frequencies commonly used in radar systems such as 24, 74, 77, and 79 GHz. However, these frequencies are susceptible to weather phenomena like severe rain and snow, which can significantly impact vehicle safety and performance. These simulation tools, including radiative transfer simulators, serve a dual purpose. Firstly, they enhance forecasts for severe weather events, contributing to autonomous vehicle safety by providing early warnings and risk assessments. Secondly, they enable the direct simulation of radar signals in autonomous vehicle driving systems, allowing researchers and engineers to evaluate radar system performance under various weather conditions. In addition to the radar signal simulator, this abstract discusses the incorporation of advanced scattering properties developed using the discrete dipole approximation (DDA). The DDA technique enhances scattering calculations for frozen hydrometeors at microwave frequencies, thereby improving the accuracy of radar signal simulations and enabling more accurate assessments of radar system performance in adverse weather conditions. In summary, this abstract explores the development and utilization of comprehensive simulation tools, emphasizing their significance in simulating microwave and radar signals and improving weather forecasts. Special attention is given to the simulation of radar signals at critical frequencies for autonomous vehicle sensing and navigation, addressing challenges posed by severe weather phenomena and their effects on signal propagation and detection

Isaac Moradi↗

NASA NTRS2004

Robotic Subsurface Analyzer and Sample Handler for Resource Reconnaissance and Preliminary Site Assessment for ISRU Activities at the Lunar Cold Traps

Since the 1960s, claims have been made that water ice deposits should exist in permanently shadowed craters near both lunar poles. Recent interpretations of data from the Lunar Prospector-Neutron Spectrometer (LP- NS) confirm that significant concentrations of hydrogen exist, probably in the form of water ice, in the permanently shadowed polar cold traps. Yet, due to the large spatial resolution (45-60 Ian) of the LP-NS measurements relative to these shadowed craters (approx.5-25 km), these data offer little certainty regarding the precise location, form or distribution of these deposits. Even less is known about how such deposits of water ice might effect lunar regolith physical properties relevant to mining, excavation, water extraction and construction. These uncertainties will need to be addressed in order to validate fundamental lunar In Situ Resource Utilization (ISRU) precepts by 2011. Given the importance of the in situ utilization of water and other resources to the future of space exploration a need arises for the advanced deployment of a robotic and reconfigurable system for physical properties and resource reconnaissance. Based on a collection of high-TRL. designs, the Subsurface Analyzer and Sample Handler (SASH) addresses these needs, particularly determining the location and form of water ice and the physical properties of regolith. SASH would be capable of: (1) subsurface access via drilling, on the order of 3-10 meters into both competent targets (ice, rock) and regolith, (2) down-hole analysis through drill string embedded instrumentation and sensors (Neutron Spectrometer and Microscopic Imager), enabling water ice identification and physical properties measurements; (3) core and unconsolidated sample acquisition from rock and regolith; (4) sample handling and processing, with minimized contamination, sample containerization and delivery to a modular instrument payload. This system would be designed with three mission enabling goals, including: (1) a self-contained, low power, low mass, "black box'' configuration for operations from a lander, various classes of rovers or a surface-based platform with human assistance or robotic anchoring mechanisms; (2) reconfigurable and scalable sample handling for delivery to various types of instrumentation, depending on mission requirements; and (3) the use of advanced automation control and diagnostic techniques that will afford local human deployed, remote teleoperation and fully autonomous intelligent operations. Though a great deal of technology has been advanced toward these objectives, the SASH system faces significant design challenges, including the low gravity environment, various levels of autonomy in operations, radiation exposure, dust contamination, and temperature extremes and deltas. Significant input from the scientific and engineering communities, as well as a significant environmental testing program, will be required to guide the design process.

Gorevan, S. P.↗

NASA NTRS2008

G-Guidance Interface Design for Small Body Mission Simulation

The G-Guidance software implements a guidance and control (G and C) algorithm for small-body, autonomous proximity operations, developed under the Small Body GN and C task at JPL. The software is written in Matlab and interfaces with G-OPT, a JPL-developed optimization package written in C that provides G-Guidance with guaranteed convergence to a solution in a finite computation time with a prescribed accuracy. The resulting program is computationally efficient and is a prototype of an onboard, real-time algorithm for autonomous guidance and control. Two thruster firing schemes are available in G-Guidance, allowing tailoring of the software for specific mission maneuvers. For example, descent, landing, or rendezvous benefit from a thruster firing at the maneuver termination to mitigate velocity errors. Conversely, ascent or separation maneuvers benefit from an immediate firing to avoid potential drift toward a second body. The guidance portion of this software explicitly enforces user-defined control constraints and thruster silence times while minimizing total fuel usage. This program is currently specialized to small-body proximity operations, but the underlying method can be generalized to other applications.

Acikmese, Behcet↗

DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2006

Intelligent Vision Systems Independent Research and Development (IR&D) 2006

This report summarizes results in conduct of research sponsored by the 2006 Independent Research and Development (IR&D) program at Marshall Space Flight Center (MSFC) at Redstone Arsenal, Alabama. The focus of this IR&D is neural network (NN) technology provided by Imagination Engines, Incorporated (IEI) of St. Louis, Missouri. The technology already has many commercial, military, and governmental applications, and a rapidly growing list of other potential spin-offs. The goal for this IR&D is implementation and demonstration of the technology for autonomous robotic operations, first in software and ultimately in one or more hardware realizations. Testing is targeted specifically to the MSFC Flat Floor, but may also include other robotic platforms at MSFC, as time and funds permit. For the purpose of this report, the NN technology will be referred to by IEI's designation for a subset configuration of its patented technology suite: Self-Training Autonomous Neural Network Object (STANNO).

Patrick, Clinton↗

NASA NTRSDate not supplied

An in-Time Aviation Safety Management System Concept of Operations and Modernization of the National Airspace System

The National Airspace System (NAS) is growing in complexity of aircraft, missions, and operations. In response, many organizations have published papers and concepts of operations (ConOps) for new and enhanced safety systems. The National Academies’ vision for an In-time Aviation Safety Management System (IASMS) is integral to Federal Aviation Administration (FAA) modernization efforts. The National Aeronautics and Space Administration (NASA) System-Wide Safety (SWS) project is conducting safety research, exploring solutions, and defining the safety needs of future missions, such as Advanced Air Mobility (AAM) and autonomous aircraft operating in a more connected, flexible, and dynamic airspace. IASMS enables and provides a path for bringing FAA’s operational vision to fruition through increasingly automated safety systems that integrate services, functions, and capabilities (SFCs). These SFCs provide the necessary responsiveness to monitor, assess, and mitigate known hazards and emergent risks. This paper describes how safety in today’s air transportation system will need to evolve, identifies key points regarding in-time safety, and explores the criticality of IASMS in the future NAS.

IASMS↗

NASA NTRSDate not supplied

An In-time Aviation Safety Management System Concept of Operations and Modernization of the National Airspace System

The National Airspace System (NAS) is growing in complexity of aircraft, missions, and operations. In response, many organizations have published papers and concepts of operations (ConOps) for new and enhanced safety systems. The National Academies’ vision for an In-time Aviation Safety Management System (IASMS) is integral to Federal Aviation Administration (FAA) modernization efforts. The National Aeronautics and Space Administration (NASA) System-Wide Safety (SWS) project is conducting safety research, exploring solutions, and defining the safety needs of future missions, such as Advanced Air Mobility (AAM) and autonomous aircraft operating in a more connected, flexible, and dynamic airspace. IASMS enables and provides a path for bringing FAA’s operational vision to fruition through increasingly automated safety systems that integrate services, functions, and capabilities (SFCs). These SFCs provide the necessary responsiveness to monitor, assess, and mitigate known hazards and emergent risks. This paper describes how safety in today’s air transportation system will need to evolve, identifies key points regarding in-time safety, and explores the criticality of IASMS in the future NAS.

Airspace↗

NASA NTRSDate not supplied

Uncrewed Lunar Surface Operations and Support Activities

A sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The number of astronauts and the availability of the surface crew to perform tasks will be limited, and extravehicular activities are burdensome and time-consuming. A balance of crewed and uncrewed surface operations will maximize crew exploration time by reducing their time dedicated to routine maintenance and support tasks. Certain sustaining activities that consume valuable crew time and preparation tasks such as staging and prepositioning equipment and materials can be performed without the crew, either before their arrival on the lunar surface or after their departure, thus improving task efficiency and mission effectiveness. This paper will define uncrewed operations and support activities and examine the functions, features, and capabilities that support sustained human and robotic surface operations. It will also discuss the extreme environmental challenges and the complexities associated with the development and operation of autonomous systems, as well as some suggested methods, techniques, and tools to overcome these challenges.

Lunar↗

NASA NTRSDate not supplied

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↗

NASA NTRS2024

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↗