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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Assessment of Spatial Navigation and Docking Performance During Simulated Rover Tasks

INTRODUCTION: Following long-duration exploration transits, pressurized rovers will enhance surface mobility to explore multiple sites across Mars and other planetary bodies. Multiple rovers with docking capabilities are envisioned to expand the range of exploration. However, adaptive changes in sensorimotor and cognitive function may impair the crew s ability to safely navigate and perform docking tasks shortly after transition to the new gravitoinertial environment. The primary goal of this investigation is to quantify post-flight decrements in spatial navigation and docking performance during a rover simulation. METHODS: Eight crewmembers returning from the International Space Station will be tested on a motion simulator during four pre-flight and three post-flight sessions over the first 8 days following landing. The rover simulation consists of a serial presentation of discrete tasks to be completed within a scheduled 10 min block. The tasks are based on navigating around a Martian outpost spread over a 970 sq m terrain. Each task is subdivided into three components to be performed as quickly and accurately as possible: (1) Perspective taking: Subjects use a joystick to indicate direction of target after presentation of a map detailing current orientation and location of the rover with the task to be performed. (2) Navigation: Subjects drive the rover to the desired location while avoiding obstacles. (3) Docking: Fine positioning of the rover is required to dock with another object or align a camera view. Overall operator proficiency will be based on how many tasks the crewmember can complete during the 10 min time block. EXPECTED RESULTS: Functionally relevant testing early post-flight will develop evidence regarding the limitations to early surface operations and what countermeasures are needed. This approach can be easily adapted to a wide variety of simulated vehicle designs to provide sensorimotor assessments for other operational and civilian populations.

Wood, S. J.↗
NASA NTRS1987

Simulation in support of advanced cockpit development

It is noted that the system complexity and high workload of the next generation of light scout/attack (SCAT) helicopters are areas of great interest to the U.S. Army. This paper describes the Crew Station Research and Development Program established by the Army to study the issues of battle captain performance for one-man vs. two-man crews. A Crew Station Research and Development Facility (CSRDF) has been contracted for which consists of a distributed computer system with several stations which play different roles in experiments. Coordination of experiments is effected from the Experimenter/Operator Console where a team of Army experimenters and NASA personnel controls and monitors the mission scenario used to test the crew members.

Lypaczewski, P. A.↗
NASA NTRS1991

Space life support engineering program

This report covers the first six months of work performed under the NASA University Grant awarded to Iowa State University to perform research on two topics relating to the development of closed-loop long-term life support systems. A comprehensive study to develop software to simulate the dynamic operation of water reclamation systems in long-term closed-loop life support systems is being carried out as part of an overall program for the design of systems for a Mars voyage. This project is being done in parallel with a similar effort in the Department of Chemistry to develop durable accurate low-cost sensors for monitoring of trace chemical and biological species in recycled water supplies. Aspen-Plus software is being used on a group of high-performance workstations to develop the steady state descriptions for a number of existing technologies. Following completion, a dynamic simulation package will be developed for determining the response of such systems to changes in the metabolic needs of the crew and to upsets in system hardware performance.

Seagrave, Richard C.↗
NASA NTRS1992

Space life support engineering program

A comprehensive study to develop software to simulate the dynamic operation of water reclamation systems in long-term closed-loop life support systems is being carried out as part of an overall program for the design of systems for a moon station or a Mars voyage. This project is being done in parallel with a similar effort in the Department of Chemistry to develop durable accurate low-cost sensors for monitoring of trace chemical and biological species in recycled water supplies. Aspen-Plus software is being used on a group of high-performance work stations to develop the steady state descriptions for a number of existing technologies. Following completion, a dynamic simulation package will be developed for determining the response of such systems to changes in the metabolic needs of the crew and to upsets in system hardware performance.

Seagrave, Richard C.↗
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↗
NASA NTRS2009

Columbia Crew Survival Investigation Report

NASA commissioned the Columbia Accident Investigation Board (CAIB) to conduct a thorough review of both the technical and the organizational causes of the loss of the Space Shuttle Columbia and her crew on February 1, 2003. The accident investigation that followed determined that a large piece of insulating foam from Columbia s external tank (ET) had come off during ascent and struck the leading edge of the left wing, causing critical damage. The damage was undetected during the mission. The CAIB's findings and recommendations were published in 2003 and are available on the web at http://caib.nasa.gov/. NASA responded to the CAIB findings and recommendations with the Space Shuttle Return to Flight Implementation Plan. Significant enhancements were made to NASA's organizational structure, technical rigor, and understanding of the flight environment. The ET was redesigned to reduce foam shedding and eliminate critical debris. In 2005, NASA succeeded in returning the space shuttle to flight. In 2010, the space shuttle will complete its mission of assembling the International Space Station and will be retired to make way for the next generation of human space flight vehicles: the Constellation Program. The Space Shuttle Program recognized the importance of capturing the lessons learned from the loss of Columbia and her crew to benefit future human exploration, particularly future vehicle design. The program commissioned the Spacecraft Crew Survival Integrated Investigation Team (SCSIIT). The SCSIIT was asked to perform a comprehensive analysis of the accident, focusing on factors and events affecting crew survival, and to develop recommendations for improving crew survival for all future human space flight vehicles. To do this, the SCSIIT investigated all elements of crew survival, including the design features, equipment, training, and procedures intended to protect the crew. This report documents the SCSIIT findings, conclusions, and recommendations.

Source record↗
NASA NTRSDate not supplied

Sensitivity of Hybrid NEP-Chemical Vehicle Mass to Assumptions for Crewed Opposition-Class Mars Missions

This research illustrates the impact of different technology choices and their associated performance on overall sizing for a hybrid Nuclear Electric Propulsion (NEP) and chemical crewed Mars transportation vehicle. A set of models for the trajectory and vehicle mass, power system specific mass(α), and power conversion performance enable the analysis. Vehicle mass required to close the mission is calculated as a function of α and specific impulse (Isp) for multiple choices of high-level assumptions including system alpha vs. power scaling and electric thruster type. System alpha is built up and compared for multiple cases of technology assumptions. A Brayton cycle model is used to illustrate the alpha sensitivities to assumptions in cycle design such as peak operating temperature and component performance. The analysis illustrates how technology performance sensitivities affect the alpha and thus vehicle sizing for a range of mission and technology assumptions.

Matthew Duchek↗
NASA NTRS1991

Pilot personality and crew coordination - Implications for training and selection

It is contended that past failures to find linkages between performance and personality were due to a combination of premature performance evaluation, inadequate statistical modeling, and/or the reliance on data gathered in contrived as opposed to realistic situations. The goal of the research presented is to isolate subgroups of pilots along performance-related personality dimensions and to document limits on the impact of crew coordination training between the groups. Three different profiles were identified through cluster analysis of personality scales that replicated across samples and predicted attitude change following training in crew coordination.

Chidester, Thomas R.↗
NASA NTRS2020

Orion Optical Navigation Performance and Testing

The Orion Optical Navigation System, which is designed to perform the navigation which will allow the crew to return safely to Earth in the event of a permanent loss of communications with the ground, has been matured through analysis and testing. This paper will detail the extensive tests and analysis that have gone into fleshing out the performance of the system in the face of numerous constraints placed on the optical navigation system.

D'Souza, Christopher↗
NASA NTRS2022

Evidence Report: Risk of Injury and Compromised Performance due to EVA Operations

During future missions to the Moon and Mars, each crewmember will most likely perform up to 24 hours a week of extravehicular activity (EVA) in support of exploration, science, construction, and maintenance tasks. Achieving mission objectives will require EVA systems and operations concepts that maximize human performance and efficiency while minimizing health and safety risks for crewmembers. Currently, over 450 EVAs have been performed in microgravity using the Extravehicular Mobility Unit (EMU). The EMU space suit has enabled the successful assembly and maintenance of the International Space Station (ISS) for over 20 years, as well as deployment of payload experiments, solar arrays, satellite launches and repairs. This EVA work was accomplished at a slower cadence than is expected for Moon and Mars, with ISS crewmembers performing no more than 7 EVAs during a single mission and without any back-to-back EVAs. Despite their success, ISS EVAs have resulted in more injury to EVA crewmembers than may be acceptable for long-duration exploration missions. The Apollo astronauts completed EVA tasks in suits that were designed for their short-duration lunar missions, although suit mobility problems were evident. The more frequent EVAs and more varied EVA tasks that are anticipated during the future longer-duration exploration missions will require EVA suits and systems that are better oriented to human health and performance than those used during the Apollo Program. Many of the problems that were encountered with the Apollo EVA suits (e.g., limited mobility and dexterity, high and aft center of gravity, and other features requiring significant crew compensation) will need to be corrected or mitigated to optimize EVA objectives of exploration missions. It is critical that we understand how EVA system design variables such as suit pressure, weight/mass, center-of-gravity location, joint ranges of motion, and biomedical monitoring, affects the ability of astronauts to perform safe, efficient, and effective EVAs. To achieve this understanding, EVA researchers will need to develop and execute an integrated human testing program across multiple environments. The research will provide objective data that will enable informed design decisions and crewmember standards, thereby ensuring EVA systems that optimize crewmember health, safety, efficiency, and performance. This report describes the risks to crew health, safety, performance, and efficiency caused by EVA operations, and it provides the evidence base to substantiate the importance of the risk.

EVA↗
DOE OSTI2022

AI Data Quality Monitoring with Hydra

Hydra is an extensible framework for training and managing AI for near real time monitoring that aims to replace the tedious and repetitive data quality monitoring activities the shift crew and online monitoring coordinator typically perform. It continuously scans incoming data in the form of monitoring plots for signs of problems, flagging them for human review. A web app was developed such that experts can efficiently label images for training. Labels are stored in a database for use in training and model validation. Backed up by a comprehensive database, it utilizes an additional web based front-end for viewing the current monitoring status from anywhere in the world. The system has been in production use for the GlueX experiment at Jefferson Lab for more than 2 years with new features still under active development.

Britton, Thomas↗
NASA NTRS1977

Apollo Soyuz mission report

A complete report on the goals and results of the Apollo-Soyuz mission is presented. Descriptions of the docking module, command and service modules and crew stations are provided. The overall performance of the Apollo spacecraft and of ground support and biomedical operations is evaluated. The pilot's report is presented, together with launch phase and anomaly summaries.

Lee, C. M.↗
NASA NTRS1977

Apollo-Soyuz pamphlet no. 1: The flight

The goals of the Apollo-Soyuz Test Project are described in this first in a series of nine pamphlets designed as a curriculum supplement for teachers, supervisors, curriculum specialists, and textbook writers as well as for the general public. Aspects of the space flight covered include descriptions of the astronaut-cosmonaut meeting and of the spacecraft and landing module; spacecraft launch; control, and rendezvous; crew work schedule; and telemetry. Experiments performed are listed in tables, and their major results are summarized.

Page, L. W.↗
NASA NTRS1982

Solar array Shuttle flight experiment - Hardware development and testing

This paper reports on the fabrication and ground testing of (a) a large area, light-weight, flexible substrate developmental solar array wing that has been built for NASA-MSFC and of (b) the supporting structure and data acquisition system (DAS) which, with the wing, will be flown in the Shuttle as an experiment in 1984. The experiment will verify the dynamics, thermodynamic, and electrical performance predictions of the array wing and will demonstrate the structural capability of the array wing for Orbiter launch and re-entry environments. The experiment hardware verification program was designed to minimize costs and risk of experiment performance degradation while maintaining Shuttle and crew safety. The previous full-scale wing hardware tests included an extension mast water table test and wing testing for random vibration, thermal vacuum, and acoustic environments. The results of these tests were used to define wing design modifications and to scope the test program for the experiment hardware.

Elms, R. V.↗
NASA NTRS1984

Cockpit management attitudes

Distinctions are drawn between personality traits and attitudes. The stability of the personality and the malleability of attitudes are stressed. These concepts are related to pilot performance, especially in the areas of crew coordination and cockpit resource management. Airline pilots were administered a Cockpit Management Attitudes questionnaire; empirical data from that survey are reported and implications of the data for training in crew coordination are discussed.

Helmreich, R. L.↗
NASA NTRS1986

Guidance law simulation studies for complex approaches using the Microwave Landing System (MLS)

This report documents results for MLS guidance algorithm development conducted by DAC for NASA under the Advance Transport Operating Systems (ATOPS) Technology Studies program (NAS1-18028). The study consisted of evaluating guidance laws for vertical and lateral path control, as well as speed control, by simulating an MLS approach for the Washington National Airport. This work is an extension and generalization of a previous ATOPS contract (NAS1-16202) completed by DAC in 1985. The Washington river approach was simulated by six waypoints and one glideslope change and consisted of an eleven nautical mile approach path. Tracking performance was generated for 10 cases representing several different conditions, which included MLS noise, steady wind, turbulence, and windshear. Results of this simulation phase are suitable for use in future fixed-base simulator evaluations employing actual hardware (autopilot and a performance management system), as well as crew procedures and information requirements for MLS.

Feather, J. B.↗