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The goal of the TU program was to maximize the development of operable hardware and systems which will be of substantial benefit to the public. Five working prototypes were developed, and a meal system for the elderly is now undergoing evaluation. Manpower utilization is shown relative to the volume of requests in work for each month. The ASTP mobile laboratories and post Skylab bedrest study are also described.
The fuel cell technology for the advanced portable life support system is reviewed, using the breadboard test data, and the design concepts are presented for the development of the improved preprototype fuel cell. Subscale test results confirm the suitability of the solid polymer electrolyte fuel cell fueled by hydride stored hydrogen and oxygen for extravehicular mobility unit (EMU) power generation. Issues verified include passive, zero-G product water removal, nonventing operation in the EMU duty cycle, and complete recovery of product water in potable form. The long cycle life and quick rechargeability are confirmed in 600 h of testing including 150 deep discharge cycles.
The Global Positioning System (GPS) is a revolutionary advance in navigation. Users can determine latitude, longitude, and altitude by receiving range information from at least four satellites. The statistical accuracy of the user's position is directly proportional to the statistical accuracy of the range measurement. Range errors are caused by clock errors, ephemeris errors, atmospheric delays, multipath errors, and receiver noise. Selective Availability, which the military uses to intentionally degrade accuracy for non-authorized users, is a major error source. The proportionality constant relating position errors to range errors is the Dilution of Precision (DOP) which is a function of the satellite geometry. Receivers separated by relatively short distances have the same satellite and atmospheric errors. Differential GPS (DGPS) removes these errors by transmitting pseudorange corrections from a fixed receiver to a mobile receiver. The corrected pseudorange at the moving receiver is now corrupted only by errors from the receiver clock, multipath, and measurement noise. This paper describes a software package that models position errors for various GPS and DGPS systems. The error model is used in the Real-Time Simulator and Cockpit Technology workstation simulations at NASA-LaRC. The GPS/DGPS sensor can simulate enroute navigation, instrument approaches, or on-airport navigation.
Shuttle processing operations are performed following prescribed instructions compiled in a Work Authorization Document (WAD). Until very recently, WADs were printed so that they could be properly executed, including the buy off of each and every step by the appropriate authorizing agent. However, with the development of EPICs, Maximo, and PeopleSoft applications, some of these documents are now available in electronic format; hence, it is possible for technicians and engineers to access them on line and buy off the steps electronically. To take full advantage of these developments, technicians need access to such documents at the point of job execution. Body wearable computers present an opportunity to develop a WAD delivery system that enables access while preserving technician's mobility, safety levels, and quality of work done. The primary objectives of this project were to determine if body wearable computers are a feasible delivery system for WADs. More specifically, identify and recommend specific brands of body wearable computers readily available on the market. Thus, this effort has field-tested this technology in two areas of shuttle processing, and it has examined the usability of the technology. Results of two field tests and a Human Factors Usability Test are presented. Section 2 provides a description of the body wearable computer technology. Section 3 presents the test at the Space Shuttle Main Engine (SSME) Shop. Section 4 presents the results of the integration test at the Solid Rocket Boosters Assembly and Refurbishing Facility (SRBARF). Section 5 presents the results of the usability test done at the Operations Support Building (OSB).
The space transportation systems required to support NASA's Exploration Initiative will demand a high degree of reliability to ensure mission success. This reliability can be realized through autonomous fault/damage detection and repair capabilities. It is crucial that such capabilities are incorporated into these systems since it will be impractical to rely upon Extra-Vehicular Activity (EVA), visual inspection or tele-operation due to the costly, labor-intensive and time-consuming nature of these methods. One approach to achieving this capability is through the use of an autonomous inspection system comprised of miniature mobile sensor platforms that will cooperatively perform high confidence inspection of space vehicles and habitats. This paper will discuss the efforts to develop a small scale demonstration test-bed to investigate the feasibility of using autonomous mobile sensor platforms to perform inspection operations. Progress will be discussed in technology areas including: the hardware implementation and demonstration of robotic sensor platforms, the implementation of a hardware test-bed facility, and the investigation of collaborative control algorithms.
The SMART (Scalable Mobile Autonomous Rocket engine Test) testbed system initiative at SSC was conceived to attempt to address many of the principal cost drivers in developing and maintaining a rocket engine test facility. The system (optimized to test engines and components generating up to 10K lbf nominal thrust) is serving as a testbed for innovative technologies and processes to provide lower cost test services with a rapid test cadence and expedient turnaround times. The system can also potentially be used as a testbed to test other related technologies relevant to surface situations (e.g., moon, Mars associated with crogenic fluid management, engine/component testing, autonomous operations, etc.). This FY20 CIF project, being conducted as part of the SMART testbed system, is developing and testing a propellant delivery system via electrically driven centrifugal pumps (obviating dependence upon Multi-Layer Pressure Vessels) with configuration and operation by a minimal number of personnel. During FY20 the team identified the requirements and worked with P3 and Masten Space Systems to develop the long lead (9 months after receipt of order) items, the 400 VDC pumps, for delivery in mid FY21. Since control of the 400 VDC pump motor is not well developed the team has established heuristics to control flows in LN2 at off nominal shaft speeds to allow deep throttling of the pump in flow test scenarios. Various test scenarios including nominal i.e. high flow high pressure, high flow low pressure, low flow high pressure, low flow low pressure, minimum throttle step change, and low inlet pressure cavitation testing were developed ahead of the anticipated hardware delivery and test. FY20 COVID Stage 3 conditions restricted access to the center and hindered lab work, so efforts focused on the system design and testing plans along with the project procurement paperwork for the hardware... now with its anticipated delivery in spring FY21. Some limited access to the center is expected by spring/summer FY21 for the continuing second year (FY21) CIF project effort meant to be focused upon system integration and initial testing.
This paper discusses the current focus of NASA’s Advanced Space Suit Pressure Garment Technology Development team’s efforts, the status of that work, and a summary of longer-term technology development priorities and activities. For the past two years, the team has focused on the development and detailed design of the Extra-vehicular Mobility Unit (xEMU) to support two parallel missions: A 2023 deadline for delivery of the xEMU International Space Station (ISS)Demonstration configuration, and a planetary walking suit configuration to support landing on the Moon in 2024. The baselined design of the xEMU will be reviewed. The results of Design Verification Testing (DVT) will be presented and its ability to provide confidence in the hardware’s ability to meet flight requirements will be discussed. To the extent possible, an impact assessment of the Exploration Extravehicular Activity Services (xEVAS) Contract will be provided. Finally, a brief review of longer-term pressure garment challenges and technology gaps will be presented to provide an understanding of the advanced pressure garment team’s technology investment priorities and needs for future exploration missions.
This paper discusses the current focus of NASA’s Advanced Space Suit Pressure Garment Technology Development team’s efforts, the status of that work, and a summary of longer-term technology development priorities and activities. For the past two years, the team has focused on the development and detailed design of the Extra-Vehicular Mobility Unit (xEMU) to support two parallel missions: A 2023 deadline for delivery of the xEMU International Space Station (ISS) Demonstration configuration, and a planetary walking suit configuration to support landing on the Moon in 2024. The baselined design of the xEMU will be reviewed. The results of Design Verification Testing (DVT) will be presented and its ability to provide confidence in the hardware’s ability to meet flight requirements will be discussed. To the extent possible, an impact assessment of the Exploration Extra-Vehicular Activity Services (xEVAS) Contract will be provided. Finally, a brief review of longer-term pressure garment challenges and technology gaps will be presented to provide an understanding of the advanced pressure garment team’s technology investment priorities and needs for future exploration missions.
The noise component of future aircraft and operations from Urban Air Mobility (UAM) vehicles is widely recognized as a challenge to community acceptance. NASA’s RVLT (Revolutionary Vertical Lift Technology) program is currently supporting research in the area of human response and psychoacoustics in an effort to augment current metrics for traditional aircraft. This talk will review current work at NASA Ames’ Human Systems Integration Division to evaluate detection, annoyance, and acceptability of UAM sound, in the context of expected ambient sound conditions.
Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles(UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with two independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10%increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by one of the two radars.
Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles (UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with three independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10% increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by two of the radars.
This report documents the completion of, and improvements made to, the software developed for automated data acquisition and automated modeling and control development on the Texas Micro rackmounted PC's. This research was initiated because a need was identified by the Metal Processing Branch of NASA Marshall Space Flight Center for a mobile data acquisition and data analysis system, customized for welding measurement and calibration. Several hardware configurations were evaluated and a PC based system was chosen. The Welding Measurement System (WMS), is a dedicated instrument strickly for use of data acquisition and data analysis. In addition to the data acquisition functions described in this thesis, WMS also supports many functions associated with process control. The hardware and software requirements for an automated acquisition system for welding process parameters, welding equipment checkout, and welding process modeling were determined in 1992. From these recommendations, NASA purchased the necessary hardware and software. The new welding acquisition system is designed to collect welding parameter data and perform analysis to determine the voltage versus current arc-length relationship for VPPA welding. Once the results of this analysis are obtained, they can then be used to develop a RAIL function to control welding startup and shutdown without torch crashing.
Mobility patterns, technology adoption and associated energy outcomes vary tremendously across settlement types. This paper explores how a highly geographically resolved exploration of the social, economic, techno-infrastructural and environmental domains of New York State is key to understanding observed variations in transportation technology adoption and associated mobility and energy outcomes. Current socioeconomic and mobility data sets at the census block group level are integrated in a hierarchical clustering approach to show how variations in mobility and energy outcomes are shaped by these domains - which may enhance progress on investments, or effectively inform planning and other decisions for state-wide goals. The clustering produces four settlement types to predict dependent variables of electric vehicle (EV) adoption rates, commute mode, vehicle fuel economy, and vehicles per household. This typology shows EV adoption rates among the core urban population, which is wealthy and highly-educated, are high - 3 EVs/1000 vehicles versus 1 EV/1000 vehicles among the urban working class. Commuting mode is closely linked with population and employment density - more than 90% of core urbanites use transit or active modes, compared with only 22% of suburbanites and 17% of rural residents. Household vehicle ownership also varies, with 2 vehicles per household in rural areas and only 0.5 in core urban settings. Important findings on differences among the rural, suburban, urban, and urban core settlement types suggest a need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems state-wide.
This paper will report the status of the Emergency Response Robotics project, a teleoperated mobile robot system being developed at JPL for use by the JPL Fire Department/HAZMAT Team. The project, which began in 1991, has been focused on developing a robotic vehicle which can be quickly deployed by HAZMAT Team personnel for first entry into an incident site. The primary goals of the system are to gain access to the site, locate and identify the hazard, and aid in its mitigation. The involvement of JPL Fire Department/HAZMAT Team personnel has been critical in guiding the design and evaluation of the system. A unique feature of the current robot, called HAZBOT III, is its special design for operation in combustible environments. This includes the use of all solid state electronics, brushless motors, and internal pressurization. Demonstration and testing of the system with HAZMAT Team personnel has shown that teleoperated robots, such as HAZBOT III, can successfully gain access to incident sites locating and identifying hazardous material spills. Work is continuing to enable more complex missions through the addition of appropriate sensor technology and enhancement of the operator interface.
For over 50 years, government fishery agencies have recognized the need to transition excess fishing capacity in coastal waters to aquaculture. For the most part, investment strategies to move wild capture and harvest efforts into aquaculture have failed since the technology and capital expense for entry, such as large fish pens, was not conducive for acceptance. In contrast, low trophic level aquaculture of shellfish and seaweeds is suitable as an addition to the livelihoods of seasonal fishing communities and to those displaced by fishery closures, especially if vessels and gear can be designed around existing fishing infrastructures, thus allowing fishers to maintain engagement with their primary fishery, while augmenting income via aquaculture. In this study, an inexpensive, lightweight, and highly mobile gear for kelp seaweed farming was developed and tested over a 3-year period in southern Maine, USA. The system was different from existing kelp farming operations used in nearshore waters that use low-scope mooring lines, and heavy, deadweight anchors. Instead, a highly mobile, easy to deploy system using lightweight gear was designed for exposed conditions. The entire system fit into fish tote boxes and was loadable onto a standard pickup truck. The seaweed system had small but efficient horizontal drag embedment anchors connected to a chain catenary and pretensioned with simple subsurface flotation. The system was able to be deployed and removed in less than 4 h by a crew of three using a 10 m vessel and produced a harvest of 12.7 kg/m over an 8-month fall-winter growth period. The target group for this seaweed research and development effort were coastal fishing communities who move seasonally into non-fishing occupations in service industries, such as construction, retail, etc. An economic assessment suggests farmers would realize an 8% return on investment after3 years and $13.50/h greater income as compared to a non-farming off season job at minimum wage. This low-cost seaweed farming system for fall-winter operations fits well into a “livelihood” strategy for fishing families who must work multiple jobs in the offseason when their main fishery is unavailable.
Interest in personal communications (PCOMM) has been stimulated by recent developments in satellite and terrestrial mobile communications. A personal access satellite system (PASS) concept was developed at the Jet Propulsion Laboratory (JPL) which has many attractive user features, including service diversity and a handheld terminal. Significant technical challenges addressed in formulating the PASS space and ground segments are discussed. PASS system concept and basic design features, high risk enabling technologies, an optimized multiple access scheme, alternative antenna coverage concepts, the use of non-geostationary orbits, user terminal radiation constraints, and user terminal frequency reference are covered.
With the Constellation Program, NASA is entering a new age of space exploration that will take us back to the Moon, to Mars, and beyond, and NASA is developing the new technology and vehicles to take us there. At the forefront are the Orion spacecraft and the Ares I launch vehicle. As NASA's gateway to space, Kennedy Space Center (KSC) will process and launch the new vehicles. This will require new systems and extensive changes to existing infrastructure. KSC is designing a new mobile launcher, a new launch control system, and new ground support equipment; modifying the Vehicle Assembly Building, one of the launch pads, and other facilities; and launching the Ares I-X flight test. It is an exciting and challenging time to be an engineer at KSC.
Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.