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A unique, highly automated thermal-vacuum facility for optical testing of lenses and cameras is described. In particular, measurements of MTF, boresight, and geometric image distortion over a large parameter space including wavelength, field of view and temperature will be discussed.
Absent a general method for mathematically sound, automated transformation of customer requirements into a formal model of the desired system, developers must resort to either manual application of formal methods or to system testing (either manual or automated). While formal methods have afforded numerous successes, they present serious issues, e.g., costs to gear up to apply them (time, expensive staff), and scalability and reproducibility when standards in the field are not settled. The testing path cannot be walked to the ultimate goal, because exhaustive testing is infeasible for all but trivial systems. So system verification remains problematic. System or requirements validation is similarly problematic. The alternatives available today depend on either having a formal model or pursuing enough testing to enable the customer to be certain that system behavior meets requirements. The testing alternative for non-trivial systems always have some system behaviors unconfirmed and therefore is not the answer. To ensure that a formal model is equivalent to the customer s requirements necessitates that the customer somehow fully understands the formal model, which is not realistic. The predominant view that provably correct system development depends on having a formal model of the system leads to a desire for a mathematically sound method to automate the transformation of customer requirements into a formal model. Such a method, an augmentation of requirements-based programming, will be briefly described in this paper, and a prototype tool to support it will be described. The method and tool enable both requirements validation and system verification for the class of systems whose behavior can be described as scenarios. An application of the tool to a prototype automated ground control system for NASA mission is presented.
Atomic oxygen generated in ground-based research facilities has been used to not only test erosion of candidate spacecraft materials but as a noncontact technique for removing organic deposits from the surfaces of artwork. NASA has patented the use of atomic oxygen to remove carbon-based soot contamination from fire-damaged artwork. The process of cleaning soot-damaged paintings with atomic oxygen requires exposures for variable lengths of time, dependent on the condition of a painting. Care must be exercised while cleaning to prevent the removal of pigment. The cleaning process must be stopped as soon as visual inspection or surface reflectance measurements indicate that cleaning is complete. Both techniques rely on optical comparisons of known bright locations against known dark locations on the artwork being cleaned. Difficulties arise with these techniques when either a known bright or dark location cannot be determined readily. Furthermore, dark locations will lighten with excessive exposure to atomic oxygen. Therefore, an automated test instrument to quantitatively characterize cleaning progression was designed and developed at the NASA Glenn Research Center at Lewis Field to determine when atomic oxygen cleaning is complete.
The MER (Mars Exploration Rover) Attitude and Pointing System Simulation Testbed Environment (RAPSSTER) provides a simulation platform used for the development and test of GNC (guidance, navigation, and control) flight algorithm designs for the Mars rovers, which was specifically tailored to the MERs, but has since been used in the development of rover algorithms for the Mars Science Laboratory (MSL) as well. The software provides an integrated simulation and software testbed environment for the development of Mars rover attitude and pointing flight software. It provides an environment that is able to run the MER GNC flight software directly (as opposed to running an algorithmic model of the MER GNC flight code). This improves simulation fidelity and confidence in the results. Further more, the simulation environment allows the user to single step through its execution, pausing, and restarting at will. The system also provides for the introduction of simulated faults specific to Mars rover environments that cannot be replicated in other testbed platforms, to stress test the GNC flight algorithms under examination. The software provides facilities to do these stress tests in ways that cannot be done in the real-time flight system testbeds, such as time-jumping (both forwards and backwards), and introduction of simulated actuator faults that would be difficult, expensive, and/or destructive to implement in the real-time testbeds. Actual flight-quality codes can be incorporated back into the development-test suite of GNC developers, closing the loop between the GNC developers and the flight software developers. The software provides fully automated scripting, allowing multiple tests to be run with varying parameters, without human supervision.
The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.
The Operating Missions as Nodes on the Internet (OMNI) project at NASA's Goddard Space flight Center (GSFC), is demonstrating the use of standard Internet protocols for spacecraft communication systems. This year, demonstrations of Internet access to a flying spacecraft have been performed with the UoSAT-12 spacecraft owned and operated by Surrey Satellite Technology Ltd. (SSTL). Previously, demonstrations were performed using a ground satellite simulator and NASA's Tracking and Data Relay Satellite System (TDRSS). These activities are part of NASA's Space Operations Management Office (SOMO) Technology Program, The work is focused on defining the communication architecture for future NASA missions to support both NASA's "faster, better, cheaper" concept and to enable new types of collaborative science. The use of standard Internet communication technology for spacecraft simplifies design, supports initial integration and test across an IP based network, and enables direct communication between scientists and instruments as well as between different spacecraft, The most recent demonstrations consisted of uploading an Internet Protocol (IP) software stack to the UoSAT- 12 spacecraft, simple modifications to the SSTL ground station, and a series of tests to measure performance of various Internet applications. The spacecraft was reconfigured on orbit at very low cost. The total period between concept and the first tests was only 3 months. The tests included basic network connectivity (PING), automated clock synchronization (NTP), and reliable file transfers (FTP). Future tests are planned to include additional protocols such as Mobile IP, e-mail, and virtual private networks (VPN) to enable automated, operational spacecraft communication networks. The work performed and results of the initial phase of tests are summarized in this paper. This work is funded and directed by NASA/GSFC with technical leadership by CSC in arrangement with SSTL, and Vytek Wireless.
Frequent or regular testing of multiconductor cables terminated in multipin conductors tedious, if not impossible, task. This inexpensive circuit simplifies open/short testing and is amenable to automation. In operation, pair of connectors selected to match pair of connectors installed on each of cables to be tested. As many connectors accommodated as required, and each can have as many conductors as required. Testing technique implemented with this circuit automated easily with electronic controls and computer interface. Printout provides status of each conductor in cable, indicating which, if any, of conductors has open or short circuit.
Advanced furnace systems are being developed for use in space. Systems are being tested for current experiment applications and modified for future experiment requirements. Future projects are: (1) fabrication and testing of the Advanced Automated Directional Solidification Furnace (AADSF) flight hardware; (2) development of a Heat Pipe Furnace (HPF) for use in space. Heat pipes will be tested for space flight qualification in conjunction with the furnace development. The HPF design will be based on the AADSF development and will be of modular design including capabilities of operating with or without heat pipes; and (3) the AADSF furnace will be modified and tested to operate at temperatures up to 1700 C in the heated cavity. This will be accomplished by developing a new hot end heating module and insulation package for the existing AADSF. Refurbishment of the Drop Tower Furnace (DTF) is under way. The DTF can operate at temperatures up to 1700 C. The sample size will be approximately 3/8 in. dia. x 5/8 in. long. Design improvements for the General Purpose Rocket Furnace (GPRF) for use in the Material Experiment Assembly (MEA) are to be accomplished.
The feasibility of using radio frequency receivers to collect data from automated weather stations to model fluxes of latent heat, sensible heat, and radiation using routine weather data collected by automated weather stations was tested and the estimated fluxes were compared with fluxes measured over wheat. The model Cupid was used to model the fluxes. Two or more automated weather stations, interrogated by radio frequency and other means, were utilized to examine some of the climatic variability of the First ISLSCP (International Satellite Land-Surface Climatology Project) Field Experiment (FIFE) site, to measure and model reflected and emitted radiation streams from various locations at the site and to compare modeled latent and sensible heat fluxes with measured values. Some bidirectional reflected and emitted radiation data were collected from 23 locations throughout the FIFE site. Analysis of these data along with analysis of the measured sensible and latent heat fluxes is just beginning.
Testing technique permits accurate measurement of temperature-dependent thermal conductivity, by virtue of the small temperature differential required across a specimen. The permissible mean insulation temperature ranges from cryogenic to 10 degrees F for the insulation under test.
The National Aeronautics and Space Administration (NASA) has completed a flight test to evaluate the performance of an onboard prototype automation system operating in future high density urban airspace. The test was part of a research investigation of the Urban Air Mobility (UAM) concept, with a focus on a future environment having hundreds of simultaneous operations over a metropolitan area. The complexity of this future UAM airspace may require automation capable of replanning an aircraft’s path in the presence of traffic and other changing constraints. A live-virtual-constructive (LVC) approach was used to conduct the test. Prototype automation technology was integrated into one of the two live aircraft, which were combined with virtual traffic to create a mixed reality environment at the target airspace density. In-flight evaluation enabled verification of the automation’s functions and discovery of any unexpected behaviors resulting from its operation in an actual flight environment. The in-flight evaluation also provided data for validation of air traffic simulations. This paper discusses the design, methodology, and challenges overcome to conduct a successful flight test. Remaining challenges, future work, and recommendations to improve the flight test capability are also discussed.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
The NASA Office of the Chief Health and Medical Officer (OCHMO) requested HRP conduct a survey of industry and government standards and guidelines for the interaction of automation/autonomy with humans. Guidance for verification testing of human-automation designs was also requested. A rapid response was requested so that the deliverables could be provided as part of a future NASA solicitation for automated systems for HLS. The project began with a broad review and analysis of prior NASA work in this area, as well as external standards related to human interaction with automation/autonomy. The team reviewed approximately 200 documents, and interviewed multiple industry and government subject matter experts (SMEs). “Gold standard” documents were selected for focus and key categories were identified. A cross-walk of standards was created, and common standards and guidelines were selected and rephrased (where necessary) to be information-rich, concise, and usable. Verification guidelines and examples were also developed. The final report includes an introduction to automation and autonomy, selected standards and guidelines, verification method guidance, and research gaps. In addition to the report, the team delivered a list of candidate standards for autonomous vehicles, as well as a list of candidate standards for a future NASA-STD-3001 update on automation/autonomy. The presentation will describe the rapid response project approach and methods, examples of standards and guidelines identified and delivered to OCHMO, and research needed to develop additional automation/autonomy design guidance where gaps currently exist.
Five flight tests of the Digital Automated Landing System (DIALS) were conducted on the Advanced Transport Operating System (ATOPS) Transportation Research Vehicle (TSRV)--a modified Boeing 737 Aircraft for advanced controls and displays research. These flight tests were conducted at NASA's Wallops Flight Center using the Microwave Landing System (MLS) installation on Runway 22. This report is primarily a collection of data plots of all performance variables recorded for the entire five flight tests. A description and source of the performance variables is included. Performance variables include inertial data, air data, automatic control commands, control servo positions, sensor data, DIALS guidance and control parameters, and Kalman filter data. This data illustrates low overshoot captures of the localizer for intercept angles of 20 deg, 30 deg, 40 deg, and 50 deg intercept angles, and low overshoot captures of the glideslope slope for 3 deg, 4.5 deg, and 5 deg glideslopes. Flare maneuvers were successfully performed from the various glideslope angles and good decrab maneuvers were performed in crosswinds of 6 knots. In 18 to 20 knot crosswind conditions rudder limiting occurred which caused lateral drifting although heading alignment was achieved.
Five flight tests of the Digital Automated Landing System (DIALS) were conducted on the Advanced Transport Operating Systems (ATOPS) Transportation Research Vehicle (TSRV) -- a modified Boeing 737 aircraft for advanced controls and displays research. These flight tests were conducted at NASA's Wallops Flight Center using the microwave landing system (MLS) installation on runway 22. This report describes the flight software equations of the DIALS which was designed using modern control theory direct-digital design methods and employed a constant gain Kalman filter. Selected flight test performance data is presented for localizer (runway centerline) capture and track at various intercept angles, for glideslope capture and track of 3, 4.5, and 5 degree glideslopes, for the decrab maneuver, and for the flare maneuver. Data is also presented to illustrate the system performance in the presence of cross, gust, and shear winds. The mean and standard deviation of the peak position errors for localizer capture were, respectively, 24 feet and 26 feet. For mild wind conditions, glideslope and localizer tracking position errors did not exceed, respectively, 5 and 20 feet. For gusty wind conditions (8 to 10 knots), these errors were, respectively, 10 and 30 feet. Ten hands off automatic lands were performed. The standard deviation of the touchdown position and velocity errors from the mean values were, respectively, 244 feet and 0.7 feet/sec.