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NASA NTRS2019

Cracking Problems in Low-Voltage Chip Ceramic Capacitors

Cracking remains the major reason of failures in multilayer ceramic capacitors (MLCCs) used in in space electronics. Due to a tight quality control of space-grade components, the probability that as manufactured capacitors have cracks is relatively low, and cracking is often occurs during assembly, handling and the following testing of the systems. Majority of capacitors with cracks are revealed during the integration and testing period, but although extremely rarely, defective parts remain undetected and result in failures during the mission. This situation is aggravated by manual soldering and rework that are often used during low volume production of circuit boards for space. Although failures of MLCCs are often attributed to the post-manufacturing stresses, in many cases they are due to a combination of certain deviations in the manufacturing processes that result in hidden defects in the parts and excessive stresses during assembly and use. This report gives an overview of design, manufacturing and testing processes of MLCCs focusing on elements related to cracking problems. The existing and new screening and qualification procedures and techniques are briefly described and assessed by their effectiveness in revealing cracks. The capability of different test methods to simulate stresses resulting in cracking, mechanisms of failures in capacitors with cracks, and possible methods of selecting the most robust to manual soldering stresses capacitors are discussed.

testing↗
NASA NTRS2018

Earthshine as an Illumination Source at the Moon

Earthshine is the dominant source of natural illumination on the surface of the Moon during lunar night, and at some locations within permanently shadowed regions (PSRs) near the poles that never receive direct sunlight. As such, earthshine has the potential to enable the scientific investigation and exploration of conditions in areas of the Moon that are either temporarily or permanently hidden from the Sun. Earthshine has also been used to refer to Earthlight reflected from the lunar surface, but in this study we use it to refer specifically to Earthlight incident at the Moon. Under certain circumstances, the heat flux from earthshine could also influence the transport and cold-trapping of volatiles present in the very coldest areas within PSRs. In this study, Earth's spectral irradiance, as it would appear at the Moon in the solar reflectance band (0.3–3.0 µm) and at thermal emission wavelengths (3–50 µm), is examined with a suite of model image cubes and whole-disk spectra created using the Virtual Planetary Laboratory (VPL) three-dimensional (latitude, longitude and altitude) modeling capability. At the Moon, the broadband, hemispherical irradiance from Earth at full-phase is approximately 0.15 W m−2 with comparable contributions from solar reflectance and thermal emission; for context, this about 0.01% that of solar irradiance and has an equivalent temperature of around 40 K. Over the simulated timeframe, spanning two lunations, Earth's thermal irradiance shows very little net change (less than a few mW m−2 resulting from cloud variability and the south-to-north motion of the sub-observer latitude on Earth). In the solar band, Earth's diurnally averaged light curve at phase angles g ≤ 60° is well-fit using a Henyey–Greenstein integral phase function. At wavelengths longward of about 0.7 µm, near the well-known vegetation “red edge”, Earth's reflected solar radiance shows significant diurnal modulation as a result of the broad maximum in projected landmass at terrestrial longitudes between 60°W and 0°, as well as from the distribution of clouds. A simple formulation with adjustable coefficients is presented, condensed from the VPL model grid, for estimating Earth's hemispherical irradiance at the Moon as a function of wavelength, phase angle and sub-observer coordinates (terrestrial latitude and longitude). Uncertainties in any one prediction are estimated to be 10–12% at 0.3 µm, rising to >25% near 2.5 µm as a result of the increasing relative brightness and unpredictable influence of clouds. Although coefficient values are derived from a suite of spring equinox models, the approximation appears to be valid for all seasons, to within the stated uncertainties. It is demonstrated that earthshine is sufficiently bright to serve as a natural illumination source for optical measurements on a robotic lander/rover, allowing the identification of water ice mixed with regolith at the percent-level of fractional area.

Surface↗
NASA NTRS2019

Bumper: A Tool for Analyzing Spacecraft Micrometeoroid and Orbital Debris Risk

“Bumper” is NASA’s computer program for analyzing spacecraft micrometeoroid and orbital debris (MMOD) risk. Bumper was developed in the late-1980s and has been continuously used and maintained since. The user base has grown from a few government entities to now include numerous commercial entities as well. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) Team is responsible for all aspects of the Bumper software. Bumper has been used to characterize MMOD risk on hundreds of spacecraft. All of the International Space Station (ISS) modules, visiting vehicles and numerous external components and systems have been analyzed. Bumper was used to analyze each of the Space Shuttle missions since STS-50. The Orion Multi-Purpose Crew Vehicle (MPCV) MMOD shielding is being developed using Bumper as well. Bumper has also been used on numerous telescopes (Hubble, James Webb, and Fermi Gamma-ray Space Telescopes), scientific probes (Stardust, New Horizons, Parker Solar Probe), and Earth observation satellites (Landsat, Joint Polar Satellite System). Bumper is also being used to analyze the micrometeoroid risk and support design of the Deep Space Gateway (DSG) and Mars Sample Return (MSR) missions. The HVIT Bumper Configuration Control Board (CCB) ensures that all changes to the code are approved, reviewed, and documented. Most of the changes are made to add new MMOD damage “ballistic limit equations” (BLEs). BLEs are typically added in response to completion of a hypervelocity impact (HVI) test series and development of an associated BLE. Other less frequent changes include updates of the debris or meteoroid environment models, feature enhancements, and feature retirement. Some BLEs are commercially sensitive and/or proprietary, so the CCB also manages code user-version control and software distribution. The current version – “Bumper 3” – is a FORTRAN executable that utilizes a 64-bit architecture. Bumper 3 has numerous features that make it a powerful tool for analyzing spacecraft MMOD risk. Bumper uses the latest orbital debris and micrometeoroid environment models. Bumper also easily processes large spacecraft geometry models, recognizes hidden surfaces, permits BLE assignment by name or number, and conducts quality checks of the spacecraft geometry model. Bumper 3 can also be used to estimate the effects of particle penetration through thin, high-standoff distance hardware components such as solar arrays and radiators. This is done using a special HVIT-developed technique know as the “3-Part Analysis.” The paper introduces the Bumper 3 MMOD risk analysis code and provides an example MMOD risk assessment showing Bumper’s role in the overall MMOD protection design process.

Lear, Dana M.↗
NASA NTRS2019

Bell Inequality Experiment for a High Brightness Time-Energy Entangled Source

A periodically poled MgO doped LiNbO3 (MgO:LN) non-degenerate photon pair source is utilized for spontaneous parametric down-conversion of 532 nm photons into time-energy entangled pairs of 800 and 1600 nm photons. The entangled photons are separated using previously detailed sorting optics, such that each wavelength is independently directed through one of two modified Mach-Zehnder interferometers also known as a Franson interferometer after which they are fiber-optically guided to high-efficiency photon detectors. Output from the detectors is sent to a high resolution time tagger, where coincidences between the entangled photons are recorded. By varying the length of the long path in one Mach-Zehnder interferometer, it is possible to observe high visibility sinusoidal fringes in the measured coincidence rates (while no variation is seen in single photon detection rates). These fringes due to interference between the photon probability amplitudes are indicative of a violation of the Bell inequality, and confirm inconsistencies with local hidden variable theory for the correlations of the time-energy entangled photon pairs.

time-energy entanglement↗
NASA NTRS2019

Bell Inequality Experiment for a High Brightness Time-Energy Entangled Source

A periodically poled MgO - doped LiNbO3 (MgO:LN) non-degenerate photon pair source is utilized for spontaneous parametric down-conversion of 532-nanometer photons into time-energy entangled pairs of 800- and 1600-nanometer photons. The entangled photons are separated using previously detailed sorting optics, such that each wavelength is independently directed through one of two modified Mach-Zehnder interferometers - also known as a Franson interferometer - after which they are fiber-optically guided to high-efficiency photon detectors. Output from the detectors is sent to a high resolution time tagger, where coincidences between the entangled photons are recorded. By varying the length of the long path in one Mach-Zehnder interferometer, it is possible to observe high visibility sinusoidal fringes in the measured coincidence rates (while no variation is seen in single photon detection rates). These fringes - due to interference between the photon probability amplitudes - are indicative of a violation of the Bell inequality, and confirm inconsistencies with local hidden variable theory for the correlations of the time-energy entangled photon pairs.

Time-Energy Entanglement↗
NASA NTRS2019

Scaling Evaluation of Ice-Crystal Icing on a Modern Turbofan Engine in PSL Using the COMDES-MELT Code

This paper presents preliminary ice-crystal icing (ICI) altitude scaling evaluation results of a Honeywell Uncertified Research Engine (HURE) that was tested in the NASA Glenn Research Center Propulsion Systems Laboratory (PSL) during January of 2018. This engine geometry features a hidden core design to keep the core less exposed. The engine was fitted with internal video cameras to observe various ice buildup processes at multiple selected locations within the engine core flow path covering the fan stator, the splitter-lip/shroud/strut, and the high pressure compressor (HPC) variable inlet guide vane (IGV) regions. The potential ice accretion risk was pre-determined to occur by using NASA’s in-house 1D Engine Icing Risk assessment code, COMDES-MELT. The code was successful in predicting the risk of ice accretion in adiabatic regions like the fan-stator of the HURE at specific engine operating points. However at several operating points during the test, liquid water was observed running along the shroud toward the variable IGV of the HPC regions with an air temperature well below freezing, thus no particle melting could have occurred due to heating from the air alone. It was reasoned that other sources of heat were present in that region. To account for these heat sources the inlet total temperature was adjusted to give a wet bulb temperature of 24 F below the standard minimum wet bulb temperature of 492 R to allow ice to accrete in the splitter-lip/shroud/strut region, which was determined from a reference case where hard ice was observed in that region. With that adjustment the COMDES-MELT code was successful in providing operating points where there was a risk of ice accretion during the test campaign. In addition to calculating possible conditions at different selected lower altitudes, simulations were run to determine potential inlet conditions that could lead to ice-crystal accretion along the prescribed stations where the cameras were available. From there, scaled test conditions were determined by best matching the following three icing related parameters of the reference condition: (1) the local air total wet bulb temperature, (2) the local ice crystal cloud melt ratio and (3) the engine fan face ice/water to air mass flux ratio of the ice crystal cloud. Instantaneous images taken from the time-lapsed movies of ice buildup were used along with the relevant thermodynamic data of air, water vapor and local icing condition to help evaluate how closely the proposed altitude scaling method could be used in ground based test facility to duplicate selected reference ICI features observed at specific location inside this engine at different scale altitudes. Discussions on observed limitation for engine icing scaling application from this test campaign and needed improvement are provided. A scaling test procedure to help identify potential ICI risk conditions and possible ice accretion locations of a new turbofan engine is evaluated in PSL.

Engine Testing in PSL↗
NASA NTRS2018

Aerodynamic and Structural Evaluation of an SMA Slat-Cove Filler Using Computational and Experimental Tools at Model Scale

Transport class aircraft produce a significant amount of airframe noise during approach and landing due to exposed geometric discontinuities that are hidden during cruise. The leading-edge slat is a primary contributor to this noise. In previous work, use of a slat-cove filler (SCF) has proven to reduce airframe noise by filling the cove aft of the slat, eliminating the circulation region within the cove. The goal of this work is to extend and improve upon past experimental and computational efforts on the evaluation of a scaled high-lift wing with a super elastic shape memory alloy (SMA) SCF. Recent turbulence measurements of the Texas A&M University 3ft-by-4ft wind tunnel allow for more accurate representation of the flow through the test section in computational fluid dynamics (CFD) analysis. The finite volume models used in CFD analysis are coupled to structural finite element models using a framework compatible with an SMA constitutive model and significant deformation, enabling fluid-structure interaction (FSI) analysis of the SCF. Both fully-deployed and retraction/deployment cases are considered. The displacement of the SCF on the experimental model is measured at various stages of retraction/deployment using a laser displacement sensor and digital image correlation system. Due to a lack of structural stiffness in the 3D-printed plastic slat during retraction and SCF stowage, a rigid steel slat is incorporated into the physical model and preliminary wind tunnel tests are conducted at multiple angles of attack in both retracted and deployed configurations.

Scholten, William↗
NASA NTRS2019

Analysis of Bioreduction Strategies for a Solid Rocket Motor on an Interplanetary Mission to Europa

The Europa Lander De-Orbit stage braking motor must comply with the Planetary Protection requirements established for a category IV mission. In its mission to Europa, a motor that hasn’t gone through bioreduction environments will carry microbial spores and other biosignature molecules that might jeopardize a mission of astrobiological concern as well as future missions to come. A motor with solid rocket propellant represents exclusive challenges associated to the calculation of high numbers of an encapsulated bioburden hidden behind a nozzle plug. Existing techniques for bioburden reduction are analysed in perspective to motor assembly facilities and the series of events that are involved in manufacturing a solid rocket motor. These techniques include antimicrobial effects of chemical components already present within the motor and bioreduction due to assembly and operational environments induced in the motor. Analysis of the manufacturing process, adhered bioburden and recent investigations into the effectiveness of microbiological techniques in finding inherent antimicrobial properties have generated a step by step outline of Planetary Protection for lander mission associated solid rocket motors.

Velez-Justiniano, Yo-Ann↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2020

Overcoming Dimensionality Barriers in Satellite Data to Elucidate SPECTRAL-SPATIAL-TEMPORAL Trends in the Ocean

The weighted harmonic mean of spectral wavelengths can be used as an index of optical water types that share similar spectral signatures.This technique can be translated to relate spectral shape between sensors of varying spectral resolution.The output along a continuum of color values enables spatio-temporal analysis of subtle spectral shifts.Directional trends in absorption and backscatter data lend a hidden context to changes in water color. Conceptualization of multiple data dimensions is imperative as passive/active sensors grow increasingly sophisticated in nature.

Vandermeulen, Ryan↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRS2018

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.

Dolph, Chester V.↗
NASA NTRS2018

International Space Station Operational Experience and Its Impacts on Future Mission Supportability

Operational experience gained on the International Space Station (ISS) has enabled significant improvements in failure rate estimates for various Orbital Replacement Units (ORUs). These improved estimates, in turn, allow more efficient and accurate spare parts allocations for future missions, enabling significant reductions in both logistics mass and risk. This paper examines the value of ISS experience to date in terms of its impact on supportability for future missions. A supportability model is presented that assesses the spares required as a function of mission endurance and risk, taking into account uncertainty in failure rate estimates. Changes in ISS Environmental Control and Life Support (ECLSS) ORU failure rate estimates are described and discussed, both in terms of the overall population of ORUs and the evolution of failure rate estimates over time for a particular item. The value of those updated failure rate estimates is assessed by calculating the estimated spares mass requirements for two cases, using the initial, pre-ISS estimates and using the estimates informed by on-orbit experience. Hidden risk resulting from underestimated failure rates is also assessed. These results indicate that, for a 1,200-day Mars mission, ISS experience has enabled a 3.9 t to 6.0 t reduction in ECLSS spares mass required and uncovered failure rate underestimates that would have resulted in an order of magnitude increase in risk had they not been discovered and corrected. The implications of these results for system development and mission planning are discussed, including approaches to accelerate the rate of failure rate refinement and the risks associated with making changes or introducing new systems. Overall, test time is a critical factor that must be carefully considered in system development, and new systems must budget appropriate time for testing in a relevant environment or accept higher risk and logistics requirements on future missions.

Owens, Andrew C.↗
NASA NTRS2019

Increasing Pilot’s Understanding of Future Automation State – an Evaluation of an Automation State and Trajectory Prediction System

A pilot in the loop flight simulation study was conducted at NASA Langley Research Center to evaluate a trajectory prediction system. The trajectory prediction system computes a five-minute prediction of the lateral and vertical path of the aircraft given the current and intent state of the automation. The prediction is shown as a graphical representation so the pilots can form an accurate mental model of the future state. Otherwise, many automation changes and triggers are hidden from the flight crew or need to be consolidated to understand if a change will occur and the exact timing of the change. Varying dynamic conditions like deceleration can obscure the future trajectory and the ability to meet constraints, especially in the vertical dimension. Current flight deck indications of flight path assume constant conditions and do not adequately support the flight crew to make correct judgments regarding constraints. The study was conducted using ten commercial airline crews from multiple airlines, paired by airline to minimize procedural effects. Scenarios spanned a range of conditions that provided evaluation in a realistic environment with complex traffic and weather conditions. In particular, scenarios probed automation state and loss of state awareness. The technology was evaluated and contrasted with current state-of-the-art flight deck capabilities modeled from the Boeing 787. Objective and subjective data were collected from aircraft parameters, questionnaires, audio/video recordings, head/eye tracking data, and observations. This paper details findings about the trajectory prediction system including recommendations about further study.

Etherington, Timothy J.↗
NASA NTRS2020

Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints

Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a ”physiological temporal fingerprint”, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states.

Napoli, Nicholas J.↗
NASA NTRS2019

Ocean Subsurface Study from ICESat-2 Mission

NASA has launched the ICESat-2 mission in September 2018 with the primary purpose of monitoring changes in the cryosphere. Fortunately, the measured photons over ocean region provide great opportunity for the ocean subsurface study. We have proposed a novel algorithm to determine ocean subsurface optical properties, such as, diffuse attenuated coefficient, kd (/m), total backscattering coefficients bb (/m), attenuated backscatter coefficient and particulate backscattering coefficients b(sub bp) (/m) from ICESat-2/ATLAS lidar measurements. Our ICESat-2 ocean subsurface results reveal high vertical resolution of these optical properties through the water column that are hidden from the passive ocean color record. Moreover, we can estimate the particulate organic carbon (POC) and phytoplankton carbon biomass based on the ICESat-2 retrieved particulate backscattering coefficients b( sub bp) results. We will present and discuss the first ICESat-2 ocean subsurface profile results, especially in polar region where passive ocean color measurements are difficult to make. We will also present the multiple scattering effects on the ocean subsurface optical properties retrieval.

Xiaomei Lu↗
NASA NTRS2020

The Next Generation X-Ray Galaxy Survey with eROSITA

eROSITA, launched on 13 July 2019, will be completing the first all-sky survey in the soft and medium X-ray band in nearly three decades. This 4-year survey, finishing in late 2023, will present a rich legacy for the entire astrophysics community and complement upcoming multi-wavelength surveys (with, e.g. the Large Synoptic Survey Telescope and the Dark Energy Survey). Besides the major scientific aim to study active galactic nuclei (AGN) and galaxy clusters, eROSITA will contribute significantly to X-ray studies of normal (i.e. not AGN) galaxies. Starting from multi-wavelength catalogues, we measure star formation rates and stellar masses for 60 212 galaxies constrained to distances of 50–200 Mpc. We chose this distance range to focus on the relatively unexplored volume outside the local Universe, where galaxies will be largely spatially unresolved and probe a range of X-ray luminosities that overlap with the low luminosity and/or highly obscured AGN population. We use the most recent X-ray scaling relations as well as the on-orbit eROSITA instrument performance to predict the X-ray emission from XRBs and diffuse hot gas and to perform both an analytic prediction and an end-to-end simulation using the mission simulation software, SIXTE. We consider potential contributions from hidden AGN and comment on the impact of normal galaxies on the measurement of the faint end of the AGN luminosity function. We predict that the eROSITA 4-year survey, will detect ≳15 000 galaxies (3σ significance) at 50–200 Mpc, which is ~ 100× more normal galaxies than detected in any X-ray survey to date.

Surveys↗
NASA NTRS1993

Demonstration of A High-Fidelity Predictive/Preview Display Technique for Telerobotic Servicing in Space

A highly effective predictive/preview display technique for telerobotic servicing in space under several seconds communication time delay has been demonstrated on a large laboratory scale in May, 1993, involving the Jet Propulsion Laboratory as the simulated ground control station and, 2500 miles away, the Goddard Space Flight Center as the simulated satellite servicing set-up. The technique isbased on a high-fidelity calibration procedure that enables a high-fidelity overlay of 3-D graphics robot arm and object models over given 2-D TV camera images of robot arm and objects. Togenerate robot arm motions, the operator can confidently interact in real time with the graphics models of the robot arm and objects overlaid on an actual camera view of the remote work site. The technique also enables the operator to generate high-fidelity synthetic TV camera views showing motion events that are hidden in a given TV camera view or for which no TV camera views are available...

Bejczy, A. K.↗