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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
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NASA NTRS2015

Advanced Bayesian Method for Planetary Surface Navigation

Autonomous Exploration, Inc., has developed an advanced Bayesian statistical inference method that leverages current computing technology to produce a highly accurate surface navigation system. The method combines dense stereo vision and high-speed optical flow to implement visual odometry (VO) to track faster rover movements. The Bayesian VO technique improves performance by using all image information rather than corner features only. The method determines what can be learned from each image pixel and weighs the information accordingly. This capability improves performance in shadowed areas that yield only low-contrast images. The error characteristics of the visual processing are complementary to those of a low-cost inertial measurement unit (IMU), so the combination of the two capabilities provides highly accurate navigation. The method increases NASA mission productivity by enabling faster rover speed and accuracy. On Earth, the technology will permit operation of robots and autonomous vehicles in areas where the Global Positioning System (GPS) is degraded or unavailable.

Center, Julian↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Coltin, Brian↗
NASA NTRS2016

Localization from Visual Landmarks on a Free-Flying Robot

We present the localization approach for Astrobee,a new free-flying robot designed to navigate autonomously on board the International Space Station (ISS). Astrobee will conduct experiments in microgravity, as well as assisst astronauts and ground controllers. Astrobee replaces the SPHERES robots which currently operate on the ISS, which were limited to operating in a small cube since their localization system relied on triangulation from ultrasonic transmitters. Astrobee localizes with only monocular vision and an IMU, enabling it to traverse the entire US segment of the station. Features detected on a previously-built map, optical flow information,and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise.Finally, we extensively evaluate the behavior of the filter on atwo-dimensional testing surface.

Coltin, Brian↗
NASA NTRS2017

Design Considerations for Attitude State Awareness and Prevention of Entry into Unusual Attitudes

Loss of control - inflight (LOC-I) has historically represented the largest category of commercial aviation fatal accidents. A review of the worldwide transport airplane accidents (2001-2010) evinced that loss of attitude or energy state awareness was responsible for a large majority of the LOC-I events. A Commercial Aviation Safety Team (CAST) study of 18 worldwide loss-of-control accidents and incidents determined that flight crew loss of attitude awareness or energy state awareness due to lack of external visual reference cues was a significant causal factor in 17 of the 18 reviewed flights. CAST recommended that "Virtual Day-Visual Meteorological Condition" (Virtual Day-VMC) displays be developed to provide the visual cues necessary to prevent loss-of-control resulting from flight crew spatial disorientation and loss of energy state awareness. Synthetic vision or equivalent systems (SVS) were identified for a design "safety enhancement" (SE-200). Part of this SE involves the conduct of research for developing minimum aviation system performance standards (MASPS) for these flight deck display technologies to aid flight crew attitude and energy state awareness similar to that of a virtual day-VMC-like environment. This paper will describe a novel experimental approach to evaluating a flight crew's ability to maintain attitude awareness and to prevent entry into unusual attitudes across several SVS optical flow design considerations. Flight crews were subjected to compound-event scenarios designed to elicit channelized attention and startle/surprise within the crew. These high-fidelity scenarios, designed from real-world events, enable evaluation of the efficacy of SVS at improving flight crew attitude awareness to reduce the occurrence of LOC-I incidents in commercial flight operations.

Ellis, Kyle K. E.↗
NASA NTRS2018

Applications of Phase-Based Motion Processing

Image pyramids provide useful information in determining structural response at low cost using commercially available cameras. The current effort applies previous work on the complex steerable pyramid to analyze and identify imperceptible linear motions in video. Instead of implicitly computing motion spectra through phase analysis of the complex steerable pyramid and magnifying the associated motions, instead present a visual technique and the necessary software to display the phase changes of high frequency signals within video. The present technique quickly identifies regions of largest motion within a video with a single phase visualization and without the artifacts of motion magnification, but requires use of the computationally intensive Fourier transform. While Riesz pyramids present an alternative to the computationally intensive complex steerable pyramid for motion magnification, the Riesz formulation contains significant noise, and motion magnification still presents large amounts of data that cannot be quickly assessed by the human eye. Thus, user-friendly software is presented for quickly identifying structural response through optical flow and phase visualization in both Python and MATLAB.

Branch, Nicholas A.↗
NASA NTRSDate not supplied

Monitoring the Calibration of the DSCOVR EPIC Instrument’s Visible Cchannels Using MODIS and VIIRS as a Reference

The Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) satellite has a constant unique view of the sunlit disk of the Earth from the Lagrange-1 (L1) point nearly a million miles away from the Earth. Due to EPIC not having any on-board calibration systems, the ten spectral channels of EPIC must be inter-calibrated using vicarious on-orbit methods, such as ray-matching with well-calibrated low Earth orbiting satellites like the Aqua-MODIS and SNPP-VIIRS radiometers. The recently released EPIC version 3 L1B data has greatly enhanced the residual navigation errors found in prior versions, thereby decreasing the calibration uncertainty in the aforementioned ray-matching calibration techniques. The automated navigation correction used by these ray-matching methods for further improving the EPIC geolocation will be compared with a new navigation correction method utilizing optical flow between the EPIC and MODIS/VIIRS images. The DSCOVR satellite went into safe mode on June 27, 2019 due to an anomaly with the satellite’s attitude control system, but operations recommenced on March 2, 2020. The calibration of the EPIC instrument before it went into safe mode and after it resumed will be analyzed in order to investigate any potential calibration shifts or discontinuities caused by the spacecraft anomaly.

Conor Haney↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Earth Observations

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary↗
NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗
NASA NTRS2020

Automatic Feature Tracking on Small Bodies for Autonomous Approach

Abstract—The autonomous approach of a spacecraft to an asteroid or comet (a small body) relies heavily on visual feature tracking to aid in estimating relative trajectories and the properties of the small body. Feature tracking for small bodies brings several challenges, including changing lighting, poor visual texture, and a concentration of features in a small part of an image. Six existing, open-source algorithms for feature tracking were tested on a simulated dataset and compared to the ground truth in the path of features. The main finding is that none of the algorithms provide all of the desired characteristics of long feature tracks with low errors and few outliers. Instead, there is a trade-off between long feature tracks and low error. The feature-matching algorithms SIFT, and BRISK provide good error characteristics, but short feature tracks, whereas the optical flow algorithm KLT provides long feature tracks, but with many features of large error. Given the challenges in feature tracking, it is recommended to focus development on each component of a feature tracking system: detection, description, and outlier rejection.

Morrell, Benjamin J↗
NASA NTRS2001

Real-Time Optical Monitoring of Flow Kinetics and Gas Phase Reactions Under High-Pressure OMCVD Conditions

This contribution addresses the real-time optical characterization of gas flow and gas phase reactions as they play a crucial role for chemical vapor phase depositions utilizing elevated and high pressure chemical vapor deposition (HPCVD) conditions. The objectives of these experiments are to validate on the basis of results on real-time optical diagnostics process models simulation codes, and provide input parameter sets needed for analysis and control of chemical vapor deposition at elevated pressures. Access to microgravity is required to retain high pressure conditions of laminar flow, which is essential for successful acquisition and interpretation of the optical data. In this contribution, we describe the design and construction of the HPCVD system, which include access ports for various optical methods of real-time process monitoring and to analyze the initial stages of heteroepitaxy and steady-state growth in the different pressure ranges. To analyze the onset of turbulence, provisions are made for implementation of experimental methods for in-situ characterization of the nature of flow. This knowledge will be the basis for the design definition of experiments under microgravity, where gas flow conditions, gas phase and surface chemistry, might be analyzed by remote controlled real-time diagnostics tools, developed in this research project.

Dietz, N.↗
NASA NTRS2006

Transient Flow Dynamics in Optical Micro Well Involving Gas Bubbles

The Lab-On-a-Chip Application Development (LOCAD) team at NASA s Marshall Space Flight Center is utilizing Lab-On-a-Chip to support technology development specifically for Space Exploration. In this paper, we investigate the transient two-phase flow patterns in an optic well configuration with an entrapped bubble through numerical simulation. Specifically, the filling processes of a liquid inside an expanded chamber that has bubbles entrapped. Due to the back flow created by channel expansion, the entrapped bubbles tend to stay stationary at the immediate downstream of the expansion. Due to the huge difference between the gas and liquid densities, mass conservation issues associated with numerical diffusion need to be specially addressed. The results are presented in terms of the movement of the bubble through the optic well. Bubble removal strategies are developed that involve only pressure gradients across the optic well. Results show that for the bubble to be moved through the well, pressure pulsations must be utilized in order to create pressure gradients across the bubble itself.

Johnson, B.↗
NASA NTRS1984

Optical tomography for flow visualization of the density field around a revolving helicopter rotor blade

In this paper, a tomographic procedure for reconstructing the density field around a helicopter rotor blade tip from remote optical line-of-sight measurements is discussed. Numerical model studies have been carried out to investigate the influence of the number of available views, limited width viewing, and ray bending on the reconstruction. Performance is measured in terms of the mean-square error. It is found that very good reconstructions can be obtained using only a small number of views even when the width of view is smaller than the spatial extent of the object. An iterative procedure is used to correct for ray bending due to refraction associated with the sharp density gradients (shocks).

Snyder, R.↗
NASA NTRS2010

Cryogenic Flow Sensor

An acousto-optic cryogenic flow sensor (CFS) determines mass flow of cryogens for spacecraft propellant management. The CFS operates unobtrusively in a high-pressure, high-flowrate cryogenic environment to provide measurements for fluid quality as well as mass flow rate. Experimental hardware uses an optical plane-of-light (POL) to detect the onset of two-phase flow, and the presence of particles in the flow of water. Acousto-optic devices are used in laser equipment for electronic control of the intensity and position of the laser beam. Acousto-optic interaction occurs in all optical media when an acoustic wave and a laser beam are present. When an acoustic wave is launched into the optical medium, it generates a refractive index wave that behaves like a sinusoidal grating. An incident laser beam passing through this grating will diffract the laser beam into several orders. Its angular position is linearly proportional to the acoustic frequency, so that the higher the frequency, the larger the diffracted angle. If the acoustic wave is traveling in a moving fluid, the fluid velocity will affect the frequency of the traveling wave, relative to a stationary sensor. This frequency shift changes the angle of diffraction, hence, fluid velocity can be determined from the diffraction angle. The CFS acoustic Bragg grating data test indicates that it is capable of accurately determining flow from 0 to 10 meters per second. The same sensor can be used in flow velocities exceeding 100 m/s. The POL module has successfully determined the onset of two-phase flow, and can distinguish vapor bubbles from debris.

Justak, John↗
NASA NTRS2010

Fiber optic liquid mass flow sensor and method

A method and apparatus are provided for sensing the mass flow rate of a fluid flowing through a pipe. A light beam containing plural individual wavelengths is projected from one side of the pipe across the width of the pipe so as to pass through the fluid under test. Fiber optic couplers located at least two positions on the opposite side of the pipe are used to detect the light beam. A determination is then made of the relative strengths of the light beam for each wavelength at the at least two positions and based at least in part on these relative strengths, the mass flow rate of the fluid is determined.

Korman, Valentin↗
NASA NTRS1991

Instabilities of line-driven stellar winds. V - Effect of an optically thick continuum

Earlier analyses of the linear instability of line-driven stellar winds are extended to the case, relevant to Wolf-Rayet stars, in which the continuum remains optically thick well above the sonic point. It is found that an optically thick flow driven by pure scattering lines is stabilized by the drag effect of the diffuse, scattered radiation. However, even a relatively small photon destruction probability can cause a flow with continuum optical thickness much greater than 1 to remain unstable, with a given growth rate. The implications of these results for the variability characteristics of winds from Wolf-Rayet stars are briefly discussed.

Owocki, Stanley P.↗