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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Terrain tracking for lander guidance using binary phase-only spatial filters

We demonstrate the use of binary phase-only spatial filters (BPOFs) for tracking target sites through a sequence of gray-scale terrain images. The filters are demonstrated in a closed loop guidance system for a laboratory lander mockup. Images of a 3D terrain board taken by the lander's video camera are preprocessed to produce binary intensity contour maps of a simulated planetary surface. A BPOF is made from a section of the current preprocessed image centered on the exact desired landing site. After the lander has descended to a lower altitude, the BPOF is correlated with a new image. The position of the correlation peak is used in making the next filter and to guide the lander so as to recenter the landing site in the camera's view. We present testbed results of the accuracy with which a site may be tracted from orbit to landing, and the maximum scale, translation, and rotation which can be tolerated between subsequent images. Application of the results to NASA's proposed Mars Rover Sample Return (MRSR) and Mars Environmental Survey (MESUR) missions is discussed.

Reid, Max B.↗
NASA NTRSDate not supplied

Celestial Mapping System

Celestial Mapping System (CMS) is a desktop application that provides a toolkit of features for visualization and analysis of celestial bodies, beginning with a 3D lunar globe. Although there are lunar mapping products publicly available, they use old data from lunar missions, have no native desktop version, and they are mostly 2D. CMS addresses this by providing high-resolution datasets from recent lunar missions, accurate elevation data to display and read 3D terrain, and is built on top of World Wind Java library which provides a rich set of functionality and the ability to easily expand to other celestial bodies. CMS is also looking at implementing features to support situational awareness, path optimization and resource assessment, which will be of use in upcoming missions to the lunar surface. CMS utilizes geospatial tools such as QGIS and GDAL to test and modify planetary data. Other features include the ability to enable specific place names, a measurement toolbox, terrain profiler, the ability to view the moon in stereo, navigation capabilities, and an interactive visit to the Apollo landing sites which showcase 3D objects of the astronauts and lunar landers. In the world of planetary mapping, CMS aims to significantly improve upon existing functionalities, and bring new tools, that will be useful to the planetary science community and mission planners.

Kaitlyn Dickinson↗
NASA NTRSDate not supplied

Aerospace Cognitive Engineering Laboratory (ACELAB) Simulator for Electric Vertical Takeoff and Landing (eVOTL) Research and Development

A new generation of aerospace innovators are looking for ways to quickly and efficiently transport people in a safe and environmentally friendly manner. In the not-too-distant future, passengers and goods are expected to routinely fly aboard a new breed of cleaner, smarter air vehicles. This represents a new and significant challenge to the Federal Aviation Agency (FAA) which is responsible for aircraft certification, pilot licensing, operating approval and airspace integration. To help streamline this process, NASA has formulated its Advanced Air Mobility (AAM) project to provide research capabilities for development and evaluation of these new concepts and an environment where industry and regulators can work together to understand the requirements and work toward consensus standards for the new market. This paper will describe the development of the Aerospace Cognitive Engineering Lab Rapid Automation Test (ACELeRATE) simulator. ACELeRATE is an adaptable fixed-base aircraft simulator focused on the investigation of the performance and interaction of pilots and increasingly automated aircraft systems. ACELeRATE can be re-configured to support various simulation environments. The simulator includes a simple reconfigurable cockpit placed within a 10-foot spherical dome with a cluster of real-time image generators, high-resolution displays and highly realistic scenery with the surrounding digital terrain and required cultural area details (e.g., hangars, runways, ramp areas, taxiways, test range apparatus, buildings with designated rooftop landing areas, and other man-made 3D structures). This paper will also describe the various hardware and software tools employed in the ACELeRATE simulator, including engineering tools used by NASA for electric Vertical Takeoff and Landing (eVTOL) vehicle equations of motion, wind-model simulation in an urban environment, as well as the various modeling techniques and tools used to quickly generate highly realistic 3D terrain models for low level flight including urban terrain and obstacle depictions.

AAM Simulation Cockpit↗
NASA NTRS2014

Planetary Rover Localization Within Orbital Maps

This paper introduces an advanced rover localization system suitable for autonomous planetary exploration in the absence of Global Positioning System (GPS) infrastructure. Given an existing terrain map (image and elevation) obtained from satellite imagery and the images provided by the rover stereo camera system, the proposed method determines the best rover location through visual odometry, 3D terrain and hori- zon matching. The system is tested on data retrieved from a 3 km traverse of the Basalt Hills quarry in California where the GPS track is used as ground truth. Experimental results show the system presented here reduces by over 60 the localization error obtained by wheel odometry.

localization↗
NASA NTRS2004

Terrain Modelling for Immersive Visualization for the Mars Exploration Rovers

Immersive environments are being used to support mission operations at the Jet Propulsion Laboratory. This technology contributed to the Mars Pathfinder Mission in planning sorties for the Sojourner rover and is being used for the Mars Exploration Rover (MER) missions. The stereo imagery captured by the rovers is used to create 3D terrain models, which can be viewed from any angle, to provide a powerful and information rich immersive visualization experience. These technologies contributed heavily to both the mission success and the phenomenal level of public outreach achieved by Mars Pathfinder and MER. This paper will review the utilization of terrain modelling for immersive environments in support of MER.

immersive environments↗
NASA NTRS2011

Multi-Modal Image Registration and Matching for Localization of a Balloon on Titan

A solution was developed that matches visible/IR imagery aboard a balloon in Saturn's moon Titan's atmosphere to SAR (synthetic aperture radar) and visible/IR data acquired from orbit. A balloon in Titan's atmosphere must be able to localize itself autonomously both globally and with respect to local terrain. The orbital data is used to provide the balloon imagery with global context. The work is novel in applying mutual information (MI) to orbital vs. aerial data. There are unique challenges in this setting. Image offsets are much higher than in medical imaging, there is local distortion due to 3D terrain relief, and the fields of regard from orbit and from the air are quite different.

Ansar, Adnan I.↗
NASA NTRSDate not supplied

Accuracy Assessment of Two Gps Fidelity Prediction Services in Urban Terrain

Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.

Andrew Moore↗
NASA NTRSDate not supplied

Accuracy Assessment of Two GPS Fidelity Prediction Services in Urban Terrain

Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.

Andrew J. Moore↗
NASA NTRS2020

A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover’s stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides roverterrain settling, realistic slip modelling, and camera rendering capability based on the rover’s NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.

Toupet, Olivier↗
NASA NTRS2000

Simulators for the X-38/CRV Parafoil and Re-Entry Phases

The X38/CRV is unique in several aspects: it does not afford the pilot a forward view through a window and it utilizes a parafoil in the final landing flight phase. As a result of some of these unique attributes the prototype on-board displays will need to provide enhanced situation awareness to aid a crew member in supervising the onboard GNC software to select a landing site, and possible obstacle avoidance during the final flight phase. In the ESA/ESTEC visualization prototype system the scene is visualized using MultiGen OpenFlight models on high-end SGI machines. The Parafoil Guidance Navigation and Control (PGNC) algorithms are being developed as a joint NASA/ESA venture. These algorithms will be used onboard the vehicle to create nominal flight trajectories for the vehicle to follow. These same algorithms are also used as parafoil simulators in our augmented situation awareness system we are developing. The output of the parafoil simulator is displayed on a Windows-based computer running the LandForm FlightVision software. The FlightVision software is used to create the synthetic environment displays and all the necessary HUD symbology. Maps, such as aeronautical charts, as well as satellite imagery are optionally overlaid on the 3D-terrain model to provide additional situation awareness for the crew.

SITUATIONAL AWARENESS↗
NASA NTRS2004

NASA Tech Briefs, December 2004

opics include: High-Rate Digital Receiver Board; Signal Design for Improved Ranging Among Multiple Transceivers; Automated Analysis, Classification, and Display of Waveforms; Fast-Acquisition/Weak-Signal-Tracking GPS Receiver for HEO; Format for Interchange and Display of 3D Terrain Data; Program Analyzes Radar Altimeter Data; Indoor Navigation using Direction Sensor and Beacons; Software Assists in Responding to Anomalous Conditions; Software for Autonomous Spacecraft Maneuvers; WinPlot; Software for Automated Testing of Mission-Control Displays; Nanocarpets for Trapping Microscopic Particles; Precious-Metal Salt Coatings for Detecting Hydrazines; Amplifying Electrochemical Indicators; Better End-Cap Processing for Oxidation-Resistant Polyimides; Carbon-Fiber Brush Heat Exchangers; Solar-Powered Airplane with Cameras and WLAN; A Resonator for Low-Threshold Frequency Conversion; Masked Proportional Routing; Algorithm Determines Wind Speed and Direction from Venturi-Sensor Data; Feature-Identification and Data-Compression Software; Alternative Attitude Commanding and Control for Precise Spacecraft Landing; Inspecting Friction Stir Welding using Electromagnetic Probes; and Helicity in Supercritical O2/H2 and C7H16/N2 Mixing Layers.

Source record↗
NASA NTRS2012

NASA Tech Briefs, June 2012

Topics covered include: iGlobe Interactive Visualization and Analysis of Spatial Data; Broad-Bandwidth FPGA-Based Digital Polyphase Spectrometer; Small Aircraft Data Distribution System; Earth Science Datacasting v2.0; Algorithm for Compressing Time-Series Data; Onboard Science and Applications Algorithm for Hyperspectral Data Reduction; Sampling Technique for Robust Odorant Detection Based on MIT RealNose Data; Security Data Warehouse Application; Integrated Laser Characterization, Data Acquisition, and Command and Control Test System; Radiation-Hard SpaceWire/Gigabit Ethernet-Compatible Transponder; Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager; High-Voltage, Low-Power BNC Feedthrough Terminator; SpaceCube Mini; Dichroic Filter for Separating W-Band and Ka-Band; Active Mirror Predictive and Requirement Verification Software (AMP-ReVS); Navigation/Prop Software Suite; Personal Computer Transport Analysis Program; Pressure Ratio to Thermal Environments; Probabilistic Fatigue Damage Program (FATIG); ASCENT Program; JPL Genesis and Rapid Intensification Processes (GRIP) Portal; Data::Downloader; Fault Tolerance Middleware for a Multi-Core System; DspaceOgreTerrain 3D Terrain Visualization Tool; Trick Simulation Environment 07; Geometric Reasoning for Automated Planning; Water Detection Based on Color Variation; Single-Layer, All-Metal Patch Antenna Element with Wide Bandwidth; Scanning Laser Infrared Molecular Spectrometer (SLIMS); Next-Generation Microshutter Arrays for Large-Format Imaging and Spectroscopy; Detection of Carbon Monoxide Using Polymer-Composite Films with a Porphyrin-Functionalized Polypyrrole; Enhanced-Adhesion Multiwalled Carbon Nanotubes on Titanium Substrates for Stray Light Control; Three-Dimensional Porous Particles Composed of Curved, Two-Dimensional, Nano-Sized Layers for Li-Ion Batteries 23 Ultra-Lightweight; and Ultra-Lightweight Nanocomposite Foams and Sandwich Structures for Space Structure Applications.

Source record↗
NASA NTRS2013

Shuttle Topography Data Inform Solar Power Analysis

The next time you flip on a light switch, there s a chance that you could be benefitting from data originally acquired during the Space Shuttle Program. An effort spearheaded by Jet Propulsion Laboratory (JPL) and the National Geospatial-Intelligence Agency (NGA) in 2000 put together the first near-global elevation map of the Earth ever assembled, which has found use in everything from 3D terrain maps to models that inform solar power production. For the project, called the Shuttle Radar Topography Mission (SRTM), engineers at JPL designed a 60-meter mast that was fitted onto Shuttle Endeavour. Once deployed in space, an antenna attached to the end of the mast worked in combination with another antenna on the shuttle to simultaneously collect data from two perspectives. Just as having two eyes makes depth perception possible, the SRTM data sets could be combined to form an accurate picture of the Earth s surface elevations, the first hight-detail, near-global elevation map ever assembled. What made SRTM unique was not just its surface mapping capabilities but the completeness of the data it acquired. Over the course of 11 days, the shuttle orbited the Earth nearly 180 times, covering everything between the 60deg north and 54deg south latitudes, or roughly 80 percent of the world s total landmass. Of that targeted land area, 95 percent was mapped at least twice, and 24 percent was mapped at least four times. Following several years of processing, NASA released the data to the public in partnership with NGA. Robert Crippen, a member of the SRTM science team, says that the data have proven useful in a variety of fields. "Satellites have produced vast amounts of remote sensing data, which over the years have been mostly two-dimensional. But the Earth s surface is three-dimensional. Detailed topographic data give us the means to visualize and analyze remote sensing data in their natural three-dimensional structure, facilitating a greater understanding of the features and processes taking place on Earth."

Source record↗
NASA NTRS2017

The Intelligent Landing System for Safe and Precise Landing on Europa

Europa, the smallest of Jupiter’s Galilean moons, is thought to harbor a vast liquid water ocean beneath its icy crust, making it one of the most scientifically intriguing targets for a robotic surface sampling mission in our Solar System. However, autonomously landing a spacecraft safely and precisely on Europa poses unique challenges, such as very little existing high-resolution reconnaissance imagery, a surface expected to be very rough and hazardous over a wide range of scales, an extremely intense ionizing radiation environment, and very limited lander resources for mass and volume. To address these challenges, we propose a novel Intelligent Landing System (ILS) combining four Guidance, Navigation & Control (GN&C) sensing functions – velocimetry, altimetry, map-relative localization, and hazard detection – that would together enable safe and precise landing on Europa’s surface. The ILS is a smart sensor system, combining an inertial measurement unit (IMU), a monocular, passive-optical camera, and a light detection and ranging (Li-DAR) sensor with dedicated computing resources as well as an onboard 3D terrain map. The ILS leverages more than a decade of technology development from programs such as the Lander Vision System, currently baselined on the Mars 2020 mission. This paper provides a detailed description of the proposed ILS architecture and concept of operations, as well as select preliminary simulation results to assess performance and robustness.

Trawny, Nikolas↗
NASA NTRS2024

Lunar Terrain Mapping Using 3D Software and Modeling Techniques for the Glenn Research Center Communication Analysis Suite

As NASA prepares to return humans to the Moon as part of upcoming Artemis missions, engineers must examine the surface features and safety of lunar terrain at various locations. The Glenn Research Center Communication Analysis Suite (GCAS) includes visualization tools developed to aid scientists in this quest to comprehend lunar terrain data, as well as spatial communication information. Visual information and understanding will be highly useful to engineers as they use the GCAS to compare the topography of regions of interest for the Artemis III and future space travel missions. Not only can three-dimensional (3D) visualization display the intricacies of the lunar terrain, but it can also demonstrate relationships such as the presence or absence of communication links between Earth ground stations, lunar crew members, and rovers, and the presence of shadows on the lunar surface. In the future, the GCAS has the potential to support the planning of other space exploration missions as well. The emerging possibility of incorporating more planetary bodies becomes especially important as NASA plans not only to return to the Moon but to press onward to Mars.

Visualization↗
NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗
NASA NTRS2012

Large Terrain Continuous Level of Detail 3D Visualization Tool

This software solved the problem of displaying terrains that are usually too large to be displayed on standard workstations in real time. The software can visualize terrain data sets composed of billions of vertices, and can display these data sets at greater than 30 frames per second. The Large Terrain Continuous Level of Detail 3D Visualization Tool allows large terrains, which can be composed of billions of vertices, to be visualized in real time. It utilizes a continuous level of detail technique called clipmapping to support this. It offloads much of the work involved in breaking up the terrain into levels of details onto the GPU (graphics processing unit) for faster processing.

Myint, Steven↗
NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗