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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Development of a machine vision system for automated structural assembly

Research is being conducted at the LaRC to develop a telerobotic assembly system designed to construct large space truss structures. This research program was initiated within the past several years, and a ground-based test-bed was developed to evaluate and expand the state of the art. Test-bed operations currently use predetermined ('taught') points for truss structural assembly. Total dependence on the use of taught points for joint receptacle capture and strut installation is neither robust nor reliable enough for space operations. Therefore, a machine vision sensor guidance system is being developed to locate and guide the robot to a passive target mounted on the truss joint receptacle. The vision system hardware includes a miniature video camera, passive targets mounted on the joint receptacles, target illumination hardware, and an image processing system. Discrimination of the target from background clutter is accomplished through standard digital processing techniques. Once the target is identified, a pose estimation algorithm is invoked to determine the location, in three-dimensional space, of the target relative to the robots end-effector. Preliminary test results of the vision system in the Automated Structural Assembly Laboratory with a range of lighting and background conditions indicate that it is fully capable of successfully identifying joint receptacle targets throughout the required operational range. Controlled optical bench test results indicate that the system can also provide the pose estimation accuracy to define the target position.

Sydow, P. Daniel↗
NASA NTRS2006

Estimating Temperature Retrieval Accuracy Associated With Thermal Band Spatial Resolution Requirements for Center Pivot Irrigation Monitoring and Management

This study explores the use of synthetic thermal center pivot irrigation scenes to estimate temperature retrieval accuracy for thermal remote sensed data, such as data acquired from current and proposed Landsat-like thermal systems. Center pivot irrigation is a common practice in the western United States and in other parts of the world where water resources are scarce. Wide-area ET (evapotranspiration) estimates and reliable water management decisions depend on accurate temperature information retrieval from remotely sensed data. Spatial resolution, sensor noise, and the temperature step between a field and its surrounding area impose limits on the ability to retrieve temperature information. Spatial resolution is an interrelationship between GSD (ground sample distance) and a measure of image sharpness, such as edge response or edge slope. Edge response and edge slope are intuitive, and direct measures of spatial resolution are easier to visualize and estimate than the more common Modulation Transfer Function or Point Spread Function. For these reasons, recent data specifications, such as those for the LDCM (Landsat Data Continuity Mission), have used GSD and edge response to specify spatial resolution. For this study, we have defined a 400-800 m diameter center pivot irrigation area with a large 25 K temperature step associated with a 300 K well-watered field surrounded by an infinite 325 K dry area. In this context, we defined the benchmark problem as an easily modeled, highly common stressing case. By parametrically varying GSD (30-240 m) and edge slope, we determined the number of pixels and field area fraction that meet a given temperature accuracy estimate for 400-m, 600-m, and 800-m diameter field sizes. Results of this project will help assess the utility of proposed specifications for the LDCM and other future thermal remote sensing missions and for water resource management.

Ryan, Robert E.↗
NASA NTRS2009

Use of the Delay-Tolerant Networking Bundle Protocol from Space

The Disaster Monitoring Constellation (DMC), constructed by Survey Satellite Technology Ltd (SSTL), is a multisatellite Earth-imaging low-Earth-orbit sensor network where captured image swaths are stored onboard each satellite and later downloaded from the satellite payloads to a ground station. Store-and-forward of images with capture and later download gives each satellite the characteristics of a node in a Delay/Disruption Tolerant Network (DTN). Originally developed for the Interplanetary Internet, DTNs are now under investigation in an Internet Research Task Force (IRTF) DTN research group (RG), which has developed a bundle architecture and protocol. The DMC is currently unique in its adoption of the Internet Protocol (IP) for its imaging payloads and for satellite command and control, based around reuse of commercial networking and link protocols. These satellites use of IP has enabled earlier experiments with the Cisco router in Low Earth Orbit (CLEO) onboard the constellation's UK-DMC satellite. Earth images are downloaded from the satellites using a custom IPbased high-speed transfer protocol developed by SSTL, Saratoga, which tolerates unusual link environments. Saratoga has been documented in the Internet Engineering Task Force (IETF) for wider adoption. We experiment with use of DTNRG bundle concepts onboard the UKDMC satellite, by examining how Saratoga can be used as a DTN convergence layer to carry the DTNRG Bundle Protocol, so that sensor images can be delivered to ground stations and beyond as bundles. This is the first successful use of the DTNRG Bundle Protocol in a space environment. We use our practical experience to examine the strengths and weaknesses of the Bundle Protocol for DTN use, paying attention to fragmentation, custody transfer, and reliability issues.

Wood, Lloyd↗
NASA NTRS2019

LANDSAT 9: Mission Status and Prelaunch Instrument Performance Characterization and Calibration

Landsat 9 is currently under development as a joint effort between NASA and the United States Geological Survey (USGS). Landsat 9 is largely a rebuild of Landsat 8 and has the same two sensors, an Operational Land Imager (OLI) and a Thermal InfraRed Sensor(TIRS). The OLI-2 on Landsat 9, being built by Ball Aerospace, has completed its pre-launch characterization and calibration and is scheduled to be delivered in the summer of 2019. The TIRS-2, with improved stray light performance and increased redundancy for improved reliability, is being built by Goddard Space Flight Center. TIRS-2is undergoing testing through Spring 2019 and is also scheduled for summer2019 delivery. Several improvements to the characterization of both instruments have been incorporated into the testing plan, including improved spectral and radiometric characterization. The instruments will then be integrated onto the spacecraft being built by Northrop Grumman Innovation Systems (NGIS). The mission is targeted to launch as early as December 2020 on an Atlas V.

Landsat↗
DOE OSTI2026

SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery

Precise registration of high-resolution synthetic aperture radar (SAR) and optical imagery is necessary for realizing the full potential and benefits of multimodal image analysis. However, two significant challenges presently exist. First, there is a lack of annotated datasets and benchmarks available for high-resolution SAR–optical image registration. Second, an assessment of efficient and reliable image registration methods that can precisely align these modalities is lacking. Here, we present a holistic description of the SpaceNet 9 Challenge and its results. We present a description of the dataset and baseline algorithm along with the results of the challenge, including a description of the winning algorithms. We release the SpaceNet 9 dataset along with open-sourcing the winning algorithms and baseline. The objective of SpaceNet 9 was to compute a dense displacement map that indicates the shift needed to align pixels in an optical image to the pixels in a SAR image. The challenge launched in April 2025 and was active for approximately two months. The top five solutions reduced image alignment error from approximately 34 m to under 13 m for public and private test data, with the best results obtaining a registration error of only 8.5 and 6.7 m on the public testing and private testing dataset, respectively. Usage of pretrained image matching models, robust outlier rejection with RANSAC, and estimating local displacement were common among the top solutions. The results of this challenge provide insight into high-resolution SAR–optical image registration and offer opportunities for future benchmarking in this domain. The baseline algorithm, winning solutions, and datasets are available at https://spacenet.ai/sn9-challenge/.

benchmark datasets↗
NASA NTRSDate not supplied

Demonstration of Lidar Sensors for Precision Safe Landing on Planetary Bodies

Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.

Precision Landing↗
NASA NTRS2013

Interagency Collaborators Develop and Implement ForWarn, a National, Near Real Time Forest Monitoring Tool

ForWarn is a satellite-based forest monitoring tool that is being used to detect and monitor disturbances to forest conditions and forest health. It has been developed through the synergistic efforts, capabilities and contributions of four federal agencies, including the US Forest Service Eastern Forest and Western Wildland Environmental Threat Assessment Centers, NASA Stennis Space Center (SSC), Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) and US Geological Survey Earth (USGS) Earth Research Observation System (EROS), as well as university partners, including the University of North Carolina Asheville's National Environmental Modeling and Analysis Center (NEMAC). This multi-organizational partnership is key in producing a unique, path finding near real-time forest monitoring system that is now used by many federal, state and local government end-users. Such a system could not have been produced so effectively by any of these groups on their own. The forests of the United States provide many societal values and benefits, ranging from ecological, economic, cultural, to recreational. Therefore, providing a reliable and dependable forest and other wildland monitoring system is important to ensure the continued health, productivity, sustainability and prudent use of our Nation's forests and forest resources. ForWarn does this by producing current health indicator maps of our nation's forests based on satellite data from NASA's MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Such a capability can provide noteworthy value, cost savings and significant impact at state and local government levels because at those levels of government, once disturbances are evident and cause negative impacts, a response must be carried out. The observations that a monitoring system like ForWarn provide, can also contribute to a much broader-scale understanding of vegetation disturbances.

Underwood, Lauren↗
NASA NTRS1994

Object recognition and pose estimation of planar objects from range data

The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and noise.

Pendleton, Thomas W.↗
NASA NTRS2012

Safehold Attitude Determination Approach for GPM

Spacecraft sating designs generally have minimal goals with loose pointing requirements. Safe pointing orientations for three-axis stabilized spacecraft are usually chosen to put the spacecraft into a thermally safe and power-positive orientation. In addition, safe mode designs are required to be simple and reliable. This simplicity lends itself to the usage of analog sun sensors, because digital sun sensors will add unwanted complexity to the safe hold mode. The Global Precipitation Measurement (GPM) Mission Core Observatory will launch into lower earth orbit (LEO) at an inclination of 65 degrees. The GPM instrument suite consists of an active radar system and a passive microwave imager to provide the next-generation global observations of rain and snow. The complexity and precision of these instruments along with the operational constraints of the mission result in tight pointing requirements during all phases of the mission. To ensure the instruments are not damaged during spacecraft safing, thermal constraints dictate that the solar pointing orientation must be maintained to better than 6.5 degrees. This requirement is outside the capabilities of a typical analog sun sensor suite, primarily due to the effects of Earth's albedo. To ensure mission success, a new analog sensor, along with the appropriate algorithms, is needed. This paper discusses the design issues involving albedo effects on spacecraft pointing and the development of a simple, low-cost analog sensor and algorithm that will address the needs of the GPM mission. In addition, the algorithms are designed to be easily integrated into the existing attitude determination software by using common interfaces. The sensor design is based on a heritage, commercial off-the-shelf analog sun sensors with a limited field-of-view to reduce the effects of Earth's albedo. High fidelity simulation results are presented that demonstrate the efficacy of the design.

Fitzpatrick, Henry↗
NASA NTRS1995

Traffic Monitor

Intelligent Vision Systems, Inc. (InVision) needed image acquisition technology that was reliable in bad weather for its TDS-200 Traffic Detection System. InVision researchers used information from NASA Tech Briefs and assistance from Johnson Space Center to finish the system. The NASA technology used was developed for Earth-observing imaging satellites: charge coupled devices, in which silicon chips convert light directly into electronic or digital images. The TDS-200 consists of sensors mounted above traffic on poles or span wires, enabling two sensors to view an intersection; a "swing and sway" feature to compensate for movement of the sensors; a combination of electronic shutter and gain control; and sensor output to an image digital signal processor, still frame video and optionally live video.

Source record↗
NASA NTRS1988

Advanced solidstate array spectroradiometer - Sensor and calibration improvements

The advanced solid-state array spectroradiometer (ASAS), an airborne imaging spectrometer with 30 spectral channels extending from 450-880 nm, is described. Modifications to the sensor mounting technique changed the ASAS from a fixed nadir viewing instrument to a sensor capable of multiple direction observations of surface bidirectional reflectance distribution functions. The system was flown on the NASA Ames C130 aircraft to obtain surface bidirectional reflectance measurements of vegetation; it demonstrated excellent reliability and radiometric stability.

Huegel, Frederick G.↗
NASA NTRS1991

Collision avoidance sensor skin

The objective was to totally eliminate the possibility of a robot (or any mechanism for that matter) inducing a collision in space operations. We were particularly concerned that human beings were safe under all circumstances. This was apparently accomplished, and it is shown that GSFC has a system that is ready for space qualification and flight. However, it soon became apparent that much more could be accomplished with this technology. Payloads could be made invulnerable to collision avoidance and the blind spots behind them eliminated. This could be accomplished by a simple, non-imaging set of 'Capaciflector' sensors on each payload. It also is evident that this system could be used to align and dock the system with a wide margin of safety. Throughout, lighting problems could be ignored, and unexpected events and modeling errors taken in stride. At the same time, computational requirements would be reduced. This can be done in a simple, rugged, reliable manner that will not disturb the form factor of space systems. It will be practical for space applications. The lab experiments indicate we are well on the way to accomplishing this. Still, the research trail goes deeper. It now appears that the sensors can be extended to end effectors to provide precontact information and make robot docking (or any docking connection) very smooth, with minimal loads impacted back into the mating structures. This type of ability would be a major step forward in basic control techniques in space. There are, however, baseline and restructuring issues to be tackled. The payloads must get power and signals to them from the robot or from the astronaut servicing tool. This requires a standard electromechanical interface. Any of several could be used. The GSFC prototype shown in this presentation is a good one. Sensors with their attendant electronics must be added to the payloads, end effectors, and robot arms and integrated into the system.

Source record↗
NASA NTRS1988

Plant health sensing

If plants are to be used as a food source for long term space missions, they must be grown in a stable environment where the health of the crops is continuously monitored. The sensor(s) to be used should detect any diseases or health problems before irreversible damage occurs. The method of analysis must be nondestructive and provide instantaneous information on the condition of the crop. In addition, the sensor(s) must be able to function in microgravity. This first semester, the plant health and disease sensing group concentrated on researching and consulting experts in many fields in attempts to find reliable plant health indicators. Once several indicators were found, technologies that could detect them were investigated. Eventually the three methods chosen to be implemented next semester were stimulus response monitoring, video image processing and chlorophyll level detection. Most of the other technologies investigated this semester are discussed here. They were rejected for various reasons but are included in the report because NASA may wish to consider pursuing them in the future.

Manukian, Ara↗
NASA NTRS2024

Expanded Signal to Noise Ratio Estimates for Validating Next-Generation Satellite Sensors in Oceanic, Coastal, and Inland Waters

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (R rs ) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and R rs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited.

signal-to-noise ratio↗
NASA NTRS2022

Drought Monitoring with Drones: A Hundred Fields at a Time

Drought frequency and severity are likely to increase due to global warming. Droughts already have a substantial negative influence on agriculture and the economy and finding ways to reduce their effects could have a monumental impact. Although NASA already has satellites deployed to collect drought data, these satellites are more for global drought indexing than local. To alleviate droughts on a local level, this paper proposes the use of drones to map soil moisture, plant health, and other drought indicators. The proposed drone design is a fixed-wing UAV equipped with a hyperspectral camera, a LiDAR sensor, and an array of weather sensors. These tools will permit it to reliably capture the necessary data to enhance suggestions on improving drought management practices. The collected data from the drones will be deployable in many ways, including for agricultural and non-agricultural applications. The hyperspectral camera has applications in monitoring the health of crops within a field to direct relief measures to the crops most in need. Thermal and LiDAR imaging can be deployed for locating leaks, predicting shortages of water bodies, and determining a field’s water needs. To implement a drought-monitoring drone, the recommended steps include building a prototype drone design that is equipped with the outlined instruments. The prototype drone could then be deployed to collect training data to guide a neural network that would provide interpretations and predictions from the data for users. After the prototype design is iterated upon, it will be ready for deployment and inform water management methods to serve the world in our battle against drought.

climate change↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2011

Optical-Path-Difference Linear Mechanism for the Panchromatic Fourier Transform Spectrometer

A document discusses a mechanism that uses flex-pivots in a parallelogram arrangement to provide frictionless motion with an unlimited lifetime. A voicecoil actuator drives the parallelogram over the required 5-cm travel. An optical position sensor provides feedback for a servo loop that keeps the velocity within 1 percent of expected value. Residual tip/tilt error is compensated for by a piezo actuator that drives the interferometer mirror. This mechanism builds on previous work that targeted ground-based measurements. The main novelty aspects include cryogenic and vacuum operation, high reliability for spaceflight, compactness of the design, optical layout compatible with the needs of an imaging FTS (i.e. wide overall field-of-view), and mirror optical coatings to cover very broad wavelength range (i.e., 0.26 to 15 m).

Blavier, Jean-Francois L.↗
NASA NTRS1994

A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included.

Kasturi, Rangachar↗