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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Mobile Autonomous Humanoid Assistant

A mobile autonomous humanoid robot is assisting human co-workers at the Johnson Space Center with tool handling tasks. This robot combines the upper body of the National Aeronautics and Space Administration (NASA)/Defense Advanced Research Projects Agency (DARPA) Robonaut system with a Segway(TradeMark) Robotic Mobility Platform yielding a dexterous, maneuverable humanoid perfect for aiding human co-workers in a range of environments. This system uses stereo vision to locate human team mates and tools and a navigation system that uses laser range and vision data to follow humans while avoiding obstacles. Tactile sensors provide information to grasping algorithms for efficient tool exchanges. The autonomous architecture utilizes these pre-programmed skills to form human assistant behaviors. The initial behavior demonstrates a robust capability to assist a human by acquiring a tool from a remotely located individual and then following the human in a cluttered environment with the tool for future use.

Diftler, M. A.↗
NASA NTRS2008

Calibrating the Helium Pressurization System for the Space Shuttle Liquid-Hydrogen Tank

Analysis of the results from the STS-114 tanking tests and subsequent launch called into question existing thermal and mass models of helium pressurization of the liquid hydrogen tank. This hydrogen tank, which makes up the bottom two-thirds of the External Tank, is pressurized prior to launch to avoid cavitation in the Shuttle Main Engine pumps. At about 2 minutes prior to launch, the main vent valve is closed, and pressurized helium flows into the tank ullage space to achieve set point pressure. As the helium gas cools, its pressure drops, calling for additional helium. Subsequent helium flows are provided in short, timed pulses. The number of pulses is taken as a rough leak indicator. An analysis of thermal models by Marshall Space Flight Center showed considerable uncertainty in the pressure-versus-time behavior of the helium ullage space and the ability to predict the number of pulses normally expected. Kennedy Space Center proposed to calibrate the dime-sized orifice, which together with valves, controls the helium flow quantity (Figure 1). Pressure and temperature sensors were installed to provide upstream and downstream measurements necessary to compute flow rate based on the orifice discharge coefficient. An assessment of flow testing with helium indicated an extremely costly use of this critical resource. In order to reduce costs, we proposed removing the orifices from each Mobile Launcher Platform (MLP) and asking Colorado Engineering Experiment Station Inc. (CEESI) to calibrate the flow. CEESI has a high-pressure air flow system with traceable flow meters capable of handling the large flow rates. However, literature research indicated that square-edged orifices of small diameters often exhibit significant hysteresis and nonrepeatability in the vicinity of choked or sonic flow. Fortunately, the MLP orifices behaved relatively well in testing (Figure 2). Using curve fitting of the air-flow data, in conjunction with ASME orifice modeling equations, a method of relating the helium mass flow to measured air flow data was obtained. This analysis showed that the highest uncertainty in flow occurred in the vicinity of the choking pressure ratio, as would be expected. In addition, analysis of typical flow pulses showed that most of the helium flow occurred either well below or well above this uncertain area. The final result is the ability to provide postlaunch estimates of helium mass flows that are within 1.5 percent of the actual value.

Source record↗
NASA NTRS2014

Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform

Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.

Prognosis↗
NASA NTRSDate not supplied

Enhancing In-Flight Structural Health Monitoring of Vertical Lift Vehicles Operating in an Urban Environment

In-situ airframe sensors have long been considered a potential solution for structural health monitoring that could change the design, certification, operation and maintenance paradigms of flight vehicles. In this approach, large networks of sensors covering the entire, or most of, an airframe throughout its operational lifetime would support real-time decisions on airworthiness and obviate the need to overbuild components or perform multiple cycles of structural qualification testing and inspections. This concept would go beyond the current practice of placing select sensors in strategic locations or relying on such sensors only during airframe qualification flights and inspections. For vehicles in the emerging urban air mobility space, reducing weight associated with overbuilds and shortening down-time associated with inspections are critical for improving safety and affordability. In practice, the wide-scale use of in-situ sensors as primary assurance for structural health has not been demonstrated to be feasible or the best solution. A sensor integration testbed was developed as a platform to evaluate the potential of multiple sensor types to enable decision making on airworthiness. We report on the initial runs of this testbed with multiple sensors attached to a common test article. Metal foil strain gauges, eddy current, fiber optic, guided wave (acoustic emission and ultrasonic)and carbon nanotube roving sensors were affixed to the test article. Baseline as well as post damage initiation and fatiguing data are presented and discussed. Despite the relative simplicity of the test article, the interpretation of the as-captured test data was generally not conclusive or did not have wide enough coverage. This result emphasizes the challenges and current limitations both in testing and the practical broad application of embedded sensors as the determinative elements in critical decision making on wide-scale structural health.

Vertical Lift Vehicles↗
NASA NTRSDate not supplied

Enhancing In-Flight Structural Health Monitoring of Vertical Lift Vehicles Operating in an Urban Environment

In-situ airframe sensors have long been considered a potential solution for structural health monitoring that could change the design, certification, operation, and maintenance paradigms of flight vehicles. In this approach, large networks of sensors covering the entire, or most of, an airframe throughout its operational lifetime would support real-time decisions on airworthiness and obviate the need to overbuild components or perform multiple cycles of structural qualification testing and inspections. This concept would go beyond the current practice of placing select sensors in strategic locations or relying on such sensors only during airframe qualification flights and inspections. For vehicles in the emerging urban air mobility space, reducing weight associated with overbuilds and shortening down-time associated with inspections are critical for improving safety and affordability. In practice, the wide-scale use of in-situ sensors as primary assurance for structural health has not been demonstrated to be feasible or the best solution. A sensor integration testbed was developed as a platform to evaluate the potential of multiple sensor types to enable decision making on airworthiness. We report on the initial runs of this testbed with multiple sensors attached to a common test article. Metal foil strain gauges, eddy current, fiber optic, guided wave (acoustic emission and ultrasonic) and carbon nanotube roving sensors were affixed to the test article. Baseline as well as post damage initiation and fatiguing data are presented and discussed. Despite the relative simplicity of the test article, the interpretation of the as-captured test data was generally not conclusive or did not have wide enough coverage. This result emphasizes the challenges and current limitations both in testing and the practical broad application of embedded sensors as the determinative elements in critical decision making on wide-scale structural health.

Vertical Lift Vehicles↗
NASA NTRSDate not supplied

Sensing Small Uncrewed Aerial Vehicles with Distributed Radars for Advanced Air Mobility Surveillance

In the context of Advanced and Urban Air Mobility major attention is being reserved to the development of sensing strategies for small Uncrewed Aerial Vehicles (sUAVs) to enable their safe operations in and around urban areas. Such strategies should rely on non-cooperative distributed sensors to strengthen the surveillance solution towards the unreliability of Global Navigation Satellite System (GNSS) positioning information, which is typical for low-altitude-flying platforms in urban regions, and increase the monitored airspace volume. To this aim, this paper proposes a fusion solution for a network of distributed ground-based radars, which can be exploited to not only increase the coverage over large airspace volumes but also improve the overall detectability and traceability of sUAVs by leveraging on multiple views over the same area. The solution exploits a centralized fusion scheme in which measurements collected by each radar are shared with a Fusion Center where Kalman Filtering is exploited to build a unique, fused track. Tests conducted on experimental data collected using two sUAVs as flying targets and three distributed radars showed that the proposed solution can produce an increase in coverage from about 20 % (single radar configuration) to about 80 % of the targets’ flight path, as well as a finer accuracy yielding meter and meter-per-second root mean square error values on position and velocity components.

Federica Vitiello↗
NASA NTRS2014

Mobilization Protocols for Hybrid Sensors for Environmental AOP Sampling (HySEAS) Observations

The protocols presented here enable the proper mobilization of the latest-generation instruments for measuring the apparent optical properties (AOPs) of aquatic ecosystems. The protocols are designed for the Hybrid Sensors for Environmental AOP Sampling (HySEAS) class of instruments, but are applicable to the community of practice for AOP measurements. The protocols are organized into eleven sections beyond an introductory overview: a) cables and connectors, b) HySEAS instruments, c) platform preparation, d) instrument installation, e) cable installation, f) test deployment, g) test recovery, h) maintenance, i) shipping, j) storage, and k) smallboat operations. Each section concentrates on documenting how to prevent the most likely faults, remedy them should they occur, and accomplishing both with the proper application of a modest set of useful tools. Within the twelve sections, there are Socratic exercises to stimulate thought, and the answers to these exercises appear in Appendix A. Frequently asked questions (FAQs) are summarized in a separate section after the answers to the exercises in Appendix B. For practitioners unfamiliar with the nautical terms used throughout this document plus others likely encountered at sea, an abbreviated dictionary of nautical terms appears in Appendix C. An abbreviated dictionary of radiotelephone terms is presented in Appendix D. To ensure familiarity with many of the tools that are presented, Appendix E provides a description of the tools alongside a thumbnail picture. Abbreviated deployment checklists and cable diagrams are provided in Appendix F. The document concludes with an acknowledgments section, a glossary of acronyms, a definition of symbols, and a list of references.

Mobilizarion↗
NASA NTRS2015

Robonaut 2 - Building a Robot on the International Space Station

In 2010, the Robonaut Project embarked on a multi‐phase mission to perform technology demonstrations on‐board the International Space Station (ISS), showcasing state of the art robotics technologies through the use of Robonaut 2 (R2). This phased approach implements a strategy that allows for the use of ISS as a test bed during early development to both demonstrate capability and test technology while still making advancements in the earth based laboratories for future testing and operations in space. While R2 was performing experimental trials onboard the ISS during the first phase, engineers were actively designing for Phase 2, Intra‐Vehicular Activity (IVA) Mobility, that utilizes a set of zero‐g climbing legs outfitted with grippers to grasp handrails and seat tracks. In addition to affixing the new climbing legs to the existing R2 torso, it became clear that upgrades to the torso to both physically accommodate the climbing legs and to expand processing power and capabilities of the robot were required. In addition to these upgrades, a new safety architecture was also implemented in order to account for the expanded capabilities of the robot. The IVA climbing legs not only needed to attach structurally to the R2 torso on ISS, but also required power and data connections that did not exist in the upper body. The climbing legs were outfitted with a blind mate adapter and coarse alignment guides for easy installation, but the upper body required extensive rewiring to accommodate the power and data connections. This was achieved by mounting a custom adapter plate to the torso and routing the additional wiring through the waist joint to connect to the new set of processors. In addition to the power and data channels, the integrated unit also required updated electronics boards, additional sensors and updated processors to accommodate a new operating system, software platform, and custom control system. In order to perform the unprecedented task of building a robot in space, extensive practice sessions and meticulous procedures were required. Since crew training time is at a premium, the R2 team took a skills‐based training approach to ensure the astronauts were proficient with a basic skill set while refining the detailed procedures over several practice sessions and simulations. In addition to the crew activities, meticulous ground procedures were required in order to upgrade firmware on the upper body motor drivers. The new firmware for the IVA mobility unit needed to be deployed using the old software system. This also provided an opportunity to upgrade the upper body joints with new software and allowed for limited insight into the success of the updates. Complete verification that the updated firmware was successfully loaded was not confirmed until the rewiring of the upper body torso was complete.

Diftler, Myron↗
NASA NTRSDate not supplied

Visual and Inertial Datasets for an eVTOL Aircraft Approach and Landing Scenario

A National Aeronautics and Space Administration (NASA) project developing computer vision algorithms for autonomous flight is producing real-world datasets with cameras mounted on aircraft. In related domains, such as autonomous driving, open datasets are key to innovation and advancement in computer vision and autonomous perception for future Advanced Air Mobility (AAM) operations. Few vision datasets, however, are publicly available in the aviation context. This paper introduces preliminary datasets containing several examples of approach and landing scenarios. The platform aircraft include a multirotor small unmanned aerial system (sUAS) and a crewed helicopter as surrogates for future electric vertical take-off and landing (eVTOL) aircraft. The dataset provides video imagery with associated inertial navigation system-global positioning system (INS-GPS) position and attitude estimates and other sensors. Surveyed locations of the visual features of the landing area are included. This dataset is the first to be released in an ongoing effort to collect and share large, diverse datasets relevant to autonomous aviation; community critique that can inform and improve future flight campaigns is welcome.

Nelson Brown↗
NASA NTRS2023

NASA's Center Innovation Fund (CIF) Internal Research and Development (IRAD) Handling Qualities Study on the Mikrolar Motion Platform Test Report

In order to support the National Aeronautics and Space Administration’s (NASA) Extravehicular Activity and Surface Mobility Program (EHP), system engineers, designers, and researchers embarked on a yearlong challenge to develop a new lunar rover-based motion table simulator to study handling qualities of lunar rovers in the lunar South Pole region, support development research for any lunar rover vendor, and provide a lunar training capability for future lunar/planetary astronauts. In 1989, the United Kingdom’s Royal Air Force Institute of Aviation Medicine (RAF-IAM) stated that motion platforms are the only simulation devices capable of fully stimulating the body motion sensors. They confirmed that motion platforms can impart accelerations to the whole body and therefore exercise the automatic motion feedback-loop that operators are used to. With both visual and motion cues handling the vehicle becomes more realistic. Strachan (2019) con-firms motion cueing from a well set-up motion platform has been found to be important especially in conditions such as night or reduced visibility where motion cues may be more relied upon

Motion Table↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRSDate not supplied

Assessing Performance of Radar and Visual Sensing Techniques for Ground-To-Air Surveillance in Advanced Air Mobility

The safe integration of Unmanned Aircraft Vehicles (UAV) within the civil airspace is of great interest to NASA’s Advanced Air Mobility project, which envisions high density of operations in and around urban areas that include both UAV and AAM aircraft. To enable safe autonomous operations of both platforms, reliable airspace surveillance strategies must be designed and experimentally validated in relevant scenarios, where multiple small UAV operate flying in low altitude conditions. An example of such a scenario is described in this paper which provides performance assessment of various sensing strategies experimentally tested during flight campaigns with four UAV completing simultaneous missions from vertiports. Such campaigns are performed by the High Density Vertiplex subproject which assesses a prototype of Urban Air Mobility ecosystem. For the purposes of this work, the flights are observed from multiple sensing nodes each with radar and camera sensors. The visual detection and tracking algorithms achieved 96.1% to 99.9% average tracking coverage of the UAV above the horizon, reaching detection ranges larger than 1 kilometer for octocopter. Radar-based tracking shows a lower coverage mainly due to ground clutter removal challenges but provides comparable detection ranges and meter-level range accuracy.

Federica Vitiello↗
NASA NTRSDate not supplied

Comparing Low-Cost Sensors with Ground-Based and Airborne In-Situ and Column Observations of NO2 and HCHO during the FRAPPE Field Campaign in Colorado, USA

Even in the presence of more reliable air quality tools, low-cost sensors have the benefit of recording data on highly localized spatial and temporal scales, allowing for multiple measurements within a single satellite pixel and on pixel boundaries. However, they are less accurate than their regulatory-grade counterparts, requiring regular co-locations with accepted instruments to ensure their validity. Thus, the addition of low-cost sensors to a field campaign – where reference-grade air quality instruments are abundant – not only provides ample opportunities for low-cost sensor co-location and calibration, but also allows the low-cost instruments to be used for sub-pixel validation, covering more surface area than the regulatory instruments alone with a network of sensors. During the summer of 2014, our low-cost sensor network was deployed during the Front Range Air Pollution and Photochemistry Experiment (FRAPPÉ) campaign conducted to sample the composition of air at and above ground level in northeastern Colorado, USA. The low-cost sensor platform included a suite of gas-phase sensors, notably NO2 and two generalized volatile organic compound (VOC) sensors, which were leveraged together to quantify speciated hydrocarbons such as formaldehyde. These key pollutants were chosen for their impacts on human health and climate change, as well as their inclusion on the TEMPO satellite launching this year. Airborne campaign measurements included slant column optical observations of formaldehyde (HCHO), nitrogen dioxide (NO2), and ozone (O3). Myriad additional in-situ instruments described chemical composition up to approximately 5 km above surface level. Ground-based instrumentation included both stationary and mobile regulatory-grade instruments, which were used for sensor calibration. Machine learning techniques such as artificial neural networks (ANNs) were used to match the low-cost signals to that of the reference-grade instruments. Here, we compare calibrated low-cost sensor data collected at ground level in a variety of locations along Colorado’s Front Range to various data sources from the FRAPPÉ campaign to better understand how well airborne and regulatory ground-based measurements can be extrapolated to other locations. Further, as the slant column measurements act as satellite simulators, we explore how low-cost instruments can be used for satellite validation purposes. Comparisons among these different data types also have important implications in data fusion.

Kristen Okorn↗
NASA NTRS1990

Airborne/spaceborne radar - Panel report

Mobile radar platforms can provide unique observations of geophysical phenomena for scientific research and for operational applications such as reconnaissance and flight safety. Airborne or spaceborne radar can provide: (1) observations where it is often impractical or too costly to place a ground-based radar, (2) surveillance over a large area or over a long time period when the system of interest is moving, (3) the ability to adjust viewing angles to minimize uncertainties due to viewing geometry, for example, along a dual-Doppler baseline, (4) measurements to complement coincident observations from other remote and in situ sensors, and (5) evaluation of ground surface properties and their impact on the evolution of the boundary-layer and their role in storm systems, hydrology, and earth surface radiation balance. However, advances in signal processing, antenna design, power amplifiers, and other critical system components will be required if radar measurements from airborne and spaceborne platforms are to become a reality.

Jorgensen, David P.↗
NASA NTRS2008

The Summer of Hydrogen

Ground crew veterans at Kennedy Space Center still talk about what they call "the summer of hydrogen"-the long, frustrating months in 1990 when the shuttle fleet was grounded by an elusive hydrogen leak that foiled our efforts to fill the orbiter's external fuel tank. Columbia (STS-35) was on Launch Pad A for a scheduled May 30 launch when we discovered the hydrogen leak during - tanking. The external fuel tank is loaded through the orbiter. Liquid hydrogen flows through a 17-inch umbilical between the orbiter and the tank. During fueling, we purge the aft fuselage with gaseous nitrogen to reduce the risk of fire, and we have a leak-detection system in the mobile launch platform, which samples (via tygon tubing) the atmosphere in and around the vehicle, drawing it down to a mass spectrometer that analyzes its composition. When we progressed to the stage of tanking where liquid hydrogen flows through the vehicle, the concentration of hydrogen approached four percent-the limit above which it would be dangerously flammable. We had a leak. We did everything we could think of to find it, and the contractor who supplied the flight hardware was there every day, working alongside us. We did tanking tests, which involved instrumenting the suspected leak sources, and cryo-loaded the external tank to try to isolate precisely where the leak originated. We switched out umbilicals; we replaced the seals between the umbilical and the orbiter. We inspected the seals microscopically and found no flaws. We replaced the recirculation pumps, and we found and replaced a damaged teflon seal in a main propulsion system detent cover, which holds the prevalve-the main valve supplying hydrogen to Space Shuttle Main Engine 3 -in the open position. The seal passed leak tests at ambient temperature but leaked when cryogenic temperatures were applied. We added new leak sensors-up to twenty at a time and tried to be methodical in our placements to narrow down the possible sources of the problem. We even switched orbiters, sending Columbia back to the Vehicle Assembly Building and bringing out Atlantis, scheduled to fly as STS-38. Two shuttles on their mobile launchers passing in the night was a majestic sight, but not one you want to see if you're trying to get an orbiter launched. None of this told us where the leak was, or if we were dealing with more than one leak source.

Weber, Philip↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRSDate not supplied

The Dark Target aerosol retrieval algorithm applied to Low Earth Orbit and GEOstationary imagers: progress towards an integrated LEO-GEO view of global aerosol

The relatively simple dark-target (DT) aerosol retrieval algorithm provides products of spectral aerosol optical depth (AOD) from measurements of multi-spectral reflectance in visible, near-infrared and shortwave infrared wavelength bands. Originally developed for Moderate-resolution Imaging Spectroradiometer (MODIS aboard Terra and Aqua) in Low-Earth Orbit (LEO), DT has been ported to Visible Infrared Imaging Suite (VIIRS aboard Suomi-NPP and NOAA-20, also in LEO), to enhanced-MODIS Airborne Simulator (eMAS, on an airborne platform), and now to sensors in GEOstationary orbit (Advanced Himawari Imager - AHI aboard Himawari-8 and Advanced Baseline Imagers – ABI aboard GOES-16 and 17). Together, these new datasets not only extend upon the 20+ year MODIS aerosol record, but also expand the temporal sampling and/or spatial resolution. Between July and October of 2019, NASA participated in two field experiments on opposite sides of the globe. These included FIREX-AQ which focused on fire and smoke in the Western U.S., and then CAMP2EX which targeted aerosol/cloud interactions around the Philippines. We have performed DT aerosol retrievals on all images from all sensors during these three months, validated against ground observations from stationary and mobile sunphotometer sites, and have begun to develop a synergy that represents semi-global observations every half hour. The resulting aerosol products are being used as context and for model assimilation, thus providing the framework for more complete characterization of global aerosol transport and lifecycle. Here, we report on progress, as well as remaining challenges such as data management, computer processing, and accounting for differences between GEO and LEO observation geometry and surface reflectance parameterization.

dark target↗
NASA NTRS2009

Carbon Nanotube Based Chemical Sensors for Space and Terrestrial Applications

A nanosensor technology has been developed using nanostructures, such as single walled carbon nanotubes (SWNTs), on a pair of interdigitated electrodes (IDE) processed with a silicon-based microfabrication and micromachining technique. The IDE fingers were fabricated using photolithography and thin film metallization techniques. Both in-situ growth of nanostructure materials and casting of the nanostructure dispersions were used to make chemical sensing devices. These sensors have been exposed to nitrogen dioxide, acetone, benzene, nitrotoluene, chlorine, and ammonia in the concentration range of ppm to ppb at room temperature. The electronic molecular sensing of carbon nanotubes in our sensor platform can be understood by intra- and inter-tube electron modulation in terms of charge transfer mechanisms. As a result of the charge transfer, the conductance of p-type or hole-richer SWNTs in air will change. Due to the large surface area, low surface energy barrier and high thermal and mechanical stability, nanostructured chemical sensors potentially can offer higher sensitivity, lower power consumption and better robustness than the state-of-the-art systems, which make them more attractive for defense and space applications. Combined with MEMS technology, light weight and compact size sensors can be made in wafer scale with low cost. Additionally, a wireless capability of such a sensor chip can be used for networked mobile and fixed-site detection and warning systems for military bases, facilities and battlefield areas.

Li, Jing↗