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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Data Processing Workflow for Fixed-wing Drone Based Radiation Mapping in the Chornobyl Exclusion Zone (CEZ) - 20119

April 2020 marks the 34. anniversary of the high-profile radiological release from the Chornobyl Nuclear Power Plant (ChNPP). The release of radioactive material from reactor number four began on the 26 April 1986 and continued over a period of about 10 days, releasing approximately 1700 PBq of radioactive material (including 85 PBq of 137-Cs) into the environment. To this day, the accident remains the most significant release of radioactive material since civil nuclear power generation began. In the years since the accident, automated and remote radiation monitoring technologies have advanced significantly in their capabilities. One such example of this is the use of unmanned aerial vehicles (UAVs) in radiation mapping investigations. In April 2019, a team of scientists from the University of Bristol showcased a novel radiation mapping system within the CEZ, specifically aiming to map radiation over a large portion of the area immediately surrounding the ChNPP. Over six days of data collection, the system flew a total distance of 583.8 km, covering an area of 14.6 sq.km with an exceptional spatial resolution (sub 20 m/pixel). The work presented herein outlines and explains the data processing procedure to convert the raw data into 137-Cs activity (kBq/sq.m) and cesium-equivalent dose-rate (CED) at 1 m above ground level (μSv/hr). A demonstration of the validity of the method is demonstrated through the successful reduction of the raw data into a single linear relationship between the measured {sup 137}Cs net peak intensity and the {sup 137}Cs activity. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Fault Detection and Performance Monitoring of Propellers in Electric UAV

Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.

Unmanned aerial vehicles↗
DOE OSTI2023

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, combining a multidisciplinary approach in the computational analysis of additive manufactured structures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency, without sacrificing performance, while demonstrating versatility to fulfill different mission profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy aerodynamic data, which is particularly important for complex aircraft concepts such as blended wing bodies. To address challenges relating to anisotropic materials, the Tsai–Wu failure criterion is applied to the structural analysis, using CFD solutions as load inputs. Aerodynamic performance results show the low-speed variant attains an endurance of 1 h, 48 min, whereas its high-speed counterpart is 29 min at a 66.7% higher cruise speed. Each variant serves different aspects of small UAS deployment, with low speed envisioned for high-endurance surveying, and high speed for long-range or time-critical missions such as delivery. The experimental and simulation results suggest room for design iteration, in wing area and geometry adjustments. Structural simulations demonstrated the need for airframe improvements to the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to be useful across multiple industries, advocating for further research and design improvements.

42 ENGINEERING↗
NASA NTRS2009

UAVSAR Phased Array Aperture

This paper describes the development of a patch antenna array for an L-band repeat-pass interferometric synthetic aperture radar (InSAR) instrument that is to be flown on an unmanned aerial vehicle (UAV). The antenna operates at a center frequency of 1.2575 GHz and with a bandwidth of 80 MHz, consistent with a number of radar instruments that JPL has previously flown. The antenna is designed to radiate orthogonal linear polarizations in order to facilitate fully-polarimetric measurements. Beam-pointing requirements for repeat-pass SAR interferometry necessitate electronic scanning in azimuth over a range of -20degrees in order to compensate for aircraft yaw. Beam-steering is accomplished by transmit/receive (T/R) modules and a beamforming network implemented in a stripline circuit board. This paper, while providing an overview of phased array architecture, focuses on the electromagnetic design of the antenna tiles and associated interconnects. An important aspect of the design of this antenna is that it has an amplitude taper of 10dB in the elevation direction. This is to reduce multipath reflections from the wing that would otherwise be detrimental to interferometric radar measurements. This taper is provided by coupling networks in the interconnect circuits as opposed to attenuating the output of the T/R modules. Details are given of material choices and fabrication techniques that meet the demanding environmental conditions that the antenna must operate in. Predicted array performance is reported in terms of co-polarized and crosspolarized far-field antenna patterns, and also in terms of active reflection coefficient.

Unmanned Aerial Vehicle (UAV)↗
NASA NTRS2018

Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

aboveground biomass↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗
DOE OSTI2020

SPRUCE Experimental Plot Top View Aerial Images beginning June 2019

This data set provides a record of the aerial images of the SPRUCE experimental plots obtained periodically using an unmanned aerial vehicle (UAV) with attached camera from 2019-05-05- to 2022-08-25. New images of the plots will be added periodically. The initial RGB images may be used for studies of greenness and snow coverage and compared with ground-based measurements and chamber-based tree and shrub Phenocam images (e.g. Schädel et al., 2022). Also included are NDVI (Normalized Difference Vegetation Index) images showing calculated difference between visible near-infrared (NIR) light reflectance from vegetation, as well as infrared (IR) and multispectral images. Files are presented in JPEG (*.jpg) or PDF (*.pdf) format inside of 28 compressed (*.zip) files. Images were collected for the 10 plots with enclosures, the 7 unchambered plots, and the environmental monitoring instrument cluster area. Images are slightly off-plot center at 12 to 15 meters above ground level or approximately 2 meters above the top of an experimental chamber.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗
DOE OSTI2020

LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame cameras are the two most commonly used sensors for 3D mapping of the object space. For example, point clouds for the area of interest can be directly derived from LiDAR sensors onboard UAVs equipped with integrated global navigation satellite systems and inertial navigation systems (GNSS/INS). Imagery-based mapping, on the other hand, is considered to be a cost-effective and practical option and is often conducted by generating point clouds and orthophotos using structure from motion (SfM) techniques. Mapping with photogrammetric approaches requires accurate camera interior orientation parameters (IOPs), especially when direct georeferencing is utilized. Most state-of-the-art approaches for determining/refining camera IOPs depend on ground control points (GCPs). However, establishing GCPs is expensive and labor-intensive, and more importantly, the distribution and number of GCPs are usually less than optimal to provide adequate control for determining and/or refining camera IOPs. Moreover, consumer-grade cameras with unstable IOPs have been widely used for mapping applications. Therefore, in such scenarios, where frequent camera calibration or IOP refinement is required, GCP-based approaches are impractical. To eliminate the need for GCPs, this study uses LiDAR data as a reference surface to perform in situ refinement of camera IOPs. The proposed refinement strategy is conducted in three main steps. An image-based sparse point cloud is first generated via a GNSS/INS-assisted SfM strategy. Then, LiDAR points corresponding to the resultant image-based sparse point cloud are identified through an iterative plane fitting approach and are referred to as LiDAR control points (LCPs). Finally, IOPs of the utilized camera are refined through a GNSS/INS-assisted bundle adjustment procedure using LCPs. Seven datasets over two study sites with a variety of geomorphic features are used to evaluate the performance of the developed strategy. The results illustrate the ability of the proposed approach to achieve an object space absolute accuracy of 3–5 cm (i.e., 5–10 times the ground sampling distance) at a 41 m flying height.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Miniature Unmanned Airborne Sunphotometer for Sun-Tracking Atmospheric Research (muSSTAR)

The miniature unmanned Airborne Sunphotometer for Sun-Tracking Atmospheric Research (muSSTAR) will provide a link between local aerosols in the boundary layer and the global measurements of Aerosol Optical Thickness (AOT). We describe a path towards a prototype small airborne sunphotometer hosted on an unmanned aerial system for enabling air-quality and climate science by profiling the lower atmospheric boundary layer over land and boundary layer in coastal environments. This project aims to miniaturize NASA Ames’ world-leading instrumentation for use on unmanned aerial vehicles.

aerosol↗
NASA NTRS2007

Small UAV Research and Evolution in Long Endurance Electric Powered Vehicles

This paper describes recent research into the advancement of small, electric powered unmanned aerial vehicle (UAV) capabilities. Specifically, topics include the improvements made in battery technology, design methodologies, avionics architectures and algorithms, materials and structural concepts, propulsion system performance prediction, and others. The results of prototype vehicle designs and flight tests are discussed in the context of their usefulness in defining and validating progress in the various technology areas. Further areas of research need are also identified. These include the need for more robust operating regimes (wind, gust, etc.), and continued improvement in payload fraction vs. endurance.

Logan, Michael J.↗
NASA NTRS2005

Apex Reference Manual 3.0 Beta

Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.

Freed, Michael A.↗
NASA NTRS2007

Automated Target Planning for FUSE Using the SOVA Algorithm

The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two IRUs and four reaction wheels. Over time through various failures, the satellite has been left with one reaction wheel on the vehicle skew axis and two gyros. To remain operational, a control scheme has been implemented using the magnetic torque rods and the remaining momentum wheel.[2] As a consequence, there are attitude regions where there is insufficient torque authority to overcome environmental disturbances (e.g. gravity gradient torques). The situation is further complicated by the fact that these attitude regions shift inertially with time as the spacecraft moves through earth s magnetic field during the course of its orbit. Under these conditions, the burden of planning targets and target-to-target slew maneuvers has increased significantly since the beginning of the mission.[3] Individual targets must be selected so that the magnetic field remains roughly aligned with the skew wheel axis to provide enough control authority to the other two orthogonal axes. If the field moves too far away from the skew axis, the lack of control authority allows environmental torques to pull the satellite away from the target and can potentially cause it to tumble. Slew maneuver planning must factor the stability of targets at the beginning and end, and the torque authority at all points along the slew. Due to the time varying magnetic field geometry relative to any two inertial targets, small modifications in slew maneuver timing can make large differences in the achievability of a maneuver.

Heatwole, Scott↗
NASA NTRS2016

Weathering the Storm: Unmanned Aircraft Systems in the Maritime, Atmospheric and Polar Environments

Unmanned systems have the potential to efficiently, effectively, economically, and safely bridge critical observation requirements in an environmentally friendly manner. As the United States Atmospheric, Marine and Arctic areas of interest expand and include hard-to-reach regions of the Earth (such as the Arctic and remote oceanic areas) optimizing unmanned capabilities will be needed to advance the United States science, technology and security efforts. Through increased multi-mission and multi-agency operations using improved inter-operable and autonomous unmanned systems, the research and operations communities will better collect environmental intelligence and better protect our country against hazardous weather, environmental, marine and polar hazards. This presentation will examine UAS (Unmanned Aircraft Systems) operations which includes developing a coordinated effort to maximize the efficiency and capabilities of unmanned systems across the federal government and research partners. Numerous intra- and inter-agency operational demonstrations and assessments have been made to verify and validated these UAS observations. The NASA-specific elements will consist of an overview of NASA UAVs (Unmanned Aerial Vehicles) available to science and recent missions.

Atmospheric↗
NASA NTRS2019

Photon Sieve Design and Fabrication for Imaging Characteristics Using UAV Flight

The Photon Sieve (PS) team at NASA Langley Research Center (LaRC) began receiving support for the development and characterization of PS devices through the NASA Internal Research & Development Program (IRAD) in 2015. The project involves ascertaining the imaging characteristics of various PS devices. These devices hold the potential to significantly reduce mission costs and improve imaging quality by replacing traditional reflector telescopes. The photon sieve essentially acts as a lens to diffract light to a concentrated point on the focal plane like a Fresnel Zone Plate (FZP). PS’s have the potential to focus light to a very small spot which is not limited by the width of the outermost zone as for the FZP and offers a promising solution for high resolution imaging. In the fields of astronomy, remote sensing, and other applications that require imaging of distant objects both on the ground and in the sky, it is often necessary to perform post-process filtering in order to separate noise signals that arise from multiple scattering events near the collection optic. The PS exhibits a novel filtering technique that rejects the unwanted noise without the need for time consuming post processing of the images. This project leverages key Langley resources to design, manufacture, and characterize a series of photon sieve specimens. After a prototype was developed and characterized in the Langley ISO5 optical cleanroom and laboratory, outside testing was conducted via the capture of images of the moon by using a telescopic setup. This next goal of the project is to design and develop a telescope and image capture system as a drone-based instrument payload. The vehicle utilized for the initial demonstration was a NASA hive model 1200 XE-8 research Unmanned Aerial Vehicle (UAV), capable of handling a 20-pound maximum payload with a 25-minute flight time. This NASA Technical Memorandum (NASA-TM) introduces preliminary results obtained using a PS-based imaging system on the UAV. The next version of the telescope structure will be designed around diffractive optical components and commercially available camera electronics to create a lightweight payload.

Kim, Hyun Jung↗