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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace system (NAS). Given the significant disparity in the number of AAM and UAM aircraft compared to commercial aircraft in the NAS, coupled with the dense AAM/UAM operations in urban environments, employing the existing ATC/ATM architectures poses considerable challenges. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗
NASA NTRSDate not supplied

Combinatorial Auction-Based Strategic Deconfliction of Federated UTM Airspace

Unmanned Aerial Vehicles (UAVs) have become commonly used to perform a wide range of commercial activities such as cinematography and medical supply delivery. Consequently, regulators have become interested in designing UAV Traffic Management systems (UTMs) to coordinate UAV traffic among a collection of UAV operators. One framework which has been recently proposed for a UTM system is a combinatorial auction. In this framework, airspace is modelled as a 4D grid of space-time cells. UAV operators bid on cells which collectively form paths for their UAVs. Ideally, an airspace auction should reveal information about the current price of flight paths to bidders, allowing bidders to identify and bid on a select number of paths instead of placing as many bids as possible in the hopes of stumbling on a cheap path. Revealing too much information, however, can allow bad actors to place bids which are intended not to win but to raise the price that a rival bidder must pay. We address these twin challenges with a new information revelation framework which provides bidders with wide-ranging pricing information while suppressing bad actors. We evaluate our framework on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to thousands of bids.

Christopher J C Leet↗
NASA NTRSDate not supplied

Design of a Propeller with Global Minimum Torque

Academia, industry, and Government are actively working towards a future where dozens of small to large Unmanned Aerial Systems (UAS) are present within urban environments. In this environment, high noise pollution and inefficiencies have the potential to render these new technologies impractical due to public annoyance and nonacceptance. This study aims to design and analyze a novel propeller design for minimum global torque using an optimization algorithm to find the full three dimensional solution of maximum efficiency. This resulted in a 𝐶 𝑙 distribution defining the global minimum torque solution for propeller design which corresponded with moving as much lift inboard as possible and reducing lift rapidly at the blade tip. The novel propeller (dubbed "Prandtl" propeller) was then compared with the current gold standard in propeller design, a minimum induced loss (MIL) propeller, which had all the same geometric properties except for blade twist and produced the same amount of thrust at the same advance ratio. The results of multiple iterations of Prandtl propellers showed an increase in efficiency of 2.0-3.3% for the Prandtl blade when compared to the equivalent MIL blade. A potential added benefit of this blade design is lower noise generation due to the lower lift loading at the tip reducing the large shear layer intensity which is the point source of noise in propellers. Two major breakthroughs for enabling widespread use of UAS in urban environments are noise reduction and vehicle efficiency, and this new propeller design has the potential to provide both.

Propeller↗
NASA NTRS2024

A Fair, Economically-Efficient, Incentive-Aligned, Scalable Airspace Auction Mechanism for UAV Traffic Management

Unmanned Aerial Vehicles (UAVs) are increasingly used in a wide range of applications such as cinematography, package delivery, and surveying. As a result, regulators have become interested in developing UAV Traffic Management (UTM) systems to coordinate UAV traffic. One possible framework for UTM is a combinatorial auction. Under this framework, airspace is modeled as a grid of space-time cells. UAV operators bid on sets of cells which collectively form flight paths for their UAVs. An ideal airspace auction should: be fair, be incentive-aligned, be scalable, allocate airspace economically-efficiently, enable price discovery, and reduce the work required to participate where possible. In this paper, we propose the first auction mechanism for airspace allocation that meets the criteria above. Our mechanism: (a) is provably economically-efficient, fair and incentive-aligned, (b) shares pricing information with bidders and (c) has features which reduce the burden of participating. We evaluate our mechanism on scenarios based on a Japan Aerospace Exploration Agency (JAXA) case study and find that it can scale to 26,000 bids.

Robert Allan Morris↗
NASA NTRSDate not supplied

UAS Traffic Management Beyond Visual Line of Sight (UTM BVLOS)

NASA is working with Industry and FAA to enable beyond visual line of sight (BVLOS) unmanned aerial systems (UAS) operations wherever it is needed. Prior to NASA involvement, the UAS Traffic Management (UTM) concept and architecture did not exist. Then, a system architecture to support UTM in the nation's airspace was pioneered by NASA and adopted externally. Now, NASA will be a dedicated partner with the ongoing Key Site Operational Evaluation in Texas. NASA is needed to help define how services can be approved and applied to enable BVLOS operations in the nation's airspace.

UTM↗
NASA NTRSDate not supplied

NASA Listening Session on Public Safety Needs for UTM

NASA is working with Industry and FAA to enable beyond visual line of sight (BVLOS) unmanned aerial systems (UAS) operations wherever it is needed. The objective of this presentation is to widen NASA's reach of Public Operator involvement in the UTM BVLOS space. Collaboration between NASA and Public Operations is needed to help define how services can be approved and applied to enable BVLOS operations in the nation's airspace.

UTM↗
NASA NTRSDate not supplied

UTM Public Operators Outreach: Drone Responders

NASA is working with Industry and FAA to enable beyond visual line of sight (BVLOS) unmanned aerial systems (UAS) operations wherever it is needed. The purpose of this presentation is to widen NASA's reach of Public Operator involvement in the UTM BVLOS space and inform them of the fundamental principles of UTM/BVLOS. Collaboration between NASA and Public Operations is needed to help define how services can be approved and applied to enable BVLOS operations in the nation's airspace.

UTM↗
NASA NTRSDate not supplied

HIRF Tolerance and Avoidance for Advanced Air Mobility Vehicles

Advanced Air Mobility (AAM), including Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles may fly in similar airspace to Transport Category Rotorcraft, thereby requiring meeting the same stringent High-Intensity Radiated Fields (HIRF) certification requirements. In a previous effort, a proposed map-based approach protects a vehicle by keeping it away from high power sources at safe distances based on its tolerance level. By designing to a lower tolerance level, significant cost savings can be achieved at the cost of slightly more complex flight planning. However, too low a threshold can result in large avoidance areas, potentially reducing the vehicle operating space. This current effort suggests a minimum threshold for vehicles operating in an urban area. It is derived from analyzing regulatory transmitter data for New York City as a representative metropolitan. As a result, a vehicle can tolerate common lower-power transmitters by default and only needs to avoid far less common high-power sources. It is also found the existing HIRF requirements may be insufficient against many powerful transmitters such as weather radars and satellite uplink transmitters, and that the map-based approach can address this concern.

HIRF↗
NASA NTRS2024

Applications of ArcticDEM for measuring volcanic dynamics, landslides, retrogressive thaw slumps, snowdrifts, and vegetation heights

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m 3 . Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 103 m 3 and a total area of 0.025 km 2 . ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m 3 and 1,160,000 ± 85,000 m 3 , highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Chunli Dai↗
NASA NTRSDate not supplied

HIRF Tolerance and Avoidance for Advanced Air Mobility Vehicles

Advanced Air Mobility (AAM), including Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles may fly in similar airspace to Transport Category Rotorcraft, thereby requiring meeting the same stringent High-Intensity Radiated Fields (HIRF) certification requirements. In a previous effort, a proposed map-based approach protects a vehicle by keeping it away from high power sources at safe distances based on its tolerance level. By designing to a lower tolerance level, significant cost savings can be achieved at the cost of slightly more complex flight planning. However, too low a threshold can result in large avoidance areas, potentially reducing the vehicle operating space. This current effort suggests a minimum threshold for vehicles operating in an urban area. It is derived from analyzing regulatory transmitter data for New York City as a representative metropolitan area. As a result, a vehicle can tolerate common lower-power transmitters by default and only needs to avoid far less common high-power sources. It is also found the existing HIRF requirements may be insufficient against many powerful transmitters such as weather radars and satellite uplink transmitters, and that the map-based approach can address this concern.

HIRF↗
NASA NTRS2024

ENLIGHTEN: Electrical Network Line Inspection Guided by High-Speed Technology & Electromagnetic Navigation

Natural disasters are a major cause of power outages, primarily due to their damage to power line infrastructure. As a result, the current state of power maintenance, with a heavy reliance on manual labor, is in trouble. Its lack of speed and autonomy must be addressed to ensure power security for households and institutions worldwide. This innovative approach explores how High-Speed Unmanned Aerial Vehicle (HSUAV) technology can inspect power line infrastructure rapidly in response to natural disaster scenarios. The HSUAV technology will be accompanied by enhanced sensory equipment including light detection and ranging (LiDAR), thermal imaging, and electromagnetic field (EMF) navigation. It would also be supplemented by innovative forms of machine learning models and recharging systems to efficiently detect anomalies in power line infrastructure. The proposed system, ENLIGHTEN, can help restore power in affected communities after a devastating natural disaster, greatly improving relief efforts. In the future, ENLIGHTEN can be expanded beyond the United States and shared worldwide, effectively mitigating the consequences on a larger scale.

UAV systems, Power line management, Natural disast↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
NASA NTRS2024

Integrating Manned Aircraft and UAVs for the Prediction, Tracking, and Eradication of Desert Locust Swarms

Desert locusts (Schistocerca gregaria) present an acute threat to the agriculture of certain regions, capable of annihilating vast expanses of farmland and destabilizing food security. In this paper, an aviation-based framework concept is introduced that integrates both manned aircraft and Unmanned Aerial Vehicles (UAVs) to preemptively forecast, localize, and eliminate locust swarms efficiently. The framework begins by leveraging predictive modeling methods to refine search areas and pinpoint high-value Points of Interest (POIs) where locust swarms are most likely to materialize. From there, the proposed two-step model uses optical, infrared, and hyperspectral sensors onboard manned aircraft to conduct long-range surveying of probable swarm locations. Upon detection of a swarm, UAVs equipped with sophisticated sensors, including pheromone detectors, thermal and optical imaging systems, are deployed directly from the aircraft to carry out targeted biopesticide applications. This approach, which capitalizes on the combined strengths of both vehicles, provides the rapid response and large-scale intervention necessary to control locust outbreaks. By targeting locust swarms during their most destructive phase, this paper aims to disrupt the locust life cycle and mitigate their severe agricultural impact, offering a scalable and sustainable solution for food security in afflicted regions.

locust monitoring systems↗
NASA NTRS2024

Cooperative and Non-Cooperative UAS Detection

This paper describes a system that enhances airspace situational awareness by detecting and identifying Unmanned Aerial Systems (UAS). This multi-domain solution tracks both cooperative scientific flights as well as non-cooperative intrusions from "bad actors." The system supports a critical push towards safety within NASA’s advanced air mobility mission. After surveying the existing technologies at Langley Research Center, the radar and visual systems were chosen for the primary and secondary detection mechanisms, respectively. These systems were tuned an upgraded to become more sensitive to UAS activity. Additionally, a Remote Identification receiver was procured and integrated into the flight surveillance system.

Lucas Barduson↗
NASA NTRSDate not supplied

Millimeter Wave Airborne Radar System for Earth Science Applications

The innovative element of this IRAD will be the merging inexpensive components into a design that yields a SWaP-C (i.e., size, weight, power, and/or cost) system suitable to be mounted on unmanned aerial vehicles (UAVs) or be integrated with larger ground-based radars to provide intermediate atmospheric sensing.

Clouds and precipitation↗
NASA NTRSDate not supplied

UTM: UAS Traffic Management

NASA is working with industry, public operators, and FAA to enable beyond visual line of sight (BVLOS) unmanned aerial systems (UAS) operations wherever it is needed. The purpose of this presentation is to widen NASA's reach of public operator involvement in the UTM BVLOS space and inform them of the fundamental principles of the UAS Traffic Management (UTM) project. Collaboration between NASA and public operations is needed to help define how services can be approved and applied to enable BVLOS operations in the Key Site and beyond.

UTM↗
NASA NTRSDate not supplied

Gap Analysis of UAS Manuals and Hazards

Emerging aviation includes the use of small Unmanned Aerial Systems (UAS) in novel operations. The manufacture and operation of these small UAS are not as regulated as today’s commercial operation, and their widespread use introduces new risks and hazards to the general public. Today, there are case-by-case approvals for sUAS operations, particularly for emergency response operations in which the potential benefits to use of sUAS is perceived to outweigh potential risks. We analyze operational approvals, procedures, and concepts of operation to identify and categorize the risks and hazards that applicants and approvers are already considering, and also identify barriers and mitigations that the operators have already put in place. This analysis may help lead to routine checklists that standardize safety analysis and lead to more routine operations.

grounded theory↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗