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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
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

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗
NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗
DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

Overflow Analysis of Unmanned Aircraft Systems

With the increasing presence of unmanned aircraft systems (UAS), or drones, in the national airspace, the management for access and operation of these vehicles is required. NASA’s management approach is being developed under the unmanned aircraft system traffic management (UTM) program. To determine the aerodynamic characteristics of drones, wind tunnel experiments and computation fluid dynamic (CFD) analysis have been conducted. These experiments and analyses are undertaken to understand the flight capabilities of these vehicles in variable head and cross wind conditions. The results of these investigations will provide metrics for the safe operation of these vehicles in and around civil populations and in urban settings. The focus of this paper is to model a drone installed in a wind tunnel for varying pitch attitudes and rotor rpm settings. Specifically, the IRIS drone is modeled in the NASA-Ames 7x10 ft wind tunnel. The tunnel mounting hardware and the tunnel enclosure are modeled along with the IRIS drone geometry. The rotors of the drone are modeled using two methodologies: a rotor disk model and full rotating rotors with moving grids. The results of the analysis are compared with available experimental data to validate the computational approach.

Stremel, Paul M.↗
NASA NTRSDate not supplied

Achieving Equilibrium for Dense, Integrated, Vehicle Navigation

Drone usage has proliferated in recent years with many applications that have market-changing potential. Applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. In this paper, the term drone is used to mean both Unmanned Aerial Vehicle and small Unmanned Aircraft System vehicles. Flight infrastructure can currently only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay atone airport sends ripple effects through the system, causing more delays and missed connections. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the NAS is expected to skyrocket to millions, potentially congesting the airspace resulting in possible separation violations. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is a safety critical property for drones in the airspace. This paper addresses separation in time and in distance for high volume corridors (en-route) and lanes(on ground). The requirements and necessary conditions for maintaining proper separation and reaching maximum throughput for a given corridor/lane are addressed assuming unidirectional corridors/lanes where an aircraft arrives from one side and departs from the opposite side. Simulation results are presented that show the presented solution guarantees a set of drones to reach the equilibrium state by adjusting their speed based on their distance to the aircraft in front of them. The equilibrium state is defined as a state when a set of n aircraft moving at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as a state when the aircraft cannot move at their maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach using a large number of drones and evaluate the scalability of the proposed solution.

Mahyar R. Malekpour↗
DOE OSTI2022

UAS Live Incursion Drills Survey

Unmanned aircraft systems (UAS/drones) are rapidly evolving and are considered an emerging threat by nuclear facilities throughout the world. Due to the wide range of UAS capabilities, members of the workforce and security/response force personnel need to be prepared for a variety of drone incursion situations. Tabletop exercises are helpful, but actual live exercises are often needed to evaluate the quick chain of events that might ensue during a real drone fly-in and the essential kinds of information that will help identify the type of drone and pilot. Even with drone detection equipment, the type of UAS used for incursion drills can have a major impact on detection altitude and finding the UAS in the sky. Using a variety of UAS, the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Security (INS) would like to offer partners the capability of adding actual UAS into workforce and response exercises to improve overall UAS awareness as well as the procedures that capture critical steps in dealing with intruding drones.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2019

Overflow Simulations of Unmanned Aircraft Systems

National airspace, the management for access and operation of these vehicles is required. This management is being developed under the unmanned aircraft system traffic management system (UTM) program. To determine the aerodynamic characteristics of drones, wind tunnel experiments and computation fluid dynamic (CFD) analysis have been conducted. These experiments and analyses are undertaken to understand the flight capabilities of these vehicles in variable head and cross wind conditions. The results of these investigations will provide metrics for the safe operation of these vehicles in and around civil populations and in urban settings. The focus of this paper is to model a drone installed in a wind tunnel for varying pitch attitudes and rotor rpm settings. Specifically, the IRIS drone is modeled in the NASA-Ames 7x10 ft. W/T. The tunnel mounting hardware and the tunnel enclosure are modeled with the IRIS drone geometry. The rotors of the drone are modeled using two methodologies: a rotor disk model and individual blade representations. The results of the analysis are compared with available experimental data to validate the computational approach.

Stremel, Paul M.↗
NASA NTRS2024

Search Technology for Optimal Rescue Missions (STORM)

Natural disasters, such as earthquakes, hurricanes, and wildfires are responsible for the deaths of 60,000 to 90,000 people per year. Today, search and rescue (SAR) operations heavily rely on humans to find and deliver life-saving supplies to those affected by these disasters. However, these operations have limits in visibility, navigation, communication systems, and data availability in the area affected, as well as endangering the SAR personnel. Search Technology for Optimal Rescue Missions (STORM) discusses a new system for SAR teams using autonomous drones able to find and deliver supplies to people, without risking more lives in the process. The concept includes the use of two drone types, STORM Search and STORM Rescue, which will survey and locate survivors and be able to drop equipment to the survivors identified, respectively. These two types of drones were optimized in drone design and durability (such as the use of dihedral wings and a toroidal propeller), detection and navigation systems (sturdy thermal and Light Detection and Ranging [LiDAR] cameras), automation and system design (Machine Learning and Computer Vision), server-drone communication (Meshnets), weight, and cost. Once implemented, the STORM concept is expected to improve, ease, and speed up SAR operations, and most important of all, rescue lives that would have never been currently possible to find.

Astha Ingole↗
DOE OSTI2025

Remote sensing images, DEM, and point clouds associated with “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds”

This data package is associated with the publication “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds” published in Frontiers in Environmental Science, Environmental Informatics and Remote Sensing (Bao et al., 2026; doi: 10.3389/fenvs.2026.1725258). This data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results from four approaches are provided: (1) unoccupied aerial vehicle (UAV, colloquially known as drone) imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. A preliminary version of this data package was published in October 2025 at the time of manuscript submission. It was updated in March 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) 8 folders; (2) the detailed flight configuration html files; (3) field metadata; (4) a readme; (5) a data dictionary; and (6) file-level metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024. The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and “LiDAR”. The ground truth measurements are in the folder called “optical_level_kit”. Lastly, results comparing the different approaches are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy. For information on using the .obj and .npy files, see the readme files within the same folder as the files.

54 ENVIRONMENTAL SCIENCES↗