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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Detection, Localization, and Tracking of Unauthorized UAS and Jammers

Small unmanned aircraft systems (UASs) are expected to take major roles in future smart cities, for example, by delivering goods and merchandise, potentially serving as mobile hot spots for broadband wireless access, and maintaining surveillance and security. Although they can be used for the betterment of the society, they can also be used by malicious entities to conduct physical and cyber attacks to infrastructure, private/public property, and people. Even for legitimate use-cases of small UASs, air traffic management (ATM) for UASs becomes of critical importance for maintaining safe and collusion-free operation. Therefore, various ways to detect, track, and interdict potentially unauthorized drones carries critical importance for surveillance and ATM applications. In this paper, we will review techniques that rely on ambient radio frequency signals (emitted from UASs), radars, acoustic sensors, and computer vision techniques for detection of malicious UASs. We will present some early experimental and simulation results on radar-based range estimation of UASs, and receding horizon tracking of UASs. Subsequently, we will overview common techniques that are considered for interdiction of UASs.

surveillance↗
NASA NTRS2018

Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

Wedescribeanew,on-board,shortrangeperceptionsystem that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

Matthies, Larry↗
NASA NTRS2023

Fighting Wildfires Using UAVs Using Autonomous Biodegradable Self-sacrificing (ABS) Drones to Combat Large Wildfires with the Assistance of Satellite Imagery

As wildfires continue to increase in number and severity due to global warming, firefighters are having a harder time combating them safely. This paper examines the application of Autonomous Biodegradable Self-sacrificing (ABS) Drones as a method of fighting wildfires that decreases the number of lives put at risk and proposes a sustainable alternative to current aerial firefighting methods. When a fire is detected or predicted through satellite imagery, ABS Drones are dispatched from firefighting stations or aerial watchtowers to fly towards designated areas and release fire retardants, preventing the fire from further spreading. The most significant feature of this drone is its ability to complete its mission without having to worry about returning safely because ABS Drones are designed to crash and release fire retardants, while not further harming the environment with water-based lithium-ion batteries and a biodegradable body. ABS Drones make it safer for firefighters as it gives firefighters one less life-threatening task to do and allows them to focus on putting out the fire. This solution aims to tackle the unpredictable nature of wildfires that often threaten the safety of firefighters and civilian communities.

ABS Drones↗
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↗
NASA NTRS2023

Plant Disease Detection: Exploring Applications in Hyperspectral Imaging and Machine Learning for Agriculture

The threat of crop disease on food security and agriculture is projected to escalate, leading to reduced crop yields, global economic loss, and endangered food availability to vulnerable populations. Early identification of plant disease is crucial to combatting the crisis but traditional manual methods for disease detection are laborious and may miss early signs of infection. The proposed solution suggests equipping a drone with a hyperspectral camera to collect images and analyzing the data with neural networks trained to flag and classify infected plants. This approach offers a faster, more accurate, and potentially more cost-effective alternative to current practices. While the expensive and complex nature of hyperspectral imaging (HSI) may be an obstacle to the adoption of the drone, rapidly advancing technologies compounded with rental-based usage may make these tools simpler, cheaper, and more accessible to a wider audience. The research further discusses the potential for automated flight paths and the expansion of disease detection to a broader range of crops.

plant disease detection↗
NASA NTRS2016

Identification and Reconfigurable Control of Impaired Multi-Rotor Drones

The paper presents an algorithm for control and safe landing of impaired multi-rotor drones when one or more motors fail simultaneously or in any sequence. It includes three main components: an identification block, a reconfigurable control block, and a decisions making block. The identification block monitors each motor load characteristics and the current drawn, based on which the failures are detected. The control block generates the required total thrust and three axis torques for the altitude, horizontal position and/or orientation control of the drone based on the time scale separation and nonlinear dynamic inversion. The horizontal displacement is controlled by modulating the roll and pitch angles. The decision making algorithm maps the total thrust and three torques into the individual motor thrusts based on the information provided by the identification block. The drone continues the mission execution as long as the number of functioning motors provide controllability of it. Otherwise, the controller is switched to the safe mode, which gives up the yaw control, commands a safe landing spot and descent rate while maintaining the horizontal attitude.

Unmanned aerial system↗
NASA NTRSDate not supplied

Flying Blind: Keeping aircraft safe without a pilot on board.

The unmanned aircraft market is one of the fastest growing sectors in the world today, poised to become a billion dollar industry in the next several years. This explosive growth in unmanned aircraft, both small and large, brings an increased risk of these vehicles interfering with current aircraft, or harming unsuspecting bystanders. This talk will discuss some of the research NASA is doing to keep the airspace safe, while allowing drone pilots the freedom to fly. The discussion will center on two NASA-developed systems: Safeguard, a platform-independent geofence; and DAIDALUS, a software suite for detect and avoid. In addition to describing what the systems are supposed to do, we'll also discuss how NASA uses formal methods to provide assurance that they actually do as intended.

unmanned aircraft↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

sensor fusion↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

object tracking↗
NASA NTRSDate not supplied

Drone Applications for Wildfires and Other Emergency Situations

Potential Solutions to Wildfires This project is looking at multiple aspects of the use of UAV’s during wildfire season. This project identifies multiple solutions to communication problems, early action response, and innovative tactics to fight wildfires during the dark hours of the second shift. This project will identify the features and innovations needed to create a second shift that can more effectively fight wildland fires at night. In addition, this project will look to find which satellite systems work best for detection of wildfires, with a special focus on remote locations with more rural communities. There will also be a focus on ways to improve current UAVs and sensors for aerial fire surveillance as well as the understanding on how to establish a safe airspace for these UAVs during wildfires. More specifically, unmanned ariel vehicles (UAV) or drones can be used during natural disasters and other situations to help first responders be a more efficient team, ultimately saving hundreds of more lives. Depending on the situation, drones can be equipped with many different accessories, each with their own advantages. When it comes to operating a drone, communication between the first responder, dispatcher, and the person flying the drone is very important when exchanging useful information. A focus on existing systems, and what changes could be made to create more novel systems will also be shown. Moreover, wildfires not only pose a hazard to human safety and a dent on communication methods, but it also has detrimental effects on the environment. Specifically, the use of common fire retardants have dangerous health effects on human health and the environment as a whole. Therefore, this project will also explore the use of alternative eco-friendly fire retardants and recent innovations to the use of sustainable retardants.

Drones↗
NASA NTRSDate not supplied

Autonomous Spacecraft Inspection with Free-Flying Drones

This paper describes a proof-of-concept mission demonstrating a multi-agent system performing visual inspection of damage sustained by a spacecraft. Free-flying satellites, simulated by unmanned aerial vehicles (UAVs), autonomously fly around a mock space module maximizing the search space for damage detection. The free-flyers are responsible for independently coordinating their flights to avoid collision with the space module and each other, while executing mission tasks. Damage analysis on the surface of the mock space module is performed in real-time using video from each free-flyer. Three-dimensional modeling is deployed offline to supplement and improve damage detection. This approach demonstrates the feasibility of deploying real space systems for damage detection, where 2D analysis can quickly determine region of interest and 3D visualization can produce a human-navigable virtual environment with depth perspective for further investigation.

unmanned aerial vehicle (UAV)↗
NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗
NASA NTRSDate not supplied

Post-Severe Thunderstorm Damage Assessment from March 2-3, 2020, Nashville, TN, Using Synthetic Aperture Radar Observations

Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.

Hannah G Pankratz↗
NASA NTRS2022

Going to Extremes: Architecting Holographic Microscopes for Extreme Environments

Bacterial life exists on earth in extreme environments. These are environments described by large temperature excur- sions, large pressure excursions, and large radiation excursions from the nominal conditions near sea level which are largely populated by humans. These extreme locales represent such places as the ocean ice, the briny pools of Death Valley and deep mines, the acidic hot springs of the High Sierra or Yellowstone, or even the clouds of our upper atmosphere. The preponderance of bacterial life in these extreme environments here on earth suggests that bacterial life might likely exist in the extreme environments of our own solar system, such as the icy moons of Europa or Enceladus. Thus, architecting instruments for detect- ing life in these extreme environments on earth builds confidence that we can architect such instruments for flight missions. In this paper, we discuss our experience with designing digital holo- graphic microscope instruments to enable detection of bacteria in several extreme environments. In particular we discuss three different instruments. The first is our field instrument which is a small, portable instrument for examination of remote sites. The second is submersible instrument which enables exploration of deep aquatic environments and is deployed on a ocean-going drone. The third is a balloon-borne instrument to examine the bacterial content of the upper atmosphere. We will provide a review of each instrument and discuss aspects of instrument engineering for each particular application.

Ramirez, Alex↗
NASA NTRSDate not supplied

Demonstration of Lidar Sensors for Precision Safe Landing on Planetary Bodies

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

Precision Landing↗
NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗
NASA NTRSDate not supplied

Topographic Quintet: Comparing Five Methods for Measuring Ultra-High Resolution Topography

We compare different methods for collecting ultra-high resolution topography data within an analog planetary, human landing site scale area. Our aim is to investigate the cost and benefits of different 3D terrain mapping techniques, their associated data collection methods, and how their different specifications (e.g., range, spatial resolution, scanning-time, mobility, operating constraints, GPS-Denied operation, etc.) might be applied to landing-site characterization and mission operations. We compare 3D terrain data collected during a field campaign in November 2021 from an outcrop at Kilbourne Hole in southern New Mexico using different Light Detection and Ranging (LiDAR) sensors on the ground and stereo-derived 3D data from framing cameras mounted on small uncrewed aerial systems (sUAS).Our foci for this experiment are ground-based, surveying, and autonomous vehicle-type 3D scanning sensors that might be used for planetary surface exploration from landed assets (e.g., lander, rover, astronaut-mounted sensors, decent imaging, hoppers, or drones).[e.g. 1]This test is not meant to benchmark these scanners against one another, nor provide a recommendation for a specific make or model. Rather, our goal is to quantify time, effort, resolution, and operational trade-offs that are important for selecting a topographic instrument/methodology for a given scope of terrain characterization. Our results indicate that each technique is capable of exceptional quality terrain characterization for planetary exploration and scientific inquiry, but we hypothesize the appropriate technique is highly dependent on the scope of operational specifications and science requirements.

P Whelley↗