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At least 127 records · Page 7

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NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

A surrogate artificial neural network/machine learning model was developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small Unmanned Aerial System (sUAS) platforms. The model was trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor-airframe configurations. Acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively, enabling a time domain reconstruction of the waveform for the range of geometric and flow parameters tested.

acoustics↗

NASA NTRSDate not supplied

An Artificial Neural Network Approach to Predict Rotor-Airframe Acoustic Waveforms

acoustics↗

NASA NTRS2016

NH11B-1726: FrankenRaven: A New Platform for Remote Sensing

Small, modular aircraft are an emerging technology with a goal to maximize flexibility and enable multi-mission support. This reports the progress of an unmanned aerial system (UAS) project conducted at the NASA Ames Research Center (ARC) in 2016. This interdisciplinary effort builds upon the success of the 2014 FrankenEye project to apply rapid prototyping techniques to UAS, to develop a variety of platforms to host remote sensing instruments. In 2016, ARC received AeroVironment RQ-11A and RQ-11B Raven UAS from the US Department of the Interior, Office of Aviation Services. These aircraft have electric propulsion, a wingspan of roughly 1.3m, and have demonstrated reliability in challenging environments. The Raven airframe is an ideal foundation to construct more complex aircraft, and student interns using 3D printing were able to graft multiple Raven wings and fuselages into FrankenRaven aircraft. Aeronautical analysis shows that the new configuration has enhanced flight time, payload capacity, and distance compared to the original Raven. The FrankenRaven avionics architecture replaces the mil-spec avionics with COTS technology based upon the 3DR Pixhawk PX4 autopilot with a safety multiplexer for failsafe handoff to 2.4 GHz RC control and 915 MHz telemetry. This project demonstrates how design reuse, rapid prototyping, and modular subcomponents can be leveraged into flexible airborne platforms that can host a variety of remote sensing payloads and even multiple payloads. Modularity advances a new paradigm: mass-customization of aircraft around given payload(s). Multi-fuselage designs are currently under development to host a wide variety of payloads including a zenith-pointing spectrometer, a magnetometer, a multi-spectral camera, and a RGB camera. After airworthiness certification, flight readiness review, and test flights are performed at Crows Landing airfield in central California, field data will be taken at Kilauea volcano in Hawaii and other locations.

remote sensing↗

NASA NTRSDate not supplied

High-Intensity Radiated Field (HIRF) Map - An Avoidance Approach for UAM, AAM, and UAS Vehicles

Advanced Air Mobility (AAM), Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles will fly in similar airspace to Transport Category Rotorcraft, thereby requiring them to meet the most severe requirements for High Intensity Radiated Fields (HIRF) certification. The environment is severe for rotorcraft, much more so than for fixed-wing aircraft, due to lower altitude operations, potentially exposing the vehicles to close and direct view of high-power transmitters on the ground. High-level HIRF exposure potentially leads to avionic system upsets, interference, and undesirable effects. Shielding and circuit protection against HIRF will be a significant barrier to size, weight, and cost, especially for emerging electric vertical take-off and landing (eVTOL) and electric short take-off and landing (eSTOL) vehicles. This paper proposes a novel approach to HIRF protection, which reduces costs by testing and certifying vehicles to a “vehicle tolerance level” that is lower than required for Transport Category Rotorcraft. The remaining protection is achieved by maintaining a safe distance from known HIRF sources, calculated based on the vehicle's tolerance level and transmitter characteristics such as transmit power, antenna beamwidth, and direction. Tailored maps are developed, identifying transmitters and avoidance zones within an operating area or along a flight path, allowing for restricted vehicle operations. The HIRF avoidance zones could be smaller for vehicles with higher tolerance levels, enabling them to operate closer to transmitters. Transmitter data can be extracted from regulatory databases like the FCC and NOAA. A map tool is developed in Matlab to calculate and plot HIRF avoidance zones from transmitters in FCC and NOAA databases as proof of concept. Illustrations are presented for AM/FM/TV transmitters, communication satellite dishes, and weather radars. Also illustrated are HIRF zones for smaller transmitters, including land-mobile radios, pagers, microwave links, and cellular towers. An example of flight planning around the transmitters is presented. This method is a substantial deviation from the standard approach and involves slightly higher flight-planning complexity, but the potential cost savings are significant. Future AAM/UAM/UAS aeronautical charts could include these new HIRF avoidance zones.

HIRF↗

NASA NTRSDate not supplied

High-Intensity Radiated Field (HIRF) Map - An Avoidance Approach for UAM, AAM, and UAS Vehicles

Advanced Air Mobility (AAM), Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles will fly in similar airspace to Transport Category Rotorcraft, thereby requiring them to meet the most severe requirements for HighIntensity Radiated Fields (HIRF) certification. The environment is severe for rotorcraft, much more so than for fixed-wing aircraft, due to lower altitude operations, potentially exposing the vehicles to close and direct view of high-power transmitters on the ground. High-level HIRF exposure potentially leads to avionic system upsets, interference, and undesirable effects. Shielding and circuit protection against HIRF will be a significant barrier to size, weight, and cost, especially for emerging electric vertical take-off and landing (eVTOL) and electric short take-off and landing (eSTOL) vehicles. This paper proposes a novel approach to HIRF protection, which reduces costs by testing and certifying vehicles to a “vehicle tolerance level” that is lower than required for Transport Category Rotorcraft. The remaining protection is achieved by maintaining a safe distance from known HIRF sources, calculated based on the vehicle's tolerance level and transmitter characteristics such as transmit power, antenna beamwidth, and direction. Tailored maps are developed, identifying transmitters and avoidance zones within an operating area or along a flight path, allowing for restricted vehicle operations. The HIRF avoidance zones could be smaller for vehicles with higher tolerance levels, enabling them to operate closer to transmitters. Transmitter data can be extracted from regulatory databases like the FCC and NOAA. A map tool is developed in Matlab to calculate and plot HIRF avoidance zones from transmitters in FCC and NOAA databases as proof of concept. Illustrations are presented for AM/FM/TV transmitters, communication satellite dishes, and weather radars. Also illustrated are HIRF zones for smaller transmitters, including land-mobile radios, pagers, microwave links, and cellular towers. An example of flight planning around the transmitters is presented. This method is a substantial deviation from the standard approach and involves slightly higher flight-planning complexity, but the potential cost savings are significant. Future AAM/UAM/UAS aeronautical charts could include these new HIRF avoidance zones.

HIRF↗

NASA NTRS2018

A VTOL Small Unmanned Aircraft System to Expand Payload Capabilities

One of the goals of Unmanned Aerial Systems (UAS) is to increase the capabilities of flight vehicles while maintaining small airframes that are also lightweight. Private package delivery companies and government agencies have an interest in vehicles that have large payload to weight ratios; thus allowing to deliver heavy payloads. Currently, UAS configurations suffer from propulsive and aerodynamic limitations that decrease the payload weight and/or mission range. In order to decrease these limitations, the Kinetic and Potential Energy Alternation for Greater Lift Enhancement (KP EAGLE) concept provides a novel way to transport payloads that may be too heavy for vehicles in a similar weight class. The concept vehicle takes off vertically without the payload. It then performs a dive maneuver that transfers the gained potential energy into kinetic energy to pick up the payload. This concept does not require special equipment during takeoff or landing and provides a better payload to weight ratio than other conventional vehicles in the same weight class.

Geuther, Steven↗

NASA NTRSDate not supplied

TPSAS-NF1676L-13523-DND

Access to the National Airspace System entails policy, architectural and certification challenges for Unmanned Aerial Systems (UAS) as they are being developed. This talk identifies these challenges, builds upon past architectural change successes, and proposes a roadmap to allow small UAS (< 50lb vehicles) to operate in an airspace underlay experiment. The intent is to demonstrate robust operations that can be scaled up to more complex systems and airspace.

Dan Williams↗

NASA NTRS2016

Simulation and Flight Test Data Collection Review for Supporting Phase 1 Detect and Avoid MOPS

RTCA Special Committee 228 is a consortium of government, industry, and academic organizations tasked to develop minimum operational performance standards for UAS detect and avoid systems. The UAS in the NAS (National Airspace System) project is studying the minimum operational performance standards for unmanned aerial systems (UAS's) detect-and-avoid (DAA) system in order to operate in the National Airspace System. Over the past 3 years, the project has executed a series of fast-time simulation, human-in-the-loop experiments, and flight tests in support of this effort. The purpose of this briefing is to summarize the models developed and data collected to overcome UAS integration barriers, so UAS can remain well clear of all traffic.

Santiago, Confesor↗

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 NTRS2012

UAS Integration Into the NAS: An Examination of Baseline Compliance in the Current Airspace System

As a result of the FAA Modernization and Reform Act of 2012, Unmanned Aerial Systems (UAS) are expected to be integrated into the National Airspace System (NAS) by 2015. Several human factors challenges need to be addressed before UAS can safely and routinely fly in the NAS with manned aircraft. Perhaps the most significant challenge is for the UAS to be non-disruptive to the air traffic management system. Another human factors challenge is how to provide UAS pilots with intuitive traffic information in order to support situation awareness (SA) of their airspace environment as well as a see-and-avoid capability comparable to manned aircraft so that a UAS pilot could safely maneuver the aircraft to maintain separation and collision avoidance if necessary. A simulation experiment was conducted to examine baseline compliance of UAS operations in the current airspace system. Researchers also examined the effects of introducing a Cockpit Situation Display (CSD) into a UAS Ground Control Station (GCS) on UAS pilot performance, workload and situation awareness while flying in a positively controlled sector. Pilots were tasked with conducting a highway patrol police mission with a Medium Altitude Long Endurance (MALE) UAS in L.A. Center airspace with two mission objectives: 1) to reroute the UAS when issued new instructions from their commander, and 2) to communicate with Air Traffic Control (ATC) to negotiate flight plan changes and respond to vectoring and altitude change instructions. Objective aircraft separation data, workload ratings, SA data, and subjective ratings regarding UAS operations in the NAS were collected. Results indicate that UAS pilots were able to comply appropriately with ATC instructions. In addition, the introduction of the CSD improved pilot SA and reduced workload associated with UAS and ATC interactions.

Fern, Lisa↗

NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗

NASA NTRS2021

Decentralized Control Synthesis for Air Traffic Management in Urban Air Mobility

Urban air mobility (UAM) refers to air transportation services within an urban area, often in an on-demand fashion. We study air traffic management (ATM) for vehicles in a UAM fleet, while guaranteeing system safety requirements such as traffic separation. Existing ATM methods for unmanned aerial systems, such as UAS traffic management, utilize alternative approaches which do not provide strict safety guarantees. No established infrastructure exists for providing ATM at scale for UAM. We provide a decentralized, hierarchical approach for UAM ATM that allows for scalability to high traffic densities as well as providing theoretical guarantees of correctness with respect to user-provided safety specifications. Our main contributions are two-fold. First, we propose a novel UAM ATM architecture that divides the control authority between vertihubs that are each in charge of all UAM vehicles in their local airspace. Each vertihub also contains a number of vertiports that are in charge of UAM vehicle takeoffs and landings. The resulting architecture is decentralized and hierarchical, which not only enables scalability, but also robustness in the event of any individual vertihub or vertiport no longer being operational. Second, we provide a contract-based correct-by-construction reactive synthesis approach that provably guarantees safety properties with respect to user-provided specifications in linear temporal logic. We demonstrate the approach on large-volume UAM air traffic data.

Urban Air Mobility↗

NASA NTRS2015

Next Generation UAS Based Spectral Systems for Environmental Monitoring

This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.

onboard processing↗

NASA NTRS2019

Model-based System Health Management and Contingency Planning for Autonomous UAS

Safe autonomous operations of an Unmanned Aerial System (UAS) requires that the UAS can react to unforeseen circumstances, for example, after a failure has occurred. In this paper we describe a model-based run-time architecture for autonomous on-board diagnosis, system health management, and contingency management. This architecture is being instantiated on top of NASA's Core Flight System (cFS/cFE) as amajor component of the on-board AutonomousOperating System (AOS). We will describe our diagnosis and monitoring components, which continuously provide system health status. Automated reasoning with constraint satisfaction form the core of our decision-making component, which assesses the current situation, aids in failure disambiguation, and constructs a contingency plan to mitigate the failure(s) and allow for a safe end of the mission. We will illustrate our contingency management system with two case studies, one for a fixed-wing aircraft in simulation, and one for an autonomous DJI S1000+ octo-copter.

Schumann, Johann M.↗

NASA NTRS2017

The Science of Drones

Drone: the public's term for any flying vehicle that doesn't have a pilot onboard. Unmanned aircraft system (UAS): preferred civil term that emphasizes the drone as a "system". Unmanned aerial vehicle (UAV): older but common term, especially in academia. Remotely piloted aircraft system (RPAS): the military's most common term for a drone, and probably the most accurate.

Drones↗

NASA NTRS2016

Assessing the Impact of Operational Constraints on the Near-Term Unmanned Aircraft System Traffic Management Supported Market

An economic impact market analysis was conducted for 16 leading sectors of commercial Unmanned Aerial System (UAS) applications predicted to be enabled by 2020 through the NASA UAS Traffic Management (UTM) program. Subject matter experts from seven industries were interviewed to validate concept of operations (ConOps) and market adoption assumptions for each sector. The market analysis was used to estimate direct economic impacts for each sector including serviceable addressable market, capital investment, revenue recovery potential, and operations cost savings. The resultant economic picture distinguishes the agricultural, pipeline and railroad inspection, construction, and maritime sectors of the nascent commercial UAS industry as providing the highest potential economic value in the United States. Sensitivity studies characterized the variability of select UAS sectors economic value to key regulatory or UTM ConOps requirements such as weight, altitude, and flight over populated area constraints. Takeaways from the analysis inform the validation of UTM requirements, technologies and timetables from a commercial market need and value viewpoint. This work concluded in August 2015 and reflects the state of the UAS industry and market projections at that time.

Air Traffic Management↗

NASA NTRS2022

Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

Kolawole Ogunsina↗

NASA NTRS2018

Giving the Public a Perspective into Unmanned Aircraft Systems' Operations

NASA is currently engaged in research to safely enable large-scale commercial applications of small Unmanned Aerial Systems (UAS) in low altitude airspace. This research effort, referred to as UAS Traffic Management (UTM), encompasses the concepts and technologies needed to accommodate the projected demand of UAS operating in the national airspace. One aspect related to the successful implementation of UTM in the future is public acceptance. Transparency will heavily influence this acceptance, and a public portal will provide much of that transparency through ease of access to information about the operations - mainly who, why, and where such operations are taking place. Related concerns to the public are individual privacy, security, and accountability of the operators. Providing the aforementioned information about operations can mitigate these concerns, but a balance will have to be achieved between the need for transparency from the public and the privacy of the operators. The proper balance and the needs of the various UTM stakeholders with regard to information access will be explored through the development and testing of a public portal as part of NASA's Technical Capability 3 (TCL 3) demonstration. Additionally, various approaches to the display of information and user interfaces will be surveyed through the development of a public portal by multiple UTM industry partners across different test sites.

UAS↗