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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗
NASA NTRSDate not supplied

Use of an Uninhabited Aircraft System (UAS) for Atmospheric Observations During an Acoustic Flight Test

A jet noise test was performed at Niagara Falls International Airport using a Calspan Learjet 25 by acoustics researchers at NASA Glenn Research Center in partnership with Calspan personnel and Uninhabited Aircraft Systems (UAS) pilots and sensor operators from NASA Langley Research Center. To account for atmospheric attenuation in the de-propagation of jet noise from a ground-based microphone array to the source for comparison with model data collected in a facility at NASA GRC, a vertical profile of atmospheric conditions was required. To that end, a sUAS was flown with a weather sensor package measuring atmospheric pressure, humidity, and temperature in a range of altitudes from ground level to 304.8 m (1,000 ft) above ground level (AGL). The sUAS flights were performed concurrently with and adjacent to the Learjet flight path. Data from these UAS flights are presented herein; additionally, comparisons with conventional balloon-borne instrumentation are made with a particular focus on the quality and capability of UAS-based observations for acoustic flight test applications. Furthermore, a ground-based light detection and ranging (LiDAR) system was deployed for collecting wind magnitude and direction for discrete altitudes up to 304.8 m (1,000 ft) AGL. Data from the LiDAR unit will be presented and discussed in the context of aircraft acoustic flight testing. This effort was supported by NASA’s Commercial Supersonic Technology project.

UAS↗
NASA NTRS2006

HALE UAS Command and Control Communications: Step 1 - Functional Requirements Document. Version 4.0

The High Altitude Long Endurance (HALE) unmanned aircraft system (UAS) communicates with an off-board pilot-in-command in all flight phases via the C2 data link, making it a critical component for the UA to fly in the NAS safely and routinely. This is a new requirement in current FAA communications planning and monitoring processes. This document provides a set of comprehensive C2 communications functional requirements and performance guidelines to help facilitate the future FAA certification process for civil UAS to operate in the NAS. The objective of the guidelines is to provide the ability to validate the functional requirements and in future be used to develop performance-level requirements.

Source record↗
NASA NTRS2012

Concepts of Integration for UAS Operations in the NAS

One of the major challenges facing the integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) is the lack of an onboard pilot that can comply with the legal requirement identified in the US Code of Federal Regulations (CFR) that pilots see and avoid other aircraft. UAS will be expected to demonstrate the means to perform the function of see and avoid while preserving the safety level of the airspace and the efficiency of the air traffic system. This paper introduces a Sense and Avoid (SAA) concept for integration of UAS into the NAS that is currently being developed by the National Aeronautics and Space Administration (NASA) and identifies areas that require additional experimental evaluation to further inform various elements of the concept. The concept design rests on interoperability principles that take into account both the Air Traffic Control (ATC) environment as well as existing systems such as the Traffic Alert and Collision Avoidance System (TCAS). Specifically, the concept addresses the determination of well clear values that are large enough to avoid issuance of TCAS corrective Resolution Advisories, undue concern by pilots of proximate aircraft and issuance of controller traffic alerts. The concept also addresses appropriate declaration times for projected losses of well clear conditions and maneuvers to regain well clear separation.

Consiglio, Maria C.↗
NASA NTRS2013

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project

NASA's UAS Integration in the NAS project, has partnered with Rockwell Collins to develop a concept Control and Non-Payload Communication system prototype radio, operating on recently allocated UAS frequency spectrum bands. The prototype radio will be used to validate initial proposed performance requirements for UAS control communications. This presentation will give an overview of the current status of the design, development, and flight test planning for this prototype radio.

Griner, James H.↗
NASA NTRS2014

Analysis of Well-Clear Boundary Models for the Integration of UAS in the NAS

The FAA-sponsored Sense and Avoid Workshop for Unmanned Aircraft Systems (UAS) defnes the concept of sense and avoid for remote pilots as "the capability of a UAS to remain well clear from and avoid collisions with other airborne traffic." Hence, a rigorous definition of well clear is fundamental to any separation assurance concept for the integration of UAS into civil airspace. This paper presents a family of well-clear boundary models based on the TCAS II Resolution Advisory logic. Analytical techniques are used to study the properties and relationships satisfied by the models. Some of these properties are numerically quantifed using statistical methods.

Upchurch, Jason M.↗
NASA NTRS2013

Human Factors Guidelines for UAS in the National Airspace System

The ground control stations (GCS) of some UAS have been characterized by less-than-adequate human-system interfaces. In some cases this may reflect a failure to apply an existing regulation or human factors standard. In other cases, the problem may indicate a lack of suitable guidance material. NASA is leading a community effort to develop recommendations for human factors guidelines for GCS to support routine beyond-line-of-sight UAS operations in the national airspace system (NAS). In contrast to regulations, guidelines are not mandatory requirements. However, by encapsulating solutions to identified problems or areas of risk, guidelines can provide assistance to system developers, users and regulatory agencies. To be effective, guidelines must be relevant to a wide range of systems, must not be overly prescriptive, and must not impose premature standardization on evolving technologies. By assuming that a pilot will be responsible for each UAS operating in the NAS, and that the aircraft will be required to operate in a manner comparable to conventionally piloted aircraft, it is possible to identify a generic set of pilot tasks and the information, control and communication requirements needed to support these tasks. Areas where guidelines will be useful can then be identified, utilizing information from simulations, operational experience and the human factors literature. In developing guidelines, we recognize that existing regulatory and guidance material will, at times, provide adequate coverage of an area. In other cases suitable guidelines may be found in existing military or industry human factors standards. In cases where appropriate existing standards cannot be identified, original guidelines will be proposed.

Guidelines↗
NASA NTRS2014

A Family of Well-Clear Boundary Models for the Integration of UAS in the NAS

The FAA-sponsored Sense and Avoid Workshop for Unmanned Aircraft Systems (UAS) defines the concept of sense and avoid for remote pilots as "the capability of a UAS to remain well clear from and avoid collisions with other airborne traffic." Hence, a rigorous definition of well clear is fundamental to any separation assurance concept for the integration of UAS into civil airspace. This paper presents a family of well-clear boundary models based on the TCAS II Resolution Advisory logic. For these models, algorithms that predict well-clear violations along aircraft current trajectories are provided. These algorithms are analogous to conflict detection algorithms but instead of predicting loss of separation, they predict whether well-clear violations will occur during a given lookahead time interval. Analytical techniques are used to study the properties and relationships satisfied by the models.

Munoz, Cesar A.↗
NASA NTRS2014

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project - Gen-4 and Gen-5 Radio Plans

NASA's UAS Integration in the NAS project, has partnered with Rockwell Collins to develop a concept Control and Non-Payload Communication (CNPC) system prototype radio, operating on recently allocated UAS frequency spectrum bands. This prototype radio is being used to validate initial proposed performance requirements for UAS control communications. This presentation will give an overview of the current plans for the prototype radio development.

communication↗
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 NTRS2016

The Impact of Suggestive Maneuver Guidance on UAS Pilots Performing the Detect and Avoid Function

This presentation discusses the results of a recent UAS Integration into the NAS human-in-the-loop simulation. In the study, 16 active UAS pilots flew a UAS through civil airspace and were tasked with maintaining well clear from other aircraft in the area. Pilots performed the task with four different detect and avoid (DAA) traffic displays, each of which varied in the form of guidance it provided to pilots The present findings focus on how the different displays impacted pilots' measured response to scripted conflicts with their aircraft. Measured response is made up of several components, each of which help inform our understanding of the pilots' role in the overall detect and avoid task.

detect and avoid↗
NASA NTRS2015

UAS Integration into the NAS: Detect and Avoid Display Evaluations in Support of SC-228 MOPS Development

This presentation provides an overview of the work the Human Systems Integration (HSI) sub-project has done on detect and avoid (DAA) displays while working on the UAS Integration into the NAS project. Much of the work has been used to support the ongoing development of minimum operational performance standards (MOPS) for UAS by RTCA Special Committee 228. The design and results of three different human-in-the-loop simulations are discussed, with particular emphasis on the role of the UAS pilot in the Self Separation Timeline.

unmanned aircraft systems↗
NASA NTRS2016

UAS Air Traffic Controller Acceptability Study-2: Effects of Communications Delays and Winds in Simulation

This study evaluated the effects of Communications Delays and Winds on Air Traffic Controller ratings of acceptability of horizontal miss distances (HMDs) for encounters between UAS and manned aircraft in a simulation of the Dallas-Ft. Worth East-side airspace. Fourteen encounters per hour were staged in the presence of moderate background traffic. Seven recently retired controllers with experience at DFW served as subjects. Guidance provided to the UAS pilots for maintaining a given HMD was provided by information from self-separation algorithms displayed on the Multi-Aircraft Simulation System. Winds tested did not affect the acceptability ratings. Communications delays tested included 0, 400, 1200, and 1800 msec. For longer communications delays, there were changes in strategy and communications flow that were observed and reported by the controllers. The aim of this work is to provide useful information for guiding future rules and regulations applicable to flying UAS in the NAS.

Comstock, James R., Jr.↗
NASA NTRS2016

DAIDALUS Observations From UAS Integration in the NAS Project Flight Test 4

In order to validate the Unmanned Aerial System (UAS) Detect-and-Avoid (DAA) solution proposed by standards body RTCA Inc., the National Aeronautics and Space Administration (NASA) UAS Integration in the NAS project, alongside industry members General Atomics and Honeywell, conducted the fourth flight test in a series at Armstrong Flight Research Center in Edwards, California. Flight Test 4 (FT4) investigated problems of interoperability with the TCAS collision avoidance system with a DAA system as well as problems associated with sensor uncertainty. A series of scripted flight encounters between the NASA Ikhana UAS and various "intruder" aircraft were flown while alerting and guidance from the DAA algorithm were recorded to investigate the timeliness of the alerts and correctness of the guidance triggered by the DAA system. The results found that alerts were triggered in a timely manner in most instances. Cases where the alerting and guidance was incorrect were investigated further.

Vincent, Michael J.↗
NASA NTRS2017

Safely Enabling UAS Operations in Low-Altitude Airspace

NASA is developing a system to safely enable low altitude unmanned aerial system (UAS) operations. The system is referred to as UAS Traffic Management (UTM). The UTM will safely enable a variety of business models and multiple operations in the same airspace. The UTM will provide services such as airspace configuration and geo-fencing, weather and wind integration, demand-capacity imbalance management, and separation management, and contingency management. The UTM research and development has been conducted in collaboration with many in industry, academia, and government. The UTM system will evolve through four builds. Each build will be collaboratively tested with partners. The final prototype will be available for persistent daily use of UAS operations beyond visual line of sight (BVLOS).

low-altitude airspace↗
NASA NTRS2017

Requirements for an Integrated UAS CNS Architecture

Communications, Navigation and Surveillance (CNS) requirements must be developed in order to establish a CNS architecture supporting Unmanned Air Systems integration in the National Air Space (UAS in the NAS). These requirements must address cybersecurity, future communications, satellite-based navigation and APNT, and scalable surveillance and situational awareness. CNS integration, consolidation and miniaturization requirements are also important to support the explosive growth in small UAS deployment. Air Traffic Management (ATM) must also be accommodated to support critical Command and Control (C2) for Air Traffic Controllers (ATC). This document therefore presents UAS CNS requirements that will guide the architecture.

Templin, Fred L.↗
NASA NTRS2017

Implicitly Coordinated Detect and Avoid Capability for Safe Autonomous Operation of Small UAS

As the airspace becomes increasingly shared by autonomous small Unmanned Aerial Systems (UAS), there would be a pressing need for coordination strategies so that aircraft can safely and independently maneuver around obstacles, geofences, and traffic aircraft. Explicitly coordinating resolution strategies for small UAS would require additional components such as a reliable vehicle-to-vehicle communication infrastructure and standardized protocols for information exchange that could significantly increase the cost of deploying small UAS in a shared airspace. This paper explores a novel approach that enables multiple aircraft to implicitly coordinate their resolution maneuvers. By requiring all aircraft to execute the proposed approach deterministically, it is possible for all of them to implicitly agree on the region of airspace each will be occupying in a given time interval. The proposed approach lends itself to the construction of a suitable feedback mechanism that enables the real-time execution of an implicitly conflict-free path in a closed-loop manner dealing with uncertainties in aircraft speed. If a network infrastructure is available, the proposed approach can also exploit the benefits of explicit information.

Balachandran, Swee↗