Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “UAs”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗
NASA NTRSDate not supplied

Effects of Unmanned Aircraft Voice Communication Delay on En Route Air Traffic Management Operations

The present study investigated the effects of remotely piloted unmanned aircraft (UA) voice communication delay on air traffic management operations with different background sector traffic-volume levels. Three one-way UA voice delay lengths of 400, 900, and 2,000 milliseconds (ms) were tested, representing the currently estimated transmission delays for terrestrial, satellite communication (SATCOM), and long SATCOM Command and Control Link System, respectively. All delay values exceeded 390 ms, the FAA’s current requirement for the maximum communication latency in the National Airspace System. Eight retired en route air traffic controllers (ATCs) and eight remote pilots (RPs) participated in a human-inthe-loop simulation study, where a simulated UA flight transitioned through an Oakland en route low-altitude sector to a local non-towered airport. The results showed that, when the 2,000-ms voice delay was present, radio transmission step-ons and detect-and-avoid (DAA) alert level elevations increased. In addition, ATC workload, ATC acceptability for the UA, and RP acceptability for the DAA were negatively affected only when the UA had a 2,000-ms voice delay under the high traffic-volume condition, but not in the other conditions.

remotely piloted unmanned aircraft, voice delay, s↗
NASA NTRS2022

Effects of Unmanned Aircraft Voice Communication Delay on En Route Air Traffic Management Operations

The present study investigated the effects of remotely piloted unmanned aircraft (UA) voice communication delay on air traffic management operations with different background sector traffic-volume levels. Three one-way UA voice delay lengths of 400, 900, and 2,000 milliseconds (ms) were tested, representing the currently estimated transmission delays for terrestrial, satellite communication (SATCOM), and long SATCOM Command and Control Link System, respectively. All delay values exceeded 390 ms, the FAA’s current requirement for the maximum communication latency in the National Airspace System. Eight retired en route air traffic controllers (ATCs) and eight remote pilots (RPs) participated in a human-in-the-loop simulation study, where a simulated UA flight transitioned through an Oakland en route low-altitude sector to a local non-towered airport. The results showed that, when the 2,000-ms voice delay was present, radio transmission step-ons and detect-and-avoid (DAA) alert level elevations increased. In addition, ATC workload, ATC acceptability for the UA, and RP acceptability for the DAA were negatively affected only when the UA had a 2,000-ms voice delay under the high traffic-volume condition, but not in the other conditions.

remotely piloted unmanned aircraft, voice delay, s↗
NASA NTRS2022

Using Trajectory Smoothness Metrics to Identify Drones in Radar Track Data

The identification of unmanned aircraft systems (UAS) using trajectory data is considered. Specifically, a number of smoothness metrics are proposed, which can be used to distinguish UAS from other aerial objects even when they are engaged in accelerative maneuvers (non-constant-velocity flight). The metrics are evaluated on a data set from a UAS sense-and-avoid field test, which contains track data of aerial objects recorded by a vehicle-board radar system during a flight test. The metrics are found to effectively differentiate UAS from other objects such as birds for this data set. In addition, an initial statistical performance analysis of one of the smoothness metrics is undertaken, using 15 data sets deriving from multiple flight tests. The smoothness metric is shown to identify the target UAS with 95% accuracy (95% true positive rate), while achieving a false positive rate of less than 9%.

Sandip Roy↗
NASA NTRSDate not supplied

Initial Assessment of Lost Command and Control Link Procedures

This paper presents an initial assessment of lost command and control (LC2L) procedures for large Uncrewed Aircraft Systems (UAS) in a simulated representative airspace environment using real airspace procedures. The experiment matrix consists of nine different flight routes: four nominal, four following current LC2L procedures, and one that routes the UAS more conservatively through less-busy airspace. For each route, eleven simulated UAS flights – in ten-minute increments – were flown into Fort Worth Alliance Airport following a real Instrument Approach Procedure. The simulated UAS flew amongst real recorded tracks of approximately 4,700 flights on January 18, 2022. The analysis focused primarily on the number of aircraft with which each UAS lost separation and where the losses occurred. This work presents a significant increase in testing capability and provides the foundation for further verification and validation of LC2L procedures using additional analysis metrics.

Uncrewed aircraft↗
NASA NTRSDate not supplied

Initial Assessment of Lost Command and Control Link Procedures

This paper presents an initial assessment of lost command and control (LC2L) procedures for large Uncrewed Aircraft Systems (UAS) in a simulated representative airspace environment using real airspace procedures. The experiment matrix consists of nine different flight routes: four nominal, four following current LC2L procedures, and one that routes the UAS more conservatively through less-busy airspace. For each route, eleven simulated UAS flights – in ten-minute increments – were flown into Fort Worth Alliance Airport following a real Instrument Approach Procedure. The simulated UAS flew amongst real recorded tracks of approximately 4,700 flights on January 18, 2022. The analysis focused primarily on the number of aircraft with which each UAS lost separation and where the losses occurred. This work presents a significant increase in testing capability and provides the foundation for further verification and validation of LC2L procedures using additional analysis metrics.

uncrewed aircraft↗
NASA NTRS2016

Application of an ADS-B Sense and Avoid Algorithm

The National Aeronautics and Space Administration Armstrong Flight Research Center in Edwards, California is leading a program aimed towards integrating unmanned aircraft system into the national airspace system (UAS in the NAS). The overarching goal of the program is to reduce technical barriers associated with related safety issues as well as addressing challenges that will allow UAS routine access to the national airspace. This research paper focuses on three novel ideas: (1) A design of an integrated UAS equipped with Automatic Dependent Surveillance-Broadcast that constructs a more accurate state-based airspace model; (2) The use of Stratway Algorithm in a real-time environment; and (3) The verification and validation of sense and avoid performance and usability test results which provide a pilot's perspective on how our system will benefit the UAS in the NAS program for both piloted and unmanned aircraft.

ADS-B↗
NASA NTRS2016

National Unmanned Aerial System Standardized Performance Testing and Rating (NUSTAR)

The overall objective of the NUSTAR Capability is to offer standardized tests and scenario conditions to assess performance of the UAS. The following are goals of the NU-STAR: 1. Create a prototype standardized tests and scenarios that vehicles can be tested against. 2. Identify key performance parameters of all UAS and their standardized measurement strategy. 3. Develop standardized performance reporting method (e.g., consumer report style) to assist prospective buyers. 4. Identify key performance metrics that could be used by judged towards overall safety of the UAS and operations. 5. If vehicle certification standard is made by a regulatory agency, the performance of individual UAS could be compared against the minimum requirement (e.g., sense and avoid detection time, stopping distance, kinetic energy, etc.).

Testing↗
NASA NTRS2016

Integrated Test and Evaluation (ITE) Flight Test Series 4

The integrated Flight Test 4 (FT4) will gather data for the UAS researchers Sense and Avoid systems (referred to as Detect and Avoid in the RTCA SC 228 ToR) algorithms and pilot displays for candidate UAS systems in a relevant environment. The technical goals of FT4 are to: 1) perform end-to-end traffic encounter test of pilot guidance generated by DAA algorithms; 2) collect data to inform the initial Minimum Operational Performance Standards (MOPS) for Detect and Avoid systems. FT4 objectives and test infrastructure builds from previous UAS project simulations and flight tests. NASA Ames (ARC), NASA Armstrong (AFRC), and NASA Langley (LaRC) Research Centers will share responsibility for conducting the tests, each providing a test lab and critical functionality. UAS-NAS project support and participation on the 2014 flight test of ACAS Xu and DAA Self Separation (SS) significantly contributed to building up infrastructure and procedures for FT3 as well. The DAA Scripted flight test (FT4) will be conducted out of NASA Armstrong over an eight-week period beginning in April 2016.

ITE↗
NASA NTRS2017

Maintain and Regain Well Clear: Maneuver Guidance Designs for Pilots Performing the Detect-and-Avoid Task

In order to support the future expansion and integration of Unmanned Aircraft Systems (UAS), ongoing research efforts have sought to produce findings that inform the minimum display information elements required for acceptable UAS pilot response times and traffic avoidance. Previous simulations have revealed performance benefits associated with DAA displays containing predictive information and suggestive maneuver guidance tools in the form of banding. The present study investigated the impact of various maneuver guidance display configurations on detect-and-avoid (DAA) task performance in a simulated airspace environment. UAS pilots ability to maintain DAA well clear was compared between displays with either the presence or absence of green DAA bands, which indicated conflict-free flight regions. Additional display comparisons assessed pilots ability to regain DAA well clear with two different guidance presentations designed to aid in DAA well clear recovery during critical encounters. Performance implications and display considerations for future UAS DAA systems are discussed.

unmanned aircraft systems (UAS)↗
NASA NTRS2017

Maintain and Regain Well Clear: Maneuver Guidance Designs for Pilots Performing the Detect-and-Avoid Task

In order to support the future expansion and integration of Unmanned Aircraft Systems (UAS), ongoing research efforts have sought to produce findings that inform the minimum display information elements required for acceptable UAS pilot response times and traffic avoidance. Previous simulations have revealed performance benefits associated with DAA displays containing predictive information and suggestive maneuver guidance tools in the form of banding. The present study investigated the impact of various maneuver guidance display configurations on detect-and-avoid (DAA) task performance in a simulated airspace environment. UAS pilots ability to maintain DAA well clear was compared between displays with either the presence or absence of green DAA bands, which indicated conflict-free flight regions. Additional display comparisons assessed pilots ability to regain DAA well clear with two different guidance presentations designed to aid in DAA well clear recovery during critical encounters. Performance implications and display considerations for future UAS DAA systems are discussed.

user interface design↗
NASA NTRS2016

Unmanned Aircraft System Traffic Management (UTM) Concept of Operations

Many applications of small Unmanned Aircraft System (sUAS) have been envisioned. These include surveillance of key assets such as pipelines, rail, or electric wires, deliveries, search and rescue, traffic monitoring, videography, and precision agriculture. These operations are likely to occur in the same airspace in presence of many static and dynamic constraints such as airports, and high wind areas. Therefore, small UAS, typically 55 pounds and below, operations need to be managed to ensure safety and efficiency of operations is maintained. This paper will describe the Concept of Operations (ConOps) for NASA's UAS Traffic Management (UTM) research initiative. The UTM ConOps is focused on safely enabling large-scale small UAS (sUAS) operations in low altitude airspace. The UTM construct supports large-scale visual line of sight and beyond visual line of sight operations. It is based on two primary mantras: (1) flexibility where possible and structure where necessary (2) a risk-based approach where geographical needs and use case indicate the airspace performance requirements. Preliminary stakeholder feedback and initial UTM tests conducted by NASA show promise of UTM to enable large-scale low altitude UAS operations safely.

autonomy↗
NASA NTRSDate not supplied

HAT m:N Cognitive Task Analysis (CTA)

This Cognitive Task Analysis (CTA) study was designed to understand the capability of the m:N Tactical Operator (TO) interfaces developed by the Human-Autonomy Teaming Laboratory at NASA Ames to support operators responsible for simplified pilot operations of 100 independently operated small UAS (sUAS) in a constrained geographic area. The m:N sUAS TO interface includes a central Tactical Situation Display (TSD) digital map with moving icons reflecting the sUAS location and planned flight route. The interface also has two side panels. The left panel includes a tabular list of UAS assets and mission tasking, a list of recently viewed assets, and a list of events and alerts. The right panel includes a tabular list of UAS assets and their associated telemetry, text-based chat communication window, and a tabbed checklist window. This CTA was adapted from the incident-based applied cognitive task analysis (Militello & Hutton, 1998) and included demographics questions, scenario-based simulations, a task diagram and knowledge audit methods. In addition to examining the support provided by this m:N sUAS TO interface, this CTA study, conducted with aviation subject matter experts in analogous roles to the future tactical operator, was designed to illuminate and project likely cognitive requirements of the tactical operator. Interviewees participated in two scenario-based simulations using the m:N sUAS TO interfaces. In the first simulation, the interviewees supervised 12 sUAS operating in downtown San Diego, California transiting to and from a central sUAS Hive, restaurant locations, and customer drop off locations. Interviewees were asked to react to a UAS Volume Reservation (UVR) event with a two-phase impact on food delivery operations. In the second scenario, the interviewees supervised 100 sUAS operating in the same airspace and with the same mission. Interviewees used the interfaces to recognize and react to two sUAS air vehicle problems. After each scenario, we asked the interviewees a semi-structured list of questions to elicit their reflections about using the interfaces. Interviewees were confident in their ability to respond to two off-nominal situations in each simulation. Interviewees felt that, given high levels of automation on the sUAS, they would be able to manage the events without requiring additional support or handing off the sUAS to a colleague or supervisor in both the n=12 or n=100 sUAS settings. In the n=12 sUAS condition, interviewees used the center map to understand the asset location and progress along with mission tasking. An additional display window, Asset Telemetry, helped interviewees understand battery state and sUAS altitude. When the number of sUAS increased, interviewees altered their behavior. Rather than maintaining awareness of individual assets, interviewees appeared to become more reactive, managing exceptions. Interviewees reported that they spent less time looking at the nominal aircraft, and focused their attention primarily on the off-nominal aircraft. In addition, in the second simulation with n=100 sUAS, interviewees reported that they relied more on the side panels (Mission Timeline and Asset Telemetry) to gather information.

human-autonomy teaming↗
NASA NTRSDate not supplied

Overview of NASA’s Extensible Traffic Management (xTM) Work

NASA’s Unmanned Aircraft Systems (UAS) Traffic Management (UTM) project introduced a new Air Traffic Management (ATM) architecture that utilizes industry’s ability to supply industry-developed, third-party services that work complementarily with the FAA-provided Air Traffic Service (ATS) to exchange relevant air vehicle information among the UAS operations and between the UTM and the conventional ATM system. The UTM architecture was used to successfully demonstrate the feasibility of safe, efficient, and scalable small UAS operations in low altitudes below 400 feet above ground level. Following the success and adoption of UTM architecture, the foundational UTM requirements and core properties were generalized to become Extensible Traffic Management (xTM) requirements to support operations of new entrants beyond small UAS, such as operations in high altitudes over 60,000 feet, designated as upper Class E in the United States National Airspace System (NAS). In this paper, the generalization of UTM to xTM and NASA’s approach for developing an xTM system for upper Class E Traffic Management (ETM) are discussed. The paper also discusses the planned research to examine the potential xTM-Air Traffic Control (ATC) interactions across multiple xTM systems and identify common coordination procedures, ATC roles/responsibilities, and data exchange requirements. This work is one of the steps for improving interoperability between the xTM systems and ATS, which is critical for safe and efficient sharing of the airspace among the new entrants served by the xTM systems and conventional ATS-serviced operations.

air traffic management↗
NASA NTRS2022

m:N Working Group

On November 29th and 30th, 2022, the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Ames Research Center in Mountain View, CA for an in person meeting. The option to dial in remotely and use Conference.IO to engage with questions was offered as well. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA and RTCA’s SC-228 Detect and Avoid. Each subgroup is run by a government/industry team.

multi-vehicle↗
NASA NTRS2023

m:N Working Group Annual Status Report

This document serves as an annual report of m:N Unmanned Aircraft Systems (UAS) subgroup activities, addressed challenges, and roadmaps for the future. The subgroups consist of small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM). Also included in this report are participant lists for each subgroup (Appendix B) and future roadmap and outreach plans.The subgroups meet in a virtual format multiple times throughout the year. Twice a year, participants from all the subgroups come together as part of the m:N working group to brief each other in person on progress, challenges, and path forward ideas for successful incorporation of UAS into the airspace. Recent m:N in person meetings include: - November 29-30, 2022 at the NASA Ames Research Center in Mountain View, CA - May 9 & 11, 2023 at AUVSI’s Xponential conference in Denver, CO - The next in person working group meeting is planned for November 28-30, 2023 at NASA Langley in Hampton, Virginia The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the Federal Aviation Administration (FAA) and Radio Technical Commission for Aeronautics (RTCA’s) SC-228 Detect and Avoid committee.

multi-vehicle↗
NASA NTRS2024

m:N Working Group: Meeting Summary March 2024

From March 26th to 28th, 2024 the m:N UAS working group and its subgroups (Evaluation Methodologies, Exceptions/Interventions, and Initial Operating Capability for Airspace Integration) met at SAIC in Washington, D.C. for an in-person meeting. The subgroups meet virtually throughout the year, and twice a year participants from all the subgroups come together to further identify and discuss challenges and paths forward for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (DroneUp) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This includes identifying requirements, use cases, metrics, and the development of white papers to support organizations including the FAA, RTCA, and ASTM. A change from last year, the Large UAS and HAPS sub working groups have disbanded while the sUAS working group continues independently, currently working on a white paper titled Personnel Selection, Roles, and Training for sUAS. For 2024 the m:N sub working groups have been refocused to cover evaluation methodologies, interventions/exceptions, and initial operating capability for airspace integration; with the premise that the outcomes from these subgroups will be white papers. These white papers can inform one another to ultimately become a master whitepaper. Each subgroup lead is called out below: Evaluation Methodologies Subgroup Jay Shively, Adaptive Aerospace Interventions/Exceptions Subgroup Andy Thurling, DroneUp (Lead) Initial Operating Capability for Airspace Integration Subgroup Andy Lacher, NASA (Lead)

m:N operations↗
NASA NTRS2003

Status Report and Lessons Learned from the Univ. of Arizona NMSD

We will present the latest generation of space mirror technology being developed at the Univ. of Arizona (UA). Unlike conventional monolithic mirrors, the UA mirrors are completely active in their operation. This allows greater flexibility in the mass, volume and performance specifications. The UA mirror design uses a thin flexible substrate for the optical surface and an actuated lightweight structure for surface accuracy and support. We provide an update on the UA NGST Mirror System Demonstrator (NMSD). The 2-m, f/5 NMSD mirror uses a 2 mm thick glass substrate and weighs 86 pounds. We review the mirror's design, discuss the mythology schemes used to actuate the figure, and present a list of the lessons learned.

Baiocchi, Dave↗