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Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project
Public version of the presentation given to the IASP group on the progress for annual year 2019.
Unmanned Aircraft System Traffic Management (UTM) Project
The presentation covers a high level summary of the UTM project and TCL 4 testing in 2019.
Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: Flight Test Series 6 Results SC-228 WG1 Outbrief
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Investigation of Longitudinal Aero-Propulsive Interactions of a Small Quadrotor Unmanned Aircraft System
Aerodynamic propulsive and airframe interactions significantly influence the forces and moments of multirotor vehicles. This paper presents recent results studying the influence of these effects on a small quadrotor vehicle in hover and forward flight. Aerodynamic interaction model development was explored utilizing system identification tools and incorporated into a previously developed NASA quadrotor flight dynamic simulation. The aerodynamic interaction model improved accuracy of the simulation in comparison to free-flight and static wind tunnel results. Comparisons between models of different fidelity were completed to demonstrate the effects of aerodynamic interaction on model accuracy and provide recommendations for modelling requirements.
Unmanned Aircraft System Traffic Management (UTM) Project
This is a slide set for use in presenting an overview of NASA's UAS Traffic Management (UTM) Project and most recent activites. The contents of the slides describe the background and need that led to the development of the UTM concept as well as a high level explanation of what UTM is and how it addresses the identified needs. The architecture that was developed to support the UTM environment and its interface with the National Airspace System is also included. The approach that the Project undertook in framing the environments and Technical Capability Levels of its research is also presented with further detail given with respect to the final, large-scale event conducted in 2019. Finally, the broad impact and influence that the project and its activites had across many areas is presented as a concluding statement.
Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project Closeout Technical Interchange Meeting September 30, 2020 - October 1, 2020
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Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project - Closeout Technical Interchange Meeting September 30, 2020 - October 1, 2020
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Scalable Traffic Management for Emergency Response Operations (STEReO)Airspace Operations Laboratory (AOL) Unmanned Aircraft Systems (UAS)Traffic Management (UTM) (AOLUSS)Tabletop Overview
The Scalable Traffic Management for Emergency Response Operations (STEReO) project requires prototyping software to extend existing Airspace Operations Laboratory (AOL) software capabilities to support research in integrating drone flight activities within an emergency response airspace environment. A presentation encompassing project AOLUSS details, communications and network design will be presented at a Workshop.
Unmanned Aircraft Systems Traffic Management (UTM) Project: Briefing to Mass Robotics
Overview of the UTM system and what it means to the UAS community
Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project Project Closeout Review November 17, 2020
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Unmanned Aircraft Systems (UAS) Traffic Management Project (UTM) Small UAS Communications and Navigation
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Unmanned Aircraft Systems (UAS) Traffic Management (UTM): Past, Present, and Future of UTM
• Develop and validate airspace operations and integration requirements to enable safe, large-scale UAS operations. • Provide prototype (software) UTM system for further FAA testing and development.
Unmanned Aircraft System Flight Test Approach Supporting the Development of Regulatory Recommendations for Integration with the National Airspace System
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UAS Pilot Assessments of Display and Alerting for the Next Generation Airborne Avoidance System
Unmanned aircraft systems (UAS) must comply with specific standards to operate in the National Airspace System (NAS). Among the requirements are the detect and avoid (DAA) capabilities, which include display, alerting, and guidance specifications. Previous studies have queried pilots for their subjective feedback on these display elements on earlier systems; the present study sought pilot evaluations with an initial iteration of the unmanned variant of a Next Generation Airborne Collision Avoidance System (ACAS XU). Sixteen participants piloted simulated aircraft with both standalone and integrated DAA displays. Their opinions were gathered using post-block and post-simulation questionnaires as well as guided debriefs. The data showed pilots had better understanding and comfort with the system when using an integrated display. Pilots also rated ACAS Xu alerting and guidance as generally acceptable and effective. Implications for further development of ACAS XU and DAA displays are discussed.
Defining the Collision Avoidance Region for DAA Systems
Unmanned aircraft systems (UAS) will be required to equip with a detect-‐and-‐avoid (DAA) system in order to satisfy the federal aviation regulations to maintain well clear of other aircraft, some of which may be equipped with a Traffic Collision Avoidance System (TCAS) to mitigate the possibility of mid-‐air collisions. As such, the minimum operational performance standards (MOPS) for UAS DAA systems are being designed with TCAS interoperability in mind by a group of industry, government, and academic institutions named RTCA Special Committee-228 (SC-228). This document will discuss the development of the spatial-‐temporal volume known as the collision avoidance region in which the DAA system is not allowed to provide vertical guidance to maintain or regain DAA well clear that could conflict with resolution advisories (RAs) issued by the intruder aircraft's TCAS system. Three collision avoidance region definition candidates were developed based on the existing TCAS RA and DAA alerting definitions. They were evaluated against each other in terms of their interoperability with TCAS RAs and DAA alerts in an unmitigated factorial encounter analysis of 1.3 million simulated pairs.
Investigating Surveillance Performance for UAS Detect-and-Avoid Systems
Most unmanned aircraft systems will be required to be equipped with a detect-and-avoid system that is capable of maintaining appropriate separation from other aircraft. One of the critical components of detect-and-avoid systems is a surveillance system that identifies potential threat aircraft in real time and tracks these aircraft so that their future trajectories may be used to predict conflicts. The performance of the detect-and-avoid system generally depends on technical parameters of the surveillance system, such as the surveillance range. The quantitative requirements for detect-and-avoid systems will be determined to meet safety metrics for the operation of unmanned aircraft systems in the National Airspace System. This study employs a sensor model comprised of the surveillance range, and horizontal and vertical fields of regard that mainly characterize the overall performance of a surveillance system. In this study, potential metrics for evaluating the performance of a surveillance system were investigated through fast-time simulation with a traffic scenario that included both proposed unmanned aircraft flights and historical visual flight rule aircraft tracks. Using the simulation results, an overall analysis of encounter geometry highlights the encounter characteristics that relate surveillance parameters to safety metrics and detect-and-avoid system performance. Then, given several candidate surveillance volumes, performance and safety metrics are derived; these metrics include the ratio of undetected and late-detected violations and the time to violation at first detection. These example metrics demonstrate the utility of the database of encounters created in this work, a database which will be useful in the derivation of required detect-and-avoid surveillance system requirements.