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OverviewArchitectureApproach and scheduleFAA-NASA Research Transition Team deliverablesTechnical Capability Level 1 (TCL1) Demonstration overview and resultsTCL2 Demonstration overview and results.
This is a quick presentation on the UAM Research Visions which include the Commerical Operating Environment and the Grand Challenge.
Increasing demand for improved capabilities of small unmanned aircraft systems (sUAS) has generated interest in improving the design techniques for these vehicles. sUAS have typically been designed using iterative methods with multiple prototypes, but advancements in aircraft design software will make it possible to generate conceptual designs of very small VTOL aircraft with reduced hardware prototyping. This paper describes a research effort to generate a conceptual design of an approximately 6-lb quadcopter using the NASA rotorcraft design software NDARC. Wind tunnel and hover test data are used to validate and refine the conceptual design results. The effects of parametric design variations on vehicle scale are shown. The design study described herein shows that the NDARC software, which was designed for full-scale rotorcraft, can be used to design and evaluate sUAS vehicles.
This presentation examines the UTMs second mantra, a risk-based approach where geographical needs and use cases determine the airspace performance requirements and draws lessons from geofence buffer sizing research and trajectory conformance requirement research.
Concept of operations; Roles, responsibilities, implications on who pays; Information architecture paved way for FAA's (Federal Aviation Administration's) RFI (Request for Information); Demonstrated initial feasibility of architecture, application-protocol-interface-based approach, and overall construct; Data exchange and protocols; Demonstration of UTM (Unmanned Aerial Systems Traffic Management) TCL1 (Tuscaloosa, AL Airport Terminal 1) with all 6 test sites; Initial demonstration of UTM TCL2 for BVLOS (Beyond Visual Line-of-Sight) requirements.
In support of NASA’s Unmanned Aircraft Systems Integration in the National Airspace System project and RTCA Special Committee 228, an analysis has been performed to provide insight in to the trade space between unmanned aircraft speed and turn capability and detect and avoid sensor range requirements. The work was done as an initial part of the effort to understand low size, weight, and power sensor requirements for aircraft that have a limited speed envelope or can limit the envelope for portions of their mission and may be able to turn at higher than “standard rate.” Range and timeline reductions coming from limiting speed range and from increasing available turn rate in some speed ranges are shown.
In support of NASA’s Unmanned Aircraft Systems Integration in the National Airspace System project and RTCA Special Committee 228, an analysis has been performed to provide insight in to the trade space between detect and avoid (DAA) Well Clear definition threshold variations, which could affect DAA sensor range and alerting requirements.
This is a redacted version of the 2018 annual report presented to NASA shareholders for general release.
The National Aeronautics and Space Administration’s (NASA) Unmanned Aircraft Systems Traffic Management (UTM) project works to develop tools and technologies essential for safely enabling civilian low-altitude small Unmanned Aerial Systems (sUAS, also known as drones) operations. This paper presents results of work completed in the paper [1] presented at the 2018 ICNS conference where proposed approaches were explored for evaluating and analyzing sUAS Command and Control (C2) links based on commercial cellular networks. This paper focuses on the UTM Project’s Technology Capability Level 3 (TCL-3) test results which address the communications portion identified within the same paper. A software defined radio (SDR) was flown as a sUAS payload to capture received signal spectrum in Long Term Evolution (LTE) frequency bands of interest. The purpose was to measure the RF environment at UTM altitudes to characterize the interference potential. The SDR payload was flown at various stationary altitudes where the LTE over-the-air complex (I/Q) samples were captured by the SDR and later post-processed. The SDR received inputs through an omnidirectional antenna. The complex samples captured were an aggregate of transmissions received from all line-of-sight (LOS) towers within the geographic area for the specific radio frequency bandwidth the SDR is programmed to capture. Using this approach, the complex samples captured do not distinguish between the various eNodeB's (Long Term Evolution (LTE) transmitting towers). The complex samples were post processed via a Discrete Fourier Transform (DFT) algorithm to view the captured spectrum along with the power levels across the captured LTE bandwidth. This SDR payload process of capturing complex samples was done at two different regions within the US: 1) NASA's Ames Research Center (ARC) in Moffett Field, CA, and 2) Griffiss Airfield in Rome, NY. The data capture at the ARC site was done at two physical locations within the Ames campus where many stationary altitude captures where done as high as 800 ft. above ground level (AGL). The data captured at the Griffiss Airport (also known as the NY Corridor Site) were acquired at one location with three specific stationary altitude levels – {Ground Level (GL), 300 ft., and 400 ft.}. The LTE spectrum power levels were captured for two LTE carriers, AT&T and Verizon, at both sites where their respective spectra and power levels were measured and compared at various altitudes. The overall results show that there is an increase in LTE spectrum power levels at higher altitudes for drones. A detailed analysis of this data and conclusions drawn from the results are presented in this paper.
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.
Recent advances in small Unmanned Aerial System (sUAS) technologies lower the barriers for use by both private and commercial entities. However, these advances are also likely to lead to greater vehicle densities, a more heterogenous mix of vehicles and equipment and greater levels of vehicle autonomy, which can increase the chance for communications disruptions. For the safe and secure operation of these vehicles, it is essential to have a robust communications network. This work is focused on harnessing the power of quantum technologies to enable this robust communications network by: (1) utilizing quantum optimization algorithms to design robust network with routing redundancy that can respond adaptively to dynamically changing real-time environment and disruptions, (2) utilize quantum optimization algorithms resource allocation for detection, localization, and tracking of mobile communication disruption agents and (3) utilize quantum key distribution (QKD) to execute secure key sharing in anti-jamming protocols for secure radio frequency (RF) communication. Efforts to map these quantum optimization algorithms to commercially available quantum annealers and soon to be available general-purpose gate-model quantum hardware architectures will be reviewed, and plans for testing the solutions to these algorithms through indoor sUAS flight tests will be discussed. Lastly, efforts to miniaturize and practically deploy Quantum Key Distribution (QKD) hardware, which could ultimately be used to securely exchange encryption keys, in sUAS networks will be reviewed.
Space is becoming increasingly congested as the number of on-orbit satellites and debris objects continues to grow. Space traffic management (STM) is critical for ensuring that the expanding orbital population operates safely and efficiently, avoiding collisions and radio-frequency interference while still facilitating widespread space operations. Recent events such as the FCC approval of SpaceX’s ~12,000 satellite constellation, the signing of Space Policy Directive 3 (which moves Space Situational Awareness responsibilities away from the Department of Defense and to a civil agency), and the growth in rideshare and small launch vehicles illustrate the rapidly changing nature of this domain. This paper will describe the concept of operations (ConOps) for a civilian STM research initiative, which has been developed from previous NASA work to enable safe operation of small unmanned aircraft systems. The STM ConOps proposes an architecture to enable efficient data sharing and coordination between participants to facilitate safe spaceflight operations. It is designed to utilize and promote the emerging field of commercial STM services, as a complement to existing government-provided STM services. The concept envisions a phased evolution that would gradually integrate additional capabilities, proposing a first phase architecture and tentative plans for a broader system. Work towards developing an STM research and prototyping platform is also discussed.
This presentation is for the Plenary Session: "Unmanned Traffic Management (UTM) and the Future of Unmanned Systems in Urban Airspace" at the ASCE flagship 2019 International Conference on Transportation and Development, "Engineering Smart Mobility for the Smart City", June 9-12, 2019. The Panel is at 8:00 am on Wednesday, June 12 and moderated by Brent Ingraham (DOD). This presentation supplements Dr. Kopardekar's short introduction on how NASA is developing an Unmanned Traffic Management system and how this UTM will change the future of shared airspace.
No abstract available
These slides present NASA's vision, historical overview , and current Unmanned Systems projects at AFRC.
Public version of the presentation given to the IASP group on the progress for annual year 2019.
No abstract available