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NASA NTRS2011

UAS Modeling of the Communication Links Study Results

There were many links calculated for this and the other scenarios. The rain was analyzed for 99.9% availability with rain rated of none, 20 mm/hr and 90 mm/hr at a height of 5 km out to 25 NM. This was done for each scenario for LOS and for BLOS links for Scenario 5 and 6. Scenario 1 was a LOS-only scenario. Use of two 3 dB Antennas on both ends. The CS2 was unable to maintain a control RF Link during the flight. The largest access gap periods between object top and bottom UA antennae were caused by terrain (ridges and hills). The CS Antenna was changed to High Gain Directional Antenna, all three CS maintained lock on vehicle. There were RF dropouts between the top and bottom UA antennae caused by aircraft obstructions (fuselage, wings, wheel assembles, etc.). Note that for this study antenna locations were placed on top and bottom center of the UA body. Future study should include actual UA antenna locations on the aircraft providing manufactures are willing to provide information. The importance of CS location(s) was demonstrated for primary or backup CS. With a second backup CS placed in a suitable location the UA was able to maintain an overall RF link. The actual location of both backup CSs required the antenna location to be place 150 ft above ground in order to establish a RF link between the UA and CS.

Birr, Richard↗

NASA NTRSDate not supplied

Spatial and Temporal Deconfliction of Trajectories in the Presence of Uncertainties

Demonstration of conflict-free movement for multi-agent teams in challenging scenarios is crucial in developing trust and trustworthiness in an autonomous transport system. Tolerance verification queries are explored as a mechanism to enforce spatial and temporal deconfliction for a cooperating team of Unmanned Aerial Systems (UAS) with prescribed heterogeneous path-following performance. Obstacles in the environment are modelled as set of polyhedra, whereas each vehicle’s trajectory is represented as a sequence of polynomial curves with C2 continuity, expressed in a Bernstein basis. Each vehicle is modelled as a point mass and a safety distance, informed by the geometry of the UAS and the worst-case path-following error. This defines a tube around the trajectories where each UAS is most likely to fly through. In addition, obstacles in the environment have an associated safety buffer around them to account for the uncertainty in their location and geometric description. The tolerance verification queries explored in this paper combine the safety distance information from each UAS and environmental hazard to compute trajectories that are contained within the safe configuration space. Tolerance verification is also compared with other proximity queries to determine the suitability of each method along the different steps of the trajectory generation algorithm. This paper analyzes the fitness and performance of three proximity queries – collision, tolerance verification, and distance computations – between polyhedral and polynomial curves to ensure deconfliction between obstacles and vehicles, but also between polynomial curves to guarantee safe separation among cooperating UAS.

trajectory generation↗

NASA NTRS2024

Concept, Design, & Implementation of a Remote Vehicle Operations Center for Autonomous Missions

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.

CERTAIN↗

NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗

NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗

NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗

NASA NTRS2020

TCL4 UTM (UAS Traffic Management) Nevada 2019 Flight Tests, Airspace Operations Laboratory (AOL) Report

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, NASA has organized a series of flight test demonstrations. Technology Capability Level-4 (TCL4) flight tests were conducted at a Nevada, USA test site, during June 2019. The testing resulted in over 300 data collection flights using eight live rotorcraft, 15 simulated vehicles, involving six flight crews and five Unmanned Aerial System (UAS) Service Suppliers (USS). The TCL4 approach was designed to demonstrate five scenarios that set up five diverse sets of UAS events and activities. The Nevada test site focused on three of these scenarios: an incoming weather front, a concert event with an incident requiring an emergency response, and a scenario where multiple vehicles experienced Communication, Navigation, and Surveillance (CNS) issues. The test site created their scenarios to each have three phases and were required to complete three executions of each scenario, for a total of nine missions per Nevada vehicle per scenario. This document presents data collected from participants during the TCL4-Nevada flight test that provides information about how much and how well operators were able to make use of UTM functions and information, with the goal of exploring what the minimum information requirements and/or best practices might be in TCL4 operations. The driving enquiry was: how do UTM tools and features support (human) operators leading to safe and effective conduct of large-scale beyond visual line of sight (BVLOS) sUAS operations in “urban canyon” environments? As with the data collected during previous similar tests (e.g., TCL3, Martin et al., 2019), the quality of the UTM information exchanged, and the meaningfulness and therefore usefulness of this information, were all focal points of the questions asked and the data collected. Data aligned with five human-system attributes to indicate that UTM provided information that contributed to users’ ability to operate safely and effectively within UTM, but that information was not always complete and was sometimes unclear.

UTM↗

NASA NTRS2014

User Interaction with the CNPC Link

As part of NASAs UAS in the NAS project, human factor guidelines are being developed for the ground control station of unmanned aircraft systems. A critical task for the pilot of a UAS is the management and control of the terrestrial radio links used to provide control of the vehicle and relay communications with air traffic control. An analysis of the task of the UAS pilot has been conducted to identify areas where guidelines are required. This analysis has identified a set of information requirements that must be supported by the radio equipment used to provide Control and Non-payload Communication (CNPC). Key requirements include the need for an alert when the link is lost, information on the current quality and latency of the link, and information to enable the future quality of the link to be anticipated as the aircraft changes location. These identified requirements can assist in the development of minimum operational performance standards for CNPC equipment.

unmanned aerial systems↗

NASA NTRS2015

Design, Development, and Testing of a Compound Wing V/TOL small UAS

This paper discusses the development and testing of an innovative small UAS (Unmanned Aircraft System). The design of the vehicle was driven by the need to both have long endurance yet still have the convenience of V/TOL (Vertical Take-Off and Landing) operation. The paper discusses some of the design considerations and configurations evaluated in searching for a configuration that met the demanding mission requirements. The paper also discusses some aspects of the compound wing and experimental testing conducted to discern the optimum parameters for the wing's design. The paper discusses the results of the preliminary flight testing and outlines further research to be conducted.

Logan, Michael J.↗

NASA NTRSDate not supplied

Design of a Propeller with Global Minimum Torque

Academia, industry, and Government are actively working towards a future where dozens of small to large Unmanned Aerial Systems (UAS) are present within urban environments. In this environment, high noise pollution and inefficiencies have the potential to render these new technologies impractical due to public annoyance and nonacceptance. This study aims to design and analyze a novel propeller design for minimum global torque using an optimization algorithm to find the full three dimensional solution of maximum efficiency. This resulted in a 𝐶 𝑙 distribution defining the global minimum torque solution for propeller design which corresponded with moving as much lift inboard as possible and reducing lift rapidly at the blade tip. The novel propeller (dubbed "Prandtl" propeller) was then compared with the current gold standard in propeller design, a minimum induced loss (MIL) propeller, which had all the same geometric properties except for blade twist and produced the same amount of thrust at the same advance ratio. The results of multiple iterations of Prandtl propellers showed an increase in efficiency of 2.0-3.3% for the Prandtl blade when compared to the equivalent MIL blade. A potential added benefit of this blade design is lower noise generation due to the lower lift loading at the tip reducing the large shear layer intensity which is the point source of noise in propellers. Two major breakthroughs for enabling widespread use of UAS in urban environments are noise reduction and vehicle efficiency, and this new propeller design has the potential to provide both.

Propeller↗

NASA NTRSDate not supplied

Design of a Propeller with Global Minimum Torque

Propeller↗

NASA NTRS2015

Urban SAFE50: Modeling, Controlling, and Testing Safe UAS Operations in Low Altitude Settings

The research and development of UAVs (Unmanned Aerial Vehicles) are quickly progressing as industries and hobbyist societies recognize their utility. NASA is focused on the technology development and safety considerations surrounding commercial use of UAVs. Urban SAFE50 (Safe Autonomous Flight Environment within the notional last 50 feet of operation of 55 pound class UAS (Small Unmanned Aircraft Systems)) is focused on the necessary real-time decision algorithms and flight models prevalent in low-altitude, high-density city environments. Construction of on-board controls to respond to motor failure and wind dynamics as well as a database of computational flight models and battery discharge profiles will help policymakers to predict and regulate unmanned aircraft flight safely and effectively.

SAFE↗

NASA NTRS2013

NASA Activity Update for the 2013 Unmanned Vehicle Systems International (UVSI) Yearbook

This year s report offers a high level perspective on some of the UAS related activities in which NASA is involved, both internal and external to the agency. Internally, NASA issued UAS operational policy on certification of NASA UAS and aircrew. A team of NASA UAS experts and operators analyzed all current procedures and best practices to design the policy. An update to the agencies Aircraft Operations Management Manual incorporated a new chapter to address UAS planning, preflight operations, flight operations, flight crew requirements, airworthiness and flight safety reviews. NASA UAS are classified into three categories based on weight and airspeed. Aircrews, including observers, are classified by how they interface with the UAS, and the policy defines qualifications, training, and currency. The NASA flight readiness approval process identifies risks and mitigations in order to reduce the likelihood and/or consequence of the risk to an acceptable level. The UAS operations process incorporates all aspects of airworthiness, flight standards and range safety exactly the same processes used for NASA manned aircraft operations. NASA has two internal organizations that routinely operate UAS. The Science Mission Directorate utilizes UAS as part of its Airborne Science Program and is the most frequent operator of NASA UAS in both national and international airspace. The Aeronautics Research Mission Directorate conducts UAS flight operations in addition to conducting research important to the UAS community. This past year the Science Mission Directorate supported the Hurricane and Severe Storm Sentimental (HS3) Mission with two NASA Global Hawk platforms. HS3 is a five-year mission specifically targeted to investigate the processes that underlie hurricane formation. During the 2012 portion of this mission the Global Hawk overflew hurricanes Leslie and Nadine in the Atlantic Ocean completing 6 flights and accumulating more than 148 flight hours. Another multi-year mission was initiated last year when the Sensor Integrated Environmental Remote Research Aircraft (SIERRA) UAS began surveying faults in California s Surprise Valley. A team of scientists and engineers from the United States Geological Survey (USGS), NASA Ames Research Center, Central Washington University, and Carnegie Mellon University will measure magnetic fields using ground surveys and the SIERRA to map the geophysics below the surface of Surprise Valley. The data collected will be used to generate 3D maps of the geophysical data of the area. The Aeronautics Mission Directorate continues its collaboration with Boeing to conduct UAS flight operations of the X-48C, a modified version of the X-48B originally built by Cranfield Aerospace, United Kingdom. The Aeronautics Mission Directorate utilizes vehicles of this size for a wide variety of research studies. Most of these operations are conducted within restricted airspace. The Aeronautics Research Mission Directorate also sponsors the UAS in the National Airspace System (NAS) Project, which is working in close cooperation with the Federal Aviation Administration (FAA) to address critical challenges associated with routine UAS operations in civil airspace. The project is focused on separation assurance and collision avoidance systems and algorithms, command and control for non-military operations including spectrum allocation requirements, human system interaction issues, and safety and certification topics.

Bauer, Jeffrey E.↗

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 NTRS2016

Effect of Vertical Rate Error on Recovery from Loss of Well Clear Between UAS and Non-Cooperative Intruders

When an Unmanned Aircraft System (UAS) encounters an intruder and is unable to maintain required temporal and spatial separation between the two vehicles, it is referred to as a loss of well-clear. In this state, the UAS must make its best attempt to regain separation while maximizing the minimum separation between itself and the intruder. When encountering a non-cooperative intruder (an aircraft operating under visual flight rules without ADS-B or an active transponder) the UAS must rely on the radar system to provide the intruders location, velocity, and heading information. As many UAS have limited climb and descent performance, vertical position andor vertical rate errors make it difficult to determine whether an intruder will pass above or below them. To account for that, there is a proposal by RTCA Special Committee 228 to prohibit guidance systems from providing vertical guidance to regain well-clear to UAS in an encounter with a non-cooperative intruder unless their radar system has vertical position error below 175 feet (95) and vertical velocity errors below 200 fpm (95). Two sets of fast-time parametric studies was conducted, each with 54000 pairwise encounters between a UAS and non-cooperative intruder to determine the suitability of offering vertical guidance to regain well clear to a UAS in the presence of radar sensor noise. The UAS was not allowed to maneuver until it received well-clear recovery guidance. The maximum severity of the loss of well-clear was logged and used as the primary indicator of the separation achieved by the UAS. One set of 54000 encounters allowed the UAS to maneuver either vertically or horizontally, while the second permitted horizontal maneuvers, only. Comparing the two data sets allowed researchers to see the effect of allowing vertical guidance to a UAS for a particular encounter and vertical rate error. Study results show there is a small reduction in the average severity of a loss of well-clear when vertical maneuvers are suppressed, for all vertical error rate thresholds examined. However, results also show that in roughly 35 of the encounters where a vertical maneuver was selected, forcing the UAS to do a horizontal maneuver instead increased the severity of the loss of well-clear for that encounter. Finally, results showed a small reduction in the number of severe losses of well-clear when the high performance UAS (2000 fpm climb and descent rate) was allowed to maneuver vertically, and the vertical rate error was below 500 fpm. Overall, the results show that using a single vertical rate threshold is not advisable, and that limiting a UAS to horizontal maneuvers when vertical rate errors are above 175 fpm can make a UAS less safe about a third of the time. It is suggested that the hard limit be removed, and system manufacturers instructed to account for their own UAS performance, as well as vertical rate error and encounter geometry, when determining whether or not to provide vertical guidance to regain well-clear.

detect and avoid↗

NASA NTRS2007

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

The NASA Ikhana unmanned aerial vehicle (UAV) is a General Atomics Ae ronautical Systems Inc. (San Diego, California) MQ-9 Predator-B modif ied to support the conduct of Earth science missions for the NASA Sci ence Mission Directorate and, through partnerships, other government agencies and universities. It can carry over 2000 lb of experiment p ayloads in the avionics bay and external pods and is capable of missi on durations in excess of 24 hours at altitudes above 40,000 ft. The aircraft is remotely piloted from a mobile ground control station (GC S) that is designed to be deployable by air, land, or sea. On-board s upport capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The Ikhana project will complete GCS d evelopment, science support systems integration, external pod integra tion and flight clearance, and operations crew training in early 2007 . A large-area remote sensing mission is currently scheduled for Summ er 2007.

Cobleigh, B.↗

NASA NTRS2007

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

The NASA Ikhana unmanned aerial vehicle (UAV) is a General Atomics Aeronautical Systems Inc. (San Diego, California) MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate and, through partnerships, other government agencies and universities. It can carry over 2000 lb of experiment payloads in the avionics bay and external pods and is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. The aircraft is remotely piloted from a mobile ground control station (GCS) that is designed to be deployable by air, land, or sea. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for Summer 2007.

Cobleigh, Brent R.↗

NASA NTRS2006

Ikhana: A NASA UAS Supporting Long Duration Earth Science Missions

NASA's Ikhana unmanned aerial vehicle (UAV) is a General Atomics MQ-9 Predator-B modified to support the conduct of Earth science missions for the NASA Science Mission Directorate through partnerships, other government agencies and universities. Ikhana, a Native American word meaning 'intelligence', can carry over 2000 lbs of atmospheric and remote sensing instruments in the payload bay and external pods. The aircraft is capable of mission durations in excess of 24 hours at altitudes above 40,000 ft. Redundant flight control, avionics, power, and network systems increase the system reliability and allow easier access to public airspace. The aircraft is remotely piloted from a mobile ground control station (GCS) using both C-band line-of-sight and Ku-band over-the-horizon satellite datalinks. NASA's GCS has been modified to support on-site science monitoring, or the downlink data can be networked to remote sites. All ground support systems are designed to be deployable to support global Eart science investigations. On-board support capabilities include an instrumentation system and an Airborne Research Test System (ARTS). The ARTS can host research algorithms that will autonomously command and control on-board sensors, perform sensor health monitoring, conduct data analysis, and request changes to the flight plan to maximize data collection. The ARTS also has the ability to host algorithms that will autonomously control the aircraft trajectory based on sensor needs, (e.g. precision trajectory for repeat pass interferometry) or to optimize mission objectives (e.g. search for specific atmospheric conditions). Standard on-board networks will collect science data for recording and for inclusion in the aircraft's high bandwidth downlink. The Ikhana project will complete GCS development, science support systems integration, external pod integration and flight clearance, and operations crew training in early 2007. A large-area remote sensing mission is currently scheduled for the Summer 2007.

Cobleigh, Brent R.↗