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NASA NTRSDate not supplied

Management of Operations under Visual Flight Rules in UTM for Disaster Response Missions

The disaster response domain has experienced an increased focus in recent years due to the rise in number and scale of events, lessons learned from past experience, and emerging technologies that make possible a more coordinated and effective response. As part of this focus, JAXA and NASA have been collaborating on the integration of manned and unmanned aircraft in support of disaster response operations through integrated testing of their respective mission planning and optimization system (Disaster Relief Aircraft Information Sharing Network, or D-NET) and an automated UAS traffic management system (e.g., UTM). In 2018, JAXA and NASA jointly participated in a large-scale disaster drill in Japan where the integration of systems was successfully demonstrated through real-time data exchanges, visualization, and decision making as part of the coordinated airspace management of a manned helicopter in VFR conditions and unmanned small UAS operating in common areas. This work details a flight test consisting of two flights that were conducted December 2019 near the Chofu Aerodrome in Tokyo, which focused on the evaluation of pilots operating under Visual Flight Rules (VFR) communicating through D-NET and sharing intent and position information within UTM. This work contributes to defining the necessary requirements for digital coordination between manned and unmanned operations. UTM requires the use of operation volumes, which are spatial and temporal volumes that encompass UAS flight trajectories and account for technical performance errors and deviations due to disturbances (e.g., wind). A series of flights, representing different missions, used landmark-based operation volumes and conformance of the aircraft to those operation volumes were tracked within UTM. Experienced disaster response helicopter pilots provided insight on the development of the operation plans and their usability during disaster response operations. Results from the flight test supported the suggested benefits of using landmarks for planning and positional awareness and highlighted the need for future research in advanced visualization capabilities to support operations that consider both system constraints and flight deck/airspace management interaction.

disaster response↗

NASA NTRSDate not supplied

UTM to AAM

This short presentation is intended to provide a a brief overview of the background of the UAS Traffic Management (UTM) concept and the legacy of work performed in the area of human factors as part of the project's research. The introduction of UTM provides the foundation for presenting the progression of the concept from low altitude airspace with small UAS to an environment with larger passenger and cargo carrying vehicles integrating into more complex airspace as part of the Advanced Air Mobility (AAM) concept. Related research and testing plans are presented to illustrate the direction of related project efforts as well as the facilities that are available to support the necessary research ahead.

UTM↗

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

Safely Enabling UAS Operations in Low-Altitude Airspace

low-altitude airspace↗

NASA NTRS2017

Safely Enabling UAS Operations in Low-Altitude Airspace

low-altitude airspace↗

NASA NTRS2018

UTM and D-NET: NASA and JAXA's Collaborative Research on Integrating Small UAS with Disaster Response Efforts

Natural disasters, such as flooding, wildfire, hurricane, tornadoes, earthquakes and tsunamis, pose challenges in preserving human life and minimizing the damages to a region. During catastrophic events, timely response of disaster relief personnel, an efficient deployment of resources in the recovery effort, and coordinated information sharing amongst different relief agencies can make a substantial difference in responding to those impacted by the disaster. Many relief activities currently utilize both ground personnel and manned airborne assets during different phases of the disaster response. Typically, multiple organizations support relief activities and this often creates logistics coordination challenges between agencies which can result in wasted time or resources. The Japan Aerospace Exploration Agency (JAXA) has been developing an "Integrated aircraft operation system for disaster relief (D-NET)", which assists collection and sharing of disaster information through the integrated operation of aircraft such as helicopters, aircraft, and satellites, for efficient and safe rescue operations by disaster relief aircraft. Due to the advancement in unmanned aircraft systems (UAS) technologies, public safety organizations have started incorporating small UAS (sUAS) as an asset in their disasters response activities. To address the airspace integration challenges of the influx of sUAS in the United States the National Aeronautics and Space Administration (NASA), under the UAS Traffic Management (UTM) project, has been engaged in research to enable large-scale commercial applications of sUAS operating in low altitude airspace. This paper presents the integration of D-NET, which incorporate sUAS in the planning, information sharing, and operation support of disasters response activities, and UTM, which provides airspace management to enable large scale high density operations. The integration of the DNET and UTM systems enables coordination, data sharing, and airspace management to improve the timeliness of the disaster response, enable relief organization to reduce cost and overhead by using UAS assets and still maintain airspace safety during the relief activities.

UTM↗

NASA NTRS2018

Information Displays and Crew Configurations for UTM Operations

In this paper we discuss how team configuration may influence how infor-mation is shared among team members for low-altitude Unmanned Aircraft Systems (UAS) operations. NASA collected and analyzed observation data gathered during a series of field tests for the UAS Traffic Management (UTM) project. The field tests were part of a larger effort aimed at advancing the UTM concept, conducted at six test-sites spread across the USA. Ground control station (GCS) concepts, flight-crew composition, and crew-size var-ied within and across test-sites. Flight crews took two strategic approaches to organizing their teams. The first of the two approaches was implemented by one third of the flight crews. These crews integrated the role of UTM opera-tor into the duties of existing crew members, merging the current roles with this new one, keeping the UTM Operator collocated with the flight crew. The remaining two thirds implemented a distributed team configuration, where a single UTM operator distributed support across multiple crews. Results from our data collection efforts revealed that UTM Operator location influenced whether flight crews used verbal communication versus displays to acquire UTM information.

teams↗

NASA NTRS2022

Evaluation and Improvement of System-of-Systems Resilience in a Simulation of Wildfire Emergency Response

Because of the increasing threat that wildfires pose, there is interest in leveraging new technologies to improve firefighting. Specifically, Unmanned Aerial Systems (UAS) and UAS Traffic Management (UTM) promise to improve firefighters’ situational awareness, coordination, communications, safety, and strategy. While these technologies could be beneficial, there has been little formal investigation into how much benefit would occur and whether these benefits would outweigh hazards introduced by these systems. To better understand the impacts of these technologies, this paper presents a high-level dynamic simulation for evaluating wildfire response performance and resilience incorporating fire propagation, surveillance and communication, response planning, and the resulting mitigation actions. This simulation is then used to study the impact of communications and surveillance improvement, considering (1) the effect on fire containment and ground crew injuries and (2) the effect of introduced and existing disruptive fault scenarios. Simulating this model over a large number of scenarios finds that these changes can improve containment and reduce ground crew injuries. While these improvements generalize over both existing and introduced single-fault scenarios and thus result in a more resilient system, they could be negated if the introduced communications infrastructure is prone to full-scale outages.

modeling↗

NASA NTRS2019

Airborne Radar for sUAS Sense and Avoid

A primary challenge for the safe integration of small UAS operations into the National Airspace System (NAS) is traffic deconfliction, both from manned and unmanned aircraft. The UAS Traffic Management (UTM) project being conducted at the National Aeronautics and Space Administration (NASA) considers a layered approach to separation provision, ranging from segregation of operations through airspace volumes (geofences) to autonomous sense and avoid (SAA) technologies for higher risk, densely occupied airspace. Cooperative SAA systems, such as Automatic Dependent Surveillance-Broadcast (ADS-B) and/or vehicle-to-vehicle communication systems provide significant additional risk mitigation but they fail to adequately mitigate collision risks for non-cooperative (non-transponder equipped) airborne aircraft. The RAAVIN (Radar on Autonomous Aircraft to Verify ICAROUS Navigation) flight test being conducted by NASA and the Mid-Atlantic Aviation Partnership (MAAP) was designed to investigate the applicability and performance of a prototype, commercially available sUAS radar to detect and track non-cooperative airborne traffic, both manned and unmanned. The radar selected for this research was a Frequency Modulated Continuous Wave (FMCW) radar with 120 degree azimuth and 80 degree elevation field of view operating at 24.55GHz center frequency with a 200 MHz bandwidth. The radar transmits 2 watts of power thru a Metamaterial Electronically Scanning Array antenna in horizontal polarization. When the radar is transmitting, personnel must be at least 1 meter away from the active array to limit nonionizing radiation exposure. The radar physical dimensions are 18.7cm by 12.1cm by 4.1cm and it weighs less than 820 grams making it well suited for installation on small UASs. The onboard, SAA capability, known as ICAROUS, (Independent Configurable Architecture for Reliable Operations of Unmanned Systems), developed by NASA to support sUAS operations, will provide autonomous guidance using the traffic radar tracks from the onboard radar. The RAAVIN set of studies will be conducted in three phases. The first phase included outdoor, ground-based radar evaluations performed at the Virginia Tech’s Kentland Farm testing range in Blacksburg, VA. The test was designed to measure how well the radar could detect and track a small UAS flying in the radar’s field of view. The radar was used to monitor 5 test flights consisting of outbound, inbound and crossing routes at different ranges and altitudes. The UAS flown during the ground test was the Inspire 2, a quad copter weighing less than 4250 grams (10 pounds) at maximum payload. The radar was set up to scan and track targets over its full azimuthal field of view from 0 to 40 degrees in elevation. The radar was configured to eliminate tracks generated from any targets located beyond 2000 meters from the radar and moving at velocities under 1.45 meters per second. For subsequent phases of the study the radar will be integrated with a sUAS platform to evaluate its performance in flight for SAA applications ranging from sUAS to manned GA aircraft detections and tracking. Preliminary data analysis from the first outdoor ground tests showed the radar performed well at tracking the vehicle as it flew outbound and repeatedly maintained a track out to 1000 meters (maximum 1387 meters) until the vehicle slowed to a stop to reverse direction to fly inbound. As the Inspire flew inbound tracks from beyond 800 meters, a reacquisition time delay was consistently observed between when the Inspire exceeds a speed of 1.45 meters per second and when the radar indicated an inbound target was present and maintained its track. The time delay varied between 6 seconds to over 37 seconds for the inbound flights examined, and typically resulted in about a 200 meter closure distance before the Inspire track was maintained. The radar performed well at both acquiring and tracking the vehicle as it flew crossing routes out past 400 meters across the azimuthal field of view. The radar and ICAROUS software will be integrated and flown on a BFD-1400-SE8-E UAS during the next phase of the RAAVIN project. The main goal at the conclusion of this effort is to determine if this radar technology can reliably support minimum requirements for SAA applications of sUAS. In particular, the study will measure the range of vehicle detections, lateral and vertical angular errors, false and missed/late detections, and estimated distance at closest point of approach after an avoidance maneuver is executed. This last metric is directly impacted by sensor performance and indicates its suitability for the task.

Szatkowski, George N.↗

NASA NTRS2017

UTM TCL 2.0 Software Version Description (SVD) Document

This is the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Technical Capability Level(TCL) 2.0 Software Version Description (SVD) document. This UTM TCL 2.0 SVD describes the following four topics: 1. Software Release Contents: A listing of the files comprising this release 2. Installation Instructions: How to install the release and get it running 3. Changes Since Previous Release: General updates since the previous UTM release 4. Known Issues: Known issues and limitations in this release

Mcguirk, Patrick↗

NASA NTRS2017

UTM TCL2 Software Requirements

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Technical Capability Level (TCL) 2 software implements the UTM TCL 2 software requirements described herein. These software requirements are linked to the higher level UTM TCL 2 System Requirements. Each successive TCL implements additional UTM functionality, enabling additional use cases. TCL 2 demonstrated how to enable expanded multiple operations by implementing automation for beyond visual line-of-sight, tracking operations, and operations flying over sparsely populated areas.

Smith, Irene S.↗

NASA NTRS2017

Safeguard: Progress and Test Results for a Reliable Independent On-Board Safety Net for UAS

As demands increase to use unmanned aircraft systems (UAS) for a broad spectrum of commercial applications, regulatory authorities are examining how to safely integrate them without compromising safety or disrupting traditional airspace operations. For small UAS, several operational rules have been established; e.g., do not operate beyond visual line-of-sight, do not fly within five miles of a commercial airport, do not fly above 400 feet above ground level. Enforcing these rules is challenging for UAS, as evidenced by the number of incident reports received by the Federal Aviation Administration (FAA). This paper reviews the development of an onboard system - Safeguard - designed to monitor and enforce conformance to a set of operational rules defined prior to flight (e.g., geospatial stay-out or stay-in regions, speed limits, and altitude constraints). Unlike typical geofencing or geo-limitation functions, Safeguard operates independently of the off-the-shelf UAS autopilot and is designed in a way that can be realized by a small set of verifiable functions to simplify compliance with existing standards for safety-critical systems (e.g. for spacecraft and manned commercial transportation aircraft systems). A framework is described that decouples the system from any other devices on the UAS as well as introduces complementary positioning source(s) for applications that require integrity and availability beyond what can be provided by the Global Positioning System (GPS). This paper summarizes the progress and test results for Safeguard research and development since presentation of the design concept at the 35th Digital Avionics Systems Conference (DASC '16). Significant accomplishments include completion of software verification and validation in accordance with NASA standards for spacecraft systems (to Class B), development of improved hardware prototypes, development of a simulation platform that allows for hardware-in-the-loop testing and fast-time Monte Carlo evaluations, and flight testing on multiple air vehicles. Integration testing with NASA's UAS Traffic Management (UTM) service-oriented architecture was also demonstrated.

Young, Steven D.↗

NASA NTRS2017

TCL2 Ocean Scenario Replay

This is a video replay of system data that was generated from the UAS Traffic Management (UTM) Technical Capability Level (TCL) 2 flight demonstration in Nevada and rendered in Google Earth. What is depicted in the replay is a particular set of flights conducted as part of what was referred to as the Ocean scenario. The test range and surrounding area are presented followed by an overview of operational volumes. System messaging is also displayed as well as a replay of all of the five test flights as they occurred.

Mohlenbrink, Christoph P.↗

NASA NTRS2018

Small Unmanned Aircraft Electromagnetic Interference (EMI) Initial Assessment

With many applications envisioned for small Unmanned Aircraft Systems (sUAS), and potentially millions of sUAS expected to be in operation in the future, the electromagnetic interference environment associated with the sUAS is of interest to understanding the potential performance impacts on the sUAS command and control communications link as well as the sUAS payload and payload links. As part of NASA's UAS Traffic Management (UTM) Project, flight experiments are planned to characterize the RF environment at altitudes up to 400 ft to better understand how UTM command and control links can be expected to perform. The flight experiments will use an RF channel sensing payload attached to an sUAS. In terms of the payload being capable of measuring relatively low level signals at altitude, electromagnetic interference (EMI) emanating from the sUAS vehicle itself could potentially complicate the measurement process. For this reason, NASA was interested in measuring the EMI performance of the sUAS planned for these flight experiments, a DJI model S1000. The S1000 was thus measured in a controlled EMI test chamber at the NASA Ames Research Center. The S1000 is a carbon fiber based platform with eight rotors. As such, the EMI test results represent potential performance of a number of similar sUAS types. sUAS platforms significantly different from the S1000 may also require EMI testing, and the method employed for NASA's S1000 EMI tests can be applied to other platforms. In this paper we describe the UTM project, the RF channel sensing payload, the EMI testing method and EMI test results for the S1000, and discuss the implications of these results.

Unmanned aircraft↗

NASA NTRS2018

Small Unmanned Aircraft Electromagnetic Interference (EMI) Initial Assessment

With many applications envisioned for small Unmanned Aircraft Systems (sUAS), and potentially millions of sUAS expected to be in operation in the future, the electromagnetic interference environment associated with the sUAS is of interest to understanding the potential performance impacts on the sUAS command and control communications link as well as the sUAS payload and payload links. As part of NASAâ€"TM"s UAS Traffic Management (UTM) Project, flight experiments are planned to characterize the RF environment at altitudes up to 400 ft to better understand how UTM command and control links can be expected to perform. The flight experiments will use an RF channel sensing payload attached to an sUAS. In terms of the payload being capable of measuring relatively low level signals at altitude, electromagnetic interference (EMI) emanating from the sUAS vehicle itself could potentially complicate the measurement process. For this reason, NASA was interested in measuring the EMI performance of the sUAS planned for these flight experiments, a DJI model S1000. The S1000 was thus measured in a controlled EMI test chamber at the NASA Ames Research Center. The S1000 is a carbon fiber based platform with eight rotors. As such, the EMI test results represent potential performance of a number of similar sUAS types. sUAS platforms significantly different from the S1000 may also require EMI testing, and the method employed for NASAâ€"TM"s S1000 EMI tests can be applied to other platforms. In this paper we describe the UTM project, the RF channel sensing payload, the EMI testing method and EMI test results for the S1000, and discuss the implications of these results.

traffic management↗

NASA NTRS2018

Rapid Trajectory Prediction for a Fixed-Wing UAS in a Uniform Wind Field with Specified Arrival Times

This paper presents an algorithm to rapidly generate trajectories for a kinematic fixed-wing Unmanned Aircraft System (UAS) model flying at constant altitude in a uniform wind field. Arrival times are specified by operators and rapid generation is accomplished via an elliptic integral problem formulation. Simulations are provided that illustrate this approach in the context of NASA's UAS Traffic Management Project.

trajectory↗

NASA NTRS2019

Exploration of Near-Term Potential Routes and Procedures for Urban Air Mobility

Urban air mobility is gaining interest as the need for On Demand Mobility in today's congested traffic is becoming high in metropolitan areas. Urban Air Mobility (UAM) is envisioned as a concept to transport passengers and cargo safely and efficiently using innovative aircraft in the urban areas. It is expected to improve mobility for the general public, decongest road traffic, reduce transport time and reduce the strain on existing public transport networks. There exist several challenges to Urban Air Mobility (UAM) such as integration of procedures with airspace and the airport, noise levels that are acceptable to the general public, public safety, public acceptance, vehicle certification, and more. Most of the research in the United States and European skies (DLR - German Aerospace Center) related to urban areas has focused on small UAS (Unmanned Aircraft Systems) flights (NASA's UTM (UAS Traffic Management) research) and their integration with the airspace and building safe operations in densely populated areas. Previous studies on UAM have focused on fast time simulations of the routes that are separated via a separation service and network of routes. Similarly, research in Europe has focused on the approach profile for these innovative aircraft, vertiports and battery life among others. UAM as a part of the On-Demand Mobility effort has provided some guidelines for operations as shown below: Does not require additional ATC (Air Traffic Control) infrastructure; Does not impose additional workload on ATC; Does not restrict operations of traditional airspace users; Will meet appropriate safety thresholds and requirements; Will prioritize operational scalability; Will allow flexibility where possible and structure where necessary. This paper explores potential routes and procedures in a Human-In-The-Loop (HITL) experiment that could be applied in the near-term to allow integration of UAM flights into the airspace as well as a large airport. The airspace that was explored was Dallas Fort Worth (DFW) airspace managed by the DFW East Tower in South Flow only. In addition, Dallas Love Field (DAL) and Addison (ADS) airspace were also part of the testbed. The initial set of routes investigated in this study were published helicopter routes in the DFW area. Figure 1 shows class B airspace in DFW area and the origin/destination city pairs where UAM flights flew along with helicopter routes shown in blue. The research focused on exploring procedures for integrating UAM flights into Class Bravo and Class Delta airspace. Three different communication procedures, evaluated with three different levels of UAM traffic, are shown in Table 1. The current day routes were evaluated with current day communication procedures were explored as the first condition. The current day routes were also evaluated in the second condition with reduced communications, which was assumed due to the presence of a Letter Of Agreement (LOA). The purpose of the LOA was to reduce the verbiage associated with pilots getting clearance to Class B airspace from the controllers, pre-assigning beacons codes to the UAM flights, separate routes by assigning altitudes and speeds to flights going in any one direction. Flights were expected to automatically change frequency when exiting Class B airspace, thus transition points for entry and exit points were also specified in the LOA.

Urban Air Mobility↗

NASA NTRS2019

Onboard Decision-Making for Nominal and Contingency sUAS Flight

This study presents an onboard decision-making architecture for small unmanned aerial systems (sUAS). The decision-maker is part of NASA's SAFE50 project that is working under the UAS Traffic Management (UTM) Technical Capability Level (TCL) 4 to provide autonomous point-to-point UAV flight in BVLOS, high-density urban environments. The decision-maker monitors various metrics to determine the safety and feasibility of the mission and categorizes flight states as Nominal, Off-Nominal, Alternate Land, and Land Now in a finite state machine. Changes in the monitored metrics serve as transitions in the state machine and trigger replanning. Navigation degradation and communication failure are simulated to show the feasibility of the decision-maker framework in appropriately switching the flight state.

Baculi, Joshua↗