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At least 325 records · Page 18

Testing Enabling Technologies for Safe UAS Urban Operations

A set of more than 100 flight operations were conducted at NASA Langley Research Center using small UAS (sUAS) to demonstrate, test, and evaluate a set of technologies and an overarching air-ground system concept aimed at enabling safety. The research vehicle was tracked continuously during nominal traversal of planned flight paths while autonomously operating over moderately populated land. For selected flights, off-nominal risks were introduced, including vehicle-to-vehicle (V2V) encounters. Three contingency maneuvers were demonstrated that provide safe responses. These maneuvers made use of an integrated air/ground platform and two on-board autonomous capabilities. Flight data was monitored and recorded with multiple ground systems and was forwarded in real time to a UAS traffic management (UTM) server for airspace coordination and supervision.

Moore, Andrew J.↗

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.↗

ATM-X: Air Traffic Management – eXploration: Urban Air Mobility Overview

The UAM market is expected to be big in the billions of dollars. And, of course, UAM will need to be able to readily access and operate in the airspace for this to happen. In the ATM-X UAM sub-project, we are focused on NASA’s top-level goal of enabling UAM Maturity Level-4 (UML-4) operational density and complexity, with hundreds of simultaneous operations in a metropolitan area managed through a UTM-like architecture with third-party provided traffic management services. We are doing this by developing a concept of operations with the FAA and the UAM community that serves as a framework and guide for community research and development. We are also doing this by conducting research to define system requirements based on the concept and developing the UAM airspace management system for the AAM National Campaign.

urban air mobility↗

NASA Advanced Air Mobility (AAM)

This presentation will provide a UTM update, AAM overview (including details on the AAM Ecosystem Working Groups), and Regional Modeling and Simulations tool description.

Advance Air Mobility↗

Upper E Traffic Management

This is a slide set as part of a meeting series with members of a working group aimed at the development of a concept that addresses needs and gaps in the management of high altitude airspace operations. This concept leverages elements developed through the UAS Traffic Management project with respect to a cooperative, service-based approach that provides services and capabilities in areas (e.g., Upper E airspace) that currently receive no or limited service from Air Traffic Control. This concept is meant to provide a safe, fair, and scalable approach to management of Upper E operations that reduces the burden on ATC while providing the flexibility and access desired by current and new users of the airspace. This set of slides includes partner feedback on the following concept elements: Upper E Traffic Management (ETM) planning processes, transit phase of operation, data sharing, time horizons for data sharing, and cooperative integration of military operations. An additional segment of the slides consists of an overview of the UAS Traffic Management (UTM) concept and its relationship to ETM.

Upper E↗

Research to Operations

10 minute introduction of Parimal Kopardekar and NASA (UTM and ETM involvement) for panel to assist NOAA in successfully implementing their unmanned systems strategy.

Parimal Kopardekar↗

The System Modeling and Analysis of Resiliency in STEReO (SMARt-STEReO)

Wildfire emergency response has remained rooted in relatively low-tech solutions for coordination between ground and aerial assets. These low-tech solutions are robust for the remote environments in which wildfires are usually fought, but limit strategic cross-organizational support and the ability to deploy and effectively utilize aerial assets. As aircraft become more advanced and new technology, including drones, become available to firefighters, a new, more modern method of asset coordination is needed. NASA is working on a project called ‘Scalable Traffic Management for Emergency Response Operations’ (STEReO) to integrate unmanned aerial systems (UAS)and UAS traffic management (UTM)into wildfire response. STEReO’s goals include simplifying the coordination of aerial assets, improving the existing UAS framework, and increasing the role of additional autonomous systems to reduce human risk and to increase system resilience. This paper describes the development of the ‘System Modeling and Analysis of Resiliency in STEReO’ (SMARt-STEReO) project, which aims to model wildfire response and to quantify the additional system resilience that STEReO technology provides firefighters. This paper verifies SMARt-STEReO and defines its scope; it includes experimental and statistical analysis of the impact that the addition of UAS has on both performance metrics and also on performance resiliency response to a given fault. SMARt-STEReO is a grid-based model of fire propagation that incorporates varying crew responses. Through the use of a Python package called ‘fmdtools’, the model easily allows for the addition of faults to the system. These faults allow analysts to investigate various response parameters. Factors including terrain, fuel type and wind speed can be modified to affect the fire propagation; additionally, the number of ground crews, engines, fixed wing aircraft, helicopters, and UAS can be changed to affect the crew response. The communication lines between actors mimic those used in real life situations. This paper explains the development of SMARt-STEReO including background research, verification and validation, and preliminary experimental analysis of system resilience to both a minor and major fault in systems with and without UAS.

Resiliency↗

Weather Intelligent Navigation Data and Models for Aviation Planning (WINDMAP)

WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere;(3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5)integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models

Koushik Datta↗

Simulations of Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to offer air taxi service as an alternative to driving on the congested roads. Integration of UAM operations into the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. In this talk, I present results from simulations performed during FY2019 to investigate if NASA’s UAS Traffic Management (UTM) architecture and its implementation are extensible for UAM operations. These simulations also tested a set of core airspace management services tailored to controlled airspace access. In the latter half of this talk, I present the efforts made towards the integration of two such services – a strategic scheduling and a tactical separation service – in a simulation environment under ideal conditions. I will conclude this talk by presenting the future work planned towards enabling UAM operations

Urban Air Mobility↗

Simulations of Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to offer air taxi service as an alternative to driving on the congested roads. Integration of UAM operations into the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. In this talk, I present results from simulations performed during FY2019 to investigate if NASA’s UAS Traffic Management (UTM) architecture and its implementation are extensible for UAM operations. These simulations also tested a set of core airspace management services tailored to controlled airspace access. In the latter half of this talk, I present the efforts made towards the integration of two such services – a strategic scheduling and a tactical separation service – in a simulation environment under ideal conditions. I will conclude this talk by presenting the future work planned towards enabling UAM operations

Urban Air Mobility↗

Informing New Concepts for UAS and Autonomous System Safety Management using Disaster Management and First Responder Scenarios

As emerging flight operations become more prevalent and increasingly automated and distributed, the capabilities for managing safety of vehicles and operations will also need to evolve. To address this challenge, the National Academies has envisioned an In-Time Aviation Safety Management System (IASMS) capability for a wide range of aviation operations including current commercial operations as well as new entrants envisioned with advanced air mobility (AAM). The suite of IASMS services, functions, and capabilities (SFCs) would be implemented in a federated approach and would address trends as well as individual operations. Through predictive modeling and data analysis, IASMS is envisioned to identify arising risks so that they can be mitigated, in-time, before a safety incident occurs. IASMS and its requisite set of SFCs must leverage a wide range of information to perform. To better understand these new needs, FSF worked with the aviation and humanitarian communities to develop and validate scenarios that include traditional aviation operations and UAS operations intermingled as they are deployed for disaster management and first responder (DMFR) situations. The three scenarios developed include: • Post Natural disaster response, such as a hurricane, involving multiple parties utilizing traditional aviation and UAS to support rescue operations, surveil damage, and locate survivors needing assistance. • Wildfire fighting in remote locations with traditional aircraft for transport and fire-retardant delivery combined with UAS for surveillance of fire locations as well as to track individual firefighter locations. • Medical Operations and AAM in Urban Environments including passenger-carrying helicopters and AAM vehicles, medical missions (such as transport of radio-pharmaceuticals), and other UAS delivery operations (such as the delivery of defibrillators). Each scenario was developed and validated by representatives with expertise in humanitarian operations, urban and rural emergency response, air traffic management, UAS operations, and traditional flight operations. The scenario definitions address roles and responsibilities of individual actors, the appropriate utilization of UAS, and the actions taken by those actors to appropriately manage risks associated with the mission and environment. The risks to aviation traffic and to people on the ground explored included potential risks arising from incompatibilities in calculating reference altitudes (eg, differing uses of AGL, MSL, barometric, or GPS-derived values), loss of command and control (C2) communications, rapid changes in weather and winds, and physical interference. For each risk, IASMS SFCs were postulated in the context of monitoring services, risk assessment capabilities, and identifying appropriate mitigation strategies. The identified SFC capabilities were envisioned from known services postulated for IASMS and for UTM. For these unique environments, IASMS SFCs are needed to address conditions such as hazardous payloads, micro-climates and urban canyons, and the need to keep uninvolved air traffic out of the area where DMFR operations are being conducted. The second phase of analysis focused on inferring the specific information needs and the SFCs for IASMS, utilizing a structure of 16 information classes to organize requirements. For each of the risks identified in the workshops, it was postulated what data sources would be necessary to monitor critical aspects of the risk (eg, surrounding air traffic, ground population, terrain, etc). to be directly measured as well as data that would be derived, which implies additional SFCs for different actors to understand what information would likely be exchanged between parties. For an IASMS to be effective, additional research is needed to develop the advanced algorithms that can address the increasingly autonomous and complex operations in differing environments and to develop means of identifying unknown risks. Looking at these scenarios highlighted a number of research issues. These include the ability to quickly "cordon off" airspace thru temporary flight restrictions (TFRs) or other means, developing clear definitions to enable automation-based algorithms for prioritizing operations, defining airspace density metrics, standardization of altitude reporting, and establishing a basis for safety data metrics definition and collection. This paper seeks to outline the development of an IASMS in the context of the DMFR scenarios and resulting demonstrations. Utilizing this contextual approach, NASA will generate recommendations for an assured safety framework for AAM operations that enables AAM operations to safely access the NAS.

In Time Aviation Safety Management System↗

Wildfire Emergency Response Hazard Extraction and Analysis of Trends (HEAT) through Natural Language Processing and Time Series

Emerging wildfire operations aim to improve safety and performance through the integration of technologies including UAS and UTM. Recent advances in natural language processing (NLP) techniques, as well as the availability of wildfire incident reports, has made possible a large-scale analysis of wildfire hazards and trends. Identifying longitudinal trends will help us target risk mitigation and safety management activities. Note: This presentation does not include sound please disregard icon.

Sequoia R. Andrade↗

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↗

Wildland Fire Mitigation: The Role of Airspace Management

Wildland fire is a significant problem on six continents. As wildland fires become increasingly widespread, dangerous, and costly, advances in aerial technology will make a significant difference. Leveraging Unmanned Aircraft Systems Traffic Management (UTM) success, NASA is conducting aerial suppression research and development (R&D) and is collaborating across government, industry, and the academic community to co-develop cutting-edge technology, procedures, and best practices. Join Dr. Parimal "PK" Kopardekar, Director of the NASA Aeronautics Research Institute (NARI), and learn more about how to maximize the utilization of airspace for wildfire management and mitigation.

Wildland fire↗

Discovery Synchronization Service (DSS) for UAM

NASA's ATM-X UAM Subproject recently completed the “X4 Strategic Conflict Management Simulation” with seven NC-1 airspace partners to develop and test initial UAM airspace management capabilities, including the Provider of Services for UAM (PSU), to enable strategic conflict management of UAM operations. The simulation environment leveraged the notional architecture from FAA NextGen UAM ConOps v1.0 and used technologies developed as part of UTM standards and applied them to UAM. One of the main technologies used was the Discovery and Synchronization Service (DSS) and some gaps in applying it to UAM were identified during X4. In this Technical Interchange Meeting (TIM), we will share the technical findings from the simulation and welcome industry feedback.

UAM↗

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗

SWS Project Relevant Research

Project Overview Tech Challenge 2 - In-Time Safety Management for Emerging Operations - (2018-19) Initial Architecture and Information Req'ts - UTM-like ecosystem; ASIAS/SWIM-like info sharing - Ref: NASA TM-2020-220440 - (2020-2021) Phase 1 - Architecture and SFCs tailored to domain risks - Build collaborations on key topics - (2021-2022) Phase 2 - Services with infrastructure for urban ops - Broader in-time risk assessment span - (2023-2024) Phase 3 - With and by partners - Urban sUAS and AAM/UAM domains

Wind modeling↗

Nonrepudiation for Drone Related Data

In order for UTM to support safe multiple UAS operations within and beyond visual line of sight, data related to weather, 3D structures, other aircraft, etc. must be made available. To support safe operations data must be collected and maintained considering security and resilience.

Security↗