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NASA NTRSDate not supplied

Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications

With the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations, there will be an increasing demand for voice and data communications within the National Airspace System (NAS). The continued use of existing VHF and UHF frequency allocations is not a sustainable approach, and as a result, a new spectrum management solution is required to support future mission needs. The proposed spectrum management concepts leverage modern advancements such as artificial intelligence (AI) and big data to dynamically optimize the spectrum utilization based on the predicted communications demand throughout the airspace. This technical investigation considers both air-ground and air-air communications networks, and can be applied to both existing applications such as the air traffic control (ATC) system, as well as future applications, such as the emerging Advanced Air Mobility (AAM). To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development and will continue to evolve to support new and advanced airspace applications. It is anticipated that this proposed spectrum concept will better serve the spectrum needs of future NAS applications.

Eric J Knoblock↗
NASA NTRSDate not supplied

Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications

With the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations, there will be an increasing demand for voice and data communications within the National Airspace System (NAS). The continued use of existing VHF and UHF frequency allocations is not a sustainable approach, and as a result, a new spectrum management solution is required to support future mission needs. The proposed spectrum management concepts leverage modern advancements such as artificial intelligence (AI) and big data to dynamically optimize the spectrum utilization based on the predicted communications demand throughout the airspace. This technical investigation considers both air-ground and air-air communications networks, and can be applied to both existing applications such as the air traffic control (ATC) system, as well as future applications, such as the emerging Advanced Air Mobility (AAM). To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development and will continue to evolve to support new and advanced airspace applications. It is anticipated that this proposed spectrum concept will better serve the spectrum needs of future NAS applications.

Eric J Knoblock↗
NASA NTRS2021

Regional Air Mobility: Leveraging Our National Investments to Energize the American Travel Experience

America is home to over 5,000 airports available for public use, yet only 30 of these airports serve over 70% of all travelers.01 Despite this vast network of airports, the majority are underutilized due to air transportation services that have trended towards consolidation by putting more people into fewer, larger aircraft on well-traveled routes. Also, these bigger jet-propelled aircraft can only take off from and land on longer airstrips. Regional Air Mobility (RAM) will fundamentally change how we travel by bringing the convenience, speed, and safety of air travel to all Americans, regardless of their proximity to a travel hub or urban center. We advocate RAM technology investment as complementary to and an accelerator for Advanced Air Mobility (AAM) and other initiatives that aspire to transform the airspace. Through targeted advanced technology investments, such as aircraft automation, enhanced operational models, more efficient aircraft and propulsion systems, and expanded airport renewable energy generation, many of which are already underway, RAM will increase the safety, accessibility, and affordability of regional travel while building on the extensive and underutilized federal, state, and local investment in our nation’s local airports.

Regional Air Mobility↗
NASA NTRS2021

High-Density Automated Vertiport Concept of Operations

The National Aeronautics and Space Administration (NASA) vision for Advanced Air Mobility (AAM) includes Urban Air Mobility (UAM) – a concept involving vertical takeoff and landing (VTOL) aircraft, decentralized (or federated) traffic management, and new infrastructure to support urban, suburban, and rural flight operations. High-density performance-based routes or corridors enable prompt transportation of people and goods from node to node, where each node represents a vertiport, defined as an identifiable ground or elevated area used for the takeoff and landing of VTOL aircraft. In the presence of uncertainty surrounding aircraft turnaround time on the ground, vertiports are the critical end points in scheduling, sequencing, and spacing (SSS) of aircraft in dense metropolitan environments. This Concept of Operations (ConOps) includes vertiports of varying sizes, configurations, service offerings, and locations. UAM air vehicles include conventional rotorcraft, unmanned VTOL aircraft, and novel piloted VTOL aircraft. This ConOps focuses on operations at a high-density vertiport, supported by a Vertiport Automation System (VAS) with high-throughput operation capabilities under conditions defined as NASA’s Urban Air Mobility Maturity Level Four (UML-4).

Urban Air Mobility↗
NASA NTRS2021

Collaborative Communications Between A Human and A Resilient Safety Support System

Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.

Advanced air mobility↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRS2021

A Systematic Approach to Developing Paths Towards Airborne Vehicle Autonomy

Advanced Air Mobility (AAM) demands greater levels of aircraft autonomy than are currently implemented today. To enable this requirement, novel aircraft functionalities and technologies as well as supporting airworthiness and operational regulations are required. A structured method to derive a comprehensive list of aircraft level decision-making functions is defined and applied. The resulting function set is programmed into an ontology, and enables autonomous decision-making through the application of a defined decision-making process. Paths to implementing the functions are generated by applying a structured four step method. By surveying current technologies, airspace, procedures and regulations, the paths generation method defines incremental paths to autonomy that the current regulatory environment can support. Opportunities to implement novel technologies and functions are identified, and regulatory mechanisms supporting their implementation are underscored. The analysis provides the tools to further define aircraft functions and paths to their implementation, while demonstrating that for particular use cases, aircraft autonomy is attainable in the medium-term.

Paul Vajda↗
NASA NTRSDate not supplied

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗
NASA NTRSDate not supplied

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis↗
NASA NTRSDate not supplied

Community Integration of Advanced Air Mobility

This presentation was given by Nancy Mendonca, NASA Aeronautics Research Mission Directorate Deputy AAM Mission Integration Manager, at the Business of Automated Mobility Forum (BAMF) on November 3, 2021. The presentation covers the topic of community integration of advanced air mobility.

Nancy Mendonca↗
NASA NTRSDate not supplied

Benchmark Problem Development for Testing Maturity of Intelligent Contingency Management Tools

Increasingly autonomous Advanced Air Mobility (AAM) vehicles will be required to handle diverse conditions with limited human intervention. Intelligent contingency management (iCM) approaches are under development to address how automated agents can handle unforeseen, unplanned, and unanticipated events. Benchmark scenario is needed to test the maturity of the developed iCM tools and techniques.

Jon Holbrook↗
NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗
NASA NTRSDate not supplied

Eye Glance Behaviors of Ground Control Station Operators in a Simulated Urban Air Mobility Environment

Research into concepts such as advanced air mobility (AAM) and urban air mobility (UAM) offers an opportunity for successfully and safely adding new classes of vehicles into the National Airspace System. However, a need exists for research into the human factors associated with these concepts. In this paper, we evaluate the gaze behaviors of three remote ground control station operators (GCSOs) conducting simulated UAM operations. The participants monitored and controlled an unmanned aircraft system (UAS) from pre-flight to landing using ground control station (GCS) software across nine scenarios within a remote UAS operations center at NASA Langley Research Center (LaRC). As this work was exploratory, descriptive statistics were calculated to provide some initial insight into GCSO gaze patterns associated with the GCS display. Scenarios that required the operator to directly interact with an airspace scheduling system off-screen resulted in fewer on-screen glances than scenarios that did not include direct interactions with the off-screen system. After investigating several areas of interest (AOIs) within the GCS display, participants primarily viewed three AOIs: the map, vehicle status, and operations checklist. The results yielded several GCS design and operational improvement recommendations to include: (a) adding altitude information to the vehicle icon, (b) adding additional traffic information, and (c) including additional GCS training, to reduce the need to scan an operations checklist, which would allow allocation of visual attention towards other AOIs.

Urban Air Mobility↗
NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗
NASA NTRS2022

Optimal Locations for Air Mobility Vertiports

The purpose of this study was to effectively solicit the public to obtain inputs on potential Advanced Air Mobility (AAM) vertiport locations for UAM and RAM operations that transport people, specifically: where they would like to see vertiports located, and how they would like to use this new mode of transport (e.g. for “local” trips across town, for more “regional” trips between cities/towns, for business-related purposes, for leisure/personal purposes, routinely or only for “special” occasions, etc.) Maven conducted an electronic survey of individuals residing in specific geographies in order to obtain a broadly representative sample of the citizenry across all demographics and population density designations (e.g. rural, exurban, suburban, and urban). For the purposes of this survey, Maven received input from over 1,500 individuals divided between the Los Angeles Metropolitan area (as a general proxy for urban residents), and the state of Ohio (as a general proxy for suburban, exurban, and rural residents).

Advanced air mobility↗
NASA NTRSDate not supplied

Multi-Vehicle (m:N) Operations in the NAS - NASA's Research Plans

The Advanced Air Mobility movement is occurring across the world with goals of enabling, affordable, efficient, accessible, and safe air transportation at a much larger scale than today’s operations, largely enabled by electrification and automation. Transformative and disruptive innovations are emerging that will support an ecosystem designed to transport goods and people to locations not traditionally served by air transportation. To realize the full vision of AAM, technology will be needed to allow a few operators to operate many vehicles (m:N). The benefits of m:N operations are described, along with the current state-of-the-art, barriers, need, and NASA’s plans to address some of the barriers, including a Multi-Vehicle (m:N) Working Group with goals of producing a community-developed operational approval roadmap for various domains.

multi-vehicle↗
NASA NTRSDate not supplied

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (i.e. urban and rural unmanned aircraft systems) ecosystem, NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV). HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with FMI to identify optimal approaches for displaying information for human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a controlled vehicle completed a takeoff, active flight, and landing sequence while automated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self-reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users would like greater configurability of the interface based on their personal information requirements, and they would like the opportunity to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could be introduced as a potential way to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

vertiplex↗
NASA NTRSDate not supplied

Lean Model-Based Systems Engineering on the NASA High-Density Vertiplex Subproject

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE)in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers(SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This paper will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V)tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This paper details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.

Demetrios Katsaduros↗