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NASA NTRSDate not supplied

Enabling Advanced Air Mobility Operations through Appropriate Trust in Human-Autonomy Teaming: Foundational Research Approaches and Applications

Emerging Advanced Air Mobility(AAM)operations will be enabled by increasingly autonomous systems, requiring technologies to take on more responsibilities and fundamentally altering traditional human-automation interaction paradigms. The growing reliance on higher levels of automation will necessitate research to identify capabilities and principles that facilitate humans and machines working and thinking better together, i.e., human-autonomy teaming (HAT). Trust is an inherent requirement in effective teams because when members work interdependently, those agents (human, automation) must be willing to accept a level of risk to rely upon each other to reach goals and contribute to team tasks. This work provides an initial approach to enabling AAM operations through appropriate trust within HAT. The main contributions of this approach resides in connecting the construct of trust to mental models. Using the outlined mental model approach, we propose novel HAT strategies, such as Adaptive Trust Calibration, and preview planned research activities derived from this approach. Additionally, we propose several practical applications that can currently be employed by AAM development communities.

Eric T Chancey↗

NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density Safety Metric

NASA’s Advanced Air Mobility (AAM) project focuses on enabling emerging aviation markets by accelerating development of safe, high-volume flight operations.[1] It involves development and validation of vehicles, airspace, and automation changes required to support concepts such as Urban Air Mobility, a vision for electric or hybrid electric, vertical or short take-off and landing vehicles that can transport passengers and cargo over an urban environment.[2] NASA’s System Wide Safety (SWS) project is coordinating with AAM by understanding how safety could be affected by these emerging operations. We approach the assessment of airspace safety by identifying threats to operations and then monitoring and predicting the evolution of those threats encoded in a set of safety metrics.[3] Toward this end, we have been developing a dynamic density metric to predict the likelihood of vehicle conflicts in the airspace.

Dynamic Density↗

NASA NTRS2020

In-time System-wide Safety Assurance (ISSA) Concept of Operations and Design Considerations for Urban Air Mobility (UAM)

Emerging operations involving Advanced Air Mobility (AAM), such as Urban Air Mobility (UAM), pose a challenge to safety assurance and to accessibility within the National Airspace System (NAS).In particular, the public has a low tolerance for risk in aviation and the current NAS tends to be labor-intensive with limited ability to scale up for UAM. In response to this landscape, NASA is collaborating with industry to define a Concept of Operations (ConOps) for In-time System-Wide Safety Assurance (ISSA) for scalable UAM involving a service-oriented architecture. This architecture focuses safety investments for technological solutions that can overcome safety related barriers for emerging operations. By working with industry, consensus can be reached on desirable system traits that are based on integration and fusion of data and leverage increasingly autonomous and automated systems. These complex systems can identify anomalies, precursors, and trends that together enable more proactive management of operational risks. AAM and UAM elevate the need for risk management in relation to increasing density and heterogeneity of vehicles and operations. Whereas safety in today’s NAS is built on a history of programs and technologies that react to incidents and accidents, AAM presents an opportunity to leverage that experience and its implications and proactively integrate safety into the earliest designs of vehicles and systems. In a perfect world AAM and UAM would not be inherently dangerous but until then ensuring the highest quality of safety requirements is the bridge to mitigating risks.

In-Time System-Wide Safety Assurance↗

NASA NTRSDate not supplied

Multirotor Configuration Trades Informed by Handling Qualities for Urban Air Mobility Application

Many contemporary Advanced Air Mobility (AAM), and more specifically, urban air mobility (UAM) vehicle designers are attracted to variable rotor speed-controlled designs with multiple rotors because of the great potential for mass savings compared to more traditional, variable blade pitch-controlled vehicles. These designs are based on the assumption that the stability and control of recreation or basic utility-sized drones can be scaled to larger passenger-sized vehicles. Previous work had shown the challenges in stabilizing passenger-sized quadcopters. In this study, power constraints were made less restrictive and varied, allowing more control power. Motor parameters such as efficiency, nominal voltage and current operating point, and rise time of the rotor speed controller step response were studied. By fixing the efficiency of the motor to 95% and assuming a motor voltage to current ratio of 2.0 (previously, assumed to be 1.0), the authors were able to stabilize the quadcopter in the roll axis because this allowed the vehicle to achieve adequate rise times between 0.4 and 0.8 s. This motor optimization was extended to a hexacopter and octocopter designed to the same payload size and mission as the quadcopter. The three vehicle configurations and their motor speed controllers were compared. It was found that while hexacopter and octocopter required more mass and overall power; all three configurations had similar margins required for control. However, the hexacopter and octocopter were able to use this power margin to achieve lower rise times (i.e. the vehicle responded more quickly to pilot inputs) than the quadcopter, with the octocopter having the lowest rotor response rise time of the three vehicle configurations studied.

Multirotor↗

NASA NTRSDate not supplied

Validation of Software Tools for the Analysis of Electrified Aircraft

The Revolutionary Vertical Lift Technology (RVLT) project is addressing the challenges involving the electrification of Advanced Air Mobility (AAM) aircraft. Electrification of aircraft has the potential to reduce fuel consumption, emissions, take-off field length, and noise. This presentation provides insight into the work being conducted by the NASA Glenn Research Center Electric Propulsion (EP) Team in the area of EP software tools, power quality analysis, hardware evaluation, and standards development.

Electrified Aircraft Propulsion (EAP)↗

NASA NTRSDate not supplied

Aerospace Cognitive Engineering Laboratory (ACELAB) Simulator for Electric Vertical Takeoff and Landing (eVOTL) Research and Development

A new generation of aerospace innovators are looking for ways to quickly and efficiently transport people in a safe and environmentally friendly manner. In the not-too-distant future, passengers and goods are expected to routinely fly aboard a new breed of cleaner, smarter air vehicles. This represents a new and significant challenge to the Federal Aviation Agency (FAA) which is responsible for aircraft certification, pilot licensing, operating approval and airspace integration. To help streamline this process, NASA has formulated its Advanced Air Mobility (AAM) project to provide research capabilities for development and evaluation of these new concepts and an environment where industry and regulators can work together to understand the requirements and work toward consensus standards for the new market. This paper will describe the development of the Aerospace Cognitive Engineering Lab Rapid Automation Test (ACELeRATE) simulator. ACELeRATE is an adaptable fixed-base aircraft simulator focused on the investigation of the performance and interaction of pilots and increasingly automated aircraft systems. ACELeRATE can be re-configured to support various simulation environments. The simulator includes a simple reconfigurable cockpit placed within a 10-foot spherical dome with a cluster of real-time image generators, high-resolution displays and highly realistic scenery with the surrounding digital terrain and required cultural area details (e.g., hangars, runways, ramp areas, taxiways, test range apparatus, buildings with designated rooftop landing areas, and other man-made 3D structures). This paper will also describe the various hardware and software tools employed in the ACELeRATE simulator, including engineering tools used by NASA for electric Vertical Takeoff and Landing (eVTOL) vehicle equations of motion, wind-model simulation in an urban environment, as well as the various modeling techniques and tools used to quickly generate highly realistic 3D terrain models for low level flight including urban terrain and obstacle depictions.

AAM Simulation Cockpit↗

NASA NTRS2021

ATTRACTOR: Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability

Autonomous systems (AS) are crucial to realizing the vision of new, complex transportation modes, such as advanced air mobility (AAM) and urban air mobility (UAM). A chief barrier to induction of AS into aviation is insufficient understanding of AS reliability in time-critical and safety-critical environments—an obstacle to certification. ATTRACTOR is aimed at building a basis for certification of classes of autonomous cyber-physical-human systems (CPHS) via establishing metrics and models of trustworthiness and trust in multi-agent team interactions, analyzable trajectories, explainability of computational algorithms (explainable artificial intelligence, or XAI), and persistent modeling and simulation, in the context of missions planning and operation. By “building a basis for certification,” we mean acquiring an understanding of when a system is trustworthy and developing computable means to estimate trustworthiness and trust in order to eventually inform functional requirements that contribute to certification. The outcomes are applicable not just to aviation but to all domains that rely on autonomous systems.

Autonomy↗

NASA NTRSDate not supplied

Urban Air Mobility (UAM) Vision Concept of Operations (ConOps) UAM Maturity Level (UML)-4 Overview

The Vision ConOps is intended as a foundation to engage members of the UAM community and provide a consensus on the future vision of UAM operations. It provides a concept for more detailed discussion and a basis for the exploration of ideas using a common framework to inform the continued development and integration of UAM as part of the broader transportation system. Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation’s role in everyday life, including the movement of goods and people. Urban Air Mobility (UAM) represents one of the most exciting and complex AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities.

Brian Hill↗

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).

UAM↗

NASA NTRSDate not supplied

An Approach for Defining IASMS Services, Functions, and Capabilities

Assuring safety in the NAS with the inclusion of new entrants, such as Advanced Air Mobility (AAM), will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using autonomous vehicles. The focus of the In-time Aviation Safety Management System (IASMS) is to overcome AAM’s safety assurance challenges. The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs) designed to manage operational risks, identify unknown risks, and inform system designs. This paper describes an approach for defining SFCs based on technology trends in research, assessment of known and unknown risks in voluntary safety reports, and causal and contributing factors in aviation accidents and incidents. This approach would identify potential SFCs that further expand the Monitor, Assess, and Mitigate (M-A-M) functionality that represents the enabling framework of the IASMS. Safety implications that will result from integration of AAM in the transformation of the National Airspace System (NAS) were addressed in National Academies committees reports on AAM and IASMS. Development of a ConOps for IASMS was a top recommendation and can be represented as a reframing of safety assurance that builds on real-time alerting such as the Traffic Alert and Collision Avoidance System, and adds the more encompassing in-time temporal parameter in recognition of the different timelines for collecting and assessing safety data for risk mitigations. For example, mining for safety trends from data sources such as the Aviation Safety Information Analysis and Sharing system occurs over a longer time period. Research on AAM operations poses that SFCs can be designed to monitor the safety margin appropriate for AAM including with regards to the distance between current flight parameters and nominal ideal conditions. These in-time comparisons will become more complex as the density of operations increases at least in certain areas and can include planned and actual 4D trajectory, and in-time comparisons having implications on conflict modeling and prediction including expected and actual departure time, fix/waypoint crossing times, and arrival time. These comparisons would be integrated as part of SFCs that redefine and inform new safety margin. An increased safety margin improves management of operational risks while reducing the potential for anomalies. An increased safety margin also has implications for operator confidence in the certainty of its operations and trust in automation. Technology trends in research could be used to refine existing SFCs and define needs for additional SFCs that provide safety improvements to the design and operation of vehicles, airspace design, and operator performance requirements. NASA is developing innovative approaches to safeguard against major accidents and incidents that have occurred in the NAS and those anticipated with the inclusion of envisioned AAM operations. The innovations use operational performance data to monitor, detect, and predict flight variations exceeding safe nominal patterns, such as would be caused by navigational error, severe weather complications, or hijacking of UAS controls. These innovative approaches have high potential to prevent accidents and incidents in the new AAM era. It is anticipated that elements of the innovations will evolve into SFCs for the IASMS. Voluntary safety reports can be monitored to identify anomalies related to design or operational performance risks. Reports could be periodically monitored and assessed for specific topics. Reports might serve as weak signals or precursors indicative of emergent risk such as when combined with other safety information. The architecture could include SFCs that are based on voluntary safety reports recognizing the periodic temporal nature of data analysis. As previously mentioned, aviation accidents with their causal and contributing precursors can inform the need for SFCs in the IASMS. Accidents and incidents at San Francisco International Airport such as Asiana 214 and Air Canada 759 illustrate how combinations of different factors lead to increased risk. These types of precursors and different factors have implications on the types of SFCs that could be needed to monitor and manage different sources and types of design and operational risk. Continuing to assure the safety of AAM as designs and operations gain in complexity can be accompanied by defining SFCs that also increase in complexity. These SFCs can leverage information from findings and recommendations synthesized across on-going research, voluntary safety reports, and accident and incident reports. These SFCs can serve to refine accuracy of algorithms and resolve limitations with current practices. The IASMS architecture represents the framework for the SFCs and their critical role in safety assurance.

In-Time Aviation Safety Management System↗

NASA NTRSDate not supplied

Acoustic and Performance Measurements Obtained in the NASA Langley Small Hover Anechoic Chamber

The emergence of small Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) vehicles has motivated the need to acoustically characterize the rotors found on these vehicle platforms. Hover chamber tests of small rotors are beneficial in assessing the noise impact of UAS, and potentially larger AAM vehicles. Various experiments have been conducted in the NASA Langley Small Hover Anechoic Chamber (SHAC), formerly known as the Small Anechoic Jet Facility (SAJF), in order to investigate the noise and performance of small rotors. One of these tests was that of an “ideally twisted rotor”, which was designed in order to simplify the theoretical rotor inflow for a target thrust condition in hover. This rotor was fabricated using state-of-the-art rapid prototyping and tested in the SHAC. Aerodynamic load and acoustic data were acquired across a range of rotation rates and rotor collective settings. Both tonal and broadband components were identified in the acoustic data, with broadband noise being a prominent contributor to the overall rotor system noise.

Nicole Pettingill↗

NASA NTRS2021

High-Density Automated Vertiport Concept of Operations

Urban Air Mobility↗

NASA NTRS2021

Methods for Recording and Documenting Ambient Environmental Sound for use in Listening Devices

The potential for implementing Advanced Air Mobility (AAM) vehicles as a viable new transportation system into communities will likely be significantly affected by the psychoacoustic impact of these new noise sources into the existing ambient soundscape. This document addresses a need within the research community for a consistent means of documenting recordings of the ambient soundscape via a metadata framework (Rizzi, et al. 2020). Such recordings can provide a cognitive context for AAM vehicle sounds, as well as a fixed condition or independent variable against which experimental manipulations of vehicle sounds can be evaluated in listening tests. This document also provides recommendations for recording and reproduction techniques of ambient sound. A brief review is made of the use of ambient recordings in prior aircraft noise studies, and of psychoacoustic motivations for its implementation is reviewed from the fields of soundscape, auditory scene analysis, and time-varying partial specific loudness research.

urban air mobility↗

NASA NTRSDate not supplied

An Approach for Identifying IASMS Services, Functions, and Capabilities From Data Sources

Assuring safety in the NAS with the inclusion of new entrants that are part of Advanced Air Mobility (AAM) will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using semi-autonomous/autonomous vehicles. Overcoming these AAM safety assurance challenges is the focus of the In-time Aviation Safety Management System (IASMS). The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs)designed to manage operational risks, identify unknown risks, and inform system design to mitigate risk. This paper describes a broad approach for identifying SFCs involving technology trends in research, assessment of known and unknown risks in safety reports, and causal and contributing factors in aviation accidents and incidents. This approach leverages these sources to identify potential SFCs that enable the Monitor, Assess, and Mitigate (M-A-M)functionality that represents the enabling framework of the IASMS.

Kyle Ellis↗

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

Ruixuan Han↗

NASA NTRSDate not supplied

Revolutionary Vertical Lift Technology (RVLT) Overview

The Aircraft Working Group enables the Advanced Air Mobility (AAM) ecosystem through vehicle development and production as well as individual vehicle management and operations. Aircraft design and operations management develop AAM-inspired concepts and technologies to help define requirements and standards addressing key challenges such as safety, integration, noise, automation, and scalability. This presentation focuses on RVLT electronic Vertical Takeoff Landing Propulsion overview.

Peggy Cornell↗