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NASA NTRSDate not supplied

Highlights of NASA’s Orbital Debris Program Office In Situ and Laboratory Measurements

NASA’s Orbital Debris Program Office (ODPO) maintains various returned spacecraft materials, capabilities, and facilities used for in situ and laboratory measurements that directly support orbital debris environmental models. In situ measurements include the analysis of exposed and returned hardware surfaces. These surfaces serve as passive sensors for the small-sized micrometeoroid and orbital debris (MMOD) flux below the sensitivity of ground-based radar and optical sensors. Various instruments and techniques are used to determine the size and depth of selected impact features, and – if feasible – the composition of the projectile material. Analysis of the impactor residues enables the differentiation of MM and OD for debris below 1 mm to support modeling the OD environment. In addition, projectiles identified as OD can be further differentiated in low-, medium-, and high- density impactors based on chemical analyses. In addition to in situ measurements, the ODPO has also worked in collaboration with the U.S. Space Force Space Systems Command (formerly the U.S. Air Force Space and Missile Systems Center), the Aerospace Corporation, and the University of Florida on a laboratory-based hypervelocity impact test, DebriSat, conducted at the Air Force Arnold Engineering Development Complex in 2014. The resulting data from this impact test series are being analyzed to assess the fragments’ sizes/masses, materials/densities, shapes, and other parameters of interest. The DebriSat project provides the data needed to update NASA’s breakup models and size estimation models using the simulated orbital breakup of a modern, low Earth orbit spacecraft. Ultimately, over 200,000 fragments from this impact test will be stored at NASA Johnson Space Center (JSC) and further analyzed by the ODPO. This project will also use machine learning techniques to infer physical parameters of fragments embedded in the soft-catch foam used in the impact experiment. Applied to X-ray imagery of the foam panels, these techniques promise to minimize human-in-the-loop processes for fragment extraction and physical characterization. A brief overview of this project and data collected will be presented. Lastly, the ODPO provides various capabilities hosted at NASA JSC for optical inspections and measurements using a variety of techniques and scientific instrumentation to support both in situ and laboratory measurements. The ODPO’s Optical Measurement Center (OMC) is an advanced facility for photometric and spectroscopic laboratory measurements of targets, including fragments from the DebriSat project. The OMC simulates telescopic observations by using space-like illumination conditions and source-target-sensor orientation techniques. Additionally, the OMC is uniquely equipped to acquire pseudo-bidirectional reflectance distribution data for broadband photometric measurements, thus removing aspect angle dependencies that can affect target size estimates using the optical size estimation model. Narrow-band surface material characterization using spectroscopic instrumentation gives insight into how the albedo parameter – also important in the optical size estimation model – may vary depending on the state of the material. The OMC also performs simulations of photometric measurements using optical ray-tracing software to model the OMC optical throughput. In addition to the OMC, the ODPO houses a start-of-the-art Fragment Analysis Facility that uses multiple microscopic inspection instruments to support in situ measurements and material characterization. An overview of both facilities will be highlighted in this paper.

Orbital Debris↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRSDate not supplied

Task Load Management in Earth Independent Medical Operations

BACKGROUND: Medical care in spaceflight carries a high task load and can easily overwhelm a small crew. Present day operations in low Earth orbit (LEO) offload most medical tasks to ground teams in mission control. This team includes dozens of flight surgeons, specialists, and engineers and supports the on-orbit crew in monitoring environmental systems, tracking medications, guiding procedures, providing expert advice, and many other tasks. However, the physical limitations of the speed of light and technical limitations of bandwidth, channel capacity, and signal processing mean that missions beyond LEO cannot rely on this level of telemedical support. The further we travel from Earth the more these tasks will fall on the shoulders of the crew and the greater the risk of task saturation to the wellbeing of the crew and the success of the mission. Exploration class space crews will need progressively more robust systems for managing task load as they progress further out in space. OVERVIEW: Medical task management systems will need to assist with two broad categories of tasks; cognitively intensive tasks and procedure execution tasks. In both cases the goal is for the systems to operate in the background with minimal human-in-the-loop intervention. To accomplish this such systems will need to be designed with careful consideration for human factors and human systems integration to maximize efficiency, minimize alarm fatigue, and avoid inadvertently increasing task loads. Finally, the key domains of space medicine tasking can be used to map present day and near future technologies to the areas where they are best suited to support and identify gaps which can be targeted for research and development. DISCUSSION: Task load is a major challenge for Earth Independent Medical Operations to overcome. It will require careful coordination between experts in a variety of fields paying attention to human factors and human systems integration as well as technical and medical expertise. If done well medical task management systems can handle many of the tasks currently run by humans in mission control and enable human crews to maintain terrestrial standards of care in the extraterrestrial environment.

Dana Levin↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes.

Human-Autonomy Teaming↗
NASA NTRS2023

Joint Augmented Reality Visual Informatics System: Concept of Operations

NASA proposed requirements for a digital display for an EVA spacesuit to provide relevant information to the crew member. The Joint Augmented Reality Visual Informatics System (Joint AR) project pursued four years of research and development towards a suit-display system in a near-eye, AR form factor. The project was responsible for developing software (custom graphics engine and core flight software), physical hardware prototyping (controls, projection display optics, suited display platform), virtual prototyping platform (a virtual reality testbed), and human-in-the-loop (HITL) operational testing informed by EVA flight controllers, crew members, and human factors engineers for con-ops definition. This document contains substantial updates to CTSD-ADV-1788 Rev. Basic. This revision was produced by the project to summarize the use-cases and and user experiences developed throughout the project, and refine the Basic revision originally drafted at the beginning of the project life cycle. The primary purpose of this document is to summarize and make available the scenario development efforts that have been pursued and explored within the Joint AR project. This includes descriptions of the scenarios themselves as well as corresponding potential of advanced informatics displays to support those specified scenarios. In doing so, this document provides a variety of approaches to deconstruct and hypothesize how future technological capabilities so that with future EVA work demands can be satisfied within future human planetary spaceflight missions.

Matthew Miller↗
NASA NTRSDate not supplied

Sim to Flight: Evaluating Flight Path Management Automation in High Density Urban Environments

Combined simulation and flight testing enable the study of single- and multi-aircraft performance of onboard automation systems for dynamic flight path management (FPM). The National Aeronautics and Space Administration (NASA) is investigating system performance and functional capabilities of such automation for immersion into complex, high density, future operations such as Urban Air Mobility (UAM). This paper provides an overview of a series of interdependent sim-to-flight research activities involving large-scale batch simulations, human-in-the-loop verification, and flight-test validation of a research prototype FPM automation system. Together, they significantly contributed to a functional assessment of FPM automation functionality in a live-virtual-constructive (LVC) operating environment characterized by two live aircraft and hundreds of virtual aircraft interacting in a modeled complex urban airspace. Initial simulation and flight test results, future work, and conclusions are presented.

Advanced Air Mobility↗
NASA NTRSDate not supplied

NASA Small Spacecraft Technology (SST) Program - Recent and Upcoming Technology Demonstrations and Development Efforts

The Small Spacecraft Technology (SST) program within NASA’s Space Technology Mission Directorate, expands the ability to execute unique missions through rapid development and demonstration of capabilities for small spacecraft applicable to exploration, science and the commercial space sector. Through targeted development and frequent in space testing, the program: • Enables execution of missions at much lower cost than previously possible • Substantially reduces the time required for development of spacecraft • Enables new mission architectures through the use of small spacecraft • Expands the reach of small spacecraft to new destinations and challenging new environments • Enables the augmentation of existing assets and future missions with supporting small spacecraft. The program achieves its objectives through: • Identification and investment in the development of new subsystem technologies to enhance or expand the capabilities of small spacecraft • Sponsorship of flight demonstrations of new technologies, capabilities and applications for small spacecraft • Promotion of the use of small spacecraft as platforms for testing and demonstrating technologies and capabilities that might have more general applications in larger-scale spacecraft and systems Technologies funded by the program that are key to advancing the utility and capability of small spacecraft were demonstrated in 2022 and 2023. Notable on-orbit demonstrations include the following. • The Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment’s (CAPSTONE) navigation technology that was demonstrated for the first time in 2023 could provide autonomous onboard navigation information for future lunar missions, minimizing or eliminating human-in-the-loop mission operations. • The optical communications technology also demonstrated in 2023 by the TeraByte InfraRed Delivery (TBIRD) payload that launched on Pathfinder Technology Demonstrator-3, achieved 200 gigabit per second (Gbps) throughput on a space-to-ground optical link between a satellite in orbit and Earth, the highest data rate ever achieved by optical communications technology. • The precision laser pointing performance of NASA’s CubeSat Infrared CrossLink A (CLICK) spacecraft’s fine steering mirror control system was successfully demonstrated. This element of the laser communications system will be used to demonstrate two-way crosslink communication in low-Earth orbit by CLICK B/C anticipated to launch in late 2024. Investments in subsystem technology development through funding to university, industry, and other government partners in a number of areas will be discussed. Among others, topic areas include propulsion, autonomous swarm technology, edge computing, and thermal control. Additionally, the status of the CubeSat-alternative platform, DiskSat, strategies for accelerating the demonstration of technology payloads via the Realizing Rapid, Reduced-cost high-Risk Research (R5) project, and the status of recently completed and upcoming on-orbit demonstrations will also be presented.

Roger C Hunter↗
NASA NTRSDate not supplied

Reach Performance Using Touchscreens Under G and Vibration Conditions

This presentation summarizes prior results from human-in-the-loop experiments that measured the accuracy and precision of human reaches to a touchscreen under a range of altered gravitational and vibrational conditions. From the data, this presentation proposes a method for estimating the worst-case reach accuracy and precision during lunar landings with an eye towards supporting engineering design decisions about human control interfaces and crew operations for lunar missions.

human performance↗
NASA NTRS2024

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

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

CERTAIN↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes

Human-Autonomy Teaming↗
NASA NTRS2024

Crew Health and Performance Integrated Data Architecture (CHP-IDA) Project

BACKGROUND: Future Human Exploration missions introduce a new paradigm as crews move further from the resupply and near real-time ground support typical of Low Earth Orbit missions today. Without immediate support from ground-based personnel, exploration crews will be more reliant on inflight data and technology to respond to emergencies and anomalies. A data architecture to support a new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to enable crew autonomy. OVERVIEW: The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project funded by NASA’s Exploration Medical Integrated Product Team (XMIPT) is laying a foundation for future in-flight informatics by providing a back-end architecture for collecting, storing, and integrating multiple sources of data generated by and around the crew. CHP-IDA provides a platform for common data models and Application Programming Interfaces to access, integrate, process, and display CHP data (e.g., environmental, exercise, medical, sleep, performance, etc.). This will facilitate the increased situation awareness and decision support required by the crew and remote support of exploration missions. This presentation will describe the currently ongoing effort to develop and evaluate a path-to-flight concept of the CHP-IDA software and its core capabilities. Current integrations will be discussed, including analytics for Extravehicular Activity metabolic rate and data ingestion from a multi-functional integrated medical device. The presentation will also provide examples of scenarios used to demonstrate the CHP-IDA through human-in-the-loop test bed activities as well as examples of appropriate system performance metrics. DISCUSSION: Today, in-flight data is often siloed, unsynchronized, and largely inaccessible in real time. Many data sets require manual entry and/or data transfer between vehicles and the ground. These issues contribute to risks in supporting exploration medical capabilities. The CHP-IDA is a back-end data system providing core capabilities needed for timely and meaningful data insights across CHP domains to crew and remote personnel to enable increased crew autonomy. Future work includes collaboration with additional CHP domains, new technology integrations, and further demonstrations of the IDA within different vehicle and communication latency contexts. LEARNING OBJECTIVES 1. The audience will understand that the CHP-IDA is a back-end system, providing a platform to facilitate access, promote decision tools, and provide meaningful insights to crew and to remote stakeholders during exploration missions. 2. The audience will gain insight into human-centered research and activities used to discover CHP domain data needs and pain points and how this information is used to guide development of the IDA.

Exploration↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRS2024

Bone Conduction Headphone Research and Testing for xEMU Communications Applications

The new space suit being developed for exploration EVAs on the surface of the Moon and in microgravity environments is referred to as xEMU. A government reference model had been developed and has undergone extensive functional and environmental testing. This suit contains new upgrades from the current EMU on ISS, such as integrated speakers and microphones, eliminating the need for astronauts to wear a Communications Carrier Assembly (CCA) on their heads during spacewalks. However, this design approach results in speaker-to-microphone acoustic coupling and communications echo and feedback. A proposed solution to this issue is to replace the integrated open speakers with an astronaut worn bone conduction headset for audio capabilities while on EVA to receive incoming voice communications from Mission Control and other EVA or IVA crew members. This would eliminate the acoustic coupling and echo effect while being more ergonomically sound than the current CCA and leave the ear open to suit-ambient sound for situational awareness. A human-in-the-loop evaluation was performed, comparing five commercially available bone conduction headsets, to evaluate comfort, fit, and adjustability for long-duration wear. Five engineering test subjects with different head sizes were utilized to wear the headsets for six hours and provide succinct feedback and score the headsets on a variety of factors, to help determine which of the headsets performed the best and could advance to future bone conduction audio testing. Three of the headsets were well-received among the diverse group of subjects, and at least one of these will advance to further testing to be considered for future use under the xEMU helmet for exploration EVAs. Rating results and evaluation methods for this bone conduction headset evaluation will be presented.

Bridget Cavanaugh↗
NASA NTRSDate not supplied

Overview of an Exploratory, Real-Time, Multi-Pilot Simulation Study of Early eVTOL Operations at Non-Towered Vertiports

This paper provides a report out on an exploratory, multi-aircraft/multi-pilot, real-time simulation study conducted by NASA of early commercial powered-lift, Urban Air Mobility (UAM) operations at a non-towered vertiport. As used in this paper, vertiport refers to the primary ground and airspace elements facilitating the takeoff and landing of electric vertical takeoff and landing (eVTOL) aircraft with central emphasis on a vertipad, i.e. the physical touch-down and lift-off area and surrounding approach , departure, local pattern procedures. The study, known as the Piloted UML-2 ConOps Study (PUCS), had two high-level goals. The first goal was providing preliminary insights and observations relevant to the piloting and flight operations of early, commercial UAM operations aligned with the initial stage of the FAA’s Advanced Air Mobility (AAM) Implementation Plan and the second level NASA’s UAM Maturity Level (UML) scale. The second goal was evaluating a novel, medium-fidelity, extensible, many-pilot, real-time simulation capability known as the UAM Flyers developed by NASA. The Flyers are intended to allow rapid development, screening, evaluation, and demonstrations of potential Concepts of Operation (ConOps) for UAM flight operations and airspace management in a modular and low-cost, real-time, human-in-the-loop rapid simulation prototyping environment. For this study, ten Flyer cockpits were configured to evaluate flight operations through a non-towered vertiport with pilot interfaces and displays (external and in-cockpit) appropriate for operations under visual flight rules (VFR) and employing flight and communication procedures representative of current operations at non-towered airports. The presented results include an achieved operational tempo; durations of individual flight tasks for approaches and departures; off-nominal events and triggers; and pilot comments regarding potential procedural and technology improvements.

Urban Air Mobility↗
NASA NTRSDate not supplied

Assistive Detect and Avoid Technology in Urban Air Mobility Environments

The use of Assistive Detect and Avoid (Assistive DAA or ADAA) technology in Urban Air Mobility (UAM) environments poses potential benefits as well as challenges. Assistive DAA refers to the leveraged use of DAA technology, originally developed to replace see-and-avoid capabilities for remotely piloted aircraft, in onboard-piloted aircraft to augment (rather than replace) pilots’ see-and-avoid abilities and thus enhance the safety and efficiency of visual flight operations. ADAA is anticipated to be especially safety-enhancing in airspace where traffic density is high or traditional air traffic services are limited, such as in future UAM environments. ADAA may also enable higher-tempo UAM operations than with only see-and-avoid capabilities, while still maintaining acceptable levels of safety. UAM concepts under development by the FAA, NASA, and industry focus on operations moving people and cargo in urban and suburban areas using innovative technologies, operations, and aircraft, including electric vertical takeoff and landing (eVTOL) aircraft. Researchers at NASA Langley Research Center, in collaboration with FAA researchers at the William J. Hughes Technical Center in Atlantic City, NJ, have conducted a series of medium-fidelity, human-in-the-loop research simulations of potential future UAM operations and concepts in both Class C and Class B airspace environments. These simulations have included use of a Langley-developed ADAA research tool called DANTi, which enables configurable ADAA displays to be presented to pilots of simulated eVTOL aircraft participating in higher-density and higher-tempo UAM operations. Experience and observations made during testing of the NASA-developed DANTi ADAA capability in the UAM NFLITE simulation environment will be reported in this paper together with a discussion of airspace integration and regulatory topics.

Detect and Avoid↗
NASA NTRSDate not supplied

Overview of an Exploratory, Multi-Pilot Simulation Study of Early eVTOL Operations at Non-Towered Vertiports

This paper provides a report out on an exploratory, multi-aircraft/multi-pilot, real-time simulation study conducted by NASA of early commercial powered-lift, Urban Air Mobility (UAM) operations at a non-towered vertiport. As used in this paper, vertiport refers to the primary ground and airspace elements facilitating the takeoff and landing of electric vertical takeoff and landing (eVTOL) aircraft with central emphasis on a vertipad, i.e. the physical touch-down and lift-off area and surrounding approach , departure, local pattern procedures. The study, known as the Piloted UML-2 ConOps Study (PUCS), had two high-level goals. The first goal was providing preliminary insights and observations relevant to the piloting and flight operations of early, commercial UAM operations aligned with the initial stage of the FAA’s Advanced Air Mobility (AAM) Implementation Plan and the second level NASA’s UAM Maturity Level (UML) scale. The second goal was evaluating a novel, medium-fidelity, extensible, many-pilot, real-time simulation capability known as the UAM Flyers developed by NASA. The Flyers are intended to allow rapid development, screening, evaluation, and demonstrations of potential Concepts of Operation (ConOps) for UAM flight operations and airspace management in a modular and low-cost, real-time, human-in-the-loop rapid simulation prototyping environment. For this study, ten Flyer cockpits were configured to evaluate flight operations through a non-towered vertiport with pilot interfaces and displays (external and in-cockpit) appropriate for operations under visual flight rules (VFR) and employing flight and communication procedures representative of current operations at non-towered airports. The presented results include an achieved operational tempo; durations of individual flight tasks for approaches and departures; off-nominal events and triggers; and pilot comments regarding potential procedural and technology improvements.

Urban Air Mobility↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗