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NASA NTRSDate not supplied

m:N Operations High Complexity Simulation

The Human Autonomy Teaming Laboratory at NASA Ames Research Center (ARC) is planning to conduct an experiment investigating system-level effects operational vertiport disruptions on Urban Air Mobility (UAM) traffic in a simulated vertiport network in Denver, CO. The simulation will involve two parties, one at NASA ARC and another at NASA Langley Research Center (LaRC), each playing the role of a UAM Operator. This study, referred to as the "High Complexity Sim," features a 3-by-2-by-2 mixed subjects design. The variables to be manipulated are the scale of the disruption (Disruption, three levels: Small, Medium, and Large, affecting 1, 2, and 3+, vertiports, respectively), m:N ratio (Ratio, two levels: 2:6 and 2:12), and the assumption of UAM corridors (Corridors, two levels: With and Without). Local and system-wide capacity (i.e., throughput), efficiency (scale and number of flight delays, filed-vs-flown flight times), and human performance metrics (workload, situation awareness, heart rate/heart rate variability, eye gaze/fixation and saccades, stress) will be measured. This presentation details the experimental design and planned timeline for the study.

multi-vehicle↗
NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

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. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be 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. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗
NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

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. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be 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. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗
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

m:N ConOps/R&R Remote Simulation

This presentation details the experimental design of an investigation of small unmanned aircraft system (sUAS) operations involving multiple vehicle management by a remote operator. The study is part of an ongoing effort to explore multiple vehicle control by multiple operators, i.e., the control of N vehicles by m operators (m:N operations). For this effort, NASA and collaborators have developed prototypes of a concept of operations (ConOps), roles and responsibilities (R&R) for operators and supervisors, and a ground control station (GCS), including software displays and interfaces. Participants in this study acted as the pilot-in-command of twelve aircraft flying pre-approved routes in a simulation of a food delivery operation utilizing sUAS in the San Diego, CA area. Each participant experienced four experimental trials. Twice within the course of each trial, participants were responsible for responding to a sudden, unanticipated, and high-priority contingency: an airspace restriction for sUAS operations known as a UAS Volume Reservation (UVR). Upon issuance of a UVR, pilots were expected to reroute affected vehicles around the airspace. The level of automation (LoA) and workload of the flight rerouting task were varied. The LoA was manipulated by providing reroute suggestions ("auto" condition) for aircraft or by requiring pilots to manually reroute ("manual" condition) affected vehicles. Workload was varied as a function of the number of vehicles affected by the UVR contingencies: 2 vehicles ("low workload" condition) versus 4 vehicles ("high workload" condition). Additionally, some vehicles required pilots to adjust for terrain conflicts while avoiding the UVR region. Due to the COVID-19 pandemic, in-person data collection for this study was not possible. Researchers adapted to this circumstance through the development of remote data collection protocol. Participants were able to view adapted GCS displays using the Microsoft Teams teleconferencing platform and responded to events by using a verbal protocol developed for the experiment. Using this protocol, participants provided instructions for actions to a researcher, referred to as the surrogate, to carry out on their behalf. This presentation describes the experiment design, including special details for remote data collection via a subject-surrogate configuration, and concludes with planned data analysis and results to be presented at a later date.

multi-UAS↗
NASA NTRSDate not supplied

System-Wide Error Attribution in Multi-Vehicle Operations: Theoretical Explanation, Implications, and Applications

In multi-vehicle aerial operation contexts, a single or several human operators (m) are responsible for managing multiple uncrewed vehicles (N). This is referred to as the m:N operational paradigm. When automation error occurs during m:N operations, especially if multiple vehicles exhibit automation error, a question is raised: What level of system globality will the error be attributed to? Globality refers to the levels within the conceptual hierarchy of a perceptual object. Significant costs could be incurred if the error is falsely attributed to a higher globality level than it should be (e.g., multiple groups of vehicles as opposed to a single vehicle), and those vehicles are subsequently subject to grounding and evaluation/maintenance. Likewise, it could be problematic if error is falsely attributed to a component (i.e., a lower globality level), resulting in ongoing, unsafe operation of vehicles that should be grounded or evaluated. Thus, to meet required safety standards and facilitate commercial viability, multi-vehicle operators must be capable of appropriately attributing the level of globality of automation error within the system they are responsible for.

Error↗
NASA NTRS2024

m:N Working Group Meeting Summary November 2023

From November 28th to 30th, 2023 the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Langley Research Center in Hampton, VA for an in person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together in person to further identify and discuss challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA, RTCA, and ASTM. Each subgroup is run by a government/industry team (see below). sUAS Subgroup Garrett Sadler (NASA) Scott Scheff (HF Designworks) Large UAS Subgroup Conrad Rory (NASA) Brandon Suarez (Reliable Robotics) HAPS Subgroup Andy Thurling (Thurling Aero Consulting) Jeff Homola (NASA) UAM Subgroup Mike Politowicz (NASA) Scott Scheff (HF Designworks), member-at-large

m:N operations↗
NASA NTRS2003

Applications of Space-Filling-Curves to Cartesian Methods for CFD

This paper presents a variety of novel uses of space-filling-curves (SFCs) for Cartesian mesh methods in CFD. While these techniques will be demonstrated using non-body-fitted Cartesian meshes, many are applicable on general body-fitted meshes-both structured and unstructured. We demonstrate the use of single theta(N log N) SFC-based reordering to produce single-pass (theta(N)) algorithms for mesh partitioning, multigrid coarsening, and inter-mesh interpolation. The intermesh interpolation operator has many practical applications including warm starts on modified geometry, or as an inter-grid transfer operator on remeshed regions in moving-body simulations Exploiting the compact construction of these operators, we further show that these algorithms are highly amenable to parallelization. Examples using the SFC-based mesh partitioner show nearly linear speedup to 640 CPUs even when using multigrid as a smoother. Partition statistics are presented showing that the SFC partitions are, on-average, within 15% of ideal even with only around 50,000 cells in each sub-domain. The inter-mesh interpolation operator also has linear asymptotic complexity and can be used to map a solution with N unknowns to another mesh with M unknowns with theta(M + N) operations. This capability is demonstrated both on moving-body simulations and in mapping solutions to perturbed meshes for control surface deflection or finite-difference-based gradient design methods.

Aftosmis, M. J.↗
NASA NTRS2003

Applications of Space-Filling-Curves to Cartesian Methods for CFD

The proposed paper presents a variety novel uses of Space-Filling-Curves (SFCs) for Cartesian mesh methods in 0. While these techniques will be demonstrated using non-body-fitted Cartesian meshes, most are applicable on general body-fitted meshes -both structured and unstructured. We demonstrate the use of single O(N log N) SFC-based reordering to produce single-pass (O(N)) algorithms for mesh partitioning, multigrid coarsening, and inter-mesh interpolation. The intermesh interpolation operator has many practical applications including warm starts on modified geometry, or as an inter-grid transfer operator on remeshed regions in moving-body simulations. Exploiting the compact construction of these operators, we further show that these algorithms are highly amenable to parallelization. Examples using the SFC-based mesh partitioner show nearly linear speedup to 512 CPUs even when using multigrid as a smoother. Partition statistics are presented showing that the SFC partitions are, on-average, within 10% of ideal even with only around 50,000 cells in each subdomain. The inter-mesh interpolation operator also has linear asymptotic complexity and can be used to map a solution with N unknowns to another mesh with M unknowns with O(max(M,N)) operations. This capability is demonstrated both on moving-body simulations and in mapping solutions to perturbed meshes for finite-difference-based gradient design methods.

Aftosmis, Michael J.↗
NASA NTRS2018

Multi-Operator Multi-UAV (MOMU) Control: Exploring the Influence of Sensor Tools and Playbook Task Delegation

New concepts of operations for Unmanned Aerial Vehicles (UAVs) will require a change from the current 2:1 operator to vehicle crew configuration. One particular control paradigm, largely driven by logistics, manpower, and training burdens, as well as the desire to force multiply, involves a single operator simultaneously managing multiple UAVs. This mode of operations has shown to significantly increase cognitive workload and decrease situation awareness, as operators are required to simultaneously attend to multiple sources of information. One potential way to mitigate potential drawbacks of multi-vehicle control by a single operator is to migrate to a multi-operator multi-UAV (MOMU) crew configuration, whereby M operators control N (> M) vehicles. This type of crew configuration can be organized in several ways to dynamically manage cognitive workload, match operator qualifications and skills to mission requirements, increase utilization of available assets, and thereby achieve maximum force multiplication. The present experiment examined task performance in a simulated MOMU environment and evaluated the potential benefits of sensor management aids ("Tools") as well as integrated sensor and flight automation ("Plays") compared to a fully manual condition ("Manual"). Tools support the operator by facilitating rapid understanding and management of sensor information, while the Plays support the operator by offloading/automating subtasks. Six pairs of participants were recruited for this study and tasked with sharing a pool of UAVs in order to conduct reconnaissance, surveillance, and target acquisition (RSTA) missions in adjacent Areas of Operation (AOs). Participants were given four tasks to accomplish, in order of priority: 1) prosecute High Value Targets (HVTs); 2) identify / track targets (military vehicles); 3) identify / mark civilian vehicles; and 4) respond to chat messages. Performance on the mission tasks was measured in terms of accuracy and reaction time. A composite mission score was also calculated using a payoff matrix that weighted each task according to priority. The results indicate that Playbook demonstrated better performance overall with higher accuracy rates and the highest composite score compared to Tools and Manual. The implications of these results to supporting future MOMU concepts of operations is discussed.

Playbook↗
NASA NTRS2023

m:N Working Group Spring Meeting Summary

On May 9th and 11th, 2023, the NASA-led MultiVehicle (m:N) Working Group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Pseudo Satellite (HAPS), and Urban Air Mobility (UAM)] met in Denver, CO at XPONENTIAL 2023, co-hosted by the Association for Uncrewed Vehicle Systems International (AUVSI) and Messe Düsseldorf North America (MDNA), for an in-person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year, however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N working group is co-chaired by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups, including the FAA and RTCA’s SC-228 Detect and Avoid.

multi-vehicle↗
NASA NTRS2022

m:N Working Group

On November 29th and 30th, 2022, the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Ames Research Center in Mountain View, CA for an in person meeting. The option to dial in remotely and use Conference.IO to engage with questions was offered as well. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA and RTCA’s SC-228 Detect and Avoid. Each subgroup is run by a government/industry team.

multi-vehicle↗
NASA NTRS2023

m:N Working Group Annual Status Report

This document serves as an annual report of m:N Unmanned Aircraft Systems (UAS) subgroup activities, addressed challenges, and roadmaps for the future. The subgroups consist of small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM). Also included in this report are participant lists for each subgroup (Appendix B) and future roadmap and outreach plans.The subgroups meet in a virtual format multiple times throughout the year. Twice a year, participants from all the subgroups come together as part of the m:N working group to brief each other in person on progress, challenges, and path forward ideas for successful incorporation of UAS into the airspace. Recent m:N in person meetings include: - November 29-30, 2022 at the NASA Ames Research Center in Mountain View, CA - May 9 & 11, 2023 at AUVSI’s Xponential conference in Denver, CO - The next in person working group meeting is planned for November 28-30, 2023 at NASA Langley in Hampton, Virginia The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the Federal Aviation Administration (FAA) and Radio Technical Commission for Aeronautics (RTCA’s) SC-228 Detect and Avoid committee.

multi-vehicle↗
NASA NTRSDate not supplied

Group 1: Evaluations Methodologies

m:N operations, multiple operators controlling mulitple vehicles, is a relatively new operating mode. As such, new evaluation methodologies are needed. In this presentation, neglect time and service time metrics are proposed as well as analysis candidates.

multiple↗
NASA NTRSDate not supplied

Predictive Model for Workload in Remote Operators During sUAS Contingency Scenarios

The increase in automated capabilities of small Uncrewed Aerial Systems (sUAS) has enabled the human operators to manage larger numbers of vehicles simultaneously. As this happens, the operational paradigm shifts to an m:N configuration where multiple operators (m) are managing multiple vehicles (N) together. However, many questions about how operators will interact with each other and share interaction across the vehicle pool are yet unanswered. Therefore, stakeholders from government and industry have partnered to develop ground control station concepts for such operations. The work presented in this paper aims to identify factors that contribute to operator workload. A supervised machine learning-based method built using Support Vector Machines and K-fold cross-validation was used to create workload prediction models for various NASA TLX subscales by leveraging features related to interactions and their relative timings during m:N operations. Results show that the models yielded fairly high predictive accuracies ranging from ~60-75%.

workload prediction↗
NASA NTRSDate not supplied

PAAV Tabletop 4 Results – Integrating m:N Remotely Piloted Operations

NASA's Pathfinding for Airspace with Autonomous Vehicles (PAAV) sub-project is investigating procedures and technologies to facilitate seamless integration of future UAS operations into the NAS. The Tabletop 4 activity solicited subject matter expertise to identify solutions to the potential challenges expected when the remote pilot-to-vehicle ratio scales from 1:1 to m:N, where one or more ground-based pilots control multiple uncrewed aircraft. This presentation details the method and high-level results of the PAAV Tabletop 4 activity.

m:N↗
NASA NTRS2021

m:N and Human Autonomy Teaming Concepts for High Density Vertiport Operations

This report focuses on the role of the Fleet Manager (FM) and, in particular, the ways in which automation could support their position as they manage multiple aircraft and operators in a highly dynamic, advanced air mobility (e.g., air taxi) environment, specifically "high-density vertiport" (HDV) regions. Similar to terminal area operations for traditional aviation, operations involving HDVs will need to be highly structured while also remaining resilient to the various contingencies that can happen in that environment. The role of the FM is consistent with an “m:N” architecture, where “m” number of operators cooperatively manage “N” number of vehicles (where “N” is always larger than “m”). In such a paradigm it is critical to provide the operator with the tools and information necessary to manage their fleet safely and navigate the known pitfalls with highly automated, complex systems (e.g., brittleness, insufficient situation awareness, skill degradation). As the field of m:N has expanded as an area of study, a set of higher-level automation concepts have emerged—namely “plays,” “working agreements,” and “human-autonomy teaming” (HAT)—that could support operators in this new role.

human autonomy teaming↗