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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

M:N Operations NASA/Uber Collaboration

In this presentation, current approaches to enable multiple-operator, multiple vehicle (M:N) operations are reviewed together with recent collaborative efforts between NASA and Uber. Topics include a review of human-automation teaming (HAT) concepts, including plays and working agreements, and a particular task-allocation method called Automation Level-based Task Allocation (ALTA). Following introductory material on HAT, an overview of a recent (July 2020) cognitive walkthrough study of M:N operations in the context of a food delivery via small-Unmanned Aircraft Systems application is provided. Initial results from this cognitive walkthrough detailing operator feedback on displays, operator and supervisor roles and responsibilities, and the overall concept of operation are reviewed. The presentation concludes with a description of a future, human-in-the-loop simulation experiment of M:N operations in a high-fidelity environment, which will examine the effects of high workload and assistive automation/tools on operator performance.

human-automation teaming↗
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 NTRSDate not supplied

Preliminary Development of Multi-Vehicle (m:N) Operations with NASA Langley’s Remote Vehicle Operations Center

To achieve the vision of Advanced Air Mobility (AAM), a transition from localized operations of aircraft to remote operations is being pursued across many use cases. This transition will allow fewer human operators to manage more increasingly autonomous aircraft (i.e., m operators managing N vehicles, or m:N). To study this operational concept, the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has developed a prototype remote vehicle operations center and ground control station (GCS) software to conduct research with simulated and real flight operations. To date, flight operations at LaRC have been limited to one vehicle per operator. However, the current paper describes initial development and considerations for enabling m:N flight operations at LaRC. Further, the research described in this paper provides the foundation for a concept of operations (ConOps) that will be developed to support remote operators managing multiple increasingly autonomous vehicles, with the goal of exploring human-autonomy teaming (HAT) concepts that enable more advanced m:N operations. Two key enablers have been identified to facilitate successful m:N operations: GCS software updates for multi-vehicle management and procedural updates for vehicle handoffs during off-nominal events. Additionally, specific modifications were identified across five key areas: technology and software, team structure, inter-team communication, contingency plans, and operator decision flows. Next steps in forming the LaRC m:N ConOps will include working with subject-matter experts to identify off-nominal scenarios, implementing the recommended GCS functionality for m:N operations, and performing integration testing of facility capabilities and new operational procedures. Although the future m:N ConOps will be tailored to the NASA LaRC remote operations facility and flight range, it is intended to be a transparent, accessible, and reality-based exemplar for external organizations seeking to create or evaluate their own m:N operational concepts.

m:N↗
NASA NTRSDate not supplied

m:N Operations of Autonomous Fleets

The presentation discusses the background and project framing for the m:N body of work in TTT. It also review the m:N technical challenge for Operations of Autonomous Fleets.

Kelley Hashemi↗
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↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗
NASA NTRS1995

A new series of oxycarbonate superconductors (Cu(0.5)C(0.5))(m)Ba(m+1)Ca(n-1)Cu(n)O2(m+n)+1

We found a new series of oxycarbonate superconductors in the Ba-CaCu-C-O system under high pressure of 5 GPa. Their ideal formula is (Cu(0.5)C(0.5)(m)Ba(m+1)Ca(n-1)Cu(n)O2)((m+n)+1) ((Cu,C)-m(m+1)(n-1)n). Thus far, n = 3, 4 members of the m = 1 series, (Cu,C)-1223 and (Cu,C)-1234, have been prepared in bulk while n = 4, 5 members, (Cu,C)-2334 and (Cu,C)-2345, have been prepared for the m = 2 series. (Cu,C)-1223 shows superconductivity below 67 K while T(sub c)'s of other compounds are above 110 K. In particular, (Cu,C)-1234 has the highest T(sub c) of 117 K.

Takayama-Muromachi, E.↗
NASA NTRSDate not supplied

Pathfinding for Airspace with Autonomous Vehicles (PAAV) + m:N Tabletop

The Pathfinding for Airspace with Autonomous Vehicles (PAAV) project is investigating how to integrate increasingly autonomous aircraft into the current air traffic management system. The project aims to help develop airspace procedures and technologies that are scalable to future autonomous operations. The purpose of this brief is to inform a working group on past and future PAAV efforts. The working group specializes in "m:N" operations, which refers to remote operations where one or more ground-based pilots cooperatively control multiple unmanned aircraft. The PAAV team will detail an upcoming tabletop exercise that is designed to elicit feedback from subject matter experts on current barriers to m:N operations for unmanned cargo operations and potential solutions to those obstacles. The working group will be given an opportunity to provide feedback on the objectives and methodology to be used in the PAAV tabletop exercise.

autonomous↗
NASA NTRSDate not supplied

m:N Working Group

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the background and progress of the m:N working group.

multi-vehicle control↗
NASA NTRSDate not supplied

m:N Operations and Future

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N).This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT).This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the barriers and requirements for future m:N operations.

multi-vehicle control↗
NASA NTRS2023

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗
NASA NTRS2022

m:N Working Group Status Report

This document reports on the status of the m:N working group including the barriers, sub-group status, DAA white paper and roadmapping exercise.

multi-vehicle control↗
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 NTRSDate not supplied

DAA Use Case for Auto Cargo m:N Operations

A detect and avoid use-case was developed to highlight detect and avoid issues associated with m:N operations in the auto cargo domain. This work is being done in conjunction with industry partners and developed for the Operational Scenario and Environmental Description (OSED) for RTCA SC-228. The detect and avoid function is critical, required technology for unmanned aircraft to operation in the national airspace. RTCA SC-228 has published MOPS (phase 1 & 2) detailing the requirements and methods of compliance. This work will help address additional operational aspects of how/when DAA will be employed by unmanned systems. Specifically, this work focuses on “auto cargo” operations. Auto cargo, in this context, refers to regularly scheduled cargo-size aircraft that are flown remotely. The Remote Pilot In Command (RPIC), in this case, is responsible for multiple aircraft, flown simultaneously. The use-case details the use of DAA in this context, the potential issues and gaps that exist.

multi-vehicle control↗
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 NTRSDate not supplied

Operator Workload and Task Allocation in m:N Operational Architectures of Uncrewed Aerial Systems

Uncrewed aerial systems (UAS) show promise in urban air transport, package delivery, and emergency services. UAS efficiency can be significantly improved by having fewer operators (m) manage a greater number of vehicles (N), or the m:N architecture of operation. The current study investigates how workload affects operators’ task-allocation decision-making and potential effects of two crucial human factors: trust and self-confidence. In the context of a simulated UAS package-delivery task, 10 participants with expertise in UAS operation were recruited. Each participant reported their preferred task-allocation strategy for a set of five subtasks while watching two sets of videos with different workload levels. Perceived workload, trust, and self-confidence were also measured after each video session. Overall, participants indicated a preference for automation for most of the subtasks under the delivery mission. Trust, rather than workload and self-confidence, played a significant role in experts’ decisions of task-allocation and assignment methods. Higher trust led to higher preference for automation.

workload↗