NASA Air Mobility Pathfinders (AMP) Project Concepts Workshop
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UAM↗
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AUVSI AAM Expo, Air Mobility Pathfinders (AMP) Project
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Ken Goodrich↗
Air Mobility Pathfinders Vehicle Flight Test Team Overview
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Gerrit Everson↗
DANTi: A Tool for Assistive Detect and Avoid Research
This paper presents DANTi, a research tool developed at NASA Langley Research Center to support the validation of Assistive Detect and Avoid (ADAA) requirements for General Aviation (GA). ADAA is a future on-board aircraft technology intended to augment a pilot’s see-and-avoid capability by helping them identify and resolve traffic conflicts earlier and more efficiently. DANTi includes a realistic Electronic Flight Bag (EFB) display and a fast-time simulation environment that can be fully customized to meet different research requirements. DANTi is currently used within NASA efforts such as the Air Mobility Pathfinders project on future air transportation systems and a joint NASA/FAA Laboratory Integrated Test Environment (NFLITE) on next-generation airspace operations in urban environments. These efforts investigate ADAA requirements in advanced urban air mobility settings where new aircraft types, new services, and new traffic patterns will be integrated in an overall crowded airspace.
Detect and Avoid↗
Optimizing Urban Air Mobility Research Through Bi-Directional Integration Processes for Effective Verification and Validation
Urban Air Mobility (UAM), a subset of Advanced Air Mobility (AAM), aims to revolutionize metropolitan transportation. This paper explores an engineering process framework within National Aeronautics and Space Administration (NASA)’s Air Mobility Pathfinders (AMP) project, which is focused on the safe scaling and seamless integration of UAM operations within the National Airspace System (NAS). Progressing through three phases — Initial, Midterm, and Mature — the Federal Aviation Administration (FAA)’s UAM Concept of Operations (ConOps) describes a proposed path for the evolution of UAM operations, presenting multifaceted challenges in technology, regulation, and stakeholder engagement. With this ConOps as a basis, the AMP project is executing research, development, test, and evaluation activities aimed at delivering validated reference architectures for the Midterm phase. This paper illuminates the pivotal role of the systems engineering process in managing these technical efforts. Robust verification and validation methods, along with systematic bi-directional integration process, enhance the ability to conduct research into the design and evolution of safe, efficient, and reliable UAM systems. In this paper a systematic framework will be presented and shown to facilitate interactions between UAM stakeholders and to effectively manage the complex architecture of UAM operations in the NAS.
National Airspace System↗
Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models
The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.
systems engineering↗
Provider of Services for Urban Air Mobility (PSU) Prototype Simulation (X5) Final Report
Urban Air Mobility (UAM) is a new air transportation service concept to carry passengers or cargo in metropolitan areas, leveraged by innovative aircraft and air traffic automation technologies. NASA has conducted a series of simulations, called the X-series simulation, to evaluate the UAM concept of operations and support the development of airspace procedures and services for UAM operations. The simulation called “X5” was conducted in 2023 to test a Provider of Services for UAM (PSU) prototype developed by NASA for UAM flight planning, strategic conflict management support, and data exchange between UAM operators. In this simulation, two strategic conflict management capabilities, Demand-Capacity Balancing and Sequencing and Scheduling, were further investigated. This document describes the UAM system architecture modeled, the X5 simulation environment to be executed (e.g., traffic scenario and UAM airspace construct), and the strategic conflict management processes developed and evaluated in this study. Then, the simulation results are provided using several system performance metrics, such as the number of operations planned and activated, demand-capacity imbalances detected and resolved, and pre-departure delays. Based on these metrics, the test findings and lessons learned from this simulation are discussed. NASA developed a PSU prototype as part of a reference implementation of UAM system architecture and evolved strategic conflict management capabilities for UAM operations from the previous collaborative simulations with industry partners. Below is the summary of the achievements: - Aligned NASA’s UAM reference architecture with the FAA’s UAM ConOps notional architecture - Extended UAM airspace management capabilities to include 1) Demand-Capacity Balancing (DCB) to ensure operators coordinate planned usage of shared vertiports, and 2) Sequencing and Scheduling (S&S) at UAM corridor entry and exit points to help facilitate an orderly flow of traffic - Defined the PSU information exchange APIs and requirements towards informing industry standards - Developed and tested a NASA PSU prototype as reference implementation to validate the requirements and APIs - Developed a prototype service connecting NASA’s PSU and the FAA system for testing future PSU-ATM interface requirements - Tested NASA-developed assumptions for UAM operations such as airspace design, procedures, vehicle performance, and strategic conflict management methods to inform future Cooperative Operating Practices (COPs) development with industry - Evaluated system performance metrics such as number of simultaneous operations and ground delays that can help define system-level requirements. The simulation results showed that the UAM traffic demand could be managed to minimize the needs of tactical separation provision with ground delays assigned by DCB and S&S. These accomplishments and the lessons learned from the PSU Prototype X5 simulation activities will be valuable inputs for the Air Mobility Pathfinders (AMP) project, which is NASA’s new project to create and evaluate a reference architecture for safe, secure, and scalable UAM operations.
Simulation↗
Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System
Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.
Mental Models↗
Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System
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Mental Models↗
Advanced physical-chemical life support systems research
A proposed NASA space research and technology development program will provide adequate data for designing closed loop life support systems for long-duration manned space missions. This program, referred to as the Pathfinder Physical-Chemical Closed Loop Life Support Program, is to identify and develop critical chemical engineering technologies for the closure of air and water loops within the spacecraft, surface habitats or mobility devices. Computerized simulation can be used both as a research and management tool. Validated models will guide the selection of the best known applicable processes and in the development of new processes. For the integration of the habitat system, a biological subsystem would be introduced to provide food production and to enhance the physical-chemical life support functions on an ever-increasing basis.
Evanich, Peggy L.↗
AAM and ATM-X Flight Activities and Demonstrations
The presentation will provide an overview of the Advanced Air Mobility efforts that are currently underway at NASA across a number of projects. The focus of the presentation is on key partnerships with industry and the associated flight tests that have been conducted under the Pathfinding for Airspace with Autonomous Vehicles subproejct and the National Campaign.
AAm↗
X-57 Maxwell Aircraft
X-57 Project Need - Advance the Nation's ability to design, test, and determine airworthiness of distributed electric and aero-propulsive coupling technologies, which are a critical enabler of emerging, advanced air mobility markets. - The value of X-57 lies in advancing hte Nation's ability to design, test, and certify electric aircraft, which will enable entirely new markets - The Mod II flight test program is a pathfinder for the experimental propulsion system performance and reliability to reduce the risk in the X-57 configuration.
Heather Maliska↗
X-57 Maxwell Aircraft EMI/EMC Integration Lessons Learned
X-57 Project Need - Advance the Nation's ability to design, test, and determine airworthiness of distributed electric and aero-propulsive coupling technologies, which are a critical enabler of emerging, advanced air mobility markets. - The value of X-57 lies in advancing hte Nation's ability to design, test, and certify electric aircraft, which will enable entirely new markets - The Mod II flight test program is a pathfinder for the experimental propulsion system performance and reliability to reduce the risk in the X-57 configuration.
Heather Maliska↗
From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype
Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.
Systems Engineering↗
Long-range Transport of Siberian Biomass Burning Emissions to North America During FIREX-AQ
Biomass burning from wildfires is a significant global source of aerosol and trace gases which impact air quality, tropospheric and stratospheric composition, and climate. During the summer of 2019, wildfire activity in central and eastern Siberia occurred during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign conducted between July 24 and September 6, 2019. Ground-based lidar observations from the Autonomous Mobile Ozone Lidar for Tropospheric Experiments (AMOLITE) system in Alberta, Canada retrieved frequent anomalous ozone (O3) and aerosol lamina in the troposphere and lower stratosphere during this campaign. Data from NASA’s GEOS Composition Forecast (GEOS-CF) coupled chemistry meteorology model, TROPOspheric Monitoring Instrument (TROPOMI), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), and ground-based insitu data were used to define the trans-Pacific and trans-Arctic transport pathway of Siberian biomass burning emissions resulting in the enhanced O3 and aerosol lamina observed by AMOLITE in western Canada. Siberian wildfires impacted North American air quality resulting in enhancements of hourly-averaged surface carbon monoxide (CO) (total CO >150 ppb) and fine particulate matter (PM2.5) (>20 μg m-3) in western Canada, however, minimal increases in surface-level O3 were measured as well as modeled by GEOS-CF. The impact in western Canada due to Siberian wildfires was much larger in the free troposphere, demonstrated by GEOS-CF and AMOLITE O3 lamina 30–40 ppb above background values and model-predicted PM2.5 lamina >30 μg m-3. In addition to tropospheric composition effects, the large wildfire activity in Siberia may have influenced the stratosphere as data from AMOLITE, CALIPSO, and GEOS-CF all suggested aerosol layers from the fires were located 13–18 km above ground level (agl). This study shows that the Siberian biomass burning emissions in the summer of 2019 impacted tropospheric/stratospheric composition in western Canada, and potentially could have influenced areas in the vicinity of FIREX-AQ airborne measurements, and future studies of FIREX-AQ chemical composition should consider how this long-range transport could have influenced the background trace gas and aerosol concentrations being investigated.
Biomass↗
Health Physics Innovations Developed During Cassini for Future Space Applications
The long history of space flight includes missions that used Space Nuclear Auxiliary Power devices, starting with the Transit 4A Spacecraft (1961), continuing through the Apollo, Pioneer, Viking, Voyager, Galileo, Ulysses, Mars Pathfinder, and most recently, Cassini (1997). All Major Radiological Source (MRS) missions were processed at Kennedy Space Center/Cape Canaveral Air Station (KSC/CCAS) Launch Site in full compliance with program and regulatory requirements. The cumulative experience gained supporting these past missions has led to significant innovations which will be useful for benchmarking future MRS mission ground processing. Innovations developed during ground support for the Cassini mission include official declaration of sealed-source classifications, utilization of a mobile analytical laboratory, employment of a computerized dosimetry record management system, and cross-utilization of personnel from related disciplines.
Nickell, Rodney E.↗
National Campaign Development Test Executive Summary
NASA’s vision for Advanced Air Mobility (AAM) is to provide safe, sustainable, accessible, and affordable aviation for transformational local and intraregional missions and includes the transportation of passengers and cargo as well as aerial work missions, such as infrastructure inspection or search and rescue operations. NASA’s technical expertise, intergovernmental relationships, and high level of public trust will help this technology come to market concurrent with infrastructure readiness, public acceptance, and constructive regulation. By advising and integrating disparate AAM efforts across the country and working collaboratively with the FAA, the National Campaign (NC) objective is to motivate industry progress and support the development of policy, regulatory, and technical standards in a manner that best ensures public safety and benefit to the American people. NC began with the Dry Run and Developmental Test (NC-DT), which served as a pathfinder for collaboration and direct involvement with industry partners in integrated simulation exercises and flight tests. NC-DT culminated with an acoustics-gathering flight test in September 2021 with industry partner Joby’s prototype S4 2.0 air vehicle, which delivered the first foundational baseline of noise levels present in Electric Vertical Takeoff and Landing (eVTOL) vehicles. Through the DT phase, the NC team built and tested the airspace and range infrastructure while assessing the readiness level of industry partners leading up to future NC events. The purpose of this paper is to provide an overview of NC-DT.
National Campaign↗
Pathfinding for Airspace with Autonomous Vehicles (PAAV) ACAS Xr Terminal Area Simulation Overview
This presentation provides an overview of an upcoming human-in-the-loop simulation planned by the Pathfinding for Airspace with Autonomous Vehicles (PAAV) subproject, as part of the Air Traffic Management eXploration project. The study will investigate Detect and Avoid (DAA) concepts in and around the DAA terminal area (DTA). The DTA has received little investigation in real-time, pilot-in-the-loop simulation. This study proposes to use current remotely piloted aircraft system (RPAS) pilots to investigate the effectiveness of current DTA requirements using the FAA's Airborne Collision Avoidance System X for Rotorcraft (ACAS Xr) as the DAA system under test. The findings of this stufy will be used to inform DTA and ACAS Xr standards development. This presentation will be provided to RTCA Special Committee 228 as an overview and as an opportunity to collect feedback from the larger RPAS/DAA community on the study design and key scenarios and metrics. The presentation provides a brief overview of open areas of research with ACAS Xr, the experimental design, the simulation environment and the simulation schedule.
advanced air mobility↗