Flight Evaluation of a Flight Path Management System for High Density Advanced Air Mobility
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Advanced Air Mobility↗
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Digital Flight - A New Cooperative Operating Mode to Complement VFR and IFR
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Air Traffic Management↗
Digital Flight - CAS Research towards “Sky for All” Vision of Cooperative and Highly Automated Operations
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Air Traffic Management↗
Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks
Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.
Henry Holbrook↗
DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated
Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.
Chester V Dolph↗
Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks
The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.
urban air mobility↗
Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks
The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.
urban air mobility↗
An Experimental System for Strategic Flight Path Management in Advanced Air Mobility
In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.
Autonomous Operations Planner↗
An Experimental System for Strategic Flight Path Management in Advanced Air Mobility
In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.
Autonomous Operations Planner↗
Airspace Operations: Vision for 2045 and Beyond
We are seeing interesting changes in airspace operations. We are experiencing growth in global aviation for passenger and cargo travel. At the same time, drones of all sizes, urban air mobility, electric aircraft, commercial space transportation, supersonics, hypersonics, and increasingly autonomous vehicles will continue to mature. These operations along with current aviation will require access to airspace operations. Such access and scalability needs will only continue to increase in the future. Given that systems and procedures that will enable and support the future density and diversity takes a considerable amount of time to build and harmonize across the globe, it is appropriate that research efforts to enable 2045 operations begin now. A perfect storm is brewing as a number of factors are coming together, including: anticipated growth in diversity and density; limitations of our current system to support the growth and diversity; lack of utilization of latest technologies in an increasingly digitized world to support air traffic management; and a long lead time to conduct research, develop requirements, and built and deploy air traffic management systems. All these factors indicate that now is the time to start thinking about the needs of 2045 and beyond. In a limited manner, Unmanned Aircraft System Traffic Management (UTM) has shown that new thinking and implementation paths for airspace operations is possible. The current system as it exists is based on many assumptions and limitations of technologies (e.g., radar, human-centered voice communications) which may not be true moving forward given the technologies around us are changing. The panel will discuss the following and related topics: 1. Expected growth in density, diversity, and needed scalability, 2. Likely requirements of air traffic management system to enable and support 2045 and beyond operations, 3. Assumptions related to air traffic management and operations that need to reevaluated based on technology trends, 4. Identification of research priorities and harmonization of research across the globe, and 5. Transition approaches from current air traffic operations to new vision 2045. The panel discussion will be useful for global air traffic management researchers, managers, strategists, airspace users, air traffic management system developers and integrators, and academic researchers.
Kopardekar, Parimal H.↗
Assured Contingency Landing Management for Advanced Air Mobility
Advanced Air Mobility (AAM) is quickly developing as a new air transportation system that moves people and packages in the regions previously not / less served by the current aviation systems. Such AAM must operate safely despite the potential to encounter hazards and experience anomalies and failures in-flight. It becomes especially important to have systematic auto-mitigation strategies to perform safe contingency actions in AAM flight operations, as pilots have limited Situational Awareness (SA) and limited time to make prompt decisions when encountering failures/anomalies in high-density low altitude airspace. This paper presents Assured Contingency Landing Management (ACLM) with an online landing strategy selection to decide between the following three options when a contingency landing is required: (1) Return-to-launch landing site, (2) Land immediately at a nearby clear but unprepared site, (3) Land at a prepared landing site from the approximate footprint. Our presented algorithm shows a real-time auto-mitigation loop with multiple threads that run simultaneously to check controllability, reachability, and intermediate decisions to hold/ loiter or continue the flight plan as the landing strategy solution is being computed. Case study simulation is demonstrated with the safety-critical propulsion system and battery system and shows how different failure scenarios impact the landing strategy selection.
Autonomous Mitigation↗
A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks
The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.
Structurally-Adaptive↗
A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks
The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.
Distributed sensing↗
Unmanned Autonomous Systems (UAS) Traffic Management
Educating California's Transportation Dept. (Caltrans) about Urban Air Mobility: what it is, how NASA is involved in this initiative, and plans for demonstrations (grand challenge).
UAM (Urban Air Mobility)↗
MBSE Execution of Scalable Autonomous Operations for a High Density Vertiplex
The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) and NASA SE processes were executed via MBSE using MagicDraw. Since the adoption of MBSE and utilization of MagicDraw, the systems engineering team has made tremendous strides in each pillar of MBSE including, requirements, behavior, and structure. Scalable Autonomous Operations (SAO) was a stage in the development of the High Density Vertiplex focusing on the autonomous terminal operations of a vertiport with sUAS aircraft. MBSE served the systems engineering team to document and verify the physical architecture and capture a logical architecture of SAO for distribution to the AAM community. This paper will detail methodologies that were created to successfully execute NASA SE processes via MBSE in the SAO stage as well as highlight challenges and lessons learned.
Demetrios Katsaduros↗
T^3 Autonomous Systems Student Overview
The Autonomous Systems (AS) Discipline is NASA's investment in the continued development of autonomy technologies in aerospace each year. Through sustained long-term development, researchers and scientists within AS identify and work towards knowledge and technology gaps limiting the application of autonomy technology in the future of aviation. This presentation gives a short overview of the advanced air mobility problem area and how AS work is currently contributing to NASA's effort to make advanced air mobility a safe, efficient, and equitable reality.
Autonomy↗
Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking
Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.
sensor fusion↗
Autonomy to Enable NASA Missions from Aeronautics to Space
2024 ASCEND Call for Sessions Session Format: Panel Session Topic: Space Exploration and Infrastructure: Exploring, Living, and Working in Space (The panel must map to one of six Session Topics - https://www.ascend.events/presenters/call-for-sessions/#sessiontopics) Title: Autonomy to Enable NASA Missions from Aeronautics to Space Short Session Description: In this panel discussion the National Aeronautics and Space Administration (NASA) will discuss the role that autonomy and Artificial Intelligence (AI) will play as humanity moves off-world. Recent advances in general autonomy tools are changing the way NASA and its partners leverage autonomy for its air and space initiatives, including Advanced Air Mobility (AAM) concepts and potential lunar and Martian operations. The panelists will consist of autonomy subject matter experts familiar with the current state of the art for autonomy across both aeronautical and space domains. They will discuss how those technologies could evolve as operations become more complex and which autonomy technologies can be used in both the space and aeronautical domains. For example, perhaps autonomy work originally developed for terrestrial applications, like Advanced Air Mobility (AAM), could be applied to off-world lunar and Martian applications and vice versa. Additionally, the panel will address common misconceptions of these technologies, obstacles to implementation, and possible solutions for overcoming those obstacles. Join NASA in exploring how research activities can align to streamline autonomy development efforts, advancing NASA's goals to expand humanity's reach beyond Earth for the benefit of all. Contact Information: • Dr. Adam Yingling • Office of Technology, Policy, and Strategy (OTPS) • Adam.j.yingling@nasa.gov, 703-416-9129 Session Length: 1.25 hours, 75 minutes Extended Description: Moderator: Dr. Adam Yingling, NASA, Office of Technology, Policy, and Strategy Panel Speakers: • Autonomy Forum Principals o Dr. Charles Norton, Deputy Chief Technologist, Jet Propulsion Laboratory (JPL) o Dr. Carolyn Mercer, Chief Technologist, Space Mission Directorate (SMD) o Dr. Parimal Kopardekar (PK), Advanced Air Mobility (AAM) Integration Manager o Danette Alan, NASA, Senior Leader for Autonomous Systems, Space Technology Mission Directorate (STMD) o Duane Armstrong, Intelligent Systems Lead, Autonomous Systems Laboratory (ASL) Panel Format: • Introduction (10 min): The panel moderator will provide a 10-minute session introduction that will include an overview NASA’s Moon to Mars architecture, AAM autonomy research, and an introduction of the principals as panel speakers. • Moderated Session Part 1 (30 min): There will be a 30-minute moderated session among the moderator and the five autonomy principals to discuss the current state of art for autonomy and how work developed in one domain may be applicable to other domains; including the merits and challenges for implementing those technologies. • Moderated Session Part 2 (25 min): The moderator will then ask the principals to consider how technologies developed across all the domains might be able to address the most salient challenges identified in the previous session. • Q&A (10 min): The session will conclude with 10-minutes of audience Q&A. Session Goals and Outcomes: The session goals are 1) to communicate the importance of autonomy for both aeronautical and space mission, 2) Investigate potential synergies across autonomy research efforts that will enable scalable operations, and 3) to receive community feedback as NASA leverages autonomy to evolve aviation on Earth and enable humanity to live and work off-world.
Aerospace↗