Search NASA⌕ Search

SEARCH · Search NASA

Results for “Urban Air Mobility/ Advanced Air Mobility”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace (NAS). Since there are numerous more AAM and UAM aircraft than commercial aircraft, it will be challenging to utilize the same ATC/ATM architectures. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗
NASA NTRSDate not supplied

Ground-Based Vision Tracker for Advanced Air Mobility and Urban Air Mobility

Advanced Air Mobility (AAM) Air Mobility and Urban Air Mobility (UAM) require aircraft surveillance and monitoring for safety and security. Persistent tracking of flying objects provides Air Traffic Control (ATC) and Air Traffic Management (ATM) continuous coverage and knowledge of the national airspace system (NAS). Given the significant disparity in the number of AAM and UAM aircraft compared to commercial aircraft in the NAS, coupled with the dense AAM/UAM operations in urban environments, employing the existing ATC/ATM architectures poses considerable challenges. A first step in creating a similar ATC/ATM architecture for AAM/UAM will require ground-based and airborne-based sensors to provide monitoring, which will be difficult in urban environments due to GPS degradation. This paper proposes a vision-based tracking method with static cameras by utilizing image subtraction and blob detection, which avoids adding additional electromagnetic interferences in the environment with sensors such as radar. The ground-based vision tracker (GBVT) outputs the detected objects' azimuth and elevation angles from unmanned aerial system (UAS) flight tests. Future and ongoing work includes sending the detected objects' azimuth and elevation angles as inputs for an extended Kalman filter (EKF) to estimate the position and velocity of the detected object.

distributed sensing↗
NASA NTRSDate not supplied

Concepts for Distributed Sensing and Collaborative Airspace Autonomy in Advanced Urban Air Mobility

Emerging concepts for advanced urban air mobility envision responsive air transportation capabilities that will safely move people and cargo in locations presently underserved by aviation. Expanding aviation services to these locales, particularly for high-density autonomous flight operations over urban centers, will require advances beyond the state-of-the-art techniques for airborne sensing. The emerging field of distributed sensing and ‘smart spaces’ – where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting – may provide attractive alternatives over traditional aviation solutions. This paper outlines the challenges and opportunities for distributed sensing and smart space concepts to meet the emerging needs of advanced urban operations in the national airspace. We present an overview of distributed sensing concepts and research currently being investigated under this endeavor.

Distributed sensing↗
NASA NTRSDate not supplied

Intercomparison of Ground and Aerial Systems for Urban Advanced Air Mobility Wind Field Campaigns

The need for accurate wind measurement data in urban areas is a rising concern for advanced air mobility such as uncrewed aircraft systems (UAS) and urban air mobility. The lack of quality observations of wind fields and other parameters presents a challenge for operability. Urban areas provide additional difficulty because of the complex terrain and varying building geometry and their associated flow fields. This has led to an extensive knowledge gap when it comes to real-time urban measurements. This paper focuses on comparison of available systems and the types of measurements for urban wind field campaigns. LiDARs and SoDARs are two of many ground instruments capable of gathering the necessary wind field information. UAS is also a viable technique of obtaining aerial wind measurements. Each of these all have specific strengths that make their measurements more suitable than others. Ground based and airborne systems are evaluated and compared to provide a thorough look into which system or instrument might provide the most accurate and definitive results for urban wind field campaigns.

Tyler L Willhite↗
NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗
NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗
NASA NTRS2024

Distributed Sensor Fusion of Ground and Air Nodes Using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds

High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. This work collected data using a distributed radar and camera sensor framework during the NASA Advanced Air Mobility High Density Vertiplex project SUAS flight operation. Node locations include on an onboard SUAS, affixed to the Landing and Impact Research Facility with approximate 200ft elevation, on a tripod on first-floor roof with an approximate elevation of 20 ft, and on a tripod on a concrete pad that is approximately 4 ft above sea level. Each node includes camera, radar, and GPS.

Beyond Visual Line of Sight↗
NASA NTRSDate not supplied

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↗
NASA NTRSDate not supplied

Identifying Common Use Cases across Extensible Traffic Management (xTM) for Interactions with Air Traffic Controllers

NASA’s Extensible Traffic Management (xTM) builds on the foundation and the architecture of Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept and extends it broadly to other domains, such as Advanced / Urban Air Mobility (AAM/UAM) and Upper Class E Traffic Management (ETM). These xTM concepts assume the ability to fly in airspace that is authorized to operate solely under xTM services and mostly without any air traffic control (ATC) support. However, they also assume circumstances in which the xTM vehicles would need to operate in conventional ATC-managed airspace, both during nominal and off-nominal scenarios. Due to the vast differences in the xTM vehicle performances and missions, there is a concern that ATC may have difficulty in safely managing the xTM traffic and providing appropriate services to all vehicles, unless a consistent set of roles, procedures, and data exchange requirements are defined across the diverse set of xTM vehicle operations. In this paper, we describe a set of use cases that have been identified in UTM, AAM/UAM, and ETM operations that are related to ATC interactions, and we propose to categorize these use cases across xTM domains based on common trigger events. Organizing the use cases from the perspective of ATC roles per each trigger event is expected to provide the first step in discovering common procedures and data requirements across xTM domains that could help ease the controllers’ cognitive task load and allow them to manage these interactions more safely.

Extensible Traffic Management (xTM)↗
NASA NTRSDate not supplied

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class-E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution. Overall, the ETM tabletop provided insights into how the community wanted to instantiate the generation and sharing of operational intent, detect strategic conflicts and resolve those conflicts using a preliminary set of procedural community-agreed upon COPs.

Upper Class-E Traffic Management (ETM)↗
NASA NTRSDate not supplied

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution.

Upper Class E Traffic Management (ETM)↗
NASA NTRS2025

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗
NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This paper introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗
NASA NTRSDate not supplied

Passenger Aggregation Network with Very Efficient Listing (PANVEL) Ridesharing Model for Urban Air Mobility

This presentation introduces a ridesharing model useful for examining the role of ridesharing in advancing Urban Air Mobility (UAM) operations. Although the success of UAM will likely require a host of new technologies in aircraft design, airspace management, autonomy, and more, operational innovations like ridesharing may also be key, especially to lower operating costs and attract a broader market. The ridesharing algorithm introduced, termed the Passenger Aggregation Network with Very Efficient Listing (PANVEL) model, estimates trip numbers and passenger occupancy aboard particular aircraft within a UAM network in a metropolitan area. This algorithm is applied within a transportation computational framework to determine the impact of ridesharing on mode choice, passenger travel patterns, and transportation costs. The algorithm aggregates passengers traveling between identical origin-destination pairs, factoring in individual-specific metrics, such as value of time and alternative mode options, to generate high load factors on UAM aircraft. The computational framework simulates passenger mode-choice selections using an effective-cost approach to determine the number of ridesharing UAM-preferred trips, and results with ridesharing are representative of a rough upper bound on UAM ridership potential for a given metro area. A case study in the Cleveland metro area is presented to demonstrate the algorithm and assess ridesharing potential.

advanced air mobility↗
NASA NTRSDate not supplied

Distributed Visual Sensing and Fusion for Advanced Air Mobility

Surveillance solutions for Advanced and Urban Air Mobility frameworks are a key factor to enable safe operations of highly automated aircraft in the civil airspace. To design solutions suitable for all types of aircraft, non-cooperative sensors can be used, though many challenges arise when the small dimensions of the vehicles and their proximity to the ground during low-altitude missions are considered. A distributed sensing concept can be efficiently applied to address these challenges by exploiting multiple sensors within a surveillance network. This paper proposes a strategy to fuse the information collected by three ground-fixed cameras within a network of multiple distributed sensors and is tested with during experimental flight tests. The solution exploits standalone tracking estimates of each camera within a fusion center that performs triangulation and three-dimensional tracking. This approach is tested in a scenario involving two small UAVs flying at low altitude. The paper deals with the challenges of associating the two objects from independent and unrelated tracks to achieve robust triangulation, which produces meter-level mean errors with respect to GNSS-based ground truth.

Federica Vitiello↗