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AAM Mission presentation given by the ARMD Associate Administrator, Robert Pearce, to the National Association of State Aviation Officials (NASAO) on March 2, 2022. The presentation includes an overview of the ARMD missions, AAM use cases, NASA's role to address AAM challenges and AAM mission priorities, National Campaign status, and the community partnerships for AAM.
The DRF team in collaboration with the California Civil Air Patrol (CAP), executed a field test that identified a preliminary set of data and reasoning service areas that can be enabled and enhanced by DRF to potentially bring forth improved accuracy and reduced latency to CAP’s mission critical decision making, in wildfire detection.
This presentation was given by Nancy Mendonca, Deputy AAM Mission Integration Manager for ARMD, at the North Central Texas Council of Governments (NCTCOG) UAS Workshop on November 6, 2021. This presentation walked through the AAM Vision, Current studies and reports and resources on AAM, and opportunities to engage in AAM activities.
Advanced Air Mobility is the safe, sustainable, affordable, and accessible aviation for transformational local and intraregional missions.
This presentation provides an overview of Human Factors issue in AAM
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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.
This presentation was given on November 8, 2021 at a pre-Showcase Webinar prior to the November 10, 2021 Ohio AAM Showcase. Topics covered in the presentation include NASA's AAM Partnership Strategy including the AAM partnerships, NASA's AAM National Campaign Timeline, and how NASA is working with Community Partners to integrated AAM into their communities.
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The purpose of this Tech Talk is to engage with the Advance Air Mobility National Campaign community on the use of a cloud provider to promote public confidence and accelerate the realization of emerging aviation markets for passenger and cargo transportation in urban, suburban, rural, and regional environments.
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Explore the source record for details and available documents.
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NASA is currently engaged in dedicated research into vertiports through its Advanced Air Mobility Project, housed within the Airspace Operations Safety Program (AOSP), through a Sub-Project referred to as High Density Vertiplex, or simply HDV. This effort’s mission is to develop a reference automation architecture for vertiports that will enable operations to scale to the density levels envisioned for future environments. Through the development of the HDV Concept of Operations and accompanying architecture documents a research roadmap was developed as part of the effort's formulation process that laid the foundations for the work and schedule to be completed as part of the HDV scope and technical challenge. This presentation provides the background of the HDV Sub-Project and outlines the concept and capabilities as it has developed to date. Summaries of the completed work package and status of current work package will be provided with details on specific capabilities and supporting integration developments.
The safe integration of Unmanned Aircraft Vehicles (UAV) within the civil airspace is of great interest to NASA’s Advanced Air Mobility project, which envisions high density of operations in and around urban areas that include both UAV and AAM aircraft. To enable safe autonomous operations of both platforms, reliable airspace surveillance strategies must be designed and experimentally validated in relevant scenarios, where multiple small UAV operate flying in low altitude conditions. An example of such a scenario is described in this paper which provides performance assessment of various sensing strategies experimentally tested during flight campaigns with four UAV completing simultaneous missions from vertiports. Such campaigns are performed by the High Density Vertiplex subproject which assesses a prototype of Urban Air Mobility ecosystem. For the purposes of this work, the flights are observed from multiple sensing nodes each with radar and camera sensors. The visual detection and tracking algorithms achieved 96.1% to 99.9% average tracking coverage of the UAV above the horizon, reaching detection ranges larger than 1 kilometer for octocopter. Radar-based tracking shows a lower coverage mainly due to ground clutter removal challenges but provides comparable detection ranges and meter-level range accuracy.
Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACC to which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.
Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.