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At least 523 records · Page 29

3D-CDTI User Manual v2.1

The 3D-Cockpit Display of Traffic Information (3D-CDTI) is a flight deck tool that presents aircrew with: proximal traffic aircraft location, their current status and flight plan data; strategic conflict detection and alerting; automated conflict resolution strategies; the facility to graphically plan manual route changes; time-based, in-trail spacing on approach. The CDTI is manipulated via a touchpad on the flight deck, and by mouse when presented as part of a desktop flight simulator.

3D CDTI↗

Performance Evaluation of the Approaches and Algorithms for Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted: one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft: Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

air traffic management↗

Space Shuttle Program (SSP) Dual Docked Operations (DDO)

This document describes the concept definition, studies, and analysis results generated by the Space Shuttle Program (SSP), International Space Station (ISS) Program (ISSP), and Mission Operations Directorate for implementing Dual Docked Operations (DDO) during mated Orbiter/ISS missions. This work was performed over a number of years. Due to the ever increasing visiting vehicle traffic to and from the ISS, it became apparent to both the ISSP and the SSP that there would arise occasions where conflicts between a visiting vehicle docking and/or undocking could overlap with a planned Space Shuttle launch and/or during docked operations. This potential conflict provided the genesis for evaluating risk mitigations to gain maximum flexibility for managing potential visiting vehicle traffic to and from the ISS and to maximize launch and landing opportunities for all visiting vehicles.

Sills, Joel W., Jr.↗

Performance Evaluation of the Approaches and Algorithms using Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted: one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft58; Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

Airport surface operations↗

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗

Performance Evaluation of the Approaches and Algorithms Using Hamburg Airport Operations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been independently developing and testing their own concepts and tools for airport surface traffic management. Although these concepts and tools have been tested individually for European and US airports, they have never been compared or analyzed side-by-side. This paper presents the collaborative research devoted to the evaluation and analysis of two different surface management concepts. Hamburg Airport was used as a common test bed airport for the study. First, two independent simulations using the same traffic scenario were conducted; one by the DLR team using the Controller Assistance for Departure Optimization (CADEO) and the Taxi Routing for Aircraft: Creation and Controlling (TRACC) in a real-time simulation environment, and one by the NASA team based on the Spot and Runway Departure Advisor (SARDA) in a fast-time simulation environment. A set of common performance metrics was defined. The simulation results showed that both approaches produced operational benefits in efficiency, such as reducing taxi times, while maintaining runway throughput. Both approaches generated the gate pushback schedule to meet the runway schedule, such that the runway utilization was maximized. The conflict-free taxi guidance by TRACC helped avoid taxi conflicts and reduced taxiing stops, but the taxi benefit needed be assessed together with runway throughput to analyze the overall performance objective.

nasa-dlr research collaboration↗

Air Traffic Management-eXploration Testbed for Urban Air Mobility Research and Development

The presentation will describe the architecture, current capabilities and some future enhancements of the testbed that is being developed at the National Aeronautics and Space Administration (NASA) to enable benefit, impact, safety and cost assessments for accelerating the deployment of air traffic management concept and technologies in the national airspace system. The testbed will support analysis of operational feasibility of urban air mobility operations, a part of NASA's Air Traffic Management eXploration project, and provide the data needed by regulatory agencies charged with public safety. Introduction of concepts and technologies, especially new concepts and technologies, is difficult and often takes decades because of the inability to assess the operational impact of the interaction between the proposed concept and technology and operationally deployed systems in terms of system-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airlines and other operators. To overcome these limitations, the testbed is developing infrastructure to enable mathematical modeling, human-in-the-loop evaluations and testing with operational systems in a simulated environment. In addition to the difficulty of establishing communications between geographically distributed systems, downloading/installing software, and management of startup, error-handling and shutdown, a major impediment for conducting simulations and human-in-the-loop testing with operational systems is the tedious manual scenario generation process. Several of these difficulties have been addressed in the current state of the testbed. The testbed can be described in terms of the following elements (1) web-based frontend and backend, (2) Testbed Builder, (3) Data Distribution Service, (4) Component Library, (5) Simulation Management, and (6) Scenario Generation. The web-based frontend and backend enable the user to interact with the testbed for tasks such as composing a simulation, running a simulation and retrieving output data. The Testbed Builder application launched from the web frontend is a graphical user interface for the user to drag-and-drop and connect predefined blocks for composing a simulation/scenario generation task. The Builder writes a set of instructions for Simulation Management based on the links between the blocks and the block properties such as the component (executable) associated with a particular block. Management of the distributed simulation is accomplished by Execution and Component Managers. Execution Manager interprets the instructions provided by the Builder to instruct the Component Managers to download components from the Component Library to specified computers and to start them up. Once started, the components communicate with each other by publishing messages and subscribing to messages that are delivered by the Data Distribution Service. The Scenario Generation capability can be used for creating traffic scenarios for Multi-Aircraft Control System, which has been used extensively at NASA for human-in-the-loop-based concept evaluations. The presentation will provide a testbed enabled example scenario of Multi-Aircraft Control System based simulation in which the urban air mobility pilot using the conflict detection and resolution system would interact with the air traffic controllers for resolving conflicts with other aircraft during terminal area operations.

Testbed↗

Multi-UAS HITL: Primary Results & Automation Workshop Summaries

This presentation covers the primary results from a recently completed human-in-the-loop (HITL) simulation conducted as part of the UAS (Unmanned Aircraft System) integration into the NAS (National Airspace System) project. The HITL examined the impact of multiple (simultaneous) UAS control while performing a demanding mission task and managing scripted conflicts. The scripted conflicts were designed to trigger the detect-and-avoid (DAA) system. This was the first time the DAA system as designed as part of the UAS-NAS project has been applied to multi-UAS control. The second part of the presentation briefly summarizes the takeaways from two workshops held on human-automation interaction considerations for UAS integration. NASA co-hosted and participated in both workshops.

unmanned aircraft systems↗

Intelligent UAS Sense-and-Avoid Utilizing Global Constraints

Sense-and-avoid (SAA) is a critical research topic for enabling the operation of Unmanned Aircraft Systems (UAS) in civilian airspace. SAA involves two planning related problems: 1) plan-recognition to predict the future trajectory of nearby aircraft, and 2) path planning to avoid conflicts with nearby aircraft that pose a threat. We have designed and built components of a novel intelligent sense-and-avoid (iSAA) reasoning framework that takes into account information about aircraft type, transponder code, communications, local routes, airports, airspace, terrain, and weather to more accurately predict near- and medium-term trajectories of nearby aircraft. By using this additional information both the on-board control software and the ground-based UAS operator can make more informed, intelligent decisions to effectively predict and avoid conflicts and maintain separation. While this capability benefits all categories of UASs operating under both Instrument Flight Rules (IFR) and Visual Flight Rules (VFR), it is absolutely essential for allowing smaller UASs to operate VFR at low altitude in uncontrolled airspace for operations such as survey work, wildlife tracking, aerial photography, utilities inspection, crop dusting, and package delivery.

unmanned aircraft systems↗

Air Traffic Management-eXploration Testbed for Urban Air Mobility Research and Development

The presentation will describe the architecture, current capabilities and some future enhancements of the testbed that is being developed at the National Aeronautics and Space Administration (NASA) to enable benefit, impact, safety and cost assessments for accelerating the deployment of air traffic management concept and technologies in the national airspace system. The testbed will support analysis of operational feasibility of urban air mobility operations, a part of NASA's Air Traffic Management eXploration project, and provide the data needed by regulatory agencies charged with public safety. Introduction of concepts and technologies, especially new concepts and technologies, is difficult and often takes decades because of the inability to assess the operational impact of the interaction between the proposed concept and technology and operationally deployed systems in terms of system-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airlines and other operators. To overcome these limitations, the testbed is developing infrastructure to enable mathematical modeling, human-in-the-loop evaluations and testing with operational systems in a simulated environment. In addition to the difficulty of establishing communications between geographically distributed systems, downloading/installing software, and management of startup, error-handling and shutdown, a major impediment for conducting simulations and human-in-the-loop testing with operational systems is the tedious manual scenario generation process. Several of these difficulties have been addressed in the current state of the testbed. The testbed can be described in terms of the following elements- (1) web-based frontend and backend, (2) Testbed Builder, (3) Data Distribution Service, (4) Component Library, (5) Simulation Management, and (6) Scenario Generation. The web-based frontend and backend enable the user to interact with the testbed for tasks such as composing a simulation, running a simulation and retrieving output data. The Testbed Builder application launched from the web frontend is a graphical user interface for the user to drag-and-drop and connect predefined blocks for composing a simulation/scenario generation task. The Builder writes a set of instructions for Simulation Management based on the links between the blocks and the block properties such as the component (executable) associated with a particular block. Management of the distributed simulation is accomplished by Execution and Component Managers. Execution Manager interprets the instructions provided by the Builder to instruct the Component Managers to download components from the Component Library to specified computers and to start them up. Once started, the components communicate with each other by publishing messages and subscribing to messages that are delivered by the Data Distribution Service. The Scenario Generation capability can be used for creating traffic scenarios for Multi-Aircraft Control System, which has been used extensively at NASA for human-in-the-loop-based concept evaluations. The presentation will provide a testbed enabled example scenario of Multi-Aircraft Control System based simulation in which the urban air mobility pilot using the conflict detection and resolution system would interact with the air traffic controllers for resolving conflicts with other aircraft during terminal area operations.

Simulation↗

Concept of Operations for Management by Trajectory

This document describes Management by Trajectory (MBT), a concept for future air traffic management (ATM) in which every flight operates in accordance with a four-dimensional trajectory (4DT) that is negotiated between the airspace user and the Federal Aviation Administration (FAA) to respect the airspace user's goals while complying with National Airspace System (NAS) constraints. In the present-day NAS, the ATM system attempts to predict the trajectory for each flight based on the approved flight plan and scheduled or controlled departure time. However, once the aircraft starts to move, controllers tactically manage the aircraft to implement traffic management restrictions, separate otherwise conflicting aircraft, and address arising NAS constraints. Tactical controller actions are not directly communicated to the automation systems or other stakeholders. Furthermore, the initial trajectory prediction does not anticipate these disruptions or how they will impact the flight. Consequently, and compounded by gaps in required data and models, trajectory predictions are less accurate than possible, which affects Traffic Flow Management (TFM) performance. A cornerstone of the MBT concept is that all air vehicles have, at all times, an assigned 4DT from their current state to their destination. These assigned trajectories consist of trajectory constraints and descriptions. Pilots and air traffic controllers, with the aid of automation, operate the aircraft to comply with the assigned trajectory, unless first negotiating a revision. Equipped aircraft have substantial responsibility for complying with the assigned trajectory without controller intervention. To maximize the operational flexibility available to the airspace user, the assigned trajectory only imposes trajectory constraints as required to achieve the ATM goals of NAS constraint compliance and aircraft separation. Trajectory descriptions are added to the assigned trajectory to ensure sufficient predictability. To further improve trajectory prediction accuracy, airspace users supplement the assigned trajectory by broadcasting intent information and updating it as necessary. Air vehicle intent is a more detailed description of the airspace user's plan for how the flight will fly the assigned trajectory. Air vehicle intent can change freely, without negotiation, as long as it remains in compliance with the assigned trajectory. Aircraft assigned trajectories, air vehicle intent, and predicted trajectories are shared, creating a common view among stakeholders. A NAS Constraint Service gathers and publishes information about all known NAS constraints, enabling airspace users to be informed participants in trajectory negotiation. Trajectory constraints in the assigned trajectory are mapped to NAS constraints to facilitate identifying which aircraft are affected when NAS constraints change. To support efficient trajectory negotiation, all aircraft provide current information about air vehicle capabilities. Assigned trajectories are constructed to satisfy all known NAS constraints, improving trajectory stability and predictability. Uncertainty and disruptions are handled by modifying the assigned trajectory as far in advance as possible. By proactively negotiating changes to the assigned trajectory, rather than relying on controller-selected tactical actions such as vectors to resolve traffic conflicts or implement miles-in-trail restrictions, MBT keeps aircraft on closed trajectories that are fully known to all stakeholders. Since reactive air traffic control actions cannot be predicted in advance, the downstream trajectory cannot be accurately predicted until they happen. Reliable trajectory predictions allow the system to identify needed modifications to trajectories further in advance, where they can be negotiated and communicated as amendments (i.e., additional or altered trajectory constraints) to the assigned trajectory. Decision Support Tools (DSTs) aid controllers in rapidly defining and communicating closed trajectories to the aircraft and support all stakeholders in trajectory negotiation. Anticipated MBT benefit mechanisms include more accurate trajectory predictions, improved ATM performance and robustness to off-nominal conditions, increased flexibility and operational efficiency, reduced impediments to emerging classes of airspace users accessing NAS resources, reduced environmental impacts, and enhanced safety.

ConOps↗

A Closer Look at Automation Behavior During a Human-in-the-Loop Simulation

A 2012 Human-In-The-Loop air traffic control simulation investigated a gradual paradigm-shift in the allocation of functions between operators and automation. Air traffic controllers staffed five adjacent high-altitude en route sectors, and during the course of a two-week experiment, worked traffic under four different function allocation concepts aligned with increasingly mature NextGen operational environments. These NextGen time-frames ranged from near current-day operations to nearly fully-automated control, in which the ground systems automation was responsible for detecting conflicts, issuing strategic and tactical resolutions, and alerting controllers to exceptional circumstances. This paper continues the investigations reported in previous publications. Analyses of data surrounding the conflict-resolution task serve as the context in which we investigate the interactions between controllers and the automation.

Human-Automation Interaction↗

Multi UAS Demo System Example Video

This presentation covers the primary results from a recently completed human-in-the-loop (HITL) simulation conducted as part of the UAS (Unmanned Aircraft Systems) integration into the NAS (National Airspace System) project. The HITL examined the impact of multiple (simultaneous) UAS control while performing a demanding mission task and managing scripted conflicts. The scripted conflicts were designed to trigger the detect-and-avoid (DAA) system. This was the first time the DAA system as designed as part of the UAS-NAS project has been applied to multi-UAS control. The second part of the presentation briefly summarizes the takeaways from two workshops held on human-automation interaction considerations for UAS integration. NASA co-hosted and participated in both workshops.

Rorie, Robert C.↗

Scenario Complexity for Unmanned Aircraft System Traffic

This work introduces an approach to estimate the complexity of a low-altitude air traffic scenario involving multiple UASs using mathematical programming. Given a set of multi-point UAS flight trajectories, vehicle dynamics, and a conflict resolution algorithm, an abstract model is developed such that it can be solved quickly using a mathematical programming optimization software without running high-fidelity simulations that can be computationally expensive and may not suit real-time applications. In the abstract model, each vehicle is represented by a time-varied vector associated with position, speed, and heading information. The total extra distance that aircraft need to divert from their original routes to avoid collisions is computed and used to setup a quadratic programming formula. The metrics including the number of conflicts and extra distances travelled by all vehicles are then utilized to estimate the complexity of a given UAS flight scenario. Results and verification against high-fidelity simulations will be provided in the final draft.

UTM↗

A Detect and Avoid System in the Context of Multiple-Unmanned Aircraft Systems Operations

NASA's Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgements about a potential threat of a loss of well clear with another aircraft. NASA's Phase 1 research supported the development of a Detect and Avoid (DAA) system that supports the ground-based pilot's ability to detect potential traffic conflicts and determine a resolution maneuver, but existing display/alerting requirements did not account for multiple UAS control (1:N). Demands for increased scalability of UAS in the NAS operations are expected to create a need for simultaneous control of UAs, and thus, a new DAA HMI design will likely be necessary. Previous research, however, has found performance degradations as the number of vehicles under operator control has increased. The purpose of the current human-in-the-loop (HITL) simulation was to examine the viability of 1:N operations with the Phase 1 DAA alerting and guidance. Sixteen UAS pilots flew three scenarios with varying number of UAs under their control (1:1, 1:3, 1:5). In addition to their supervisory and sensor mission responsibilities, pilots were to utilize the DAA system to remain DAA well clear (DWC) during scripted conflicts of mixed severity. Measured response times, separation performance, mission task data, and subjective feedback were collected to assess how the multi-UAS control configuration impacted pilots' ability to maintain DAA well clear and perform the mission tasks. Overall, the DAA system proved surprisingly adaptive to multi-UAS control for preventing losses of DAA well clear (LoDWC). The findings suggest that, while multi-UAS operators are able to maintain safe separation (DWC) from other traffic, their ability to efficiently perform missions drastically decreases with their number of controlled vehicles. Pilot feedback indicated that, for this context, the use of automation support tools for completing and managing mission tasks would be appropriate and desired, especially for ensuring efficient use of assets. Finally, human-machine interface (HMI) design considerations for multi-UAS operations are discussed.

Monk, Kevin J.↗

Enabled International Collaborations in EDL: Lessons Learned and Recommendations

The modern day National Airspace System (NAS) is powered by System Wide Information Management (SWIM) which is a real-time digital data sharing infrastructure that provides a high fidelity view of the lifecycle of a flight. The newly available data within the SWIM feeds can be leveraged to help drive efficiencies in the NAS. In this talk, we investigate the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We begin with a high level description of NASA's Airspace Technology Demonstration 2 which is built upon the real-time SWIM feeds and produces the data used in our investigation. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach.

William J. Coupe↗

Mission Planner Algorithm for Urban Air Mobility – Initial Performance Characterization

In this paper, an initial characterization was performed of the Mission Planner algorithm developed by NASA for Urban Air Mobility (UAM) operations research. The algorithm plans conflict-free trajectories for flights to support a given set of UAM passenger trips. The UAM trips are planned in an on-demand, first-come, first-served manner, such that any given trip is subject to the constraints imposed by previously planned trips. For this analysis, the mission planning algorithm considered only the trajectory constraints from previously-planned trips in one test condition and added vertiport constraints for the second test condition. The conflict and constraint resolution strategies used by the Mission Planner were characterized by their percentage contribution to planning iterations, their percentage effectiveness in those iterations, and their contributions to the departure delay applied to each UAM trip’s flight. With the exception of the climb and descent vertical speed strategies, most strategies showed reasonable or good performance in all test scenarios. In the test condition with vertipad constraints enabled, both the total number of iterations executed, and the number of flights that required planning iterations, was reduced for all scenarios. This was the result of the natural conditioning of the traffic achieved with scheduling and the additional information available to the Mission Planner from the vertiport scheduler. The next steps for this work will include improvements to the mission planning strategies and analyses with additional constraints and under other demand scenarios.

Guerreiro, Nelson M.↗