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NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗

NASA NTRS2015

Cubesat Constellation Design for Air Traffic Monitoring

Suitably equipped global and local air traffic can be tracked. The tracking information may then be used for control from ground-based stations by receiving the Automatic Dependent Surveillance-Broadcast (ADS-B) signal. The ADS-B signal, emitted from the aircraft's Mode-S transponder, is currently tracked by terrestrial based receivers but not over remote oceans or sparsely populated regions such as Alaska or the Pacific Ocean. Lack of real-time aircraft time/location information in remote areas significantly hinders optimal planning and control because bigger "safety bubbles" (lateral and vertical separation) are required around the aircraft until they reach radar-controlled airspace. Moreover, it presents a search-and-rescue bottleneck. Aircraft in distress, e.g. Air France AF449 that crashed in 2009, take days to be located or cannot be located at all, e.g. Malaysia Airlines MH370 in 2014. In this paper, we describe a tool for designing a constellation of small satellites which demonstrates, through high-fidelity modeling based on simulated air traffic data, the value of space-based ADS-B monitoring and provides recommendations for cost-efficient deployment of a constellation of small satellites to increase safety and situational awareness in the currently poorly-served surveillance area of Alaska. Air traffic data has been obtained from the Future ATM Concepts Evaluation Tool (FACET), developed at NASA Ames Research Center, simulated over the Alaskan airspace over a period of one day. The simulation is driven by MATLAB with satellites propagated and coverage calculated using AGI's Satellite ToolKit(STK10).

cubesats↗

NASA NTRS2013

A Cockpit-Based Application for Traffic Aware Trajectory Optimization

The Traffic Aware Planner (TAP) is a cockpit-based advisory tool designed to be hosted on a Class 2 Electronic Flight Bag and developed to enable the concept of Traffic Aware Strategic Aircrew Requests (TASAR). This near-term concept provides pilots with optimized route changes that reduce fuel burn or flight time, avoids interactions with known traffic, weather and restricted airspace, and may be used by the pilots to request a trajectory change from air traffic control. TAP's internal architecture and algorithms are derived from the Autonomous Operations Planner, a flight-deck automation system developed by NASA to support research into aircraft self-separation. This paper reviews the architecture, functionality and operation of TAP.

TASAR↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Milad Memarzadeh↗

NASA NTRS2014

Aircraft Trajectory Design Based on Reducing the Combined Effects of Carbon-di-oxide, Nitrogen and Contrails

Aircraft operations need to meet the combined requirements of safety, efficiency, capacity and reduced environmental impact. Aircraft routes can be made efficient by flying wind optimal routes. However, the desire to reduce the impact of aviation emissions and contrails may result in trajectories, which deviate from wind optimal trajectories leading to extra fuel use. The lifetime associated with different emissions and contrails varies from a few hours to several hundred years. The impact of certain gases depends on the amount and location of the emission, and the decision-making horizon, in years, when the impact is estimated. The Absolute Global Temperature Potential (AGTP) is used as a metric to measure the combined effects of emissions and contrails. This paper extends earlier work by the authors to include the effect of oxides of nitrogen in the development of aircraft trajectories to reduce the combined effects of carbon dioxide, oxides of nitrogen (NOX) and contrails. The methodology is applied to air traffic in the continental US. The paper shows the trade-offs between reducing emissions and the cost of extra fuel using a fuel sensitivity index, defined as the reduction in AGTP per kg of fuel. The paper shows the performance of the optimization strategy for decision intervals of 10, 25 and 100 years. Based on the simplified models, the inclusion of NOX emissions has a slight influence on the minimal climate impact trajectories when the decision horizons are around 25 years.

Sridhar, Banavar↗

NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

This paper examines strategic conflict resolution strategies for pair-wise high-altitude balloon, airship encounters. By strategic is meant that the conflict is identified (detected) at the time of flight plan submission or flight plan alteration through a service, such as an ETM Service Supplier (ESS). We investigate optimal control solutions for two classes of problems: non-cooperative, where the balloon must alter its ascent trajectory in order to avoid the airship and cooperative, where both vehicles make maneuvers through a negotiation protocol.

airship balloon negotiation optimal control↗

NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

airship balloon negotiation optimal control↗

NASA NTRS2013

Experiment Description and Results for Arrival Operations Using Interval Management with Spacing to Parallel Dependent Runways (IMSPiDR)

The predicted increase in the number of commercial aircraft operations creates a need for improved operational efficiency. Two areas believed to offer increases in aircraft efficiency are optimized profile descents and dependent parallel runway operations. Using Flight deck Interval Management (FIM) software and procedures during these operations, flight crews can achieve by the runway threshold an interval assigned by air traffic control (ATC) behind the preceding aircraft that maximizes runway throughput while minimizing additional fuel consumption and pilot workload. This document describes an experiment where 24 pilots flew arrivals into the Dallas Fort-Worth terminal environment using one of three simulators at NASA?s Langley Research Center. Results indicate that pilots delivered their aircraft to the runway threshold within +/- 3.5 seconds of their assigned time interval, and reported low workload levels. In general, pilots found the FIM concept, procedures, speeds, and interface acceptable. Analysis of the time error and FIM speed changes as a function of arrival stream position suggest the spacing algorithm generates stable behavior while in the presence of continuous (wind) or impulse (offset) error. Concerns reported included multiple speed changes within a short time period, and an airspeed increase followed shortly by an airspeed decrease.

Baxley, Brian T.↗

NASA NTRS2019

Exploring Human Factors Issues for Urban Air Mobility Operations

Urban air mobility (UAM) is currently receiving increased attention in the aviation literature as a new entrant into the airspace. Although the introduction of UAM offers the potential for significant benefits, it also creates the potential for fundamental change to the current air traffic management system. Several concepts are being explored to enable the development of a safe and efficient UAM system for near, mid and far term operations. A concept of operations for near term operations proposes several assumptions. Concepts for roles and responsibilities of human operators such as air traffic controllers propose different degrees of involvement. Identifying and exploring human factors issues is therefore a critical next step in the forward progression of concept development. A human-in-the-loop air traffic control simulation was used to investigate the effect of UAM traffic density and changes in current airspace routes and communication procedures on subjective controller workload and efficiency-related task performance. Findings indicate that although subjective workload was manageable for low density operations, medium and high density operations led to unmanageable levels of workload, leading to refusals to allow more vehicles into controlled airspace. By implementing a letter of agreement, verbal communications were reduced which were associated with reduced workload. Optimized routes were also associated with reduced workload and increased performance efficiency. Although these adjustments can positively support controller performance, workload still remained high during the high density UAM traffic scenarios. It is therefore suggested that, in order for UAM operation to become scalable, human operators will be required to work differently compared to current air traffic controllers. Future research should focus on the level and type of human operator or controller involvement and mated systems, to ensure safety and efficiency within UAM operations.

Air Traffic Management↗

NASA NTRS2013

Flight-Deck Strategies and Outcomes When Flying Schedule-Matching Descents

Recent studies at NASA Ames Research Center have investigated the development and use of ground-based (air traffic controller) tools to manage and schedule air traffic in future terminal airspace. An exploratory study was undertaken to investigate the impacts that such tools (and concepts) could have on the flight-deck. Ten Boeing 747-400 crews flew eight optimized profile descents in the Los Angeles terminal airspace, while receiving scripted current day and futuristic speed clearances, to ascertain their ability to fly schedulematching descents without prior training. Although the study was exploratory in nature, four variables were manipulated: route constraints, winds, speed changes, and clearance phraseology. Despite flying the same scenarios with the same events and timing, there were significant differences in the time it took crews to fly the approaches. This variation is the product of a number of factors but highlights potential difficulties for scheduling tools that would have to accommodate this amount of natural variation in descent times. The focus of this paper is the examination of the crews' aircraft management strategies and outcomes. This includes potentially problematic human-automation interaction issues that may negatively impact arrival times, speed and altitude constraint compliance, and energy management efficiency.

Flight Management System↗

NASA NTRSDate not supplied

Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.

Nathan Schimpf↗

NASA NTRS2018

Airborne Trajectory Management for Urban Air Mobility

Urban Air Mobility (UAM) has captured the imagination of the public and the aviation industry for someday soon moving people and goods through and around metropolitan areas using Unmanned Aircraft Systems (UAS) that are electrically powered, environmentally friendly, and autonomously operated. Significant investment and rapid development of vehicles for this activity is taking place, with package delivery services already beginning in some areas. However, the ability to manage thousands of these vehicles safely in a congested urban area presents a challenge unprecedented in air traffic management. Initial studies of this problem led by NASA under the UAS Traffic Management (UTM) initiative have primarily focused on geo-fencing and centralized reservation of airspace for individual flights. This paper proposes an extension of UTM using a de-centralized approach employing airborne surveillance, self-separation, and a minimized “design separation” approach to permit the optimization and safety of each flight in very high traffic densities and close proximities. The concept employs Airborne Trajectory Management (ABTM) principles and a novel new concept for variable separation criteria to manage the angular velocity of a passing vehicle, thus eliminating the "startle factor" and perceived hazard of very close operations. ABTM also accomplishes most of the services required for safe planning and execution of normal flights and recovery from abnormal or emergency operations while accommodating conventional piloted flights using the current air traffic control paradigm. The environment for UAM operations is described along with the proposed means for autonomous, tactical separation of the vehicles. Sample geometries of traffic conflicts and resolutions are shown and the airspace definitions, rules for flight within them, and additions and exemptions to the rules for these flights are listed and explained.

Cotton, William↗

NASA NTRS2010

Spinoff 2010

Topics covered include: Burnishing Techniques Strengthen Hip Implants; Signal Processing Methods Monitor Cranial Pressure; Ultraviolet-Blocking Lenses Protect, Enhance Vision; Hyperspectral Systems Increase Imaging Capabilities; Programs Model the Future of Air Traffic Management; Tail Rotor Airfoils Stabilize Helicopters, Reduce Noise; Personal Aircraft Point to the Future of Transportation; Ducted Fan Designs Lead to Potential New Vehicles; Winglets Save Billions of Dollars in Fuel Costs; Sensor Systems Collect Critical Aerodynamics Data; Coatings Extend Life of Engines and Infrastructure; Radiometers Optimize Local Weather Prediction; Energy-Efficient Systems Eliminate Icing Danger for UAVs; Rocket-Powered Parachutes Rescue Entire Planes; Technologies Advance UAVs for Science, Military; Inflatable Antennas Support Emergency Communication; Smart Sensors Assess Structural Health; Hand-Held Devices Detect Explosives and Chemical Agents; Terahertz Tools Advance Imaging for Security, Industry; LED Systems Target Plant Growth; Aerogels Insulate Against Extreme Temperatures; Image Sensors Enhance Camera Technologies; Lightweight Material Patches Allow for Quick Repairs; Nanomaterials Transform Hairstyling Tools; Do-It-Yourself Additives Recharge Auto Air Conditioning; Systems Analyze Water Quality in Real Time; Compact Radiometers Expand Climate Knowledge; Energy Servers Deliver Clean, Affordable Power; Solutions Remediate Contaminated Groundwater; Bacteria Provide Cleanup of Oil Spills, Wastewater; Reflective Coatings Protect People and Animals; Innovative Techniques Simplify Vibration Analysis; Modeling Tools Predict Flow in Fluid Dynamics; Verification Tools Secure Online Shopping, Banking; Toolsets Maintain Health of Complex Systems; Framework Resources Multiply Computing Power; Tools Automate Spacecraft Testing, Operation; GPS Software Packages Deliver Positioning Solutions; Solid-State Recorders Enhance Scientific Data Collection; Computer Models Simulate Fine Particle Dispersion; Composite Sandwich Technologies Lighten Components; Cameras Reveal Elements in the Short Wave Infrared; Deformable Mirrors Correct Optical Distortions; Stitching Techniques Advance Optics Manufacturing; Compact, Robust Chips Integrate Optical Functions; Fuel Cell Stations Automate Processes, Catalyst Testing; Onboard Systems Record Unique Videos of Space Missions; Space Research Results Purify Semiconductor Materials; and Toolkits Control Motion of Complex Robotics.

Source record↗

NASA NTRS2005

Analysis of Aircraft Clusters to Measure Sector-Independent Airspace Congestion

The Distributed Air/Ground Traffic Management (DAG-TM) concept of operations* permits appropriately equipped aircraft to conduct Free Maneuvering operations. These independent aircraft have the freedom to optimize their trajectories in real time according to user preferences; however, they also take on the responsibility to separate themselves from other aircraft while conforming to any local Traffic Flow Management (TFM) constraints imposed by the air traffic service provider (ATSP). Examples of local-TFM constraints include temporal constraints such as a required time of arrival (RTA), as well as spatial constraints such as regions of convective weather, special use airspace, and congested airspace. Under current operations, congested airspace typically refers to a sector(s) that cannot accept additional aircraft due to controller workload limitations; hence Dynamic Density (a metric that is indicative of controller workload) can be used to quantify airspace congestion. However, for Free Maneuvering operations under DAG-TM, an additional metric is needed to quantify the airspace congestion problem from the perspective of independent aircraft. Such a metric would enable the ATSP to prevent independent aircraft from entering any local areas of congestion in which the flight deck based systems and procedures may not be able to ensure separation. This new metric, called Gaggle Density, offers the ATSP a mode of control to regulate normal operations and to ensure safety and stability during rare-normal or off-normal situations (e.g., system failures). It may be difficult to certify Free Maneuvering systems for unrestricted operations, but it may be easier to certify systems and procedures for specified levels of Gaggle Density that could be monitored by the ATSP, and maintained through relatively minor flow-rate (RTA type) restrictions. Since flight deck based separation assurance is airspace independent, the challenge is to measure congestion independent of sector boundaries. Figure 1 , reproduced from Ref. 1, depicts an example traffic situation. When the situation is analyzed by sector boundaries (left side of figure), a Dynamic Density metric would identify excessive congestion in the central sector. When the same traffic situation is analyzed independent of sector boundaries (right side of figure), a Gaggle Density metric would identify congestion in two dynamically defined areas covering portions of several sectors. The first step towards measuring airspace-independent congestion is to identify aircraft clusters, i.e., groups of closely spaced aircraft. The objective of this work is to develop techniques to detect and classify clusters of aircraft.

Bilimoria, Karl D.↗

NASA NTRS2016

Traffic Aware Planner for Cockpit-Based Trajectory Optimization

The Traffic Aware Planner (TAP) software application is a cockpit-based advisory tool designed to be hosted on an Electronic Flight Bag and to enable and test the NASA concept of Traffic Aware Strategic Aircrew Requests (TASAR). The TASAR concept provides pilots with optimized route changes (including altitude) that reduce fuel burn and/or flight time, avoid interactions with known traffic, weather and restricted airspace, and may be used by the pilots to request a route and/or altitude change from Air Traffic Control. Developed using an iterative process, TAP's latest improvements include human-machine interface design upgrades and added functionality based on the results of human-in-the-loop simulation experiments and flight trials. Architectural improvements have been implemented to prepare the system for operational-use trials with partner commercial airlines. Future iterations will enhance coordination with airline dispatch and add functionality to improve the acceptability of TAP-generated route-change requests to pilots, dispatchers, and air traffic controllers.

Woods, Sharon E.↗

NASA NTRSDate not supplied

Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications

With the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations, there will be an increasing demand for voice and data communications within the National Airspace System (NAS). The continued use of existing VHF and UHF frequency allocations is not a sustainable approach, and as a result, a new spectrum management solution is required to support future mission needs. The proposed spectrum management concepts leverage modern advancements such as artificial intelligence (AI) and big data to dynamically optimize the spectrum utilization based on the predicted communications demand throughout the airspace. This technical investigation considers both air-ground and air-air communications networks, and can be applied to both existing applications such as the air traffic control (ATC) system, as well as future applications, such as the emerging Advanced Air Mobility (AAM). To support the evaluation of the proposed concepts and technologies, a modeling and simulation capability is currently under development and will continue to evolve to support new and advanced airspace applications. It is anticipated that this proposed spectrum concept will better serve the spectrum needs of future NAS applications.

Eric J Knoblock↗

NASA NTRSDate not supplied

Investigation and Evaluation of Advanced Spectrum Management Concepts for Aeronautical Communications

Eric J Knoblock↗