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Overview of developments and results of a NASA ATD-2 prototype system that enables Flight Operators and Air Traffic Management to manage reroute of departures in Dallas, TX.
This study supports re-design of airspace and procedures for management of Urban Air Mobility (UAM) fleet operations in a given metropolitan area. A goal of UAM is to maintain geographic separation of UAM flight tracks from densely populated areas in order to reduce the potential negative community impact of UAM and manage flight trajectory in anticipation of allowable noise levels and current land use. A UAM route inventory and a demographic-data-optimized topographic map are developed for the Dallas-Fort Worth metroplex to identify routes or baseline route segments for reduction of noise impact. Noise contours are computed for baseline route and alternative route design given the desired UAM demand level. The impact of UAM fleet noise is predicted based on predicted number of people and noise-sensitive facilities located inside the noise contours.
This study supports re-design of airspace and procedures for management of Urban Air Mobility (UAM) fleet operations in a given metropolitan area. A goal of UAM is to maintain geographic separation of UAM flight tracks from densely populated areas in order to reduce the potential negative community impact of UAM and manage flight trajectory in anticipation of allowable noise levels and current land use. A UAM route inventory and a demographic-data-optimized topographic map are developed for the Dallas-Fort Worth metroplex to identify routes or baseline route segments for reduction of noise impact. Noise contours are computed for baseline route and alternative route design given the desired UAM demand level. The impact of UAM fleet noise is predicted based on predicted number of people and noise-sensitive facilities located inside the noise contours.
New forms of highly automated Advanced Air Mobility (AAM) aircraft, such as electric vertical take-off and landing (eVTOL) vehicles, could transform transportation, cargo delivery, and a variety of public services. The National Aeronautics and Space Administration (NASA) conducted a series of flight demonstrations in collaboration with the Defense Advanced Research Projects Agency (DARPA) and Sikorsky Aircraft (a Lockheed Martin company) to progressively evaluate autonomous technologies. The autoland flight test research is a first in series for investigating the world’s first procedural descending-decelerating automated landing with vertical guidance Instrument Flight Procedures (IFP). The Sikorsky Optionally Piloted Vehicle (OPV) experimental UH-60 Black Hawk was used to evaluate a flight path’s four-dimensional trajectory (4DT) management into primitive commands and then follow those commands to a Point-in-Space (PinS) landing to the ground. All flight procedures were manually flown to the ground at 12 degrees with a 20-knot tail wind to ensure flight safety before automation was engaged. New and novel high precision approach procedures could pave the way for all future VTOL operations.
This presentation provides a summary of August 2017 Newark airport simulation, with focus on impact of varying Trajectory Options Set (TOS) "participation levels" (i.e., the percentage of flights that submit TOSs), as well as the results from March 2018 LaGuardia simulation with Flow Evaluation Team, which focused on benefits to individual carriers of participation. During the study, we demonstrated that both the participating and non-participating airlines benefited when TOSs but that the participating airlines benefited most and the improvement seen by participants was greatest when overall participation was lower.
Trajectory-based operations with self-separation involve the aircraft taking the primary role in the management of its own trajectory in the presence of other traffic. In this role, the flight crew assumes the responsibility for ensuring that the aircraft remains separated from all other aircraft by at least a minimum separation standard. These operations are enabled by cooperative airborne surveillance and by airborne automation systems that provide essential monitoring and decision support functions for the flight crew. An airborne automation system developed and used by NASA for research investigations of required functionality is the Autonomous Operations Planner. It supports the flight crew in managing their trajectory when responsible for self-separation by providing monitoring and decision support functions for both strategic and tactical flight modes. The paper focuses on the latter of these modes by describing a capability for tactical intent-based conflict resolution and its role in a comprehensive suite of automation functions supporting trajectory-based operations with self-separation.
The Next Generation Air Transportation System (NextGen) incorporates collaborative air traffic management and Trajectory-Based Operations (TBO) in order to significantly increase the capacity, efficiency, and predictability of operations in the National Airspace System (NAS), without decreasing safety. This is enabled by airspace users and service providers sharing knowledge about operations that allows prediction of the complete 4D flight trajectory with as little uncertainty as possible. Additionally, new software and hardware technology is critical to reaching NextGen goals, especially with regard to TBO. What if the technologies that are critical for TBO were to be impaired or fail completely? Should there be a malfunction of a piece of the technology, it must be ensured that the whole system does not break down completely or suffer severe impairment. Instead, operations need to be maintained proportionally to the problem and safety needs to be ensured (graceful degradation). This paper proposes a systematic framework to investigate the vulnerability of TBO to technology disruption, and determine the impact of technological impairment on TBO. Two representative technologies are chosen for detailed investigation and the impact of their impairment on the degradation of TBO is illustrated using a weather-related scenario. XXXX There are several possible directions of future work. We believe it is desirable to develop methods to quantitatively assess the impact of technological disruption on TBO and to have the simulation tools to validate the impact. The availability of prognostics and health management methods could be leveraged to predict technological failure/disruption, thus predicting how TBO will be a ected, and possibly pro-actively mitigating the impact. It is important to develop large-scale scenarios where the e ect of technological impairment is prominent, and identify methods to quantitatively assess the extent of TBO degradation. An important goal of such an investigation is the development of failure-resistant resilient trajectory-based oper- ations. Resilience14, 15 is the property of a system to \bounce back" and resume at least a signi cant portion of its functionalities after degradation due to technological impairment(s). A systems resilience includes properties such as \bu ering capacity" (quantifying disruptions the system can absorb or adapt to without a fundamental breakdown in performance or in the systems structure), \ exibility" (ability to restructure itself in response to external changes or pressures), "margin" (how closely the system is currently operating rela- tive to one or another kind of performance boundary), \tolerance" (whether the system gracefully degrades as stress/pressure increase, or collapses quickly when pressure exceeds adaptive capacity), etc. Future work needs to focus on quantifying and improving the resilience of TBO, and identifying resilient design solutions for aviation.
Integrated Demand Management (IDM), is a NASA developed Traffic Flow Management (TFM) concept that uses Collaborative Trajectory Options Program (CTOP) to precondition traffic flows into the Time Based Flow Management (TBFM) region, helping traffic planners manage imbalances between demand and capacity in the National Airspace (NAS). A workshop held at NASA was conducted to demonstrate how individual airlines can be impacted by using Trajectory Options Sets (TOS) during IDM operations. A primary concern that was specifically addressed in a part-task Human-in-the-Loop simulation was who received the greater benefit, TOS participating or non-TOS participating airlines? The results showed that TOS participating airlines received greater benefit in terms of ground delay, number of reroute options, and additional flight time, than non-TOS participating airlines. However, this result was dependent on the number and location of flights. Therefore, it was advantageous for airlines to equip TOS. In addition, we found that non-TOS participating airlines also benefitted from other airlines participating in TOS, because the total system-wide ground delay was reduced as more TOS were introduced into the system. The evidence suggests that the benefits were distributed fairly, and there were no unfair disadvantages for airlines who did not equip TOS.
Trajectory-Based Operations (TBO) is one of the essential paradigm shifts in the NextGen transformation of the National Airspace System. Under TBO, aircraft are managed by 4-dimensional trajectories, and airborne and ground-based metering, merging, and spacing operations are key to managing those trajectories. This paper presents the results of a study of potential metering, merging, and spacing operations within a future TBO environment. A number of operational scenarios for tactical and strategic uses of metering, merging, and spacing are described, and interdependencies between concurrent tactical and strategic operations are identified.
A flight demonstration of arrival traffic management automation was conducted in partnership between NASA, FAA and Boeing as an element of the latter’s ecoDemonstrator2020 flight program. For the demonstration, NASA’s prototype Tailored Arrival Manager(TAM) was used to compute efficient trajectory-based solutions to traffic management problems representing those encountered during time-based metering operations today. TAM solutions involving route modifications were uplinked to a Boeing 787 airplane using Controller Pilot Data Link Communications and seamlessly loaded into the airplane’s Flight Management System (FMS). The paper describes the concept and technology behind TAM along with the data exchanges and procedures involved with the demonstration. All TAM solutions were delivered in a timely manner and successfully integrated with the airplane’s FMS, thereby demonstrating the basic feasibility of trajectory-based arrival management using currently available data communications and avionics equipage. Although TAM solutions were not executed during this initial demonstration, a limited study of trajectory prediction accuracy was possible given that the airplane flew uninterrupted, automated descents with known route and speed intent. Analysis revealed that TAM meter-fix arrival time predictions were accurate to within ± 30 seconds for time horizons of 30 minutes or less, which matched closely with FMS predictions acquired through real-time data exchange. Top-of-descent predictions over similar time horizons were found accurate to within ± 5 nautical miles.
A flight demonstration of arrival traffic management automation was conducted in partnership between NASA, FAA and Boeing as an element of the latter’s ecoDemonstrator2020 flight program. For the demonstration, NASA’s prototype Tailored Arrival Manager(TAM) was used to compute efficient trajectory-based solutions to traffic management problems representing those encountered during time-based metering operations today. TAM solutions involving route modifications were uplinked to a Boeing 787 airplane using Controller Pilot Data Link Communications and seamlessly loaded into the airplane’s Flight Management System (FMS). The paper describes the concept and technology behind TAM along with the data exchanges and procedures involved with the demonstration. All TAM solutions were delivered in a timely manner and successfully integrated with the airplane’s FMS, thereby demonstrating the basic feasibility of trajectory-based arrival management using currently available data communications and avionics equipage. Although TAM solutions were not executed during this initial demonstration, a limited study of trajectory prediction accuracy was possible given that the airplane flew uninterrupted, automated descents with known route and speed intent. Analysis revealed that TAM meter-fix arrival time predictions were accurate to within ± 30 seconds for time horizons of 30 minutes or less, which matched closely with FMS predictions acquired through real-time data exchange. Top-of-descent predictions over similar time horizons were found accurate to within ± 5 nautical miles.
The Efficient Universal Trajectory Language (EUTL) is a language for specifying and representing trajectories for Air Traffic Management (ATM) concepts such as Trajectory-Based Operations (TBO). In these concepts, the communication of a trajectory between an aircraft and ground automation is fundamental. Historically, this trajectory exchange has not been done, leading to trajectory definitions that have been centered around particular application domains and, therefore, are not well suited for TBO applications. The EUTL trajectory language has been defined in the Prototype Verification System (PVS) formal specification language, which provides an operational semantics for the EUTL language. The hope is that EUTL will provide a foundation for mathematically verified algorithms that manipulate trajectories. Additionally, the EUTL language provides well-defined methods to unambiguously determine position and velocity information between the reported trajectory points. In this paper, we present the EUTL trajectory language in mathematical detail.
The air traffic management system lacks integration among its elements often due to using inconsistent information, models, and metrics about the traffic. Transitioning to trajectory-based operations, whereby flights are managed by full trajectories in space and time, will enable more integration, with the help of increased automation. Building on trajectory-based operations, an "accrued delay" metric is proposed, which continuously measures the amount of delay that a flight has accumulated up to the current time, including delays incurred during the current flight and inherited from previous flights through the turnaround process. Through a time-based metering and scheduling example, we show how using accrued delay as a metric can help integrate the decision-making across multiple decision horizons, leading to more efficient and balanced access to airspace services. We show that when prioritizing flights that have already accrued high delay because of a constrained runway resource, significant gains are achieved in terms of reducing total delay and its variance. We studied the sensitivity of these gains to numerous factors, such as time-based versus distance-based horizons, horizon size, and errors in conformance to scheduled times.
Inaccurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. Current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) techniques for the simultaneous prediction of three key APM parameters (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture the relationship between APM parameters and flight-related features with good accuracy.
The air traffic management system lacks integration among its elements often due to using inconsistent information, models, and metrics about the traffic. Transitioning to trajectory-based operations, whereby flights are managed by full trajectories in space and time, will enable more integration, with the help of increased automation. Building on trajectory-based operations, an "accrued delay" metric is proposed, which continuously measures the amount of delay that a flight has accumulated up to the current time, including delays incurred during the current flight and inherited from previous flights through the turnaround process. Through a time-based metering and scheduling example, we show how using accrued delay as a metric can help integrate the decision-making across multiple decision horizons, leading to more efficient and balanced access to airspace services. We show that when prioritizing flights that have already accrued high delay because of a constrained runway resource, significant gains are achieved in terms of reducing total delay and its variance. We studied the sensitivity of these gains to numerous factors, such as time-based versus distance-based horizons, horizon size, and errors in conformance to scheduled times.
A new operational concept has been proposed in Upper Class E airspace at or above 60,000 feet (Flight Level / FL600), which will allow operators of diverse vehicle characteristics to cooperatively manage and share their operational intents with neighboring operators to avoid conflict. There is a consensus in the community that negotiation for strategic deconfliction is needed, but there are no specific guidelines for how the negotiation should be conducted. There is a need for a structured and cooperative way to resolve the conflict between a wide variety of aircraft projected to be operating in Upper Class E for the negotiation to be carried out routinely. The use of negotiation models are a promising solution that can resolve conflict risks during flight in real-time while taking into account the uncertainty of future vehicle positions and dynamic business considerations. A two-party negotiation model has been researched, but as the traffic demand grows, there is a higher likelihood of conflict involving multiple aircraft that would require a method to handle multi-party conflict. This paper proposes a cooperative multi-party negotiation model inspired by game theory concepts for application to flight trajectory negotiation in Upper Class E traffic management. This model can be applied in flight with operators communicating directly after a potential conflict is detected. Some of the model’s benefits include allowing business costs to be private to operators, allowing operators to collaborate together to find conflict-free flight trajectories, and being compatible with different aircraft and operation types. This model provides a structured procedure for conflict resolution that can handle conflict involving multiple parties, assuming each operator is willing to take on a small cost to themselves in order to reduce the total cost to the group.
One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.
In air traffic management, conflict detection algorithms are used to determine whether or not aircraft are predicted to lose horizontal and vertical separation minima within a time interval assuming a trajectory model. In the case of linear trajectories, conflict detection algorithms have been proposed that are both sound, i.e., they detect all conflicts, and complete, i.e., they do not present false alarms. In general, for arbitrary nonlinear trajectory models, it is possible to define detection algorithms that are either sound or complete, but not both. This paper considers the case of nonlinear aircraft trajectory models based on polynomial functions. In particular, it proposes a conflict detection algorithm that precisely determines whether, given a lookahead time, two aircraft flying polynomial trajectories are in conflict. That is, it has been formally verified that, assuming that the aircraft trajectories are modeled as polynomial functions, the proposed algorithm is both sound and complete.