Engineering topics

Karr, David A.

Publications and source records attributed to Karr, David A..

8 records

NASA NTRS2017

Point-Mass Aircraft Trajectory Prediction Using a Hierarchical, Highly-Adaptable Software Design

A highly adaptable and extensible method for predicting four-dimensional trajectories of civil aircraft has been developed. This method, Behavior-Based Trajectory Prediction, is based on taxonomic concepts developed for the description and comparison of trajectory prediction software. A hierarchical approach to the "behavioral" layer of a point-mass model of aircraft flight, a clear separation between the "behavioral" and "mathematical" layers of the model, and an abstraction of the methods of integrating differential equations in the "mathematical" layer have been demonstrated to support aircraft models of different types (in particular, turbojet vs. turboprop aircraft) using performance models at different levels of detail and in different formats, and promise to be easily extensible to other aircraft types and sources of data. The resulting trajectories predict location, altitude, lateral and vertical speeds, and fuel consumption along the flight path of the subject aircraft accurately and quickly, accounting for local conditions of wind and outside air temperature. The Behavior-Based Trajectory Prediction concept was implemented in NASA's Traffic Aware Planner (TAP) flight-optimizing cockpit software application.

Karr, David A.↗

NASA NTRS2016

Deploying a Route Optimization EFB Application for Commercial Airline Operational Trials

The Traffic Aware Planner (TAP), developed for NASA Langley Research Center to support the Traffic Aware Strategic Aircrew Requests (TASAR) project, is a flight-efficiency software application developed for an Electronic Flight Bag (EFB). Tested in two flight trials and planned for operational testing by two commercial airlines, TAP is a real-time trajectory optimization application that leverages connectivity with onboard avionics and broadband Internet sources to compute and recommend route modifications to flight crews to improve fuel and time performance. The application utilizes a wide range of data, including Automatic Dependent Surveillance Broadcast (ADS-B) traffic, Flight Management System (FMS) guidance and intent, on-board sensors, published winds and weather, and Special Use Airspace (SUA) schedules. This paper discusses the challenges of developing and deploying TAP to various EFB platforms, our solutions to some of these challenges, and lessons learned, to assist commercial software developers and hardware manufacturers in their efforts to implement and extend TAP functionality in their environments. EFB applications (such as TAP) typically access avionics data via an ARINC 834 Simple Text Avionics Protocol (STAP) server hosted by an Aircraft Interface Device (AID) or other installed hardware. While the protocol is standardized, the data sources, content, and transmission rates can vary from aircraft to aircraft. Additionally, the method of communicating with the AID may vary depending on EFB hardware and/or the availability of onboard networking services, such as Ethernet, WIFI, Bluetooth, or other mechanisms. EFBs with portable and installed components can be implemented using a variety of operating systems, and cockpits are increasingly incorporating tablet-based technologies, further expanding the number of platforms the application may need to support. Supporting multiple EFB platforms, AIDs, avionics datasets, and user interfaces presents a challenge for software developers and the management of their code baselines. Maintaining multiple baselines to support all deployment targets can be extremely cumbersome and expensive. Certification also needs to be considered when developing the application. Regardless of whether the software is itself destined to be certified, data requirements in support of the application and user interface elements may introduce certification requirements for EFB manufacturers and the airlines. The example of TAP, the challenges faced, solutions implemented, and lessons learned will give EFB application and hardware developers insight into future potential requirements in deploying TAP or similar flight-deck EFB applications.

Roscoe, David A.↗

NASA NTRS2013

Costs of Limiting Route Optimization to Published Waypoints in the Traffic Aware Planner

The Traffic Aware Planner (TAP) is an airborne advisory tool that generates optimized, traffic-avoiding routes to support the aircraft crew in making strategic reroute requests to Air Traffic Control (ATC). TAP is derived from a research-prototype self-separation tool, the Autonomous Operations Planner (AOP), in which optimized route modifications that avoid conflicts with traffic and weather, using waypoints at explicit latitudes and longitudes (a technique supported by self-separation concepts), are generated by maneuver patterns applied to the existing route. For use in current-day operations in which trajectory changes must be requested from ATC via voice communication, TAP produces optimized routes described by advisories that use only published waypoints prior to a reconnection waypoint on the existing route. We describe how the relevant algorithms of AOP have been modified to implement this requirement. The modifications include techniques for finding appropriate published waypoints in a maneuver pattern and a method for combining the genetic algorithm of AOP with an exhaustive search of certain types of advisory. We demonstrate methods to investigate the increased computation required by these techniques and to estimate other costs (measured in terms such as time to destination and fuel burned) that may be incurred when only published waypoints are used.

TASAR↗

NASA NTRS2012

Autonomous Operations Planner: A Flexible Platform for Research in Flight-Deck Support for Airborne Self-Separation

The Autonomous Operations Planner (AOP), developed by NASA, is a flexible and powerful prototype of a flight-deck automation system to support self-separation of aircraft. The AOP incorporates a variety of algorithms to detect and resolve conflicts between the trajectories of its own aircraft and traffic aircraft while meeting route constraints such as required times of arrival and avoiding airspace hazards such as convective weather and restricted airspace. This integrated suite of algorithms provides flight crew support for strategic and tactical conflict resolutions and conflict-free trajectory planning while en route. The AOP has supported an extensive set of experiments covering various conditions and variations on the self-separation concept, yielding insight into the system s design and resolving various challenges encountered in the exploration of the concept. The design of the AOP will enable it to continue to evolve and support experimentation as the self-separation concept is refined.

Karr, David A.↗

NASA NTRS2009

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Karr, David A.↗

NASA NTRS2009

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Karr, David A.↗

NASA NTRS2006

Conflict Detection Using Variable Four-Dimensional Uncertainty Bounds to Control Missed Alerts

Decision-support tools for maintaining pairwise aircraft separation rely on conflict detection to alert the operator when the predicted trajectories of aircraft will result in a loss of separation. But aircraft frequently do not follow their predicted trajectories exactly. This can cause missed alerts and the failure of strategic separation procedures. We present a technique for modeling a bounded region of uncertainty around a four-dimensional predicted trajectory and an algorithm for detecting conflicts between trajectories modeled in this way that avoids missed alerts as long as the aircraft remain within the specified regions of uncertainty. In addition, we present an algorithm for detecting the intrusion of a trajectory modeled in this way into an area hazard modeled as a polygonal region. The size of the region of uncertainty can vary along the trajectory continually and independently in the along-path, cross-track, and vertical dimensions, providing an opportunity to reduce the likelihood of false alerts while protecting against typical prediction errors. The algorithm has been implemented in the Autonomous Operations Planner, a NASA Langley prototype decision support tool for airborne self-separation.

Karr, David A.↗

NASA NTRS2006

Pattern-Based Genetic Algorithm for Airborne Conflict Resolution

NASA has developed the Autonomous Operations Planner (AOP) airborne decision support tool to explore advanced air traffic control concepts that include delegating separation authority to aircraft. A key element of the AOP is its strategic conflict resolution (CR) algorithm, which must resolve conflicts while maintaining conformance with traffic flow management constraints. While a previous CR algorithm, which focused on broader flight plan optimization objectives as a part of conflict resolution, had successfully been developed, new research has identified the need for resolution routes the users find more acceptable (i.e., simpler and more intuitive). A new CR algorithm is presented that uses a combination of pattern-based maneuvers and a genetic algorithm to achieve these new objectives. Several lateral and vertical maneuver patterns are defined and the application of the genetic algorithm explained. A new approach to defining a conflicted fitness function using estimates of the local conflict region around a conflicted trajectory is also presented. Preliminary performance characteristics of the implemented algorithm are provided.

Vivona, Robert A.↗