We present the mapping of a class of simplified air traffic management problems (strategic conflict resolution) to quadratic unconstrained Boolean optimization problems. The mapping is performed through an original representation of the conflict-resolution problem in terms of a conflict graph, where the nodes of the graph represent flights and the edges represent a potential conflict between flights. The representation allows a natural decomposition of a real-world instance related to wind-optimal trajectories over the Atlantic Ocean into smaller subproblems that can be discretized and are amenable to be programmed in quantum annealers. In this paper, we tested the new programming techniques, and we benchmark the hardness of the instances using both classical solvers and the D-Wave 2X and D-Wave 2000Q quantum chip. The preliminary results show that for reasonable modeling choices, the most challenging subproblems which are programmable in the current devices are solved to optimality with 99% of probability within a second of annealing time.

Traffic flow management (TFM) allocates the various airport, airspace, and other resources to maintain an efficient traffic flow consistent with safety. TFM is a complex area of research involving the disciplines of operations research, guidance and control, human factors, and software engineering. Hundreds of human operators make TFM decisions that involve tens of thousands of aircraft, en route air traffic control centers, the Federal Aviation Administration's System Command Center, and many airline operation centers. This paper provides an overview of how TFM decisions are made today and challenges facing the system in the future, and reviews modeling and optimization approaches for facilitating system-wide modeling, performance assessments, and system-level optimization of the national airspace system in the presence of both en route and airport capacity constraints.

Initiatives in air traffic management both in the United States and in Europe are aimed at providing air traffic controllers with automation tools to separate traffic, meet time constraints required for traffic flow and accommodate route preferences of users such as airlines. These efforts are expected to result in removal of restrictions on users preferred routes without compromising safety. Thus, aircraft will be able to fly optimal routes such as great circle and wind-optimal routes. NASA has developed the design for a new automation tool, referred to as the direct-to tool, which advises the controller on direct time-saving routes for any aircraft irrespective of levels of equipage. In contrast to earlier studies on the potential benefits of direct routes in the National Airspace System, the objective of the paper is to evaluate the benefits based on a controller tool. The paper describes the benefits of applying this algorithm to the 20 air route traffic control centers within the United States. Benefits are measured in terms of the total time savings accrued by flying the direct route. Results are described for three different implementations dependent on the search region bounding each air route traffic control center. The first region exactly encloses the air route traffic control center airspace, the second is the smallest rectangular bounding region while the third is a bigger rectangular bounding region approximately twice as large as the second region. It is shown that the application of the direct-to routing algorithm does not significantly alter the number of conflicts and their spatial distribution compared to the case in which the aircraft fly along the airways. The results presented in the paper suggest that the direct-to routing algorithm can provide significant cost savings to the users without adversely impacting the air traffic management functions.

This paper proposes a method for detecting obstacles on a runway by controlling their expected flow disparities. The runway is modeled as a planar surface. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obstacle-free runway is described by the data from on-board sensors (OBS). The initial residual flow field (RFF) is obtained after warping (or stabilizing) the image using the initial MFF. The error variance of the initial MFF is estimated. The initial RFF and the error variance are first used to identify the pixels corresponding to the obstacle-free runway and then to noniteratively estimate the MFF and RFF. Obstacles are detected by comparing the expected residual flow disparities with the RFF. Expected temporal and spatial residual disparities are obtained from the use of the OBS. This allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. Experimental results for two real flight image sequences are presented.

This paper proposes a method for detecting obstacles on a runway by controlling their expected disparities. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obstacle-free runway is described by the data from on-board sensors (OBS). The error variance of the initial MFF is computed and used to estimate the MFF. Obstacles are detected by comparing the expected residual flow disparities with the residual flow field (RFF) estimated after warping (or stabilizing) an image using the MFF. Expected temporal and spatial disparities are obtained from the use of the OBS. This allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. Experimental results for two real flight image sequences are presented.

For automatic obstacle avoidance guidance during rotorcraft low altitude flight a reliable model of the nearby environment is needed. Such a model may be constructed by applying surface fitting techniques to the dense range map obtained by active sensing using radars. However, for covertness passive sensing techniques using electro-optic sensors is desirable. As opposed to the dense range map obtained via active sensing, passive sensing algorithms produce reliable range at sparse locations and, therefore, surface fitting techniques to fill the gaps in the range measurement are not directly applicable. Both, for automatic guidance and as a display for aiding the pilot, these discrete ranges need to be grouped into sets which correspond to objects in the nearby environment. The focus of this paper is on using Monte Carlo methods for clustering range points into meaningful groups. We compare three different approaches and present results of application of these algorithms to an image sequence acquired by onboard cameras during a helicopter flight. Starting with an initial grouping, these algorithms are iteratively applied with a new group creation algorithm to determine the optimal number of groups and the optimal group membership. The results indicate that the simulated annealing methods do not offer any significant advantage over the basic Monte Carlo method for this discrete optimization problem.

This paper proposes a method for detecting obstacles on a runway from monocular image sequences. The surface of the runway is modelled as a plane and the model flow field corresponding to the runway is described by 8 coefficients. The set of 8 coefficients describing the initial model flow field is given by the data from inertial navigation system (INS). The uncertainties of this initial model flow field are estimated and used to obtain the accurate model flow field. The residual flow field after warping images by the accurate model flow field is computed by solving overdetermined gradient-based optical flow equations using a singular value decomposition (SVD). This SVD gives us a new insight of the uncertainties inherent in the optical flow computation. Those pixels with large residual flow vectors are considered as obstacles. Experimental results for two real image sequences are presented.

This paper investigates the the feasibility of using visual and infrared imaging sensors to aid in the location of the aircraft position during operations such as landing in bad weather. The choice of the airport model used is crucial to algorithms which are used for position estimation based on pattern recognition. In this paper we describe the effects the choice of a model has on the behaviour of such matching algorithms. Three basic models are chosen: a line segment based model, an area based model and a texture based model. It is seen that a sparse line segment based model is not adequate to identify the runway since it matches a number of false artifacts in the image. An enhanced line segment based model containing a large number of features compares favourably with the area based model. The texture based model is seen to need a number of camera and weather dependent parameters and the performance of such a scheme is not seen to be substantially better. Thus either a proper area based model or a pseudo-area based model (based on a very large number of line features) can be seen to provide the best performance for such landmark identification and position determination algorithms.>

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten images per second to thirty or more images per second depending on the vehicle speed. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for automated helicopter flight. The algorithm can track hundreds of features in multiple image sensors using an extended Kalman filter to estimate the feature's location in a master sensor coordinate frame. The feature-tracking algorithm has reached relative maturity in the laboratory and is now being ported to several real-time architectures to support autonomous helicopter navigation research, The focus of this paper is not the core theory of the vision algorithm itself, but those aspects of it that affect the method of parallelization. The performance of the parallel algorithm is analyzed, with respect to three load balancing schemes, on both a distributed-memory and shared-memory parallel computer.>

A system capable of supporting autonomous helicopter navigation must extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second. A parallel implementation of the obstacle detection/ranging method is required. The authors describe an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer.>

Computer-vision-based methods provide one general approach for obstacle detection and range estimation for pilot assistance during low-altitude flight. Results obtained using helicopter flight data with a feature-based range estimation algorithm are presented. A method for recursively estimating range using a Kalman filter with a monocular sequence of images and knowledge of the camera's motion is described. The helicopter flight experiment and one of four resulting datasets are briefly discussed. The performance of the range estimation algorithm is examined by comparing the range estimates with true range measurements collected during the flight experiment.>

Some of the issues in the location of objects using a sequence of images from a passive sensor are examined. Image-object differential equations for a rotorcraft executing an arbitrary maneuver are developed. Assuming an onboard inertial navigation system for rotorcraft and state estimation, this study considers how object location is affected by the choice of Kalman filter estimation technique, the rotorcraft, and the object. Simulation results are presented.>