Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Simulation and Performance Evaluation of Algorithms for Unmanned Aircraft Conflict Detection and Resolution

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency

Evaluation of remain well clear and collision avoidance for drones

One of the cornerstones that should enable inserting unmanned aircraft into the airspace is the development of Detect and Avoid (DAA) systems. DAA systems will improve the Remote Pilot (RP) situational awareness by means of electronic conspicuity devices, providing them with the necessary means to Remain Well Clear (RWC) from other traffic and, if necessary, avoid Mid-Air collisions (MAC). DAA systems will compensate for the loss of a pilot on board, which drastically reduces the capacity to keep a safe separation from traffic, making current Rules of the Air very challenging to achieve. Given the growing popularity of drone operations for commercial and recreational purposes, new standards should include them in the not-too-distant future. Since current DAA standards and algorithms (DO-365 and ED-258) are being developed targeting large, mostly military Remotely Piloted Aircraft Systems (RPAS), this project proposes a new set of detection volumes and alert thresholds for U-Space users according to an aircraft type classification. This will allow adapting the existing DAA algorithms to small drones, complying with the new European framework of services and applications for drones (U-Space). Because testing new safety nets (such as new DAA algorithms) on real aircraft would be dangerous and inadequate, radar reports and computer-based simulations allow for a risk-free and faster evaluation of safety net performances. Due to the current lack of real drone radar tracks, this project has developed a multi-rotor drone encounter generator tool (called DEG). This software is able to generate a large number of synthetic pairwise quadcopter drone conflict tracks, simulating the instant prior to a MAC. The way trajectories are generated by DEG strongly depends on the type of operation being flown (inspection/surveillance flights and logistic flights) and the aircraft type (including a DJI F450 and a faster version called DJI F450 FAST). The results of this project include a drone conflict trajectory example generated with DEG and an investigation of the performance and effectiveness of the DEG tool using a tailored existing DAA algorithm (DAIDALUS).Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Full Automation of Air Traffic Management in High Complexity Airspace

The thesis is that automation of en-route Air Traffic Management in high complexity airspace can be achieved with a combination of automated tactic planning in a look-ahead time horizon of up to two hours complemented with automated tactic conflict resolution functions. The literature review reveals that no significant results have yet been obtained and that full automation could be approached with a complementary integration of automated tactic resolutions AND planning. The focus shifts to ‘planning for capacity’ and ‘planning for resolution’ and also – but not only – for ‘resolution’. The work encompasses a theoretical part on planning, and several small scale studies of empirical, mathematical or simulated nature. The theoretical part of the thesis on planning under uncertainties attempts to conceive a theoretical model which abstracts specificities of planning in Air Traffic Management into a generic planning model. The resulting abstract model treats entities like the planner, the strategy, the plan and the actions, always considering the impact of uncertainties. The work innovates in specifying many links from the theory to the application in planning of air traffic management, and especially the new fields of tactical capacity management. The second main part of the thesis comprises smaller self-containing works on different aspects of the concept grouped into a section on complexity, another on tactic planning actions, and the last on planners. The produced studies are about empirical measures of conflicts and conflict densities to get a better understanding of the complexity of air traffic; studies on traffic organisation using tactical manoeuvres like speed control, lateral offset and tactical direct using fast time simulation; and studies on airspace design like sector optimisation, dynamic sectorisation and its optimisation using optimisation techniques. In conclusion it is believed that this work will contribute to further automation attempts especially by its innovative focus which is on planning, base on a theory of planning, and its findings already influence newer developments

Control of aircraft in the terminal manoeuvring area using parallelised sequential Monte Carlo

This paper reports on the use of a parallelised Model Predictive Control, Sequential Monte Carlo algorithm for solving the problem of conflict resolution and aircraft trajectory control in air traffic management specifically around the terminal manoeuvring area of an airport. The target problem is nonlinear, highly constrained, non-convex and uses a single decision-maker with multiple aircraft. The implementation includes a spatio-temporal wind model and rolling window simulations for realistic ongoing scenarios. The method is capable of handling arriving and departing aircraft simultaneously including some with very low fuel remaining. A novel flow field is proposed to smooth the approach trajectories for arriving aircraft and all trajectories are planned in three dimensions. Massive parallelisation of the algorithm allows solution speeds to approach those required for real-time use.This work was supported by EPSRC (Engineering and Physical Sciences Research Council - UK) Grant No. EP/G066477/1AIAA Conference on Guidance, Navigation and Control 201

Conflict resolution algorithms for optimal trajectories in presence of uncertainty

Mención Internacional en el título de doctorThe objective of the work presented in this Ph.D. thesis is to develop a novel method to address the aircraft-obstacle avoidance problem in presence of uncertainty, providing optimal trajectories in terms of risk of collision and time of flight. The obstacle avoidance maneuver is the result of a Conflict Detection and Resolution (CD&R) algorithm prepared for a potential conflict between an aircraft and a fixed obstacle which position is uncertain. Due to the growing interest in Unmanned Aerial System (UAS) operations, CD&R topic has been intensively discussed and tackled in literature in the last 10 years. One of the crucial aspects that needs to be addressed for a safe and efficient integration of UAS vehicles in non-segregated airspace is the CD&R activity. The inherent nature of UAS, and the dynamic environment they are intended to work in, put on the table of the challenges the capability of CD&R algorithms to handle with scenarios in presence of uncertainty. Modeling uncertainty sources accurately, and predicting future trajectories taking into account stochastic events, are rocky issues in developing CD&R algorithms for optimal trajectories. Uncertainty about the origin of threats, variable weather hazards, sensing and communication errors, are only some of the possible uncertainty sources that make jeopardize air vehicle operations. In this work, conflict is defined as the violation of the minimum distance between a vehicle and a fixed obstacle, and conflict avoidance maneuvers can be achieved by only varying the aircraft heading angle. The CD&R problem, formulated as Optimal Control Problem (OCP), is solved via indirect optimal control method. Necessary conditions of optimality, namely, the Euler-Lagrange equations, obtained from calculus of variations, are applied to the vehicle dynamics and the obstacle constraint modeled as stochastic variable. The implicit equations of optimality lead to formulate a Multipoint Boundary Value Problem (MPBVP) which solution is in general not trivial. The structure of the optimality trajectory is inferred from the type of path constraint, and the trend of Lagrange multiplier is analyzed along the optimal route. The MPBVP is firstly approximated by Taylor polynomials, and then solved via Differential Algebra (DA) techniques. The solution of the OCP is therefore a set of polynomials approximating the optimal controls in presence of uncertainty, i.e., the optimal heading angles that minimize the time of flight, while taking into account the uncertainty of the obstacle position. Once the obstacle is detected by on-board sensors, this method provide a useful tool that allows the pilot, or remote controller, to choose the best trade-off between optimality and collision risk of the avoidance maneuver. Monte Carlo simulations are run to validate the results and the effectiveness of the method presented. The method is also valid to address CD&R problems in presence of storms, other aircraft, or other types of hazards in the airspace characterized by constant relative velocity with respect to the own aircraft.L’obiettivo del lavoro presentato in questa tesi di dottorato è la ricerca e lo sviluppo di un nuovo metodo di anti collisione velivolo-ostacolo in presenza di incertezza, fornendo traiettorie ottimali in termini di rischio di collisione e tempo di volo. La manovra di anticollisione è il risultato di un algoritmo di detezione e risoluzione dei conflitti, in inglese Conflict Detection and Resolution (CD&R), che risolve un potenziale conflitto tra un velivolo e un ostacolo fisso la cui posizione è incerta. A causa del crescente interesse nelle operazioni che coinvolgono velivoli autonomi, anche definiti Unmanned Aerial System (UAS), negli ultimi 10 anni molte ricerche e sviluppi sono state condotte nel campo degli algoritmi CD&R. Uno degli aspetti cruciali per un’integrazione sicura ed efficiente dei velivoli UAS negli spazi aerei non segregati è l’attività CD&R. La natura intrinseca degli UAS e l’ambiente dinamico in cui sono destinati a lavorare, impongono delle numerose sfide fra cui la capacità degli algoritmi CD&R di gestire scenari in presenza di incertezza. La modellizzazione accurata delle fonti di incertezza e la previsione di traiettorie che tengano conto di eventi stocastici, sono problemi particolarmente difficoltosi nello sviluppo di algoritmi CD&R per traiettorie ottimali. L’incertezza sull’origine delle minacce, zone di condizioni metereologiche avverse al volo, errori nei sensori e nei sistemi di comunicazione per la navigazione aerea, sono solo alcune delle possibili fonti di incertezza che mettono a repentaglio le operazioni degli aeromobili. In questo lavoro, il conflitto è definito come la violazione della distanza minima tra un veicolo e un ostacolo fisso, e le manovre per evitare i conflitti possono essere ottenute solo variando l’angolo di rotta dell’aeromobile, ovvero virando. Il problema CD&R, formulato come un problema di controllo ottimo, o Optimal Control Problem (OCP), viene risolto tramite un metodo indiretto. Le condizioni necessarie di ottimalità, vale a dire le equazioni di Eulero-Lagrange derivanti dal calcolo delle variazioni, sono applicate alla dinamica del velivolo e all’ostacolo modellizato come una variabile stocastica. Le equazioni implicite di ottimalità formano un problema di valori al controno multipunto, Multipoint Boundary Value Problem(MPBVP), la cui soluzione in generale è tutt’altro che banale. La struttura della traiettoria ottimale viene dedotta dal tipo di vincolo, e l’andamento del moltiplicatore di Lagrange viene analizzato lungo il percorso ottimale. Il MPBVP viene prima approssimato con un spazio di polinomi di Taylor e successimvamente risolto tramite tecniche di algebra differenziale, in inglese Differential Algebra (DA). La soluzione del OCP è quindi un insieme di polinomi che approssima il controllo ottimo del problema in presenza di incertezza. In altri termini, il controllo ottimo è l’insieme degli angoli di prua del velivolo che minimizzano il tempo di volo e che tenendo conto dell’incertezza sulla posizione dell’ostacolo. Quando l’ostacolo viene rilevato dai sensori di bordo, questo metodo fornisce un utile strumento al pilota, o al controllore remoto, al fine di scegliere il miglior compromesso tra ottimalità e rischio di collisione con l’ostacolo. Simulazioni Monte Carlo sono eseguite per convalidare i risultati e l’efficacia del metodo presentato. Il metodo è valido anche per affrontare problemi CD&R in presenza di tempeste, altri velivoli, o altri tipi di ostacoli caratterizzati da una velocità relativa costante rispetto al proprio velivolo.El objetivo del trabajo presentado en esta tesis doctoral es la búsqueda y el desarrollo de un método novedoso de anticolisión con osbstáculos en espacios aéreos en presencia de incertidumbre, proporcionando trayectorias óptimas en términos de riesgo de colisión y tiempo de vuelo. La maniobra de anticolisión es el resultado de un algoritmo de detección y resolución de conflictos, en inglés Conflict Detection and Resolution (CD&R), preparado para un conflicto potencial entre una aeronave y un obstáculo fijo cuya posición es incierta. Debido al creciente interés en las operaciones de vehículos autónomos, también definidos como Unmanned Aerial System (UAS), en los últimos 10 años muchas investigaciones se han llevado a cabo en el tema CD&R. Uno de los aspectos cruciales que debe abordarse para una integración segura y eficiente de los vehículos UAS en el espacio aéreo no segregado es la actividad CD&R. La naturaleza intrínseca de UAS, y el entorno dinámico en el que están destinados a trabajar, suponen un reto para la capacidad de los algoritmos de CD&R de trabajar con escenarios en presencia de incertidumbre. La precisa modelización de las fuentes de incertidumbre, y la predicción de trayectorias que tengan en cuenta los eventos estocásticos, son problemas muy difíciles en el desarrollo de algoritmos CD&R para trayectorias óptimas. La incertidumbre sobre el origen de las amenazas, condiciones climáticas adversas, errores en sensores y sistemas de comunicación para la navegación aérea, son solo algunas de las posibles fuentes de incertidumbre que ponen en peligro las operaciones de los vehículos aéreos. En este trabajo, el conflicto se define como la violación de la distancia mínima entre un vehículo y un obstáculo fijo, y las maniobras de anticolisión se pueden lograr variando solo el ángulo de rumbo de la aeronave, es decir virando. El problema CD&R, formulado como problema de control óptimo, o Optimal Control Problem (OCP), se resuelve a través del método de control óptimo indirecto. Las condiciones necesarias de optimalidad, es decir, las ecuaciones de Euler-Lagrange que se obtienen a partir del cálculo de variaciones, son aplicadas a la dinámica de la aeronave y al obstáculo modelizado como variable estocástica. Las ecuaciones implícitas de optimalidad forman un problema de valor de frontera multipunto (MPBVP) cuya solución en general no es trivial. La estructura de la trayectoria de optimalidad se deduce del tipo de vínculo, y la tendencia del multiplicador de Lagrange se analiza a lo largo de la ruta óptima. El MPBVP se aproxima en primer lugar a través de un espacio de polinomios de Taylor, y luego se resuelve por medio de técnicas de álgebra diferencial, en inglés Differential Algebra(DA). La solución del OCP es un conjunto de polinomios que aproximan los controles óptimos en presencia de incertidumbre, es decir, los ángulos de rumbo óptimos que minimizan el tiempo de vuelo teniendo en cuenta la incertidumbre asociada a la posición del obstáculo. Una vez que los sensores a bordo detectan el obstáculo, este método proporciona una herramienta muy útil que permite al piloto, o control remoto, elegir el mejor compromiso entre optimalidad y riesgo de colisión con el obstáculo. Se ejecutan simulaciones de Monte Carlo para validar los resultados y la efectividad del método presentado. El método también es válido para abordar los problemas de CD&R en presencia de tormentas, otras aeronaves u otros tipos de obstáculos caracterizados por una velocidad relativa constante con respecto a la propia aeronave.Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira i VirgiliPresidente: Carlo Novara.- Secretario: Lucia Pallotino.- Vocales: Manuel Sanjurjo Rivo; Yoshinori Matsuno; Alfonso Valenzuela Romer

Sequential Monte Carlo Optimisation for Air Traffic Management

This report shows that significant reduction in fuel use could be achieved by the adoption of `free flight' type of trajectories in the Terminal Manoeuvring Area (TMA) of an airport, under the control of an algorithm which optimises the trajectories of all the aircraft within the TMA simultaneously while maintaining safe separation. We propose the real-time use of Monte Carlo optimisation in the framework of Model Predictive Control (MPC) as the trajectory planning algorithm. Implementation on a Graphical Processor Unit (GPU) allows the exploitation of the parallelism inherent in Monte Carlo methods, which results in solution speeds high enough to allow real-time use. We demonstrate the solution of very complicated scenarios with both arrival and departure aircraft, in three dimensions, in the presence of a stochastic wind model and non-convex safe-separation constraints. We evaluate our algorithm on flight data obtained in the London Gatwick Airport TMA, and show that fuel saving of about 30% can be obtained. We also demonstrate the flexibility of our approach by adding noise-reduction objectives to the problem and observing the resulting modifications to arrival and departure trajectories

Conflict-free trajectory optimization with target tracking and conformance monitoring

This is a postprint (author final draft) deposit on institutional repository UPCommons from UPC, thanks to AIAA. Original version can be found on: https://arc.aiaa.org/doi/10.2514/1.C034251This paper proposes an optimization framework that computes conflict-free optimal trajectories in dense terminal airspace, while continuously monitoring trajectory conformance in an effort to improve predictability. The objective is to allow, as much as possible, continuous vertical trajectory profiles without impacting negatively on airspace capacity. Given automatic dependent surveillance–broadcast intent information, the future state of potential intruder aircraft are predicted, and this nominal trajectory is used as a constraint in the ownship trajectory optimization process. In it, a continuous multiphase optimal control problem is solved, taking into account spatial and temporal constraints. Additionally, a linearized Kalman filter keeps track of the target by estimating the deviations of its actual trajectory from its nominal trajectory, issuing a warning when an appropriate threshold is exceeded. This may be due to unexpected events, biases in the performance and weather models, wrong parameter assumptions, etc. An illustrative example is given, based on a computer simulation of two hypothetical trajectories in the Barcelona terminal maneuvering area. The results show how this framework resolves the problem of uncertainties in the trajectory predictions and results in a more efficient conflict resolution.Peer ReviewedPostprint (author's final draft

Aircraft Noise

Noise generated by aircraft continues to be a pressing issue for society, as an increasing number of people residing in close proximity to airports make noise complaints on a regular basis. The reduction in aircraft noise is therefore a very important engineering task that would require the careful identification of different acoustic sources around the airplane, the understanding of noise source behavior and ranking along flight trajectories, sophisticated measurement techniques, and robust and accurate numerical tools aimed at predicting the generation of noise, the propagation through the atmosphere, and the resulting noise impact along approach and departure flights. For an overall assessment of the situation, it has to be assessed along entire flight trajectories rather than assessing limited operating conditions only. Furthermore, it is highly recommended to apply multiple acoustic metrics and account for different and widespread observer locations along the flight. Only then can the overall situation be adequately captured. Obviously, this is a highly multidisciplinary effort and no single discipline can address this problem. This reprint includes selected research studies with that multidisciplinary context that deal with numerical or experimental investigations that range from the investigation of specific noise sources to the assessment of noise generated by the overall aircraft in operation. Both basic and applied research studies involving the modelling and simulation of aircraft noise are included