A risk-based UAM airspace capacity assessment method using Monte Carlo simulation

Inspired by risk analysis assistance service and dynamic capacity management service in U-space service, this paper investigates a risk-based UAM airspace capacity assessment method using Monte Carlo simulation for future urban air mobility. The quantitative risk assessment of the flight plan is divided into three parts: the ground / air risks of the flight plan and the mid-air collision risk between UAM. Using the comprehensive risk assessment method, this paper generates several simulation scenarios in the airspace to be evaluated in terms of the type of participants, the presence of the detect and avoid system, and the total number of participants in the airspace, conducts Monte Carlo simulations, and records the simulation data for analysis. Through the analysis of simulation data, it is found that the maximum risk of UAM in airspace increases with the increase of the number of airspace invaders and the total number of UAM. However, the maximum risk of UAM in airspace decreases when the aircraft in airspace contains the detection and avoid system with the same other conditions. Based on simulation data, this paper informatively proposes the concept of a 3D risk surface and a risk-based airspace capacity envelope, using the horizontal surface formed by a specific risk threshold to cut the 3D risk surface to form an airspace capacity envelope, which visually describes the number of aircraft that can be contained in the airspace under a specific risk threshold

Fe(3): An Evaluation Tool for Low-Altitude Air Traffic Operations

The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) are introducing high-density operations in low altitude airspace in closer proximity to populated areas than conventional high-altitude air traffic. The Flexible engine for Fast-time Evaluation of Flight Environments (Fe (sup 3)) provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system under numerous scenarios, such that stake holders can study impacts of factors in the low-altitude high-density traffic system and define requirements, policies, and protocols needed to support a safe yet efficient traffic system, and even assess operational risks and optimize flight schedules without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and sample application in UAM and UTM are also presented

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

Autonomous terminal area operations for unmanned aerial systems

After many years of successful operation in military domains, Unmanned Aerial Systems (UASs) are generating significant interest amongst civilian operators in sectors such as law enforcement, search and rescue, aerial photography and mapping. To maximise the benefits brought by UASs to sectors such as these, a high level of autonomy is desirable to reduce the need for highly skilled operators. Highly autonomous UASs require a high level of situation awareness in order to make appropriate decisions. This is of particular importance to civilian UASs where transparency and equivalence of operation to current manned aircraft is a requirement, particularly in the terminal area immediately surrounding an airfield. This thesis presents an artificial situation awareness system for an autonomous UAS capable of comprehending both the current continuous and discrete states of traffic vehicles. This estimate forms the basis of the projection element of situation awareness, predicting the future states of traffic. Projection is subject to a large degree of uncertainty in both continuous state variables and in the execution of intent information by the pilot. Both of these sources of uncertainty are captured to fully quantify the future positions of traffic. Based upon the projection of future traffic positions a self separation system is designed which allows an UAS to quantify its separation to traffic vehicles up to some future time and manoeuvre appropriately to minimise the potential for conflict. A high fidelity simulation environment has been developed to test the performance of the artificial situation awareness and self separation system. The system has demonstrated good performance under all situations, with an equivalent level of safety to that of a human pilot

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Structured urban airspace capacity analysis: four drone delivery cases

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators

Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case

A Novel Collision Avoidance Logic for Unmanned Aerial Vehicles Using Real-Time Trajectory Planning

An effective collision avoidance logic should prevent collision without excessive alerting. This requirement would be even more stringent for an automatic collision avoidance logic, which is probably required by Unmanned Aerial Vehicles to mitigate the impact of delayed or lost link issues. In order to improve the safety performance and reduce the frequency of false alarms, this thesis proposes a novel collision avoidance logic based on the three-layer architecture and a real-time trajectory planning method. The aim of this thesis is to develop a real-time trajectory planning algorithm for the proposed collision avoidance logic and to determine the integrated logic’s feasibility, merits and limitations for practical applications. To develop the trajectory planning algorithm, an optimal control problem is formulated and an inverse-dynamic direct method along with a two stage, derivative-free pattern search method is used as the solution approach. The developed algorithm is able to take into account the flyability of three dimensional manoeuvres, the robustness to the intruder state uncertainty and the field-of-regard restriction of surveillance sensors. The testing results show that the standalone executable of the algorithm is able to provide a flyable avoidance trajectory with a maximum computation time less than 0.5 seconds. To evaluate the performance of the proposed logic, an evaluation framework for Monte Carlo simulations and a baseline approach for comparison are constructed. Based on five Monte Carlo simulation experiments, it is found that the proposed logic should be feasible as 1) it is able to achieve an update rate of 2Hz, 2) its safety performance is comparable with a reference requirement from another initial feasibility study, and 3) despite a 0.5 seconds computation latency, it outperforms the baseline approach in terms of safety performance and robustness to sensor and feedback error

Engage D3.5 Opportunities for innovative ATM research (interim report)

This document reports on the topics and academic disciplines of past Exploratory Research projects, notably SESAR Workpackage E (long-term and innovative research) and SESAR Exploratory Research (ER) with a view of tracing the evolution of research as well as opportunities for future research. This analysis is complemented with relevant activities in Engage, such as the Engage thematic challenges

Landing site reachability in a forced landing of unmanned aircraft in wind

Autonomous contingency management systems, such as a forced landing system which reacts appropriately to an engine failure is important for the safe operation of Unmanned Aircraft Systems (UAS). This paper details a method to ascertain the reachability of any possible emergency landing site for a forced landing in steady uniform wind conditions. With knowledge of the aircraft’s state, such as speed heading location and orientation of a landing site, a method to calculate a minimum height loss path is developed based on aircraft glide performance. Wind direction and speed are taken into account using a trochoidal approach by defining the minimum height loss turn path. To facilitate real-time implementation, simplified gliding equations are developed without accuracy loss. The reachability of each site can be calculated, as well as how much safety margin an aircraft would have. This method is generic and could also provide decision support for human pilots in forced landing situations. Two types of aircraft Airbus A320-400 and the Cessna 172 have been investigated to demonstrate the usefulness of the method, using Monte Carlo simulations in a synthetic X-Plane R simulation environment, in order to demonstrate the performance and effectiveness of the proposed approaches