102 research outputs found

    Haptic-Multimodal Flight Control System Update

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    The rapidly advancing capabilities of autonomous aircraft suggest a future where many of the responsibilities of today s pilot transition to the vehicle, transforming the pilot s job into something akin to driving a car or simply being a passenger. Notionally, this transition will reduce the specialized skills, training, and attention required of the human user while improving safety and performance. However, our experience with highly automated aircraft highlights many challenges to this transition including: lack of automation resilience; adverse human-automation interaction under stress; and the difficulty of developing certification standards and methods of compliance for complex systems performing critical functions traditionally performed by the pilot (e.g., sense and avoid vs. see and avoid). Recognizing these opportunities and realities, researchers at NASA Langley are developing a haptic-multimodal flight control (HFC) system concept that can serve as a bridge between today s state of the art aircraft that are highly automated but have little autonomy and can only be operated safely by highly trained experts (i.e., pilots) to a future in which non-experts (e.g., drivers) can safely and reliably use autonomous aircraft to perform a variety of missions. This paper reviews the motivation and theoretical basis of the HFC system, describes its current state of development, and presents results from two pilot-in-the-loop simulation studies. These preliminary studies suggest the HFC reshapes human-automation interaction in a way well-suited to revolutionary ease-of-use

    Robust aircraft trajectory optimization under meteorological uncertainty

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    Mención Internacional en el título de doctorThe Air Traffic Management (ATM) system in the busiest airspaces in the world is currently being overhauled to deal with multiple capacity, socioeconomic, and environmental challenges. One major pillar of this process is the shift towards a concept of operations centered on aircraft trajectories (called Trajectory-Based Operations or TBO in Europe) instead of rigid airspace structures. However, its successful implementation (and, thus, the realization of the associated improvements in ATM performance) rests on appropriate understanding and management of uncertainty. Due to its complex socio-technical structure, the design and operations of the ATM system are heavily impacted by uncertainty, proceeding from multiple sources and propagating through the interconnections between its subsystems. One major source of ATM uncertainty is weather. Due to its nonlinear and chaotic nature, a number of meteorological phenomena of interest cannot be forecasted with complete accuracy at arbitrary lead times, which leads to uncertainty or disruption in individual air and ground operations that propagates to all ATM processes. Therefore, in order to achieve the goals of SESAR and similar programs, it is necessary to deal with meteorological uncertainty at multiple scales, from the trajectory prediction and planning processes to flow and traffic management operations. This thesis addresses the problem of single-aircraft flight planning considering two important sources of meteorological uncertainty: wind prediction error and convective activity. As the actual wind field deviates from its forecast, the actual trajectory will diverge in time from the planned trajectory, generating uncertainty in arrival times, sector entry and exit times, and fuel burn. Convective activity also impacts trajectory predictability, as it leads pilots to deviate from their planned route, creating challenging situations for controllers. In this work, we aim to develop algorithms and methods for aircraft trajectory optimization that are able to integrate information about the uncertainty in these meteorological phenomena into the flight planning process at both pre-tactical (before departure) and tactical horizons (while the aircraft is airborne), in order to generate more efficient and predictable trajectories. To that end, we frame flight planning as an optimal control problem, modeling the motion of the aircraft with a point-mass model and the BADA performance model. Optimal control methods represent a flexible and general approach that has a long history of success in the aerospace field. As a numerical scheme, we use direct methods, which can deal with nonlinear systems of moderate and high-dimensional state spaces in a computationally manageable way. Nevertheless, while this framework is well-developed in the context of deterministic problems, the techniques for the solution of practical optimal control problems under uncertainty are not as mature, and the methods proposed in the literature are not applicable to the flight planning problem as it is now understood. The first contribution of this thesis addresses this challenge by introducing a framework for the solution of general nonlinear optimal control problems under parametric uncertainty. It is based on an ensemble trajectory scheme, where the trajectories of the system under multiple scenarios are considered simultaneously within the same dynamical system and the uncertain optimal control problem is turned into a large conventional optimal control problem that can be then solved by standard, well-studied direct methods in optimal control. We then employ this approach to solve the robust flight plan optimization problem at the planning horizon. In order to model uncertainty in the wind and estimating the probability of convective conditions, we employ Ensemble Prediction System (EPS) forecasts, which are composed by multiple predictions instead of a single deterministic one. The resulting method can be used to optimize flight plans for maximum expected efficiency according to the cost structure of the airline; additionally, predictability and exposure to convection can be incorporated as additional objectives. The inherent tradeoffs between these objectives can be assessed with this methodology. The second part of this thesis presents a solution for the rerouting of aircraft in uncertain convective weather scenarios at the tactical horizon. The uncertain motion of convective weather cells is represented with a stochastic model that has been developed from the output of a deterministic satellite-based nowcast product, Rapidly Developing Thunderstorms (RDT). A numerical optimal control framework, based on the pointmass model with the addition of turn dynamics, is employed for optimizing efficiency and predictability of the proposed trajectories in the presence of uncertainty about the future evolution of the storm. Finally, the optimization process is initialized by a randomized heuristic procedure that generates multiple starting points. The combined framework is able to explore and as exploit the space of solution trajectories in order to provide the pilot or the air traffic controller with a set of different suggested avoidance trajectories, as well as information about their expected cost and risk. The proposed methods are tested on example scenarios based on real data, showing how different user priorities lead to different flight plans and what tradeoffs are then present. These examples demonstrate that the solutions described in this thesis are adequate for the problems that have been formulated. In this way, the flight planning process can be enhanced to increase the efficiency and predictability of individual aircraft trajectories, which would lead to higher predictability levels of the ATM system and thus improvements in multiple performance indicators.El sistema de gestión del tráfico aéreo (Air Traffic Management, ATM) en los espacios aéreos más congestionados del mundo está siendo reformado para lidiar con múltiples desafíos socioeconómicos, medioambientales y de capacidad. Un pilar de este proceso es el gradual reemplazo de las estructuras rígidas de navegación, basadas en aerovías y waypoints, hacia las operaciones basadas en trayectorias. No obstante, la implementación exitosa de este concepto y la realización de las ganancias esperadas en rendimiento ATM requiere entender y gestionar apropiadamente la incertidumbre. Debido a su compleja estructura socio-técnica, el diseño y operaciones del sistema ATM se encuentran marcadamente influidos por la incertidumbre, que procede de múltiples fuentes y se propaga por las interacciones entre subsistemas y operadores humanos. Uno de los principales focos de incertidumbre en ATM es la meteorología. Debido a su naturaleza no-linear y caótica, muchos fenómenos de interés no pueden ser pronosticados con completa precisión en cualquier horizonte temporal, lo que crea disrupción en las operaciones en aire y tierra que se propaga a otros procesos de ATM. Por lo tanto, para lograr los objetivos de SESAR e iniciativas análogas, es imprescindible tener en cuenta la incertidumbre en múltiples escalas espaciotemporales, desde la predicción de trayectorias hasta la planificación de flujos y tráfico. Esta tesis aborda el problema de la planificación de vuelo de aeronaves individuales considerando dos fuentes importantes de incertidumbre meteorológica: el error en la predicción del viento y la actividad convectiva. Conforme la realización del viento se desvía de su previsión, la trayectoria real se desviará temporalmente de la planificada, lo que implica incertidumbre en tiempos de llegada a sectores y aeropuertos y en consumo de combustible. La actividad convectiva también tiene un impacto en la predictibilidad de las trayectorias, puesto que obliga a los pilotos a desviarse de sus planes de vuelo para evitarla, cambiado así la situación de tráfico. En este trabajo, buscamos desarrollar métodos y algoritmos para la optimización de trayectorias que puedan integrar información sobre la incertidumbre en estos fenómenos meteorológicos en el proceso de diseño de planes de vuelo en horizontes de planificación (antes del despegue) y tácticos (durante el vuelo), con el objetivo de generar trayectorias más eficientes y predecibles. Con este fin, formulamos la planificación de vuelo como un problema de control óptimo, modelando la dinámica del avión con un modelo de masa puntual y el modelo de rendimiento BADA. El control óptimo es un marco flexible y general con un largo historial de éxito en el campo de la ingeniería aeroespacial. Como método numérico, empleamos métodos directos, que son capaces de manejar sistemas dinámicos de alta dimensión con costes computacionales moderados. No obstante, si bien esta metodología es madura en contextos deterministas, la solución de problemas prácticas de control óptimo bajo incertidumbre en la literatura no está tan desarrollada, y los métodos propuestos en la literatura no son aplicables al problema de interés. La primera contribución de esta tesis hace frente a este reto mediante la introducción de un marco numérico para la resolución de problemas generales de control óptimo no-lineal bajo incertidumbre paramétrica. El núcleo de este método es un esquema de conjunto de trayectorias, en el que las trayectorias del sistema dinámico bajo múltiples escenarios son consideradas de forma simultánea, y el problema de control óptimo bajo incertidumbre es así transformado en un problema convencional que puede ser tratado mediante métodos existentes en control óptimo. A continuación, empleamos este método para resolver el problema de la planificación de vuelo robusta. La incertidumbre en el viento y la probabilidad de ocurrencia de condiciones convectivas son modeladas mediante el uso de previsiones de conjunto o ensemble, compuestas por múltiples predicciones en lugar de una única previsión determinista. Este método puede ser empleado para maximizar la eficiencia esperada de los planes de vuelo de acuerdo a la estructura de costes de la aerolínea; además, la predictibilidad de la trayectoria y la exposición a la convección pueden ser incorporadas como objetivos adicionales. El trade-off entre estos objetivos puede ser evaluado mediante la metodología propuesta. La segunda parte de la tesis presenta una solución para reconducir aviones en escenarios tormentosos en un horizonte táctico. La evolución de las células convectivas es representada con un modelo estocástico basado en las proyecciones de Rapidly Developing Thunderstorms (RDT), un sistema determinista basado en imágenes de satélite. Este modelo es empleado por un método de control óptimo numérico, basado en un modelo de masa puntual en el que se modela la dinámica de viraje, con el objetivo de maximizar la eficiencia y predictibilidad de la trayectoria en presencia de incertidumbre sobre la evolución futura de las tormentas. Finalmente, el proceso de optimizatión es inicializado por un método heurístico aleatorizado que genera múltiples puntos de inicio para las iteraciones del optimizador. Esta combinación permite explorar y explotar el espacio de trayectorias solución para proporcionar al piloto o al controlador un conjunto de trayectorias propuestas, así como información útil sobre su coste y el riesgo asociado. Los métodos propuestos son probados en escenarios de ejemplo basados en datos reales, ilustrando las diferentes opciones disponibles de acuerdo a las prioridades del planificador y demostrando que las soluciones descritas en esta tesis son adecuadas para los problemas que se han formulado. De este modo, es posible enriquecer el proceso de planificación de vuelo para incrementar la eficiencia y predictibilidad de las trayectorias individuales, lo que contribuiría a mejoras en el rendimiento del sistema ATM.These works have been financially supported by Universidad Carlos III de Madrid through a PIF scholarship; by Eurocontrol, through the HALA! Research Network grant 10-220210-C2; by the Spanish Ministry of Economy and Competitiveness (MINECO)'s R&D program, through the OptMet project (TRA2014-58413-C2-2-R); and by the European Commission's SESAR Horizon 2020 program, through the TBO-Met project (grant number 699294).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 iPresidente: Damián Rivas Rivas.- Secretario: Xavier Prats Menéndez.- Vocal: Benavar Sridha

    Adaptive Airborne Separation to Enable UAM Autonomy in Mixed Airspace

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    The excitement and promise generated by Urban Air Mobility (UAM) concepts have inspired both new entrants and large aerospace companies throughout the world to invest hundreds of millions in research and development of air vehicles, both piloted and unpiloted, to fulfill these dreams. The management and separation of all these new aircraft have received much less attention, however, and even though NASAs lead is advancing some promising concepts for Unmanned Aircraft Systems (UAS) Traffic Management (UTM), most operations today are limited to line of sight with the vehicle, airspace reservation and geofencing of individual flights. Various schemes have been proposed to control this new traffic, some modeled after conventional air traffic control and some proposing fully automatic management, either from a ground-based entity or carried out on board among the vehicles themselves. Previous work has examined vehicle-based traffic management in the very low altitude airspace within a metroplex called UTM airspace in which piloted traffic is rare. A management scheme was proposed in that work that takes advantage of the homogeneous nature of the traffic operating in UTM airspace. This paper expands that concept to include a traffic management plan usable at all altitudes desired for electric Vertical Takeoff and Landing urban and short-distance, inter-city transportation. The interactions with piloted aircraft operating under both visual and instrument flight rules are analyzed, and the role of Air Traffic Control services in the postulated mixed traffic environment is covered. Separation values that adapt to each type of traffic encounter are proposed, and the relationship between required airborne surveillance range and closure speed is given. Finally, realistic scenarios are presented illustrating how this concept can reliably handle the density and traffic mix that fully implemented and successful UAM operations would entail

    Aircraft Trajectory Planning Considering Ensemble Forecasting of Thunderstorms

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    Mención Internacional en el título de doctorConvective weather poses a major threat that compromises the safe operation of flights while inducing delay and cost. The aircraft trajectory planning problem under thunderstorm evolution is addressed in this thesis, proposing two novel heuristic approaches that incorporate uncertainties in the evolution of convective cells. In this context, two additional challenges are faced. On the one hand, studies have demonstrated that given the computational power available nowadays, the best way to characterize weather uncertainties is through ensemble forecasting products, hence compatibility with them is crucial. On the other hand, for the algorithms to be used during a flight, they must be fast and deliver results in a few seconds. As a first methodology, three variants of the Scenario-Based Rapidly-Exploring Random Trees (SB-RRTs) are proposed. Each of them builds a tree to explore the free airspace during an iterative and random process. The so-called SB-RRT, the SB-RRT∗ and the Informed SB-RRT∗ find point-to-point safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. In a second instance, the Augmented Random Search (ARS) algorithm is used to sample trajectories from a directed graph and deform them iteratively in the search for an optimal path. The aim of such deformations is to adapt the initial graph to the unsafe set and its possible changes. In the end, the ARS determines the population of trajectories that, on average, minimizes a combination of flight time, time in storms, and fuel consumption Both methodologies are tested considering a dynamic model of an aircraft flying between two waypoints at a constant flight level. Test scenarios consist of realistic weather forecasts described by an ensemble of equiprobable members. Moreover, the influence of relevant parameters, such as the maximum number of iterations, safety margin (in SB-RRTs) or relative weights between objectives (in ARS) is analyzed. Since both algorithms and their convergence processes are random, sensitivity analyses are conducted to show that after enough iterations the results match. Finally, through parallelization on graphical processing units, the required computational times are reduced substantially to become compatible with near real-time operation. In either case, results show that the suggested approaches are able to avoid dangerous and uncertain stormy regions, minimize objectives such as time of flight, flown distance or fuel consumption and operate in less than 10 seconds.Los fenómenos convectivos representan una gran amenaza que compromete la seguridad de los vuelos, a la vez que incrementa los retrasos y costes. En esta tesis se aborda el problema de la planificación de vuelos bajo la influencia de tormentas, proponiendo dos nuevos métodos heurísticos que incorporan incertidumbre en la evolución de las células convectivas. En este contexto, se intentará dar respuesta a dos desafíos adicionales. Por un lado, hay estudios que demuestran que, con los recursos computacionales disponibles hoy en día, la mejor manera de caracterizar la incertidumbre meteorológica es mediante productos de tipo “ensemble”. Por tanto, la compatibilidad con ellos es crucial. Por otro lado, para poder emplear los algoritmos durante el vuelo, deben de ser rápidos y obtener resultados en pocos segundos. Como primera aproximación, se proponen tres variantes de los “Scenario-Based Rapidly-Exploring Random Trees” (SB-RRTs). Cada uno de ellos crea un árbol que explora el espacio seguro durante un proceso iterativo y aleatorio. Los denominados SB-RRT, SB-RRT∗ e Informed SB-RRT∗ calculan trayectorias entre dos puntos respetando un margen de seguridad impuesto por el usuario. Además, los dos últimos métodos convergen en soluciones de mínima distancia de vuelo. En segundo lugar, el algoritmo “Augmented Random Search” (ARS) se utiliza para muestrear trajectorias de un grafo dirigido y deformarlas iterativamente en busca del camino óptimo. El fin de tales deformaciones es adaptar el grafo inicial a las zonas peligrosas y a los cambios que puedan sufrir. Finalmente, el ARS calcula aquella población de trayectorias que, de media, minimiza una combinación del tiempo de vuelo, el tiempo en zonas tormentosas y el consumo de combustible. Ambas metodologías se testean considerando un modelo de avión volando punto a punto a altitud constante. Los casos de prueba se basan en datos meteorológicos realistas formados por un grupo de predicciones equiprobables. Además, se analiza la influencia de los parámetros más importantes como el máximo número de iteraciones, el margen de seguridad (en SB-RRTs) o los pesos relativos de cada objetivo (en ARS). Como ambos algoritmos y sus procesos de convergencia son aleatorios, se realizan análisis de sensibilidad para mostrar que, tras suficientes iteraciones, los resultados coinciden. Por último, mediante técnicas de paralelización en procesadores gráficos, se reducen enormemente los tiempos de cálculo, siendo compatibles con una operación en tiempo casi-real. En ambos casos los resultados muestran que los algoritmos son capaces de evitar zonas inciertas de tormenta, minimizar objetivos como el tiempo de vuelo, la distancia recorrida o el consumo de combustible, en menos de 10 segundos de ejecución.Programa de Doctorado en Ingeniería Aeroespacial por la Universidad Carlos III de MadridPresidente: Ernesto Staffetti Giammaria.- Secretario: Alfonso Valenzuela Romero.- Vocal: Valentin Polishchu

    A novel coordination framework for multi-robot systems

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    Having made great progress tackling the basic problems concerning single-robot systems, many researchers shifted their focus towards the study of multi-robot systems (MRS). MRS were shortly found to be a perfect t for tasks considered to be hard, complex or even impossible for a single robot to perform, e.g. spatially separate tasks. One core research problem of MRS is robots' coordinated motion planning and control. Arti cial potential elds (APFs) and virtual spring-damper bonds are among the most commonly used models to attack the trajectory planning problem of MRS coordination. However, although mathematically sound, these approaches fail to guarantee inter-robot collision-free path generation. This is particularly the case when robots' dynamics, nonholonomic constraints and complex geometry are taken into account. In this thesis, a novel bio-inspired collision avoidance framework via virtual shells is proposed and augmented into the high-level trajectory planner. Safe trajectories can hence be generated for the low-level controllers to track. Motion control is handled by the design of hierarchical controllers which utilize virtual inputs. Several distinct coordinated task scenarios for 2D and 3D environments are presented as a proof of concept. Simulations are conducted with groups of three, four, ve and ten nonholonomic mobile robots as well as groups of three and ve quadrotor UAVs. The performance of the overall improved coordination structure is veri ed with very promising result
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