102 research outputs found
Haptic-Multimodal Flight Control System Update
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
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
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
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
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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