4 research outputs found
Informed scenario-based RRT* for aircraft trajectory planning under ensemble forecasting of thunderstorms
Thunderstorms represent a major hazard for flights, as they compromise the safety of both the
airframe and the passengers. To address trajectory planning under thunderstorms, three variants
of the scenario-based rapidly exploring random trees (SB-RRTs) are proposed. During an iterative
process, the so-called SB-RRT, the SB-RRT* and the Informed SB-RRT* find safe trajectories by
meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions
of minimum flight length. Through parallelization on graphical processing units the
required computational times are reduced substantially to become compatible with near real-time
operation. The proposed methods are tested considering a kinematic model of an aircraft flying
between two waypoints at constant flight level and airspeed; the test scenario is based on a
realistic weather forecast and assumed to be described by an ensemble of equally likely members.
Lastly, the influence of the number of scenarios, safety margin and iterations on the results is
analyzed. Results show that the SB-RRTs are able to find safe and, in two of the algorithms, closeto-
optimum solutions.This work has received funding from (1) the Spanish Government (Project RTI2018-098471-B-C32) and (2) the SESAR Joint
Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287
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
Robotics 2010
Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development