48 research outputs found
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Vision-Based navigation system for unmanned aerial vehicles
Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles
(UAVs) with a robust navigation system; in order to allow the UAVs to perform
complex tasks autonomously and in real-time. The proposed algorithms deal with
solving the navigation problem for outdoor as well as indoor environments, mainly
based on visual information that is captured by monocular cameras. In addition,
this dissertation presents the advantages of using the visual sensors as the main
source of data, or complementing other sensors in providing useful information; in
order to improve the accuracy and the robustness of the sensing purposes.
The dissertation mainly covers several research topics based on computer vision
techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of
the UAV. This algorithm is based on the combination of SIFT detector and FREAK
descriptor; which maintains the performance of the feature points matching and decreases
the computational time. Thereafter, the pose estimation problem is solved
based on the decomposition of the world-to-frame and frame-to-frame homographies.
(II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to
sense and detect the frontal obstacles that are situated in its path. The detection
algorithm mimics the human behaviors for detecting the approaching obstacles; by
analyzing the size changes of the detected feature points, combined with the expansion
ratios of the convex hull constructed around the detected feature points
from consecutive frames. Then, by comparing the area ratio of the obstacle and the
position of the UAV, the method decides if the detected obstacle may cause a collision.
Finally, the algorithm extracts the collision-free zones around the obstacle,
and combining with the tracked waypoints, the UAV performs the avoidance maneuver.
(III) Navigation Guidance, which generates the waypoints to determine
the flight path based on environment and the situated obstacles. Then provide
a strategy to follow the path segments and in an efficient way and perform the
flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in
order to achieve the flight stability as well as to perform the correct maneuver; to
avoid the possible collisions and track the waypoints.
All the proposed algorithms have been verified with real flights in both indoor
and outdoor environments, taking into consideration the visual conditions; such as
illumination and textures. The obtained results have been validated against other
systems; such as VICON motion capture system, DGPS in the case of pose estimate
algorithm. In addition, the proposed algorithms have been compared with several
previous works in the state of the art, and are results proves the improvement in
the accuracy and the robustness of the proposed algorithms.
Finally, this dissertation concludes that the visual sensors have the advantages
of lightweight and low consumption and provide reliable information, which is
considered as a powerful tool in the navigation systems to increase the autonomy
of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados
(UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar
tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos
tratan de resolver problemas de la navegacion tanto en ambientes interiores como
al aire libre basandose principalmente en la informacion visual captada por las camaras
monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores
visuales bien como fuente principal de datos o complementando a otros sensores
en el suministro de informacion util, con el fin de mejorar la precision y la
robustez de los procesos de deteccion.
La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas
de vision por computador: (I) Estimacion de la Posicion y la Orientacion
(Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion
en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el
descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia
y disminuye el tiempo computacional. De esta manera, se soluciona el
problema de la estimacion de la posicion basandose en la descomposicion de las
homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion
colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales
que se encuentran en su camino. El algoritmo de deteccion imita comportamientos
humanos para detectar los obstaculos que se acercan, mediante el analisis de la
magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado
con los ratios de expansion de los contornos convexos construidos alrededor
de los puntos caracteristicos detectados en frames consecutivos. A continuacion,
comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo
decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo
extrae las zonas libres de colision alrededor del obstaculo y combinandolo
con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de
vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona
una estrategia para seguir los segmentos del trazado de una manera eficiente
y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer
soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en
la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como
realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de
referencia.
Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes
exteriores e interiores, tomando en consideracion condiciones visuales como
la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros
sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del
algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos
han sido comparados con trabajos anteriores recogidos en el estado del arte
con resultados que demuestran una mejora de la precision y la robustez de los algoritmos
propuestos.
Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener
un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo
hace una poderosa herramienta en los sistemas de navegacion para aumentar la
autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver
A review: On path planning strategies for navigation of mobile robot
This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics
Intelligent Navigational Strategies For Multiple Wheeled Mobile Robots Using Artificial Hybrid Methodologies
At present time, the application of mobile robot is commonly seen in every fields of science and engineering. The application is not only limited to industries but also in thehousehold, medical, defense, transportation, space and much more. They can perform all kind of tasks which human being cannot do efficiently and accurately such as working in hazardous and highly risk condition, space research etc. Hence, the autonomous navigation of mobile robot is the highly discussed topic of today in an uncertain environment. The present work concentrates on the implementation of the Artificial Intelligence approaches for the mobile robot navigation in an uncertain environment. The obstacle avoidance and optimal path planning is the key issue in autonomous navigation, which is solved in the present work by using artificial intelligent approaches. The methods use for the navigational accuracy and efficiency are Firefly Algorithm (FA), Probability- Fuzzy Logic (PFL), Matrix based Genetic Algorithm (MGA) and Hybrid controller (FAPFL,FA-MGA, FA-PFL-MGA).The proposed work provides an effective navigation of single and multiple mobile robots in both static and dynamic environment. The simulational analysis is carried over the Matlab software and then it is implemented on amobile robot for real-time navigation analysis. During the analysis of the proposed controller, it has been noticed that the Firefly Algorithm performs well as compared to fuzzy and genetic algorithm controller. It also plays an important role inbuilding the successful Hybrid approaches such as FA-PFL, FA-MGA, FA-PFL-MGA. The proposed hybrid methodology perform well over the individual controller especially for pathoptimality and navigational time. The developed controller also proves to be efficient when they are compared with other navigational controller such as Neural Network, Ant Colony Algorithm, Particle Swarm Optimization, Neuro-Fuzzy etc
Feasible, Robust and Reliable Automation and Control for Autonomous Systems
The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences
Control and communication systems for automated vehicles cooperation and coordination
Mención Internacional en el título de doctorThe technological advances in the Intelligent Transportation Systems (ITS) are exponentially
improving over the last century. The objective is to provide intelligent and innovative services
for the different modes of transportation, towards a better, safer, coordinated and smarter
transport networks. The Intelligent Transportation Systems (ITS) focus is divided into two
main categories; the first is to improve existing components of the transport networks, while
the second is to develop intelligent vehicles which facilitate the transportation process. Different
research efforts have been exerted to tackle various aspects in the fields of the automated
vehicles. Accordingly, this thesis is addressing the problem of multiple automated vehicles
cooperation and coordination. At first, 3DCoAutoSim driving simulator was developed
in Unity game engine and connected to Robot Operating System (ROS) framework and
Simulation of Urban Mobility (SUMO). 3DCoAutoSim is an abbreviation for "3D Simulator
for Cooperative Advanced Driver Assistance Systems (ADAS) and Automated Vehicles
Simulator". 3DCoAutoSim was tested under different circumstances and conditions, afterward,
it was validated through carrying-out several controlled experiments and compare
the results against their counter reality experiments. The obtained results showed the efficiency
of the simulator to handle different situations, emulating real world vehicles. Next
is the development of the iCab platforms, which is an abbreviation for "Intelligent Campus
Automobile". The platforms are two electric golf-carts that were modified mechanically, electronically
and electrically towards the goal of automated driving. Each iCab was equipped
with several on-board embedded computers, perception sensors and auxiliary devices, in
order to execute the necessary actions for self-driving. Moreover, the platforms are capable
of several Vehicle-to-Everything (V2X) communication schemes, applying three layers of
control, utilizing cooperation architecture for platooning, executing localization systems,
mapping systems, perception systems, and finally several planning systems. Hundreds of
experiments were carried-out for the validation of each system in the iCab platform. Results
proved the functionality of the platform to self-drive from one point to another with minimal
human intervention.Los avances tecnológicos en Sistemas Inteligentes de Transporte (ITS) han crecido de forma
exponencial durante el último siglo. El objetivo de estos avances es el de proveer de sistemas
innovadores e inteligentes para ser aplicados a los diferentes medios de transporte, con el fin
de conseguir un transporte mas eficiente, seguro, coordinado e inteligente. El foco de los ITS
se divide principalmente en dos categorías; la primera es la mejora de los componentes ya
existentes en las redes de transporte, mientras que la segunda es la de desarrollar vehículos
inteligentes que hagan más fácil y eficiente el transporte. Diferentes esfuerzos de investigación
se han llevado a cabo con el fin de solucionar los numerosos aspectos asociados con
la conducción autónoma. Esta tesis propone una solución para la cooperación y coordinación
de múltiples vehículos. Para ello, en primer lugar se desarrolló un simulador (3DCoAutoSim)
de conducción basado en el motor de juegos Unity, conectado al framework Robot Operating
System (ROS) y al simulador Simulation of Urban Mobility (SUMO). 3DCoAutoSim ha
sido probado en diferentes condiciones y circunstancias, para posteriormente validarlo con
resultados a través de varios experimentos reales controlados. Los resultados obtenidos
mostraron la eficiencia del simulador para manejar diferentes situaciones, emulando los
vehículos en el mundo real. En segundo lugar, se desarrolló la plataforma de investigación
Intelligent Campus Automobile (iCab), que consiste en dos carritos eléctricos de golf, que
fueron modificados eléctrica, mecánica y electrónicamente para darle capacidades autónomas.
Cada iCab se equipó con diferentes computadoras embebidas, sensores de percepción y
unidades auxiliares, con la finalidad de transformarlos en vehículos autónomos. Además,
se les han dado capacidad de comunicación multimodal (V2X), se les han aplicado tres
capas de control, incorporando una arquitectura de cooperación para operación en modo
tren, diferentes esquemas de localización, mapeado, percepción y planificación de rutas.
Innumerables experimentos han sido realizados para validar cada uno de los diferentes sistemas
incorporados. Los resultados prueban la funcionalidad de esta plataforma para realizar
conducción autónoma y cooperativa con mínima intervención humana.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Francisco Javier Otamendi Fernández de la Puebla.- Secretario: Hanno Hildmann.- Vocal: Pietro Cerr
Feature Papers of Drones - Volume I
[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin